Advancing AI Development

Deep stuff! We uncovered a startling link between #entropy, a bedrock concept in #physics, and how #AI can discover new ideas without stagnating. In an era where reasoning models can reflect on problems for days at a time (rather than generating quick, single-step solutions), our study shows how semantic entropy (the spread of meanings) and structural entropy (how evenly its links between concepts generated by the AI are distributed) together hold the secret to ongoing exploration as the model thinks through a problem. Specifically, we measured structural entropy using Von Neumann graph entropy (applied to the adjacency Laplacian), while semantic entropy came from a similarity-based embedding deep language embedding matrix. The key insight? Although semantic entropy consistently outpaces structural entropy, they remain in a near-critical balance—fueling "surprising edges" that introduce relationships between distant concepts. This mirrors physical systems on the brink of a phase transition, where a little bit of "disorder" keeps the process dynamic yet avoids chaos. The result is an AI that doesn’t just keep pace with known solutions but actively creates new pathways of thought over extended “thinking” sessions. As reasoning models become ever more capable—undertaking extended, multi-day "thought processes"—understanding fundamental principles is crucial. By weaving these insights into reinforcement learning strategies, we can reward models not just for correctness, but for venturing into novel conceptual ground. This opens the door to AI systems that actively cultivate new insights, rather than settling into narrow patterns or endlessly rehashing the same knowledge. Going Deeper When physicists describe entropy, they refer to the measure of "disorder" in a system: the number of ways particles can rearrange without altering the system’s energy. Yet entropy transcends molecules and heat. In this research, it emerges as the engine that drives AI reasoning models to keep generating fresh ideas over extended periods. The observed dynamics as the AI thinks about a problem reflects self-organized criticality—a state where systems hover between rigid order and random chaos. Much like a sand pile teetering on the edge of collapse, the AI preserves enough organizational structure to remain coherent, yet stays flexible enough to generate unexpected leaps in meaning. The fraction of "surprising edges" remains stable, offering evidence that the model naturally integrates new, distant ideas without toppling into confusion.

🚨 Elon Musk and Nvidia CEO Jensen Huang are urging students to look beyond just learning how to code. As AI gets better at handling repetitive tasks, both believe the real advantage will come from understanding how the world works. Through physics and math. Jensen Huang recently said that if he were graduating today, he'd focus on physics. He explained that future AI systems will need to work with the physical world, not just digital spaces. This means knowing how things move, how forces interact, and how systems behave in real life. Elon Musk has echoed the same idea. When asked about useful skills for the future, he pointed to physics, backed by math. At Tesla and SpaceX, his thinking is rooted in solving problems from the ground up using core principles, not just following existing methods. They’re not saying coding is useless. It still matters. But the next big opportunities will go to people who understand the systems AI is meant to model, control, and improve. In simple terms, learn how the world really works. Study the tough stuff. Physics and math build the kind of thinking that machines can’t easily replace. ------- Do you agree?

A new World Economic Forum report, written in collaboration with Accenture, offers one of the clearest pictures of what it takes to move from AI experimentation to real impact.   What stands out most is how sharply the gap is widening between organizations that are still running pilots and those that are now delivering measurable business value. The differentiator isn’t model performance or access to technology. It’s whether leaders can align their organizations around AI as a core capability, not a side project.   The companies pulling ahead are doing a few things differently. They’re embedding AI into strategic decision‑making, redesigning workflows so people and AI can collaborate meaningfully, and investing in the foundations that make scale possible: data, platforms, responsible governance, and modern engineering practices. They treat AI less as a promise and more as a system they are actively building. This is exactly what we’re seeing with Copilot across customers of every size. When strategy, data, security, operations, and culture all move together, AI creates compounding value. See the full report: 

I recently spoke to Gartner about what is next in #AI. Here are my thoughts: We have seen impressive progress in #llm by scaling data and compute. Will this continue to hold? Yes, I believe so, but most of those gains will be in reasoning tasks where we have precise metrics to measure uplift, as well as the ability to have synthetic data to train further, and also the freedom to trade off computation for accuracy at test time. This is seen in the recent o1 model. For reasoning tasks, we will also be able to remove hallucination when we can construct accurate verifiers that can certify every statement that #llm makes. We have been doing this in our Leandojo project for mathematical theorem proving. However, there is one area of reasoning where #llm will never be good enough: understanding the physical world. This is because language is only high-level knowledge, and cannot simulate the complex physical phenomena needed in many applications. For instance, LLMs can talk about playing tennis or look up a weather app, but they cannot internally simulate any of these processes. While images and videos can help improve their knowledge of the physical world, models like Sora learn physics by accident, and hence, still produce physically wrong outputs. How can we overcome this? By teaching AI physics from the ground up. We are building AI models that are trained in a physics-informed manner at multiple scales. They are several orders of magnitude faster than traditional simulations, and can also generate novel designs that are physically valid. You can watch some of those examples in my recent TED talk.

