You have 23 game points for eight people and up to three chip partners. Each person costs 1 to 5 points. Each chip partner costs 0 to 3.
A cheaper pick
leaves more room.
Spend 5 points on one seat and you have 18 for the other seven seats and your chips. Choose a 2-point card instead and you have three points more to work with.
Spend the extra points on another seat or on chips, or leave them unspent.
The draft blocks a pick that would leave too few points for your empty seats. The card stays visible and says why it does not fit.
This team spends all 23 points, chips included. Swap one pick to save three.
5 points does not mean a better scientist.
Points for people are provisional settings for fictional recruitment difficulty and opportunity cost. Opportunity cost is what your lab gives up when it hires one person instead of another. A 5-point researcher is harder or costlier to recruit in the game than a 1-point researcher. Points do not measure ability, quality, prestige or future impact.
I set point values by hand for a fictional frontier-AI talent market, based on recruitment difficulty and opportunity cost. The factors I imagine include current compensation and equity, institutional position, competing demand, scarcity and switching friction. Research interests, mission, autonomy and willingness to move also matter. These are provisional game settings, not calculations from pay or recruitment data.
Social Leads cost 1 or 2 points. Not every role pool has every point value. Roles are seats in this game, not anyone’s real job.
- Building tools
- Built or maintained software, research tools, datasets, benchmarks or developer products that others use.
- Open work
- Research, code, tools, data or writing shared in public. Open weights are not required.
The rules in brief
Pick eight people and their chips within 23 game points to make a lab card. Unspent points earn a Bootstrapped badge. Optional challenges check your team as you pick. Lab names and commentary are jokes about the team.
Corrections and removals
Message @falseconjecture on X with the card name and what should change. A public source helps with a factual correction.
Look up a card
127 peopleSources support the work descriptions. They do not set the points.
127 cards shown
- Research Lead
Aidan Gomez
3 ptsTransformer co-author; Cohere co-founder and CEO
Work and sources for Aidan Gomez
Leads Cohere as chief executive; in April 2026, Cohere agreed to combine with the German AI company Aleph Alpha. Co-authored Attention Is All You Need as one of the eight researchers who introduced the Transformer architecture for sequence tasks. Co-founded Cohere in 2019, building a company around language models and tools for working with text, including generation and semantic search. Aidan Gomez announced Cohere's merger with Aleph Alpha to expand sovereign AI services.
- Research Lead
Albert Gu
3 ptsState space models and Mamba co-creator; CMU and Cartesia
Work and sources for Albert Gu
Is a professor at Carnegie Mellon and co-founded Cartesia, which builds state-space models for real-time voice. In March 2026, co-authored Mamba-3, a new version of the Mamba state-space model, with Tri Dao and six other researchers. With Tri Dao, wrote Mamba, introducing selective state-space models that decide which information to retain as they process a sequence. Co-created the accompanying Mamba library, releasing code for the model and its selective state-space operations so others could run and study the approach.
- Research Lead
Alec Radford
5 ptsFirst author of GPT, GPT-2 and CLIP; led Whisper
Work and sources for Alec Radford
Was first author of the GPT and GPT-2 papers, the language models that started OpenAI's GPT line, and led Whisper, its speech-recognition model. Was first author of CLIP, training image and text representations together so a model could recognize categories described in words. Co-authored Proximal Policy Optimization, a reinforcement-learning method that reuses collected experience for several training updates and was tested on games and simulated movement. Alec Radford co-authored a paper on filtering individual training tokens to reduce unwanted language model capabilities.
- Social Lead
Alex Albert
1 ptClaude developer relations at Anthropic; posts the launch threads
Work and sources for Alex Albert
Works in Claude developer relations at Anthropic and is a familiar source for new model and API launch announcements. Posted Anthropic product news on X in June 2026. Albert discussed his work as a research product manager developing Claude models in a Behind the Craft interview.
- Wildcard only
Alexandr Wang
3 ptsFounded Scale AI; Meta's Chief AI Officer, runs Superintelligence Labs
Work and sources for Alexandr Wang
Meta Superintelligence Labs, which he leads, released its first model, Muse Spark, in April 2026, and Muse Spark 1.1 in July. Co-founded Scale to provide training data for machine-learning teams. Its early APIs handled image annotation, transcription and other data tasks. The company served applications including self-driving vehicles, intelligent assistants and voice analytics.
- Safety Lead
Amanda Askell
2 ptsThe philosopher behind Claude's character; revised constitution, Jan 2026
Work and sources for Amanda Askell
With Joe Carlsmith, was a lead author of Claude's revised constitution, the document of values that Anthropic published in January 2026. Co-authored A General Language Assistant as a Laboratory for Alignment, using an assistant model to study how training changes its responses. The experiments compared prompting with learning from preferences, examining how each approach affected helpfulness, honesty and harmlessness in the assistant's behavior.
- Social Lead
Amjad Masad
2 ptsReplit CEO; Agent 4 and a $9B valuation in March 2026
Work and sources for Amjad Masad
Replit, which he leads, raised $400 million at a $9 billion valuation and released Agent 4 in March 2026. Leads Replit as chief executive and launched Replit Agent, which builds working apps from requests written in plain language. Replit CEO; discussed production agents at SaaStr AI 2026.
- Research Lead
Andrej Karpathy
5 ptsOpenAI founding member, ex-Tesla Autopilot; joined Anthropic in May 2026
Work and sources for Andrej Karpathy
Was a founding member of OpenAI, then led Tesla's Autopilot vision team from 2017 to 2022, before returning to OpenAI. Coined "vibe coding" in a February 2025 post: describing what you want and letting the model write the code. Joined Anthropic's pretraining team in May 2026 to build a group that uses Claude to accelerate pretraining research. Co-authored the ImageNet challenge report, documenting how image recognition systems were evaluated on classification and object detection.
- Chief Scientist
Andrew Chi-Chih Yao
4 ptsTuring Award, founded Tsinghua's Yao Class, trained a generation
Work and sources for Andrew Chi-Chih Yao
Won the 2000 Turing Award for contributions to the theory of computation, including communication complexity and pseudorandom numbers. Developed research in computational complexity and cryptography, including work on communication complexity and secure computation: how parties can calculate together without revealing all their inputs. Founded Tsinghua's Yao Class in 2005, creating an undergraduate program focused on computer science. Yao coauthored the 2026 Singapore Consensus on Global AI Safety Research Priorities.
