Ilya’s 30u30 Part 25: Superintelligence
This paper introduces an equation that is not computable, and that equation takes as input 2 other things that are also not computable. That is a little bit like me saying: “Here is the definitive equation for comparing unicorns to were-people.” Anyways. Here is the long and short of it. The paper defines intelligence as the ability to achieve goals well across a wide variety of environments. So imagine you drop an AI agent into Doom. Can it beat the game? Now take that exact same agent and drop it into MechWarrior. Can it beat that game too? Then drop it into something completely different. Can it figure that out too? That, roughly speaking, is their definition of intelligence. An intelligent agent is not just really good at one thing. It can enter lots of different environments, figure out how they work, and achieve its goals. And, naturally, they give us an equation that supposedly assigns a number to this intelligence. Unfortunately, the equation is not computable. So we cannot actually use it to calculate anyone’s intelligence. But there is an interesting idea underneath all of this. Imagine our AI agent is playing Doom. The agent makes an observation: A zombie grunt is walkin
Ilya’s List #24: Learning Is Compression
A Tutorial Introduction to the Minimum Description Length Principle Hello again, all you Sutskevites. We are now at number 24 on Ilya Sutskever’s list: A Tutorial Introduction to the Minimum Description Length Principle , by Peter Grünwald. Ok. This paper kind of blew my mind, not because the main idea is complicated. The main idea is almost suspiciously simple. In fact, let’s start with this: 2 4 6 8 10 12 I could describe that sequence to you verbatim: 2, then 4, then 6, then 8, then 10, then 12. Or I could say: Start with 2. Add 2 five times. The second description is shorter. I compressed the data. But something else happened too. I learned the rule. And because I learned the rule, I can now ask: What comes next? That is the basic idea behind Minimum Description Length, and also the idea behind Machine Learning: learn the rule, then predict something using that rule. Learning is compression Suppose I give you a giant pile of data. You are trying to find an explanation for it. MDL basically says: Choose the explanation that gives you the shortest total description of everything you saw. In the easiest version, you can think of it like this: Total description = description of the
Ilya’s List #22 and #23, Part 4: The Overall Transformer Architecture
Hello all you Sutskevites. In the last three posts, we talked about the fundamentals of transformers, including attention. Now we are going to take a step back and look at the overall architecture, and how shit flows from one part to another. So let’s go back to our sentence: My cat is We have three embedding vectors: My cat is Now what? Multiple attention heads If we have multi-headed attention, that means we have multiple mechanisms doing the attention thing at the same time. Let’s pretend for a second that our three embedding vectors are 1,000 elements long each. Let’s also pretend that we have two attention heads. All three vectors go into both attention heads. So: My, cat, is → Attention Head 1 and: My, cat, is → Attention Head 2 Each head independently does the attention process we talked about in Part 3. Remember those linear transformations that make the Key, Query, and Value vectors? Those transformations can also make the vectors smaller. For our simplified example, each attention head can take our 1,000-element token vector and create 500-element Key, Query, and Value vectors. Why would we do that? Because we have two attention heads. Each head is going to produce its ow
Ilya’s List #22 and #23, Part 3: How Attention Actually Works
Ok. We have finally arrived at attention. We have the sentence: My cat is And for every word, we have three vectors: Key Query Value Now we are going to use them to blend information between the words. Again, I do not entirely agree with this approach. To me, parts of it still feel like we are just multiplying shit together until intelligence falls out. That may be due to my own ignorance. Anyway. Let’s focus on the word: My How much should “My” pay attention to “cat”? We take the Query vector for “My”. Then we compare it with the Key vector for “cat.” The comparison gives us a number. That number is an attention score. For our fake example, let’s say: “My” Query × “cat” Key = 8 Then we compare the same Query for My with the Key for is: My Query × is Key = 3 Then we do something that initially struck me as strange. We compare the Query for My with the Key for My itself. Maybe that score is: My Query × My Key = 10 So now we have three scores: My → 10 cat → 8 is → 3 These numbers tell us how strongly those tokens should contribute to the new representation we are building for My. Why does a word pay attention to itself? This bothered me for a while. Isn’t a word already itself? Why d
