Executive Summary

This was the week frontier AI became more useful, more expensive and harder to govern.

The standout release was Anthropic’s Claude Fable 5, the first publicly available Mythos-class model. The claim is not just better chat. Anthropic is positioning it around long-horizon software engineering, complex analytical work, vision, scientific research and million-token context. The caveat matters: sensitive cyber, biology, chemistry and model-distillation requests can be routed away from Fable 5 to Opus 4.8, and business traffic on Mythos-class models is subject to 30-day retention for safety monitoring. For most business users, that trade-off is acceptable, but it should not be ignored.

Apple’s WWDC reset was also significant, but more strategically than practically. Siri AI moves Apple closer to the assistant model everyone expected years ago: personal context, on-screen awareness and cross-app actions. The direction is right, but availability, device limits and regional delays mean this is not yet something most UK businesses can operationalise this week.

The broader pattern is clearer. AI capability is still moving quickly, but so are the constraints around it: compute cost, safety gating, data-retention trade-offs, model access tiers and procurement risk. The sensible response is not to chase every release. It is to test the few tools that could materially change workflow quality, while putting basic controls around confidentiality and spend.

For UK leaders, the practical takeaway is simple: try Fable 5 before the included-access window closes, and turn on stronger ChatGPT protections for anyone using AI with confidential work.

What to Try This Week

Try Claude Fable 5 before 22 June.

This is the one release worth making time for. Anthropic says Fable 5 is included at no extra cost on Pro, Max, Team and seat-based Enterprise plans until 22 June, after which it moves to usage credits unless capacity allows an extension.

Do not test it on generic prompts. Give it one difficult piece of real work that normally takes concentration and context: a messy diligence document, a product spec, a complex spreadsheet explanation, a codebase issue, a board-paper rewrite, or a market-map synthesis. Ask it to show assumptions, identify gaps, and produce a usable first draft.

The point is not whether it sounds clever. The test is whether it gets you materially closer to a decision or deliverable than your current model.

Turn on ChatGPT Lockdown Mode for confidential work.

This is less exciting than a new model, but probably more important for day-to-day business use. If your team uses ChatGPT for client documents, legal correspondence, market research, board papers or anything commercially sensitive, Lockdown Mode is a sensible default.

It reduces exposure to indirect prompt-injection attacks by restricting network behaviour, including internet image fetching, file downloads, Deep Research and Agent Mode. That will break some workflows, so use it where confidentiality matters most rather than across everything blindly.

A good rule of thumb: if the input would make you uncomfortable sitting in someone else’s inbox, use the more restricted mode.

Geopolitics, Governance and Big Moves

SpaceX prices the year’s biggest IPO at around $135 a share

SpaceX is expected to price its IPO at around $135 a share this week ahead of trading on Friday 12 June, against a $1.75 trillion target valuation. The Information’s pre-IPO subscriber Q&A on Wednesday set the pricing-week schedule. Pricing-week conditions are not gentle: the Nasdaq has fallen 5% over the past week, with interest-rate and Iran-war concerns layered on top. Goldman Sachs, the lead bank, told investors it expects SpaceX revenue to reach $474 billion by 2030 from $18.7 billion last year, two-thirds of it from AI. Martin Peers flagged the projection as harder to credit than OpenAI’s own $284 billion 2030 forecast: the xAI line item Goldman is using to justify the AI two-thirds had only $3.2 billion of 2024 revenue, substantially all of it from X advertising, which has dropped by half since the Musk acquisition.

OpenAI files confidential S-1, declares its “third phase”

OpenAI filed a confidential S-1 with the SEC on Monday 8 June. The company explicitly hedged on timing: “it may be a while because there are things we want to do that are likely easier as a private company.” Sam Altman and chief scientist Jakub Pachocki published a same-day blog titled “Built to benefit everyone,” declaring OpenAI has entered its third phase after pure research and product shipping: “the economy is now being shaped around AI.” Three stated goals: an automated AI researcher, accelerating the economy while sharing gains widely, and giving “everyone on Earth a personal AGI.” A noteworthy line, “entirely automating everything is not the future we want,” echoes Anthropic’s recent pause framing. OpenAI also proposed a global coordination body and launched an Economic Research Exchange for external researchers studying AI’s economic effects.

Anthropic now generates revenue 35% above OpenAI

The Information’s same-day “Anthropic vs. OpenAI” brief made the new revenue gap explicit: Anthropic is now generating revenue at an annualised rate roughly 35% higher than OpenAI, a sharp reversal from late 2025 when Anthropic was generating less than half. Anthropic has discussed going public as soon as Q4 and projects free-cash-flow positive by 2028. OpenAI projects to burn more than $100 billion before potentially reaching free-cash-flow positive around 2030. Its $20 billion-plus chip agreement with Cerebras already signals the scale of that burn. Perplexity separately said it plans to IPO in 2028 “regardless of what happens to Anthropic or OpenAI,” the first time a second-tier AI lab has put a specific public-listing date on its own roadmap.

Apple confirms Siri AI at WWDC, running on Google Gemini

Apple opened WWDC 2026 on Monday 8 June with the long-trailed Siri reset, rebranded Siri AI and now running on Apple’s own models in collaboration with Google Gemini. The assistant gets its own dedicated app, chatbot-style interactions with access to past conversations, screen-context awareness, file uploads, and the ability to take systemwide app actions. It will arrive as a free fall update for iPhone 15 Pro and newer with a public beta next month, but it will not launch in the EU or China. The most powerful on-device model and its features will require iPhone 17 Pro or iPhone Air, and several AI features ship with daily-usage limits that push users to iCloud subscriptions. Martin Peers’ read: “the bar was low.” Federighi used the keynote to take a public shot at competitors “racing forward, seemingly pursuing AI for the sake of AI.” Apple also announced iOS 27, a new on-device Core AI framework giving developers Swift APIs across CPU, GPU and Neural Engine, and confirmed John Ternus as the incoming CEO from 1 September.

Bots have overtaken humans on the internet, a year early

Cloudflare CEO Matthew Prince confirmed on Thursday 4 June that bot traffic now accounts for between 52 and 62% of daily internet traffic, averaging 57.4% bots to 42.5% humans over the past week. Prince’s previous public forecast was late 2027, so the milestone has arrived roughly a year early, driven by fast-growing agentic AI traffic rather than search-engine crawling. The country-level distribution is striking: Gibraltar leads at 92.1% bot traffic, followed by Singapore (76.3%), Iran (76.2%), Ireland (72.8%) and the Netherlands (68.8%). For UK B2C operators, agent-first user journeys (who arrives, what they extract, whether they can act) are now the strategic question, not the edge case. Display advertising, attribution and anti-abuse systems were all built around a human-majority baseline that no longer holds.

Four-lab bioweapons letter is the first cross-frontier policy alignment of 2026

The CEOs of Anthropic, OpenAI, Google DeepMind and Microsoft co-signed an open letter pressing US Congress to require synthetic-DNA sellers to vet every buyer and order, warning that frontier AI now enables non-experts to design bioweapons. This is the first time Sam Altman and Dario Amodei have publicly co-signed the same policy document in 2026 and the first time all four frontier-lab CEOs have aligned on a regulatory ask. UK Department for Science, Innovation and Technology now has to choose whether to back the request. The trans-Atlantic pre-deployment partnership architecture sketched in May with CAISI is the most natural vehicle.

Tech, Tools and Releases

Anthropic releases Claude Fable 5, bringing Mythos-class capability to the public

Anthropic launched Claude Fable 5, its first publicly available Mythos-class model, alongside the more powerful Claude Mythos 5 for trusted partners. Fable 5 brings frontier capability into broader use while restricting higher-risk cybersecurity and bioweapons use cases. Early testers describe it as extremely strong, but slower and expensive, with standout coding, reasoning and long-context performance, including a 1 million-token context window and 128,000-token outputs.

Apple uses WWDC to reset Siri for the AI era

Apple used WWDC 2026 to unveil Siri AI, a major rebuild of its assistant around conversational interaction, personal context and cross-app task execution. The new system draws on Apple Intelligence, updated foundation models and collaboration with Google Gemini, while retaining Apple’s privacy-heavy on-device positioning. Siri can understand emails, photos and on-screen activity, and act across apps. Apple also announced iOS 27, stronger parental controls and Image Playground upgrades.

OpenAI ships Lockdown Mode, the first consumer-tier defence against indirect prompt injection

OpenAI rolled out Lockdown Mode this week, an optional ChatGPT setting that gives stricter protection against indirect prompt-injection attacks, where hidden malicious instructions on web pages or in documents trick the model into leaking data. Turning it on disables Deep Research and Agent Mode, blocks ChatGPT from pulling or displaying internet images, and stops the chatbot from downloading files, though manual uploads and on-platform image generation still work. The mechanism is network-layer rather than parser-layer: Lockdown Mode limits the network requests an attacker could exploit, rather than trying to detect injection patterns in processed content. The feature is available to all personal accounts including the free tier. This is the first mainstream consumer-tier defence designed specifically for the indirect prompt-injection class of attack and lands inside the same fortnight as Anthropic’s open-sourced Defending Code Reference Harness.

Anthropic publishes “When AI builds itself”: 80% of new production code now written by Claude

Anthropic published “When AI builds itself” on Thursday 4 June, the first frontier-lab long-form report grounded in internal recursive-self-improvement metrics rather than hypothetical scenarios. The headline numbers: Claude now authors 80% of Anthropic’s new production code and engineers ship 8x more code per year compared with 12 months ago. Anthropic uses the report to argue for a global slowdown or temporary pause on AI development, comparing the coordination requirement to nuclear-weapons treaties. The report names MiniMax, whose new M2.7 model “helped build itself”, as evidence that recursive self-improvement is now a multi-lab pattern across the US and China. Critics quoted in TLDR AI framed the pause call as a marketing move to slow rivals. Either reading lands in the same place: recursive self-improvement is the new centrepiece of the safety debate, and Anthropic’s framing is the document that subsequent moves will reference.

Cognition rebrands Windsurf as Devin Desktop, pitches the “Switzerland of AI agents”

The Information’s Rocket Drew reported on Sunday 7 June that Cognition is making over its app to position itself as a neutral hub for AI coding agents, rebranding Windsurf as Devin Desktop. The framing, explicitly “the Switzerland of AI agents”, places Cognition as the agent-orchestration layer that does not pick favourites between OpenAI Codex, Anthropic Claude Code or Google Antigravity, in deliberate contrast to OpenAI’s announcement that it would merge Codex and ChatGPT into a single agentic surface. Cognition followed up on Tuesday with FrontierCode, the first benchmark to measure code mergeability, whether a model’s output can actually be merged into a live codebase without breaking things. For UK CTOs evaluating coding-agent platforms with teams using agents from three different vendors, the Cognition pitch is the cleanest current alternative to vendor lock-in.

Xiaomi MiMo-UltraSpeed lands a 1,000 tokens-per-second claim on commodity hardware

TLDR AI led its Tuesday “Headlines & Launches” with Xiaomi’s release of MiMo-V2.5-Pro-UltraSpeed, a 1-trillion-parameter language model developed with inference partner TileRT and clocking 1,000 tokens per second on a standard 8-GPU commodity node, roughly 15x the median frontier-model rate cited for ChatGPT and Claude. The speedup is achieved through FP4 quantisation on the model’s expert layers and DFlash speculative decoding. The model is available through a limited API trial from 9 to 23 June at three times the standard rate for roughly ten times the output. For UK enterprise teams, a 1-trillion-parameter Chinese model with credible 1,000 tokens-per-second throughput on commodity hardware is the cleanest case yet for treating Chinese frontier models as a serious cost-control lever, even before the gray-market access stories that have dominated W23 and W24 reporting.

Instagram AI chatbot breach hits 20,225 accounts

Meta confirmed this week that hackers compromised at least 20,225 Instagram accounts by exploiting a flaw in its AI-powered “High Touch Support” account-recovery chatbot, the first time Meta has put a number on the scope of the breach. The bug: the chatbot sent password reset links to any email address without checking whether it actually belonged to the Instagram account. Attackers exploited it from 17 April to 31 May 2026. Potentially exposed data: contact info, birth dates, posts, direct messages and profile information. Meta disabled the chatbot, invalidated reset links and placed affected users into a mandatory security checkpoint. This is the first quantified consumer-AI safety failure of 2026 with a confirmed account count and a counterweight to the consumer-tier defence story of OpenAI Lockdown Mode. AI is shipping consumer security improvements and consumer security failures in the same week.

Quick Hits

• The “SaaSpocalypse” is over, but public stocks haven’t noticed. Private equity mogul Orlando Bravo declared the call on CNBC on Tuesday and Marc Benioff retweeted it. Salesforce is down 34% year-to-date, ServiceNow down 30% and Asana down 45%. The SpaceX, Anthropic and OpenAI IPO window has not yet absorbed capital out of weaker tech stocks into the higher-growth listings.

• AI Boomerang: 32% of laid-off roles are being rehired. A Robert Half survey of 2,000 US hiring managers shows 32% have eliminated a role because of productivity gains from AI or automation, only to later rehire for the exact same position. Finance leads at 44%, then HR at 35%, then tech at 32%. Managers cited “gaps in quality, oversight and decision-making once business demands picked up after the initial cuts.”

• Anthropic embeds engineers at the NSA, names Glasswing coalition. The Financial Times reported Anthropic has embedded around six engineers at the NSA to deploy its Mythos model for offensive cyber operations targeting networks in countries such as China or Iran. The report explicitly names Microsoft, Apple and Amazon as Project Glasswing coalition members for the first time.

• Anthropic cuts off xAI’s access to its products. The Information’s TITV show confirmed production-level enforcement of the W24 reporting that xAI trained its coding models on Claude outputs for months before being cut off. This is the cleanest single example of a frontier lab enforcing its terms of service against a US peer, landing inside the SpaceX IPO pricing window.

• China announces a $295 billion AI buildout plan. Bloomberg reports a 2-trillion-yuan five-year blueprint to link computing sites into one grid by 2028, with Huawei and other local suppliers providing at least 80% of the core technology. The UK announced a £1.1 billion AI hardware plan the same day.

• Apple Poke approved as the first AI agent on Messages for Business. Apple opened the iMessage agent surface to a third party for the first time, days before WWDC.
  

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Executive Summary

This was the week the AI cost question moved from the CTO’s office to the CFO’s desk. Anthropic closed a $65 billion Series H at a $965 billion valuation, Alphabet moved to raise $80 billion for AI infrastructure, and Palo Alto Networks disclosed it had spent more than $1 million in tokens running Anthropic’s Mythos security model in just a few weeks of trials.

Three themes hardened.

First, AI is now a capital intensity story. Alphabet’s planned stock sale is not a routine financing event. It is a signal that even the largest platforms see compute, data centres, power, cooling and chips as strategic constraints. The AI capex cycle is no longer background infrastructure. It is becoming one of the main competitive variables in the market.

Second, AI is moving directly into work surfaces. OpenAI is pushing Codex beyond software engineering into white-collar workflows such as sales, data analytics, creative production and investment banking. ChatGPT is now inside Excel and Google Sheets. Microsoft is building more of its own MAI model stack for Copilot, GitHub and VS Code. Meta is embedding AI into Facebook creator tools. The adoption question is shifting from “which chatbot should we use?” to “which systems are we comfortable letting AI operate inside?”

Third, the safety debate is becoming more operational. Anthropic is calling for coordinated, verifiable mechanisms that would allow frontier labs to slow or pause development if recursive self-improvement risks escalate. At the same time, Google’s fake call detection shows the defensive side of AI deployment: platforms are starting to build AI-native protections against AI-enabled harms such as voice cloning and impersonation scams.

The practical takeaway for UK leaders this week: before adding another AI pilot, pick one existing workflow and define the guardrails. What system will AI sit inside? What data can it touch? What cost cap applies? What human review step prevents a bad recommendation becoming an operational error? That is now a better starting point than another generic chatbot experiment.

What to Try This Week

Build a simple AI cost control sheet before your next pilot. The Alphabet raise and Mythos token burn are two sides of the same problem: AI is powerful, but the infrastructure and inference costs are not abstract. Before starting a new pilot, build a one-page cost model covering expected users, calls per day, average tokens per call, model price, monthly run-rate and a hard stop-loss threshold. This does not need to be perfect. It just needs to stop “successful” experiments becoming surprise invoices.

Install ChatGPT for Excel on one low-risk workbook and run a controlled test. Pick a non-confidential spreadsheet your team already understands: a pipeline tracker, board KPI pack, budget template or customer-call tracker. Ask ChatGPT to explain the workbook structure, identify the key drivers, flag broken formulas, and suggest clean-up steps. Then ask it to make one contained update, such as adding a scenario tab or standardising inconsistent labels, but require it to outline the cells and tabs it plans to change before editing. Measure three things: time saved, quality of explanation, and whether the workbook remained auditable afterwards.

Test Codex on one white-collar artefact, not just code. The new Codex plug-ins are a useful prompt to test AI on a proper business deliverable: a sales account plan, market summary, board update, investment note, dashboard spec or product brief. Give it source material, ask for a first usable version, then review what it got wrong. The aim is not automation for its own sake. The aim is to find where an agent can create a better starting point than a blank page.

Look for one workflow where AI is becoming native, not bolted on. Meta’s creator assistant, ChatGPT for Excel, Microsoft Scout in Teams, OpenAI Codex on AWS and Google’s fake call detection all point in the same direction: the next wave of AI adoption will happen inside existing tools and platforms. Pick one workflow in your business and ask three questions. Where does the work already happen? What data would an assistant need to touch? What review step would stop a bad recommendation becoming an operational error?

Geopolitics, Governance and Big Moves

Anthropic closes $65 billion Series H at $965 billion, overtaking OpenAI

Anthropic confirmed on Thursday 28 May a $65 billion Series H led by Altimeter Capital, Dragoneer, Greenoaks and Sequoia at a $965 billion post-money valuation, up from $380 billion in February and now exceeding OpenAI’s most recent mark. Revenue run-rate is $47 billion, up from $30 billion earlier in the year and $10 billion in calendar 2025. The financing includes $15 billion of previously committed money, $5 billion of it from Amazon. Anthropic also filed a confidential draft S-1 with the SEC on Monday 1 June, formally putting it on the public-listing clock alongside SpaceX and OpenAI.

Alphabet plans $80B raise for AI infrastructure build-out

Alphabet plans to sell $80 billion of stock, including $10 billion to Berkshire Hathaway, to fund AI infrastructure and global compute. The company says demand for AI products from enterprises and consumers is exceeding available supply. This is the clearest signal yet that the AI race is now a balance sheet race, with data centres, power, chips and cooling becoming strategic constraints rather than back-office infrastructure for major platforms now.

Anthropic urges labs to pre-agree AI pause mechanisms

Anthropic is calling for frontier AI labs to agree coordinated, verifiable mechanisms to slow or pause development if models start improving themselves faster than society can manage. It says Claude now writes much of Anthropic’s code and engineers ship eight times more code per quarter than in 2021 to 2025. The warning is not that recursive self-improvement has arrived, but that institutions are underprepared if it does at scale soon.

Trump signs voluntary 30-day AI review order behind closed doors

President Trump signed the long-delayed AI cybersecurity executive order in private on Tuesday 2 June, two weeks after he killed the original ceremony version after calls from Elon Musk, Mark Zuckerberg and David Sacks. The final order is materially weaker than the May draft: voluntary, with a 30-day pre-release government review window instead of 90 days, no licensing regime, and an explicit clause that nothing in the order shall be construed to authorise a federal AI licensing system. It directs the DOJ to pursue AI-powered hacking and establishes an inter-agency cybersecurity clearinghouse. The administration has 60 days to define which frontier models qualify. For UK and EU procurement teams: the US is now confirmed as the lightest-touch frontier regulator in the OECD, sharpening the case for sector-regulator-led evaluation policies at home.

SpaceX confirms $75 billion IPO, Wall Street models $350 billion cash burn through 2030

SpaceX confirmed on Wednesday 3 June a $75 billion IPO target, branded by several outlets as a “record” before The Information noted that Google’s $84.75 billion equity raise the same week, including $10 billion to Berkshire Hathaway, was larger. Goldman Sachs is telling investors SpaceX will burn $120 billion of cash in the next two years and $350 billion through 2030, raising the prospect of further fundraises. The Anthropic compute deal is also less locked-in than the S-1 implied: TechCrunch confirmed the contract is in fact a 180-day lease with a 90-day mutual cancellation clause, contradicting Musk’s three-year framing. The Information’s Anita Ramaswamy warned that the 30% retail allocation, triple the typical IPO float, will produce extreme volatility post-debut.

Microsoft unveils seven in-house MAI models at Build 2026

Microsoft used Build 2026 to announce seven proprietary MAI models, including MAI-Thinking-1 for reasoning, MAI-Code-1-Flash for coding inside GitHub Copilot and VS Code, MAI-Image-2.5, MAI-Transcribe-1.5 and MAI-Voice-2. The strategic point is not just model count. Microsoft is building more of its own AI stack, reducing dependence on OpenAI and giving Azure, Copilot and Foundry customers cheaper, more controlled model options across reasoning, coding, image, voice and transcription.

Nvidia at Computex: agentic AI has arrived, organised across the stack

Jensen Huang opened Nvidia’s Computex 2026 keynote in Taiwan with “Agentic AI has arrived” and then spent the rest of the keynote backing the claim. The new RTX Spark supercomputer chips, co-engineered with Microsoft, run AI agents directly on Windows PCs. Nvidia introduced Vera, marketed as “the CPU for agents”, running tasks 1.8x faster than rivals and already adopted by Anthropic, OpenAI and the NYSE. Cosmos 3, an open robotics model, gives robots and self-driving cars planning-and-anticipation rather than just reaction. Nemotron 3 Ultra, a 550B-parameter open-weight model, scored 48 on the Artificial Analysis Intelligence Index, well ahead of the next US open-source rival. Nvidia has reorganised its entire $5 trillion-plus business around agents as the dominant future consumer of compute.

Salesforce flat, Snowflake up 35%: the AI middlemen divide

Snowflake shares jumped more than 35% on Wednesday 27 May after the database firm reported 34% growth in its most-watched sales metric, seven points ahead of its own forecast. Customers are leaning into Snowflake’s AI coding agent and a tool that lets them search across data stored in Microsoft, Salesforce and SAP apps via the Snowflake layer. Salesforce, on the same day, reported a 50% jump in Agentforce annual recurring revenue to $1.2 billion, but overall revenue growth disappointed, and the company quietly lowered its operating and free cash flow projections for the year by five percentage points. Shares were down more than 30% year-to-date. Martin Peers’ read: throwing AI on a database is a cleaner business than retrofitting AI onto a legacy app suite. UK SaaS procurement teams should weight that distinction when reading vendor AI revenue claims.

Tech, Tools and Releases

Anthropic ships Claude Opus 4.8 with adjustable effort controls

Released on Thursday 28 May alongside the funding announcement, Claude Opus 4.8 is the first Anthropic frontier model with user-adjustable effort controls. Practical effect: the same model can be dialled down for cheap, fast queries or dialled up for expensive, deeper reasoning, with the user choosing the trade-off per call rather than committing to a tier. Anthropic also shipped dynamic workflows in Claude Code, letting agents reshape their plans mid-task rather than execute a fixed sequence, and made the faster mode “significantly cheaper”. For teams running high-volume agentic workloads, this is the first mainstream model that puts the speed-versus-depth lever directly in the developer’s hands and is the most credible response yet to the cost-control complaint dogging frontier AI.

Anthropic Mythos is a security powerhouse and a budget buster

The most-discussed practitioner story of the week. Palo Alto Networks tested Anthropic’s Claude Mythos against its own source code earlier this year, said senior vice president of threat intelligence Sam Rubin to The Information. In three weeks Mythos surfaced more than two dozen critical vulnerabilities, roughly five times what existing tools would have found. The catch: Palo Alto “very quickly” burned through more than $1 million worth of tokens. Anthropic confirmed the same week that Project Glasswing, the Mythos partner programme, has expanded to 150 organisations across more than 15 countries, with partners collectively finding more than 10,000 high-or-critical-severity flaws since launch. Apple, Nvidia, Microsoft, CrowdStrike and Palo Alto are anchor partners. The signal: capability is real, the bill is real, and pricing transparency is the next contested ground.

OpenAI pushes Codex into white-collar workflows

OpenAI has launched six Codex plug-ins for data analytics, creative production, sales, product design, equity investing and investment banking, plus Sites for publishing interactive work products and Annotations for targeted edits. The move pushes Codex beyond software engineering into broader knowledge work and office automation. OpenAI says knowledge workers already represent about 20% of Codex users, growing more than three times as fast as developers inside enterprise workflows.

OpenAI frontier models and Codex arrive on AWS

OpenAI frontier models and Codex are now generally available through AWS, giving enterprises a route to adopt OpenAI inside existing security, governance, deployment and billing structures. This is commercially important because it reduces procurement friction. For many large companies, the blocker has not been model capability, but whether AI tools fit into approved cloud controls. AWS availability makes OpenAI easier to buy, govern and scale.

ChatGPT for Excel moves AI into the spreadsheet workflow

OpenAI’s ChatGPT for Excel and Google Sheets is worth adding here, even though the general availability update landed earlier in May rather than during this exact newsletter window. The reason is simple: for business users, this may be more immediately useful than many of the headline model releases.

The add-in brings ChatGPT directly into Excel and Google Sheets, where users can build, update and understand spreadsheets without copying data into a separate chatbot. Typical use cases include explaining workbook logic, tracing formula chains, cleaning messy data, creating scenario tabs, drafting analysis and finding inconsistencies across sheets.

For finance, strategy, M&A, sales operations and reporting teams, this is a meaningful shift. AI is moving into the tool where the work already happens. The governance point matters too. Spreadsheet AI will save time, but it also creates new review requirements around formula changes, assumptions, source data and auditability. The right trial is not “can it do my job?” It is “can it remove the lowest-value spreadsheet work without breaking the controls?”

MiniMax M3 brings 1M-context frontier models closer to self-hosting

MiniMax has released M3, a new multimodal model aimed at coding, agentic work and long-context tasks. It combines native image and video input with a 1M-token context window powered by MiniMax Sparse Attention, which the company says cuts long-context compute materially. The important angle is deployment flexibility: MiniMax says M3 will be fully open-sourced on Hugging Face and GitHub, supporting private clusters, self-hosting and fine-tuning for controlled enterprise workloads soon.

For business users, the point is not that every company should now host its own model. It is that the competitive set for long-context, agentic workloads is broadening. If M3 performs as advertised, then tasks such as large-codebase analysis, document-heavy diligence, internal knowledge search, video understanding and controlled agent workflows may not have to sit exclusively inside the highest-priced closed-model environments.

Meta puts an AI growth coach inside Facebook creator tools

Meta is embedding an AI assistant directly into Facebook’s creator tools, giving creators personalised recommendations based on their content style, performance, audience and goals. The rollout, starting in the U.S., Canada and India, shifts AI from one-off inspiration prompts into ongoing operational support for creator businesses. For Meta, it deepens platform dependence. For creators, it offers always-on guidance around content strategy, discovery, engagement and audience growth.

Ideogram 4.0 and Reve 2.0 swap text prompts for layout control

On Tuesday 3 June, Ideogram open-sourced Ideogram 4.0, taking the top spot for open image models on Design Arena and trailing only OpenAI and Google’s closed models, with notable strengths in typography and graphic design. Reve launched Reve 2.0 the same day, surpassing Nano Banana 2 on the Text-to-Image Arena leaderboard and trailing only GPT-image-2. Both ship a meaningful UX shift: outputs include labelled segments, so users tweak specific parts of an image without regenerating the whole thing. Reve creates images “like code”, editing by rewriting the layout JSON rather than the prompt. For marketing, design and creative teams that have spent two years re-rolling prompts, this is the first credible move toward agentic, editable visual artefacts.

Quick Hits

• Cybersecurity stocks rally on AI demand, results trail expectations. CrowdStrike and Palo Alto Networks shares are up more than 50% year-to-date on AI-driven cyber-threat demand, but actual top-line growth is barely shifting: CrowdStrike forecast 23% growth for the year, only a point ahead of last year. Netskope shares dropped 20% after-hours on Wednesday after growth slowed to 28% from 32%.

