Artificial Intelligence

Any pursuit of superintelligence has to be grounded in the core principle that if the AI we build is not helping humanity and under human control, it's not worth pursuing. We also need to accelerate and spread the benefits of AI, such that they are diffused broadly across countries, communities, and companies. This requires a frontier ecosystem in which both closed and open-source models can thrive. And for firms, it’s imperative that they retain full control over their unique and tacit knowledge. Every organization should be able to build its own continuous learning loop/hill climbing machine, without becoming dependent on any one model provider, and have the ability to embed its own knowledge into models and weights they control. So, in this context, we welcome the research, focus, and deliberate pacing needed to get alignment right as the design goal. We also welcome ideas like "embedded evaluators" and the broader efforts to develop the mechanisms to make this more than just talk. The key is that this cannot be controlled by a handful of entities, but must have broad representation across the ecosystem, countries, and fields, including academia. This is the approach we are taking: broad access and choice at every layer of the AI stack; enterprise control of learning loops and models; and the “Code of Conduct” that underlies our own first party MAI models that we’ll publish tomorrow for public consultation.

AI adoption is one thing, transformation is something else entirely.    Few companies have been bold enough to fundamentally change how they are organized and how decisions are made. At Pfizer, we are determined to break through that inertia and be the most AI-forward company in our industry because of what this technology will do for patients.    In R&D, as I said on our earnings call this week, our ambition is to build an AI-native organization where every insight, from target discovery through medical evidence, continuously informs the next decision.    We are making great progress, so I'd like to share more about how we think about it overall: why our 177 years of data is our alpha, why I chose a federated model over a central team, and why we are certifying every eligible colleague, including me, in AI fluency.

Of course you need to use open-source models if you’re an enterprise leader. Close model providers, that are now forcing data retention, are gaining immense leverage on your business if you don’t. As you connect models to your business context, they see it and learn from it, and have a track record of going after their most successful customers thanks to this information. But that’s not enough, you also need to store your data and records in open systems, or your software vendors might block you from building AI systems outside of the walled garden they have set up for you. If you can’t convince them to give you complete access to the data they manage for you, AI fortunately allows you to migrate quite fast. Once you’ve got hold of your data, you’ll need to manage how AI systems can access this data on behalf of human users, because you don’t always want Bob to see what Alice is doing in your company. That’s hard and merciless, since AI models are great at finding need-to-know errors. It takes systems that check hard access rules and models that check soft access rules. Now comes the most important part. You need to set up your own continuous training flywheel, so that you can improve your AI systems based on their interaction with your employees and your users. This is how you turn the edges of your business into AI systems your vendors and competitors cannot replicate. It’s also how you reduce deployment cost as well, as you can shrink models according to model input distribution. Those bills are getting substantial, we need to collectively become efficient if we want AI development to continue, so that matters. All of these efforts might seem daunting – they are. This is both a complete replatforming of your IT, and a complete change in the way you’re developing software, and operating your business. AI lifecycle management requires understanding human behavior and gradient descent, that’s a stretch. At Mistral, we facilitate that work by providing all primitives that you need in a single control plane, Studio, and a training platform, Forge. With our applied AI engineers and scientists working hand-in-hand with our customers, we ensure that we transfer knowledge, and that we can disappear once the systems are up and running. We deploy on our customers' infrastructure, or through our zero-data-retention hosted services, so that your edges remain your edges, and the switch button can be fully in your hand. Frontier AI can accelerate the growth of your business, but if it’s not in your hands, it’s not going to be your growth.

🚨 Zero Trust for AI Agents Anthropic just released "Zero Trust for AI Agents." As we're thinking about agentic permissions, applying a Zero Trust discipline is critical to secure adoption. AI agents interpret goals, call tools, chain actions, delegate to other agents, and maintain context across sessions. The trust surface is different. The paper introduces "least agency" — a concept that OWASP has been promoting — and the distinction from least privilege is worth sitting with. 👉 "Least privilege" asks what an identity can access. 👉 "Least agency" asks what an agent can do, under what conditions, with which tools, and with what level of oversight. Autonomous agents introduce action risk alongside access risk — and the boundaries around behavior need to be architecturally enforced, not assumed. The paper includes a design test worth writing down: 🔥 Does the control make the attack impossible, or merely tedious?🔥 The practical controls follow directly from Zero Trust fundamentals — cryptographic agent identity, short-lived credentials, tool allow-listing, sandboxed execution, and full traceability from prompt to action to outcome. None of this is new doctrine. It's existing architecture applied to a harder problem. Full disclosure: the paper cites NIST SP 800-207 on Zero Trust Architecture and the CISA Zero Trust Maturity Model, both of which I co-authored during my time supporting Federal Zero Trust efforts at CISA. Zero Trust is built for a world where we have to remove implicit trust. Agentic AI is the next version of that same problem — valid identities, valid credentials, legitimate-looking actions, and still no basis for assumed trust. Access is earned. Actions are constrained. Agency must be governed. 👉🏼 Link to Anthropic's paper in the comments.

