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.
  

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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.
  

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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.
  

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

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

  

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

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.

How We Actually Work With AI

We are long past the "wow" phase of Artificial Intelligence. We are now in the integration phase.

Before diving into specific tools, let’s address something that might surprise those still dipping their toes into AI waters: for us, AI isn’t a tool we occasionally consult. It’s open all day, every day. It’s ambient.

Whether it’s a chat window pinned to the corner of the screen, a desktop application running in the background, or voice mode activated while we’re making coffee, AI has become the colleague that never clocks off. “Hey, can you look this up?” “What does this term actually mean?” “Summarise this document.” These micro-interactions compound into enormous productivity gains over a working week.

We’re not casual users. We maintain top-tier subscriptions across Claude, ChatGPT, and Gemini. We run local LLMs for sensitive work and regularly test different open-source models. And yes, we’re deep into trialling the Chinese models, Qwen, Kimi, GLM, and MiniMax, because ignoring what’s happening in that market would be professionally negligent.

For research specifically, Google NotebookLM has become our absolute go-to, with Perplexity currently in another round of testing (more on our complicated relationship with that one later).

Our Ranking System

We’ve categorised our tools into three tiers:

• Must Have - Essential. Non-negotiable. Woven into our daily operations.

• On The Watch List - Promising, but not yet proven enough for full commitment.

• Cancelled - Tried, tested, and found wanting.

The LLM Big Three: Claude, ChatGPT & Gemini

The Verdict: Must Have (All Three)

Here’s the truth that vendor loyalty won’t tell you: you need all three. We aren’t brand loyal to any of them and we have a love-hate relationship with each of them, but they all excel at different things. Running the same prompt through all three and comparing results has become standard practice, and interestingly, as a side note Perplexity has now built this comparative functionality directly into its offering.

Claude.ai

Claude has genuinely come into its own this year. The introduction of Cowork has been transformative, this isn’t just chatbot interaction anymore; it’s genuine agentic work operating from your local files.

The Microsoft plugins for Excel and PowerPoint deserve special mention. even still in Beta, they’re exceptional, seamlessly integrating Claude’s capabilities where you’re already working rather than forcing context-switching.

Real-World Use Case: Last weekend, I found myself splitting time between parenting duties and pressing work tasks. A large document needed formatting, but we had family plans.

Here’s what I did:

I opened Claude inside PowerPoint, explained the tasks required, told it I’d be away from my desk for three hours, and asked it to gather all the questions and permissions it needed upfront. After a short Q&A and granting necessary accepts, I left for a family afternoon.

I came back three hours later. The work was done.

Similarly, we had five NDAs to prepare. I gave Claude Cowork access to our template, provided the company URLs, and specified the purpose for each. When I returned? Five NDAs, fully detailed, company name, address, company numbers added and ready to send out.

These aren’t hypothetical use cases or demo scenarios. This is handing off real tasks to AI and returning to find them completed. That’s the bar Claude has set.

Gemini

Gemini earns its place through sheer breadth of ecosystem integration. It’s a strong chat model in its own right, but the connections matter: linked to image generation tools, to Veo3 for video, and critically, to Google NotebookLM.

When your AI assistant can seamlessly tap into Google’s entire product suite, from search to workspace to creative tools, it becomes indispensable almost by default. The individual components might not always be best-in-class, but the integration is unmatched.

ChatGPT

ChatGPT occupies an interesting position. Version 4.0 set the standard everyone else chased. Version 5.2 was, frankly, disappointing. We’re hopeful 5.3 brings meaningful improvements.

One genuine frustration: Pro mode’s processing time compared to competitors has become noticeable. When you’re used to near-instant responses, waiting feels like regression.

However, and this is significant, ChatGPT’s Atlas feature, particularly when connected as an AI companion watching over your shoulder, is brilliant. Need to debug a broken AI workflow? Want real-time guidance on where you’re going wrong? Atlas excels here. Google has similar functionality, and Claude’s attempt at this wasn’t particularly strong.

The Watch List: Chinese Models

Kimi, Qwen, GLM & MiniMax

The Chinese AI market is producing genuinely impressive models at significantly lower token costs. For vanilla tasks without sensitive data concerns, they each demonstrate areas of real brilliance.

Kimi: Presentation capabilities are remarkably good. The agentic swarm approach to market research produces surprisingly thorough results.

Qwen: Lightning fast. When speed matters more than absolute accuracy, Qwen delivers.

MiniMax & GLM-5 (z.ai): Each with their own strengths, worth testing against established players on specific task types.

The Challenge: Hallucinations remain the critical weakness. Even when explicitly requesting referenced sources, these models forge links convincingly. When you’re spending as long fact-checking outputs as you would have spent researching manually, the productivity promise evaporates. That’s the tipping point we keep hitting.

For now, we’re testing these on smaller task subsets, useful for benchmarking, interesting for exploration, but not yet ready for production workflows where accuracy is non-negotiable.

Cancelled: What Didn’t Make the Cut

Grok

Grok arrived with enormous promise. Multi-agent workflows. Federated approaches. A study-group methodology to answering queries. The concept was genuinely innovative.

Three problems killed it for us:

First: Even on top-tier subscriptions, responses were never detailed enough or high-quality enough for business applications. When you’re paying premium prices, you expect premium outputs.

Second: Compare the Grok Reddit page to other which will give you a pulse for how people use it. What should be discussions about coding, business applications, and productivity has been overwhelmed by NSFW image generation content. Even the voice modes trend toward “unhinged” configurations. That’s not the community or product direction we want to align with.

Third: Cost. Grok became the most expensive option across our entire AI stack without the output quality to justify the premium.

Manus

Manus a Singapore-based startup (recently acquired by Meta for $2bn) that started brilliantly. Innovative approaches, genuine excitement about the product direction, thinsg we had never seen before (this was 12 - 18 months ago).

Then rivals did it better and cheaper. For the price point, we moved on. Perhaps Meta’s acquisition will revitalise the platform, but for now, it’s not earning a place in our toolkit.

Research Tools: The Game-Changers

Google NotebookLM - Must Have

NotebookLM is simply the best research tool available. Full stop.

For those unfamiliar: NotebookLM allows you to upload documents, websites, and various data sources, then interact with that corpus through natural language. It’s not just answering questions, it’s synthesising information from your specific sources with remarkable accuracy.

Recent expansions have taken it further. Beyond chat-based Q&A, NotebookLM now generates:

• Video summaries

• Podcast-style audio content

• Comprehensive reports

• Presentation decks

One great use case I use a lot - give it a youtube video link and then ask anything about the content of that video - imagine its a 3 hour video (who has time to watch those) - you can get the key takeaways, or ask it about the part you’re specifically interested.

Honest assessment: the videos and podcasts are interesting novelties but not particularly helpful for business use, too little control over output. However, the presentations and reports are excellent.

Example slide from a featured notebook.

Our Presentation Use Case: We use NotebookLM to see designs emerge from data. The output is PDF (non-editable), but that’s actually fine, what we’re getting is creative direction. With image generation plugged in, the design layouts and visual choices are genuinely impressive. No paid presentation tool we’ve tested competes on creativity or quality. Yes, dedicated tools give you editable PowerPoint files rather than PDFs, but the quality difference is night and day. We’ll take inspiration over editability.

Perplexity - On The Watch List

Our relationship with Perplexity is... complicated. We use it, cancel it, use it, cancel it. Currently, it’s renewed through February, with March decision pending.

What works: The configured morning briefs are genuinely valuable. The additional capabilities they’re rolling out show real product thinking. The new comparative LLM feature (running prompts across multiple models simultaneously) addresses a real workflow need.

What gives us pause: Can we achieve similar results by refining prompts with our existing LLM subscriptions? Probably. Unlike the Big Three, Perplexity isn’t a no-brainer renewal. It’s proving value, but not yet proving indispensable.

The Bottom Line for Business Users

If you take nothing else from this article, take these three recommendations:

Google NotebookLM - Focused, exceptional at what it does. The deliverables it creates (reports, presentations) are outstanding even if output formats are inflexible. The PDF or final-cut nature of exports is a minor frustration against genuinely impressive quality.

Claude - The Microsoft plugins are exceptional. Cowork represents genuinely agentic AI that operates from your local files and completes real tasks autonomously. The use cases we described, leaving for hours and returning to completed work, aren’t edge cases. That’s Tuesday.

The Big Three Together - Claude, ChatGPT, and Gemini each have strengths the others lack. Running comparative prompts across all three has become standard practice. Committing exclusively to one means accepting blind spots.

Coming Next

This is part one of our AI toolkit breakdown. Future articles will cover:

• Image Generation - Midjourney, Nano Banana, and the evolving landscape

• Video Creation - Veo 3, Sora, Kling, Runway and what actually delivers

• Coding Assistants - Claude Code, Codex, Cursor, and the developer experience

• Automation Platforms - N8N, Zapier, Crew.ai, Gumloop and building workflows

• Voice & Audio - ElevenLabs, Murf, and the voice synthesis revolution

• AI Avatars - HeyGen, Hedra, Syntheisa, Descript and virtual presenter tools

The landscape moves fast. We’ll keep testing so you don’t have to test everything yourself.

Have questions about our AI stack? Tools you’d like us to evaluate? Drop us a message, we read everything (well, Claude helps with that too).

Executive Summary

2025 was all about the hype and mislabelling of Agentic AI, 2026 is so far being characterised by a fierce push toward operationalising (true) agentic AI systems at scale. With OpenAI launching Frontier and GPT-5.3-Codex, and Anthropic unveiling Claude Opus 4.6 with multi-agent teams and extended context, the race to build AI co-workers that seamlessly integrate into enterprise workflows has intensified significantly.

Meanwhile, foundational shifts in AI hardware and supply chains were underscored by strategic chip production moves from ByteDance and a massive $650B capex forecast from hyperscalers. In parallel, the intensifying competitive dynamics surfaced through Anthropic’s ad-free stance against OpenAI’s ad experiments, and geopolitical fault lines deepened as Chinese open-source models gain traction amidst Western retrenchment. Together, these developments signal a transformative year where AI transitions from research marvels to embedded enterprise and industrial infrastructure, with growing considerations around governance, security, and sustainability reshaping the competitive landscape.

Why this matters to businesses

• If you are running a business in 2026, this is no longer about “AI strategy decks”. It is about operational execution.

  • Audit your workflows – this week
    Identify 3–5 high-cost, high-volume workflows (legal review, reporting, marketing ops, customer support). Ask: could an agent complete 60–80% of this autonomously? If yes, pilot it.

  • Reassess your software stack
    Seat-based SaaS models are being challenged. Review where you are paying per-user for tools that could be replaced or augmented by AI agents.

  • Prepare for enterprise-grade AI integration
    Frontier-style platforms mean AI will plug directly into CRM, HR, finance and data systems. Ensure your data architecture is clean and permissioned.