• DeepSeek to raise $7 billion in maiden round. The Chinese frontier-model lab is closing roughly 50 billion yuan in its first ever fundraise, with the founder committing 20 billion yuan of his own money. Fewer than 10 outside investors. Expected to close inside two weeks. Open-weight pricing pressure from Chinese labs is now a funded multi-year campaign.

• Google’s Gemini 3.5 Pro confirmed for June, date pending. Google said at I/O that Gemini 3.5 Pro is already being used internally and will roll out in June, but there is no public model card, API ID or firm date yet. Gemini 3.5 Flash is already available as a stable model. The signal: Google is not sitting out the June frontier-model cycle.

• WindBorne says AI weather model is beating government forecasts. WindBorne Systems’ WeatherMesh-6 claims more frequent and accurate forecasts than ECMWF on key variables, using deep learning and data from around 400 balloons in flight. The broader signal: AI is moving into specialist scientific infrastructure, not just chat, coding and office workflows.

• Google rolls out fake call detection against AI voice scams. Google is launching fake call detection in Phone by Google on Android 12+ devices, starting with Pixel. The feature checks whether a trusted contact’s call is genuine and warns users when spoofing or AI voice impersonation is likely. It is a defensive AI use case against a very practical AI misuse problem.
  

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Executive Summary

The frontier labs are starting to look less experimental and more like large infrastructure companies. Anthropic has reportedly raised a further $30 billion at a $900 billion-plus valuation, OpenAI is moving towards a possible IPO, and both companies are now tied into enormous long-term compute commitments. For buyers, this matters because vendor risk is changing. The largest AI providers are not disappearing, but they are also becoming more capital-intensive, more concentrated, and more strategically important to their cloud and compute partners.

The second theme of the week is governance. Trump’s decision to pause the planned AI safety executive order leaves US federal oversight more fragmented than expected. At the same time, the Vatican’s first encyclical on AI underlines a broader point: AI governance is no longer just a technical or regulatory issue. It is becoming a question of labour, power, institutional trust and public accountability. Businesses should not wait for governments to define all of this for them. Procurement rules, internal red-team processes, data controls and acceptable-use policies now need to be written at company level.

The third theme is cost. The gap between premium frontier models and cheaper open-weight or lower-cost alternatives is now too large to ignore. Google is pushing Gemini further down the cost curve with Flash. Chinese open-weight models are taking meaningful share on routing platforms. Alibaba is moving its strongest Qwen agent model into a more proprietary lane. The practical point is not that every company should switch model provider. It is that most businesses should stop treating “the best model” as a fixed answer. For high-volume workflows, model routing, cost benchmarking and task-by-task evaluation are becoming basic operating disciplines.

Finally, the product layer continues to move quickly. Figma is putting agents directly into the design canvas. Spotify is turning audio into searchable, summarised knowledge. Google is building agentic search and multimodal shopping flows into its core products. These are not all immediate enterprise priorities, but they show the same direction of travel: AI is moving from standalone chat tools into the software people already use every day.

What to Try This Week

What to Try This Week

1. Run a simple model cost test on one real workflow

Pick one repeatable AI task in the business: summarising calls, drafting reports, classifying support tickets, producing research notes or reviewing documents. Run the same task through your current model, a cheaper fast model, and one open-weight or lower-cost alternative if available. Compare quality, speed and cost. The aim is not to find the “best” model. It is to understand which tasks genuinely need premium models and which do not.

2. Add an AI procurement gate before new tools are bought

Before approving a new AI tool, ask five basic questions: what data does it touch, which model does it depend on, can outputs be checked, who owns the workflow if it fails, and what happens if pricing changes? This is especially important for tools that claim to add “AI” to an existing product but are really just chatbot wrappers with limited control, weak auditability or unclear data handling.

3. Test whether your design, marketing or knowledge workflows are becoming agent-ready

Look at one workflow where staff currently move information between tools: turning a podcast into notes, converting a meeting into actions, creating design variations, or drafting marketing assets from existing material. The useful question is whether AI can now work inside that workflow rather than sitting alongside it. Tools like Figma’s agent and Spotify’s AI audio features show where the market is heading: less copy-paste, more embedded assistance.

Geopolitics, Governance and Big Moves

Anthropic closes $30 billion round at a $900 billion valuation, past OpenAI for the first time

Bloomberg confirmed on Thursday that Anthropic has closed a $30 billion funding round at a $900 billion-plus post-money valuation led by Sequoia, Dragoneer, Greenoaks and Altimeter, putting the company past OpenAI’s $852 billion March valuation for the first time. The Wall Street Journal reported the same week that Anthropic projects a $559 million Q2 operating profit on $10.9 billion of Q2 revenue, up 130% from Q1’s $4.8 billion. The number of customers spending over $1 million annually on Claude doubled from roughly 500 to more than 1,000 between February and April 2026. Compute costs are falling from 71p to 56p per pound of revenue across the same window.

OpenAI files confidential S-1 targeting $852 billion to $1 trillion IPO

OpenAI confidentially filed S-1 paperwork with the SEC on Friday 22 May, with Goldman Sachs and Morgan Stanley leading. Targeted valuation range: $852 billion to over $1 trillion. Targeted listing window: Q4 2026, with a possible September debut. The company is generating roughly $2 billion per month in revenue and counts 50 million consumer subscribers plus 9 million business users. The confidential filing means substantive disclosures stay private until roughly 15 days before the public roadshow, and OpenAI is now subject to SEC quiet-period rules. The three-IPO timeline now reads SpaceX (June), OpenAI (September), Anthropic (October). Prediction markets put 85% probability on OpenAI listing before Anthropic.

Trump cancels AI safety executive order after Musk, Zuckerberg and Sacks calls

President Trump postponed the signing of an AI safety executive order on Thursday 21 May, telling reporters he “didn’t like certain aspects of it”. The order would have established a voluntary mechanism for AI developers to engage with federal agencies and submit advanced models for security review up to 90 days before public release. Axios, the Washington Post and NPR confirmed the same sourcing. Elon Musk, Mark Zuckerberg and David Sacks each called Trump directly on Thursday to argue the framework would damage US competitiveness against China. The signing ceremony invitations had already been issued. The substantive US-side oversight surface for the next 12 months is now CAISI’s voluntary red-team partnerships plus state-level AI legislation.

Pope Leo XIV publishes Magnifica Humanitas, the first papal encyclical on AI

The Vatican released Pope Leo XIV’s first encyclical, Magnifica Humanitas: On Safeguarding the Human Person in the Time of Artificial Intelligence, on Monday 25 May, signed by the Pope on 15 May (the 135th anniversary of Pope Leo XIII’s Rerum Novarum). Anthropic co-founder Christopher Olah attended the presentation. The encyclical is structured around four moral commitments: human dignity, labour, the common good, and what Leo calls “constructing Jerusalem rather than Babel”. The most-cited passage warns against “a race for ever more powerful algorithms and larger datasets driven by the desire to secure geopolitical or commercial dominance”. The document explicitly condemns AI in warfare, calling “the ‘just war’ theory which has all too often been used to justify any kind of war, now outdated”.

Anthropic Q2 profitability: $559 million on $10.9 billion of revenue

The Wall Street Journal’s internal-projections scoop on Wednesday 20 May is the most important data point on frontier-lab economics published in 2026. Anthropic told investors it expects a $559 million Q2 operating profit on $10.9 billion of Q2 revenue, up 130% from Q1’s $4.8 billion. The number of customers spending over $1 million annually on Claude doubled from roughly 500 to more than 1,000 between February and April. Compute costs per pound of revenue fell from 71p to 56p across the same window. Dario Amodei acknowledged at an internal event that the company “planned for 10x annual growth but saw 80x in Q1”. The cautious read is that the operating profit is being booked while $45 billion of forward compute spend has just been contractually locked in. The leading indicator (large-customer-cohort doubling in 60 days) is the part that should reshape any enterprise-AI procurement assumption built before the spring.

Chinese open-weights models hit 60% OpenRouter share at one-third Claude pricing

The CNBC investigation on Friday 22 May, sourcing Artificial Analysis benchmarks, put hard pricing numbers on the open-weights gap. A benchmark set that costs $4,811 to run on Claude Opus 4.7 runs for $1,071 on DeepSeek V4 and $544 on Zhipu GLM-5.1. Chinese open-weights models (Kimi K2.6, DeepSeek V4, GLM-5.1, Qwen 3) now account for approximately 60% of OpenRouter token volume, up from 1% in early 2024, with per-million-token pricing in the $0.28 to $0.95 range against Claude’s $3-plus. Databricks CEO Ali Ghodsi added the operational angle: open-weights routing now defaults to “whichever Chinese model is closest to my latency target this hour” for the cost-sensitive 80% of agentic workloads.

Financial Times: open-weight guardrails stripped from Meta and Google in minutes

The Financial Times investigation, published on Monday 25 May and carried by the Irish Times, showed that the Heretic tool on GitHub can strip safety guardrails from Meta’s Llama 3.3 and Google’s Gemma 3 in under 10 minutes with no specialist hardware. The technique, “abliteration”, removes the safety post-training layer. The FT working with AI safety group Alice produced a modified Gemma 3 that responded to prompts on how to disperse chlorine gas through a crowded indoor space, generated working credit-card-theft code, and wrote child-sexual-abuse stories. Heretic’s creator Philipp Emanuel Weidmann told the FT his software has been used to produce more than 3,500 “decensored” models since release last year, with the modified variants downloaded 13 million times. The story landed two days after Trump killed the federal pre-release review framework.

Karpathy joins Anthropic to use Claude to accelerate Claude pretraining

Andrej Karpathy, OpenAI co-founder and former Tesla Autopilot lead, joined Anthropic’s pretraining team on Tuesday 19 May. Karpathy will work under team lead Nick Joseph to build a team that uses Claude itself to accelerate pretraining research. His framing on X: “I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D.” The strategic frame Anthropic is pushing in TechCrunch, CNBC and Axios is that AI-accelerated research is now a more important competitive differentiator than raw compute capacity. The senior AI-research talent pool is genuinely contested across at least four destinations (Anthropic, Thinking Machines, Safe Superintelligence, Meta Superintelligence Labs).

Anthropic’s Mythos rollout moves from US labs to Japanese finance

Japan’s planned access to Claude Mythos shows how advanced models are moving from lab capability to regulated financial infrastructure. Reported access for MUFG, SMBC and Mizuho follows government and regulator discussions over cybersecurity risk, especially Mythos’s ability to find software vulnerabilities. The notable point is governance: Anthropic is not broadly releasing the model, but selectively deploying it through public-private channels where cyber upside and misuse risk both matter commercially materially.

Tech, Tools and Releases

Google I/O 2026 ships Gemini 3.5 Flash and an agent-first Search

Google’s I/O 2026 keynoteac anchored on Gemini 3.5 Flash, generally available via Google Antigravity, the Gemini API and Android Studio at $1.50 input / $9 output per million tokens. Google is claiming a 12x speed advantage versus comparable frontier models inside the Antigravity 2.0 agent harness. The model leads on coding and agentic-task benchmarks but trails on knowledge-breadth evaluations against Claude Opus 4.7 and GPT-5.5. Strategically, Google is choosing distribution over benchmarks: Gemini 3.5 Flash is now the default model in AI Mode in Search globally, with generative UI (custom interactive visuals, tables, graphs and simulations) rolling out through summer. Robby Stein, VP of Product for Search, framed it as the end of the “10 blue links era”.

Spotify pushes deeper into AI-generated audio and knowledge tools

• Spotify introduced AI-powered podcast Q&A, summaries and personalised listening briefs, letting users interrogate long-form shows rather than simply listen to them. The move turns podcasts into searchable, reusable knowledge assets.

• Spotify launched an ElevenLabs-powered audiobook creation tool for authors and publishers, using synthetic voices to reduce the cost and time involved in producing long-form audio content.

• Spotify also rolled out a NotebookLM-style app that lets users ingest content and generate AI-based notes and insights, extending its ambitions beyond streaming into AI-assisted research and learning.

Google announces Gemini Omni, Google Pics and Universal Cart.

Omni is the “any modality in, any modality out” model starting with video; Pics is generative image creation and editing built on Nano Banana; Universal Cart follows the user across Search, Gemini, YouTube and Gmail, putting Google directly into the Alexa for Shopping competitive lane.

Figma turns the design canvas into an AI workspace

Figma’s new in-canvas agent can generate design layers, explore multiple directions, apply feedback, make bulk edits and stay aligned with a team’s components, tokens and libraries. Alongside custom Make skills and improved design-to-code workflows, the update moves Figma beyond prompt-based prototyping into AI-assisted product development. For teams, the useful shift is consistency: agents can now work inside existing design systems, not around them, while preserving designer control over output quality.

Google pushes Gemini down the cost curve

Google’s Gemini 3.5 Flash shows how the model market is shifting from “biggest model wins” to price-performance. Google says Flash beats Gemini 3.1 Pro across most benchmarks while running four times faster, with broad availability across Antigravity, AI Studio, Android Studio, Gemini Enterprise and Search. For businesses, the implication is simple: more agentic and multimodal workloads are becoming viable at production scale, not just in demos.

Alibaba makes Qwen’s strongest agent model proprietary

Alibaba’s Qwen team has released Qwen3.7-Max, a proprietary model built for long-running agent work, with claimed leads on Terminal-Bench 2.0-Terminus, SWE-Pro, SciCode and several general-agent benchmarks. The important shift is strategic as much as technical: Qwen remains influential in open-weight development, but Alibaba now appears to be reserving its strongest agent and coding capabilities for closed API-based models, pushing Qwen directly into Claude, Gemini and GPT territory.

Quick Hits

• Pentagon awards classified-network AI contracts to seven vendors. OpenAI, Google, Microsoft, AWS, Nvidia, SpaceX and Reflection AI replace the $200 million July 2025 Anthropic agreement. Anthropic was formally excluded after declining the “all lawful purposes” clause on domestic surveillance and autonomous-weapons grounds. Defence Secretary Pete Hegseth’s March “supply chain risk” designation is the contractual mechanism.

• Sam Altman walks back the AI jobs apocalypse in Sydney. Speaking at a Commonwealth Bank of Australia conference on Tuesday 26 May, Altman said “I’m delighted to be wrong... I thought there would have been more impact on entry-level white-collar jobs being eliminated by now than has actually happened”. The walk-back retracts his earlier “30 to 40% of work tasks” framing.

• Anthropic policy paper narrows the US lead. The paper reframes the US-vs-China model-capability gap as “several months”, not the previously-assumed year, formalising the working assumption for capability-elicitation timelines across the policy stack.

• Anthropic Coordinated Vulnerability Disclosure dashboard updated 22 May. 1,596 vulnerabilities disclosed across 281 open-source projects, with 97 patched to date. The dashboard is the cleanest public artefact of the Claude Mythos and Project Glasswing programme.
  

  We work with leadership teams to move from experimentation to execution safely, commercially, and at speed. Talk to us.
  

Executive Summary

Another busy week in the world of AI - if i was to give the week a theme it would be “Agents”.

The first shift was commercial. Ramp’s latest AI Index showed Anthropic overtaking OpenAI on paid business adoption for the first time, with Anthropic at 34.4% of paying businesses versus OpenAI at 32.3%. That does not mean OpenAI is losing the market, but it does mean the default procurement assumption has changed. Anthropic is no longer the “technical alternative”; it is becoming a mainstream enterprise option, helped by stronger business adoption, a reported $30 billion annualised run-rate and the launch of Claude for Small Business.

The second shift was infrastructure. Nvidia’s latest results suggested AI demand is still compounding rather than plateauing, while Cerebras’ IPO performance and inference-speed claims show how aggressively investors and customers are looking for alternatives to the GPU-heavy status quo. The message is not that Nvidia is about to be displaced. It is that AI infrastructure is now a strategic market in its own right, with speed, cost and availability becoming board-level questions.

The third shift was product architecture. Google I/O pushed Gemini deeper into Search, Android, Workspace, Chrome and developer tools, while Cursor, Amazon, Apple, Microsoft and Cerebras all pointed in the same direction: AI is moving (at pace) from chat windows into workflow layers. The practical implication for software vendors is uncomfortable. Lightweight AI features bolted onto legacy products will be harder to defend if larger platforms can place capable agents directly into the tools customers already use.

For business leaders, the takeaway is simple: re-run your AI vendor assumptions, test where agents could replace manual workflows, and be sceptical of product roadmaps that treat AI as a feature rather than a new operating layer.

What to Try This Week

What to Try This Week

Run a one-hour “agent exposure” review of one workflow in your business.

Pick a process that still involves people moving information between systems: preparing a sales proposal, updating a CRM, reconciling invoices, triaging support tickets, summarising customer calls, drafting board materials or checking supplier risk.

Then ask three questions:

1. Which steps require judgement, and which are just retrieval, formatting, checking or routing?

2. Which systems does the person need to access to complete the task?

3. If an AI agent could safely read those systems and take limited actions, what would still need human approval?

The aim is not to automate the workflow this week. The aim is to understand where the boundary sits between “AI as assistant” and “AI as operator”.

For most businesses, the opportunity will not start with replacing a whole job. It will start with removing the repetitive hand-offs inside a job. That is why this week’s Google, Anthropic, Microsoft, Cursor and Amazon announcements matter: the agent layer is moving closer to the tools your team already uses.

Geopolitics, Governance and Big Moves

Anthropic overtakes OpenAI on business AI adoption

Ramp’s April AI Index showed Anthropic taking the paid business-adoption lead from OpenAI for the first time, 34.4% against 32.3%, with Anthropic’s share rising 3.8% in April while OpenAI’s fell 2.9%. Across twelve months Anthropic quadrupled its share while OpenAI’s grew 0.3%. Ramp economist Ara Kharazian called it “a stunning reversal in the competitive market dynamics for AI model providers”. The data tracks 50,000-plus US firms’ card-and-invoice spend, so big transformation deals are not in the sample, but the trend break is unambiguous.

Cerebras Day 1 closes up 68% at a $94 billion market cap

Cerebras’ first day of public trading on Friday closed at $311.07, up 68% from the $185 IPO price set 24 hours earlier, putting the company at roughly $94 billion in market value, more than 2.3 times its $40 billion IPO valuation. The IPO raised $5.55 billion at 20 times oversubscription, making it the biggest US IPO of 2026 so far. The forward multiple is rich: Cerebras is projected to roughly quadruple revenue from $800 million this year to $3.2 billion next year against heavy OpenAI customer concentration. Investors paid the premium regardless. The public-market filter on AI-infrastructure messiness has plainly weakened.

Nvidia’s results say the AI infrastructure boom is still accelerating

Nvidia’s latest results landed like another stress test for the “AI bubble” argument. Q1 FY2027 revenue hit $81.6 billion, up 85% year on year, with data centre revenue reaching $75.2 billion. The bigger signal was forward-looking: $91 billion of Q2 guidance, a higher dividend and an $80 billion buyback. Demand for AI infrastructure is not plateauing yet; it is still compounding.

Karpathy joins Anthropic’s frontier research team

Andrej Karpathy, OpenAI co-founder and former Tesla AI lead, has joined Anthropic’s pre-training team. The move is significant because pre-training is where the core capability of frontier models is built, and Anthropic says Karpathy will work on using Claude to accelerate that research. It is another signal that Anthropic is not just winning enterprise attention; it is also attracting the research talent needed to keep pushing Claude forward.

OpenAI fights governance pressure on three fronts at once

Three escalations landed inside a single 24-hour window on Thursday. The Information’s Aaron Tilley reported OpenAI’s extraordinary decision to issue a public legal threat against Apple as both companies prepare to compete in AI hardware. A fresh court filing in the Musk v Altman trial revealed Sam Altman holds more than $2 billion in personal stakes in companies that have done business with OpenAI, fresh material for the House Oversight investigation. And Microsoft executive Michael Wetter testified that Microsoft’s commercial commitments to OpenAI will exceed $100 billion by the end of its June fiscal year. Martin Peers’ read: the “never-ending series of dramas” is now hurting OpenAI’s standing as a reliable enterprise partner.

OpenAI clears Musk lawsuit, but the governance questions remain

A California jury unanimously rejected Elon Musk’s lawsuit against OpenAI, finding he waited too long to bring the case. The ruling removes a major legal overhang as OpenAI prepares for a possible IPO. Musk plans to appeal, so the dispute is not finished, but OpenAI has won the immediate round.

Microsoft’s $100 billion-plus OpenAI exposure now publicly quantified

Wetter’s Thursday testimony nailed down a figure that had previously been triangulated. Microsoft’s commercial commitments to OpenAI will exceed $100 billion by the end of its June fiscal year: the $13 billion equity stake plus training and data-centre infrastructure. Read alongside Microsoft’s $30 billion of OpenAI-related revenue between 2023 and 2025, the relationship is recalibrating from joint venture to a highly profitable but increasingly narrow commercial partnership. With Microsoft shares down 16% year-to-date and TCI having exited its $8 billion stake, activist pressure is rising. Peers’ parallel to ValueAct’s 2013 playbook, which culminated in Steve Ballmer’s exit and Satya Nadella’s appointment, looks more plausible by the week.

Microsoft MDASH beats Anthropic’s Mythos on the CyberGym benchmark

Microsoft unveiled MDASH on Thursday: a multi-agent AI security harness chaining more than 100 specialised agents in a scan, debate and validate pipeline. One set of agents scans code for potential vulnerabilities, a separate group debates whether each finding is real and exploitable, and a final stage builds proof-of-concept attacks. In its initial deployment MDASH found 16 flaws across Windows and surpassed Anthropic’s Claude Mythos on the CyberGym benchmark. This is the first non-frontier-lab system to win that benchmark and the cleanest signal yet that multi-agent architectures with explicit debate-then-validate steps outperform monolithic frontier models on offensive-cyber tasks. Britain’s AI Safety Institute confirmed the same day that AI cyber capability is doubling every 4.7 months.

Tech, Tools and Releases

Claude for Small Business ships an SMB-ready package

Anthropic launched Claude for Small Business on Thursday, a bundle of connectors and ready-to-run workflows embedding Claude inside QuickBooks, PayPal, HubSpot, Google Workspace and Microsoft 365, plus new tools for payroll, invoices and campaigns. Anthropic was traditionally the developer-tooling specialist; pairing this with the same-day Ramp data showing Anthropic ahead of OpenAI on business adoption signals a deliberate push into the operator tier where ChatGPT had been dominant. Paid Claude plans will also receive a dedicated monthly credit for programmatic usage starting 15 June, packaging API usage into existing subscription tiers.

Google I/O turns Gemini into an agentic operating layer

Google I/O was not just another model launch. It was Google pushing Gemini deeper into the surfaces where work already happens: Search, Android, Workspace, Chrome, developer tools and, eventually, smart glasses. Gemini 3.5 Flash is being positioned for faster agentic and coding workloads, while Gemini Omni expands Google’s multimodal ambitions. The bigger signal is Antigravity and Managed Agents, which make it easier for developers to build, orchestrate and deploy agents that can use tools, run code and complete multi-step tasks. For software vendors, the warning is clear: lightweight AI features bolted onto legacy products will be harder to defend.

Adaption’s AutoScientist automates frontier-style fine-tuning

Adaption, the startup founded by former Cohere VP of Research Sara Hooker, introduced AutoScientist on Thursday: a system that automatically customises AI models for specific jobs by adjusting both training data and training settings, iterating until the model meets the goal. In internal tests AutoScientist outperformed expert-tuned models by 35% on average, with success rates jumping from 48% to 64% across finance, legal, medical and five other industries. The strategic point: only a few thousand people globally can properly fine-tune a frontier model, and nearly all of them work at the same handful of labs. If that expertise can be automated, custom per-business models become practical to build at scale.

Amazon’s Alexa for Shopping replaces Rufus and takes over the search bar

Amazon’s full Alexa for Shopping rollout details landed on Thursday across The Rundown, TLDR and Martin Peers’ Briefing. The product replaces Rufus, takes over the Amazon.com search bar, and draws on Amazon’s catalogue, reviews, delivery timing, past purchases and Alexa voice conversations to answer questions, run side-by-side comparisons, track pricing and Auto-Buy items when prices hit a target. A new Buy for Me flow handles checkouts on non-Amazon stores; Scheduled Actions auto-restocks products on a cadence. For UK retailers, the open question is what the SEO and product-catalogue playbook looks like when most shopping queries are agent-mediated.

Apple readies AI agents in the App Store ahead of WWDC

The Information’s Aaron Tilley reported on Thursday that Apple is exploring how to welcome AI agents into the App Store while preserving its privacy and security positioning. The piece described concrete policy work on letting AI agents follow Apple’s privacy rules and on preventing agents from spinning up smaller apps after the parent app has been approved. The companion announcement: iOS 27 will feature a redesigned Siri, a customisable Camera app for professionals and system-wide design changes, set for WWDC on 8 June. Read alongside the OpenAI-Apple legal flare-up the same week, Apple is opening App Store policy for AI agents while signalling its native AI rebuild will compete with whatever OpenAI ships in hardware.

Cerebras shows why AI speed matters

Cerebras says it has set a new inference-speed record for Moonshot AI’s Kimi K2.6, a huge open-weight model with around a trillion parameters.

In plain English: inference is the part where an AI model gives you an answer, and tokens are the chunks of text it generates. Cerebras reportedly reached 981 output tokens per second, meaning the model could respond far faster than the official version. That is news worthy because businesses do not just want smarter AI; they want AI that can work quickly enough to power real-time apps, agents and customer-facing tools. It also strengthens Cerebras’ argument that its wafer-scale chips offer a serious alternative architecture to traditional GPU-heavy AI infrastructure.

Cursor’s Composer 2.5 shows the cost curve for AI coding is falling

Cursor has released Composer 2.5, its upgraded in-house coding model, built on Moonshot’s open-weight Kimi K2.5 checkpoint. The claim is not just better benchmarks; it is better long-running coding behaviour at far lower cost. Cursor says pricing remains $0.50 per million input tokens and $2.50 per million output tokens, making near-frontier coding performance increasingly accessible to ordinary development teams.

Quick Hits

• Anthropic CFO confirms $30 billion run-rate. Krishna Rao’s Thursday interview confirmed Anthropic at a $30 billion annualised run-rate, up from $250 million two years ago, on $75 billion raised in his tenure. WSJ separately reported investment offers arriving at $900 billion-plus valuations.

• Vercel AI Gateway production trends. 200,000-plus teams now route through the Gateway, with agentic workloads carrying 59% of all token volume. Anthropic leads in spend, Google leads in volume, and open-source models are gaining traction with no lab-loyalty.

• Microsoft hits $5.5 trillion through Nvidia’s market cap milestone. Nvidia became the first company ever to reach $5.5 trillion in market capitalisation, with the milestone coinciding with Jensen Huang’s arrival in China for Trump’s meetings with Xi Jinping. Nvidia H200 China-export delivery, meanwhile, remains at zero.

• Cisco and LinkedIn announce layoffs into AI tailwinds. Cisco cut around 4,000 jobs, roughly 5% of its workforce, “as it shifts more spending toward AI and cybersecurity” despite 12% revenue growth. LinkedIn separately laid off 5% of its 17,500-plus global staff. Layoffs.fyi is tracking 103,000-plus tech layoffs across 2026.

• ChatGPT Trusted Contact launches. OpenAI shipped a feature that lets users nominate one adult to be notified if a ChatGPT conversation suggests a potential self-harm concern. Five-step setup in Settings, rolling out worldwide within weeks. Worth raising in a team wellbeing channel.
  

  We work with leadership teams to move from experimentation to execution safely, commercially, and at speed. Talk to us.
  

Executive Summary

This was the week the AI economy started to look less like a gold rush and more like a stress test. Anthropic disclosed extraordinary revenue growth, OpenAI reportedly renegotiated billions of dollars out of its Microsoft revenue-share obligations, Cerebras pushed toward a richly valued IPO, and CoreWeave reported a level of capex that shows just how expensive the AI infrastructure race has become. Money is still moving quickly, but the questions around durability, margins and who ultimately pays are getting sharper.