Ilya’s List #22 and #23, Part 2: Why Transformers Need Keys, Queries, and Values
Last time, we took a word like “cat” and turned it into an embedding. Basically: cat ↓ token ↓ embedding vector So now our transformer has a long list of numbers representing the word cat. What do we do with it? We make three more vectors. Three little neural networks We have three learned linear transformations. What is a linear transformation? For our purposes, think of it as an extremely simple neural network with no activation function. Input. Weights. Output. That is about it. We take the embedding for cat and send it through the first transformation. That gives us a Key vector. Then we go back to the original embedding and send it through another transformation. That gives us a Query vector. Then we do it one more time. That gives us a Value vector. So now, for the word cat, we have: the embedding the Key the Query the Value Wonderful. Now let’s make things worse by adding more words. My cat is Imagine that our sentence so far is: My cat is And we want the transformer to eventually figure out what might come next. We turn all three words into tokens and look up their embeddings. Then we run each embedding through the same three learned transformations. So now we have: My: emb
Ilya’s List #22 and #23, Part 1: What the Hell Is an Embedding?
Hello friends. Now it is finally time to get around to the Transformers paper. This concept took me about five years to understand because it is so complex. So if you do not understand it, do not feel bad. It is just a difficult thing to wrap one’s head around. Fully connected neural networks? Easy. Convolutional neural networks? Easy. Transformers will turn your brain inside out. Even though they are currently the dominant architecture for building Large Language Models, I hold a grudge against them because it took me so long to understand them. Also, there are a few things about them that I do not agree with, but that is probably due to my ignorance and not a flaw in the Transformer architecture. At least, that is what ChatGPT tells me. But now I am remembering that ChatGPT is a transformer, so maybe I should not trust it. Anywho. Let’s get started. I did not want to write this one even though it is pretty high on Ilya’s reading list, so I avoided it as long as I could. But I am running out of papers. Attention is all you need? In my personal opinion, transformers are basically a word blender. They allow words to interact with each other so that information gets mixed around. Tha
Ilya’s 30u30 v21: Neural Turing Machines
What if a neural network had a little database it could read from and write to? Ok, this one is a little bit complex. But the core idea is actually pretty cool. Before we get into Neural Turing Machines, we first have to answer an important question: What the hell is a Turing machine? You know those old computers from the 1970s with the giant tape reels spinning around? A Turing machine is kind of like that. Conceptually, you have a long tape containing information. You can: read from the tape write to the tape move around on the tape That sounds incredibly primitive now, but the idea was groundbreaking. Alan Turing showed that an absurdly simple machine like this could, in principle, perform any computation that a computer can perform. Now fast-forward several decades. We have neural networks. And somebody asks: What if we gave a neural network its own little tape? That is basically the idea behind a Neural Turing Machine. Remember LSTMs? We already talked about LSTMs. My favorite way of thinking about them is that an LSTM is basically an RNN with a smart manager watching over its shoulder. The manager has gates that control information: What should I remember? What should I forge
Ilya’s List, Part 14: Pointer Networks