  • Stress-test your commercial model
    If AI can compress delivery costs, how does that affect your pricing power? Consider moving from time/seat-based models to outcome-based pricing.

  • Monitor regulatory exposure
    UK and US frameworks are tightening around frontier models. Assign internal ownership for AI governance and compliance now, not later.

  • Budget for AI infrastructure
    Hyperscaler capex signals long-term cost curves will fall. Allocate experimental budget today so you are not forced into reactive adoption in 12 months.

• The bottom line: AI is shifting from “assistive tool” to “operational co-worker” with genuine use cases to back this up. Businesses that operationalise early will compound efficiency gains; those that hesitate may find their cost base structurally uncompetitive.

This Week’s Policy & Regulation Brief

~$1 trillion erased from software and services stocks

The S&P 500 software and services index dropped by approximately 3.1% on Thursday alone, extending a seven-session sell-off triggered by fears of AI disruption. The decline accelerated following Anthropic’s launch of Claude Opus 4.6 and Cowork plugins on 5 February, which demonstrated advanced capabilities in legal, marketing, and data workflows. Goldman Sachs noted that while near-term earnings remain important signals, they are insufficient to disprove long-term downside risks. Furthermore, short interest in SaaS and cybersecurity stocks has been rising for three months.

Nvidia H200 exports to China remain in limbo

Despite the US administration giving the green light in mid-January for H200 exports under strict new conditions (including third-party testing, a 50% cap versus US supply, and a ban on military use), Chinese customs officials have reportedly blocked imports and discouraged further orders. Chinese customers have requested over two million units, but Nvidia cannot confirm when shipments will begin. The stock rebounded by 7.8% earlier in the week due to broader capital expenditure narratives, yet the China overhang persists.

UK AI regulation evolving

Ministers have signalled their intention to introduce a Frontier AI Bill. This legislation would grant the AI Security Institute statutory powers, including the authority to conduct pre-deployment model testing. The Institute previously published its first Frontier AI Trends Report in December 2025.

Federal AI pre-emption framework advances

The December 2025 Executive Order, which directs US federal agencies to challenge burdensome state AI laws, continues to unfold. The Secretary of Commerce is required to publish an evaluation of these state laws by 11 March 2026. Concurrently, Federal Communications Commission proceedings are underway regarding a federal AI disclosure standard that could pre-empt state rules.

Amazon leads Big Tech rout, down >5% on Friday

Amazon rattled investors by forecasting $200 billion in 2026 capital expenditure, which is over $50 billion above consensus estimates. Over the past week, Amazon, Microsoft, Nvidia, Meta, Alphabet, and Oracle collectively lost over $1 trillion in market capitalisation, with Amazon alone shedding approximately $300 billion.

Nvidia approaching $5 trillion

Despite export uncertainty in China, Nvidia projects approximately $500 billion in revenue for 2026. The company initially crossed the $5 trillion market capitalisation mark on 29 October 2025, and analysts forecast a year-end range of $7 trillion to $9 trillion if execution remains strong.

Broader rotation out of tech

The recent tech sell-off triggered a market rotation into value-oriented sectors such as consumer staples, energy, and industrials. This volatility was further amplified by the unwinding of leveraged positions. Both the IMF and the Bank of England have issued warnings regarding the risks of an AI bubble.

Accenture acquiring Faculty (UK)

Accenture has agreed to acquire the UK-based firm Faculty. This acquisition brings over 400 AI professionals to Accenture, alongside the Faculty Frontier decision-intelligence product. Marc Warner, the CEO of Faculty, will step into the role of Accenture CTO. The deal remains subject to regulatory approval.

Anthropic’s ad-free campaign takes aim at OpenAI

Anthropic used a 2026 Super Bowl advert and a public pledge to position Claude as a permanently ad-free AI assistant. This approach directly contrasts with OpenAI’s decision to introduce adverts in ChatGPT, framing a broader industry debate over business models, ethics, and the best way to monetise consumer AI access.

Model & Platform Updates

Anthropic launches Claude Opus 4.6

Released 5 February. Anthropic's most capable model, featuring extended thinking (visible chain-of-thought), 1M token context window (beta), 128K max output tokens, agentic coding that tops SWE-bench, and multi-agent "agent teams." Ranked #1 on Finance Agent benchmark. The release triggered a ~$300B software stock selloff as investors grasped AI's potential to replace enterprise workflows.

Anthropic's Cowork plug-ins, the ones that triggered 'SaaSpocalypse' market sell-off

Following Anthropic's recent launch of 11 open-sourced agent plug-ins for its Cowork platform, enterprise software stocks experienced a brutal sell-off early last week. These customisable plug-ins allow businesses to automate department-specific workflows across legal, marketing, data analysis, and customer support without writing any code. The advanced capabilities, particularly the legal contract review plug-in, severely spooked investors. This wiped billions in market capitalisation from traditional SaaS and professional services firms as fears of AI replacing seat-based software models intensified.

OpenAI launches “Frontier” enterprise agent platform

Unveiled 5 February. A managed platform for deploying AI agents as “enterprise coworkers” with CRM/HR integration, SOC 2 Type II compliance, and multi-agent orchestration. Early customers include Intuit, Thermo Fisher, Oracle, and Uber. Enterprise Frontier Programme pairs OpenAI engineers directly with customer teams.

Mistral releases Voxtral Transcribe 2

Two speech-to-text models: Voxtral Mini Transcribe V2 (batch, $0.003/min — 5× cheaper than ElevenLabs) and Voxtral Realtime (sub-200ms latency, Apache 2.0 open source). 13-language support, on-device deployment for GDPR/HIPAA compliance.

DeepSeek-V4: Open-weight LLM with 1M+ context and advanced coding capabilities

DeepSeek-V4 is a highly anticipated pre-release open-weight model expected to launch in mid-February 2026. While not yet officially released, phased grey-scale testing began on the DeepSeek app on 11 February, expanding the model's context window from 128K to over one million tokens. Early internal tests suggest the model will outperform leading competitors in complex code generation. Furthermore, recent analyst reports confirm V4 integrates DeepSeek's newly published "Engram" memory module alongside advanced sparse attention mechanisms to improve long-context reasoning. Although no official download link is available yet, its high efficiency and open-weight nature could significantly lower the barrier for local or enterprise deployment once launched, despite ongoing geopolitical hurdles.

Perplexity Launches Model Council for Multi-Model Insights

Perplexity has introduced Model Council, a new feature for its Max subscribers that simultaneously runs queries across multiple leading AI models (like Claude, GPT, and Gemini). Recognising that model performance varies by task, a synthesiser evaluates the diverse outputs, resolves conflicts, and delivers a unified answer. This highlights areas of model agreement and divergence, offering users increased confidence and balanced perspectives for complex research, decision-making, verification, and creative brainstorming.

ByteDance stuns the AI video world with Seedance 2.0

ByteDance has launched the beta version of Seedance 2.0, a highly advanced multimodal AI video model. The system generates 15-second, 2K resolution clips with natively synchronised audio. Early testers report that its cinematic storytelling and temporal consistency are easily rivalling top Western systems, boasting a massive leap in usable output rates on the very first prompt.

Quick Hits

• Apple to integrate third-party AI chatbots into CarPlay - Users will be able to access OpenAI, Anthropic, and other chatbots within CarPlay. Cannot change Siri activation, but apps can auto-enter voice mode.

• MIT study warns LLM ranking platforms are unreliable – Highlights the fragility of user-driven LLM benchmarks and calls for better evaluation methodologies.

• Agentic AI promises $450B value in life sciences marketing by 2028 – Autonomous AI agents are set to transform pharma engagement and commercial workflows.

If you’ve been following AI.Proof, you know we don’t do hype. We test tools, evaluate the hype vs the reality and tell you what actually works for getting things done in a business. So when we say that Anthropic’s Claude Cowork might be the most practically useful AI product we’ve seen in 2026, we mean it.

Not because it’s clever. Because it’s useful. Properly, boringly, get-the-job-done useful.

What Is Cowork, and Why Should You Care?

Claude Cowork is best understood as "Claude Code for everyone else." Claude Code, launched earlier last year, allowed software developers to delegate coding tasks to an AI agent that could plan, execute and debug across multiple files. Cowork applies the same agentic architecture to general business work.

Let’s start with what it isn’t. It isn’t another chatbot. It isn’t another “ask me anything” text box. And it isn’t another demo that looks brilliant on stage but falls apart when you try to use it with real files and real deadlines.

Cowork is a new mode inside the Claude Desktop app (macOS for now) that gives Claude direct access to a folder on your computer. You point it at your files, describe what you need done, and it gets on with it. It reads documents. It creates spreadsheets with working formulas. It builds PowerPoint decks. It organises, renames, analyses, and produces finished work product.

Cowork is an AI agent built by Anthropic that works directly with the files and folders on your computer. Think of it less like talking to an assistant and more like leaving a brief for a competent colleague who happens to work incredibly fast.

The tool launched on 12 January 2026 as a “research preview” for Claude Max subscribers, and has since expanded to everyone. It’s built on the same architecture as Claude Code, which has been Anthropic’s breakout hit with developers since late 2024. The crucial difference is that Cowork wraps all that capability in an interface that doesn’t require you to open a terminal or write a single line of code.

Cowork turns unstructured inputs into polished business outputs without manual formatting.

What It Actually Does

Here’s where it gets interesting for anyone running a business or managing a team.

Cowork doesn’t just suggest what you could do. It does the work. Point it at a folder of receipt photos and ask for an expense report. You’ll get back a formatted Excel spreadsheet with categories, totals, and working formulas. Give it a pile of research notes and ask for a board paper. You’ll get a Word document with proper headings, structure, and citations.

The built-in “skills” handle the file formats most businesses live in: .xlsx, .pptx, .docx, and .pdf. These aren’t basic exports. The Excel files come with VLOOKUP formulas, conditional formatting, and multiple tabs. The PowerPoint decks read your existing templates and match your fonts and layouts. The PDFs can be merged, split, or have forms filled in.

You can also queue up multiple tasks and let Cowork run through them in parallel. It plans its approach, keeps you updated on progress, and asks for clarification when it needs it. If you’ve ever left a decent brief for a junior analyst and come back to find the work done, that’s the experience they’re aiming for.

Critically, you can build custom plugins using the "Plugin Creator" tool. This allows organisations to encode their own standard operating procedures into reusable skills that any authorised team member can deploy. For enterprises, this promises to capture institutional knowledge that typically walks out the door when experienced staff leave.

Eleven open-source plugins cover every major business function, and you can build custom ones using the Plugin Create tool.

The Stock Market Reaction

Here’s where the story gets dramatic. When those plugins dropped, investors took one look at the implications and started selling.

Thomson Reuters fell 15.8% on the Tuesday. LegalZoom dropped nearly 20%. RELX, the parent company of LexisNexis, saw double-digit declines. FactSet Research Systems dropped 10%. Salesforce and Workday both took significant hits. The Goldman Sachs software basket fell 6%, and the Nasdaq had its worst two-day tumble since April.