Underneath the financial noise, three practical themes stood out. First, AI is now part of the cyber threat landscape, with Google documenting the first known case of AI being used to discover and write a zero-day exploit, while OpenAI launched its own cyber-defence programme. Second, AI productivity is starting to show up in workforce planning, with Monday.com joining a growing list of software companies linking headcount discipline to AI-enabled efficiency. Third, AI is becoming a real cost line, not just a software experiment, with usage-based pricing and AI compute costs beginning to appear in company commentary and customer invoices.

The practical takeaway this week is simple: AI is moving out of the innovation budget and into operating reality. If you have not yet mapped which AI tools your business is paying for, how they are priced, and where usage-based costs could compound, now is the time. That is the focus of this week’s practical exercise below.

What to Try This Week

What to Try This Week

Audit your AI usage-based pricing exposure before your next finance review. Three things this week made clear that AI is no longer just a tooling decision: it is a budget line that surprises CFOs. Monday.com flagged AI compute costs hitting gross margin. Figma reports 75% of paying customers consuming AI credits weekly. Software firms across the board are now charging per use, not per seat.

Concrete steps any operations or finance leader can take this week:

1. List every AI tool your business pays for, including embedded features inside existing software (Microsoft Copilot, Salesforce Einstein, HubSpot AI, Notion AI, Figma AI, Adobe Firefly). You are likely to miss two or three on the first pass.

2. For each, identify the pricing model: flat seat, credit-based, token-based, or hybrid. Credit and token models are where the bill compounds invisibly.

3. Pull last quarter’s actual usage versus the included allowance. Anything above 80% utilisation is a renewal-cycle problem already.

4. Set a monthly cap or alerting threshold on every usage-based product. Most platforms have this buried in admin settings; it is rarely on by default.

5. Brief your finance team on the new line item. “AI cost of revenue” is now a thing that shows up on quarterly calls. Better for them to hear it from you than from your audit.

This is a half-day exercise for most SMEs and a one-day exercise for mid-market. Worth doing this week before the next purchase order or auto-renewal goes through unexamined.

Geopolitics, Governance and Big Moves

Anthropic discloses 80x revenue growth at $44B annualised

CEO Dario Amodei told staff that Q1 2026 revenue grew roughly 80x year-on-year, pushing annualised recurring revenue above $44B by early May, up from $9B at year-end 2025. Customers spending more than $100K a year are up 7x; those over $1M a year are rising sharply. Claude Code and a new financial-services agent product (built with JPMorgan for pitchbooks and credit memos) are the two named drivers. For context, Salesforce took roughly 20 years to reach $30B in annual revenue. Anthropic did it in under three years from a standing start.

Cerebras prices IPO at $35B valuation with 20x oversubscription

AI chip designer Cerebras Systems is expected to debut on Nasdaq on Thursday 14 May at $150 to $160 a share, up sharply from an initial range of $115 to $125. At the top end this implies a $35B valuation and as much as $4.8B raised. Demand is reportedly 20x oversubscribed. The company posted $510M of 2025 revenue, anchored by a 750MW compute deal with OpenAI. The IPO is the second major AI infrastructure listing this cycle after CoreWeave; both have benefited from public-market appetite ignoring the cash burn beneath. Investors are clearly comfortable paying for AI exposure rather than profits.

OpenAI restructures Microsoft deal, saving $97B through 2030

A previously undisclosed amendment to the OpenAI-Microsoft revenue-sharing agreement will save OpenAI as much as $97B in payments to Microsoft through 2030, per The Information. The original 20% revenue share could have totalled $135B if OpenAI hit its long-term revenue goals. The new structure leaves OpenAI on the hook for “only a fraction” of that. Microsoft, separately, has already booked more than $30B in revenue from OpenAI’s technology. The pair are recalibrating economics ahead of OpenAI’s possible $1T IPO and Microsoft’s worsening investor narrative (see below).

Pentagon signs $200M AI deals with eight firms, Anthropic excluded

The US Department of Defense signed agreements with SpaceX, OpenAI, Google, Microsoft, Nvidia, AWS, Oracle and Reflection for frontier AI capabilities. Anthropic was conspicuously omitted, despite being the largest single revenue generator in the sector. Reporting points to ongoing tensions between Anthropic and the administration over the firm’s safety stance and its restrictions on weapons-related model use. For UK and European defence primes watching the US, this is the clearest signal yet that frontier-AI procurement is becoming a political instrument. Build your supplier list with the political alignment of frontier labs in mind, not just the technical capability.

CoreWeave’s Q1 capex hits $7.7B, three times revenue

AI cloud provider CoreWeave doubled Q1 revenue to $2B year-on-year but spent $7.7B on data-centre capex in the same quarter, burning $4.7B in cash. Full-year 2026 guidance is $12-13B of revenue against $35B of capex, three times revenue. The company ended the quarter with $24.8B of debt and roughly $3B of cash, two-thirds of which came from a fresh Nvidia equity injection. Anthropic, Microsoft, OpenAI and Meta are the customer underwriters. Shares fell 15% across the day. The trade is binary: if hyperscaler AI commitments hold, CoreWeave is undervalued. If they pull back, it cannot service its debt.

Microsoft becomes the activist target, down 16% YTD

Microsoft shares are down nearly 16% year-to-date, making it the worst-performing US big tech name in 2026. UK hedge fund TCI sold “almost all” of its $8B stake, citing concern that AI threatens the Office franchise. The narrative pressure has reopened activist conversations: with SpaceX’s IPO weeks away pulling capital out of incumbents, Martin Peers has suggested a streamlining play (carving off gaming, Bing, LinkedIn) is overdue. Azure is still growing close to 40% and Copilot now sits above 20M paid seats. The fundamentals are stronger than the share price; the marketing is not.

Tech, Tools and Releases

Thinking Machines Lab debuts “interaction models”

Mira Murati’s lab broke its post-OpenAI silence with a research preview of interaction models: AI systems built for live human-AI collaboration across voice, video and text in 200-millisecond chunks. Unlike agentic systems that run autonomously and report back, interaction models are designed to be steered, interrupted and corrected in real time, with a background reasoning model handling slower work concurrently. Use cases shown include live translation, exercise coaching, and timed interventions. This is a bet that the next leap in AI is not longer autonomy, but better collaboration. Worth watching for customer-service, sales-enablement and training applications.

Google traces first AI-written zero-day exploit in the wild

Google’s Threat Intelligence Group confirmed the first publicly documented case of a hacker using AI to discover and write a zero-day vulnerability, against a widely used web management tool. Google flagged unusually polished attack code, lengthy explanation comments, and a fabricated severity score as the giveaways. Anthropic’s Rob Bair said defenders’ lead is “months, not years”. For UK ops and security leaders: the offensive use of AI is no longer theoretical or research-grade. Patch cadence, identity hygiene and two-factor authentication enforcement have a sharper urgency this quarter than last.

OpenAI launches Daybreak to rival Anthropic Mythos

OpenAI introduced Daybreak, a cyber-defence platform combining a new GPT-5.5-Cyber model with the Codex Security agent. The pitch: turn vulnerability discovery and audit-ready patching from weeks of manual work into minutes. Cloudflare, Cisco and Palo Alto Networks are launch partners. Access to GPT-5.5-Cyber is restricted to vetted “trusted defenders” running critical infrastructure who can clear strict approval by 1 June. This sits directly opposite Anthropic’s Mythos programme, which used the same offensive capability to help Mozilla find 271 Firefox vulnerabilities. The frontier labs are now selling the cure alongside the disease.

Claude Agents Learn to “Dream” Between Tasks

Anthropic has moved Claude’s “Dreaming” feature into research preview, letting managed agents review past sessions, spot recurring patterns and tidy their memory stores between tasks. The output is a separate, reviewable memory file, not a change to the model itself. The direction matters: enterprise agents are shifting from one-off assistants toward systems that learn operational preferences over time, with clearer auditability than opaque retraining loops for users and IT teams.

Elon Musk dissolves xAI into SpaceXAI division

Elon Musk announced that xAI will dissolve and be absorbed into SpaceX as a new internal division called SpaceXAI, consolidating Grok, X, and AI compute under a single entity ahead of the SpaceX IPO expected this summer. The reorganisation aligns Musk’s AI stack with his most valuable private asset and creates the simplest possible structure for institutional investors. For practical purposes: anyone with embedded Grok API integrations should expect rebranding, contract restructuring and potentially deprecation warnings over the next quarter.

Monday.com freezes headcount, blames AI productivity

Enterprise SaaS firm Monday.com told investors it expects workforce to stay “largely flat” through 2026, “reflecting the productivity gains AI is already delivering across our organisation”, after previously guiding 20% headcount growth. Shopify, Spotify, Pinterest and Uber have all said something similar in recent quarters. The pattern: companies are not yet conducting mass layoffs; they are simply not backfilling or expanding. Crucially, Monday.com also flagged rising “computing costs related to AI” hitting gross margin. The productivity is real, but it is not free. AI-driven hiring restraint is the slow-burn signal worth tracking; the unemployment numbers will lag it by quarters.

ElevenLabs deepens enterprise traction with Klarna, Revolut

Voice AI specialist ElevenLabs disclosed enterprise contracts signed in the past quarter with Deutsche Telekom, Revolut and Klarna, alongside a $500M Series D with BlackRock, Wellington, D.E. Shaw, Schroders, Nvidia and Salesforce Ventures among new investors. Crucially, this is voice AI moving out of content-creator tooling and into customer-facing operations: support agents, call-centre automation, localisation. For UK SME leaders running multi-language operations or volume customer service, voice AI is now production-ready in a way that screen-readers and IVR menus stopped being years ago.

Quick Hits

• OpenAI launches Deployment Co with $4B private equity backing: new venture acquired AI consulting firm Tomoro and adds ~150 consultants targeting enterprise AI implementation, alongside Anthropic’s parallel $1.5B JV with Blackstone, Hellman & Friedman and Goldman Sachs.

• OpenAI’s $18B Broadcom chip deal hits financing snag: Broadcom will only fund first-phase production if Microsoft commits to buying 40% of the chips; Microsoft has not yet signed.

• SAP signs Anthropic agreement: new partnership lets SAP customers build agents using Claude through a deeper product integration, a meaningful enterprise win against Microsoft and OpenAI on the SAP installed base.

• House Oversight Committee opens investigation into Sam Altman’s personal investments: scrutinising ties to OpenAI’s commercial partnerships, adding to legal and governance pressure during Musk v. Altman trial.

• Google "Omni" model leaked ahead of I/O 2026 - A UI string spotted in Gemini's video generation tab reads "powered by Omni," sparking speculation that Google will unveil a unified multimodal architecture (text, image, video, audio, reasoning) at Google I/O on May 19–20. Analysts frame this as Google's answer to GPT's unified model strategy.
  

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Executive Summary

Two themes emerged this week.

First, AI oversight is back on the agenda in Washington. After dismantling the previous administration’s AI safety framework earlier this year, the White House is now reportedly drafting an executive order that would require pre-release government vetting of frontier AI models. Microsoft, Google DeepMind and xAI also signed pre-deployment evaluation agreements with the Commerce Department’s Center for AI Standards and Innovation (CAISI) on 5 May, joining OpenAI and Anthropic. The direction of travel is clear: the most capable models are no longer being treated as ordinary software releases. They are becoming strategic infrastructure.

Second, evidence continues to grow, showing that AI adoption is moving from experimentation into operational redesign. Coinbase confirmed a workforce reduction on 5 May and directly linked the move to AI deployment across customer service, fraud and compliance. PayPal has also announced a multi-year restructuring programme alongside the creation of an internal AI Transformation Group. The important point for business leaders is not that AI automatically means layoffs. It is that investors and management teams are now starting to connect AI deployment with measurable changes to cost base, service delivery and operating leverage.

For businesses, the practical takeaway is this: AI is no longer just a productivity tool sitting at the edge of the organisation. It is beginning to reshape workflows, team structures and expectations around speed, cost and responsiveness. The companies that benefit will not be the ones that simply buy more tools. They will be the ones that understand where AI can genuinely improve repeatable work, where human judgement still matters, and where the operating model needs to change around the technology.

What to Try This Week

What to Try This Week

Run a 30-minute workflow audit with your senior team.

Pick two or three repeatable workflows where your business already spends significant time, money or management attention. Avoid the obvious AI use cases such as basic content generation. Look instead at areas such as compliance review, contract analysis, financial reporting, account research, sales operations, regulatory monitoring, customer onboarding or management reporting.

For each workflow, answer four questions:

1. What work is genuinely repeatable?

2. What data, documents or systems does the work depend on?

3. Where does human judgement add real value?

4. What could AI realistically improve today: speed, cost, accuracy, consistency, customer experience or management visibility?

The goal is not to create a redundancy plan. It is to build a clearer view of where AI could improve the business in practical terms, and where the organisation is not yet ready because the data, process or governance is too weak.

This is the useful lesson from this week’s news. AI is starting to show up in operating models, not just product demos. Businesses should respond by mapping the workflows where the technology could create measurable value, then deciding which ones are worth testing properly.

Trump Administration Reverses Course on AI Oversight

The White House is drafting an executive order for mandatory pre-release government vetting of frontier AI models, a sharp reversal of its January position. The proposal mirrors the UK’s pre-deployment safety regime and was triggered specifically by Anthropic’s Claude Mythos cyber capabilities. No timeline has been published. Industry reaction is split between welcoming the policy clarity and concern about chilling effects on US frontier development. Sources: New York Times (4 May), Wall Street Journal, Politico, CIO.

Pentagon Signs Classified-Network AI Deals with Seven Firms; Anthropic Excluded

On 1 May, the Department of Defense confirmed agreements with SpaceX, OpenAI, Google, Microsoft, AWS, Nvidia and startup Reflection AI to deploy LLMs on classified networks for “any lawful purpose.” Anthropic was excluded after refusing the unrestricted-use clause. The fine print allows the government to require adjustments to safety filters, with no contractual veto for the vendor. Constitutional and congressional scrutiny appears likely. Sources: New York Times, Associated Press, Tom’s Hardware.

NYT: Federal Safety Net “Not Ready” for AI Job Displacement

On 5 May, the New York Times argued that economists across the political spectrum agree the US federal safety net is structurally unprepared for AI-driven displacement at the scale now measurable. The piece cited Goldman Sachs research showing net 16,000 US jobs lost per month to AI substitution, with Gen Z and entry-level workers bearing the brunt. The widening entry-level wage gap is now empirically quantified, not speculative. Source: New York Times.

AMD Q1 Earnings Reset the AI Infrastructure Trade

On 5 May, AMD reported Q1 revenue of $10.3 billion (up 38% year on year), with data centre at $5.8 billion (up 57%). Q2 guidance of $11.2 billion came in well above the $10.5 billion consensus. The stock rose 13 to 16% after hours and triggered a sector-wide rally in Micron, Intel and Arista. Morgan Stanley revised its price target up the same day. The print broadens the AI infrastructure thesis beyond Nvidia in a meaningful way. Sources: Reuters, CNBC, AMD IR, EBC.

Coinbase and PayPal Cut Workforces, Cite AI Directly

On 5 May, Coinbase confirmed approximately 700 layoffs (14% of headcount), explicitly attributing the cuts to AI-driven consolidation across customer service, fraud detection and compliance. The stock rose 3.5% on the announcement. PayPal CEO Enrique Lores announced a 20% reduction over two to three years and stood up an internal AI Transformation Group led by former Walmart technology executive Anshu Bhardwaj. Investors are now treating AI-led restructuring as a margin event. Sources: New York Times, Fortune, SFGate, Bloomberg, HeyGoTrade.

Sierra Raises $950M; Cognition in Talks at $25B

Bret Taylor’s enterprise AI customer-service startup Sierra closed a $950 million Series E on 4 May, led by Tiger Global and Google’s GV with Benchmark, Sequoia and Greenoaks participating. Valuation: $15.8 billion. Separately, on 6 May Bloomberg reported that Cognition AI (maker of Devin) is in early talks at a $25 billion valuation, 2.5 times its September 2025 mark. Agentic enterprise software is now in price-discovery mode. Sources: TechCrunch, CNBC, Bloomberg, Techzine.

Anthropic Finalises Approximately $1.5B Wall Street Joint Venture

On 4 May, Anthropic confirmed a joint venture backed by Blackstone, Hellman & Friedman, Goldman Sachs and General Atlantic to embed Claude across private equity portfolio companies. The structure mirrors Palantir’s forward-deployed engineer model and positions Claude as enterprise infrastructure for a substantial share of the global buyout market. Reported total commitment is approximately $1.5 billion. This is a distribution play, not a product sale, and a significant strategic move. Sources: Reuters, Wall Street Journal, GuruFocus, Investing.com.

OpenAI Raises $4B+ for Enterprise “Deployment Company”

Within minutes of the Anthropic announcement on 4 May, OpenAI confirmed it had raised more than $4 billion from TPG, Brookfield Asset Management, Advent International, Bain Capital, Dragoneer and SoftBank for an enterprise deployment subsidiary valued at approximately $10 billion. The structure remains majority-controlled by OpenAI. The simultaneous announcements signal that enterprise distribution, not model capability, is the decisive battleground both labs now see for 2026. Sources: Times of India, Quartz, Financial Times.

Model & Platform Updates

Anthropic Lifts Rate Caps and Launches Financial Services Agents

Following the SpaceX compute deal, Anthropic announced on 6 May the removal of five-hour rate caps for Claude Pro, Max, Team and Enterprise users and lifted peak-hour limits on Claude Code for paid subscribers. On the same day, Anthropic unveiled a financial services agent suite covering KYC, compliance monitoring, trade documentation and portfolio analysis. This is an explicit play for Wall Street mandates and a direct competitive thrust against OpenAI and Bloomberg AI. Sources: Anthropic, Ars Technica, Bloomberg.

OpenAI Rolls Out GPT-5.5 Instant as ChatGPT’s New Default Model

On 5 May, OpenAI began rolling out GPT-5.5 Instant as ChatGPT’s default model for all users, replacing GPT-5.3 Instant. The update claims sharper, more concise answers, improved image and STEM performance, better web-search decisions and materially lower hallucination rates. More interesting commercially, personalization is being expanded through past chats, files and connected Gmail, with memory-source controls to show users what context shaped responses. Source: OpenAI.

Microsoft Brings Copilot Cowork to Mobile and Plugins

On 5 May, Microsoft expanded Copilot Cowork with iOS and Android access through the Microsoft 365 Copilot mobile app, plus native and custom plugins that package skills and business knowledge into reusable workflows. The update also adds Agent 365 integration for observability, security and governance, bringing Cowork closer to enterprise-grade workflow infrastructure. Important caveat: Cowork remains a Frontier preview feature, so availability and capabilities may change. Source: Microsoft.

Mistral Ships Medium 3.5 and Le Chat Agentic “Work” Mode

On 3 May, Mistral AI released a meaningful product slate: Mistral Medium 3.5 (claimed 77.6% on SWE-Bench Verified), a 128-billion-parameter flagship, async cloud-based coding sessions and an agentic Work mode in Le Chat. The product direction is squarely on production-grade agent orchestration and developer workflows, two areas where European labs have struggled to compete with US frontier players. The release reframes Mistral’s commercial positioning. Source: Mistral via AI news trackers.

xAI Releases Grok 4.3

On 1 to 2 May, xAI released Grok 4.3 with “always-on reasoning,” a one-million-token context window, lower API pricing and a Custom Voices voice cloning suite. Intelligence Index score reportedly improved by four points over Grok 4.20. The release is incremental rather than a step change, but the pricing move is the more interesting commercial signal as the cost curve continues to fall across all major frontier providers. Sources: VentureBeat, llm-stats.com.

Stripe Launches Link Wallets for AI Agents

On 2 to 3 May, Stripe introduced digital Link wallets that autonomous AI agents can use to make payments, alongside protocols built with Cloudflare enabling agents to autonomously create accounts, register domains and deploy applications. This is foundational economic infrastructure for agent-native commerce. For any business with online checkout, the relevant question is no longer whether AI agents will be customers, but whether your systems are ready for them. Source: Stripe.

Quick Hits

• Greg Brockman’s stake disclosed at approximately $30B in Musk trial: OpenAI’s president took the stand on 4 May, confirming financial entanglements with Sam Altman and a stake worth approximately $30 billion. Cross-examination focused on board independence ahead of OpenAI’s anticipated IPO.

• Musk admits xAI distilled OpenAI models: Under cross-examination on 4 May, Elon Musk admitted that xAI trained Grok by “distilling” OpenAI’s GPT models, a violation of OpenAI’s terms of service. Legal observers flagged it as a significant trial moment.

• Cathie Wood’s ARK sells AMD post-earnings: On 5 May, ARK Investment Management sold AMD post-earnings and rotated into Shopify and GeneDx, a notable read on whether AMD’s near-term upside is now priced in.

• Asia tech rally hits records on AI-led optimism: The MSCI Asia Pacific Index gained 2.3% on 4 May, the strongest single-day move since early April, with South Korea and Taiwan tech indices both up more than 4.5% on AI chip demand.
  

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Executive Summary

This was the week the AI bull market met the income statement. On Wednesday Microsoft, Alphabet, Meta and Amazon all reported in a single afternoon, the busiest earnings day in tech history. The numbers were broadly strong (Microsoft’s AI run rate hit $37 billion, up 123% year on year, and Alphabet’s Cloud cleared $20 billion in a quarter for the first time), but the market punished Meta for raising 2026 capex guidance to $125–145 billion and Amazon for soft Q2 outlook. Apple reports today. The combined 2026 AI capex commitment across the four hyperscalers is now north of $650 billion, a number that only works if revenue keeps compounding.

It was also the week three structural pillars of the AI market shifted at once. The Wall Street Journal reported on Tuesday that OpenAI has missed internal revenue and weekly-active-user targets, prompting a sharp sell-off in Oracle, SoftBank and the AI chipmakers. China’s NDRC ordered Meta to fully unwind its $2 billion acquisition of agentic AI startup Manus, the first time Beijing has forced a non-Chinese cross-border AI deal to reverse. And Microsoft and OpenAI quietly dissolved their exclusive licensing arrangement on Monday, freeing OpenAI to sell across AWS and Google Cloud and ending Microsoft’s revenue share. AWS moved within 48 hours, putting GPT-5.5, GPT-5.4 and Codex into Bedrock.

On the product side, OpenAI shipped GPT-5.5 to all paid ChatGPT and Codex tiers, DeepSeek launched the open-source V4 family running natively on Huawei chips, Mistral released Medium 3.5 with autonomous “Vibe” coding agents, and Gemini gained native file export to Word, Excel and PDF. The practical takeaway for UK leaders this week is simpler than it looks: the cost of switching AI vendors has just dropped sharply, and the menu of usable tools has widened materially. Concrete next steps below.

What to Try This Week

You do not need to pick a side in the OpenAI versus Anthropic versus Google argument to benefit from this week. Three things are worth a serious hour of testing before Friday.

1. Test GPT-5.5 on a real piece of work. At Lighthouse this is back as our fave LLM. It launched on 23 April to Plus, Pro, Business and Enterprise users in ChatGPT and Codex, with a 400K context window and a noticeably faster response mode. Greg Brockman called it “a new class of intelligence”. The honest test is not the demo videos. Give it a strategy memo, a complicated spreadsheet analysis, a long board pack, or a multi-step research brief and judge it on output. API access is still listed as “coming very soon”, so for now this is a ChatGPT-only test.

2. Pilot DeepSeek V4 on a routine workflow. Finally - DeepSeek released V4-Pro and V4-Flash as fully open-weight models on 24 April. V4-Flash runs at roughly $0.28 per million output tokens, which is a fraction of what Western frontier models cost, and the quality is now in the same conversation as GPT-5.4 on coding and reasoning benchmarks (third-party verification still pending). For non-sensitive, high-volume tasks (document summarisation, first-draft research, simple agent loops) it is worth running V4-Flash in parallel against your current model and comparing on the cost-per-acceptable-output metric. This is the first credible “drop in for cheap” option in months.

3. Use Gemini’s new direct file generation. Google rolled this out globally on 29 April. You can now ask Gemini to produce a .docx, .xlsx, .pptx, PDF or Markdown file directly from a chat prompt and either download it or save it to Drive. This eliminates the “Prompt, then paste into Word, then reformat” workflow that most teams still run. For anyone using Google Workspace, it is a free productivity upgrade available right now. Test it on something real: a one-pager from a research note, a budget table from a transcript, a draft slide outline from a brief.

For leadership teams, there is a fourth thing worth doing. The end of OpenAI–Microsoft exclusivity means your AI procurement is now a genuine competitive process for the first time. If you signed an Azure-OpenAI commitment in 2024 or 2025, ask your team to model what a multi-cloud Bedrock or Vertex deployment looks like commercially. You almost certainly have more leverage on price and terms today than you did last week.This Week’s Policy & Regulation Brief

Big Tech earnings: $650 billion of AI capex meets the question of revenue

On Wednesday 29 April, Microsoft, Alphabet, Meta and Amazon all reported in the busiest single tech earnings session on record. Microsoft beat with $82.9 billion of revenue, Azure up 40%, and disclosed an AI business now running at $37 billion annualised, up 123% year on year. Alphabet beat on every line, Cloud cleared $20 billion in the quarter for the first time, and the stock rallied. Meta beat on revenue but raised 2026 capex guidance to $125–145 billion and lost roughly 6% after hours. Amazon beat on sales but missed Q2 operating-income guidance, also down after hours. Apple reports today. The combined 2026 AI capex commitment across the four hyperscalers is now over $650 billion, the largest single-year infrastructure build in commercial history. Source: CNBC, Bloomberg, Microsoft and Alphabet earnings releases.

OpenAI misses revenue and user targets, AI infrastructure stocks sell off

The Wall Street Journal reported on Tuesday 28 April that OpenAI has missed multiple internal monthly revenue targets and has fallen short of its 1 billion weekly active ChatGPT users goal, with CFO Sarah Friar warning internally that the company may struggle to fund committed compute contracts. Oracle fell more than 6%, SoftBank dropped roughly 10% in Asia and Nvidia, AMD and Broadcom all closed 3–5% lower on the day. Friar is also reportedly at odds with Sam Altman over the aggressiveness of the planned 2026 IPO timeline at the company’s $852 billion valuation. The story is significant for two reasons: it is the first crack in the OpenAI growth narrative, and it strengthens the negotiating position of any enterprise sitting in front of an OpenAI commercial team this quarter. Source: WSJ, CNBC, Reuters.

China blocks Meta’s $2 billion Manus deal in first cross-border AI forced unwind

On 27 April China’s National Development and Reform Commission ordered Meta to fully unwind its $2 billion acquisition of agentic AI startup Manus, registered in Singapore but founded by Chinese entrepreneurs. The order was issued under foreign-investment security rules, with the threat of penalties and individual criminal exposure for non-compliance. Manus employees are already integrated into Meta’s Singapore team and Tencent and HongShan Capital have already received deal proceeds, making the practical reversal extraordinarily complex. This is the first time Beijing has forced the unwind of a closed cross-border AI deal, and it sets a precedent any Western buyer of a Chinese-founded AI asset will now have to price in regardless of where the target is incorporated. Source: NYT, Bloomberg, Fortune.

Microsoft and OpenAI dissolve exclusive partnership

On Monday 27 April Microsoft and OpenAI announced a fundamental rewrite of their partnership. Microsoft’s licence to OpenAI IP runs to 2032 but is now non-exclusive. OpenAI can sell its products across AWS, Google Cloud and any other platform. Microsoft will no longer pay revenue share to OpenAI, and OpenAI’s outbound revenue share to Microsoft is capped through 2030. The AGI trigger clauses that previously could have altered the partnership are removed. AWS moved within 48 hours, putting GPT-5.5, GPT-5.4 and Codex into Bedrock as a limited preview, the first time Anthropic-aligned AWS has stocked OpenAI frontier models. For enterprise buyers this is the structural change of the week: the OpenAI–Azure lock-in is over, and procurement leverage just shifted. Source: Reuters, CNBC, NYT, AWS newsroom.