I know for a fact that I studied this one early on, I thought I wrote a blog post about it but I guess not. Pointer networks are a pretty cool idea. You put in a string of words. The output is just the position of one of the words. For example: Is Ridley a cat or a dog? If we put this into a pointer network, it would return 3, because in position 3 of our sentence above is the word Cat, and Ridley is a cat. This is kind of a cool way of doing word processing, because the results can ONLY be words from the input. While there is still a chance of hallucination here, that hallucination is limited because we can only use words for the output that we used on the input. For example, if we ask whether Ridley is a cat or a dog, the model can’t respond with “Star Wars.” Here is kind a how I see this in my head: I also think of it kinda like a Ouija board, because you can only select the limited words that are already there. The AI model is like the spirit of a dead ancestor, guiding the selector to the right word. Not a whole heck of a lot to see here. On to the next! Ilya’s List, Part 14: Pointer Networks was originally published in Ilya Sutskever’s 30 Foundational Papers of AI, Explained
Ilya’s 30 Foundational Papers, Part 5: GPipe
Hello all. Apparently I missed number 5 and 14 in my quest to understand all that is Ilya Sutskever’s AI knowledge foundation. So anyways, let’s talk about the GPipe paper. I think the best way to explain the paper is using animation. Normally, how you train an AI is, you get your neural network, you load it onto a GPU, and you run some data through it. If the results are bad, then you backpropagate and change the weights and biases so that the results might be better next time. But AI models are getting bigger and bigger. Kimi K3 just came out and it is 2.8 trillion parameters, each one in 16 bits of precision. That is like… a lot of terabytes. And as of right now, even the best GPUs in the world can only hold around 100 gigabytes. So we are going to have to split our huge model up, so that the first few layers are on one GPU, the next few layers are on another GPU, etc. until we have the whole thing loaded onto GPUs. Why do we do this? Why are we loading the model onto GPUs? Because a GPU can train an AI model 60 times faster than a regular old CPU. Instead of taking hours, training can take minutes. Instead of taking 2 months, training can take 1 day. However, today’s models are
Ilya’s List, Number 20: Scaling Laws for Neural Language Models
This paper starts out not very surprising. More data makes language models better. More training makes language models better. More parameters make language models better. Absolutely shocking. Please remain seated. The paper is Scaling Laws for Neural Language Models , and it is one of the papers from Ilya Sutskever’s famous reading list. The big idea is that language model performance does not improve randomly. It follows predictable mathematical patterns. That is the important part. If model performance is predictable, then training a giant AI model becomes less like wizardry and more like engineering. Still expensive engineering, but engineering. Loss, or: How Embarrassed Is the Model? The paper talks about cross entropy loss. Very roughly, this measures how surprised the model is when it sees the correct next token. If the sentence is: The cat sat on the ___ And the correct next token is “mat,” a good model assigns “mat” a high probability. A bad model confidently guesses “spreadsheet,” “mitochondria,” or “Belgium.” The lower the loss, the less embarrassed the model should be. For language models, loss is related to perplexity: Perplexity = e^loss So a loss of 5 means the model
Many Tongues, One Intelligence: Building the AI Commons for Language Diversity | AI House Davos 2026
How can we ensure that the next generation of AI systems reflects the world's linguistic and cultural diversity rather than narrowing it? What infrastructure is needed to preserve and amplify language diversity in the age of AI? This interactive evening session explores how shared AI infrastructures, from open data to equitable compute access, can empower linguistic diversity and prevent AI systems from homogenizing global languages. Through short expert inputs and open networking, participants exchange ideas on inclusive AI innovation for the common good, examining how multilingual AI, inclusive datasets, and accessible compute can create an AI Commons that serves all languages and cultures, not just dominant ones. Speakers Annette Oxenius (ETH Zurich, Vice President Research and Member of Executive Board) Stéphanie Lacour (EPFL, Vice President Support to Strategic Initiatives) Antoine Bosselut (Swiss AI, Co-Lead of Apertus Project) Martin Tisné (Current AI, Founder and Chair) Alexandra Baumann (Swiss Federal Department for Foreign Affairs, Ambassador, Head of Division for Prosperity & Sustainability) Moderator Nina Frey (ICAIN, ETH Zurich, Executive Director) (c) AI House Davos 2