- Wildcard only
Andrew Ng
3 ptsTaught Stanford's ML MOOC; DeepLearning.AI, AI Fund, LandingAI
Work and sources for Andrew Ng
DeepLearning.AI, which he founded, released the short course Agent Skills with Anthropic in January 2026. Worked on Stanford's autonomous helicopter project, applying reinforcement learning to flight control. The project studied how learning algorithms could produce control policies for demanding maneuvers on a real helicopter, taking questions about an agent's behavior beyond simulations and into physical flight. Ng released Context Hub, an open-source tool that gives coding agents current API documentation.
- Infra Lead
Andrew Tulloch
2 ptsHelped train GPT-4o and o3, and left Meta in September 2026
Work and sources for Andrew Tulloch
Left Meta in September 2026, about eleven months after he joined from Thinking Machines Lab, according to Semafor. Worked on machine-learning systems at Meta and contributed to training GPT-4o, GPT-4.5 and o3 at OpenAI, as recorded in his own biography. Studied mathematics at Sydney and Cambridge before his work on the systems used to train large models. Semafor reported that Tulloch was leaving Meta after the launch of its Muse AI products.
- Wildcard only
Arthur Mensch
3 ptsMistral co-founder; raised a €3B Series D led by Samsung in Sept 2026
Work and sources for Arthur Mensch
Was a researcher at DeepMind, where he co-authored the Chinchilla paper on compute-optimal training, before co-founding Mistral AI in 2023. Mistral AI, which he co-founded and leads, announced a €3 billion Series D led by Samsung Electronics in September 2026. Co-authored the Mistral 7B report, describing a language model built with grouped-query and sliding-window attention. The team released a base model and an instruction-following version. The report explains the architecture choices behind a model intended to use compute and memory efficiently.
- Evals Lead
Arvind Narayanan
3 ptsCo-wrote AI Snake Oil; argues AI is normal technology; ICML 2026 keynote
Work and sources for Arvind Narayanan
Gave a keynote at the ICML machine-learning conference in Seoul in July 2026, titled "What will be left for us to work on?" Worked on Princeton's Web Transparency and Accountability Project, studying how companies collect and use people's information. His research has also examined cultural stereotypes in machine learning. Co-wrote AI Snake Oil to help readers assess what different kinds of AI can actually do. Narayanan co-authored a paper that proposes twelve measures of AI agent reliability.
- Research Lead
Ashish Vaswani
3 ptsFirst author of Attention Is All You Need, reportedly now at NVIDIA
Work and sources for Ashish Vaswani
In June 2026, reportedly joined NVIDIA's Nemotron model group with members of Essential AI, the company he co-founded. Co-authored the 2017 Transformer paper with seven collaborators, introducing a sequence model driven by attention. The team built an encoder and decoder around attention rather than recurrence, then tested the architecture on machine translation and reported how it changed training efficiency. Ashish Vaswani co-authored a paper that measures when a language model starts to memorize training data instead of generalizing.
- Post-training Lead
Barret Zoph
3 ptsLed post-training at OpenAI; DeepMind VP of Research since Aug 2026
Work and sources for Barret Zoph
Led post-training at OpenAI, the stage that turns a pretrained model into an assistant. Joined Google DeepMind as vice president of research in August 2026, after he co-founded Thinking Machines Lab and briefly returned to OpenAI. With Quoc Le, developed neural architecture search, training a controller to propose network designs and improve them using measured validation results. Co-authored Switch Transformers with William Fedus and Noam Shazeer, routing each token to a selected expert network to limit the computation used.
- Evals Lead
Beth Barnes
3 ptsFounded METR, which measures how long AI agents can work on their own
Work and sources for Beth Barnes
METR, which she founded, published a March 2026 note on how modeling choices change its time-horizon estimates for AI agents. Co-authored METR's randomized study of experienced open-source developers using early-2025 AI tools. The study compared task completion times with and without those tools in repositories the developers already knew, testing productivity claims against work in familiar, existing codebases. Barnes co-authored METR's external review of Anthropic's Claude Opus 4.6 sabotage risk report.
- Social Lead
Boris Cherny
2 ptsCreated Claude Code; said in June 2026 he no longer writes code by hand
Work and sources for Boris Cherny
Said at Fortune Brainstorm Tech in June 2026 that on some days he manages thousands, or tens of thousands, of AI agents. Created Claude Code, Anthropic's command-line coding agent, and leads it as a product; he also shares notes on how he works with it. Head of Claude Code at Anthropic; spoke at Code with Claude London, May 2026. Cherny said his team used Claude Code to build Cowork, Anthropic's new assistant for general work tasks.
- Safety Lead
Buck Shlegeris
2 ptsCo-founded Redwood Research, CEO; works on the AI control agenda
Work and sources for Buck Shlegeris
Is chief executive of Redwood Research, which he co-founded, focused on AI control. Co-authored AI Control, a study that tests safeguards while assuming a model may deliberately try to get around them. The team evaluated protocols for getting useful work from an untrusted model, measuring how well the safeguards held up against attempts to undermine them. Shlegeris co-authored AI Security Priorities: A Field-Wide Agenda, which proposed priorities for protecting AI systems and infrastructure.
- Social Lead
Cat Wu
1 ptHead of Product for Claude Code at Anthropic; keynoted Code with Claude
Work and sources for Cat Wu
Spoke in the opening keynote of Code with Claude in London in May 2026. Leads product for Claude Code, the command-line coding agent that Anthropic builds for developers. Recorded a one-year Claude Code retrospective with Boris Cherny in June 2026. Wu discussed Claude Code and Cowork product development in an interview on Lenny's Podcast.
- Infra Lead
Chris Lattner
3 ptsCreated LLVM and Swift; Mojo hit 1.0 after Qualcomm bought Modular
Work and sources for Chris Lattner
Became an executive vice president for AI software at Qualcomm after it bought Modular, the company he co-founded, in July 2026. Co-created LLVM and co-wrote its compiler-framework paper with Vikram Adve, using a shared intermediate representation to analyze and transform programs. Started and led Swift's language design and implementation, also helping design its compiler architecture and interactive programming tools.
- Chief Scientist
Chris Manning
2 ptsCo-created GloVe, taught Stanford's CS224N, ACL president 2015
Work and sources for Chris Manning
Is a Stanford professor and a past president of the Association for Computational Linguistics; his textbooks on statistical NLP and information retrieval are standard references. Developed GloVe with Jeffrey Pennington and Richard Socher. The project released code and pretrained word vectors learned from word co-occurrence counts. Taught Stanford's CS224N course on natural language processing with deep learning, with public lectures and assignments covering how neural models work with language. Manning and Jasper Jian published Humans and transformer LMs: Abstraction drives language learning in the EACL 2026 proceedings.
