Technology

Hot take: the #legalengineer is now the most critical role in the in-house legal department. Not the GC. Not the deputy. Not the head of legal ops. The person who sits at the intersection of legal process expertise, technology fluency, and change management and who can re-engineer how legal work gets done as AI reshapes what's possible is what separates the teams that will come out of this period ahead from the ones that will have a lot of expensive technology and not much to show for it. In-house legal is redesigning itself right now. What goes to outside counsel? What does AI handle? How do we staff? You can't answer those questions or execute on the answers without someone who can architect the new model. I've been in this space for over two decades. This is the role I'd prioritize above almost anything else right now. https://lnkd.in/gCy6tQr5

𝗧𝗵𝗲 𝗽𝗮𝗿𝗮𝗱𝗼𝘅 𝗼𝗳 𝗺𝗼𝗱𝗲𝗿𝗻 𝗵𝗲𝗮𝗹𝘁𝗵 𝘁𝗲𝗰𝗵: 𝗧𝗵𝗲 𝗺𝗼𝗿𝗲 𝘄𝗲 𝗺𝗼𝗻𝗶𝘁𝗼𝗿, 𝘁𝗵𝗲 𝗺𝗼𝗿𝗲 𝗮𝗻𝘅𝗶𝗼𝘂𝘀 𝘄𝗲 𝗯𝗲𝗰𝗼𝗺𝗲. We track our bodies 24/7. Count every calorie. Measure sleep, HRV, glucose, stress. From Apple Watch. To Oura Ring. To the latest “temple” device. Somewhere along the way, awareness turned into obsession. Here’s the paradox no one talks about: We have the best health-tracking tools in history, and some of the worst health outcomes. Something doesn’t add up. 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘀𝗵𝗼𝘄𝘀 𝗦𝗹𝗲𝗲𝗽 𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴 𝗰𝗮𝗻 𝘄𝗼𝗿𝘀𝗲𝗻 𝘀𝗹𝗲𝗲𝗽 Studies on orthosomnia (an obsession with “perfect” sleep metrics) show that people who fixate on sleep scores experience more sleep anxiety, lighter sleep, and poorer recovery—even when objective sleep doesn’t improve. Trying to optimize sleep can literally break it. 𝗛𝗥𝗩 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝘀 𝘀𝘁𝗿𝗲𝘀𝘀 𝗳𝗼𝗿 𝗺𝗮𝗻𝘆 𝘂𝘀𝗲𝗿𝘀 HRV is a useful trend marker—but daily fluctuations are normal. Research shows that constant HRV checking can heighten health anxiety and perceived stress, especially when users don’t understand variability or context. Ironically, stressing about HRV often lowers HRV. 𝗠𝗼𝗿𝗲 𝗱𝗮𝘁𝗮 ≠ 𝗯𝗲𝘁𝘁𝗲𝗿 𝗵𝗲𝗮𝗹𝘁𝗵 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 Behavioral science research consistently finds that excessive self-monitoring leads to hypervigilance, loss of bodily trust, and decision fatigue. When every sensation becomes a data point, people stop listening to internal cues and start deferring to dashboards. In short: 𝗢𝘃𝗲𝗿-𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲𝘀 𝗮𝘄𝗮𝗿𝗲𝗻𝗲𝘀𝘀 𝘄𝗶𝘁𝗵 𝗮𝗻𝘅𝗶𝗲𝘁𝘆. So what actually creates health? The same fundamentals that worked 5,000 years ago: • Deep, peaceful sleep • Regular sunlight • Real, nourishing food • Daily movement • Time with people you love These don’t need algorithms. They need presence. Use wearables if they serve you—I do, occasionally. But don’t let them become your master. Your life isn’t an algorithm waiting to be optimized. It’s a system meant to be felt, explored, and course-corrected. The best health coach you’ll ever have is already inside you. Trust it.