The fear wasn’t irrational. When an AI tool can review contracts, build financial models, prepare competitive briefs, and produce formatted reports, it starts to overlap with a very large number of enterprise software products that charge substantial subscription fees to do those same things.

As Dan Ives at Wedbush noted, scaling these tools across large enterprises with thousands of employees and entrenched processes isn’t something that happens overnight. And Gartner analysts wrote that predictions of the death of SaaS are “premature.” But the direction of travel was clear enough to shake markets worldwide, including significant drops across Indian IT outsourcing stocks.

The timing got worse for incumbents when Anthropic followed up on 5 February with Claude Opus 4.6, a new model with a million-token context window (roughly the equivalent of being able to read and process several thousand pages at once), plus the ability to coordinate teams of AI agents working in parallel. Software stocks took another leg down.

The ‘SaaSpocalypse’: Software stocks cratered after Anthropic’s plugin launch on 30 January.”

How It Was Built (And Why That Matters)

Here’s a detail that says more about where we are with AI than any benchmark score. According to Boris Cherny, the inventor of Claude Code, Cowork was built using Claude Code itself. Product Manager Felix Rieseberg confirmed the app came together in roughly a sprint and a half, about one and a half weeks.

Read that again. Anthropic used its own AI coding tool to build a new product in under two weeks. The team had done some prototyping and exploration beforehand, and the current release still has rough edges. But the core product was assembled by the same AI agent technology it’s now packaging for everyone else.

This is what “eating your own cooking” looks like in 2026. And it’s a powerful signal about what these tools can do when pointed at real work.

Practical Use Cases for Your Business

We’ve been testing Cowork at Lighthouse, and here are the scenarios where it delivers clear value today:

Sales Operations: Before a client meeting, a salesperson can instruct Cowork to review the prospect’s website, analyse previous email threads stored locally and compile a briefing document with talking points tailored to the specific opportunity.

Legal Operations: Rather than paying external counsel to review standard NDAs, an in-house team can route contracts through Cowork’s legal plugin. The tool flags unusual indemnity clauses or jurisdiction issues, allowing senior lawyers to focus on negotiation rather than first-pass review. It doesn’t replace the need for legal expertise but for more commodity documents like NDA’s it’s very useful.

Meeting and interview analysis. Drop a batch of meeting notes into a folder. Cowork will extract themes, key quotes, action items, and areas of consensus or disagreement across the full set. While this use case isn’t new, it’s another subscription you don’t need, just record audio of meetings and the analysis and actions is covered.

Expense and receipt processing. Photograph your receipts, drop them in a folder, and Cowork produces a categorised expense spreadsheet with totals and breakdowns. It handles the OCR, the categorisation, and the formatting in one pass.

Competitive research. Pair Cowork with Claude in Chrome (the browser extension) and it can research competitors online, then compile findings into a formatted document or presentation, all without you manually copying information between tabs and documents.

Report and presentation production. Give it an outline and supporting data. Get back a formatted Word document or PowerPoint deck. The outputs aren’t perfect every time, but they’re a dramatically better starting point than a blank page.

What You Need to Know Before You Start

A few practical considerations.

Availability. Cowork is currently macOS only via the Claude Desktop app. The plugins are free and open-source on GitHub.

Security. Cowork runs in a sandboxed environment. It can only access the specific folder you grant it permission to. It asks before taking significant actions. That said, Anthropic is upfront that it can take destructive actions (like deleting files) if instructed to, so keep backups of anything important and be specific in your instructions.

Limitations. It’s a research preview, which means there are rough edges. It’s built for local files, so cloud-first teams using Google Workspace will need to sync files locally. And while the plugins are described as “no-code,” customising them for your specific workflows does involve editing some JSON and markdown files.

The opportunity cost of waiting. This is still early. The teams and businesses who start experimenting now will have a meaningful head start when these tools mature. You don’t need to rebuild your entire operation. Start with one repetitive task, one folder of messy files, one report that takes too long to produce. See what happens.

The Value Creation: Beyond the Hype

The stock market reaction to Cowork wasn’t just about one product from one company. It was a recognition that the gap between “AI can chat about your work” and “AI can do your work” just got a lot smaller.

Strip away the market hysteria and you are left with a practical reality. Claude Cowork offers something that has been promised for decades but rarely delivered: the elimination of mundane work without requiring technical expertise.

For businesses, the immediate value lies in time reclamation. Knowledge workers spend approximately twenty per cent of their week on administrative tasks: formatting documents, reorganising files, transferring data between formats, compiling reports from scattered sources. Cowork appears capable of handling significant portions of this workload, not by replacing the human judgment but by executing the mechanical implementation.

The second value lies in standardisation. When junior staff create spreadsheets or presentations, quality varies. When an AI agent applies the same skill template each time, output becomes consistent. This reduces error rates and ensures compliance with internal standards without requiring exhaustive training.

Third, there is the democratisation of automation. Previously, automating a workflow required either expensive software engineers or expensive enterprise software licences. Cowork allows non-technical professionals to automate their own repetitive tasks through conversation. A finance director who cannot write Python can nonetheless instruct Cowork to build a complex financial model.

Cowork won’t replace your legal team, your analysts, or your consultants. But it will level them up and change what you need them to spend their time on. The mundane, repetitive, format-and-compile work that eats up hours every week? That’s exactly what tools like this are built for.

And unlike most of what passes for AI strategy advice, this isn’t theoretical. You can download it today, point it at a folder, and see for yourself.

That’s the AI.Proof approach. Less hype. More doing.

Executive Summary

This week marks a pivotal moment in the AI industry as key players recalibrate strategies around integrated AI ecosystems, bringing unprecedented convergence between AI development, hardware innovation, and cloud infrastructure.

The landmark mergers and multi-billion-dollar partnerships are driving a new era where hyperscalers, chipmakers, and AI startups interlock capabilities to accelerate customised, scalable AI solutions.

Elon Musk consolidating xAI into SpaceX signals a bold bet on space-based AI infrastructure, while Amazon and OpenAI discussions hint at more exclusive and bespoke foundation models, reshaping competitive dynamics.

Chinese AI firms continue to disrupt through cost-competitive, high-performance models that influence Western deployments, highlighting geopolitical shifts in AI power.

Meanwhile, OpenAI’s strategic model retirements and new tools like Codex for macOS underscore an intense focus on refining developer experiences and AI product ecosystems. Together, these moves illustrate an industry rapidly maturing from exploratory innovation to integrated, high-stakes competition and infrastructure dominance worldwide.

Why this matters to businesses

• Agentic AI is moving from demo to deployment
  Autonomous agents embedded in browsers, apps, and commerce platforms are becoming real operational tools. Firms should identify repeatable workflows (research, customer support, procurement, internal reporting) that can be safely automated within 6–12 months.

• Developer productivity is becoming a competitive advantage
  Tools like Codex for macOS signal a shift toward AI-native development environments. Organisations should equip engineering teams with AI-assisted tooling now, as productivity gaps will widen quickly between adopters and laggards.

• Cost pressure will increasingly favour non-US and open models
  Chinese and open-weight models are now competitive on performance and significantly cheaper. Businesses should trial alternative models for non-sensitive workloads to reduce inference costs and dependency on premium providers.

• Expect tighter coupling between AI models and infrastructure
  Hyperscalers and model providers are moving toward exclusive or semi-exclusive partnerships. Businesses should assume less vendor neutrality over time and proactively review concentration risk across cloud, AI models, and tooling.

This Week’s Policy & Regulation Brief

Elon Musk Merges xAI Into SpaceX, Creating a Trillion-Dollar AI-Aerospace Giant

Elon Musk’s merger of xAI into SpaceX forms a $1.25T-valued private company combining rockets, satellite networks, and AI models, aiming to build solar-powered space AI data centers. This vertical integration uniquely positions Musk to deploy AI services globally, challenging both aerospace and AI incumbents.

ElevenLabs Raises $500M at $11B Valuation, Dominating Generative AI Voice Sector

The AI voice startup’s fresh funding round demonstrates surging investor confidence in synthetic audio technologies as a core AI frontier with broad creative and enterprise applications.

Chinese AI Chipmakers Show Financial Strength and IPO Ambitions

Cambricon reported its first profitable year with a $316M net profit, while Axera Semiconductor prepares to raise nearly $380M in a Hong Kong IPO. These milestones mark significant maturation in China’s AI chip sector, signaling intensifying competition with global semiconductor players and expanding investor interest.

Microsoft and AI Startup Perplexity Forge a $750M, Three-Year Cloud Partnership

Perplexity’s $750 million deal with Microsoft to deploy AI models through Azure Foundry exemplifies the fierce cloud provider competition to anchor AI-native workloads. The partnership also signals startups diversifying away from AWS, consolidating Microsoft’s influence in the AI infrastructure ecosystem.

China’s Hyperscalers Aggressively Invest in Agentic AI for Commerce

Leading Chinese tech companies Alibaba, Tencent, and ByteDance are embedding autonomous AI agents within super apps for seamless commerce workflows. This focused investment could disrupt Western AI market models and redefine enterprise adoption of agentic AI worldwide.

Amazon Eyes Tens-of-Billions Equity Stake in OpenAI for Customized AI Models

Reports reveal Amazon is considering a massive equity investment in OpenAI tied to agreements to develop tailored AI models exclusively for Amazon. Such a partnership would redefine hyperscaler and foundation model competition by driving exclusive, deeply integrated AI innovation.

Chinese AI Startup DeepSeek R1 Upends Global AI Market with Cost-Effective Models

DeepSeek’s GPT-4-level models, available at substantially lower costs, are catalyzing a shift in global AI sourcing as top Western firms adopt Chinese and open-source alternatives despite export controls. This disrupts traditional vendor landscapes and complicates geopolitical AI competition.

Model & Platform Updates

Google DeepMind Launches Project Genie for Real-Time Text-to-3D Worlds

Google DeepMind’s Project Genie demonstrates how text prompts can generate fully explorable 3D environments using autoregressive world models. Rather than static scenes, Genie creates interactive worlds that respond to user movement and actions. While still a research project, it signals progress toward AI-generated simulations for gaming, robotics training, and digital twins. The work highlights DeepMind’s focus on environment-level intelligence rather than single-task models.

Kling 3.0 Releases Its Most Advanced Multimodal Model

Kling 3.0 introduces improved multimodal video generation with stronger consistency of characters and visual elements across shots. The model supports short, cinematic clips of up to 15 seconds with controlled camera movements, transitions, and shot composition. While positioned primarily for creative and marketing use cases, the release underscores rapid advances in controllable generative video, narrowing the gap between experimental AI video tools and production-ready workflows.