- Safety Lead
Chris Olah
4 ptsCo-founded Anthropic and leads its interpretability work
Work and sources for Chris Olah
Co-founded Anthropic in 2021 and leads its interpretability research, which maps the features and circuits inside Claude. Spoke at the Vatican presentation of Pope Leo XIV's first encyclical, a formal papal letter, on AI in May 2026. Co-authored Distill's Feature Visualization work, which generates images to investigate the patterns that activate parts of a neural network. Co-authored the Circuits research series, tracing connections between neurons to study how simpler visual features combine into more complex behavior.
- Wildcard only
Clem Delangue
3 ptsCo-founded Hugging Face; testified to Congress; makes the open case on TV
Work and sources for Clem Delangue
Argued in a May 2026 interview that AI regulation should target bad actors instead of restricting the technology. Gave congressional testimony in 2023 about Hugging Face's platform for sharing models and datasets. He described its approach to open science and argued for lowering barriers to participation in AI development, explaining why researchers and developers outside a few large organizations should be able to contribute. Delangue announced an app store for Hugging Face's Reachy Mini robot, with about 200 apps available at launch.
- Evals Lead
Clémentine Fourrier
2 ptsCo-built the Open LLM Leaderboard; maintains the public GAIA results
Work and sources for Clémentine Fourrier
Updated the public results dataset for GAIA, a benchmark for AI assistants, on Hugging Face in October 2026. Helped maintain the Open LLM Leaderboard, a service for comparing models through a shared evaluation process. Co-authored Zephyr, which trained a smaller assistant using preferences supplied by a larger model. The team released its models and training recipe so others could study the approach.
- Evals Lead
Dan Hendrycks
4 ptsCreated MMLU; directs the Center for AI Safety
Work and sources for Dan Hendrycks
Directs the Center for AI Safety, which organised the 2023 statement on AI extinction risk; he also introduced the GELU activation. Co-authored MMLU, a benchmark that tests language models across 57 academic and professional subjects. The study examined differences in performance across subjects and whether models could recognize when they were wrong, going beyond whether a model answered individual questions correctly. Hendrycks published Eigenism: Ethics for a Human-AI Future, which proposed an ethical framework for AI copies, updates, and human-AI identity.
- Wildcard only
Dario Amodei
4 ptsCo-founded Anthropic; will apparently ban you if you insult its model
Work and sources for Dario Amodei
Co-founded Anthropic in 2021 and is its chief executive; at OpenAI he led the GPT-2 and GPT-3 work and co-authored the scaling-laws paper. Published the essay The Adolescence of Technology in January 2026, about the risks of powerful AI and whether institutions are ready. Co-authored Deep Reinforcement Learning from Human Preferences, studying how people could guide agents by comparing examples of their behavior. The team used those comparisons to train a reward model. This explored a way to specify goals that are hard to capture in a reward function written by hand.
- Chief Scientist
David Silver
3 ptsLed AlphaGo and AlphaZero; left DeepMind to found Ineffable Intelligence
Work and sources for David Silver
Left Google DeepMind in January 2026 to found Ineffable Intelligence, a London lab that studies learning from experience instead of human data. Led AlphaGo research and co-authored its original paper, combining neural networks with tree search to choose moves in Go. Co-authored AlphaZero, showing how one reinforcement-learning approach could learn chess, shogi and Go through self-play without training on expert games. Silver's Ineffable Intelligence raised $1.1 billion at a $5.1 billion valuation.
- Post-training Lead
Daya Guo
3 ptsFirst-named author on the DeepSeek-R1 reasoning paper
Work and sources for Daya Guo
Was first-named author of the DeepSeek-R1 research, which investigated reinforcement learning for language-model reasoning. The team compared R1-Zero's reinforcement-learning-only approach with R1's pipeline using initial supervised examples, and published the training recipe and its reasoning evaluations. Daya Guo's research position at DeepSeek ended in April, according to his personal website.
- Chief Scientist
Demis Hassabis
5 ptsDeepMind co-founder, Nobel laureate; now Alphabet's Chief Scientist
Work and sources for Demis Hassabis
Shared the 2024 Nobel Prize in Chemistry with John Jumper for AlphaFold, the protein-structure prediction system. Co-founded DeepMind in 2010 and led it as chief executive until August 2026. Became chair of Google DeepMind and chief scientist of Alphabet in August 2026, after leaving the DeepMind chief executive role. Co-authored the original AlphaGo paper. The team combined neural networks, tree search and self-play to build a program that defeated professional Go player Fan Hui.
- Wildcard only
Dwarkesh Patel
2 ptsHosts the podcast where researchers, even Musk, share their timelines
Work and sources for Dwarkesh Patel
Published podcast interviews with Elon Musk and Dario Amodei in February 2026, and with Jensen Huang in April. Publishes long interviews and written research questions about AI, alongside conversations about science and history. His Questions about the Future of AI essay draws out questions about changing capabilities and deployment, giving readers a way to follow the uncertainties behind the interview discussions. Patel published a discussion with Noam Brown about agent groups, alignment, and recursive self-improvement.
- Infra Lead
Dylan Patel
2 ptsSemiAnalysis founder; tracks who gets which accelerators, and when
Work and sources for Dylan Patel
Co-authored InferenceX v2, a February 2026 SemiAnalysis report on its open benchmark for the hardware that runs AI models. Founded SemiAnalysis, a research publication covering the chips and datacenters behind AI. Its reporting follows semiconductor supply chains and the costs of training and running models, connecting hardware decisions with the software workloads they need to support. Substrate published a profile of Patel and SemiAnalysis's influence on AI infrastructure decisions.
- Safety Lead
Eliezer Yudkowsky
3 ptsFounded MIRI; has been warning you since 2008, now in Spanish too
Work and sources for Eliezer Yudkowsky
In March 2026, Ediciones Destino published the Spanish edition of If Anyone Builds It, Everyone Dies, his book with Nate Soares. Wrote about logical uncertainty: how an agent should reason about a mathematical statement when it has not yet worked out the answer. His MIRI discussions connect that problem with artificial agents that must make decisions despite limits on the time and computation available for reasoning.
- Wildcard only
Elon Musk
5 ptsCo-founded OpenAI, founded xAI, then folded it into SpaceX in 2026
Work and sources for Elon Musk
Co-founded OpenAI in 2015 and left its board in 2018; he founded xAI in 2023 and later sued OpenAI over its structure. SpaceX, which he leads, acquired xAI, the maker of Grok, in February 2026 and listed on Nasdaq in June. Wrote Tesla's 2006 master plan, setting out a staged approach to developing electric cars. The plan began with an expensive sports car and described using that path to reach more affordable vehicles. It connected those product choices with a longer-term move toward sustainable energy.