The Long Horizon: A Swiss Morning on the Future of Intelligence | AI House Davos 2026
After a week of conversation at the AI House under the theme A Human Intelligence Shift, Friday turns our attention to The Long Horizon. The Swiss Morning sets the stage for looking beyond immediate breakthroughs to ask how intelligence may evolve, whether it will converge across cultures, fragment into competing systems, or diversify in new and surprising ways. Building on Switzerland's neutrality and strong innovation heritage, the day connects global debates to local realities, exploring how a small, future-focused nation can translate worldwide conversations into meaningful action at home while offering a steady voice to the international community. Speakers Bernhard Kratzwald (EthonAI, Co-Founder and CTO) Davide Scaramuzza (University of Zurich, Professor of Robotics and Perception) Harald Kröll (Chipmind AG, Founder and CEO) Nadja Christoffel (Prättigau–Davos, Head of Regional Development) Philipp Hölzenbein (Raven, Co-Founder) Rolf Pfister (Lab42, Co-Founder & Director of Research) Pascal Kaufmann (Mindfire, Founder and CEO) Tilman Eberle (Davos Tech Summit, GM) Moderators Florian Herzog (FHGR, Director of Studies AI in Software Engineering) Rebecca Brauchli (Zurich Universi
From Co-Pilots to Agentic Autonomous Economies | AI House Davos 2026
The emergence of agent economies is inevitable. The rapid rise of autonomous AI agents is leading to a new economic layer of agent-agent interactions beyond human pace. These agents are beginning to act not just as tools, but as autonomous participants, transacting, negotiating, and coordinating in digital environments at scale. This shift brings immense potential for efficiency and innovation, but also serious risks and questions around fairness, control, and societal impact. This panel convenes AI researchers, economic game designers, policymakers, and ethicists to explore how we can proactively shape these emerging economies and build the critical infrastructure, identity, and oversight that will foster trust and coordination in these high-speed ecosystems. Speakers David Evan Harris (UC Berkeley, Chancellor's Public Scholar) Gordon Liao (Circle Internet Group, Chief Economist and Head of Research) James Landay (Stanford HAI, Co-Director) Rod Beckstrom (Investor, Author, Tech CEO, Geopolitician) Moderator Kenneth Cukier (The Economist, Deputy Executive Editor) (c) AI House Davos 2026 Founders & Strategic Partners: ETH AI Center, Merantix, G42, Hewlett Packard Enterprise, EPFL AI
Sustainable AI: From Climate Science to Efficient Computing | AI House Davos 2026
As climate pressures intensify, AI is emerging as a critical tool for understanding risks, strengthening adaptation, and guiding smarter decisions. This session explores how new frugal AI models are improving climate efficiency, how advanced optimization tools can enhance energy systems and industrial processes, and how next-generation forecasting is reshaping resilience planning. The discussion highlights where AI delivers real environmental impact today and what breakthroughs are needed to scale sustainable solutions for tomorrow. Speakers Eric Enselme (Independent Board Advisor) Varun Sivaram (Emerald AI, Founder and CEO) Himanshu Gupta (ClimateAi, Co-Founder and CEO) Moderator Ghjulia Sialelli (ETH AI Center, PhD Fellow) (c) AI House Davos 2026 Founders & Strategic Partners: ETH AI Center, Merantix, G42, Hewlett Packard Enterprise, EPFL AI Center, The University of Tokyo Presenting Partner: KPMG
Unprecedented Scale – Building Startups in the Age of AI | AI House Davos 2026