OpenAI Debuts Codex Mac App for AI-Assisted Software Development

OpenAI’s Codex macOS app brings AI-assisted coding into a native desktop environment, supporting multi-agent workflows, local execution, and tighter developer feedback loops. Designed to streamline complex software tasks, it allows developers to plan, write, test, and refactor code with reduced manual context switching. The launch reflects OpenAI’s strategic push beyond APIs toward integrated developer tools that compete directly with established IDE-centric ecosystems.

Mistral Unveils Ultra-Fast Translation and Speech Models Under Open Licences

Mistral has released new real-time translation and speech models focused on speed, efficiency, and open accessibility. By offering open-weight or permissive licences, Mistral positions itself as a challenger to proprietary US-based labs, particularly for enterprises seeking cost control and on-premise deployment. The models target practical use cases such as live translation and speaker diarisation, reinforcing Mistral’s strategy of performance-efficient, developer-friendly AI systems.

Chrome Rolls Out Agentic AI Upgrades Powered by Google Gemini

Google is embedding Gemini-powered agentic capabilities directly into Chrome, including autonomous browsing tasks, contextual sidebars, and multimedia assistance. These features move the browser from a passive interface to an active AI agent that can research, summarise, and act on user intent. The update signals Google’s ambition to make agentic AI a default layer of consumer software, blurring the line between applications and intelligent assistants.

Intel Moves into Discrete GPUs, Challenging Nvidia’s AI Dominance

Intel’s push into discrete GPUs represents a strategic attempt to diversify AI compute supply beyond Nvidia’s dominant ecosystem. While Nvidia remains the market leader, Intel’s roadmap aims to offer competitive alternatives for data centres and large-scale AI workloads. Increased competition could ease supply constraints and pricing pressure for enterprises, though Intel still faces challenges in software maturity and developer adoption compared with Nvidia’s established CUDA platform.

Anthropic Adds Plugins to Claude Cowork

Anthropic has introduced plugin support for Claude Cowork, expanding the assistant’s ability to integrate with external tools and enterprise workflows. Plugins allow Claude to interact with data sources, applications, and services beyond chat, positioning it more clearly as a workplace productivity agent. This move aligns Anthropic with broader industry trends toward tool-augmented AI, where value increasingly comes from execution and integration rather than model capability alone.

Quick Hits

• OpenAI Rumoured to Release ChatGPT 5.3 - I’ll leave thi shere for another week, its coming - Online speculation suggests OpenAI may be preparing a release dubbed ChatGPT 5.3 as early as next week. However, OpenAI has made no official announcement confirming the existence, timing, or scope of such an update.

• GitHub Integrates Anthropic Claude and OpenAI Codex as Multi-Agent Coding Interfaces - Enhances developer workflows with choice and assignment of AI coding agents across platforms.

• xAI Introduces Grok Imagine 1.0 - The latest update allows users to create videos with a length of up to 10 seconds at 720p resolution.

• Anthropic is too rumoured to be releasing it’s next model Sonnet 5 -
  Excellent benchmarks yadayada - but a much lower token cost, so a more powerful model and cheaper - we shall see.

Three hours. That’s how long my first experiment with a computer-use AI agent lasted before I found myself padding downstairs at midnight to physically unplug my new Mac Mini from the wall.

Not because anything had gone wrong. But because the possibilities of what could go wrong had finally sunk in, and to be honest in my excitement about the possibilities. much to my realisation later on, I had put in place no guardrails.

We have spent the better part of two years discussing Large Language Models (LLMs) as if they were oracles. We query them, they answer. It is a transactional relationship that works, like a very advanced search engine or a clever librarian. But the narrative is shifting. The industry is pivoting, it was certainly the buzzword of 2025 "Agentic AI”. Software that doesn't just talk, but acts.

This week, I want to share my hands-on experience deploying OpenClaw, one of the new breed of “computer-use” AI agents.

These tools represent a genuine shift in how AI can operate, moving from responding to prompts to actually doing things on your computer. But as with most advances in AI, the reality sits somewhere between the breathless hype and the dystopian warnings.

Here’s what I learned, what surprised me, and what I think it means for businesses trying to figure out where these tools actually fit.

What Actually Is It?

Before we get to the part where I physically unplugged Jeff, we need to define what we are dealing with.

If you’ve been following AI developments, you’ll have seen announcements from Anthropic, OpenAI, and Google about AI systems that can “use computers like humans do” giving them hands. The concept is straightforward: instead of just generating text or answering questions, these agents can see your screen, move a mouse cursor, click buttons, type into applications, and navigate between programs.

Think of it as the difference between asking a colleague to explain how to do something versus asking them to actually do it for you.

Several open-source projects have emerged to make this capability accessible. OpenClaw, has been around for a few months but has just gone viral, it is an agent (and if you read our article on agents - it is an agent) that runs on your own hardware, connected to the AI model of your choice. They provide the scaffolding: the screen capture, the input simulation, the agent loop that lets an AI observe, think, and act.

The pitch is compelling. Imagine an AI that could handle your social media posting, research competitors, book meetings, update your CRM, format documents, or manage routine admin tasks - a Siri that actually does something. Not just by integrating with each application’s API, but simply by using them the same way you would.

But here’s where the hype needs tempering. What you’re actually getting, at least today, is something more modest but still genuinely useful.

The Security Wake-Up Call

Let me return to that midnight unplugging incident.

ClawedBot’s (what is was called at the time) capabilities sounded amazing, I rushed out and bought a dedicated Mac Mini. This was a deliberate choice. I didn't want this running on my primary work laptop, a decision that proved to be the smartest thing I did all week.

Dedicated hardware, isolated from my main work systems. Smart, I thought. Within a few hours of setup, I had an agent running, talking to me, connected to a capable AI model, with access to a browser and basic applications, not I might add to passwords and other systems, but certainly enough to be dangerous.

Then I started thinking through the implications. The "Cold Sweats" kicked in.

This agent could see my screen. It could type. It could click. It could navigate to websites. It could fill in forms. If I logged into any service, it would have those credentials available in the browser. If it decided (or was manipulated into deciding) to visit a malicious website, it could download files. It could potentially execute code.

I’m not an alarmist about AI. But I am someone who’s spent decades in corporate environments where security isn’t optional. And the traditional security model, where you trust the user and control what software runs, doesn’t map cleanly onto an AI agent that is the user.

The scary use cases you read about online are real. Agents are susceptible to prompt injection (where a malicious website instructs the agent to do something bad) and simple incompetence (as i have proven above). If you deploy an agent into your business infrastructure without guardrails, you are effectively giving a stranger your login credentials and hoping they are in a good mood.

The next morning, I powered the Mac Mini back up and I started again. This time with proper precautions:

Isolation first. The Mac Mini is now pretty much sandboxed, we used strict containerisation (Docker) to ensure that the agent could play in its sandbox but couldn't touch the rest of the playground. Things like sharing files have to be a little more deliberate than it simply having access my default. Yes it does dull down its full potential of what is possible, but it also dulls down on what can go wrong.

No stored credentials. If a task requires logging into something, It has its own credentials separate to our and it doesn’t have access to what they are. Tedious, but necessary.

Audit everything. Every action the agent takes is logged. I review these regularly, not because I expect problems, but because understanding what the agent actually does is essential to improving how I use it.

Is this overkill? Possibly. But the attack surface of a computer-use agent is fundamentally different from a chatbot. It’s not generating text that you then act on. It’s acting directly. That demands a different risk posture.

If you’re considering experimenting with these tools, I’d strongly recommend reading up on the security considerations first. Several thoughtful articles have been published on the risks and mitigations.

The key message: treat systems like Openclaw with the same caution you’d apply to giving a stranger remote access to your machine. Because functionally, that’s what you’re doing.

The Economics: Affordable but needs monitoring

One part of the reboot was switching the underlying AI model powering Jeff. I initially started with Anthropic’s Opus 4.5, but even with fairly lightweight, conversational use over Telegram, it became obvious that Jeff was burning through tokens at an alarming rate, faster than Tom Cruise as Maverick burns through fuel in his fighter jet on full afterburner.

I have heard some people online run these models on max plans without much concern, but I’d also heard that Anthropic had begun clamping down on excessive usage, including suspending accounts. Given how heavily we already rely on Claude elsewhere in the business, I wasn’t prepared to take that risk.

I switched to MiniMax, one of the lower-cost AI models that still handles computer-use tasks adequately, albeit as some people have commented online, with less personality than Opus 4.5. After a full week of regular use, my token costs came to roughly five dollars.

That’s not nothing, but it’s far from prohibitive. For context, that’s less than a single cup of coffee at most London cafes. If Jeff saves me even an hour of work per week, the ROI is absurdly positive.

The economics will vary depending on which model you choose and how intensively you use the agent. More capable models from the major providers cost more. Complex tasks that require many iterations burn through tokens faster. But the barrier to entry is lower than I expected.

This matters because it means experimentation is cheap. You don’t need to commit significant budget to find out whether computer-use agents work for your situation. A few pounds and a weekend of tinkering will tell you a lot.

There is a concept i haven’t tried yet of configuring Jeff to use multiple AI models, each configured for different tasks.

The Initial Reality: A Junior Team Member, Not a Replacement

Now for the honest assessment of what the Jeff actually delivers.

Once the panic subsided and the security was tightened, we got to work. In its first few days, Jeff felt like... a very junior team member. One who needed explicit instructions, followed them literally, and didn’t show much initiative beyond the immediate task.

Ask Jeff to draft a social media post? Jeff would. Ask him to develop a content strategy? He would produce something generic and unhelpful.

This wasn’t dramatically different from what I could achieve with a one-shot prompt to a standard LLM. The computer-use capability added the ability to execute, but the thinking remained firmly task-level.

I’ll be honest: after the first few days, I wondered whether the security hassle and setup time were worth it. Jeff worked but he wasn’t going to win employee of the month.

The Shift: When Role Definition Changed Everything

Then something interesting happened.

I stopped giving the agent random tasks and I spent time defining a specifc role in the business - giving a clear, consistent role (job spec): digital marketing support, onboarding him if you like, as you would a real employee. I gave Jeff a personality, more context, examples, teaching him the role. Specifically i got him to start monitoring relevant conversations, identifying content opportunities, drafting social post content ideas, and flagging interesting developments in the AI space, which we could explore and collaborate on jointly.

With that focus, Jeff’s behaviour shifted noticeably.

He became more curious. Instead of just completing assigned tasks, he started surfacing things I hadn’t asked for. Interesting threads on X. Relevant newsletter posts from others in the space. Potential content angles based on trending discussions. Testing initial assumptions about the role I had shared and challenging them.

The outputs improved too. With consistent context about AI-Proof’s positioning, audience, and voice, the content drafts became more helpful. Still requiring editing, but providing genuine starting points rather than generic filler.

I’m cautious about anthropomorphising AI systems. The agent didn’t “learn” in any meaningful sense, and it doesn’t have curiosity the way a human does. But the combination of a well-defined role, consistent context, and accumulated interaction history seemed to produce qualitatively better results.