- Social Lead
Ethan Mollick
1 ptWharton professor; One Useful Thing; Co-Existence lands 20 October 2026
Work and sources for Ethan Mollick
Wrote Co-Existence, a follow-up to Co-Intelligence that Penguin lists for publication in October 2026. A Wharton School professor who writes the newsletter One Useful Thing and wrote Co-Intelligence, a book about working alongside AI. Teaches at the Wharton School and writes the One Useful Thing newsletter.
- Safety Lead
Evan Hubinger
2 ptsAnthropic alignment science lead; stress-tests models for deception
Work and sources for Evan Hubinger
Leads alignment stress-testing at Anthropic, including the Sleeper Agents study of models trained to behave deceptively. Co-authored Risks from Learned Optimization, examining whether training can produce a model that pursues an internal objective of its own. The paper distinguishes the objective used to train a model from the one its learned computations might pursue, and examines how those can come apart. Anthropic's new Claude constitution credited Hubinger with drafting language on inoculation prompting and suggesting revisions.
- Chief Scientist
Fei-Fei Li
4 ptsCreated ImageNet and World Labs, which AMD is buying for about $8.2B
Work and sources for Fei-Fei Li
Leads World Labs, which AMD agreed to buy in September 2026; she is to become AMD's chief scientist when the deal closes. Created ImageNet with collaborators, building an image dataset that researchers could use to train and compare visual recognition systems. Co-authored the ImageNet challenge report, documenting the benchmark's image classification and object detection tasks, evaluation methods and remaining difficulties. Li's World Labs raised $1 billion to develop spatial intelligence technology.
- Research Lead
François Chollet
4 ptsCreated Keras and ARC-AGI; co-founded Ndea; ARC-AGI-3 stumped the models
Work and sources for François Chollet
Co-founded Ndea in 2025, a lab working on program synthesis as a route to more general AI. Co-founded the ARC Prize Foundation, which released ARC-AGI-3, a benchmark of interactive reasoning tasks for AI agents, in March 2026. Created Keras, a deep-learning API that lets developers assemble, train and reuse neural-network components. Designed the ARC benchmark and wrote On the Measure of Intelligence, using small visual puzzles to examine how a system learns unfamiliar tasks from a few examples.
- Wildcard only
Gary Marcus
2 ptsFounded Geometric Intelligence, testified to the Senate, posts the doubts
Work and sources for Gary Marcus
Founded Geometric Intelligence, acquired by Uber in 2016, and testified to the US Senate on AI oversight in May 2023; he wrote Rebooting AI. Wrote in April 2026 that Claude Code was a major advance and evidence for neurosymbolic methods, which mix neural networks and symbolic rules. Wrote Deep Learning: A Critical Appraisal in 2018, setting out concerns about what deep learning methods could and could not do. The paper argues for combining those methods with other approaches. It separates success on particular benchmarks from the wider problem of building general intelligence.
- Chief Scientist
Geoffrey Hinton
5 ptsTuring and Nobel deep learning pioneer who lectures on living with AI
Work and sources for Geoffrey Hinton
Won the 2024 Nobel Prize in Physics with John Hopfield for foundational discoveries that enable machine learning with neural networks. Shared the 2018 Turing Award with Yoshua Bengio and Yann LeCun for deep learning. His earlier work includes backpropagation, Boltzmann machines and dropout. Received an honorary Doctor of Science degree from Harvard University in May 2026. Co-authored AlexNet with Alex Krizhevsky and Ilya Sutskever, documenting how a deep convolutional network trained on GPUs could recognize objects in ImageNet.
- Infra Lead
Georgi Gerganov
3 ptsCreated llama.cpp and joined Hugging Face, keeping it MIT-licensed
Work and sources for Georgi Gerganov
Joined Hugging Face with his ggml.ai team in February 2026; llama.cpp and ggml stay under the MIT license. Created llama.cpp, a C and C++ project for running language models locally instead of relying on a hosted service. The project provides quantized model formats and support for different hardware, giving developers practical controls over memory use and where inference runs.
- Infra Lead
Greg Brockman
4 ptsOpenAI co-founder and president; kickstarted OpenAI Five, the Dota 2 run
Work and sources for Greg Brockman
Is OpenAI's president and a co-founder; he was its first chief technology officer. As of August 2026, oversees OpenAI's product, enterprise, consumer and most core infrastructure work, after several executives left. Co-founded OpenAI and co-wrote its 2015 introduction with Ilya Sutskever, setting out the new research organization's plans. Co-authored the OpenAI Five report, documenting the distributed training system and sustained self-play used to build a Dota 2 agent.
- Social Lead
Guillermo Rauch
2 ptsVercel CEO; co-created Next.js; says the IPO is likely in 2027
Work and sources for Guillermo Rauch
Told Bloomberg Línea in April 2026 that Vercel will likely list its shares on a stock exchange in 2027. Co-created the Next.js web framework and, as chief executive of Vercel, leads v0, the company's app builder. CEO of Vercel in 2026; the company also makes the v0 app builder. Rauch said Vercel was ready for an initial public offering as AI-generated applications increased its revenue.
- Social Lead
Harrison Chase
1 ptCreated LangChain, then the company around it; Interrupt 2026 host
Work and sources for Harrison Chase
Was listed to speak on the future of agents at LangChain's Interrupt conference in San Francisco in May 2026. Created LangChain, an open-source framework for building applications on language models, then co-founded the company of the same name. CEO and co-founder of LangChain, listed as a 2026 AI Engineer speaker. Chase launched LangChain Labs, an applied research effort focused on continual learning for AI agents.
- Safety Lead
Helen Toner
3 ptsOn OpenAI's board through the 2023 crisis; now leads CSET at Georgetown
Work and sources for Helen Toner
Sat on OpenAI's board from 2021 until November 2023, through the week in which the board removed and then reinstated Sam Altman. Leads Georgetown's Center for Security and Emerging Technology (CSET); in January 2026 she co-authored its report When AI Builds AI. Co-authored CSET's Preparing for AI Agents report, examining systems that can take actions beyond a chat conversation. The report considers how those systems are developing and what risks they can amplify, then discusses places where technical safeguards or governance could intervene.
- Infra Lead
Horace He
1 ptFlexAttention co-developer; founding engineer at Thinking Machines Lab
Work and sources for Horace He
Was a founding engineer at Thinking Machines Lab after years on PyTorch's compiler team at Meta. Co-authored PyTorch's introduction to FlexAttention, which lets developers write their own attention patterns without starting from a custom GPU kernel. The interface compiles those patterns into efficient kernels. It gives model developers a way to experiment with attention while reusing the surrounding PyTorch tools.