Every cloud provider faces the same AI infrastructure challenge: chips need to be positioned close together to exchange data quickly, but they generate intense heat, creating unprecedented cooling demands. We needed a strategic solution that allowed us to use our existing air-cooled data centers to do liquid cooling without waiting for new construction. And it needed to be rapidly deployed so we could bring customers these powerful AI capabilities while we transition towards facility-level liquid cooling. Think of a home where only one sunny room needs AC, while the rest stays naturally cool – that’s what we wanted to achieve, allowing us to efficiently land both liquid and air-cooled racks in the same facilities with complete flexibility. The available options weren't great. Either we could wait to build specialized liquid-cooled facilities or adopt off-the-shelf solutions that didn't scale or meet our unique needs. Neither worked for our customers, so we did what we often do at Amazon… we invented our own solution. Our teams designed and delivered our In-Row Heat Exchanger (IRHX), which uses a direct-to-chip approach with a "cold plate" on the chips. The liquid runs through this sealed plate in a closed loop, continuously removing heat without increasing water use. This enables us to support traditional workloads and demanding AI applications in the same facilities. By 2026, our liquid-cooled capacity will grow to over 20% of our ML capacity, which is at multi-gigawatt scale today. While liquid cooling technology itself isn't unique, our approach was. Creating something this effective that could be deployed across our 120 Availability Zones in 38 Regions was significant. Because this solution didn't exist in the market, we developed a system that enables greater liquid cooling capacity with a smaller physical footprint, while maintaining flexibility and efficiency. Our IRHX can support a wide range of racks requiring liquid cooling, uses 9% less water than fully-air cooled sites, and offers a 20% improvement in power efficiency compared to off-the-shelf solutions. And because we invented it in-house, we can deploy it within months in any of our data centers, creating a flexible foundation to serve our customers for decades to come. Reimagining and innovating at scale has been something Amazon has done for a long time and one of the reasons we’ve been the leader in technology infrastructure and data center invention, sustainability, and resilience. We're not done… there's still so much more to invent for customers.

A milestone in quantum physics — rooted in a student project What began as a student's undergraduate thesis at Caltech — later continued as a graduate student at MIT — has grown into a collaborative experiment between researchers from MIT, Caltech, Harvard, Fermilab, and Google Quantum AI. Using Google’s Sycamore quantum processor, the team simulated traversable wormhole dynamics — a quantum system that behaves analogously to how certain wormholes are predicted to work in theoretical physics. Here’s what they did: Implemented two coupled SYK-like quantum systems on the processor that represent black holes in a holographic model. Sent a quantum state into one system. Applied an effective “negative energy” pulse to make the simulated wormhole traversable. Observed the state emerge on the other side — consistent with quantum teleportation. This wasn’t just classical computer modeling — it ran on real qubits, using 164 two-qubit quantum gates across nine qubits. Why it matters: The results are consistent with the ER=EPR conjecture, which suggests a deep link between quantum entanglement and spacetime geometry. In the holographic picture, patterns of entanglement can be interpreted as wormhole-like “bridges.” This experiment shows how quantum processors can begin to probe aspects of quantum gravity in a laboratory setting, complementing astrophysical observations and theoretical work. While no physical wormhole was created, this is a step toward using quantum computers to explore some of the most fundamental questions in physics. What breakthrough in science excites you most? Share your thoughts below — and let’s discuss how quantum computing is reshaping our understanding of reality. ♻️ Repost to help people in your network. And follow me for more posts like this. CC: thebrighterside

Two strikingly similar headlines surfaced this past week that should make every leader pause: • “Companies Are Pouring Billions Into A.I. It Has Yet to Pay Off.” — New York Times • “Companies Are Pouring Billions Into AI. Here’s Why They’re Not Seeing Returns” — Forbes The NYT points to the human side: employees resist tools they don’t trust. Forbes focuses on the technical side: most AI still can’t understand the context of work. Both are true, and they’re related. When AI lacks context, employees lose trust. It can’t tell the latest doc from last year’s draft. It summarizes a customer conversation but drops the follow-ups buried in the thread. It pulls a response from Slack while ignoring the context in Google Drive. Employees realize it creates more work than it saves, and stop using it. Pilots stall, deployments fade, and projects slide into the “trough of disillusionment" as the NYT describes. Unfortunately, that's the reality for many organizations. At Glean, we work hard to make sure AI understands the enterprise context the way a human does. If a subject matter expert says something, I trust it more. If something’s old, I double-check it. That’s how people think, and it’s how AI should work too. Yet every enterprise has its own documentation culture and quirks, so sometimes we struggle at first. But we persist and co-develop with customers until the system reaches the quality they need. Then we take those learnings to make it work automatically for the next customer. We’ve seen this approach deliver measurable impact for customers: • Booking.com: Glean Agents give teams faster access to customer insights, cutting video production time by 75% and doubling monthly output. • Confluent: Glean’s AI-powered search saves 15,000+ hours/month, boosts support satisfaction by 13%, and cuts ticket investigation time by 10 minutes. • Fortune 100 telecom company: Glean surfaces instant knowledge during support calls, reducing call resolution time by 17 seconds across 800+ agents. • Leading global consultancy: Glean Agents automate RFP workflows, cutting consulting project proposals from 4 weeks to a few hours (97% faster). • Wealthsimple: Glean gives employees instant access to policies and knowledge, driving $1M+ in annual productivity gains. When AI understands the real context of work—across people, tools, and workflows— employees trust it and use it. Instead of falling into the trough of disillusionment, companies climb a slope toward productivity gains and real ROI.