The lesson here maps to what we’ve discussed before about AI deployment generally: specificity and starting small matters. A vague instruction to “help with marketing” produces vague results. A clear brief about role, responsibilities, and context produces something you can actually use.

Practical Guidance: Is This Worth Your Time?

So should you experiment with OpenClaw or an equivalent? Here’s my honest assessment:

Consider it if:

You have specific, repetitive tasks that involve multiple applications or websites, and you’ve already exhausted simpler automation options. Social media management, competitive monitoring, routine research, and data gathering from multiple sources are reasonable starting points.

You’re comfortable with technical setup and have the security awareness to deploy these tools responsibly. This isn’t plug-and-play territory yet.

You have patience for iteration. The first attempts will disappoint. Value emerges through refinement.

Hold off if:

You’re expecting an autonomous digital employee who needs minimal oversight. That’s not where the technology is today (my view).

Your potential use cases involve sensitive data, customer information, or systems where mistakes have significant consequences. The risk profile doesn’t justify it yet.

You don’t have isolated hardware or a sandboxed environment to experiment in. Running a computer-use agent on your primary work machine is asking for trouble.

What Comes Next

I’m continuing to develop the Jeff’s role. I want to explore the concept of additional AI co-workers for Jeff (sub agents), still of a bit of research to do before we’re ready for that though.

The current focus remains digital marketing: content ideation, social media monitoring, SEO/Adword tracking and first draft generation. Small, contained use cases where mistakes are recoverable and value is measurable.

If that proves consistently valuable, I’ll consider expanding scope. But carefully, and with the same methodical approach we’d apply to any new team member: prove yourself in the small things before taking on the big ones.

The technology is genuinely novel. For the first time, AI systems can interact with our digital tools the same way we do. That opens possibilities that API integrations and chatbots simply can’t address.

But novel doesn’t mean mature. These are early days. The tools are rough, the risks are real, and the gap between demos and daily utility remains significant.

For businesses serious about AI implementation, computer-use agents are worth watching and worth experimenting with in controlled environments. They’re not yet worth betting operations on.

I’ll report back as the experiment continues. In the meantime, if you’ve been testing these tools yourself, I’d love to hear about your experience. What worked? What didn’t? What surprised you?

The conversation continues.

Until next week,

Les

Executive Summary

This week’s top AI industry news reveals a pronounced shift towards open innovation and strategic diversification beyond the dominant large language model (LLM) paradigm.

AI is moving decisively from experimentation to execution, but with higher stakes. The shift toward agentic systems, alternative model architectures, and embedded AI (from browsers to chips) means competitive advantage will increasingly come from how AI is operationalised, not which model is chosen.

At the same time, regulation, security risk, and geopolitical fragmentation are becoming first-order constraints. CEOs should treat AI less as a technology bet and more as an operating-model decision that touches governance, data access, talent, and long-term platform dependency.

This multifaceted evolution underscores a critical inflection point where innovation, governance, and market consolidation will define AI’s trajectory through 2026. Industry leaders must now navigate not only technological breakthroughs but also the ethical, legal, and geopolitical complexities steering the AI landscape’s next chapter.

Why this matters to businesses

• Regulation is becoming a design constraint
  EU actions against Google and xAI show regulation is no longer reactive, it actively shapes product architecture, distribution, and data access. AI strategy must now be built with regulatory exposure in mind, especially for platform-dependent businesses.

• The LLM monoculture is breaking
  World models, JEPA architectures, and specialised agents challenge the assumption that bigger LLMs always win. This widens the strategic landscape: differentiation will increasingly come from architecture, integration, and domain fit, not just model size.

• Agentic AI is moving from experiments to execution (in some areas)
  Autonomous, multi-agent systems (Kimi, Gemini, Moltbot) signal a shift from AI as a tool to AI as an operator. Businesses should expect AI to increasingly execute workflows, not just assist decisions, reshaping productivity, software design, and competitive advantage. However use of agentic AI is causing IT experts real concern over security as agents want access to our data and systems.

This Week’s Policy & Regulation Brief

Yann LeCun’s new venture is a contrarian bet against large language models

Yann LeCun launched Advanced Machine Intelligence (AMI), focusing on world models and JEPA-based AI architectures as alternatives to LLMs, aiming to create a global, open-source AI platform beyond US-China dominance. This could reshape frontiers of autonomous agents and robotics grounded in real-world understanding.

EU opens formal Digital Services Act investigation into xAI over Grok’s sexualiSed images

The EU initiated a regulatory probe into xAI’s Grok for generating sexualized images of women and children, threatening fines up to 6% of global revenue. This escalates regulatory vigilance on generative AI content moderation and accountability.

Anthropic CEO Dario Amodei outlines AI dangers and calls for stronger controls

Amodei highlights risks from AI including bioterrorism and mass job losses, urging chip export bans and transparency measures. His essay frames AI’s next years as pivotal for societal impact and governance.

The EU orders Google to remove Android AI search barriers to rivals

The European Commission has told Alphabet’s Google to remove barriers that give its own AI search services an edge on Android, ordering it to grant rival AI and search providers equal access to key system features and anonymised search data. The move, under the bloc’s Digital Markets Act, gives Google a six-month deadline to define how it will comply. Regulators say the step is meant to foster fair competition and innovation, while Google warns of potential impacts on privacy and security.

OpenAI appoints Barret Zoph to lead enterprise push in 2026

Anthropic’s hire to lead its Bengaluru hub illustrates intensified global talent competition and regional expansion strategies as AI firms race to secure leadership in key international markets.

Ricursive raises $335M at $4B valuation to automate chip design

Ricursive’s strong fundraising round for AI-powered chip design reflects investor enthusiasm in specialised AI chip infrastructure to accelerate next-generation hardware.

Mistral CEO calls claims of Chinese AI lag a “fairy tale” amid open-source competition

Mistral leadership challenges Western narratives on Chinese AI progress, highlighting intense open-source AI innovation in China. This underscores rising geopolitical competition in AI technology leadership.

Inferact raises $150M seed to build cross-hardware efficient AI infrastructure

By focusing on optimised AI execution across diverse hardware, Inferact aims to unify AI model performance and cost-effectiveness across CPUs, GPUs, and accelerators.

UK Government Launches National AI Training Push to Accelerate Business Adoption

The UK government has announced a nationwide AI training initiative aimed at boosting adoption across businesses and the public sector. The programme focuses on upskilling workers, improving AI literacy among leaders, and reducing barriers to practical deployment, particularly for SMEs. By pairing training with broader digital reform efforts, the government hopes to close the UK’s AI productivity gap and accelerate real-world impact beyond pilot projects and experimentation.

Model & Platform Updates

Moonshot releases Kimi K2.5, a trillion-token multimodal AI agent swarm model

Moonshot’s Kimi K2.5 combines large-scale multimodal pretraining with an orchestrated swarm of specialised sub-agents, extending autonomous and distributed AI capabilities well beyond conventional LLM architectures. The model represents a significant step forward in agentic system design, tightly integrating vision, language, and coordinated decision-making at scale.

Anthropic publishes Claude’s Constitution, setting a safety and ethics framework for AI assistants

Claude’s Constitution guides AI reasoning with a principled approach prioritizing ethics, safety, and compliance, reflecting growing focus on AI alignment and responsible deployment.

From Clawdbot to Moltbot: Open-Source AI Agent Sparks Global Buzz

Moltbot, the open-source personal AI assistant originally called Clawdbot, has exploded in popularity across developer communities and social media. Built to run locally and automate tasks via familiar messaging apps like WhatsApp and Telegram, it offers persistent memory and proactive actions, not just chat. A trademark nudge from Anthropic forced a rebrand, but the momentum hasn’t slowed, with massive GitHub interest and tech buzz highlighting its potential, alongside rising security and misuse concerns.

MiniMax Unveils M2.1: Major Multilingual Coding & AI Agent Upgrade

Chinese AI developer MiniMax has launched M2.1, a significant update to its open-source M2 model designed for real-world programming and agentic workflows. The upgrade expands multilingual coding support, including Rust, Go, JavaScript, Kotlin and more, and improves performance on complex instruction following and office automation tasks, making it more practical for production use. Benchmarks show stronger robustness and broader applicability across developer and enterprise scenarios, reinforcing MiniMax’s push into competitive open-weight AI models.

Google integrates Gemini AI tightly into Chrome with new autonomous agentic features

Google has significantly expanded Gemini’s integration into Chrome, introducing autonomous, agent-like capabilities directly within the browser. The update allows Gemini to proactively assist with tasks such as summarising webpages, navigating workflows, managing forms, and executing multi-step actions on behalf of users.

By embedding agentic AI at the browser layer, Google is positioning Chrome as an execution environment for AI assistants, signalling a shift from passive AI tools to always-on, task-oriented digital agents.

Quick Hits

• OpenAI Rumoured to Release ChatGPT 5.3 - Online speculation suggests OpenAI may be preparing a release dubbed ChatGPT 5.3 as early as next week. However, OpenAI has made no official announcement confirming the existence, timing, or scope of such an update.

• Tiny startup Arcee AI releases Trinity, a 400B-parameter open-source LLM - This significant open-source model release from a small US startup challenges incumbent open LLMs and could catalyse further democratisation and competition in high-end AI models.

• OpenAI launches Prism, a GPT-5.2-powered LaTeX workspace for scientists
  Prism integrates AI deeply into complex research workflows, offering scientists advanced assistance with drafting, math reasoning, citations, and figures, potentially revolutionising scientific productivity and collaboration.

However, even with perfect prompts, you don’t have full control.

You craft a structured prompt, press send, and hand over control. The AI decides the sequence. The AI decides what to prioritise. If something goes wrong halfway through, you don’t know where or why. You simply get a subpar result and have to start again.

Structured prompting solves the input problem. It doesn’t solve the control problem.

This week, we are looking at what does: workflows.

The Limitation of Prompt-First Thinking

When you run a complex task through a single prompt, the AI handles everything in one pass. Research, analysis, synthesis, formatting. You cannot see the intermediate steps. You cannot intervene if the research is thin but the synthesis is fine. You cannot swap out one component without re-running the whole thing.

If the output is wrong, your only option is to tweak the prompt and try again. You are essentially debugging a black box.

This matters less for simple tasks. Drafting an email, summarising a document, or brainstorming ideas are fine with single-pass prompting. But for anything with multiple stages, dependencies, or real-world consequences, you need more than a good prompt.

You need a process you can see, test, and improve piece by piece.

What Workflows Actually Give You

A workflow breaks a task into discrete steps. Each step has a defined input, a defined output, and a clear job to do. The AI might handle one step, or several, but it does not control the overall sequence. You do.

This delivers four advantages that prompting alone cannot match:

1. Control over sequence. You define what happens first, second, third. The AI handles specific nodes (like summarising text or extracting data) but the orchestration is yours. If step three should only run after a human reviews step two, you build that in.

2. Visibility and debugging. When something fails, you know which step failed. You can inspect the input and output at each stage. Instead of re-running an entire prompt and hoping for a different result, you fix the specific component that broke.