In many parts of the world, the standard $20,000 fetal monitor simply isn't an option. This portable alternative uses AI to help health workers get accurate readings at a fraction of the cost—no specialized training required. It can spot warning signs like a slow fetal heartbeat in time to consult a specialist, begin treatment, or get a mother to a hospital. In maternal health, that can mean the difference between a dangerous complication and a healthy birth.

Gartner just surveyed 350 large enterprises deploying AI. 80% cut jobs. Some by as much as 20%. The result? The companies that cut the most showed nearly identical financial returns to the ones that cut the least. In several cases, the ones that cut less performed better. No correlation between AI-driven layoffs and improved ROI. None. Gartner's Helen Poitevin was direct: "Workforce reductions may create budget room, but they do not create return." Cutting people frees up cash. It does not generate value. Most leadership teams are conflating the two. So what actually works? Upskilling staff to work alongside AI. Redesigning roles around what humans do well vs. what AI does well. Building operating models where people guide autonomous systems instead of getting replaced by them. There's a real difference between using AI to do the same work with fewer people and using AI to unlock work that was previously impossible. The first saves money on paper. The second compounds over time. We've already seen the pattern. Klarna cut 700 CS roles, watched quality decline, and started rehiring. IBM automated HR functions and reversed course. The Commonwealth Bank of Australia reversed 45 AI-driven layoffs after realizing those roles were never redundant. Gartner predicts half of companies that attributed headcount cuts to AI will rehire under new titles by 2027. If someone in your org is building an AI business case around headcount reduction, share this data. The assumption that fewer people equals better margins equals better returns is not supported by the evidence. AI is not leading to a jobs apocalypse. It's changing the shape of what people do. The companies that understand that difference will be the ones worth working for, and buying from, three years from now. Read the full piece on State of Brand here: https://lnkd.in/ggH-NXyM

Cognitive Surrender is how engineers quietly accumulate comprehension debt My latest free deep-dive: https://lnkd.in/gzwBNDXh ✍ When we use AI coding agents and orchestrate, the line between delegating and surrendering moves under our feet daily. Based on recent research, the distinction is critical for anyone shipping code: - Cognitive Offloading: You hand off the how and keep the what. You judge whether the result is sensible and intervene when it isn't. - Cognitive Surrender: You stop constructing the answer entirely. The AI's output becomes "your" output, and you inherit its confidence without doing the underlying reasoning. In software engineering, surrender is the mechanism by which comprehension debt accumulates. Every 600-line PR we casually approve, or every complex stack trace we let the agent fix without understanding the root cause—these are tiny, compounding loans. The codebase grows, but our mental model of the system shrinks. Surface correctness is not systemic correctness. To resist surrender, we have to build friction and calibration into our workflows. Here are a few heuristics I use: 1. Construct an expectation first: Before running the agent, decide what the answer should roughly look like. If it doesn't match, you have a real choice to make. 2. Read the diff like a junior wrote it: "Seems right" is not a code review. The job hasn't changed but the author has. 3. Ask the model to argue against itself: This breaks the borrowed-confidence effect and forces you to evaluate the tradeoffs. 4. Solo time at the keyboard: Write code without the agent weekly. It's the ultimate calibration exercise to ensure offloading hasn't become surrender. The goal isn't to stop using AI tools - I use them every day to ship faster. The goal is mutual amplification, where the agent acts as the second engineer in the room, not the only one. #ai #programming #softwareengineering