- Post-training Lead
Hyung Won Chung
3 ptsInstruction tuning and reasoning research; Meta Superintelligence Labs
Work and sources for Hyung Won Chung
Worked on reasoning models at OpenAI before joining Meta Superintelligence Labs in 2025. Co-authored the Flan instruction-tuning study, testing how model size, task variety and examples containing reasoning steps affected the resulting models. The team released Flan-T5 checkpoints alongside the research, letting others run the instruction-tuned models and examine their behavior.
- Chief Scientist
Ilya Sutskever
5 ptsAlexNet co-author, OpenAI co-founder; runs Safe Superintelligence, quietly
Work and sources for Ilya Sutskever
Co-founded OpenAI in 2015 and was its chief scientist until 2024, co-leading work from GPT to GPT-4. Has led Safe Superintelligence as chief executive since July 2025, after co-founder Daniel Gross left. Co-authored AlexNet with Alex Krizhevsky and Geoffrey Hinton. Their 2012 paper described a convolutional network trained on GPUs to recognize objects in ImageNet. With Oriol Vinyals and Quoc Le, co-wrote the 2014 sequence-to-sequence paper on using recurrent networks for translation.
- Infra Lead
Ion Stoica
4 ptsCo-created Spark and Ray; Databricks, Anyscale and now Inferact for vLLM
Work and sources for Ion Stoica
Co-created Apache Spark and co-founded Databricks, then Anyscale around Ray; he is a Berkeley professor. Co-founded Inferact, a company for the open-source vLLM serving engine; Inferact launched with $150 million in seed funding in January 2026. Co-authored the Ray paper, describing a distributed framework for running parallel tasks and programs that keep their own state. The team tested that shared interface on reinforcement-learning workloads, where collecting experience, training models and running simulations place different demands on a cluster.
- Safety Lead
Jack Clark
3 ptsCo-founded Anthropic, writes Import AI, leads the Anthropic Institute
Work and sources for Jack Clark
Has led the Anthropic Institute since its launch in March 2026; it combines the Frontier Red Team, Societal Impacts and Economic Research teams. Co-authored the GPT-3 report, which studied whether a language model could learn tasks from examples included in its prompt. The report also examined limitations and societal implications of large text-generation systems. His Import AI newsletter explains research and policy developments for a public audience. Anthropic announced that Clark would lead the Anthropic Institute and become Head of Public Benefit.
- Evals Lead
Jacob Steinhardt
3 ptsForecasting and evaluation research; co-founded Transluce, CEO
Work and sources for Jacob Steinhardt
Is a Berkeley professor whose research includes forecasting AI progress and measuring model capabilities. Co-founded Transluce with Sarah Schwettmann; its January 2026 funding page says more than 25 organizations used its Docent tool for reviewing agent transcripts. Co-authored a mechanistic study of grokking in small Transformers trained on modular arithmetic. The team reconstructed the algorithm the models learned and used it to measure progress before strong test performance appeared, connecting an internal computation with the later change in generalization.
- Evals Lead
Jaime Sevilla
3 ptsCo-founded Epoch AI, which charts compute and training trends
Work and sources for Jaime Sevilla
Co-authored Compute Trends Across Three Eras of Machine Learning, collecting historical evidence about the resources used to train models. Co-founded Epoch AI, which began by organizing model and compute data. The work gives researchers a record for studying changes in training scale over time. Sevilla co-authored an Epoch AI analysis of whether AI companies can become profitable. Sevilla co-authored an Epoch AI analysis that compares growth in token demand with inference capacity.
- Chief Scientist
Jakub Pachocki
3 ptsOpenAI chief scientist who led GPT-4 and wrote An Alien Mind
Work and sources for Jakub Pachocki
Has been OpenAI's chief scientist since 2024, succeeding Ilya Sutskever. Published the essay An Alien Mind in September 2026, about AI alignment, monitoring and shared safety thresholds between labs. Led the development of GPT-4 and OpenAI Five, according to OpenAI's account of his research contributions. Co-authored the OpenAI Five report, documenting how a distributed reinforcement-learning system learned Dota 2 through self-play despite hidden information, long games and many possible actions.
- Post-training Lead
Jan Leike
4 ptsRLHF pioneer who led OpenAI's superalignment team, now at Anthropic
Work and sources for Jan Leike
Co-led OpenAI's superalignment team until 2024, then joined Anthropic to lead alignment research. His earlier work helped establish reinforcement learning from human feedback (RLHF). Co-authored Deep Reinforcement Learning from Human Preferences, studying how people's comparisons of behavior clips could train a reward model. Later co-authored InstructGPT, applying human demonstrations and preferences over text responses to the problem of training language models to follow instructions. Jan Leike co-authored Constitutional Classifiers++, a system that detects attempts to bypass AI safety controls.
- Chief Scientist
Jared Kaplan
3 ptsScaling laws co-author, Anthropic co-founder, Responsible Scaling Officer
Work and sources for Jared Kaplan
Co-founded Anthropic in 2021 and is its chief science officer; he also led its Responsible Scaling Policy, which ties safety measures to model capability levels. In an April 2026 Free Press interview, explained why Anthropic limited access to its Claude Mythos model. Co-authored Scaling Laws for Neural Language Models, measuring how model size, training data and compute relate to prediction loss and training-budget allocation. Also co-authored the GPT-3 paper, which tested whether examples in a prompt could specify tasks without updating the model's weights.
- Research Lead
Jason Wei
3 ptsChain-of-thought and emergent abilities papers; Meta Superintelligence Labs
Work and sources for Jason Wei
Moved from OpenAI to Meta Superintelligence Labs in 2025. Was first author of the chain-of-thought prompting study, testing examples that show intermediate reasoning steps on arithmetic, commonsense and symbolic tasks. Co-authored Emergent Abilities of Large Language Models, examining behaviors observed at larger model scales and whether smaller models' results predicted them. Jason Wei co-authored WearableQA, a benchmark that tests how AI models reason about data from wearable devices.
- Chief Scientist
Jeff Dean
4 ptsMapReduce, TensorFlow and TPU veteran who co-founded Discovery Loop
Work and sources for Jeff Dean
Left Google in August 2026 to co-found Discovery Loop, an AI-for-science startup, with Sanjay Ghemawat, Oriol Vinyals and Quoc Le. Co-created MapReduce with Sanjay Ghemawat. Their system let programmers process large datasets while its runtime handled how work was split, scheduled and recovered after machine failures. Co-authored the TensorFlow systems paper, describing software for running machine-learning computations across different devices and distributed machines.