AI is reshaping the startup playbook, with vibe coding and instant access to powerful models lowering the barrier to build. This panel brings founders, investors, and tech leaders together to explore how speed, data, and automation now drive advantage for AI-native companies and how unicorn-style growth is transforming markets. The discussion examines shifting economic dynamics across industries, venture, government, and ecosystem partners, how investors identify real potential, and what it takes to stand out when anyone can ship fast. Panelists share strategies for scaling responsibly, competing globally, and building impactful, sustainable AI-first organizations in this new era of company creation. Speakers Alex Ilic (ETH AI Center, Co-Founder and Executive Director) Andrew Ng (DeepLearning.AI, Founder) Andy Hock (Cerebras Systems, Chief Strategy Officer) Laura Modiano (OpenAI, Head of Startups EMEA) Moderator Nicole Büttner (Merantix Momentum, Founder and CEO) (c) AI House Davos 2026 Founders & Strategic Partners: ETH AI Center, Merantix, G42, Hewlett Packard Enterprise, EPFL AI Center, The University of Tokyo Presenting Partner: KPMG
The Fabric of Society: AI in Critical Infrastructure | AI House Davos 2026
AI is reshaping critical infrastructure, from data centers and power grids to satellite and communication networks. Rising compute demand, tighter reliability requirements, and deeper automation are forcing rapid changes in how these systems are designed and managed. With atomic energy remaining a vital source of stable baseload power, its role grows even more important as AI-driven systems increase overall electricity demand and dependence on reliable generation. This panel examines how AI can improve efficiency and operational resilience, while also creating new vulnerabilities, cyber risks, and capacity challenges. Speakers Francesco Sciortino (Proxima Fusion, Co-Founder and CEO) Frederic Werner (ITU, Chief Strategic Engagement Division, AI for Good) Rafael Mariano Grossi (International Atomic Energy Agency, Director General) Moderator Vijay V. Vaitheeswaran (The Economist, Global Energy and Climate Innovation Editor) (c) AI House Davos 2026 Founders & Strategic Partners: ETH AI Center, Merantix, G42, Hewlett Packard Enterprise, EPFL AI Center, The University of Tokyo Presenting Partner: KPMG
Shadow AI: The Hidden Layer of Intelligence | AI House Davos 2026
This panel explores "Shadow AI" models, tools, and autonomous agents operating outside formal oversight. As AI proliferates beyond institutional control, risks around security, governance, and accountability intensify. The discussion examines how organizations and policymakers can detect, audit, and manage hidden AI systems, and how to balance open innovation with the systemic risks posed by unmonitored or unauthorized intelligent agents. Speakers Menna El-Assady (ETH Zurich and Faculty, ETH AI Center, Professor) Navrina Singh (Credo AI, Founder and CEO) Petar Tsankov (LatticeFlow AI, Co-Founder and CEO) Moderator Akhilesh Tuteja (KPMG, Global Leader, Cyber Security) (c) AI House Davos 2026 Founders & Strategic Partners: ETH AI Center, Merantix, G42, Hewlett Packard Enterprise, EPFL AI Center, The University of Tokyo Presenting Partners: KPMG
Sovereignty vs. Collaboration: Who Owns the Future of Intelligence? | AI House Davos 2026
In a world where data is currency and AI drives progress, sovereignty is no longer just a buzzword. It's a battleground. Who truly controls the future of intelligence: nations, corporations, or the networks that span them? This session challenges the assumption that independence and global collaboration are mutually exclusive. Is the quest for sovereign AI a path to resilience, or a recipe for isolation and stagnation? We confront the hard questions: Can secure, compliant, air-gapped data coexist with the open exchange that fuels innovation? Does sovereignty demand walls, or can it thrive on bridges? This panel debates whether sovereignty is a shield or a cell and explores how bold, pragmatic strategies can reconcile autonomy with the undeniable benefits of global collaboration. Speakers Denise Wong (IMDA, Assistant Chief Executive) Juan Ramón Troncoso-Pastoriza (Tune Insight, Co-Founder and CEO) Talal Al Kaissi (G42 & Core42, Group Chief Global Affairs Officer & Interim CEO) Thierry Pienaar (HPE, Chief Tech Officer, HPC & AI WW) Moderator Irena Bednarich (Vice President, International Government Relations and Strategic Partnerhips, HPE) (c) AI House Davos 2026 Founders & Strategic
Inclusive Intelligence: Empowering Societies to Shape AI | AI House Davos 2026