A few self-driving taxis in San Francisco just demonstrated the real problem with autonomy. They were too rational. For a brief moment, several robotaxis aligned at an intersection and created a perfectly polite deadlock. No aggression. No improvisation. No human-style “you go, I’ll go.” Just algorithms waiting for clarity. And that is exactly why this moment matters. What interests me is that this was not a failure of sensing. The vehicles could see. The problem was social judgment. Because cities are not just physical systems. They are negotiation systems. They run on: → tiny signals → hesitation → assertiveness → eye contact → imperfect timing That is where autonomy gets much harder than people think. We are not only teaching machines how to detect objects and follow lanes. We are asking them to operate inside messy human environments where the right move is not always the most logical one. To me, that is the deeper lesson. The next frontier in self-driving is not just better perception. It is better judgment under uncertainty. And that is a much more difficult problem. What do you think matters more for autonomous vehicles now: seeing the road better, or learning how to navigate human ambiguity? #AI #AutonomousVehicles #SelfDrivingCars #FutureOfMobility #Innovation #Technology #SmartCities #MachineLearning #FutureOfWork

🪂 How To Make Your Design System AI-Ready (https://lnkd.in/dtnpy7CM), a practical guide on how to reduce drifts, minimize mistakes, maintain context and improve the quality of AI-generated prototypes — with structured spec files, automated auditing and token layers. Put together by Hardik Pandya from Atlassian. --- 🔹 1. Design Decisions Are Infrastructure AI-generated prototypes often don't deliver consistently decent results because of tiny inconsistencies scattered all across a design system. Often it's decisions made but not documented, hard-coded values never cleaned up, or relying too much on AI making sense of mock-ups or design flows on its own. Unsurprisingly, better AI prototypes come from better data — but also from better human guidance. We shouldn’t assume that AI knows how to choose the right component, and how to design with accessibility in mind. It needs priorities, a clear path on how we make decisions, design principles, examples, do's and don'ts. In fact, we should treat design decisions as infrastructure. That means that every time we make a decision — not just a design decision, but even decision on how actually prioritize our work and how we make decisions around here — it must find a path into the spec file that is then consumed by AI. --- 🔶 2. Three Layers: Spec Files + Token Layer + Audit To ensure quality, we establish design principles, guidelines, rules in a form of “spec files”). It's structured Markdown files that include spacing rules, color choices, component usage guidelines, priorities etc. AI is going to read and reuse that spec file every time it's going to generate a prototype. Because the spec files are text files, it's much more cost-effective, but also much more accurate just because we don't rely on AI recognizing or decoding patterns from mock-ups, but gets specific guidelines instead. In fact, extending code is often a more effective way than generating code from mock-ups. Token layer lists and keeps updated all tokens used throughout the design system. AI always chooses from a closed set of named variables instead of inventing plausible values ad-hoc. An audit script catches what AI gets wrong. It scans the prototype and flags every hard-coded value and flags it if necessary. It can be a regular software doing that, with AI waiting for its feedback to come back. Finally, when a design system ships updates, a sync routine flags which spec files need updating. The goal is to make sure that AI always reads up-to-date, current specs, not the ones written against an outdated version. --- 🔺 3. Examples of AI-Ready Design Systems ⌾ Atlassian: https://lnkd.in/dVsGc3Cp ⌾ Carbon: https://lnkd.in/d4zq4WWb ⌾ CMS Design System: https://lnkd.in/dHHzV3en ⌾ Nordhealth: https://lnkd.in/d8C4j2ZA Yet again, AI can’t magically resolve technical debt or design debt — it needs guidance, decisions, priorities and principles.

One of the things that has struck me in recent conversations with British businesses is how often AI projects stall for reasons that have nothing to do with the technology itself. A new report from the Stanford Digital Economy Lab reaches a similar conclusion. They looked at 51 companies that had deployed AI and, in most cases, the problems came from having to change internal processes, sort out data and generally just get the organisation to operate differently. It also explains why outcomes vary so much. Two companies can try to do the same thing with AI and end up in completely different places. One moves quickly, the other gets stuck. The difference tends to come down to leadership and how the organisation is set up to adopt it, which reflects what I’ve seen more broadly on the ground here in the UK. The businesses making the most progress are not necessarily the ones with access to better technology, but the ones prepared to adapt how they operate around it. That should give some confidence. This isn't about having access to something others don’t - it's simply about how you use it. If more businesses are able to make that shift, there's a real opportunity here for British companies to move faster and compete more effectively than many may expect.