- Social Lead
Jeremy Howard
1 ptCo-founded fast.ai and Answer.AI; created ULMFiT; still teaching for free
Work and sources for Jeremy Howard
Co-founded fast.ai, which teaches deep learning for free, and co-created ULMFiT, an early method for fine-tuning language models. Founding CEO of Answer.AI; keynote speaker at AI Engineer Melbourne 2026. Howard and Luke Versweyveld published an analysis of OpenAI's contract language on autonomous weapons.
- Research Lead
Jie Tang
3 ptsLed Tsinghua's GLM and ChatGLM model family; Zhipu AI co-founder
Work and sources for Jie Tang
Co-founded Zhipu AI, which released GLM-5.1, its new flagship language model, in April 2026. Co-authored GLM, developing a language-model pretraining approach with collaborators that learns by generating missing spans of text. The team tested whether this shared training method could support both language understanding and text generation, instead of designing a separate pretraining setup for each task. Jie Tang co-authored the GLM-5 technical report, which describes a model for long agent tasks and software engineering.
- Research Lead
Jim Fan
2 ptsCreated Voyager, led NVIDIA's GR00T robot project
Work and sources for Jim Fan
Co-leads GEAR, NVIDIA's robotics research group behind Isaac GR00T; in June 2026, NVIDIA announced an open GR00T humanoid robot design for researchers. Co-authored Voyager under the name Linxi Fan, helping build a Minecraft agent that uses a language model to write executable skills. The team designed a growing skill library and feedback loop so the agent could revise its code and reuse what it had learned on later tasks. Jim Fan co-authored RoboTTT, a robot policy that learns from longer sequences of observations and actions.
- Research Lead
Joelle Pineau
2 ptsRan Meta FAIR, open science champion; Cohere's Chief AI Officer since 2025
Work and sources for Joelle Pineau
Ran Meta's Fundamental AI Research lab (FAIR) as vice president of AI research until 2025, and championed reproducibility in machine learning. Became Cohere's chief AI officer in August 2025; in April 2026, she posted that Cohere would always remain headquartered in Canada. Co-authored the report on the NeurIPS 2019 reproducibility program, examining code submission, a community replication challenge and an experimental reporting checklist. The team documented what these efforts revealed and proposed ways for researchers and conferences to make machine-learning results easier to inspect and reproduce.
- Post-training Lead
John Schulman
5 ptsCo-developed PPO, InstructGPT co-author; Thinking Machines chief scientist
Work and sources for John Schulman
Co-founded OpenAI, co-developed TRPO and PPO, and helped lead ChatGPT's post-training; he is now chief scientist at Thinking Machines Lab. Co-developed Proximal Policy Optimization, a reinforcement-learning method that allows several training updates from the same batch of experience. Co-authored InstructGPT, combining human-written examples with rankings of model responses to train language models to follow instructions more closely. John Schulman co-authored an ICML paper that measures how much new information language models learn during training.
- Research Lead
Junyang Lin
3 ptsLed Alibaba's Qwen; left in March 2026 to found Pragmatik Labs
Work and sources for Junyang Lin
In August 2026, founded Pragmatik Labs, a Shanghai lab that researches AI agents, after he left Alibaba's Qwen team in March 2026. Led work on the Qwen open-model family and co-authored its technical report, covering pretrained language models and versions trained to follow instructions. The team's release included chat and code-specialized models, with published evaluations on language, mathematics and programming tasks. Junyang Lin announced his departure as head of Alibaba's Qwen AI division.
- Wildcard only
Kai-Fu Lee
3 ptsWrote AI Superpowers, founded 01.AI; eyeing a Hong Kong listing in 2027
Work and sources for Kai-Fu Lee
Wrote AI Superpowers (2018), founded Sinovation Ventures and 01.AI, and earlier led Google China and Microsoft Research Asia. Told Bloomberg in July 2026 that 01.AI plans a private funding round before a Hong Kong stock listing in 2027. Wrote his Carnegie Mellon doctoral thesis on continuous speech recognition with a large vocabulary. The research addressed speaker independence: recognizing connected speech without building a separate system around one person's voice. It focused on continuous utterances rather than asking a speaker to pause between isolated words.
- Chief Scientist
Kaiming He
3 ptsResNet lead author; MIT professor, part-time at Google DeepMind
Work and sources for Kaiming He
Is a professor at MIT, after years at Facebook AI Research, where he also co-created Momentum Contrast (MoCo) and masked autoencoders for self-supervised learning. Co-authored two 2026 technical reports: Generative Modeling via Drifting in February and ELF: Embedded Language Flows in May. Was first author of ResNet, introducing residual connections with collaborators to make very deep neural networks easier to train for image recognition. Co-authored Mask R-CNN, extending object detection so the system could also mark the pixels belonging to each detected object.
- Social Lead
Lee Robinson
1 ptLed developer education at Cursor; now trains models at SpaceX
Work and sources for Lee Robinson
Works on machine learning at SpaceX and helps train AI models, after SpaceX acquired Cursor in August 2026. Led developer education at Cursor; before that he led developer relations at Vercel. Gave a talk on recursive model improvement at AI Engineer in July 2026. Robinson announced that Cursor had joined SpaceX and that he would work on Grok's model behavior.
- Post-training Lead
Lewis Tunstall
2 ptsCo-wrote NLP with Transformers and open recipes at Hugging Face
Work and sources for Lewis Tunstall
Is a researcher at Hugging Face and co-wrote the book Natural Language Processing with Transformers. Co-created the original Open LLM Leaderboard, giving people a shared place to compare openly available language models on published evaluations. Co-authored Zephyr, training chat models with AI-generated preference rankings and releasing the models, code and recipes for studying direct preference optimization. Lewis Tunstall co-authored physics-intern, a framework that uses AI agents for theoretical physics research.
- Post-training Lead
Liam Fedus
3 ptsSwitch Transformer co-author; Periodic Labs sought a $7.5B valuation
Work and sources for Liam Fedus
In May 2026, Forbes reported that Periodic Labs, which he co-founded, was in talks to raise money at a $7.5 billion valuation. Co-authored Switch Transformers with Barret Zoph and Noam Shazeer, using selected expert networks so a large model does not activate every parameter for every token. Publishing as William Fedus, also co-authored the study of emergent abilities in language models, examining behaviors observed as model scale increased. Liam Fedus's Periodic Labs announced Neon, an AI model trained to analyze experimental results from its materials laboratories.
- Wildcard only
Liang Wenfeng
4 ptsFounded DeepSeek; V4 shipped open weights at 1.6T params in April 2026
Work and sources for Liang Wenfeng
Co-founded High-Flyer, the quantitative hedge fund whose GPU clusters funded DeepSeek, which he founded in 2023. DeepSeek, which he founded, released preview versions of DeepSeek V4 with open weights in April 2026; V4-Pro has 1.6 trillion parameters. Co-authored the DeepSeek-V2 report, where he is listed as Wenfeng Liang. The model combines a mixture-of-experts design with attention that uses less memory. The report studies how those choices can reduce the resources needed to run a language model.