Website traffic was a valuable metric correlated to growth. Now it may be a vanity metric, not correlated to growth. Search has been disrupted. Visits to your website are declining. So, marketers - what now? The search landscape was already shifting (I talked about this at INBOUND last year). Now, the change is accelerating dramatically: - AI Overviews appear in 43% of Google searches – when they do, organic CTR drops by nearly 35%. - Google’s AI Mode and audio AI overviews are coming – they will cause clicks to collapse further. - More buyers are using LLMs to find information, ChatGPT search in Europe grew 3.7x in six months. So, what should marketers do? And how can AI help? 1. Be everywhere and diversify your channels The days of relying solely on Google search are way over. You need to show up on YouTube, LinkedIn, Instagram, podcasts, and in niche communities. The good news? AI makes multi-channel, multi-format content creation scalable – even for small teams. 2. Be specific with context In the past, broad informational content was the way to rank in Google. Today, buyers expect results deeply relevant to them, whether they’re on Google, LLMs, or Reddit. You need specific content that reflects your expertise and resonates with your buyers. 3. Optimize for conversion, not clicks Traffic was once the lever you could pull. Now, conversion is where the opportunity lies. AI enables you to deliver personal messages that drive better conversion. Don’t ask, “How do we get more blog visits?” Ask, “How do we convert more prospects into customers across all channels?” The changes in search are sending shockwaves across marketing teams and media companies everywhere. The era of traffic-based marketing is ending. But a new era full of opportunity is just beginning. Super exciting times for marketers to reinvent the playbook!

The New York Times profiled a start-up with 28 employees serving nearly 50 million users. That company is us. The traditional startup playbook: raise massive funding, hire hundreds of employees, and worry about profitability "later." But there's another way. Everyone at Gamma could fit in a small restaurant. We're not just surviving—we've been profitable for 15+ consecutive months, with revenue growing month over month, and lifetime negative net burn (we have more money in the bank than we've raised). This isn't an accident. We've deliberately designed our organization to maximize impact per person. Instead of creating specialist silos, we hire versatile generalists who can solve problems across domains. Rather than building management hierarchies, we find player-coaches who both lead and execute. Our team leverages AI tools throughout our workflow - Claude for data analysis, Cursor for coding efficiency, NotebookLM for customer research synthesis. These aren't just productivity hacks; they're force multipliers. Examples: — When our growth PM needed better analytics, he didn't file a ticket with a data team—he built a self-serve system that anyone can use without SQL knowledge. — When our marketing lead needed to understand our customers better, she fed thousands of interactions into an LLM and created actionable personas that now guide our entire strategy. — When our design team needs to test a hypothesis, we create a rapid prototype and show it to our power users. What we're seeing isn't just about "doing more with less." It's about fundamentally changing what's possible per person. The most valuable employees aren't specialists who excel in narrow domains - they're resourceful problem-solvers who continuously expand their capabilities. This approach creates remarkable resilience. Since everyone understands multiple functions, we don't have single points of failure when someone leaves or moves to another project. If you're building today, the question isn't how quickly you can scale headcount … it's how much impact you can create with the smallest possible team. The future belongs to tiny teams of extraordinary people.

𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗮𝗻 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆, 𝘆𝗼𝘂 𝗳𝗶𝗿𝘀𝘁 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗮 𝘀𝗼𝗹𝗶𝗱 𝗱𝗮𝘁𝗮 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝗻𝗱 𝗲𝗻𝗳𝗼𝗿𝗰𝗲 𝘀𝘁𝗿𝗶𝗰𝘁 𝗱𝗮𝘁𝗮 𝗵𝘆𝗴𝗶𝗲𝗻𝗲. Getting your house in order is the foundation for delivering on any AI ambition. The MIT Technology Review — based on insights from 205 C-level executives and data leaders — lays it out clearly: 𝗠𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗱𝗼 𝗻𝗼𝘁 𝗳𝗮𝗰𝗲 𝗮𝗻 𝗔𝗜 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗧𝗵𝗲𝘆 𝗳𝗮𝗰𝗲 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗶𝗻 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗿𝗶𝘀𝗸 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁. Therefore, many firms are still stuck in pilots, not production. Changing that requires strong data foundations, scalable architectures, trusted partners, and a shift in how companies think about creating real value with AI. Because pilots are easy, BUT scaling AI across the enterprise is hard. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: ⬇️ 1. 95% 𝗼𝗳 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗮𝗿𝗲 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 — 𝗯𝘂𝘁 76% 𝗮𝗿𝗲 𝘀𝘁𝘂𝗰𝗸 𝗮𝘁 𝗷𝘂𝘀𝘁 1–3 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀:   ➜ The gap between ambition and execution is huge. Scaling AI across the full business will define competitive advantage over the next 24 months. 2. 𝗗𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗹𝗶𝗾𝘂𝗶𝗱𝗶𝘁𝘆 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀: ➜ Without curated, accessible, and trusted data, no AI strategy can succeed — no matter how powerful the models are. 3. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗽𝗿𝗶𝘃𝗮𝗰𝘆 𝗮𝗿𝗲 𝘀𝗹𝗼𝘄𝗶𝗻𝗴 𝗔𝗜 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 — 𝗮𝗻𝗱 𝘁𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗴𝗼𝗼𝗱 𝘁𝗵𝗶𝗻𝗴:   ➜ 98% of executives say they would rather be safe than first. Trust, not speed, will win in the next AI wave. 4. 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲𝗱, 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗔𝗜 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 𝘄𝗶𝗹𝗹 𝗱𝗿𝗶𝘃𝗲 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝘃𝗮𝗹𝘂𝗲:  ➜ Generic generative AI (chatbots, text generation) is table stakes. True differentiation will come from custom, domain-specific applications. 5. 𝗟𝗲𝗴𝗮𝗰𝘆 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗮𝗿𝗲 𝗮 𝗺𝗮𝗷𝗼𝗿 𝗱𝗿𝗮𝗴 𝗼𝗻 𝗔𝗜 𝗮𝗺𝗯𝗶𝘁𝗶𝗼𝗻𝘀:  ➜ Firms sitting on fragmented, outdated infrastructure are finding that retrofitting AI into legacy systems is often more costly than building new foundations. 6. 𝗖𝗼𝘀𝘁 𝗿𝗲𝗮𝗹𝗶𝘁𝗶𝗲𝘀 𝗮𝗿𝗲 𝗵𝗶𝘁𝘁𝗶𝗻𝗴 𝗵𝗮𝗿𝗱: ➜ From GPUs to energy bills, AI is not cheap — and mid-sized companies face the biggest barriers. Smart firms are building realistic ROI models that go beyond hype. 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗳𝘂𝘁𝘂𝗿𝗲-𝗿𝗲𝗮𝗱𝘆 𝗔𝗜 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝗰𝗵𝗮𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗺𝗼𝗱𝗲𝗹 𝗿𝗲𝗹𝗲𝗮𝘀𝗲.   𝗜𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝘀𝗼𝗹𝘃𝗶𝗻𝗴 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 — 𝗱𝗮𝘁𝗮, 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗥𝗢𝗜 — 𝘁𝗼𝗱𝗮𝘆.