3. Human-in-the-loop by design. Not everything should be automated. Workflows let you decide where humans review, approve, or intervene. The AI does the heavy lifting while a person signs off before anything goes out the door.

4. Incremental improvement. Want to upgrade the model for one step? Swap it. Want to add a new data source? Add a node. You improve the system over time without rebuilding from scratch.

What This Looks Like in Practice

At Lighthouse, we have moved a number of repeatable tasks into workflows. Two examples stand out.

Market Intelligence We used to pull signals from Google News, industry feeds, and several other sources. The information was useful, but someone still had to sift through it every morning to separate noise from signal. Now that process runs automatically. An AI-powered workflow pulls from the same sources, reviews and sorts the content, and delivers a daily brief each morning. Relevant, factual, and fully curated.

Company Profiling This involves researching a business before a call or meeting. We already had a process for this: check the website, pull Companies House data, scan recent news, and compile a summary. The workflow didn’t invent a new process; it codified the one we already had. Same result, same quality, just automated.

Between these two workflows alone, we have recovered roughly half a day per week of one person’s time. That is not theoretical. That is time we have measured and redeployed.

Choosing a Tool

We have tried several platforms, including Gumloop and CrewAI. To properly evaluate them, we built the exact same workflow in each tool to compare the results directly.

For our specific requirements, n8n consistently came out ahead in two critical areas.

First, the token cost. Even when using the exact same models across platforms, we found the cost to run the workflow was significantly higher on other tools. The efficiency of n8n made the unit economics far more viable.

Second, hallucination control. In one instance during our testing on a different platform, the AI generated a company profile that was false in every aspect. It even fabricated the source links. While this may have been a configuration issue on our end rather than a platform fault, the reality is that we have never encountered that level of instability with n8n.

Make (formerly Integromat) and Zapier are approachable if you are starting from scratch. But if you are serious about building AI workflows that run reliably in production, n8n is worth the learning curve.

A Practical Example: The Company Research Workflow

Let’s walk through a simple workflow you can build this week. The goal: enter a company’s website and receive a structured research briefing in your inbox within minutes.

Here is the structure:

• Step 1: Input. The workflow starts with a manual trigger. You enter a company website URL through a simple form or directly in n8n. This is the only human input required.

• Step 2: Website Research. A web scraping node fetches key pages (homepage, About page, leadership team). An LLM node then summarises what the company does, who leads it, and what they sell. You are not asking the AI to “research the company”; you are giving it specific text and asking for a structured summary.

• Step 3: Companies House. A separate node calls the Companies House API to pull official data, such as the registered address, directors, and recent filings. This is factual, structured data with no AI interpretation needed.

• Step 4: Web Search. A search node queries recent news and funding announcements. The raw results pass through an LLM node that filters for relevance and summarises key developments. You set the parameters (e.g., last 90 days, exclude job postings).

• Step 5: Synthesis. We meger these parallel streams and let an LLM interpret and structure the data into a useful format.

• Step 6: Email Summary. A final node compiles everything into a templated briefing and sends it to your inbox.

The entire workflow runs in a few seconds. What used to take 30 minutes of tab-switching now happens in the background.

For the full build, including node settings and prompts, watch the companion video which will be on our youtuber channel shortly.

A Note of Caution: The Maintenance Tax

It is important to be realistic. Automated workflows are not “set and forget.” If you deploy them, you must be prepared to monitor them.

Breakages typically occur at the endpoints. An API might change its structure, login credentials expire (e.g. google’s last 1 week). Firms are increasingly guarding their data, so web scrapers may stop working, or access to data might be explicitly turned off for bots.

When these break (and they will break), they need fixing. The knowledge of how the workflow operates must be shared across your team. If only one person understands the logic and that person is away, your automated process becomes a bottleneck. Treat this as software: it requires maintenance.

The Verdict: Is This Ready for Prime Time?

Yes. With a caveat.

For firms looking to start with AI, structured prompting and/or workflows such as N8N, Zapier, Gumloop and Crewai are great. Start small target a specific workflow and automate it, try different tools (free tiers) to see which one works best for you.

Workflows are production-ready for well-defined, repeatable tasks. The tooling has matured. The barrier isn’t technology; it’s clarity. You need to know what you are automating before you automate it.

Having spoken to lots of companies the ones that go big fast, surprisingly dont generate the same value as those who look inwards and expand after inital small scale pilots.

So, start with one workflow that saves real time. Prove the value. Then expand.

Prompting is how you talk to AI. Workflows are how you put AI to work.

Clawdbot - Breaking News

As I write this on a Saturday night, I have been monitoring a lot of discussion online about a new agent - Clawdbot.

Clawdbot was created by Austrian-born developer Peter Steinberger, a software engineer and open-source lead best known for building developer tools like PSPDFKit (now Nutrient). He launched the project in late 2025, early 2026 and maintains it with community contributions.

Clawdbot is a locally run AI agent that automates research and monitoring tasks on your machine. It can review spreadsheets, check company data, monitor inboxes, and update records on a schedule, acting like a junior analyst working overnight without relying on paid APIs.

From what you read it sounds great (almost too great) however enough for me to buy a Mac Mini and start using it. Min is called Arthur and i talk to it via Whatsapp.

In this slot next week I will share my fist week’s experience with it.

Executive Summary

This week underscored the growing intersection between AI technological breakthroughs and the broad regulatory, geopolitical, and infrastructure challenges shaping the industry’s future.

As major players accelerate AI hardware innovation and integration, such as SiFive embedding Nvidia’s NVLink Fusion for next-gen heterogeneous chips, governments and regulators grapple with the complexities of AI’s rapid expansion.

From the UK Treasury’s urgent call for proactive financial sector AI regulation to emerging global disputes over harmful AI content and energy infrastructure strain driven by AI workloads, the week showcased a landscape where technological leadership and governance must evolve in tandem.

Notably, China’s workaround tactics to access cutting-edge AI chips amidst export controls and strategic regional expansions by firms like Anthropic highlight a world where AI competitiveness is increasingly geopolitically charged. This dynamic interplay between innovation, regulation, and global market pressures marks a pivotal moment in defining AI’s safe and balanced integration into society.

Why this matters to businesses

• Agentic AI is moving from demos to real workflows

  Experiments like Cursor’s agent-built browser and xAI’s “human emulators” show AI increasingly performing end-to-end tasks. Firms should keep close to this developing area with a view to redesigning workflows around supervision, not execution.

• Open models are accelerating competitive pressure
  Open-source releases like Flux.2 lower barriers to entry. Differentiation will come less from access to models and more from proprietary data, integration depth, and speed of execution.

• AI advantage is becoming increasingly geopolitical
  Export controls, regional compute access, and talent hubs matter. Businesses reliant on AI vendors should assess geographic concentration risk and supply-chain resilience for models and infrastructure.

This Week’s Policy & Regulation Brief

UK Treasury Committee Warns “Wait-and-See” AI Approach Risks Serious Harm

A UK parliamentary Treasury Committee has warned that the government, Bank of England and Financial Conduct Authority risk exposing consumers and the financial system to “serious harm” by maintaining a passive “wait-and-see” stance on AI in finance. With about 75% of financial firms already using AI, MPs urged AI-specific stress tests, clearer regulatory guidance and accountability rules, highlighting risks including opaque algorithms, exclusion of vulnerable customers, fraud and market instability.

Silicon Valley’s Messiest Breakup Moves Toward Court Battle

Elon Musk’s lawsuit against OpenAI and Microsoft advances as motions to dismiss are denied, potentially setting precedent for governance and fiduciary duties in AI labs amid commercial pressures.

Trump and Governors Push Emergency Power Auction Due to AI Demand

The surge in AI data center power requirements is prompting U.S. regional grid operators to hold auctions for new power plant funding, highlighting AI’s direct impact on energy policy and infrastructure.

Chinese AI Firms Rent Compute Abroad to Circumvent Export Controls

By leasing compute resources in Southeast Asia and the Middle East, Chinese AI companies like Alibaba attempt to access Nvidia’s Rubin chips, signalling broadening AI technology divides spurred by geopolitical tech restrictions.

Anthropic Appoints Former Microsoft India MD to Head Bengaluru Expansion

Anthropic’s hire to lead its Bengaluru hub illustrates intensified global talent competition and regional expansion strategies as AI firms race to secure leadership in key international markets.

Global Regulators Criticise xAI Over AI-Generated Exploitative Content

Despite growing regulatory backlash worldwide over xAI’s model generating explicit images non-consensually, U.S. authorities remain largely inactive, exposing enforcement gaps in AI content safety governance.

Applied Compute, Founded by Ex-OpenAI Researchers, in Talks for $1.3 Billion Valuation

Applied Compute, an AI startup founded by three former OpenAI researchers, is in early discussions to raise a new funding round that could value the company at about $1.3 billion, more than doubling its implied $500 million valuation from just months earlier. The firm builds tailored AI models and agents for enterprise use, and the talks, though not final, signal strong investor appetite for specialised AI infrastructure businesses.

xAi to build human emulators

Elon Musk’s AI company xAI is reportedly developing “human emulators”, AI systems designed to mimic real employees performing digital tasks like keyboard and mouse interactions, automating standard desk work. Former xAI engineer Sulaiman Ghori discussed these virtual workers in a detailed interview, noting internal tests where they acted like staff on org charts. The initiative aims to scale AI-driven automation for white-collar roles, raising productivity and workforce-impact questions.

Anthropic’s New "Economic Primitives" Reveal Rapid US Diffusion and a Shift Back to Augmentation

Anthropic’s latest analysis offers rare, data-driven insight into how AI is actually being adopted across the economy, and where businesses are seeing real, measurable impact.

• AI adoption is spreading unusually fast, plan now, not later
  AI usage across U.S. regions is diffusing ~10× faster than previous general-purpose technologies. Waiting for “market maturity” risks falling behind peers who are already embedding AI into day-to-day workflows.

• Automation works best for narrow, repeatable tasks
  API usage shows AI excels at structured automation (coding, data processing, support triage). Businesses should prioritise task-level automation, not full role replacement.

• Productivity gains are real but incremental
  AI could lift productivity by roughly ~1% annually at an economy level. For firms, gains compound when AI is embedded across many small processes rather than one “big” transformation.

• AI impact will differ by function, not just by industry
  Variation in AI use is greater within companies than between sectors. Leaders should audit tasks function-by-function (finance, sales, ops), not assume blanket applicability.

• Measurement matters more than hype
  Anthropic’s “economic primitives” highlight the importance of tracking who uses AI, how often, and for what tasks. Firms that instrument usage will adapt faster and waste less spend.

Model & Platform Updates

Anthropic Reveals “Assistant Axis” Neural Pattern Governing LLM Behavior

Anthropic’s research identifies a neural activity pattern outlining LLM assistant-like persona and helpfulness, advancing AI interpretability and hinting at new safety and control mechanisms.