From CES this week, one thing is clear: we are moving into the era of physical AI — intelligence that operates in the real world.    Robotics, including humanoid and non-humanoid systems, are getting a lot of attention right now. This is familiar territory for Autodesk. We have decades of experience working with manufacturing, AI, and industrial design leaders who build in the physical world.    MarketWatch recently explored this momentum and included some of my perspective: https://lnkd.in/e_DN9HwC    Progress will not come from machines that just look like us, nor just language. It will come from AI that understands physics, objects, and three-dimensional space. That’s why work on world models, like what Fei-Fei Li and others are doing, matters. These systems learn from sensory data to build a usable understanding of their environment.    Physical AI will change how every industry that makes things designs, simulates, and executes. That is core to Autodesk’s mission, and I am optimistic about what is ahead.    Who is ready to put physical AI to work across everything we design and build? 

In this latest Forbes article, I draw a compelling line from Ada Lovelace’s 19th-century foresight to today’s AI-driven enterprise transformations. Lovelace envisioned machines augmenting human creativity—a vision now realized as #generativeAI reshapes industries. Accenture's experience with over 2,000 gen AI projects reveals that only 13% of companies achieve significant enterprise-wide value, while 36% are scaling AI for industry-specific solutions. Success in this new era hinges on more than just technology investment. Companies must also invest in their people, prioritize industry-specific AI applications, and embed responsible AI practices from the outset. Organizations adopting agentic architecture - digital teams comprising orchestrator, super, and utility agents—are 4.5 times more likely to realize enterprise-level value. Here are five key lessons we’ve learned: 1. Lead with value from the top: Executive sponsorship is crucial. Companies with CEO sponsorship achieve 2.5 times higher ROI from their #AI investments.  2. Invest in people, not just technology: Empower your workforce with the skills to harness AI. Organizations excelling in AI transformation invest in broad AI upskilling, adopt dynamic workforce models, and enable human + agent collaboration.  3. Prioritize industry-specific AI solutions: Tailor AI applications to your sector’s unique needs. Companies creating enterprise-level value are 2.9 times more likely to have a comprehensive data strategy to support their AI efforts.  4. Design and embed AI responsibly from the start: Ensure ethical and effective AI integration. Organizations creating enterprise-level value are 2.7 times more likely to have responsible AI principles and governance in place across the AI lifecycle.  5. Reinvent continuously: Stay adaptable in the face of ongoing change. Companies with advanced change capabilities are 2.1 times more likely to achieve successful transformations. These lessons should serve as a practical playbook for navigating the complexities of #AI integration and achieving sustainable growth. Please read the full article to explore how Lovelace’s visionary ideas are shaping the future of business through #generativeAI. https://lnkd.in/gEVzQeRA

AI field note: Reducing the 'mean time to ah-ha' (MTtAh) is critical for driving AI adoption—and unlocking the value. When it comes to AI adoption, there's a crucial milestone: the "ah-ha moment." It's that instant of realization when someone stops seeing AI as just a smarter search tool and starts recognizing it as a reasoning and integration engine—a fundamentally new way of solving problems, driving innovation, and collaborating with technology. For me, that moment came when I saw an AI system not just write code but also deploy it, identify errors, and fix them automatically. In that instant, I realized AI wasn’t just about automation or insights—it was about partnership. A dynamic, reasoning collaborator capable of understanding, iterating, and executing alongside us. But these "ah-ha moments" don’t happen by accident. Systems like ChatGPT or Claude excel at enabling breakthroughs, but it really requires us to ask the right questions. That creates a chicken-and-egg problem: until users see what’s possible, they struggle to imagine what else is possible. So how do we help people get hands-on with AI, especially in enterprise organizations, without relying on traditional training? Here are some approaches we have tried at PwC: 🤖 AI "Hackathons" or Challenges: Host short, low-stakes events where employees can experiment with AI on real problems. For example, marketing teams could test AI for campaign ideas, while operations teams explore process automation. ⚙️ Sandbox Environments: Provide low-friction, risk-aware access to AI tools within a dedicated environment. Let users explore capabilities like text generation, workflow automation, or analytics without worrying about “messing something up.” 🚀 Pre-built Use Cases: Offer ready-to-use templates for specific challenges, such as drafting a client email, summarizing documents, or automating routine reports. Seeing results in action builds confidence and sparks creativity. At PwC we have a community prompt library available to everyone, making it easier to get started. 🧩 Embedded AI Mentors: Assign "AI champions" who can guide teams on applying AI in their work. This informal mentorship encourages experimentation without formal, structured training. We do this at PwC and it's been huge. ⚡️ Integrate AI into Existing Tools: Embed AI into everyday platforms (like email, collaboration tools, or CRM systems) so users can naturally interact with it during routine workflows. Familiarity leads to discovery. Reducing the mean time to ah-ha—the time it takes someone to have that transformative realization—is critical. While starting with familiar use cases lowers the barrier to entry, the real shift happens when users experience AI’s deeper capabilities firsthand.