How can societies actively shape the future of AI through inclusion, literacy, and broad civic participation? What mechanisms enable informed communities to influence AI governance? This panel explores how societies can move from passive recipients to active shapers of AI futures. The discussion examines strategies to strengthen public engagement in AI policymaking, close knowledge gaps through accessible education programs, and build tools that enable transparent, participatory governance. Speakers share insights on how informed communities can influence the development, deployment, and oversight of AI systems that impact daily life and democratic decision-making—from university-led education initiatives to civic assemblies and cross-border research collaboration. Speakers Baroness Joanna Shields (Responsible AI Future Foundation, Executive Chair) Basma AlBuhairan (C4IR Saudi Arabia, Managing Director) Joël Mesot (ETH Zürich, President) Thomas Hofmann (Technical University of Munich, President) Moderator Spriha Srivastava (CNBC, VP & International Executive Editor of Digital) (c) AI House Davos 2026 Founders & Strategic Partners: ETH AI Center, Merantix, G42, Hewlett Packard Enter
AGI Night | AI House Davos 2026
An evening to rethink what AGI truly means. Rather than seeing intelligence as stored knowledge or fixed patterns, we explore it as a dynamic, evolving process, one that has shaped human cognition across millions of years. How close are we to creating machines that don't just perform tasks but continuously reshape their own models of the world? What risks and possibilities arise when intelligence becomes augmented, accelerated, or even decoupled from biology as we know it now? We examine the promises and perils of transformative AI, and widen the discussion into cultural, philosophical, and spiritual dimensions. What does it mean for humanity when a new kind of mind emerges alongside us? And how might this shift redefine creativity, purpose, and the future of human evolution? Speakers Gary Marcus (NYU, Professor Emeritus) Max Tegmark (MIT & Future of Life Institute, Professor & President) Richard Socher (you.com, Co-Founder and CEO) Moderator Jack Symes (Durham University, Philosopher) (c) AI House Davos 2026 Founders & Strategic Partners: ETH AI Center, Merantix, G42, Hewlett Packard Enterprise, EPFL AI Center, The University of Tokyo Presenting Partners: KPMG
AI Generation: Rethinking Primary and Secondary Education | AI House Davos 2026
In recent years, the use of AI, including generative AI, has been expanding in primary and secondary education. This panel discusses the use of AI in education from a wide range of perspectives, including educational benefits and concerns, impacts on children's development, challenges in implementation and operation, national and local policies, and global trends. Speakers Masami Hagiya (Institute for AI and Beyond, Director) Don Passey (Lancaster University, Professor, School of Social Sciences) Mary Webb (King's College London, Professor of AI in Education) Moderator Toshinori Saito (Seisa University, Professor, Graduate School of Education) (c) AI House Davos 2026 Founders & Strategic Partners: ETH AI Center, Merantix, G42, Hewlett Packard Enterprise, EPFL AI Center, The University of Tokyo Presenting Partners: KPMG
The Next Wave of Deep Tech and Future Use | AI House Davos 2026
As generative and agentic AI move from early adoption into true mainstream use, a new set of frontiers is rapidly coming into view. This session explores the next wave of AI innovation beyond today's paradigms, examining where the technology is heading, which breakthroughs still stand between us and the next leap forward, and how these advances could reshape entire industries throughout the 2030s. From multimodal intelligence and autonomous experimentation to large-scale simulation and the emergence of new human-machine interfaces, the panel maps out trajectories that could redefine how AI is built, deployed, and experienced. Speakers Adrian Locher (Merantix Capital, Co-Founder and General Partner) Andrey Khusid (Miro, Founder and CEO) Deepak Pathak (Skild AI, Co-Founder and CEO) Nal Kalchbrenner (Project Prometheus, Founding Member) Moderator Jamie Heller (Business Insider, Editor in Chief) (c) AI House Davos 2026 Founders & Strategic Partners: ETH AI Center, Merantix, G42, Hewlett Packard Enterprise, EPFL AI Center, The University of Tokyo Presenting Partners: KPMG
Quantum Computing as the Next Frontier in AI Capability | AI House Davos 2026