SiFive Integrates Nvidia’s NVLink Fusion With RISC-V Processor IP

This integration enables high-bandwidth CPU-GPU communication optimized for AI workloads, pushing forward next-generation heterogeneous compute architectures for AI training and inference.

Introducing ChatGPT Go, now available worldwide

OpenAI has launched ChatGPT Go globally, a lower-cost tier designed to widen access to its latest GPT-5.2 Instant model. The plan offers faster responses, higher usage limits and significantly expanded memory, enabling longer-running tasks and more personalised interactions. OpenAI positions ChatGPT Go as a bridge between free access and enterprise offerings, targeting professionals, small businesses and teams seeking reliable AI capabilities without full enterprise pricing.

Gemini's ‘Personal Intelligence’ upgrade

Google has begun rolling out “Personal Intelligence,” a new feature for its Gemini AI that lets the assistant optionally access and reason across users’ Gmail, Google Photos, YouTube and Search histories to deliver deeply personalized responses. Initially available in beta to paid subscribers in the U.S., the feature aims to make Gemini more context-aware and useful for everyday tasks, while giving users control over what data is connected and how it’s used.

Cursor Runs Massive AI Agent Experiment to Build Web Browser at Scale

Cursor’s CEO disclosed that hundreds of autonomous AI coding agents, powered by GPT-5.2, collaborated continuously for nearly one week to produce a web browser from scratch, reportedly writing millions of lines of code and core subsystems including the rendering engine. The result is experimental, proof-of-concept work rather than a production-ready product, highlighting both AI’s rapid advances and ongoing questions about practical usability and code quality.

Quick Hits

• OpenAI to launch chatbot ads in February – OpenAI plans to introduce advertising within its chatbot products from February, signalling a strategic shift toward monetisation and platform commercialisation.

• Black Forest Labs releases open-source Flux.2 - expanding access to advanced image generation models and intensifying competition across the generative AI ecosystem.

Most of us can remember the excitement when generative AI first went mainstream. Teams rushed to experiment with tools like ChatGPT, often ending up in a prompt frenzy of ad-hoc experiments. Marketers, for example, collected “magic prompts” and jumped between AI models in hopes of content nirvana. The result has been familiar across industries. Hours spent re-prompting. Inconsistent outputs. Results that look impressive one minute and unusable the next.

The problem is not the models. It is the way we are using them. One-off prompting does not scale. It creates no consistency, no shared learning, and no reliable capability. Teams get stuck in a cycle of trial and error, where every task starts from scratch and success depends on individual experimentation rather than a repeatable approach.

The businesses seeing real value from AI have made a simple shift. They have moved away from ad-hoc prompting and towards structured prompting. It takes more effort upfront, but it delivers better results, faster, and at scale.

As one practitioner put it, ad-hoc prompts “leave behind zero institutional knowledge”. In practice, that means each new project starts from scratch, with no continuous improvement. Worse, the inconsistency can hurt your brand: one B2B SaaS company found their lead quality dropped 23% after six months of unstructured AI content, because prospects were confused by wildly different tones in the messaging. It’s a common pattern across industries: without structure, AI efforts become just another tool that eats your valuable time and even introduce risk (imagine trying to explain to how an AI made a decision when you have no record of the prompt or rationale).

Why Ad-Hoc AI Prompting Falls Short

To understand the need for a new approach, let’s examine why ad-hoc prompting fails to deliver business value (in the long term). Some common issues include:

• Lack of context: Users assume the AI understands their business, audience, or constraints. It does not. Without background, the model fills in the gaps with generic assumptions.

• Vague instructions: Ad-hoc prompts tend to be loosely worded – “Summarise this report” or “Help with marketing”. Such ambiguity leads to unpredictable outputs. The AI isn’t told what success looks like, so each response might focus on different aspects or miss the mark entirely.

• Inconsistent output guidance: One prompt might yield a formal essay, the next a casual list, because there’s no guidance on format or tone. People often forget to specify the desired style or structure for the answer. The result is output chaos – unusable formats that require heavy editing.

• One-and-done thinking: Many teams treat an AI query as a one-off exchange rather than part of a larger process. They might copy a “magic prompt” they found online that works once, but it breaks when the context changes. There’s no framework to iterate or handle edge cases, just trial and error each time.

• No success criteria: Perhaps the biggest gap is the absence of any quality framework. Teams rarely define what a good outcome should include (or avoid) in advance. With no criteria, there’s no reliable way to judge or improve the outputs beyond subjective guesses.

This is why ad-hoc prompting wastes time and creates risk. It produces outputs that are difficult to explain, hard to replicate, and impossible to govern. In regulated or brand-sensitive environments, “we asked ChatGPT” is not an acceptable process.

Content created via one-shot prompts, for example, often swings in tone and quality from piece to piece, undermining brand trust. (One enterprise found that their Monday blog post sounded corporate and formal, while Thursday’s social media copy read like a teenager wrote it.) Such inconsistency is not just cosmetic; it directly impacts business outcomes.

From Ad-Hoc Prompts to AI‑Proof Workflows

Clearly, businesses need to move beyond ad-hoc prompting. The solution is to treat AI not as a toy or a one-time trick, but as part of a structured workflow. This is the core of AI‑Proof: making your AI usage robust and repeatable by design.

An AI‑Proof workflow means that whenever you use AI for a business task - whether it’s generating a report, drafting an email, or analysing data, you follow a consistent process with defined steps and standards. Instead of relying on individual ingenuity for each prompt, you create a system that anyone on your team can follow to get reliable, consistent and repeatable results.

Think of it as moving from chatting with AI to orchestrating AI.

In a conversation, anything goes; in a workflow, you have stages, checkpoints, and handoffs between human and machine. This kind of structure brings the same rigor to AI that we expect from any business process. In fact, companies that have adopted structured AI workflows report transforming their AI efforts from sporadic experiments into a dependable competitive advantage, with measurable ROI and maintained quality at scale.

At the heart of the AI‑Proof approach is a structured framework. These are the three pillars that turn prompt-crafting into a disciplined workflow.

• Designing Structured Prompts instead of free-form queries,

• Providing Explicit Context rather than assuming the AI knows anything, and

• Defining Outputs so the AI (and your team) know what a good result looks like.

The Shift: From Prompting to Prompt Structures

Teams that get consistent results from AI do not rely on better wording or clever tricks. They rely on structure.

Instead of improvising each time, they use repeatable prompt structures that force clarity before the AI ever responds. These structures make users slow down, think through the task, and provide the information the model actually needs.

At AI-Proof, we see two prompt structures used most effectively in business settings.

Structure 1: The Core Business Prompt

This structure is ideal for everyday tasks such as drafting content, summarising documents, or generating analysis.

<Role>        Expertise the AI should adopt  
<Context>     Relevant background and situation  
<Goal>        What needs to be done, and for whom  
<Format>      How the output should be structured  
<Constraints> Rules, tone, length, or exclusions  

This structure forces five critical decisions before the AI starts writing.

1. Who is it acting as?

2. What does it need to know?

3. What is success?

4. What should the output look like?

5. What boundaries must it respect?

The key is don’t add context that is not relevant to the task, provide relevant information. In addition to goal or the task - be very specific - what do you want.

Compared to a one-line prompt, this feels like more work (and it is). In practice, it removes guesswork and dramatically improves first-draft quality.

Structure 2: The Workflow Prompt

For more complex or higher-risk tasks, teams extend the structure to guide the AI through a sequence rather than a single response.

<Role>        Expertise the AI should adopt  
<Context>     Background and business situation  
<Task>        The primary task to complete  
<Steps>       How the task should be approached  
<Audience>    Who the output is for  
<InputData>   Data, documents, or facts to use  
<Format>      Required structure of the output  
<Constraints> Rules, checks, and guardrails  

This structure turns an AI interaction into a lightweight workflow. Instead of asking the model to do everything at once, you tell it how to think about the task, what to prioritise, and what standards it must meet.

In practice, this is how teams move from “chatting with AI” to orchestrating AI.

Structured Prompts

The first pillar of SPEC is Structured Prompts. This means developing a standardised format or template for your prompts, rather than writing them haphazardly. A structured prompt is more like a well-formed instruction sheet than a casual question. It typically includes sections or components that ensure all necessary information is covered. For example, a structured prompt template for a report summary might always have: 1) a statement of your role or goal, 2) the specific task or question, 3) relevant details or data points to include, and 4) the desired format of the answer. By using the same template each time (with new content plugged in), you impose order and clarity on the AI’s job.

In practice, template-driven prompts make your interactions with AI far more consistent. Rather than letting each user prompt in their own style, the team might establish: “For any customer email draft, always start the prompt by stating the customer’s context and problem, then clearly ask for the resolution steps, and finally specify the tone and format of the email reply.” This structure leaves little to guesswork. It’s analogous to providing a form for the AI to fill out, instead of a blank page. Well-designed prompt templates often include slots for context setting, task specification, output formatting, and even guardrails or disclaimersxenoss.io – ensuring that the AI has everything it needs to produce the right output and nothing important is omitted.

Crucially, structured prompts can be reused and improved over time. They become assets for your organisation. If one department crafts a great prompt template for, say, analyzing sales data, that template can be shared and standardised across the company. Teams end up building a library of proven prompts (sometimes called prompt libraries or playbooks) rather than every employee improvising. This not only saves time, but it means best practices propagate quickly. When prompts are structured and documented, you can also version-control them and track changes, much like code – enabling continuous refinement. In short, “template standardisation” turns prompt engineering into a scalable, team-based practice, with each template incorporating business logic and quality controls for consistent behaviourxenoss.io.

Finally, a structured approach makes it easier to troubleshoot and optimise prompts. If the output isn’t right, you can pinpoint which section of the prompt needs adjustment (e.g. maybe the task description was too vague, or the format instructions weren’t clear enough) instead of scrapping the whole thing. This modularity and clarity are the foundation on which the rest of SPEC builds.

Explicit Context

Explicit Context, is all about information. Context means any background, facts, or parameters the AI should know to do the task properly. “Explicit” means you spell it out every time, you do not rely on the AI to infer or remember things you haven’t told it (AI’s memory as of today is still very patchy). One of the most common failure modes in prompt usage is when users provide a bare instruction with either no context or irrelevant context, essentially forcing the model to make assumptions. For example, asking “Help me draft a proposal” in isolation could yield a generic proposal unrelated to your business. The AI has the ability to generate text, but it has no inherent knowledge of your specific situation unless you provide it.

Structured prompts allow you to explicitly feed in the who/what/why of the task: Who is the audience or end-user of the output? What are you trying to achieve? Why does it matter or what constraints should the AI keep in mind? By giving this context, you significantly increase the chances of a relevant and useful result.