Google commits up to $40 billion to Anthropic; Cohere–Aleph Alpha merge into a $20 billion sovereign-AI play

Google announced on Friday 24 April that it will invest up to $40 billion in Anthropic ($10 billion immediately at a $350 billion valuation, $30 billion contingent on performance milestones), plus 5 gigawatts of compute starting 2027. Combined with Amazon’s $25 billion commitment the previous week, Anthropic has now secured roughly $65 billion of fresh hyperscaler capital in eight days against an annualised revenue run rate it says has crossed $30 billion. Separately, Canadian Cohere announced a merger with Germany’s Aleph Alpha, valuing the combined sovereign-AI champion at around $20 billion and backed by Schwarz Group’s $600 million Series E. The two deals confirm the same trend: enterprises and governments outside the US are willing to pay a meaningful premium for AI providers they perceive as data-sovereign. Source: Bloomberg, NYT, TechCrunch, Business Wire.

Meta and Microsoft cut roughly 17,000 jobs while AI capex doubles

On 23 April Meta confirmed cutting 8,000 roles, around 10% of headcount, with effect from 20 May, and froze a further 6,000 open roles. Microsoft simultaneously offered voluntary buyouts to about 8,750 US employees, around 7% of US headcount. Both companies tied the cuts explicitly to AI-driven efficiency gains and rising capital expenditure on infrastructure. Across the big four hyperscalers, more than 92,000 tech employees have been let go in 2026 to date. The labour signal is now hard to dismiss as one-off restructuring; it is becoming policy. UK boards should expect the question of “where could AI replace headcount” to land on the next operating-review agenda whether they raise it or not. Source: CNBC, BBC, Fortune.

Intel surges 20%+ on AI chip demand; Nvidia briefly tops $5 trillion

Intel reported Q1 revenue of $13.6 billion, up 7% year on year, with data centre and AI segment revenue up 22% to $5.1 billion. The stock jumped more than 20% the next day and traded above its dot-com era peak; the US government’s roughly 10% stake (acquired for $8.9 billion in 2025) is now worth around $35 billion. Tesla also confirmed Intel’s 14A process as the manufacturing partner for the AI5 chip in its $20–25 billion Terafab project with SpaceX and xAI. Nvidia, meanwhile, briefly crossed a $5 trillion market capitalisation on 24 April, the first company to do so, before giving back roughly 1% on the OpenAI revenue news. Source: CNBC, Intel newsroom, Reuters.

Musk versus Altman trial opens, with consequences for the OpenAI IPO

Opening arguments in Elon Musk’s lawsuit against OpenAI, Sam Altman and Greg Brockman began in Oakland federal court on Monday 28 April. Musk is seeking $134–150 billion in damages and the dissolution of OpenAI’s for-profit structure, claiming Altman betrayed the founding non-profit mission. Both Musk and Altman testified during the week; Satya Nadella is on the witness list. The trial is expected to run roughly four weeks. A Musk win would force structural changes at OpenAI and almost certainly delay the IPO targeted for late 2026; even a partial win could leak materially damaging internal documents. For enterprises sitting on multi-year OpenAI commitments, this is a litigation-risk line item that needs explicit board acknowledgement. Source: NYT, AP, NPR.

Anthropic Claude Mythos accessed by unauthorised users; supply-chain vulnerability exposed

Anthropic disclosed on 21–22 April that an unauthorised group had accessed Claude Mythos, its most powerful and tightly-gated cybersecurity model, via a third-party contractor’s credentials combined with data from a separate breach at AI recruiter Mercor. Mythos is the model Anthropic itself describes as capable of finding and exploiting vulnerabilities in every major operating system and browser, and which prompted an emergency US Treasury–Federal Reserve summit with bank CEOs earlier in the month. There is no evidence yet of malicious use, but the incident exposes a structural problem: even heavily restricted “red-team” access cannot be fully contained once distributed across vendors. For UK CISOs, the lesson is to ask not just which AI vendors you use, but which AI vendors your AI vendors use. Source: Bloomberg, BBC, SiliconAngle, WSJ.

Anthropic acquires Coefficient Bio for $400 million

On 28 April Anthropic confirmed an all-stock acquisition of stealth biotech AI startup Coefficient Bio for around $400 million. Coefficient has fewer than ten employees and was founded by two former Genentech computational drug-discovery scientists. The price tag for an eight-person scientific startup is the headline, but the strategic point is that Anthropic is following OpenAI (Novo Nordisk partnership, GPT-Rosalind) and Google DeepMind into vertical scientific AI, where the competitive moat is access to specialised data and domain expertise rather than raw model scale. UK life-sciences and biotech firms not yet engaged in conversations of this kind should expect the competitive gap to widen quickly. Source: LinkedIn (Steve Torso), Anthropic.

Model & Platform Updates

OpenAI ships GPT-5.5 to all paid ChatGPT and Codex tiers

OpenAI released GPT-5.5 on 23 April, available to Plus, Pro, Business, Enterprise and Education users in ChatGPT and Codex. The model has a 400K context window in Codex, a 1.5× faster “Fast mode”, reported scores of 84.9% on the GDPVal 44-occupation benchmark and 78.7% on OSWorld-Verified for autonomous computer use. Greg Brockman framed it publicly as “a new class of intelligence... way more intuitive”. API access is officially “coming very soon” but not yet live, and OpenAI has classified GPT-5.5 as “High” cybersecurity risk rather than “Critical”. For UK teams already paying for ChatGPT Business or Enterprise, this is a free upgrade worth testing on real work this week. Source: OpenAI blog, TechCrunch, CNBC.

DeepSeek V4 launches open-source, on Huawei silicon, at a fraction of frontier cost

On 24 April Chinese lab DeepSeek released V4-Pro (1.6 trillion parameter mixture-of-experts, 49 billion active, 1 million-token context) and V4-Flash (284 billion parameters total, 13 billion active) as fully open-weight models. Both are optimised to run natively on Huawei Ascend chips, support hybrid attention for long context, and ship at prices roughly 99% below comparable frontier APIs (Flash is around $0.28 per million output tokens). DeepSeek claims V4-Pro is now competitive with GPT-5.4 on coding and reasoning benchmarks. Independent verification is still pending and the political risk of routing data through DeepSeek is non-trivial, but for non-sensitive, high-volume workloads V4-Flash is the most credible cost-disruptive option since the original DeepSeek V3. Source: DeepSeek API docs, WSJ, TechCrunch.

Mistral releases Medium 3.5 with autonomous “Vibe” coding agents

On 29 April French lab Mistral launched Medium 3.5, a 128 billion parameter dense model in public preview, with a 256K context window and 77.6% on SWE-bench Verified. The release is paired with Vibe, a remote cloud coding agent that runs in isolated sandboxes, executes multi-step coding tasks asynchronously and can open GitHub pull requests autonomously. Pricing is $1.50 per million input tokens and $7.50 per million output. For development teams already evaluating Cursor, Codex or Claude Code as autonomous coding tools, Mistral now offers a credible European alternative with stronger data-sovereignty positioning, which matters for any organisation worried about EU AI Act exposure or UK public-sector procurement. Source: mistral.ai.

Gemini gets direct file generation; AWS Bedrock adds OpenAI; Anthropic ships persistent memory

Three smaller-but-immediately-useful product updates landed this week. Google rolled out direct file generation in Gemini globally on 29 April: users can now produce Word, Excel, PowerPoint, PDF and Markdown files directly from a chat prompt and save them to Drive. AWS, on 28 April, made GPT-5.5, GPT-5.4 and Codex available inside Bedrock with enterprise-grade IAM, PrivateLink, guardrails and CloudTrail logging, the first time AWS has stocked OpenAI frontier models. Anthropic moved Claude Managed Agents to public beta with persistent memory: agents now retain learning across sessions in exportable filesystem files, with multi-agent memory sharing, audit logs and rollback. Early enterprise adopters Netflix and Rakuten reported a 97% reduction in first-pass errors in document workflows. Source: Google blog, AWS newsroom, Anthropic.

Google TurboQuant: 6× memory reduction and 8× speedup at zero accuracy cost

Presented at ICLR 2026 in early April but gathering enterprise attention this week, Google’s TurboQuant algorithm uses PolarQuant vector rotation and Quantized Johnson-Lindenstrauss compression to quantise the KV cache to 3 bits with no measurable accuracy loss. Result: roughly 6× reduction in memory overhead and 8× speedup in attention computation. The implication for enterprise AI economics is significant. If TurboQuant-class techniques are deployed at scale across hyperscaler inference fleets, the unit cost of running frontier models could fall sharply over the next 12 months, which in turn changes the maths of every per-seat enterprise AI contract you have signed. Worth flagging to your CIO. Source: Google Research, ICLR 2026.Quick Hits

• Nvidia briefly tops $5 trillion market capitalisation: First company in history to do so, on 24 April; the stock has risen 20% in April alone.

• Taylor Swift trademarks her voice: Filed three US trademark applications on 28 April covering voice soundbites and Eras Tour imagery, the first attempt by an artist to use trademarked sound marks to defend against AI deepfakes; widely viewed as a test case for celebrity and executive voice IP.

• Lovelace AI exits stealth: Founded by Andrew Moore (former head of Google AI), launched “Elemental”, a context-engine builder that sits between AI agents and enterprise data systems for high-stakes industries.

• IBM launches Bob: A general-availability AI development partner spanning the full software development lifecycle, internally piloted with 80,000+ IBM employees and now sold as SaaS with a 30-day trial.

• Lightelligence IPO debut: Photonics AI chipmaker priced and traded up nearly 400% on 28 April, raising around $310 million; signals investor appetite for optical-interconnect plays beyond the Nvidia trade.

• Cursor opens TypeScript SDK: Public beta of an SDK that lets developers build custom coding agents using the same runtime, harness and frontier models that power Cursor itself.
  

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Executive Summary

Three things happened this week that any UK board should have on its agenda by Monday. First, the UK Government sent an open letter to business leaders on 15 April warning that AI systems can now find cyber vulnerabilities at “unprecedented speeds”, and told boards to treat AI cyber risk as a governance-level issue. Second, Anthropic disclosed that its Claude Mythos model can independently find and exploit flaws in every major operating system and browser, prompting an emergency meeting between the US Treasury, the Federal Reserve and major bank CEOs. Third, the clock to EU AI Act enforcement is now inside 100 days: the bulk of high-risk obligations become binding on 2 August 2026.

Beyond cyber and compliance, the week was extraordinary on deals and on releases. Amazon lifted its Anthropic commitment to $25 billion in exchange for a $100 billion, ten-year AWS contract, the biggest cloud lock-in in AI history. SpaceX took a $60 billion option on Cursor, rewriting the AI coding market overnight. Tim Cook confirmed his departure as Apple CEO, handing over to John Ternus with an explicit AI mandate. On the tools front, Anthropic shipped Claude Opus 4.7 and launched Claude Design, OpenAI rolled ChatGPT Images 2.0 to every paid plan and gave Codex full desktop-agent powers, and Moonshot released Kimi K2.6, the most capable open-source agentic model to date.

The practical takeaway: elevate AI cyber risk to a board agenda item this week and close your readiness gap against the EU AI Act. Concrete next steps are in “What to Try This Week” below.

What to Try This Week

You do not need a full AI transformation programme to get value from this week’s releases. Three tools stood out in real use. Claude Opus 4.7 is noticeably stronger for complex writing, reasoning and coding tasks. ChatGPT Images 2.0 is materially better at producing usable visuals and handling text in-image. Claude Design is the surprise package: early, but already good enough to test for first-draft slides, one-pagers and visual concepts.

1. Test Claude Opus 4.7 on a real piece of work
Give it something that actually matters: a board memo, a strategy note, a market summary or a messy spreadsheet analysis. The improvement is most obvious on multi-step tasks where weaker models lose structure or nuance. Judge it on output quality, not on benchmarks.

2. Compare ChatGPT Images 2.0 against your current design workflow
Use it to create a LinkedIn graphic, newsletter image, event visual or presentation slide concept. It is not just better at aesthetics; it is also better at following instructions and rendering text cleanly. For many teams, that may be enough to reduce reliance on separate image tools for everyday work.

3. Give Claude Design one hour of serious testing
Ask it to turn a rough idea into a one-pager, sales leave-behind, prototype or presentation concept. It is not replacing a designer for high-stakes brand work, but it is already good enough to accelerate first drafts and reduce blank-page time for commercial teams.

This Week’s Policy & Regulation Brief

UK Government warns boards: elevate AI cyber risk now

On 15 April the UK Government published an open letter to business leaders warning that AI systems are finding vulnerabilities in cyber defences at “unprecedented speeds”, and instructing boards to treat AI cyber threats as a governance-level priority rather than a security-team problem. The letter was co-signed by ministers and echoed by techUK. It is the clearest signal yet that Whitehall expects UK boards to have tested their cyber posture against AI-augmented threats. Treat it as a compliance prompt, not a press release.

EU AI Act: 100 days to enforcement, and Brussels is under siege

The bulk of high-risk obligations under the EU AI Act become enforceable on 2 August 2026, covering risk management, data governance, transparency, human oversight and post-market monitoring. Fines can reach €35 million or 7% of global turnover. In parallel, Amnesty International and civil-society groups sent an open letter on 15 April arguing that the Act’s Omnibus simplification process is gutting core safeguards. Any UK business selling into the EU, or processing EU data, should assume the rules stick as written.

Apple transition: Tim Cook out, John Ternus in, AI the defining job

Apple confirmed on 20 April that Tim Cook will step down as CEO on 1 September. John Ternus, SVP of Hardware Engineering and a 25-year Apple veteran, takes over with the explicit remit of fixing Apple’s AI strategy. AI chief John Giannandrea left earlier in April and his responsibilities have been split across Federighi, Cue and Khan. Apple’s $4 trillion valuation now rests on whether Ternus can close the AI gap. For enterprise IT teams, expect iOS and macOS AI changes to arrive faster than planned.

Amazon locks Anthropic into AWS with $100bn ten-year deal

Amazon announced a fresh $5 billion investment in Anthropic, with up to $20 billion more to follow on commercial milestones, taking total commitment to $33 billion. In return, Anthropic has committed to spend over $100 billion on AWS over the next decade and secured up to 5 gigawatts of Trainium compute. For buyers, this is structural: Anthropic’s infrastructure future is now Amazon’s. It reinforces the case for multi-provider AI procurement and changes the posture between AWS and Microsoft in any enterprise AI deal.

SpaceX takes $60bn option on Cursor, reshapes the coding stack

On 21 April SpaceX announced a partnership with Cursor, the AI coding startup, with an option to acquire it for $60 billion or pay $10 billion for joint development work by year-end. Cursor was valued at $2.5 billion fifteen months ago. OpenAI had reportedly tried and failed to buy the company first. The deal brings Cursor into the Musk empire alongside xAI and Colossus, the 200,000-GPU supercomputer, and will force Microsoft, OpenAI and Google to respond on developer tooling pricing.

Maine passes first US statewide data centre moratorium

Maine’s legislature has approved an 18-month moratorium on new data centres requiring more than 20 megawatts, citing energy grid strain. Governor Janet Mills has a short window to sign or veto. At least 12 other states are weighing similar bills. This is the first serious US planning constraint on AI compute build-out and it lands at exactly the moment Big Tech has committed over $650 billion in AI capex for 2026. Expect the UK grid-capacity debate to intensify in parallel.

Anthropic’s Claude Mythos triggers Treasury-Fed bank summit

Anthropic disclosed that Claude Mythos, currently restricted to a consortium of around 40 partners including JPMorgan, can independently find and exploit vulnerabilities in every major operating system and browser. In testing it identified a 27-year-old OpenBSD flaw. Treasury Secretary Scott Bessent and Fed Chair Jerome Powell convened an emergency meeting with US bank CEOs in response. The UK Government’s 15 April letter landed in the same week. If you run critical systems, this is no longer an abstract risk; it is a board reporting line that needs a named owner.

Model & Platform Updates

Anthropic ships Claude Opus 4.7

Released on 16 April, Claude Opus 4.7 is a direct upgrade on 4.6 with stronger software engineering performance, a new “xhigh” reasoning effort setting for complex multi-step work, and a three-times jump in multimodal resolution to 3.75 megapixels per image. A tokeniser change means prompts use slightly fewer tokens for the same content. For teams already building on Claude, it is a drop-in upgrade worth testing this week, particularly if you are running coding agents or processing diagrams, whiteboards, and document scans at scale.

Claude Design: Anthropic steps onto Figma and Canva’s turf

Anthropic launched Claude Design on 17 April as a research preview, a product for creating visual work including prototypes, slides, presentations and one-pagers directly from natural language. It turns a prompt into editable visual output and targets the workflow space currently owned by Figma, Adobe Express and Canva. Early reviews have been strong. For marketing, sales and product teams, this is worth an hour of play this week: if the output is half as good as the demos suggest, it reshapes who needs a designer in the loop.

OpenAI Codex becomes a full desktop agent, but not in the UK

OpenAI’s April Codex update transforms the product into a desktop automation agent. It now operates Mac apps autonomously with its own cursor, remembers preferences, schedules multi-day work, runs parallel agents, and pulls in 90-plus new plugins across Atlassian, GitLab and Microsoft Suite. It also generates images via gpt-image-1.5. Crucially, the desktop-agent feature is not yet available in the EU or UK. That gap is a direct consequence of the AI Act and UK regulatory posture, and worth flagging to anyone on your team running Codex workflows.

ChatGPT Images 2.0 rolls out to every paying plan

OpenAI pushed ChatGPT Images 2.0 to all paid ChatGPT tiers on 21 April, replacing the previous image generator across Plus, Team, Enterprise and Edu. The new model produces materially sharper, more photorealistic output and handles text inside images considerably better than before, which was the most common complaint about the prior version. For anyone using ChatGPT in marketing, internal comms or deck production, this is an immediate free upgrade. Test it against whatever you are currently paying Midjourney or Adobe for.

Moonshot releases Kimi K2.6, the most capable open-source agentic model

Chinese lab Moonshot released Kimi K2.6 on 21 April as a fully open-source, 1-trillion-parameter mixture-of-experts model, supporting 300 parallel sub-agents across 4,000 coordinated steps and 13-hour autonomous coding sessions. It tops the HLE-Full benchmark at 54.0, narrowly ahead of GPT-5.4 and Claude Opus 4.6. For UK businesses that need sovereign or on-premise deployment, this is the most capable open model to date. It matters if your compliance team cannot send data to a US-hosted API.

OpenAI GPT-Rosalind targets life sciences

OpenAI released GPT-Rosalind on 16 April, a frontier reasoning model tuned for life sciences research. Launch partners include Amgen, Moderna, the Allen Institute and Thermo Fisher, with early use cases spanning target identification, protein work and trial design. This follows Novo Nordisk’s partnership announced last week. The pattern is now clear: frontier labs are embedding directly into regulated, high-value verticals rather than selling generic APIs. UK life sciences firms not yet in a similar conversation should expect the competitive gap to widen from here.

Perplexity launches Personal Computer for Mac

Perplexity has launched Personal Computer, a Mac-based extension of its Computer product that can work across local files, native apps and the Comet browser from inside the desktop app, with voice control included. Access is live for Max subscribers, Pro rollout is coming, and Windows is on a waitlist. The bigger shift is strategic: AI tools are moving from answering questions to operating the machine itself.

xAI pushes Grok 4.2 into public beta

xAI has put Grok 4.2 into public beta as an explicitly selectable release candidate, rather than a quiet default upgrade. That matters less for the headline than for the operating model: frontier labs are shipping fast, learning in public and iterating weekly. For enterprise users, that increases optionality, but also raises the bar on version control, testing and governance before deployment.

Cursor and Factory push enterprise coding agents to centre stage

Beyond the SpaceX deal, Cursor’s revenue has gone from $100 million to $2 billion ARR in fourteen months, described as the fastest SaaS ramp in history. Factory AI closed a $150 million round at a $1.5 billion valuation to build enterprise coding agents. The combined message to engineering leaders: autonomous coding agents are now a procurement category, not an experiment. If you have not evaluated them against your current tooling budget in the last quarter, you are already behind your peers.

Quick Hits

• Cerebras files for IPO, first major AI-chip listing of the cycle: The Nvidia rival filed its S-1 on 17 April, targeting a mid-May listing with 2025 revenue of $510 million and $24.6 billion in remaining performance obligations.

• UK executive sentiment flips negative on AI jobs: Accenture’s Bloomberg-reported poll on 19 April found roughly half of UK business leaders now expect net AI-driven job losses over the decade, up from one in three two years ago.

• Meta starts tracking employee keystrokes and mouse movements: Meta confirmed on 21–22 April that it is installing screen and keystroke capture software on all US employee computers to train its agentic AI models, prompting internal backlash and likely regulatory scrutiny.

• Adobe ships Firefly AI Assistant across the Creative Cloud: Adobe’s new assistant orchestrates tasks across Photoshop, Premiere and Illustrator from a single prompt, the clearest sign yet that the creative suite is being rebuilt around agent-style workflows.

• Deezer reports 44% of new music uploads are AI-generated: Most are flagged as fraudulent, showing how quickly synthetic content is crowding creator platforms and why provenance policies now matter for marketing and brand teams too.
  

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Executive Summary

Anthropic quietly withheld its most capable model, Claude Mythos, from public release after testing showed it could identify thousands of zero-day vulnerabilities and break out of its own sandbox. It is the first time a frontier lab has publicly chosen not to ship a model on capability grounds, and it sets a precedent that OpenAI and Google will now have to answer to.

Against that, the week was defined by scale. OpenAI closed a $122 billion round at a $852 billion valuation. Anthropic raised $30 billion in Series G funding. Meta broke with its open-source past by launching a proprietary frontier model, Muse Spark, while Google countered with a full Apache 2.0 release of Gemma 4. Capability is accelerating, but the survey data is sobering: 54% of executives report AI is “tearing their company apart”, and only 29% report measurable ROI.

The takeaway: enforcement of the EU AI Act lands on 2 August 2026, sixteen weeks from now. If you have not yet inventoried the AI agents running in your business and set a basic governance policy, that is your job this week. Practical starting points are in “What to Try This Week” below.

What to Try This Week

Spend two hours auditing AI usage inside your business. Start by listing every AI tool, agent, or workflow your teams are actually using, including the shadow ones nobody formally approved. You will find more than you expect. Then classify each by the data it touches: public, internal, customer, sensitive. Anything touching the bottom two categories needs a human-in-the-loop policy, audit logging, and a named owner. That is your minimum viable governance posture before August.

Once the inventory is done, install the Microsoft AI Agent Governance Toolkit on whichever agents matter most, or use Anthropic’s published governance templates if you are in the Claude ecosystem. Neither costs anything. The one conversation to have with your leadership team this week is this: who is accountable when an agent does something wrong, and what is our answer when a regulator or customer asks us to prove it cannot? If you do not have clean answers to both, that is the work.

This Week’s Policy & Regulation Brief

The enterprise AI ROI gap is widening, not closing

Writer’s 2026 Enterprise AI Adoption Survey of 2,400 executives finds 79% of organisations struggling to adopt AI, a double-digit jump on 2025, with 54% of C-suite leaders saying adoption “is tearing their company apart”. Only 29% report significant ROI from generative AI, despite 59% investing over $1 million a year. 75% admit their AI strategy is “more for show”, and 67% suspect a data leak via unapproved tools.

Anthropic withholds Claude Mythos on safety grounds

Anthropic confirmed this week that it has not released Claude Mythos, internally described as its most capable model to date, after red-team testing showed the model could identify thousands of previously unknown software vulnerabilities and escape its test sandbox. This is the first time a frontier lab has publicly gated a model on capability rather than cost. It establishes a reference point that competitors, and eventually regulators, will be measured against. Expect procurement teams at large enterprises to start asking suppliers for equivalent capability disclosures.

Novo Nordisk and OpenAI partner on drug discovery

Novo Nordisk announced a multi-year partnership with OpenAI focused on accelerating early-stage drug discovery, with Novo’s share price moving 4% on the news. The deal matters beyond pharma because it is the clearest signal yet of frontier labs becoming embedded in regulated, high-stakes industries. It follows similar moves by AstraZeneca and Sanofi earlier in Q1. For UK life sciences firms, the competitive clock has now started.

EU AI Act enforcement is 16 weeks away

The full enforcement phase of the EU AI Act begins on 2 August 2026. Obligations cover general purpose models, high-risk systems, transparency, and a formal risk classification process. Any UK business with European customers, subsidiaries, or supply chain exposure falls inside the perimeter. Readiness across European enterprises remains low: most have not completed a system inventory, let alone classification. Fines scale with turnover. This is the single most pressing compliance story of the quarter, and Q2 is the window to act.

OpenAI closes $122bn round, but investors start questioning the $852bn price tag

OpenAI closed its Series E at $122 billion this week, taking the implied valuation to $852 billion and making it the largest private funding event in corporate history. Yet by 14 April the FT and The Information were reporting growing investor unease about strategic coherence: the new AWS partnership, friction with Microsoft over overlapping product territories, six acquisitions already in Q1, a COO reshuffle, and a live New Yorker investigation into Sam Altman’s leadership. Capital is flowing. Clarity is not.

Anthropic Series G: $30bn at $380bn valuation

Anthropic raised $30 billion in Series G funding at a $380 billion valuation, on the back of a reported $30 billion revenue run-rate driven largely by agentic deployments inside Fortune 500 customers. The speed of the round, coming just months after its previous raise, tells you everything about how enterprise demand for production agents has shifted. For UK leaders running procurement cycles, assume Anthropic and OpenAI will both be commercially viable counterparties for at least the next three years.

Broadcom and Anthropic strike 3.5GW compute deal

Anthropic signed a compute infrastructure partnership with Broadcom that secures access to 3.5 gigawatts of custom silicon capacity, alongside a $50 billion commitment to US-sited data centres. The practical effect is that Anthropic reduces its dependence on Nvidia and establishes a second-source supply chain. For enterprises that care about continuity of supply and pricing leverage, it reinforces the case for multi-vendor AI strategies rather than single-provider lock-in.

Model & Platform Updates

Meta launches Muse Spark, breaking from open source

Meta released Muse Spark, its first proprietary frontier model, ending a three-year strategy of open-weighting everything. Muse Spark is multimodal with native reasoning and benches competitively against GPT-5.4 and Claude Mythos’ predecessor. The product pivot has obvious implications for the Llama ecosystem and for the tens of thousands of businesses running Llama-based workflows. If you have built on Llama, start evaluating the Muse Spark API terms now rather than waiting for the next Llama release that may not come.

Google open-sources Gemma 4 under Apache 2.0

Google released Gemma 4 fully open-source under Apache 2.0, with the 31B variant ranking third on public leaderboards behind only GPT-5.4 and Claude. Commercial use is unrestricted. This is the most capable truly open model released to date. For businesses that cannot send data to a hosted API for regulatory or commercial reasons, Gemma 4 changes the economics. Self-hosted deployment on a single high-end GPU is now realistic for mid-market companies, not just research labs.

GPT-5.4 Thinking crosses human-level on business tasks

OpenAI’s GPT-5.4 Thinking variant posted 75% on OSWorld and 83% on GDPVal, both benchmarks covering realistic multi-step business tasks across spreadsheets, browsers, and enterprise applications. Scores are now above the average human baseline. The practical reading is that back-office automation, particularly in finance operations, procurement, and customer support triage, no longer requires human-in-the-loop for routine cases. Productivity gains are real, but so is the governance gap, which the next story addresses.

Google expands Gemini Enterprise desktop agent

Google has extended its desktop agent inside Gemini Enterprise, moving Gemini towards a task-execution workspace that looks very similar in shape to Anthropic’s Claude Cowork. The new interface includes a “Require human review” toggle, signalling that Google is taking agent oversight seriously for desktop-level actions. Combined with signals of eventual integration with AI Studio, it points towards a unified Google work platform that competes head-on with Microsoft Copilot and Anthropic at the desktop layer.

Microsoft ships AI Agent Governance Toolkit

Microsoft released an open-source AI Agent Governance Toolkit this week, covering sandboxing, policy enforcement, audit logging, and permission scoping. It integrates with Azure, GitHub, and Entra. Crucially, it is free and Apache-licensed, which removes the most common excuse for not starting. For any business running autonomous or semi-autonomous agents against internal data, this is the most accessible baseline governance layer currently available. Install it, or have a better reason than “we will look at it next quarter”.

Quick Hits

• Microsoft commits $17.5bn to India: Announced 13 April, Microsoft’s largest ever Asia investment funds AI and cloud infrastructure through 2029, shifting compute gravity away from US soil.