Quantum computing is emerging as the next frontier in AI, promising new forms of capability far beyond today's systems. This panel explores what breakthroughs might become possible when quantum techniques are applied to machine learning, and how close we truly are to achieving useful quantum advantage for AI. The discussion also looks at the broader implications for security, accelerating human discovery and global governance as quantum power grows. Speakers Grégoire Ribordy (IonQ, VP Science & Technology, Founder, ID Quantique) Marina Marinkovic (ETH Zurich, Professor Computational Physics) Nathan Baker (Microsoft, Lead Architect of Quantum) Steve Suarez (HorizonX, Founder and CEO) Moderator Anu Unnikrishnan (ETH Zurich, Executive Director Quantum Center) (c) AI House Davos 2026 Founders & Strategic Partners: ETH AI Center, Merantix, G42, Hewlett Packard Enterprise, EPFL AI Center, The University of Tokyo Presenting Partners & Guest Country: KPMG, The University of Tokyo, Poland
From Orbit to Insight: AI’s Role in Shaping a Smarter Planet | AI House Davos 2026
Space exploration isn't just about reaching new frontiers - it's about unlocking breakthroughs that transform life on Earth. This panel explores how AI in space is accelerating innovation and driving real-world impact across smart cities, precision agriculture, disaster response, climate modeling, and global sustainability. From analysing satellite imagery to training AI models aboard the International Space Station, and even the future development of orbital data centres, space-powered intelligence is transforming global challenges into opportunities for a smarter and more resilient world. Speakers Samantha Cristoforetti (European Space Agency, Astronaut) Bernhard von Weyhe (European Space Agency,Senior Media Relations Officer) Mark Mozena (Planet,Vice President of Government Affairs) Moderator Kirk Bresniker (HPE Labs, HPE Fellow, Vice President & Chief Architect) (c) AI House Davos 2026 Founders & Strategic Partners: ETH AI Center, Merantix, G42, Hewlett Packard Enterprise, EPFL AI Center, The University of Tokyo Presenting Partners: KPMG
What is a Job? NextGen Organizational Strategy, Structure, and Scale | AI House Davos 2026
AI is redefining how organizations create, coordinate, and capture value. As enterprises become AI-native, traditional organizational models are being dismantled and the boundaries of what a 'job' is are being reshaped entirely. Collaboration between humans and AI agents is transforming organizations from rigid structures into dynamic systems of intelligence. Acting as co-workers and autonomous economic actors, they reshape work, markets, and accountability. This panel focuses on how human–agent organizations are transforming industries and redefining business models and what this means for strategy, leadership, and the future of work. Speakers May Habib (WRITER, Co-Founder and CEO) Alessandro de Luca (Merck, Head of Digital Enterprise Solutions and Group CIO) Judith Wiese (Siemens, Chief People and Sustainability Officer and Member of the Managing Board) Moderator Ashish Madan (KPMG Germany, Managing Partner, CTO) (c) AI House Davos 2026 Founders & Strategic Partners: ETH AI Center, Merantix, G42, Hewlett Packard Enterprise, EPFL AI Center, The University of Tokyo Presenting Partner: KPMG
NYT Op-ed: Silicon Valley is at an inflection point
2025. ‘Terrified’ federal workers are clamming up , 2025. The foundations of America’s prosperity are being dismantled , 2025. Microsoft’s hypocrisy on AI , 2024. AI is taking water from the desert , 2024. Inside the chaos at OpenAI , 2023 The new AI panic , 2023. Cleaning up ChatGPT takes heavy toll on human workers , 2023 The U.S. is turning away from its biggest scientific partner , 2023 Artificial intelligence is creating a new world order , 2022. U.S.-China tensions fuel outflow of Chinese scientists from U.S. universities , 2022. How Facebook got addicted to spreading misinformation , 20…
What is ChatGPT? What to know about the AI chatbot
2023. China seeks a quantum leap in computing , 2022. AI has cracked a key mathematical puzzle for understanding our world , 2020. This is how AI bias really happens—and why it’s so hard to fix , 2018.
The hidden workforce that helped filter violence and abuse out of ChatGPT
podcast, 2023 Can you make AI fairer than a judge? Play our courtroom algorithm game , interactive, 2019. We analyzed 16,625 papers to figure out where AI is headed next , data viz, 2019. What is AI? We drew you a flowchart , graphic, 2018. What is machine learning? We drew you another flowchart , graphic, 2018.