AI is rapidly moving from passive text generators to active decision-makers. To understand where things are headed, it’s important to trace the stages of this evolution. 1. 𝗟𝗟𝗠𝘀: 𝗧𝗵𝗲 𝗘𝗿𝗮 𝗼𝗳 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗙𝗹𝘂𝗲𝗻𝗰𝘆 Large Language Models (LLMs) like GPT-3 and GPT-4 excel at generating human-like text by predicting the next word in a sequence. They can produce coherent and contextually appropriate responses—but their capabilities end there. They don’t retain memory, they don’t take actions, and they don’t understand goals. They are reactive, not proactive. 2. 𝗥𝗔𝗚: 𝗧𝗵𝗲 𝗔𝗴𝗲 𝗼𝗳 𝗖𝗼𝗻𝘁𝗲𝘅𝘁-𝗔𝘄𝗮𝗿𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 Retrieval-Augmented Generation (RAG) brought a major upgrade by integrating LLMs with external knowledge sources like vector databases or document stores. Now the model could retrieve relevant context and generate more accurate and personalized responses based on that information. This stage introduced the idea of 𝗱𝘆𝗻𝗮𝗺𝗶𝗰 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗮𝗰𝗰𝗲𝘀𝘀, but still required orchestration. The system didn’t plan or act—it responded with more relevance. 3. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜: 𝗧𝗼𝘄𝗮𝗿𝗱 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 Agentic AI is a fundamentally different paradigm. Here, systems are built to perceive, reason, and act toward goals—often without constant human prompting. An Agentic system includes: • 𝗠𝗲𝗺𝗼𝗿𝘆: to retain and recall information over time. • 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴: to decide what actions to take and in what order. • 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲: to interact with APIs, databases, code, or software systems. • 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆: to loop through perception, decision, and action—iteratively improving performance.    Instead of a single model generating content, we now orchestrate 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗮𝗴𝗲𝗻𝘁𝘀, each responsible for specific tasks, coordinated by a central controller or planner. This is the architecture behind emerging use cases like autonomous coding assistants, intelligent workflow bots, and AI co-pilots that can operate entire systems. 𝗧𝗵𝗲 𝗦𝗵𝗶𝗳𝘁 𝗶𝗻 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 We’re no longer designing prompts. We’re designing 𝗺𝗼𝗱𝘂𝗹𝗮𝗿, 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 capable of interacting with the real world. This evolution—LLM → RAG → Agentic AI—marks the transition from 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 to 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲.

MAJOR AI LEGAL NEWS. The revised EU Product Liability Directive came into force yesterday, 8 December 2024. It represents a fundamental shift in how liability for AI systems and software is addressed. The Directive could directly impact organisations using and developing AI, and they may wish to consider if they need to reassess their contracts, policies, and operational approaches to liability management. Under the new framework, AI system providers (treated as manufacturers in the legislation) are liable for defects in AI systems and software that cause harm, potentially including defects that emerge after deployment. This potentially includes harm linked to updates, upgrades, or the evolving behaviour of machine-learning systems. Organisations should also consider the liability implications for failing to have sufficient AI literacy among their staff which is a requirement under the AI Act from 2 February. AI training may now be a business imperative for some organisations. The Directive’s approach to defectiveness considers not only when a product is placed on the market but also whether the manufacturer retains control over it post-market, such as through updates or connected services. This means manufacturers may be held liable for defects that arise after deployment if they could reasonably foresee and mitigate risks but fail to act. Organisations, particularly those providing software or AI systems, should look at ongoing compliance and risk management to meet evolving safety expectations. The Directive's coverage of potential liability for post-market defects could have big implications for contracts. Organisations should consider whether their agreements with suppliers, integrators, and distributors include clear terms governing responsibility for defects. The focus is on whether the product provides the safety consumers are entitled to expect. A proactive approach to risk management, extending beyond initial product deployment to encompass ongoing updates and system monitoring may be prudent. Software providers should take note that they potentially could be held liable even if their product operates as a component of a larger system. This liability regime incentivises stronger warranties, indemnities, and cooperation agreements to allocate risk effectively across supply chains. Companies should review existing contracts to confirm they reflect the Directive's requirements and renegotiate where necessary to close gaps in accountability. The Directive also works in tandem with EU regulations like the AI Act. Businesses that fail to meet mandatory product safety requirements under the likes of the AI Act risk facing presumptions of defectiveness under the Product Liability Directive. With the AI Liability Directive in progress, organisations should also prepare for further changes that will make it easier for claimants to bring AI-related liability claims.