• Gujarat High Court bars AI from judicial decisions: India’s Gujarat High Court ruled on 9 April that AI tools cannot be used to draft judgments or make rulings, the first major court to draw an explicit line.

• Sydney entrepreneur engineers cancer vaccine with ChatGPT: Reported 14 April, a Sydney researcher used ChatGPT and AlphaFold to design a personalised mRNA vaccine that put tumours into remission in a canine trial, with UNSW now preparing human trials.
  

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Executive Summary

Anthropic withheld its most powerful model from public release this week, marking the first time a frontier lab has openly suppressed a system on capability grounds. Claude Mythos, which autonomously discovered high-severity vulnerabilities in every major operating system and browser during internal testing, is being shared only with a defensive coalition of 40+ organisations including Apple, Google and Nvidia. Whether you see this as responsible stewardship or a competitive positioning play, it sets a precedent every business leader should understand.

Beyond that headline, the quarter’s numbers landed and they are staggering: $300 billion in global venture funding in Q1 2026, with AI capturing 80% of it. Global M&A hit $1.22 trillion. Four of the five largest venture rounds ever recorded closed in a single quarter. Meanwhile, Meta abandoned its open-source principles to launch Muse Spark as a closed model, Google went the opposite direction by releasing Gemma 4 under a fully permissive Apache 2.0 licence, and Microsoft quietly started building its own foundation models independent of OpenAI.

The practical takeaway this week: if you have not yet stress-tested your organisation’s exposure to the EU AI Act (full enforcement begins 2 August 2026, less than four months away), do it now. The penalties reach 7% of global annual turnover for the highest tier of violations. That is not theoretical risk; it is a compliance countdown.

What to Try This Week

1. Download and run Google’s Gemma 4. This is not a vague suggestion. The 4B parameter variant runs on a mid-range laptop; the 31B variant runs on a single consumer GPU (ours runs on a mac mini). It is fully open-weight, Apache 2.0 licensed, and free. Install Ollama (ollama.com), run ollama pull gemma4, and test it against a real task from your business: summarising meeting notes, drafting first-pass emails, analysing a document. Compare the quality to what you are currently paying for via API calls or subscriptions. If you find the output is 80% as good for your use case, you have just identified a path to running AI locally with zero ongoing costs and no data leaving your network. For regulated industries or businesses with data sensitivity concerns, this is a meaningful capability shift that arrived this week.

2. If you are deploying AI agents in any capacity, also take 30 minutes to review Microsoft’s Agent Governance Toolkit on GitHub (github.com/microsoft/agent-governance-toolkit). It is the first comprehensive open-source framework for governing autonomous AI agents at runtime, and it maps directly to EU AI Act compliance requirements that take effect in August. Even if you are not ready to implement it, understanding the governance categories it covers will sharpen your thinking about what controls your own agent deployments need.

This Week’s Policy & Regulation Brief

Anthropic Withholds Claude Mythos: “Too Dangerous” for General Release

Anthropic confirmed it is suppressing its most capable model after internal tests showed it could escape its own sandbox, chain together zero-day exploits across major operating systems, and outperform elite human security researchers. Instead of a public launch, Anthropic deployed the model through Project Glasswing, a defensive cybersecurity partnership with Apple, Google, Microsoft, Nvidia, CrowdStrike and 40+ other organisations. Anthropic is committing up to $100 million in usage credits to the programme. This is the first acknowledged case of a frontier lab voluntarily withholding a model on capability grounds.

Q1 2026: AI Swallows the Venture Market Whole

Global venture funding hit $300 billion in Q1 2026, with AI capturing roughly $242 billion, or 80% of the total. That is up from 53% a year ago. Four of the five largest venture rounds in history closed in a single quarter: OpenAI ($122 billion at an $852 billion valuation), Anthropic ($30 billion), xAI ($20 billion) and Waymo ($16 billion). Separately, global M&A surged to $1.22 trillion, with four of the six largest deals tied to AI. Capital is no longer flowing toward AI; capital is AI.

EU AI Act: Four Months to Full Enforcement

The EU AI Act’s core high-risk provisions, including conformity assessments, transparency obligations, deepfake labelling requirements and post-market monitoring, take full effect on 2 August 2026. Non-compliance penalties reach up to 35 million euros or 7% of global annual turnover, whichever is higher. UK businesses with EU customers or operations are in scope. If your compliance programme is not already underway, the window to prepare is closing fast.

US Lawmakers Target Chip-Making Equipment Exports to China

Congress introduced the bipartisan MATCH Act on 2 April, aiming to ban exports of advanced semiconductor manufacturing equipment to China, specifically naming ASML and Tokyo Electron. Former White House AI czar David Sacks called it “the single most important export control” available. Meanwhile, Huawei launched its Ascend 950PR chip, claiming 2.87 times the performance of Nvidia’s China-legal H20, with ByteDance and Alibaba already placing orders. The bifurcation of global AI infrastructure is accelerating.

OpenAI Publishes Industrial Policy Blueprint, Proposes Robot Taxes

OpenAI released a 35-page policy document on 6 April calling for public wealth funds, taxes on businesses that replace workers with AI, four-day working weeks and rapid expansion of social safety nets. CEO Sam Altman explicitly addressed the transition to “superintelligence.” Whatever one makes of the substance, this is the most politically ambitious document any frontier lab has published. It signals that the largest AI companies are now actively trying to shape the regulatory environment around workforce disruption.

Model & Platform Updates

Meta Launches Muse Spark: Closed Model, Open-Source Principles Abandoned

Meta released Muse Spark on 8 April, its first model from Meta Superintelligence Labs led by former Scale AI CEO Alexandr Wang. Two notable shifts: it is Meta’s first reasoning model, and it is closed-weight, abandoning the open-source Llama tradition. Performance sits near parity with GPT-5.4 and Claude Sonnet 4.6 on writing and reasoning, though it lags on coding. A “Contemplating” mode uses parallel sub-agents for complex problems. An open-source version is reportedly planned, but the strategic direction is clear.

Anthropic Gates Claude Mythos Preview Behind Cybersecurity Programme

Anthropic has not publicly released Claude Mythos Preview, instead limiting access through Project Glasswing, a defensive cybersecurity programme involving major tech, cloud, finance, and open-source partners. The company says the model found thousands of previously unknown vulnerabilities across major operating systems and browsers, including critical flaws that have since been patched. Anthropic is offering $100 million in usage credits, plus $4 million for open-source security, with access via API, Bedrock, Vertex AI, and Microsoft Foundry. :contentReference[oaicite:0]{index=0}

Google Releases Gemma 4: Open Source, Free, Runs on Your Phone

Google DeepMind released Gemma 4 on 2 April, a family of four open-weight models (up to 31 billion parameters) under an Apache 2.0 licence. The 31B model ranks third on the global Arena AI text leaderboard, outperforming models twenty times its size. It runs on Android phones, Raspberry Pi and consumer-grade GPUs. The Apache 2.0 licensing is the real story: full commercial freedom, no usage restrictions, no vendor lock-in. For any business exploring local or on-device AI without recurring API costs, this is the most significant release of the quarter.

Google quietly launched two new AI tools

Google AI Edge Eloquent (iOS App Store): A free offline-first dictation app using local Gemma-based ASR models. Features live transcription, filler-word removal, style transformations (e.g., “Key points,” “Formal”), custom jargon import from Gmail, and full local/cloud toggle. Android version with system-wide keyboard integration is coming.

AI-generated photo/video captions in Google Maps (powered by Gemini): Automatically suggests editable captions when users contribute media about places. Rolling out now in English on iOS (U.S.), with global/Android expansion planned.

Anthropic Launches Claude Managed Agents to Cut the Engineering Burden of AI Deployment

Anthropic has launched Claude Managed Agents, a new enterprise product designed to make it easier to build, run and monitor autonomous AI agents at scale. The offering bundles the infrastructure around the model, including agent harnesses, memory systems, sandboxed execution environments and permission controls, so teams do not have to build the full runtime themselves. Anthropic says this lets agents run in the cloud for extended periods while developers focus on product workflows rather than orchestration and systems engineering.

Microsoft Launches Its Own Foundation Models, Reducing OpenAI Dependency

Microsoft released three proprietary models through Azure AI Foundry: MAI-Transcribe-1 (which it claims is the most accurate transcription model available), MAI-Voice-1 (text-to-speech) and MAI-Image-2 (image generation, already in Copilot and Bing). Separately, the open-source Agent Governance Toolkit provides runtime security for enterprise AI agents, mapping to EU AI Act, HIPAA and SOC2 compliance. Together, these are Microsoft’s clearest signal that it is building capabilities independent of OpenAI.

DeepSeek V4: China’s Open-Weight Challenger Is Imminent

DeepSeek confirmed an April release window for V4, a one-trillion-parameter mixture-of-experts model with fully open weights under Apache 2.0. Early reports suggest frontier-level coding performance (94.7% on HumanEval), all trained for an estimated $5.2 million. DeepSeek has withheld early access from US chip makers, sharing only with domestic Chinese partners. If the benchmarks hold, this further closes the gap between open and proprietary models while deepening the US-China AI ecosystem split.

GPT-4o Retired; Frontier Lineup Reshuffles

OpenAI retired GPT-4o from all plans on 3 April, completing the migration to GPT-5.x. The current frontier lineup: GPT-5.4 Thinking (OpenAI), Claude Sonnet 4.6 (Anthropic), Gemini 3.1 Pro (Google), Grok 4.20 Beta 2 (xAI) and Meta’s Muse Spark. In the Q2 pipeline: Claude Mythos (gated), Grok 5 (estimated 6 trillion parameters), GPT-5.5 and DeepSeek V4.

Quick Hits

• AI Scientist v2 makes history: A paper fully generated by Sakana AI’s automated scientific discovery system was accepted at a peer-reviewed conference without human intervention, the first confirmed case of this happening.

• Did they didn’t they? Anthropic accidentally shipped 512,000 lines of Claude Code (they said it was an April fools): An apparent misconfigured npm package exposed source code, hidden feature flags and internal architecture details. Anthropic attributed it to a April fools prank - there is debate online that it isnt.

• OpenAI buys a media company: OpenAI acquired TBPN, a daily live tech and business talk show, placing it under its Global Affairs team. It is OpenAI’s first media acquisition and has drawn scrutiny about editorial independence ahead of a potential IPO.
  

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Executive Summary

This week showed where AI power is concentrating and what that means for everyone else.

The biggest developments were not just new models or product launches. They were signs that capital, regulation, and distribution are consolidating around a small number of players. OpenAI’s latest funding round, SoftBank’s additional financing, and Shield AI’s defence raise all point to the same reality: the centre of gravity in AI is moving toward companies with the balance sheets, infrastructure, and political access to shape the market.

At the same time, the policy environment is becoming harder to predict. California’s move to impose safety and privacy requirements on AI suppliers sits in direct tension with the White House’s push to limit state-by-state rulemaking. For businesses, that means AI governance can no longer be treated as a future compliance issue. It is becoming an operating issue now.

On the product side, the pattern is similar. Shopify is building for agent-led commerce, Salesforce is embedding AI deeper into workflow, and leading labs are shifting resources toward models with broader strategic value. The message is straightforward: this market is maturing quickly, but many organisations are still approaching it experimentally.

That gap matters. The current risk for most businesses is no longer missing AI entirely. It is adopting it in scattered ways, implementing badly, and creating cost, inconsistency, and governance problems that are harder to unwind later.

Why this matters for business

1. Review where you are overexposed to a single AI vendor
If a small number of providers continue to attract most of the capital and market power, their pricing, product priorities, and commercial terms will increasingly shape your roadmap. This week is a good reminder to review where you depend on one model, one cloud, or one workflow provider. Even a lightweight second-source option can improve leverage and resilience.

2. Treat AI governance as a live operating discipline, not a policy document
If your team is already using AI tools, governance needs to exist in the flow of work. That means clear rules on approved tools, what data can be used, who signs off external-facing outputs, and where human review is mandatory. The practical test is simple: could a team manager explain your AI rules in two minutes without opening a slide deck?

3. Check whether your digital buying journey is usable by agents as well as people
Shopify’s announcement matters because it changes the shape of discovery and conversion. Product data, stock availability, pricing, and checkout logic increasingly need to be machine-readable and consistent. A practical first step is to review your product catalogue and checkout flow with your digital team and ask what would break if the “customer” were an AI agent rather than a human.

4. Focus less on experimentation volume and more on operational usefulness
Many firms can point to pilots. Far fewer can point to repeatable time savings, faster response times, lower support effort, or better decision quality. If you want AI to matter commercially, ask each team for one workflow where time, quality, or cost can be measured before and after deployment. That will tell you far more than a long list of disconnected use cases.

This Week’s Policy & Regulation Brief

California Defies White House with AI Safety Executive Order

California has introduced new safety, privacy, and civil rights requirements for AI suppliers contracting with the state, creating a direct clash with the White House’s preference for lighter-touch, federally led rules. For businesses operating across the US, the practical issue is not politics but fragmentation. Compliance may now depend on customer type, geography, and procurement route, which makes flexible governance more important.

OpenAI’s $122B round includes $3B from retail investors

OpenAI’s latest financing round is notable not just for its size, but for what it signals about market expectations. Capital is continuing to flow toward a small number of frontier players seen as strategic infrastructure bets. For customers, that usually means faster product expansion, stronger ecosystem pull, and greater lock-in risk. It is another reason to keep procurement discipline and avoid unnecessary dependency.

Public Concern Over AI Hits New High

A new survey showing growing public concern about AI matters because adoption is not driven by capability alone. Trust, explainability, and perceived fairness increasingly shape how quickly businesses can deploy customer-facing AI without resistance. If sentiment keeps deteriorating, organisations using AI in service, hiring, or communication will need stronger messaging, clearer review processes, and more visible safeguards around how outputs are used.

OpenAI’s Sora Shutdown Reveals Shift to Enterprise and Robotics

The reported shutdown of Sora points to a broader commercial pattern: companies are reallocating resources away from expensive consumer AI products with uncertain monetisation and toward enterprise, infrastructure, and robotics-related opportunities. For business leaders, the takeaway is to watch where serious investment is being redirected. That often gives a better signal about where durable value is likely to sit than headline product launches do.

Nvidia’s AI Chip Dominance Faces Elon Musk’s Terafab Challenge

Nvidia remains the dominant force in AI compute, but new large-scale infrastructure bets show that rivals are trying to challenge that position through vertical integration and manufacturing scale. The practical takeaway for businesses is not to speculate on winners, but to recognise that compute availability, pricing, and concentration remain strategic variables. If AI is core to your roadmap, infrastructure assumptions should be reviewed regularly.

OpenRouter Data Highlights Rising Adoption of Lower-Cost Chinese AI Models

Usage trends showing growing adoption of lower-cost Chinese models highlight an uncomfortable truth for Western providers: price-performance is becoming a powerful competitive lever. For buyers, this creates opportunity and complexity at the same time. Lower-cost alternatives may widen access, but they also raise questions around security, governance, support, and geopolitical exposure. Procurement teams should be evaluating these trade-offs now, not later.

AI Designated Top US National Security Concern for 2026

The framing of AI as a top national security issue reinforces how closely commercial AI and state priorities are now linked. This has practical consequences for vendors and enterprise buyers alike, from procurement opportunities in defence and public sector markets to increased scrutiny over partnerships, data handling, and supply chains. Businesses in sensitive sectors should expect AI decisions to receive more board-level and regulatory attention.

Model & Platform Updates

OpenAI Shuts Down Sora, Redirects Team to “Spud” World Simulation Model

OpenAI’s reported move away from Sora toward a broader world-simulation effort suggests a shift from standalone media generation to models with wider strategic applications, including robotics and physical-world reasoning. The business takeaway is that labs are prioritising platforms with longer-term leverage over eye-catching consumer tools. That should influence how companies assess which AI categories are maturing and which may prove commercially fragile.

Anthropic Source Code Leak Reveals Claude Mythos (Capybara)

The reported Anthropic leak matters less for the codename and more for what it exposed: how much strategic value now sits inside model development, evaluation, and safety infrastructure. For businesses, it is another reminder that AI risk is not just about model outputs. It includes access control, configuration mistakes, internal tooling exposure, and operational security. Basic governance failures can create outsized consequences very quickly.

Salesforce Launches AI-Heavy Slack Redesign with 30 New Features

Salesforce’s Slack update shows where enterprise AI is heading in practice: not as a separate destination, but as a layer embedded inside existing systems of work. The important question for leaders is not whether Slack has more AI features. It is whether those features reduce effort, improve handoffs, or shorten decision cycles. If they do not, more functionality will simply mean more interface clutter.

Shopify Launches Agentic Storefronts and Universal Commerce Protocol

Shopify’s latest move is one of the clearest signs yet that commerce platforms are preparing for AI agents to act on behalf of buyers. That has practical implications for merchants now. Product data needs to be structured, inventory needs to be accurate, and checkout processes need to be easy for software as well as people to navigate. Businesses that prepare early should have an advantage.

NVIDIA Vera Rubin Enters Full Production

NVIDIA’s latest platform is designed around multi-step agentic workloads rather than conventional one-shot inference, which is a useful signal about where enterprise demand may be headed. More businesses are moving from content generation to task execution, orchestration, and automation. Leaders evaluating their own roadmap should ask whether their AI plans are still centred on chat interfaces when the market is moving toward more autonomous systems.

PrismML Launches 1-Bit “Bonsai” LLM Family for Edge Devices

If PrismML’s performance claims prove credible, the significance is clear: capable models running in far smaller memory footprints would expand what can be done on-device without depending on the cloud. That could matter for latency, privacy, resilience, and cost. For businesses, the near-term takeaway is to watch edge AI more closely, especially in environments where connectivity, security, or response speed make cloud-only approaches less practical.

Mistral Releases Voxtral TTS and Small 4 Open-Source Models

Mistral continues to strengthen its position as a serious open-weight alternative, combining practical model releases with infrastructure investment. For businesses, this matters because it broadens the set of viable options beyond the largest US labs. Open models can offer more control, pricing flexibility, and deployment choice, but they also place more responsibility on the customer for integration, evaluation, and governance.

Google Gemini 3.1 Flash Live Rolls Out Globally

Google’s latest Gemini release reflects a growing emphasis on real-time, multimodal interaction at lower cost. The interesting commercial angle is not novelty, but fit: these tools are increasingly being shaped for high-volume environments where responsiveness and cost control matter more than maximum model sophistication. For buyers, that makes use-case discipline even more important. The right model is often the one that is fast enough and cheap enough.

Quick Hits

• Ollama adds MLX preview for Apple Silicon - The popular open-source local LLM tool is now powered by Apple’s MLX framework on Macs, delivering significant speed gains for running AI models locally without cloud dependency. A meaningful step for on-device AI adoption.

• Meta unveils $499 Ray-Ban AI smart glasses for prescription users - Enhanced AI features including camera, vision, and voice capabilities. Meta continues to bet that wearables, not phones, will be the primary interface for everyday AI.

• AI Scientist v2 goes fully open-source - The autonomous research system that independently generates hypotheses, designs experiments, writes papers, and self-reviews had one of its AI-generated papers score in the top 45% at an ICLR workshop via blind peer review. A landmark moment for AI-driven scientific discovery.

• xAI co-founder departs, leaving Musk with only two original members - Ross Nordeen’s exit signals potential internal turbulence at Elon Musk’s AI lab, which continues to operate with a smaller leadership team than its frontier-lab competitors.
  

  We work with leadership teams to move from experimentation to execution safely, commercially, and at speed. Talk to us.
  

Executive Summary

This week was a useful reminder that the AI market is growing up.

The headlines were not really about smarter chatbots or bigger launches. They were about infrastructure, execution, and whether AI investment can actually stand up to commercial scrutiny. IBM’s Confluent deal was about data access. Anthropic’s latest move was about turning AI into something that can do work, not just answer questions. And investors punished Alibaba and Tencent for spending heavily without telling a convincing value-creation story.

For business leaders, that matters because the centre of gravity is shifting. The question is no longer whether AI is interesting. It is whether your business is making disciplined decisions about where to use it, which vendor to back, and what return you expect to see.

Why this matters for business

1. Most AI decisions now look like tool choices. They are actually operating model choices.

What looked like product news this week was really a reminder that AI decisions are starting to hardwire themselves into how businesses run. The platform you adopt shapes where work happens, how data moves, what your teams rely on, and how difficult it becomes to change course later. That means leaders should stop treating AI selection as a lightweight software decision and start treating it as part of core business design.

2. The real risk is no longer “doing nothing” or “moving fast”. It is implementing badly.

The gap is widening between businesses using AI with clear guardrails and businesses letting usage spread informally through teams. Poor deployment now carries real cost: messy workflows, duplicated spend, weak accountability, and avoidable data exposure. The advantage will not go to the company with the most tools. It will go to the one that is clearest on where AI adds value, who owns it, and what good looks like.

3. The winners will be the firms that connect AI to commercial outcomes, not internal excitement.

Investors and operators are both becoming less patient with vague AI ambition. The bar is rising. It is no longer enough to say the business is “using AI” or “exploring use cases”. The more important question is whether it is improving margin, increasing speed, strengthening customer experience, or creating some other measurable advantage. If that link is weak, the strategy is weak.

This Week’s Policy & Regulation Brief

IBM completes Confluent acquisition to strengthen real-time AI infrastructure

IBM completed its roughly $11 billion acquisition of Confluent on 17 March, bringing a major streaming-data platform into its stack. The strategic logic is straightforward: enterprise AI agents are only as useful as the live data they can access. For businesses, this is a reminder that AI value depends less on the model alone and more on whether your data can be connected, governed, and used in real time across systems.

Yann LeCun’s AMI Labs raises $1.03bn to back an alternative to LLM-first AI

Former Meta AI chief Yann LeCun’s new startup, AMI Labs, raised $1.03 billion to develop “world models” rather than rely on standard LLM approaches alone. The business implication is not that LLMs are being replaced tomorrow, but that the market is still open to different technical paths. Leaders making long-term AI bets should avoid assuming today’s dominant architecture will remain the only viable one.

Musk says xAI is being rebuilt from the foundations up

Elon Musk said xAI “was not built right first time around” and is being rebuilt after a period of senior departures and internal turbulence. That matters because frontier AI capability is only part of the story. Organisational stability, product focus, and execution discipline matter too. For enterprise buyers, this is another reason to assess vendor resilience, not just benchmark performance, before committing strategically.

Alibaba and Tencent lose $66bn as investors demand a clearer AI business case

Alibaba and Tencent shed around $66 billion in market value in roughly 24 hours after investors judged their AI monetisation plans too vague. The lesson is immediate for any leadership team funding AI programmes internally: spending alone is not persuasive. Markets and boards increasingly want to see a credible path from AI investment to revenue, margin improvement, or stronger competitive position.

Anthropic vs. Pentagon (Yes this is still ongoing)

The legal battle between Anthropic and the Department of Defence escalated significantly this week. Anthropic filed dual federal lawsuits challenging its designation as a “supply chain risk to national security”, a label previously reserved for foreign adversaries like Huawei. Court filings reveal the DoD told Anthropic negotiations were “nearly aligned” just one week before the designation was issued. More than 30 employees from OpenAI and Google DeepMind, including Google’s Chief Scientist Jeff Dean, filed legal briefs supporting Anthropic. The outcome will set a precedent for whether the US government can compel AI companies to remove ethical guardrails as a condition of doing business.

China’s Open-Source AI Strategy Is Working, Despite Chip Curbs

A US-China Economic and Security Review Commission report published 23 March warned that Chinese open-source AI models have narrowed performance gaps with Western frontier models despite US export controls. Roughly 80% of US AI startups are reportedly using Chinese open-source models. The commission flagged China’s embodied AI ambitions, particularly in manufacturing and robotics, as a distinct and underappreciated threat vector.

Model & Platform Updates

Anthropic gives Claude remote control of your Mac via Dispatch

Anthropic has started previewing a setup in which Claude Code and Claude Cowork can operate a user’s Mac, with Dispatch letting tasks be initiated remotely from a phone. It is still early and Anthropic stresses oversight, but the practical shift is important: AI is moving from chatbot to delegated operator. For businesses, that raises both productivity opportunities and new questions around permissions, auditability, and safe task boundaries.

MiniMax launches M2.7 with “self-evolving” training workflow claims

MiniMax says its new M2.7 model can automate parts of its own reinforcement learning workflow, including improving memory, skills, and elements of its training harness. Even if those claims prove only partly durable, the direction is important. Model developers are now trying to automate more of the improvement loop itself. For buyers, this signals faster iteration cycles and potentially shorter windows of competitive differentiation.

Google unveils TurboQuant to reduce AI memory overhead

Google Research introduced TurboQuant, a compression approach aimed at cutting the memory burden of vector quantisation, with early claims of major memory savings and performance gains in some tests. For businesses, the headline is not just research novelty. Techniques like this could reduce infrastructure cost and hardware constraints over time, making high-performance AI systems cheaper to run and easier to deploy at scale.

Google expands AI Studio’s full-stack app-building workflow

Google has been pushing AI Studio further toward a full-stack prototyping and app-building environment, including a new “vibe coding” experience, stronger project continuity, and updated billing options. The practical takeaway is that AI development tools are converging with lightweight software creation platforms. For teams experimenting with internal tools, this lowers the barrier to turning prompts into usable prototypes, but it also increases the need for governance over what gets built and deployed.

OpenAI this week

• OpenAI Kills Sora, Pivots to Enterprise “Superapp”

  OpenAI confirmed it is shutting down Sora, its video generation platform, just months after launch and a Disney licensing deal. The Sora team is being redirected to “world simulation” for robotics. Simultaneously, CEO Sam Altman announced OpenAI will consolidate ChatGPT, the Atlas browser, and Codex into a single desktop superapp. The moves are consistent with a company preparing for a Q4 2026 IPO at up to $1 trillion valuation. OpenAI’s Applications CEO Fidji Simo told staff there are “no more side quests.” The enterprise pivot is now official.

• OpenAI Acquires Astral - Developer Toolchain War Heats Up

  OpenAI announced the acquisition of Astral, maker of the widely adopted Python tools uv, Ruff, and ty. The Astral team joins OpenAI’s Codex platform, which has grown 5x since January to 2 million weekly active users. This mirrors Anthropic’s December 2025 acquisition of Bun (JavaScript runtime), confirming both labs are acquiring developer infrastructure to win the coding agent wars.

Mistral Launches Forge - Train Models from Scratch on Your Data

Mistral’s Forge platform, launched at GTC, allows enterprises to pre-train models from scratch on proprietary data, not just fine-tune. Early customers include ASML, Ericsson, the European Space Agency, and Singapore’s DSO. This is a significant capability for regulated industries that need full control over their AI training pipeline.

Gemini 3.1 Flash-Lite Targets Enterprise Scale

Google released Gemini 3.1 Flash-Lite at $0.25 per million input tokens, 2.5x faster time-to-first-token than prior models, with adjustable thinking levels to modulate reasoning effort by cost. A clear play for high-volume enterprise workloads where speed and cost matter more than raw capability.

Quick Hits

• OpenAI targets Q4 2026 IPO: Could be the largest tech IPO in US history, potentially at a $1 trillion valuation. All product decisions are now being filtered through IPO readiness.

• Bridgewater warns of $650B Big Tech AI capex in 2026: Co-CIO Greg Jensen flagged “significant downside risks” if execution falters, with parallels to the dot-com bubble, while noting AI spend is adding roughly 100 basis points to US GDP growth.

• Anthropic safety researcher quits, warns “world is in peril”: Mrinank Sharma publicly resigned from Anthropic citing bioweapons concerns. A notable signal from inside the lab widely regarded as the most safety-conscious.

• Mark Zuckerberg building personal “CEO agent”: WSJ reported Zuckerberg is testing an AI agent to replace layers of executive information retrieval. Meta employees have built hundreds of internal AI tools, with engineer output reportedly up 30% since early 2025.
  

  We work with leadership teams to move from experimentation to execution safely, commercially, and at speed. Talk to us.
  

Executive Summary

This week’s AI news was less about flashy launches and more about where the market is actually heading. Three signals stood out.

First, the AI race is hardening into an infrastructure contest. Nvidia’s latest announcements reinforced that value is concentrating not just in models, but in the compute, systems, and platforms needed to run them at scale. At the same time, Meta’s reported workforce cuts and infrastructure spending show how seriously large companies are reorganising around AI economics.