- Social Lead
Logan Kilpatrick
2 ptsProduct lead for Google AI Studio and the Gemini API
Work and sources for Logan Kilpatrick
In May 2026, at Google I/O, Google AI Studio, the product he leads, added a way to build Android apps from prompts. Led developer relations at OpenAI before moving to Google, where he became a public voice for the Gemini API and Google AI Studio. In 2026 he posts Gemini launch news; a June 2026 podcast lists him as technical staff at Google DeepMind. Kilpatrick coauthored Google's I/O announcement of Gemini API updates, Antigravity tools, and native Android support in AI Studio.
- Post-training Lead
Long Ouyang
3 ptsFirst author of InstructGPT, the paper that made models follow instructions
Work and sources for Long Ouyang
Was first author of InstructGPT, documenting a training pipeline that starts with human-written examples and then learns from people's rankings of model responses. The study compared instruction-tuned models with larger base models, measuring human preferences and reporting remaining problems with truthfulness and harmful outputs.
- Research Lead
Lucas Beyer
3 ptsVision Transformer co-author; Google, OpenAI, now Meta Superintelligence
Work and sources for Lucas Beyer
Moved from Google DeepMind to OpenAI's Zurich office in 2024, then to Meta Superintelligence Labs in 2025. Co-authored the Vision Transformer paper, testing whether a Transformer could recognize images presented as sequences of small patches. With collaborators, developed SigLIP's training objective for matching images and text, and released models so others could use and evaluate the method.
- Post-training Lead
Luke Metz
2 ptsLearned optimizers; Thinking Machines co-founder, at Meta since August 2026
Work and sources for Luke Metz
Works on learned optimizers: neural networks trained to update other networks' parameters. Left Thinking Machines Lab and rejoined OpenAI in January 2026, with Barret Zoph and Sam Schoenholz. Co-authored research that trains neural networks to make optimizer updates, testing whether learning across many tasks could transfer to unseen problems. Was first author of VeLO, scaling this approach into an optimizer that reads gradients and produces parameter updates for another neural network.
- Social Lead
Lydia Hallie
1 ptTeaches Claude Code at Anthropic; shipped a free course on it in May 2026
Work and sources for Lydia Hallie
Published a free Claude Code course on master.dev in May 2026, covering skills, hooks, plugins and the Model Context Protocol. Teaches developers to use Claude Code at Anthropic, after developer-experience roles at Bun and Vercel. On the Claude Code team; taught a Claude Code workshop on 21 April 2026. Hallie published Anthropic's explanation of Claude usage limits and advice on reducing token use.
- Safety Lead
Max Tegmark
2 ptsMIT professor who runs the Future of Life Institute and its pause letter
Work and sources for Max Tegmark
Is an MIT physics professor and co-founded the Future of Life Institute, which published the 2023 letter calling for a pause on training systems beyond GPT-4. The Future of Life Institute, which he co-founded and chairs, released its Summer 2026 AI Safety Index of nine AI companies in July 2026. Co-authored Towards Guaranteed Safe AI, a proposal for using formal verification to support safety claims about AI systems. The framework separates the model of the world from the safety requirements and the process used to check them, so each assumption can be examined.
- Social Lead
Michael Truell
2 ptsCursor co-founder and CEO; SpaceX bought Cursor for $60B in Aug 2026
Work and sources for Michael Truell
Cursor, the company he co-founded, became part of SpaceX in August 2026, when SpaceX completed the acquisition. Co-founded Anysphere, the company behind the Cursor code editor, and serves as its chief executive. Profiled by Fortune in April 2026 as Cursor's chief executive.
- Evals Lead
Mike Knoop
3 ptsCo-founded Zapier, ARC Prize and Ndea; ARC-AGI-3 launched in 2026
Work and sources for Mike Knoop
Co-founded Ndea with François Chollet in 2025 to pursue program synthesis. In March 2026, the ARC Prize Foundation he co-founded launched ARC-AGI-3, an interactive benchmark of turn-based environments with no instructions. Co-founded Zapier, a platform for automating work between software applications. Co-founded the ARC Prize Foundation, which supports research through reasoning benchmarks and competitions. Its stated focus is how systems adapt to unfamiliar problems, rather than how well they reproduce solutions to familiar tasks.
- Safety Lead
Miles Brundage
3 ptsAI policy and deployment readiness; founded AVERI for independent audits
Work and sources for Miles Brundage
Was OpenAI's head of policy research and then senior adviser for AGI readiness until October 2024. Launched the AI Verification and Evaluation Research Institute (AVERI) in January 2026, a nonprofit that argues for independent audits of frontier AI models. Co-authored Toward Trustworthy AI Development, a report on making claims about AI systems easier to verify. The report proposes technical and institutional checks for developers' claims, asking what evidence would let other people assess safety rather than rely on the developer's assurances alone.
- Wildcard only
Mira Murati
3 ptsEx-OpenAI CTO; Thinking Machines Lab previewed its first models in May 2026
Work and sources for Mira Murati
Was OpenAI's chief technology officer from 2022 to 2024, overseeing ChatGPT, DALL-E and the GPT-4 launches. Thinking Machines Lab, which she co-founded and leads, previewed its first in-house models, called interaction models, in May 2026. Co-founded Thinking Machines Lab, whose published mission includes making AI systems easier for people to customize. The company's founding description centers collaboration between people and machines, and explains its research and product work in terms of giving users more ways to guide a system.
- Post-training Lead
Nathan Lambert
1 ptWrote the RLHF Book; led open post-training at Ai2 until June 2026
Work and sources for Nathan Lambert
Has advised the companies Mercor and Arcee AI on research and open-source strategy since June 2026. Wrote the openly available RLHF Book, explaining preference data, reward models and reinforcement learning for language-model post-training. Was first author of the Tulu 3 report, releasing post-training methods with collaborators along with models, training data and code for others to inspect and reuse. Nathan Lambert announced Trillium Labs, a nonprofit that develops open AI post-training research and tools.
- Safety Lead
Neel Nanda
3 ptsLeads mechanistic interpretability at Google DeepMind; TransformerLens
Work and sources for Neel Nanda
Leads the mechanistic interpretability team at Google DeepMind, studying how models compute internally. Was listed as a mentor for the summer 2026 cohort of MATS, a research training program for AI alignment. Created TransformerLens, a library for inspecting the internal computations of Transformer language models. Co-authored a study that reconstructed how small Transformers learned modular arithmetic. The team used that mechanism to track learning before the models' test accuracy showed generalization.