A company I know deployed an AI agent in 3 days. No boundaries defined. No guardrails. No sandbox testing. No failure playbook. Week 1: It sent 400 unapproved emails to clients. This is not a horror story. This is what happens when excitement outpaces engineering. The companies succeeding with AI agents in 2026 all follow the same principle: Scaling follows confidence, not excitement. They start small. They define limits. They test adversarial scenarios. They build human approval gates. They observe before they expand. Here’s the step-by-step deployment path serious teams follow - Start with a safe, low-risk use case - Define the agent’s boundaries clearly - Map structured workflows (no guessing) - Ground it with trusted data sources - Apply least-privilege access - Add guardrails before autonomy - Choose the right architecture - Test in simulation (normal + edge cases) - Deploy in a sandbox first - Introduce human approval gates - Add observability and monitoring - Roll out gradually - Create a failure playbook - Build continuous learning loops - Implement governance & compliance controls Safe AI isn’t about slowing down innovation. It’s about engineering trust. Constrain → Ground → Test → Observe → Expand. 15-step framework. Swipe through. Your team needs this before the next sprint planning meeting. What’s the biggest mistake you’ve seen in AI agent deployment? Drop it below 👇

"AI data centers represent the most significant opportunity for grid economics in a generation. Today’s electric grid operates at less than 40% utilization for much of the year. When AI data centers are interconnected strategically to leverage existing capacity, they don’t strain the system— they optimize it. By spreading fixed grid costs across substantially more kilowatt-hours, these AI facilities become catalysts for lower rates and accelerated infrastructure investment." "Our analysis of a 1 GW of data center deployment in a representative mid-sized electric utility with one million customers shows: - Customer rates can decrease by nearly 5%—providing tangible relief to millions of Americans. - Over $1.35 billion in new capital investment becomes justifiable— without any rate increases. - Critical grid modernization accelerates—funded by new revenue streams rather than ratepayer burden." - GridCARE

𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝗶𝘁 𝗿𝗲𝗮𝗹𝗹𝘆 𝘁𝗮𝗸𝗲 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗵𝗮𝘁 𝘄𝗼𝗿𝗸 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝘄𝗼𝗿𝗹𝗱? 𝗡𝗼𝘁 𝗷𝘂𝘀𝘁 𝗱𝗲𝗺𝗼𝘀. 𝗡𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮𝗴𝗲𝗻𝘁𝘀 𝗰𝗮𝗹𝗹𝗶𝗻𝗴 𝗮 𝘁𝗼𝗼𝗹. To build robust, autonomous AI systems, you need to understand and orchestrate the full stack of agentic capabilities — from tool use and memory to inter-agent communication and orchestration logic. That’s why I created the ABC of Agentic AI. A visual A-to-Z glossary of 26 foundational concepts every builder, architect, or researcher should be familiar with. Here’s a breakdown of some important but often overlooked building blocks: 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲 (𝗗𝗱): Agents don’t just respond — they act. That means selecting the right tool (e.g., browser, API, DB) at the right time, based on context. 𝗟𝗼𝗻𝗴-𝗧𝗲𝗿𝗺 𝗠𝗲𝗺𝗼𝗿𝘆 (𝗟𝗹): Stateless LLMs are limited. Agents need persistent memory across tasks, sessions, and even agent lifecycles to behave coherently. 𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 (𝗠𝗺): This is where the field is headed — standardizing how agents manage context, tools, and memory to make them modular and interoperable. 𝗜𝗻𝘁𝗲𝗿-𝗔𝗴𝗲𝗻𝘁 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 (𝗜𝗶): Multi-agent systems require agents to talk, collaborate, and share knowledge using structured protocols like A2A or OAP. 𝗥𝗲𝗳𝗹𝗲𝘅𝗶𝗼𝗻 (𝗥𝗿): This isn’t a buzzword. Reflexive reasoning lets agents reflect on what they did, why it didn’t work, and how to improve next time — all on their own. 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗧𝗼𝗼𝗹𝘀 (𝗢𝗼): If you can’t debug it, you can’t scale it. Platforms like Langfuse and Helicone let you monitor agent behavior, memory usage, and failure modes in production. 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 (𝗪𝘄): Think beyond single agents. Tools like LangGraph, LangFlow, or n8n help you coordinate complex, multi-step, multi-agent tasks. 𝗨𝘁𝗶𝗹𝗶𝘁𝘆 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 (𝗨𝘂): How do agents make choices? Scoring functions help agents evaluate options and pick the best action — crucial for planning and optimization. 𝗧𝗵𝗶𝘀 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 “𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁𝘀.” It’s about engineering intelligent systems with modular reasoning, structured memory, task decomposition, and real-time decision-making. Whether you're:  • Building AI copilots  • Designing task agents for enterprise workflows  • Experimenting with agent memory and planning  • Or just getting started… This glossary will help you speak the language of Agentic AI with clarity and confidence. Which concept here do you think is most underutilized today?