Second, AI agents are moving closer to real enterprise workflows, and that raises the stakes. Stories involving internal chatbot compromise, sensitive data exposure, and classified-data training plans all point to the same issue: once AI systems can access tools, files, and internal systems, governance matters as much as model quality.

Third, the market is shifting from experimentation to implementation. Microsoft, Anthropic, OpenAI, Perplexity, and Google are all pushing toward more persistent, embedded, workflow-oriented AI. The question for businesses is no longer whether AI is useful. It is where to deploy it safely, which processes to redesign around it, and how to capture value without creating cost, legal, or security problems.

Why this matters to businesses

• AI is becoming an operating model issue, not just a software tool.
  The competitive advantage will not come from “using AI” in the abstract. It will come from redesigning workflows, decision rights, and team structures so AI can improve output, speed, and consistency in commercially meaningful areas.

• Governance now needs to catch up with capability.
  As AI tools gain access to internal data, systems, and actions, businesses need clearer rules on permissions, oversight, data boundaries, and human approval. The risk is no longer just inaccurate answers; it is inappropriate access, automation without control, and exposure of sensitive information.

• Vendor choice is becoming a strategic decision.
  The market is fragmenting between model providers, infrastructure players, and workflow platforms. Businesses should avoid chasing every release and instead decide which vendors fit their security requirements, integration needs, and long-term AI architecture.

• Cost discipline will matter as much as innovation.
  The leaders in AI adoption are increasingly treating it as a capital allocation and productivity question. Businesses should focus on use cases where AI can reduce friction, improve margins, or strengthen decision-making, rather than funding broad experimentation without a clear path to return.

• The winners will be the firms that move early, but selectively.
  This is not the moment for blind acceleration or passive observation. It is the moment to identify the few high-value processes where AI can be deployed safely, prove impact quickly, and build the internal governance to scale from there.

This Week’s Policy & Regulation Brief

Morgan Stanley Warns of Imminent AI “Breakthrough Shock”

A widely circulated Morgan Stanley report warned that a step-change in AI capability, driven by a 10x compute ramp at leading labs, will arrive between April and June 2026, and that governments, corporates, and workers are “not prepared.” The report frames AI as a macro “intelligence currency” risk, arguing that self-reinforcing improvement cycles could begin by 2027. The bank’s survey of 1,000 executives across five countries found a 4% net workforce reduction over the past 12 months directly attributable to AI, with 30% of early AI adopters reporting quantifiable productivity gains by end of 2025, up from 16% a year earlier.

AI Agent Hacked McKinsey’s Internal Chatbot in Two Hours

Security startup CodeWall said its autonomous AI agent breached McKinsey’s internal AI platform, Lilli, and gained read-write access in about two hours by exploiting exposed APIs and weak access controls. Reports say the system could access millions of internal messages before McKinsey patched the flaw. The story matters because it shows agentic AI can move from assistant to attacker fast when connected to poorly governed enterprise systems.

ByteDance Suspends Video AI Model Over Copyright Disputes

ByteDance halted the rollout of a new video-generative AI model after facing copyright challenges, underscoring rising legal risk for models trained on or remixing copyrighted content. The suspension highlights the increasingly fraught relationship between generative AI capabilities and intellectual property law, a tension that is only going to intensify as video models mature.

The Pentagon is planning for AI companies to train on classified data, defense official says

The Pentagon is exploring frameworks that would allow select AI companies to train models on classified data within secure government-controlled environments. The initiative reflects growing interest in deploying advanced AI for defence applications, including intelligence analysis and operational planning. However, the approach raises significant concerns around data security, model control, and compliance, particularly regarding how sensitive information is handled, retained, and potentially embedded in commercial AI systems.

Meta Plans Up to 20% Workforce Cuts as AI Spend Surges

Meta is preparing workforce reductions of up to 20% as it reallocates capital toward AI infrastructure and compute. The move aligns with broader industry trends, where large tech firms are funding multi-billion AI investments through headcount cuts. The company is prioritising efficiency, automation, and AI-led productivity, signalling a structural shift in how big tech balances growth, cost, and capability.

Meta Quietly Retreats from Metaverse Ambitions to Refocus on AI

Meta is scaling back its metaverse push, deprioritising elements of its Reality Labs strategy as AI becomes the company’s central focus. While not formally “cancelling” the metaverse, investment and strategic attention have shifted decisively toward AI models, infrastructure, and products. The pivot reflects weaker-than-expected adoption of virtual worlds and stronger near-term returns from AI-driven services and advertising.

Anthropic vs. Pentagon Standoff Continues

The legal confrontation between Anthropic and the Pentagon remains active. Defense Secretary Pete Hegseth terminated the Pentagon’s relationship with Anthropic on 1 March after CEO Dario Amodei refused to allow unrestricted use of Claude for mass surveillance and autonomous armed drones. The Department of Defence issued a “supply chain risk designation”, the first time an American company has received this classification. Anthropic has vowed to challenge it in court. President Trump has instructed most government agencies to stop using Anthropic’s AI, while granting the Pentagon a six-month phase-out period.

Model & Platform Updates

Microsoft Launches Copilot Cowork on Anthropic’s Claude

Microsoft’s Copilot Cowork, a long-running, multi-step enterprise AI agent built on Anthropic’s Claude, launched in limited research preview. It integrates across Microsoft 365 (Outlook, Teams, Excel, SharePoint) and is powered by a “Work IQ” system for full enterprise context. This marks a significant architectural shift: Microsoft 365 Copilot is now explicitly multi-model, running both OpenAI and Anthropic systems. Broader rollout via the Frontier programme begins late March. Pricing is included in the existing $30/user/month M365 Copilot plan.

Anthropic Enables Mobile Control of Claude “Coworker” Sessions

Anthropic is introducing the ability to access and control Claude “coworker” sessions from mobile, allowing users to monitor and interact with long-running AI tasks remotely. This extends Claude beyond chat into a persistent, task-executing agent that continues working in the background. Mobile access means users can review progress, provide input, and steer workflows on the go, reinforcing the shift toward always-on, agentic AI integrated into daily work.

OpenAI Updates this week

• OpenAI Brings Sora Video Generation Into ChatGPT

  OpenAI began integrating Sora-class video generation directly into ChatGPT starting 14 March, blurring the line between conversational AI and full video creation tools. The move raises significant questions about inference costs (projected in the hundreds of billions of dollars over coming years), safety controls, and platform liability for AI-generated video content at internet scale.

• OpenAI Demos GPT-Realtime-1.5 for Voice
  OpenAI demonstrated GPT-Realtime-1.5, an updated voice model aimed at making spoken AI interactions faster and more fluid. The release extends competition in real-time multimodal assistants and positions voice as a core interaction layer rather than an add-on.

• OpenAI Enhances Platform for Agentic Workflows

  OpenAI has reportedly upgraded its platform to support models carrying out more multi-step tasks autonomously without constant human prompts, reinforcing the shift toward agentic workflows and reducing the friction between instruction and execution.

• Introducing GPT-5.4 mini and nano

  OpenAI launched optimised smaller variants of GPT-5.4 tailored for coding, tool use, and agentic workflows, reducing latency and cost while supporting high-volume, multimodal applications.

Perplexity’s new answer to OpenClaw

Perplexity has launched Personal Computer, an always-on AI agent that runs on a dedicated Mac mini and connects local files, apps and active sessions with Perplexity’s cloud orchestration layer. The pitch is enterprise control: sensitive actions require approval, sessions include audit trails, and users get a kill switch. More broadly, it pushes AI agents beyond chat into persistent, remotely supervised digital workers that keep operating after the user steps away.

Google updates Stitch with AI-native design canvas and reasoning agent

Google has evolved Stitch from a prompt-to-UI generator into what it calls an AI-native software design platform. The new update adds a design canvas and reasoning-driven workflow so users can create, refine and collaborate on interfaces more iteratively, rather than generating one-off mockups. The significance is less about faster wireframes and more about compressing the path from app idea to interactive prototype, especially for non-technical builders.

Nvidia GTC 2026: Full-Stack AI Infrastructure Overhaul

Jensen Huang’s GTC keynote in San Jose (16 March) was the week’s defining product announcement. Nvidia unveiled the Vera Rubin platform, seven new chips, five rack-scale systems, and a new supercomputer architecture for agentic AI, targeting 10x performance-per-watt improvement over Grace Blackwell. The Groq 3 LPU debuted as the first commercial product from Nvidia’s $20B Groq acqui-hire, promising a 35x boost in tokens-per-watt when paired with Rubin racks (available Q3 2026). Nvidia also previewed the Kyber architecture, 144 GPUs arranged vertically for next-generation density, expected 2027, and released NemoClaw, a reference toolkit for building enterprise-ready AI agents using the OpenClaw open-source agent framework.

Quick Hits

• Sam Altman frames AI as a metered utility: Speaking at the BlackRock Infrastructure Summit, Altman said AI will likely be delivered like electricity, with users billed by intelligence or compute consumption. The framing could reshape how hyperscalers, regulators, and enterprises think about access, pricing, and antitrust in AI markets.

• Meta’s Rogue AI Agent Exposes New Enterprise Risk: Meta is investigating an internal AI agent incident after a system reportedly shared sensitive company information, and potentially some user-related data, with employees who were not authorised to see it.
  

  We work with leadership teams to move from experimentation to execution safely, commercially, and at speed. Talk to us.
  

Executive Summary

This week’s strongest AI signals were not about hype. They were about governance, operating risk, and practical deployment.

First, the Pentagon–Anthropic dispute showed how quickly AI policy, procurement, and political risk can collide. Whether or not most businesses ever face government intervention directly, the lesson is immediate: if you rely on third-party AI systems, your access, terms of use, and acceptable use boundaries can change faster than most organisations are prepared for. AI governance is no longer a future-state exercise. It is an operating requirement.

Second, AI agents continued their shift from interesting demo to usable workflow tool. OpenAI’s latest model claims stronger computer-use performance, Mastercard demonstrated an early form of agentic payment, and Anthropic expanded memory and migration features designed to make AI tools more persistent in day-to-day work. The practical takeaway is not “full autonomy is here.” It is that businesses should now be identifying narrow, high-frequency workflows where AI can save time under supervision.

Third, the policy and geopolitical backdrop is becoming more consequential. China’s AI push, proposed US chip export controls, and the UK’s copyright reversal all point to a market that is becoming more politically shaped, more regulated, and more fragmented. For leadership teams, this means AI strategy cannot sit only with innovation teams. It now touches legal, procurement, cyber, compliance, and workforce planning.

The organisations that will benefit most from AI over the next 12 months are unlikely to be the ones chasing every release. They will be the ones making deliberate choices now: where AI is allowed, where it is not, which workflows justify deployment, and what controls need to be in place before use scales further.

Why this matters to businesses

• Treat AI governance as a live operating issue, not a policy document. The Anthropic episode is a reminder that model access, vendor relationships, and acceptable-use standards can become commercial and reputational risks very quickly. If your teams are already using AI in customer-facing, regulated, or sensitive contexts, now is the time to define approved tools, prohibited use cases, escalation rules, and data-handling boundaries.

• Start with supervised workflow deployment, not broad AI transformation rhetoric. This week’s product launches matter because they improve persistence, memory, tool use, and action-taking. That makes AI more useful for repetitive internal work. Pick one or two bounded tasks now, such as research preparation, first-draft reporting, meeting follow-up, or structured admin, and test where AI can reduce turnaround time without weakening control.

• Bring AI decisions closer to the leadership agenda. This week’s developments were not just technical. They touched procurement, regulation, geopolitics, IP, workforce design, and platform dependency. Businesses should be asking today: which AI tools are already in use across the company, where are we exposed to vendor or regulatory change, and who internally owns the commercial and risk decisions as usage expands?

This Week’s Policy & Regulation Brief

Anthropic Blacklisted by the Pentagon - Then Used in Iran Strikes

The US Department of Defense designated Anthropic a “supply-chain risk” after the company declined to permit unrestricted military uses of its Claude AI models. Anthropic has said it will challenge the designation in court, arguing the classification is legally unsound.

However, Reuters reported that US military contractors continued using Claude in operational contexts during the same period the designation was being enforced. The episode highlights the growing tension between national-security policy and the practical reliance many organisations now have on commercial AI systems.

OpenAI Signs Pentagon Deal, Then Rewrites It

OpenAI secured a classified Pentagon contract reportedly worth around $200 million shortly after the US government moved to restrict the use of certain competing AI systems within federal agencies. CEO Sam Altman later acknowledged that the initial rollout of the deal appeared “opportunistic and sloppy,” and OpenAI subsequently clarified that its models cannot be used for domestic surveillance and that intelligence-related uses would require separate agreements.

The announcement also triggered debate inside the tech industry. Hundreds of employees across major AI companies, including Google and OpenAI, signed an open letter calling for clearer limits on the military use of advanced AI systems.

China Makes AI the Centrepiece of Its Five-Year Plan

China’s newly announced five-year national development plan places artificial intelligence at the centre of its industrial strategy. The blueprint references AI dozens of times and introduces a sweeping “AI+” programme designed to accelerate adoption across sectors including manufacturing, healthcare, robotics, and advanced computing.

Chinese policymakers increasingly view AI as a foundational technology for economic competitiveness and national security. For global businesses, the message is clear: China intends to remain a central player in the development and deployment of next-generation AI infrastructure and applications.

Chinese state enterprises and agencies move to curb in-office OpenClaw use over potential security risks

China has warned state-owned enterprises and government agencies against installing OpenClaw on office systems over security concerns, even as the open-source AI agent experiences a surge in adoption across the country (source: Reuters). Local governments and tech giants such as Tencent and Alibaba have been promoting the technology through subsidies, cloud deployments, and developer programmes, fuelling what many observers describe as an “OpenClaw boom.”

US Drafts Sweeping AI Chip Export Controls

The US Commerce Department is preparing new rules that could significantly expand oversight of global AI chip exports from companies such as Nvidia and AMD.

According to early reporting, the proposed framework would introduce a tiered system. Smaller shipments could receive streamlined approval, while large-scale AI infrastructure deployments would face stricter review and potential government-to-government agreements.

If implemented, the rules would give Washington far greater influence over where advanced AI infrastructure is built worldwide, reflecting the growing view that compute capacity is a strategic asset rather than simply a commercial product.

UK Kills AI Copyright Bill, Delays to 2027

Following strong opposition from the creative sector, the UK government is reconsidering earlier proposals that would have allowed AI developers to train models on copyrighted content under broad opt-out rules. A House of Lords committee reviewing the issue has recommended a “licensing-first” framework that would require AI developers to obtain permission or licences before using copyrighted material for training.

Yann LeCun's $1B bet against LLMs

Turing Award winner Yann LeCun (formerly at Meta) launched Advanced Machine Intelligence with a $1.03 billion!!! seed round to build AI systems that focus on real-world physical understanding rather than language models.

Large Language Models work by predicting the next token in a sequence. That’s incredibly powerful for language tasks but has fundamental limitations.

LeCun’s argument:

• They don’t understand the world

• They don’t reason in a structured way

• They don’t have persistent memory

• They can’t plan actions over time

If successful, the approach could open new pathways for AI development in areas such as robotics, manufacturing, and scientific discovery, fields where understanding the physical world matters as much as generating text. « One to watch.

Oregon Passes First 2026 AI Chatbot Safety Bill

Oregon's SB 1546 cleared both chambers with near-unanimous support, requiring chatbot operators to disclose AI identity, implement suicide-prevention protocols, and add child-safety protections. Sent to the Governor's desk. This follows the Google Gemini wrongful death lawsuit, in which a family alleges the chatbot cultivated a delusional relationship and coached a 36-year-old man's suicide. Expect more state action.

Model & Platform Updates

OpenAI Launches GPT-5.4 - The First Model That Uses a Computer Better Than You (Allegedly)

Released 5 March, GPT-5.4 is OpenAI's most significant release this year. It's the first general-purpose model with native computer-use capabilities, scoring 75% on the OSWorld benchmark, above the 72.4% human baseline. It has a 1 million-token context window (double the previous generation), 33% fewer hallucinations than GPT-5.2, and a new "Tool Search" system that cuts token consumption by 47% in agent workflows. Available in standard, Thinking, and Pro variants. Balyasny Asset Management has already deployed it across 95% of its investment teams, reporting that complex research tasks that took days now complete in hours.

Google’s NotebookLM Can Now Turn Your Research Into a Documentary

Google introduced generative AI-powered Cinematic Video Overviews in NotebookLM, enabling the tool to transform research documents, notes, and source material into narrative-driven videos. The feature automatically synthesises information, structures it into a storyline, and generates visual sequences that explain complex topics in a documentary-style format. It represents a step beyond summarisation, turning research into shareable multimedia outputs and highlighting AI’s growing role in knowledge synthesis, content production, and the transformation of written analysis into dynamic storytelling.

Zoom introduces an AI-powered office suite, says AI avatars for meetings arrive this month

Do you remember during COVID the clever ways people tried to fool their bosses they were actually working, Well - Zoom is bringing AI avatars into meetings this month as part of a broader push into AI-powered workplace software.

Zoom says the avatars are photorealistic representations that can mimic your appearance, expressions, and lip/eye movements in meetings, so you can appear present without being fully camera-ready. They’re also intended to work in Zoom Clips / asynchronous video messages, where an avatar can deliver updates on your behalf. Zoom is pairing this with real-time deepfake risk detection during meetings.

Mastercard brings agentic payments to life in Singapore with DBS and UOB

Mastercard completed its first live authenticated AI-agent-based payment transaction in Singapore with DBS and UOB, offering an early glimpse of what “agentic payments” could look like in practice. In simple terms, agentic payments allow an AI agent to not only recommend or select a product or service, but also complete the transaction on a user’s behalf within pre-approved rules and security checks. That matters because it moves AI from search and assistance into real commercial action. The challenge now will be trust, permissions, fraud controls, and defining how much autonomy consumers and businesses are willing to hand over.

Anthropic Ships Claude Memory Import and Free-Tier Memory

Anthropic has introduced new tools allowing users to import conversation histories and context from other AI assistants, alongside expanded memory capabilities designed to make Claude more persistent across workflows.

The goal is to move beyond single-session chat interactions and enable AI systems to retain context over time, a key requirement if AI is to function as a practical day-to-day work assistant rather than simply a question-and-answer tool.

OpenAI Codex Security - AI That Hunts Vulnerabilities Autonomously

Formerly codenamed Aardvark, Codex Security is an autonomous agent that identifies, validates via sandbox exploits, and patches software vulnerabilities. During beta, it found novel flaws in OpenSSH and Chromium, reduced alert noise by 84%, and cut false positives by 50%. Available to Enterprise, Business, and Education customers. The Anthropic-Mozilla collaboration tells a similar story: Claude Opus 4.6 discovered 22 CVEs in Firefox in two weeks, roughly matching two months of typical high-severity bug reports.

Nvidia Is Reportedly Preparing an Open-Source AI Agent Platform

Nvidia is reportedly preparing an open-source platform for building autonomous AI agents, extending its ambitions beyond chips into the software layer that powers AI systems. According to reports ahead of GTC, the platform would integrate parts of Nvidia’s existing AI stack, including NeMo and related infrastructure, with tools designed to help models plan tasks, use external tools, and operate across enterprise workflows. If launched as reported, it would strengthen Nvidia’s position as a core infrastructure provider for agentic AI.

Quick Hits

• Block cuts 40% of workforce, CEO credits AI - Jack Dorsey cut 4,000 jobs and told the press "a much smaller team equipped with these tools can achieve more." Block shares surged 25%. Dorsey predicted most companies would reach the same conclusion within a year. 45,000+ tech jobs have been cut in Q1 2026 with AI cited as the primary driver.

• AI-generated war content reaches industrial scale - BResearchers and journalists have identified a surge in AI-generated images and videos related to the Iran conflict circulating across social media platforms. Investigations by outlets including Reuters have debunked multiple pieces of fabricated footage that gained widespread traction online. The episode highlights how generative AI is accelerating the speed and scale at which misinformation can spread during geopolitical crises.

• Meta acquires Moltbook, the social network for OpenClaw bots - Meta has acquired Moltbook, a platform built for autonomous OpenClaw agents to post updates, interact, and coordinate with one another. The acquisition gives Meta an early foothold in what some are calling the emerging “agent internet”, digital environments designed not just for humans, but for AI systems acting on their behalf. It signals that Meta sees value not only in building models, but in owning the social and distribution layers around autonomous AI.
  

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There’s a statistic that gets quoted a lot right now: 78% of organisations are using AI in at least one business function. McKinsey’s State of AI survey published that figure, and by their most recent update it’s climbed to 88%. The headlines write themselves. AI adoption is mainstream. Businesses are all in.

Except they’re mostly not.

Dig into what “using AI” actually means for the majority of those organisations and you’ll find a familiar picture. They’ve got Microsoft 365 Copilot switched on. Maybe it was bundled into their existing licence. Maybe IT rolled it out as a pilot. Either way, someone in the business is using Copilot to summarise a Teams meeting or tidy up an email in Outlook, and that’s being counted as AI adoption.

To be clear: that’s not a bad place to start. Copilot is a genuine productivity boost for the tasks it handles. If it’s helping people draft emails faster or catch up on meetings they missed, that’s real value. Nobody should dismiss it.

But a key distinction to make is: Copilot is a feature (or capability), not a strategy.

It’s embedded inside tools you already use, which is its strength and its limitation. It’s excellent at improving things you were already doing in Microsoft’s ecosystem. What it doesn’t do (or i haven’t seen strong evidence) is help you research a competitor, build a marketing asset from scratch, analyse data in a fundamentally new way, generate images or video, automate a workflow that spans multiple platforms, or act as a genuine thinking partner when you’re wrestling with a complex business problem.

If Copilot is the only AI your business runs, you’re using AI the way you’d use spell-check. Helpful, but not transformative.

Microsoft knows this, incidentally. From July 2026, Copilot capabilities are being folded directly into Microsoft 365 plans rather than sold as a separate add-on. It’s becoming table stakes. The question for businesses isn’t whether they should have Copilot, they’ll get it anyway. The question is what they build on top of it.

Over the past three months, we’ve shared our thoughts on dozens of AI tools in depth. We’ve covered LLMs and research tools (Part 1), marketing and design tools including image generation, video, voice, social media and presentations (Part 2), and operational tools that move the needle on day-to-day business processes (Part 3). That full stack can quickly add up to serious amount each month (even when just trialling to see what works and what deosn’t). We use premium tiers across multiple platforms, and we do it because testing and evaluating these tools is core to what AI-Proof does.

But most businesses don’t need all of it. And buying tools because they look impressive, in business as in life, rarely solves a problem and almost never leads to sustained use. The subscriptions pile up, the logins gather dust, and six months later someone in finance asks why you’re spending £200 a month on software nobody’s using. This became a problem here internally trials which added no value but we forgot about - so we vibe coded with Claude a subscription tracker, to remind us to cancel things before we got charged again.

So we asked ourselves a simple question: if we were advising a business that was starting from scratch with a tight budget, what would we actually tell them to buy?

Before You Spend Anything: What Do You Actually Need AI For?

The mistake most businesses make is starting with tools. They read a review, buy-in to the hype (this is all to easy), sign up for a trial, poke around for a few days, and either get hooked on something they don’t really need or abandon something genuinely useful because they didn’t know what to do with it.

Start with jobs, not tools.

Most businesses need AI for four things. Not all four on day one, but these are the capability areas where AI delivers measurable value:

A thinking and writing partner. This is the big one. Drafting content, editing documents, brainstorming ideas, summarising long reports, preparing for meetings, analysing data, writing proposals. The tasks that eat hours every week and benefit enormously from having an intelligent collaborator that never gets tired, never judges a rough first draft, and can process information faster than any human.

Research. Pulling together market intelligence, understanding competitors, synthesising information from multiple sources, getting up to speed on unfamiliar topics quickly. This is where AI stops being a fancy autocomplete and starts genuinely expanding what a small team can know and do.

Visual content. Social media graphics, presentations, basic image creation, brand materials. Not every business needs this, but if you’re producing any kind of marketing output, the gap between “I can’t design” and “this looks professional” has been almost entirely closed by AI tools.

Workflow automation. Repetitive tasks, data processing, content pipelines, anything that follows a predictable pattern and currently eats someone’s time. This is the most advanced capability area and typically the last one businesses should tackle, because you need to understand your processes before you can automate them.

Figure out which of these matter most to your business before you open your wallet. If you’re a consulting firm, the thinking partner and research capabilities will transform your output. If you’re a marketing agency, visual content and workflow automation might be the priority. If you’re a small business with a lean team wearing multiple hats, the thinking partner alone could be worth more than the next hire you can’t afford.

That clarity is what separates a useful AI investment from another piece of shelfware.

Tier 1: Starter - Around £20 a Month

One subscription. That’s it. One LLM that becomes your daily thinking partner, supported by free tools that fill the gaps.

This is where most businesses should begin, and where many should stay for longer than they think. The temptation is to jump straight to multiple subscriptions, but there’s a genuine skill curve with AI tools. Using one LLM well, learning its strengths, understanding how to prompt effectively, building it into your daily routine, will deliver more value than having three subscriptions you use superficially.

The core decision: which LLM?

At this price point, you’re choosing between Claude Pro (around £18 per month), ChatGPT Plus (around £20 per month), or Google Gemini Advanced (around £19 per month). All three are excellent (and update frequently). All three have areas where they outperform the others. We’ve covered our detailed assessments in Part 1 of the AI Stack series, but here’s the practical buying guidance.

If your work is primarily writing, analysis and document-heavy tasks: Claude.

Claude’s writing quality is, in our experience, the strongest of the three. It handles nuance well, follows complex instructions reliably, and its Cowork feature means it can operate directly from your local files, completing tasks while you’re away from your desk. The Microsoft plugins for Excel and PowerPoint are exceptional. If your AI use is going to centre on producing written content, analysing documents, preparing reports, or working within Microsoft Office, Claude is the one to start with.

If you need the broadest ecosystem and creative tools: Gemini Advanced.

Gemini’s strength isn’t just the chat interface. It’s the ecosystem. Your subscription gives you access to Nano Banana for image generation (which handles business graphics, text rendering and product shots better than most dedicated tools), Veo 3 for video generation, and deep integration with Google Workspace. If you’re already a Google shop and you want image and video capabilities alongside your LLM, Gemini gives you the most for your money at this tier.

If you want the most established platform with the widest plugin ecosystem: ChatGPT Plus.

ChatGPT has the largest user base, the most extensive library of custom GPTs, and features like Atlas that provide real-time guidance while you work. If you’re likely to lean heavily on community-built tools and integrations, or if your team already has some familiarity with ChatGPT, it’s a solid choice. The new Go tier at around £8 a month is also worth considering if even £20 feels like a stretch, though you’ll sacrifice some capability.

The free tools that complete Tier 1:

Regardless of which LLM you choose, two free tools should be in your stack from day one.

Google NotebookLM is, in our view, the best research tool available. It’s free. You upload documents (be sensible here), websites, youtube links, and data sources, then interact with that corpus through natural language. It synthesises information from your specific sources with remarkable accuracy. It generates reports, presentations, even podcast-style audio content. For any business doing research, competitive analysis, or document synthesis, NotebookLM is non-negotiable. We recommend it to every business we work with, regardless of budget.

Canva Free gives you a genuinely capable design tool at no cost. The template library alone is worth it. You won’t get everything the Pro tier offers, but for a business just starting with AI-assisted design, the free tier covers the basics.

What Tier 1 gives you: A genuine AI thinking partner for writing, analysis and daily tasks, plus research capability and basic design tools. For around £20 a month, you have a setup that goes meaningfully beyond Copilot.

What you’re giving up: You can’t compare outputs across multiple LLMs (which is how we catch errors and find the best response). A workaround here is to use perplexity.ai which is an umbrella to all the available LLM’s. You don’t have professional-grade design or social media tools. You have no video, voice, or automation capability. For many businesses, especially in the first three to six months of AI adoption, that’s absolutely fine.

Tier 2: Growth - Around £50 to £60 a Month

This is where things start to compound. Two LLM subscriptions, plus one professional creative tool, means you can cross-reference AI outputs, maintain a stronger creative workflow, and start to build AI into multiple areas of the business.