For instance, instead of saying “Summarise this report,” an explicit-context prompt might say: “<Role>You are an assistant helping a financial analyst. <Goal>Summarise the attached quarterly report in plain English for a non-financial executive, focusing on revenue trends and any risks. The executive should grasp the company’s financial health in under 5 minutes of reading.” Here we have provided role, audience, purpose, and key focus areas.

Explicit context can include specific data and references as well. If you have numbers, names, or excerpts the AI should consider, include them in the prompt or as attachments, this is where the <InputData> is often used.

The guiding principle is: never make the AI assume. If the tone should be friendly but formal, say that explicitly. If the advice must align with UK regulations, state that. If the AI’s answer should refer to a specific product or scenario, describe it. By being explicit, you prevent the AI from filling gaps with its own (often incorrect) assumptions. This not only improves relevance, but also reduces risks like misinformation. In a business setting, persistent, governed context is what elevates AI from a nifty demo to a reliable tool – you maintain control over what knowledge the AI uses.

Over time, capturing context in your prompts also serves as a form of documentation; new team members can read past prompt+context setups and quickly understand the situation and solution, which again builds institutional knowledge.

Criteria-Driven Outputs

The format and the goal outputs addresses the “what does good look like?” question head-on. In traditional prompting, users often accept whatever the AI returns and then judge it by feel. By contrast with this approach, you define the success criteria before the AI generates the output. You effectively tell the model how to format or judge its answer, and you give yourself (or your team) a checklist to evaluate the result.

What do criteria-driven outputs look like in practice?
They can be specific instructions about the form of the answer, such as:
“The output should be a bulleted list of 5 key findings, each 1–2 sentences long.”

They can also include content requirements:
“Make sure to mention our three core product benefits and include a call-to-action in the final paragraph.”

They might involve style and tone guidelines:
“Use a professional tone, avoid jargon, and write in short paragraphs.”

And importantly (in the constraints section), they can outline what not to do:
“Do not make up any data; if you don’t have a certain detail, state that it’s not available.”

By encoding these criteria into the prompt (or having them clearly in mind to verify against), you set a quality bar for the AI’s work.

Many teams fail to do this, resulting in outputs that wander or miss key points. and then they wonder why the AI didn’t deliver. The truth is that vague prompts lead to vague outputs. As experts note, effective prompt engineering starts with clear, specific requirements: you must tell the model exactly what task to perform, under what constraints (length, format, tone, etc.), and ideally how success will be measured.

For example, instead of prompting “Write a press release for our new product” (and hoping it’s decent), a criteria-driven prompt would say:
<Role> You are an experienced PR Director and press release copywriter for fintech and banking trade media.
<Context> [FictionalProduct] is a SaaS platform for banks that detects anomalous transactions and account behaviour in near real time, streamlines fraud investigations, and creates audit-ready case files for compliance. It integrates with core banking systems and payment rails via API connectors. Launch date: 15 February 2026 (fictional) Pricing: from £4,500 per month (fictional)
<Goal> Write a one-page press release announcing the product launch, aimed at banking and financial crime industry media.
<Format> AP-style press release structure: Headline, dateline, lead paragraph, body, two executive quotes, release date and pricing, boilerplate, media contact.
<Constraints> One page (450–650 words). British English. Professional and factual. No hype or buzzwords. Use placeholders for any missing facts. Do not invent data beyond what’s in this prompt.
This prompt doesn’t guarantee a perfect output, but it significantly raises the floor, the draft will contain the key elements you specified, because you told the AI those were needed.

By defining output criteria, you also make it easier to evaluate and refine the results. You can check the AI’s answer against your checklist: Does it have all the parts we asked for? Does it follow our format and tone? If not, you know exactly what to fix (either adjusting the prompt or doing a quick edit).

In some cases, you can even have the AI self-check its output by providing criteria as a separate prompt, for example, asking:

• “List any ways the above output fails to meet the criteria”,

• “Play back what I have requested and highlight any ambiguities”,

which is only possible if you had clear criteria in the first place.

Criteria-driven outputs turn prompting into a criteria-driven dialogue with the AI. You’re effectively giving the model a target to hit. Without targets, the AI is shooting in the dark. This approach echoes practices from software and quality engineering: think of it as akin to having acceptance criteria for a user story, or test cases that the output should pass. The result is more consistent and measurable quality. Rather than just saying “this looks good,” teams can quantify improvements (for instance, did the output follow the template? include all 5 required points? stay within 500 words?). Over time, these criteria can be refined to raise the quality bar further. In short, if Structured Prompts and Explicit Context set the AI up for success, Criteria-Driven Outputs define what success means.

Why the Extra Effort Pays Off

Structured prompting feels slower at first. But it creates three compounding advantages.

Consistency
Outputs follow the same logic, tone and format every time. Brand voice stabilises. Quality stops swinging.

Speed at scale
Once a prompt structure exists, it can be reused, refined, and shared. What takes ten minutes to set up saves hours of re-prompting later.

Institutional learning
Prompt structures become assets. Teams improve them over time instead of starting from zero. Knowledge compounds rather than disappearing.

This is the difference between using AI as a novelty tool and using it as part of a business process.

What This Means for Businesses and Professionals

AI will not replace good thinking. But it will amplify poor thinking at speed.

The professionals and teams who benefit most from AI are not those with the cleverest prompts, but those with the clearest structure. They invest a little more effort upfront so that results become repeatable, explainable, and scalable.

If your AI usage still relies on ad-hoc questions and trial-and-error, the ceiling is low. If you adopt structured prompting, AI becomes something your whole organisation can use with confidence.

That is how teams move from experimentation to capability. And that is how AI stops being unpredictable and starts being useful.

Turn prompt structures into a shared team capability

One reason ad-hoc prompting fails inside organisations is that it often lives in silos, people’s heads. Someone finds a “good prompt”, it works once, and then it disappears into a Slack thread or a personal notes file.

A practical way to fix this is to standardise the environment your team works in, not just the wording of individual prompts.

Modern tools like ChatGPT & Claude.ai now support two simple building blocks that make this much easier: Projects and custom GPTs (ChatGPT).

Projects are shared workspaces where you can group related chats, upload reference files, and add custom instructions so the AI stays on-topic and remembers what matters for that specific workstream. You can also share projects with teammates, so everyone is working with the same context, files, and house rules.

Custom GPTs are reusable versions of ChatGPT that can be configured with instructions, knowledge files, conversation starters, and optional capabilities, so that users do not have to reinvent the setup every time. For a business, this is a lightweight way to bake in your brand tone, prompting structure, and definition of “good”. (OpenAI Help Center)

Put simply:

• Projects are great for ongoing initiatives where context builds over time.

• Custom GPTs are great for repeatable tasks where you want consistent behaviour on demand.

If your goal is to share and refine prompting strategies across a team, you can combine both.

A simple implementation you can do this week

1. Create a Project called “Prompt Playbook”
   Upload a one-page brand tone guide, your do and do not list, and your two preferred prompt structures:

• <Role> <Context> <Goal> <Format> <Constraints>

• <Role> <Context> <Task> <Steps> <Audience> <InputData> <Format> <Constraints>

2. Add Project Instructions that enforce the structure
   Example: “Always request missing inputs. Always respond using the chosen template. Always confirm audience and output format. Never guess facts.”

3. Create one custom GPT for a high-frequency use case
   For example: “Sales Email Drafter” or “Board Pack Summariser”. In the GPT instructions, hard-code your prompt structure and tone rules, then add a few conversation starters that make the right behaviour the default.

4. Make ownership a team sport
   Where your plan supports it, enable shared edit access so prompt improvements are not trapped with one individual. That is how prompt quality compounds over time.

The outcome is simple but powerful. Instead of relying on individuals to “be good at prompting”, you give the whole team a tuned environment where good prompting is the default. That is how structured prompting becomes repeatable at scale.

Next Steps: Making Your Business AI‑Proof

Adopting the AI‑Proof approach is a strategic move, but you can start with small, practical steps. Here are some concrete next steps to get started with workflow-based AI in your organisation:

1. Identify a High-Value Use Case: Begin with one area where AI is currently used in an ad-hoc way (or could be used) and where consistency matters, for example, generating minutes of meetings, drafting standard emails, or creating marketing content. Talk to the team involved and pinpoint the pain points (e.g. “We spend too much time editing the AI’s output” or “The style keeps coming out wrong”).

2. Create a structured prompt, project or Custom GPT for that Workflow: Assemble the team and design a structured prompt template for the chosen use case. Write out what sections the prompt should have: context/background information, the specific task request, and the output guidelines/criteria. Don’t worry about getting it perfect on the first try. Include explicit context (who is the audience or any data the AI needs) and define the output format and quality criteria as clearly as possible. You might draw from existing best prompts your team has used, but now you’ll formalise them. Document the template in a place everyone can access. This template will be the starting point every time the task is run. (If you’re using an AI platform that allows custom instructions or chaining prompts, you can encode the template there as well.)

3. Test, Tweak, and Roll Out: Run a few real-world tasks through the new structured prompt workflow. Evaluate the results against your criteria – did the AI output meet the expectations? Gather feedback from users: Was anything unclear in the template? Did we forget any context or constraints? Adjust the prompt template and criteria based on this feedback. You may do a couple of iterations until the workflow reliably produces good results. Once it’s working, formally adopt it as the new standard process for that task. Train the relevant team members on using the template (which might simply be walking them through filling it in, since it should be straightforward). Encourage them to suggest improvements as they use it. Over time, make sure to capture those improvements in the template (versioning changes as needed). Finally, you can scale up: apply SPEC to additional use cases, and build a library of AI workflow templates across departments. Regularly review these workflows to ensure they still align with business goals and update them if requirements change or if a better prompting technique is discovered.

By taking these steps, you’ll begin to AI-proof your business processes, turning sporadic AI experiments into dependable components of your operations. Remember, the goal is not to remove the creativity or flexibility of AI, but to channel it through well-defined processes so that it consistently delivers value.

As you implement AI-Proof workflows, you’ll likely find not only efficiency gains and better output, but also a cultural shift: your teams will start thinking in terms of systems and capabilities rather than one-off tricks. In an era where every company is looking to harness AI, those that build solid, workflow-based foundations will leap ahead. AI-Proofing your business today means you’re not just chasing the latest AI hype, you’re creating an enduring capability, one structured prompt at a time.

Next stop: The key is to keep the momentum: iterate on your workflows, share successes internally, and gradually expand AI-Proof practices to more areas. By doing so, you ensure that AI becomes a trusted ally in your business – delivering results predictably, efficiently, and on your terms.

What’s coming next and why you should subscribe

If structured prompting is how you get better answers, workflows are how you get repeatable outcomes.

In the next AI-Proof edition, we’ll show you how teams are turning good prompts into end-to-end workflows using tools like n8n and Zapier, plus the workflow features now built into platforms like ChatGPT and Google.

Not theory. Practical patterns you can copy.
The goal is simple: take a task that currently eats hours each week and turn it into a workflow that runs reliably in the background.

If you want that walkthrough, subscribe so you don’t miss it.