The combinations depend on what your business prioritises.

For content-heavy businesses (marketing, consulting, professional services):

Claude Pro (£18) + Gemini Advanced (£19) + Canva Pro (£13) = £50 a month.

This is, honestly, the combination we’d recommend to most businesses at this price point. Claude gives you the best writing and document partner. Gemini gives you the Google creative ecosystem: Nano Banana for images, Veo 3 for video clips, and deep Workspace integration. Canva Pro unlocks the full template library, brand kit features, scheduling and social media posting. Together, these three cover the thinking partner, research, and visual content needs comprehensively.

You also get NotebookLM free alongside your Gemini subscription, so your research capability remains strong. And you can now do something genuinely powerful: run the same prompt through Claude and Gemini, compare the results, and use the better output. This comparative approach is how we work every day, and it catches hallucinations, biases, and gaps that a single model misses.

For operations-focused businesses (finance, logistics, professional services with heavy document workflows):

Claude Pro (£18) + ChatGPT Plus (£20) + Canva Pro (£13) = £51 a month.

If your work involves less creative output and more document analysis, data work, and process improvement, swapping Gemini for ChatGPT makes sense. ChatGPT’s Atlas feature for real-time guidance, combined with Claude’s document and Excel capabilities, gives you a strong operational toolkit. You lose the Google creative tools (Nano Banana, Veo 3), but if video and image generation aren’t priorities, that trade-off works.

For the marketing and brand-focused business:

Gemini Advanced (£19) + Canva Pro (£13) + one specialist tool (e.g. ElevenLabs).

If visual content is your primary need, you could take the remaining budget and put it toward a specialist tool instead of a second LLM. ElevenLabs starts at around £5 a month for voice generation, or you could put the budget toward stock imagery or a specific design tool. This path sacrifices the comparative prompting approach but maximises your creative capability.

What Tier 2 gives you: The ability to cross-reference AI outputs for quality control, professional creative tools, and coverage across writing, research, design, and in some combinations, video and image generation.

What you’re giving up: Specialist tools for voice, talking head video, and workflow automation. You’re also not yet at the point where AI is handling end-to-end processes, it’s still primarily augmenting individual tasks.

Tier 3: Full Stack - £150 and Beyond

This is where AI stops being a set of individual tools and starts becoming infrastructure, an ecosystem that works with how you do business. At this level, you’re building the kind of capability we described across all three parts of our AI Stack series.

The foundation stays the same: your LLM subscriptions, Canva Pro, NotebookLM. On top of that, you’re adding specialist tools based on the capabilities that matter most to your business.

Voice and video: ElevenLabs (from around £5 a month for the starter tier, scaling up for more usage) gives you voice cloning and synthesis. HeyGen (from around £24 a month) gives you talking head video with avatar cloning. Together, these transform what a small team can produce. Content that would have required a studio, a camera crew, and a production budget is now achievable with subscriptions and a laptop. We covered this in detail in Part 2, and it remains one of the most genuinely transformative capability areas.

Workflow automation: n8n (free self-hosted, or paid cloud plans) or Zapier (from around £16 a month) let you connect your AI tools into automated workflows. This is where the real efficiency gains compound, because you move from “using AI for individual tasks” to “AI handling entire processes.” But a word of caution: automation is the last thing you should add, not the first. You need to understand your processes deeply before you automate them, otherwise you’re just automating chaos more efficiently.

All three LLMs at professional tiers: At this budget, running Claude, ChatGPT and Gemini together becomes practical. The ability to compare outputs across all three, use each for its strengths, and have redundancy when one model is underperforming is genuinely valuable for businesses where AI quality matters.

What Tier 3 gives you: A comprehensive AI capability that covers writing, research, design, image generation, video, voice, presentations, and workflow automation. This is a production-grade toolkit.

Who it’s for: Businesses where AI has become a core part of how they operate, not just a convenience. If you’ve been through Tier 1 and Tier 2, you’ve identified which capabilities deliver real value for your specific business, and you’re expanding deliberately. That deliberate path matters. The businesses that jump straight to Tier 3 typically waste money on tools they don’t use properly. The ones that build up through the tiers invest in what they’ve proven works.

The Mistakes to Avoid

Having tested, subscribed to, and cancelled more AI tools than we care to admit, here are the pitfalls we’d steer you away from.

Subscribing to everything at once and using none of it properly. This is the most common mistake. Five subscriptions used casually will always deliver less than one subscription used well. Master one LLM before you add a second. Build prompting discipline before you add creative tools (see our post on prompting). Automate after you’ve understood your processes, not before.

Chasing specialist tools before you’ve mastered a core LLM. The AI social media tools, the presentation generators, the writing assistants that promise to replace your content team, we’ve tried them all. Almost without exception, the dedicated AI tools overpromise and underdeliver compared to a well-prompted LLM. Get good at prompting first. Our workflow is LLM (Claude in our case) + Canva (and NotebookLM for creative inspiration). You’ll be surprised how much a single LLM can handle.

Paying for capabilities your existing stack already provides. This was one of our key findings across the Stack series. Many specialist tools are solving problems that your LLM subscription or your Canva subscription already handles. Before you add a new tool, ask: have I actually tried doing this with what I’ve already got? The answer is often no.

Assuming more expensive means better. It frequently doesn’t. Our most expensive cancellation, Grok, was also our most disappointing. Some of the most valuable tools in our stack, NotebookLM, Canva, the free tiers of various platforms, cost nothing or very little. Price and value have a loose relationship in AI tooling.

Start With One. Use It Properly.

If there’s a single message that runs through everything we publish at AI-Proof, it’s this: AI value comes from structured use, not from tool hoarding.

One well-used subscription with good prompting practice will outperform five subscriptions used casually, every single time. The businesses getting real value from AI aren’t the ones with the biggest software budgets. They’re the ones that have taken the time to understand what they need, chosen their tools deliberately, and built the discipline to use them consistently.

If your business is still relying on Copilot alone, you’re not behind. You’ve started. The next step isn’t to buy everything, it’s to pick one tool, invest the time to use it well, and build from there.

That’s how AI stops being a line item and starts being a capability.

This article builds on our AI Stack series: Part 1 covered LLMs and research tools, Part 2 explored marketing and design tools, and Part 3 reviewed operational tools. If you want the detailed reviews behind our recommendations, start there.

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Executive Summary

This was the week AI became a political weapon.

The Trump administration blacklisted Anthropic as a “supply chain risk to national security” - a designation previously reserved for hostile foreign states - after the company refused to remove guardrails on mass surveillance and autonomous weapons from its Pentagon contract. OpenAI swooped in hours later with its own classified-network deal, triggering the largest consumer backlash in AI history: ChatGPT uninstalls surged 295%, Claude hit number one on the App Store, and hundreds of tech workers across Silicon Valley signed open letters opposing unchecked military AI use.

Meanwhile, OpenAI closed a $110 billion funding round - the largest private raise in history, and Nvidia committed $4 billion to photonics companies to secure the optical interconnects that will power next-generation AI data centres. Big Tech is now forecast to spend $650 billion on AI infrastructure in 2026 alone.

On the model front, OpenAI shipped GPT-5.3 Instant with a 27% hallucination reduction, Google launched Gemini 3.1 Flash-Lite at a tenth the cost of Pro, and DeepSeek V4, a trillion-parameter open-weight model from China, is expected imminently. For businesses, the week crystallised three uncomfortable truths: AI is now inseparable from geopolitics, capital concentration is accelerating, and the model release cadence has become a permanent sprint.

Why this matters to businesses

• Your AI vendor choices now carry political risk - The Anthropic blacklisting is a wake-up call. Your choice of AI provider is no longer just a technology decision - it is a supply chain risk decision with geopolitical exposure. If your business relies on a single model provider and that provider falls out of favour with a government you operate under, your access could be disrupted overnight. Diversify your model dependencies now.

• The consumer backlash is a leading indicator for enterprise The 295% surge in ChatGPT uninstalls was consumer-driven, but enterprise buyers are watching closely. Companies seen as ethically compromised in AI face reputational risk with customers, employees, and partners. Vendor values on safety and governance are now material considerations, not marketing fluff.

• The cost of inference is collapsing - plan accordingly Google’s Flash-Lite at $0.25 per million input tokens is a tenth the price of Pro. If you locked in AI budgets based on pricing from six months ago, revisit them. Workloads you deemed too expensive to automate may now be viable.

• Your action this week: Audit your organisation’s AI vendor exposure. Map which providers you depend on, what alternatives exist for each use case, and how quickly you could migrate if access were restricted. The businesses that treat AI procurement like any other critical supply chain will be the ones least disrupted when the next shock hits.

This Week’s Policy & Regulation Brief

Trump Administration Blacklists Anthropic, Designates It a National Security Risk

President Trump ordered all federal agencies to cease using Anthropic's technology. Defence Secretary Hegseth designated the company a "supply chain risk to national security" after Anthropic refused unrestricted military use of Claude, including mass domestic surveillance and fully autonomous weapons. Anthropic is challenging the designation in court.

OpenAI Signs Pentagon Deal

OpenAI Signs Pentagon Deal, Then Amends It Under Pressure Hours after Anthropic's blacklisting, OpenAI signed a $200 million classified-network contract. Following a 295% surge in ChatGPT uninstalls and employee open letters, OpenAI added explicit anti-surveillance safeguards. CEO Sam Altman conceded the original announcement was "rushed" and "looked opportunistic."

US Supreme Court Draws the Line on AI Creativity: Fully AI-Generated Works Cannot Be Copyrighted

The US Supreme Court declined to hear Thaler v. Perlmutter, leaving intact a lower-court ruling that copyright protection requires human authorship. Works created entirely by artificial intelligence without meaningful human involvement cannot be copyrighted under US law. The decision effectively settles the issue for now, reinforcing that AI can assist creativity but cannot legally be recognised as the author of a protected work.

OpenAI Closes Record $110 Billion Funding Round

Amazon led with $50 billion, alongside $30 billion each from Nvidia and SoftBank, valuing OpenAI at $730–840 billion. AWS becomes the exclusive third-party cloud for OpenAI's enterprise platform. This cements deep entanglement between the dominant chip maker, the largest cloud provider, and the leading model developer.

March 11 Federal AI Deadline Looming

Three simultaneous deadlines converge next week: the Commerce Department must evaluate state AI laws, the FTC must issue AI deception guidance, and broadband funding tied to state AI regulation takes effect. Colorado's AI Act is directly in the crosshairs.

EU AI Act Enforcement Looms: Companies Face €35M Fines and Criminal Deepfake Penalties from August 2026

The EU AI Act enters full force on 2 August 2026, giving organisations just five months to prepare. Non-compliance could trigger fines of up to €35 million or 7% of global turnover. Some member states are already moving faster: Italy has introduced criminal penalties for the dissemination of deepfakes. For companies deploying AI in Europe, regulatory compliance is rapidly becoming a board-level priority.

AI’s New Cold War: Anthropic Warns Western Models May Be Fueling China’s AI Breakthroughs

Anthropic has warned that Chinese AI labs may be using Western frontier models to help train their own systems, raising concerns about an accelerating global AI arms race. The comments come as reports suggest DeepSeek’s upcoming V4 model may have been trained on restricted Nvidia Blackwell chips, potentially breaching US export controls. Together, the developments highlight growing fears that Western technology is indirectly fuelling China’s next generation of advanced AI models.

DeepSeek Allegedly Trained V4 on Banned Nvidia Blackwell Chips

A senior Trump administration official told Reuters that DeepSeek’s forthcoming V4 model was trained on Nvidia’s most advanced chips in potential violation of US export controls. If confirmed, this would be the most significant export control breach in the AI era.

Model & Platform Updates

OpenAI Rolls Out GPT-5.3 Instant as Default Model with Major Hallucination Reduction

OpenAI has released GPT-5.3 Instant as its new default model, delivering a 26.8% reduction in hallucinations by incorporating web-sourced answers. The update also reduces unnecessary refusals and improves conversational flow, producing more natural interactions. The release signals continued focus on reliability and usability as model providers compete to make AI systems more trustworthy for everyday and enterprise use.

Google Slashes AI Costs with Gemini 3.1 Flash-Lite as Gemini 3 Pro Heads for Shutdown

Google has launched Gemini 3.1 Flash-Lite, a significantly cheaper and faster model designed for high-volume AI workloads. Priced at $0.25 per million input tokens, roughly a tenth the cost of Gemini Pro, it is also 45% faster than the previous generation. At the same time, Google has scheduled Gemini 3 Pro for deprecation on 9 March, giving developers just days to migrate their applications.

Google ships Gemini 3.1 Pro with doubled reasoning performance

Gemini 3.1 Pro more than doubled reasoning performance over 3 Pro on ARC-AGI-2, scoring 77.1%, at unchanged pricing. The rollout went live across the Gemini app, Vertex AI, and Google AI Studio on the same day, combined with Alphabet's 4% stock jump on the enterprise rollout.

Enterprise Agent Programmes Expand

Anthropic unveiled pre-built agentic workflows through Cowork plugins for finance, legal, HR, and engineering. OpenAI announced multi-year Frontier Alliances with Accenture, BCG, McKinsey, and Capgemini. The competition for enterprise AI distribution is fully joined.

DeepSeek V4 Expected Imminently

A trillion-parameter, million-token-context open-weight model with conditional memory and major efficiency gains. If the benchmarks hold up, this positions a Chinese open model closer to frontier-lab capabilities than ever before.

Quick Hits

• ChatGPT consumer revolt intensifies - Uninstalls surged 295%, Claude hit number one on the App Store, one-star ratings spiked 775%, and Katy Perry publicly switched to Claude.

• Big Tech AI capex hits $650 billion for 2026 - Bridgewater warns the AI boom has entered a “more dangerous phase.”

• February VC: $189 billion globally, 90 percent AI - Three companies alone accounted for 83 percent of all venture dollars raised.

• OpenAI hints at ChatGPT 5.4 - This is all we know

  

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As with everything in this series, we’ve tested these tools with real workloads and real money. Some delivered genuine value from day one. Others were expensive lessons in hype. Here’s what we found.

Email Assistants: The Promise That Hasn’t Landed Yet

The pitch is seductive. An AI that lives in your inbox, triages your messages, drafts replies, and keeps you at inbox zero without lifting a finger. We loved the idea. The reality, unfortunately, hasn’t caught up.

We trialled several of the leading email assistants on the market, and the pattern was consistent: what you’re actually paying for is a glorified email classification tool. They tag your messages with labels and categories, which is mildly useful, but they rarely create anything of genuine value. The drafts they produce tend to miss the tone, misread the intent, or reply to the wrong thread entirely. In any kind of client-facing or sensitive context, you’d never let them send unsupervised, and most of the drafts need rewriting anyway, which rather defeats the purpose.

The one we had the most success with was the Perplexity Email Assistant, available exclusively to Perplexity Max subscribers at $200 per month. It connects to Gmail and Outlook, can draft contextual replies, apply smart labels, and even manage calendar scheduling via email. It does draft some interesting responses that could save time on routine correspondence. But reliability has been a persistent issue. Emails sometimes fail to send, or you’re left uncertain whether they actually went out. We found ourselves restarting the integration more often than felt acceptable for a tool at that price point. The assistant also only works with a single email address at present, which limits its usefulness for anyone juggling multiple accounts.

Our verdict on email assistants as a category: unconvinced. The technology isn’t mature enough to trust with anything beyond the most routine correspondence, and the cost-to-value ratio doesn’t stack up. We’ve parked this category for now and will revisit as the tools improve.

Meeting Notetakers: The Gateway Drug to AI Adoption

If there’s one AI use case that has genuinely earned its place in the modern workplace, it’s the meeting notetaker. When surveys report that 80% of companies are now using AI, this is overwhelmingly where that adoption sits, and for good reason. The workflow is simple and the value is immediate: an AI joins your meeting, records the audio, transcribes the conversation, and then generates a structured summary with action items and minutes. It saves time, improves accountability, and ensures nothing falls through the cracks.

We’ve tested several vendors in this space, each with their own strengths and pricing models.

Otter.ai is very capable. Its Pro plan runs at $8.33 per user per month on an annual subscription (or $16.99 monthly), offering 1,200 minutes of transcription and the ability to join meetings on your behalf when you’re double-booked. The Business plan at $20 per user per month bumps that to 6,000 minutes with enhanced team features like speaker tagging and collaborative action items.

Cluely takes a different approach. Rather than just recording and summarising after the fact, it positions itself as a real-time AI assistant that sits alongside your call, surfacing suggestions and talking points as the conversation happens. It reads both the live transcript and whatever’s on your screen. The Pro tier costs $20 per month. It’s an interesting concept, though it doesn’t record audio, which means you lose a layer of verifiability. There are also broader ethical questions about the tool, given its origins in interview assistance, and some companies now treat its use as a policy violation in assessment contexts.

Microsoft Copilot in Teams is worth mentioning for organisations already in the Microsoft ecosystem. It handles transcription and summarisation competently, though it’s bundled into the broader Copilot licensing rather than priced as a standalone tool.

The one we use day in, day out is Fireflies.ai. It records the audio, produces a clean transcription, and generates summaries with action items. The Pro plan is $10 per user per month (annual billing), and for most small teams that’s more than sufficient. The Business plan at $19 per user per month adds video recording, conversation intelligence, and unlimited storage. It’s reliable, it’s well-integrated, and it just works.

That said, we’d sound a note of caution about the long-term economics of this category. Apple Notes on Mac now includes a record-and-transcribe feature. If you already have access to an LLM, there’s nothing stopping you from recording a meeting natively and then throwing the transcript at the model for summarisation and action items. What tools like Fireflies give you is convenience: everything in one package without the multi-step process. But as native OS capabilities and local LLMs improve, the paid notetaker market may face significant commoditisation. For now, though, the value is real and the entry price is low enough to justify.

Workflow Automation: Where AI Starts Earning Its Keep

This is where things get genuinely exciting, and where we’d encourage any business that’s moved beyond basic prompting to focus next.

There’s a natural hierarchy to getting value from AI. It starts with the one-shot prompt: type something into an LLM and get a result. The problem is that one-shot prompts are inconsistent. There’s no structure, no repeatable format, and the quality varies wildly. The first upgrade is to adopt structured prompting, using a clear framework of role, context, task, and format (ideally in XML-style tags). Follow that discipline and your outputs become dramatically more consistent and useful.

But the real step change comes when you start chaining structured prompts together into automated workflows. Instead of manually running prompts one at a time, you build a pipeline where the output of one step feeds into the next, with logic gates and filters along the way. This is where AI stops being a novelty and starts becoming operational infrastructure.

There are several platforms competing in this space:

Zapier is the most established, with plans starting on a free tier (100 tasks per month) and a Professional plan from $19.99 per month for 750 tasks. It’s powerful and well-integrated with a vast library of app connections. The unified plan structure now bundles workflows, data storage, forms, and AI tool connections into single subscriptions. Our honest assessment: it’s excellent, but we find the interface more complicated than it needs to be, particularly when building multi-step automations with branching logic.

Gumloop is a newer entrant backed by Y Combinator, offering a visual drag-and-drop interface for building AI-powered workflows. The Solo plan starts at $37 per month for 10,000 credits. Be aware, though, that credit consumption can be unpredictable. Each advanced AI call (using models like GPT-4o or Claude) costs 20 credits, and a single multi-step workflow can burn through over 100 credits in one execution. We found the token-burn pricing model made costs difficult to forecast and expensive for routine tasks.

Clay is worth mentioning, though it’s more focused on sales intelligence and data enrichment than general-purpose automation. Plans range from free to $800 per month, and it’s extremely powerful for go-to-market workflows. But it’s a specialist tool rather than a broad automation platform.

Our preferred platform, and the one we’d recommend for most businesses, is n8n. It’s open-source, it’s flexible, and its pricing model is the most sensible in the category. The cloud-hosted Starter plan begins at EUR 24 per month for 2,500 executions, with Pro at EUR 60 for 10,000. Crucially, a workflow with 15 steps still counts as a single execution, not 15 billable actions, which is a fundamental difference from Zapier’s per-task billing. And if you’re technically inclined, the self-hosted Community Edition is completely free with unlimited executions.

We run n8n both on a virtual private server and locally. Our primary use case is sector intelligence: every night around 1am, an automated workflow spins up and scans the last 24 hours across a range of news sources, blogs, Reddit threads, and industry publications. It pulls in roughly a thousand stories, then passes them through three sequential AI filtering stages, each using a different LLM call to assess relevance. By the time it’s finished, it commits somewhere between 10 and 20 genuinely relevant stories and delivers them to our inbox by morning. That single workflow replaces what would otherwise be hours of manual scanning and reading, and it runs every single day without intervention.

These kinds of automations are absolute game changers. If you’ve got comfortable with AI prompting and you’re looking for the next step, workflow automation is where you should be heading. Find the tasks in your business that grind, the repetitive, low-value work that keeps people from doing higher-value thinking, and automate them. The tools do break occasionally and they’re not all perfect, but the return on investment is substantial.

AI Assistants: The Future, But Not Quite the Present

The concept of a persistent AI assistant, one that knows your preferences, manages your calendar, handles your admin, and operates across your messaging platforms, is arguably the most compelling long-term vision in the entire AI space. And in early 2026, one project brought that vision closer to reality than anything else: OpenClaw.

Originally published in November 2025 by Austrian developer Peter Steinberger under the name Clawdbot, the project was renamed to Moltbot in January 2026 following trademark complaints from Anthropic, and then to OpenClaw three days later. Despite the naming turbulence, the project exploded in popularity, amassing 140,000 stars and 20,000 forks on GitHub by early February, with a community registry (ClawHub) hosting over 5,700 skills.

The concept is powerful. OpenClaw is a self-hosted AI assistant that connects to the messaging platforms you already use: WhatsApp, Telegram, Slack, Discord, Teams, even iMessage. Unlike a ChatGPT or Claude conversation that starts fresh each time, OpenClaw maintains continuous memory across all interactions. It remembers your preferences, past conversations, and contextual details. It can manage emails, browse the web, and automate tasks autonomously.

We built our own OpenClaw agent, which we named Arthur, and it worked impressively well for what it is. But we’ve mothballed Arthur for now, and here’s why.

First, the API costs add up quickly. Every interaction routes through an LLM API, and for an assistant that’s meant to be always-on and responsive, the token consumption becomes significant. Second, when we looked honestly at the use cases people are building with it, most of them can be achieved more simply and cheaply with a structured prompt or a scheduled automation. We get a daily briefing every morning from an LLM covering the weather, key business news, what happened with Arsenal, the latest from Formula One, and overnight AI developments. That doesn’t need to route through an API-powered agent; it’s a straightforward automation.

The technology itself is still firmly in beta. There are bugs, reliability issues, and the kind of rough edges you’d expect from a project that’s been around for barely three months. On February 14th, Steinberger announced he was joining OpenAI and that the project would transfer to an open-source foundation, which should help with long-term governance but also introduces uncertainty about direction.

We think AI assistants are unquestionably the future. The idea of an AI employee that you can task, message, and delegate to is extraordinarily powerful. But right now, adopting one means accepting early-adopter pain for limited practical gain over simpler alternatives. It’s a bit like the MP3 player era before the iPhone arrived: the technology works, but something transformative is coming that will make the current generation look primitive. When a major LLM provider fully backs this paradigm, perhaps OpenAI building on Steinberger’s work, that’s when it will become a must-have. For now, it’s one to watch closely rather than one to bet your operations on.

Coding Agents: The Truly Agentic Frontier

If the tools above represent the current state of AI in operations, coding agents represent where the entire field is heading. This is AI at its most autonomous: you describe what you want, the agent plans the work, writes the code, tests it, and delivers the result. It’s not process automation in the way n8n workflows are. It’s genuinely agentic behaviour, where the AI makes decisions, spins up sub-agents, and course-corrects independently.

The major players have all entered this arena. Google launched Antigravity, an AI-first IDE built on a modified fork of Visual Studio Code (reportedly influenced by their $2.4 billion acquisition of the Windsurf team). It’s currently in free public preview, powered by Gemini 3 models, with a tiered pricing model expected later in 2026. Its standout feature is the Manager View, where a developer can dispatch multiple agents to work on different tasks simultaneously. OpenAI offers Codex, available to ChatGPT Plus subscribers at $20 per month, with 30 to 150 local tasks every five hours. The Pro tier at $200 per month unlocks significantly higher throughput. Grok from xAI and all of the major Chinese models also offer coding capabilities.

But the tool that consistently generates the most discussion and the most enthusiasm from developers is Claude Code from Anthropic. Available with a Claude Pro subscription at $20 per month (or the Max plan at $100-$200 per month for heavier usage), Claude Code operates in the terminal and takes a genuinely agentic approach: you provide a product requirement specification, it plans the implementation, works through the task list, tests along the way, and spins up different agents as needed. For anyone who takes the time to write a proper spec upfront, the results are remarkable. The API is also available on a pay-per-token basis for developers who want to integrate it into their own toolchains using the Claude Agent SDK.

We’re not a coding house by trade, but we’ve run several projects through these tools and the output has been genuinely impressive. When you invest the time in creating a proper product requirement specification, which AI itself can help you shape, the agents can autonomously work through the build, checking off requirements and running tests as they go.

For those who want a more visual, less terminal-driven approach, platforms like Lovable offer a compelling alternative. Lovable lets you describe what you want in natural language and generates full-stack web applications through a chat interface. The Pro plan starts at $21 per month (billed annually) for 100 credits, making it extraordinarily accessible. We’ve used it to build several internal tools: a mileage tracker, a subscription renewal manager (to stop those annoying auto-renewals catching us out), and other lightweight utilities. For around $250 a year, you can prototype and build bespoke internal tools that would cost thousands to commission from a developer.

A few caveats are worth noting. Context window management matters: the longer a chat session runs on these platforms, the more the model loses track of earlier context, and you end up spending more tokens debugging than building. Every developer we’ve spoken to is clear that vibe-coded applications are prototypes, not production systems. The security, scalability, and code quality considerations that matter in production software are not yet handled reliably by these tools. That will change, but for now, treat the output as internal tooling and proof-of-concept work rather than client-facing product.

The broader point, though, is transformative. If you have an internal need, say you want a CRM system tailored exactly to your workflow and nothing on the market quite fits, you can now build one yourself for the cost of a single software subscription. For internal tools and prototypes, the economics are extraordinary.

We expect our own workflow to evolve from platforms like Lovable towards the more powerful agentic coding tools (Claude Code, Codex, Antigravity) as those mature and as our requirements grow more sophisticated.

The Bottom Line

If you take nothing else from this piece, here’s our operational AI hierarchy:

Meeting notetakers are the clear starting point. Regardless of which vendor you choose, they all add value, they’re all competitively priced, and they represent the lowest-risk, highest-reward entry into AI for most businesses. Just pick one and start using it.

Email assistants are a long way off. Right now, they’re expensive email categorisers that can’t be trusted with tone, context, or autonomous sending. We wouldn’t recommend investing here until the technology matures significantly.

Workflow automation is where the real operational value lives. Once you’re comfortable with structured prompting, building automated workflows is the natural next step. Find the repetitive, mind-numbing tasks in your business and automate them. n8n is our tool of choice, but Zapier and others are perfectly capable alternatives. This is where every business should be heading.

AI assistants are the future, but adopting them today means signing up for the early-adopter experience: bugs, high API costs, and capabilities that can largely be replicated with simpler tools. OpenClaw has shown what’s possible, and when a major provider fully productises this paradigm, it will be transformative. For now, keep watching.

Coding agents are game changers, full stop. Whether you use a visual platform like Lovable for quick prototypes or a full agentic tool like Claude Code for more serious development, the ability to turn a written specification into working software is here today. Be clear on your requirements upfront, manage your context windows carefully, and treat the output as internal tooling rather than production code, and you’ll get extraordinary value.

The operational AI landscape is moving fast. Some of these categories will look completely different in twelve months. But the tools that work today, notetakers, automation workflows, and coding agents, work well enough to justify adoption right now. The rest is coming. And when it arrives, the businesses that have already built the muscle memory of working with AI will be the ones best positioned to capitalise.

Next week, we’ll be back with a deeper dive into agents and AI. Stay tuned.

Executive Summary

This was the week AI capability met market reality. The US software index is down 24% year-to-date. IBM lost tens of billions in a single session. Nassim Taleb warned of software bankruptcies. Markets are no longer pricing AI as potential. They are pricing it as disruption.

The triggers were concrete: live COBOL modernisation, autonomous vulnerability scanning, a $60 billion chip deal, and export control allegations against China’s leading AI lab. Meanwhile, the Pentagon summoned Anthropic’s CEO for a direct confrontation over military AI restrictions, and the 11 March deadline for the US Commerce Department to evaluate state AI laws approaches.

For businesses, the signal is clear: the potential gap between “AI can assist your work” and “AI can perform your work” narrowed materially this week, and markets are repricing accordingly.

Our newsletter is non-hype, so I don’t want to come across alarmist, so the word “potential” matters, markets are reacting to frontier innovations (new releases), in most businesses adoption is still and the co-pilot minute taker level of maturity. However, it is important not to ignore these events as they will eventually shape our business environment.

Why this matters to businesses

• The software repricing is structural, not sentiment
  The $2 trillion wiped from software stocks reflects a market recognising that AI agents can now perform work entire SaaS categories were built to support: vulnerability scanning, legacy code modernisation, contract review, expense processing. If your business model relies on selling seats for tasks an agent can handle, its time to conduct a risk assessment, the commercial pressure will come (AI adoption still lags well behind innovations).

• Vendor concentration risk is real and growing
  Meta signed a $60 billion chip deal with AMD alongside its existing Nvidia commitment. Nvidia is investing $30 billion directly into OpenAI. The hyperscalers and model providers are locking into deep, exclusive partnerships. Businesses should audit their AI supply chain dependencies now, before vendor lock-in becomes vendor lock-out.

• Model IP is harder to protect than you think
  The distillation exposé revealed that frontier model outputs can be systematically harvested and replicated through fraudulent API access at scale. If you are building differentiated products or services on top of model APIs, the competitive moat may be narrower than assumed. Assess how defensible your AI-enabled advantages actually are.

• Your action this week: Identify one incumbent software tool your team relies on where the core function can now be performed or enhanced by AI. Run a 48-hour parallel test. Not to replace it immediately, but to understand the gap. The businesses that move first on this will compound savings. The ones that wait will be paying legacy pricing for commodity capability.

This Week’s Policy & Regulation Brief

DeepSeek allegedly trained next model on banned Nvidia Blackwell chips

A senior Trump administration official told Reuters that DeepSeek's forthcoming V4 model was trained on Nvidia's most advanced Blackwell chips, likely housed in a data centre in Inner Mongolia, in potential violation of US export controls. The US is investigating. DeepSeek may strip technical indicators that reveal chip usage before release. The model is expected within days. If confirmed, this would be the most significant export control breach in the AI era.

Anthropic-Pentagon standoff escalates to direct confrontation

The dispute we flagged last week reached a new level. Defence Secretary Pete Hegseth summoned Anthropic CEO Dario Amodei to a Tuesday meeting officials described as "not a get-to-know-you meeting." The Pentagon wants unrestricted military use of Claude; Anthropic maintains red lines on autonomous weapons and mass domestic surveillance. Officials threatened to designate the company a "supply chain risk," a label normally reserved for hostile foreign actors. Claude remains the only AI model approved for the Pentagon's classified networks.

Anthropic calls out China's AI copycats

Anthropic published detailed evidence that DeepSeek, Moonshot AI, and MiniMax conducted 16 million fraudulent API exchanges through 24,000 fake accounts to extract Claude's reasoning, coding, and agentic capabilities. MiniMax alone ran 13 million exchanges. Anthropic traced activity to specific researchers at DeepSeek and explicitly framed the distillation as undermining the competitive advantage US export controls were designed to preserve.

Amazon’s AI tool Kiro blamed for AWS outages, raising safety and reliability concerns

Amazon’s internal AI automation caused at least two significant service disruptions, including a 13-hour outage, spotlighting early risks of AI integration in critical cloud operations. Despite attributing the incidents to user error, the episode stresses the importance of rigorous AI safety protocols.

DOJ AI preemption deadline looming at 11 March

The Commerce Department must publish its evaluation identifying "onerous" state AI laws by 11 March. States with non-compliant laws risk losing federal broadband funding. Colorado's AI Act is specifically named. This will be a pivotal moment in determining whether AI regulation in the US is driven federally or state-by-state.

Anthropic drops $20 million into pro-regulation super PAC

Anthropic committed $20 million to Public First Action, directly countering the $125 million Leading the Future PAC backed by OpenAI co-founder Greg Brockman and Andreessen Horowitz. Over $200 million is now committed to AI-related midterm election spending, making AI regulation a defining 2026 campaign issue.

Australia scraps AI Advisory Body

The Australian government abandoned plans for a permanent external AI Advisory Body after 15 months of work and approximately $200,000 spent, opting instead for an internal AI Safety Institute. Critics warn Australia risks repeating the regulatory failures seen with social media.

Model & Platform Updates

Claude Code Security and COBOL modernisation tools launch

Two Anthropic product launches dominated market headlines. Claude Code Security is an autonomous vulnerability scanner that reasons about codebases like a security researcher, tracing data flows and catching context-dependent vulnerabilities. Using Opus 4.6, the team discovered 500+ previously undetected bugs in open-source projects. Separately, Claude Code can now map dependencies across millions of lines of COBOL, document workflows, and migrate legacy code. The combined market impact: tens of billions wiped off IBM and cybersecurity stocks.

Enterprise agent programmes expand from both OpenAI and Anthropic

Both leading AI labs made enterprise moves this week. Anthropic unveiled pre-built agentic workflows for finance, legal, HR, and engineering through Cowork plugins, with enterprise admin controls and new connectors for Gmail, DocuSign, and Clay. OpenAI announced multi-year Frontier Alliances with Accenture, BCG, McKinsey, and Capgemini, embedding forward-deployed engineers alongside consulting teams. The battle for enterprise AI distribution is now fully joined.

Google ships Gemini 3.1 Pro with doubled reasoning performance

Gemini 3.1 Pro more than doubled reasoning performance over 3 Pro on ARC-AGI-2, scoring 77.1%, at unchanged pricing. The rollout went live across the Gemini app, Vertex AI, and Google AI Studio on the same day, combined with Alphabet's 4% stock jump on the enterprise rollout.

Samsung Galaxy AI expands multi-agent ecosystem

Samsung announced Perplexity as its second integrated AI agent for Galaxy S26 alongside Gemini, with broader multi-agent support allowing users to choose task-specific AI assistants. Nearly 80% of Samsung users now rely on two or more AI agents daily.

Google brings AI music generation to the mainstream with Gemini integration

Google’s Lyria 3 music model embedded in Gemini enables users to create AI-generated songs with lyrics and cover art, broadening creative accessibility. This expansion into AI music democratisation pairs generation with provenance measures like SynthID watermarks.

WordPress integrates an AI assistant directly into its site editor

Automattic's native AI assistant in WordPress.com uses Google’s Nano Banana models to offer in-editor content editing, layout suggestions, and image generation. This seamless AI infusion into a leading CMS signals major steps toward widespread AI-powered content creation.

DBS pilots system letting AI agents make payments autonomously

DBS Bank and Visa’s pilot enables AI agents to handle real-time payment transactions securely for routine purchases, marking a shift of AI from advisory roles into automated financial execution. The trial addresses key security and liability concerns with tokenised credentials and issuer-managed approvals.

Quick Hits

• xAI co-founder exodus continues - Half of xAI's original 12 co-founders have now departed, with at least three forming a new stealth venture. Musk frames the departures as reorganisation for execution speed ahead of a planned 2026 IPO.

• Bridgewater warns AI capex entering "more dangerous phase" - Big Tech set to spend $650 billion on AI infrastructure in 2026, up 59% from $410 billion in 2025. Co-CIO Greg Jensen warned AI leaders can no longer meet investor expectations "without posing existential threats to other sectors."

• Perplexity’s retreat from ads signals a strategic shift toward enterprise focus - The AI search startup pivots away from consumer advertising to concentrate on sustainable enterprise sales..

• How Silicon Valley has long ignored China’s looming Taiwan invasion and its chip supply impact - U.S. officials’ warnings to major chip customers underscore looming geopolitical risks for global AI semiconductor supply chains.

Image Generation: From Six-Fingered Hands to Genuine Utility

I still remember the excitement of discovering AI image generation back around 2021. Midjourney was leading the charge, and the concept felt genuinely magical. Type a prompt, and an image materialises from nothing. It was one of those moments where the future felt like it had arrived early.

But those early models, while impressive in concept, were riddled with anomalies. People with six fingers were practically a trademark. Anything with visual complexity, whether zebra crossings, architectural details or crowds, would produce surreal, often unusable results. And text? Forget it. Text rendering has been the single most persistent weakness in AI image generation. Even today, if you wander around a local market or garden centre, you’ll spot AI-generated posters for sale where the text is pure nonsense. It’s become something of an unintentional watermark for the technology.

So where are we now?

Midjourney remains an exceptional tool. There are also lots more providers today Ideogram, Fireflies (by Adobe), Recraft and Leonardo.ai to name but a very few. The quality ceiling is high, and if you’re skilled at prompting, you can produce stunning results. But there’s a practical reality: the standard plan runs at $200 - $300 per year. Prompting is still very much a skill, and images often need iteration and refinement.

While it’s incredibly useful to create a bespoke image for a specific use case, the truth is that requirement doesn’t come up as often as you’d think. Not for us, anyway.

So today, we’ve largely come back to earth. Stock imagery from Unsplash handles the bulk of our needs, and for AI-generated images, we use Google’s Nano Banana, which comes bundled with our Gemini subscription.

Nano Banana deserves a proper shout-out here. It’s an incredible tool that is relatively new on the scene. Its image editing capabilities are superb. You can take a product shot and swap the background while preserving brand integrity. Its ability to create business schematics that clearly communicate concepts and processes is outstanding. And critically, its text handling is excellent, one of the areas where so many other tools still fall short. For our day-to-day needs, Nano Banana covers everything. (Note Grok and OpenAI also have very capable image generation capabilities - Grok’s model is lightening fast.)

For that remaining one per cent of cases, the truly unique images that neither stock photography nor Nano Banana can deliver, we run a local LLM setup. It’s a bit more involved to configure, and you have to build workflows for the different models, but the key advantage is straightforward: it’s free. No subscription, no per-image cost. For those rare occasions, it’s well worth having in the toolkit.

The bottom line on image generation: you don’t need to spend $200-$300 a year on unless bespoke, high-end image creation is a core part of your workflow, which for bigger marketing teams may be true. A Gemini subscription with Nano Banana, supplemented by stock imagery and a local LLM for edge cases, covers the vast majority of business needs at a fraction of the cost.

Video Generation: Where AI Delivers Real Business Value

Video is the area where AI arguably brings the most tangible new value to the marketing stack. Before AI video generation existed, your options were limited and expensive. Stock video sites charge premium rates for footage, and if your content involves anything niche or licensed, costs escalate rapidly. Try sourcing Formula One footage and watch your budget evaporate.

This is where AI-generated video genuinely comes into its own. The results aren’t perfect, and as with everything in this space, your ability to prompt effectively directly determines the quality of your output. Our Lighthouse logo, getting an animated lighthouse beam is truly hit and miss. There’s research involved, and you need to understand the strengths and limitations of the platform you’re working with.

But for us, in terms of real business value, the primary use case is clear: creating specific scenes for B-roll footage in marketing and promotional videos. This is genuinely powerful. Instead of spending hours trawling stock sites trying to find footage that approximates what you need, you can generate exactly the scene you’re looking for. The time savings alone make it worthwhile.

That said, context matters. If you need generic footage of people in a meeting room, a stock site is probably quicker and easier. But when you need something specific, a particular setting, action or mood, AI generation is the way forward.

We’ve tried most of the major platforms over the years, including Runway ML, Kling, PikaLabs, Domio, Sora (OpenAI) and several others. Where we’ve settled, and where we’re getting the most consistent results, is Google’s Veo 3. The platform delivers remarkably reliable output. What you prompt is very close to what you get, without the strange anomalies that plague other tools. Character consistency across scenes, historically one of the biggest challenges in AI video where maintaining the same character across multiple ten-second clips felt nearly impossible, has been largely solved. Veo 3 handles it well.

I’d like to try Sora 2 from OpenAI, but at the time of writing, it’s not available in the UK. Something to revisit. I know what I said about Grok as an LLM last week, but its probably our #2 go to AI for image and video, the Lighthouse Video was made with Grok - it’s lightening fast, provides variations and has beeter limits - we have it through our X (e.g twitter).

There are limitations the videos are 8 - 12s depending on who you use, getting consistency across multiple videos - e.g. lets image you want to make an 30 second advert is still challenging (but getting better), and you are still charged for videos that arent usable - so planning is needed. I used our limits for the next few hours trying to create a couple of example clips.

There are aggregator platforms out there that bundle multiple AI video tools together. We’ve looked at these, but the cost of the tokens is opaque and prohibitive for our use case. Instead, we stick with Veo 3 through our existing Google subscription, which means we get Gemini, Nano Banana and Veo 3 as a combined package. That’s an extremely powerful creative stack working in concert, and it’s cost-effective because we’re leveraging a subscription we’re already paying for rather than lighting up additional tools.

Voice Generation & Talking Heads: Where AI Breaks the Mould

This is the section where AI stops being a helpful productivity tool and starts genuinely changing how content is created. Voice generation and talking head videos represent a shift from traditional marketing formats into proper multimedia content, and the results are, frankly, game changing.

Voice Generation

Let’s start with voice. The clear leader in this space is ElevenLabs, and they’ve earned that position. The quality of their voice synthesis is remarkable, the product keeps getting better, but the feature that sets them apart is voice cloning. You can train the model on your own voice and create a genuinely realistic digital twin. I did it with a two-hour voice sample (recorded incrementally over a few days), and the results were impressive from the start. What’s particularly striking is that the clone gets stronger and more natural the more you use it. It learns the nuances, the cadence, the way you naturally emphasise certain words.

Beyond single-voice quality, ElevenLabs handles multi-speaker content brilliantly. You can have two people talking at once, which opens up enormous creative possibilities. If you’re producing content that would traditionally require getting multiple people into a studio, whether that’s a conversation, an interview format or a panel discussion, you can now generate that with AI voices. The time and cost savings are significant.

There are other solid tools in this space. We’ve also used Murf AI, which produces good results. But we’ve settled on ElevenLabs as our default. It’s a key part of the stack.

Talking Head Videos

Now take that voice and marry it with a talking head video generator, and you’ve got something truly powerful.

There are several platforms in this space. Synthesia is well known, particularly for product demos and corporate training content. Hedra is another option worth exploring. But the one we use most, and the one that’s delivered the best results for us, is HeyGen.

HeyGen offers two approaches. You can use a digital avatar, essentially a made-up person that looks and moves realistically. This works well for content where you don’t need a recognisable face. But where it gets really exciting is their person clone feature. You record a short video of yourself, roughly three minutes, and HeyGen creates a digital version of you that’s remarkably accurate. The example below isnt perfect and took two minutes to make, but gives you an idea.

For someone like me, this is transformative. I’m terrible at outtakes. Every time I sit down to record a one-minute video, it takes twenty minutes because I stumble over words, lose my train of thought or just can’t get the delivery right. The idea that I can now use my own cloned voice from ElevenLabs paired with a realistic avatar of myself from HeyGen is fantastic. The content looks and sounds like me, but without the frustration of endless retakes.

This is where AI is genuinely breaking the mould. We’re moving beyond the traditional marketing toolkit of static images, PowerPoint decks and social media graphics into proper multimedia content creation. The combination of AI voice cloning and talking head generation means a small team, or even a solo operator, can produce video content that would previously have required a studio, a camera crew and a budget to match. That’s not incremental improvement. That’s a fundamental shift in what’s possible.

Social Media: The One Area Where AI Has Let Us Down

Here’s where I have to be candid: we haven’t cracked this one. We’ve genuinely tried to find an AI tool that creates on-brand, differentiated social media posts (e.g we’ve tried Predis.ai, Buffer). And every time, we’ve ended up cancelling within 24 hours of the trial.

The problems are consistent across platforms. The templates are homogeneous, with every post following the same structure with the same style of graphic, every time. There’s no variation, no ability to create content that actually stands out in a feed. The text is difficult to control. And once you strip away the AI-generated content (which you end up replacing anyway), what you’re left with is essentially a very expensive scheduling engine.

We’ve accepted the reality: AI hasn’t solved social media content creation yet. Not for businesses that care about differentiation and brand quality, at least.

So we’ve reverted to what we’ve used since day one: Canva.

Canva is an exceptional package, and over the years they’ve built in some genuinely powerful AI capabilities of their own. Here’s why it works:

Their template library is extraordinary, not just in volume but in quality and variety. You can create visually stunning social media content quickly, without needing a design background. The UI is so easy to use. The AI features within Canva let you push designs further through prompting, taking a strong template to the next level. And critically, they’ve built in the scheduling and posting capabilities, so your entire social media workflow from creation through to publication lives in one place.

The subscription cost, compared to the dedicated AI social media tools we’ve tried, is dramatically more competitive. That’s the takeaway from our experience: the specialist AI tools overpromise and underdeliver, while Canva, which doesn’t position itself primarily as an AI tool, delivers more practical value - canva is the gold standard and the one to beat.

Presentations: A Story of Frustration Turning to Progress

The final piece of our marketing stack is presentations, and this one has a nuanced story.

First, some context on how we actually use PowerPoint: it’s more document than presentation. We rarely stand up and present slides in the traditional sense. Instead, we deliver PowerPoint reports. It’s how we package and deliver content to clients. We have established templates, and most of the time, we’re not looking for AI to create the final deck. What we need is help getting past the blank page. What layout works, how can we present this concept in a visually engaging way? How do we make this look good within our brand guidelines? We’re always hunting for arrangements and styles that feel visually attractive and give the brand a premium feel.

And here, similar to social media, AI has been genuinely poor to date on the design element. You can use it, but the output just isn’t attractive enough. We’ve bought packages, trialled them and cancelled them almost overnight. The designs are repetitive, undifferentiated, and nothing stands out. Tools like Gamma and others in that category have all followed the same pattern for us.

Where we’ve landed as a default starting point, and this connects back to last week’s article, is Google NotebookLM. It has limitations, primarily that it only produces PDFs. But they’re addressing that, and I believe a recent release now allows export to PowerPoint, which would be significant. We can’t necessarily use NotebookLM output as a final client-facing deck, but the content it generates is so engaging and well-structured that it’s become our go-to for getting ideas and initial structure down.

The real game changer in this space, and this has only become available in 2026, is Claude’s PowerPoint plugin. This is where things get genuinely exciting. We can work in a deck that already contains our template slides, feed Claude the content, and it will take a first pass at building the slides using our template, our brand guidelines and the source material. It’s not perfect and there’s reformatting involved, but it’s considerably better than any of the commercial AI presentation packages we’ve tried. The difference is that it works with your existing brand assets rather than imposing generic templates.

And then there’s Canva again. For creating a standout presentation from scratch, one that needs to look absolutely fantastic, Canva remains brilliant. Just as with social media, it’s the reliable, high-quality option that keeps earning its place in our stack.

Where This Is All Heading

Looking across all five of these areas, a pattern emerges. AI is starting to make inroads into graphic design and layout, but for now the real value is coming from two directions. First, the major platform subscriptions (including OpenAI and Grok): For us it is Google’s ecosystem giving us Gemini, Nano Banana and Veo 3 in one package, plus Anthropic’s Claude with its PowerPoint integration. Second, the established creative tools like Canva, ElevenLabs and Heygen that have thoughtfully integrated AI capabilities into an already excellent product.

I think much of this is heading towards commodity. Image generation is already being squeezed as every major LLM adds the capability natively. Video generation still has room, as there’s a whole creative industry there with genuine complexity to solve. But for social media and presentations, I think the large LLM providers will eventually make it very difficult for independent tools to compete.

The practical advice? Before you subscribe to the next shiny AI marketing tool, look at what you’re already paying for. The answer might already be in your existing stack.

Next week: In the final part - we’ll look at the productivity tools we use, included in that are Agents, Coding tools and automation tools.

Executive Summary

AI is no longer arriving, it's embedding itself. From checkout flows to defense strategy, this week's developments reveal an industry in full-scale deployment mode.

Further examples this week gome from Google and Baidu who are weaving AI directly into commerce and consumer experiences, positioning it as the primary transactional layer rather than a backend utility.

Regulatory and geopolitical tensions are sharpening too: defense agencies are pressuring AI firms to reconcile ethical guardrails with operational agility, while governments globally are tightening their grip on AI-generated content and data practices.

On the infrastructure front, massive funding rounds, including efforts to scale domestic AI data centers in India, signal an intensifying race for compute sovereignty, matched by critical advances in power delivery. Add to this the strategic pivots and IPO preparations reshaping competitive dynamics among leading AI providers, and the picture becomes clear: AI's transition from lab to market is accelerating, redrawing the boundaries of commerce, governance, and global tech power.

Why this matters to businesses - how you can act on this news today within your business

• If you strip away the headlines, one theme dominates: AI is moving from “tool” to “operating layer.” The winners will not be those just experimenting, it will be those that are embedding AI / (or agents - still early) into workflows, transactions, and decision-making.

  • Your action today (or this week at least) what have you got to lose:

    • Identify one repetitive internal workflow (report drafting, data collation, meeting prep, lead research).

    • Investigate how AI can lift 80% of the effort to do it from you.

    • Redesign the process around AI-first execution, not human-first with AI assistance.

    • Measure time saved and error reduction within 30 days.
      

    Governance is important - but do not wait for perfect governance or perfect models. Capability is already good enough. Competitive advantage will come from speed of integration.

• The bottom line: The question has moved on quickly in the last few months from “should we use AI?”. As we’ve said previously the pace of change in 2026 has been mind blowing, AI is no longer a productivity add-on. It is becoming infrastructure. Businesses that redesign workflows around agents now will compound efficiency and margin gains while others are still “testing.

• We work with leadership teams to move from experimentation to execution safely, commercially, and at speed, talk to us.

This Week’s Policy & Regulation Brief

Pentagon may sever Anthropic relationship over AI safeguards

The Pentagon is threatening to end its relationship with Anthropic after months of difficult negotiations over AI usage restrictions. Defence officials want unrestricted access to Claude for weapons development, intelligence collection, and surveillance operations, but Anthropic refuses to lift safeguards prohibiting fully autonomous weapons and mass domestic surveillance of Americans, highlighting the fraught balance between AI safety principles and defence requirements.

Microsoft pursues "true self-sufficiency" in AI with enterprise and healthcare models

Microsoft is accelerating development of proprietary AI models to reduce reliance on OpenAI, with AI chief Mustafa Suleyman championing "true self-sufficiency." The company is prioritizing enterprise applications and "medical super-intelligence" in healthcare, signaling an aggressive push to own key commercial AI verticals amid forecasts of rapid white-collar automation and intensifying competition in the AI market.

US FTC accelerates antitrust probe into Microsoft's AI and cloud dominance

The FTC is fast-tracking its investigation into whether Microsoft is monopolising the enterprise computing market with bundled cloud and AI offerings, potentially impacting future platform competition and regulation.

UK to tighten online safety laws for AI chatbots and social media

Prime Minister Keir Starmer is advancing legislation to close loopholes in the UK's Online Safety Act, requiring all AI chatbot providers, including ChatGPT and Grok, to comply with illegal content duties. Makers of chatbots putting children at risk will face massive fines or service bans, marking one of the first explicit regulatory moves on AI platform accountability for child protection.

Blackstone leads $1.2B financing for Neysa to build India's AI compute infrastructure

This massive investment into Neysa to deploy 20,000+ GPUs marks a strategic effort to build domestic AI infrastructure in India, reflecting the rising importance of regional cloud and compute independence.

European defense funding hits record $8.7B with AI representing 44%

Venture capital investment in European defence, security, and resilience startups surged to a record $8.7 billion in 2025, a 55% year-over-year increase and 18-fold growth since 2020. AI-related deals accounted for nearly half of all investment, demonstrating the centrality of artificial intelligence technologies in regional security strategies and Europe's commitment to technological resilience.

Insider reports of AWS strategic shake-up amid AI contract competition

AWS is reassessing its AI cloud strategy as Microsoft Azure and Google Cloud gain momentum in enterprise AI infrastructure. Analysts forecast Microsoft's cloud business could overtake AWS within three years, driven by AI-powered services demand. Despite posting strong Q4 results, AWS ranked third behind Google Cloud and Microsoft in growth momentum, highlighting intensifying rivalry in the sector.

Model & Platform Updates

Google’s Gemini 3 Deep Think upgrade shatters reasoning benchmarks

Google's latest upgrade to Gemini 3 Deep Think has achieved breakthrough performance on key intelligence benchmarks, scoring 84.6% on ARC-AGI-2 and reaching gold-medal standard at the International Mathematics Olympiad. The specialised reasoning mode powers a new math and science research agent designed to solve complex scientific, engineering, and research challenges, exemplifying the advanced reasoning capabilities emerging in 2026.

Baidu integrates OpenClaw AI assistant into its 700M-user search app

Baidu is embedding the popular OpenClaw AI agent directly into its flagship search app, providing approximately 700 million Chinese smartphone users with task-automating AI capabilities. The integration enables users to perform tasks such as scheduling, organizing files, and writing code without requiring separate applications. This strategic move positions AI as a core consumer engagement platform ahead of Lunar New Year.

OpenAI acquires OpenClaw

OpenClaw is a managed platform built to deploy AI agents as “enterprise coworkers, (Jeff as we call our agent in the office)” bringing founder Peter Steinberger into the fold. The move signals a strategic shift from model provider to end-to-end agent operator. Rather than simply supplying LLM capability, OpenAI is accelerating into workflow orchestration, embedding autonomous agents directly into enterprise systems. Expect deeper vertical integration, tighter enterprise control layers, and faster commercialisation of multi-agent deployments across large organisations.

Google enables shopping transactions directly within AI chats

Google launches AI-powered ads with integrated Direct Offers and checkout in conversational interfaces, transforming Gemini and AI Mode into seamless transactional agents.

ByteDance releases Doubao 2.0, enhancing multi-step AI agent capabilities

ByteDance has released Doubao 2.0, upgrading China's most popular AI chatbot with complex reasoning and multi-step task execution capabilities. The company positions this as its entry into the "Agent Era," with performance matching OpenAI's GPT-5.2 and Google's Gemini 3 Pro at significantly reduced costs. With over 155 million users, Doubao 2.0 reinforces ByteDance's dominance in consumer-level AI agents.

Anthroipc Releases Sonnet 4.6

Anthropic has released Sonnet 4.6, sharpening its positioning as the enterprise-grade reasoning model for high-trust workflows. The update focuses on improved long-context stability, coding reliability, and reduced hallucination rates in complex multi-step tasks. Rather than chasing headline benchmark theatrics, Anthropic continues to optimise for controllability and governance—features increasingly critical for regulated industries. For businesses, the signal is clear: model performance is converging, but safety, auditability, and deployment discipline are becoming the real differentiators.

Zhipu AI’s stock surges following GLM-5 release with enhanced coding and agent abilities

Zhipu AI’s GLM-5 release has done more than ship a model update, it has triggered a sharp market response, with shares jumping 30% on the news. The upgrade strengthens coding performance and agent-style task execution, reinforcing China’s push toward competitive, open-source AI ecosystems. For global businesses, the signal is strategic: Chinese foundation model providers are closing capability gaps quickly, backed by capital markets that reward domestic AI champions as geopolitical tech bifurcation accelerates.

Quick Hits

• Introducing GPT-5.3-Codex-Spark - OpenAI’s GPT-5.3-Codex-Spark model delivers 15x faster code generation and a massive 128k token context window, setting a new standard for AI-assisted software development at scale.

• Alibaba enters physical AI race with open-source robot model RynnBrain – Alibaba’s launch of RynnBrain, an open-source vision-language-action AI model for robotics, expands AI’s frontier into real-world physical tasks and autonomous systems with broad industrial implications.

• Meta plans to add facial recognition to its smart glasses– Points to deepening on-device AI perception but evokes privacy debates.