- Research Lead
Noam Brown
4 ptsSuperhuman poker and Diplomacy AI; reasoning model research at OpenAI
Work and sources for Noam Brown
Works on reasoning models at OpenAI, where he was part of the team behind the o1 series. Created the poker systems Libratus and Pluribus with his Carnegie Mellon advisor, studying decisions in games where players cannot see each other's cards. Helped develop CICERO with teammates, combining strategic planning with natural-language negotiation to play the board game Diplomacy. Noam Brown said GPT-5.6 performed AI research better than most human interns.
- Research Lead
Noam Shazeer
4 ptsTransformer co-author, founded Character.AI, co-led Gemini after returning
Work and sources for Noam Shazeer
Co-founded Character.AI in 2021, then returned to Google in 2024 to co-lead Gemini. Left Google for OpenAI in June 2026, according to Agence France-Presse reporting on Alphabet's leadership changes. Co-authored the Transformer paper, replacing recurrent sequence processing with attention in a model demonstrated on machine translation. Was first author of the sparsely gated mixture-of-experts paper, developing a layer that selects a small number of expert networks for each input instead of using every expert.
- Evals Lead
Ofir Press
2 ptsCo-created SWE-bench, the coding-agent exam, and SWE-agent
Work and sources for Ofir Press
Co-created SWE-agent, which lets a language model work inside a repository, and ALiBi, a positional method that lets models read longer inputs. Co-authored SWE-bench, a benchmark built around real issues in existing software repositories. The tasks ask a model to understand an issue and the code around it, then propose a change that is checked with tests. The benchmark uses actual repository maintenance problems. Press and his co-authors released a revised SWE-fficiency paper on optimizing real software repositories.
- Social Lead
Omar Sanseviero
1 ptLead for AI developer experience at DeepMind; Gemma 4 landed in March 2026
Work and sources for Omar Sanseviero
Works on Gemma launches at Google DeepMind; Google released the first Gemma 4 open models in March 2026. Works on developer experience for the Gemma open models and AI Studio at Google DeepMind, after years at Hugging Face. Leads developer experience for AI Studio, Gemini API and Gemma; presented Gemma 4 in 2026. Sanseviero and Ian Ballantyne published Google's developer guide for DiffusionGemma.
- Research Lead
Oriol Vinyals
4 ptsCo-wrote seq2seq, led AlphaStar, co-led Gemini, now at Discovery Loop
Work and sources for Oriol Vinyals
Led AlphaStar, the agent that reached Grandmaster level in StarCraft II, and later co-led Gemini at Google DeepMind. Left Google DeepMind in August 2026 to co-found Discovery Loop with Jeff Dean, Sanjay Ghemawat and Quoc Le. Co-wrote the sequence-to-sequence paper with Ilya Sutskever and Quoc Le, using recurrent networks to translate an input sentence into an output sentence. Also co-authored the AlphaFold paper, contributing to the team's published system for predicting protein structures from amino-acid sequences.
- Social Lead
Paige Bailey
1 ptDevRel engineering lead at Google DeepMind; demos Gemini and Gemma live
Work and sources for Paige Bailey
Was the guest in The wonder-driven builder, a Posit video interview published in June 2026. Leads AI developer relations at Google DeepMind and demonstrates the Gemini and Gemma models for developers. Gave Google DeepMind model and tool talks through September 2026. Bailey explained Google's full-stack AI development approach in a video published on Google's blog.
- Safety Lead
Paul Christiano
4 ptsPioneered RLHF, advises CAISI and sits on the OpenAI Foundation board
Work and sources for Paul Christiano
Founded the Alignment Research Center in 2021, then served as head of AI safety at the US AI Safety Institute, which later became the Center for AI Standards and Innovation (CAISI). Joined the OpenAI Foundation board and its Safety and Security Committee in September 2026. Co-authored the 2017 paper Deep Reinforcement Learning from Human Preferences, an early study of training agents from people's feedback. The team asked people to compare short behavior clips. A learned reward model then used those comparisons to guide agents without a reward function written for each task.
- Evals Lead
Percy Liang
4 ptsCo-created SQuAD and HELM; Marin open lab; Chief Scientist at Simile
Work and sources for Percy Liang
Is a Stanford professor who co-created SQuAD, the reading-comprehension benchmark, and directs the Center for Research on Foundation Models. Is co-founder and chief scientist of Simile, a startup that simulates human behavior; it raised $100 million in February 2026. Co-authored HELM, an evaluation framework for comparing language models across different scenarios. The team measured accuracy alongside robustness, fairness and other properties. It made the evaluation methods explicit so readers could see which parts of model behavior a result did, and did not, measure.
- Social Lead
Philipp Schmid
1 ptDeepMind developer relations; Gemini guides on his blog all year
Work and sources for Philipp Schmid
Published a developer guide to Gemini Managed Agents on his blog in May 2026. Writes practical guides to building with Gemini as a Google DeepMind developer relations engineer; he previously worked at Hugging Face. Developer relations engineer at Google DeepMind; spoke at AI Engineer World's Fair 2026. Schmid and Mark McDonald released a Gemini API developer skill and published its evaluation results.
- Social Lead
Poteto (Lauren Tan)
1 ptReact Compiler core team; joined Cursor in March 2026, now under SpaceX
Work and sources for Poteto (Lauren Tan)
A member of the React Compiler core team, the group behind the optimizing compiler for React. Posted on X in March 2026 that she joined Cursor, which became part of SpaceX in August 2026. Tan published a recorded talk about verification and code structure for AI agents that submit production changes.
- Research Lead
Quoc Le
3 ptsseq2seq and AutoML; left Google after 15 years to co-found Discovery Loop
Work and sources for Quoc Le
Co-created neural architecture search, the AutoML approach of training a controller to design networks. Left Google in August 2026 to co-found Discovery Loop with Jeff Dean, Sanjay Ghemawat and Oriol Vinyals. Co-wrote the sequence-to-sequence paper with Ilya Sutskever and Oriol Vinyals, using recurrent networks to learn translation from paired sentences. With Barret Zoph, developed neural architecture search, training a controller to propose network designs and learn from their measured performance.
- Chief Scientist
Richard Sutton
4 ptsTuring-winning RL pioneer who wrote The Bitter Lesson, now at Oak Lab
Work and sources for Richard Sutton
Shared the 2024 Turing Award with Andrew Barto for developing the conceptual and algorithmic foundations of reinforcement learning. Wrote The Bitter Lesson, the 2019 essay arguing that general methods that use more compute beat hand-built knowledge. Left Keen Technologies and co-founded Oak Lab with former student Khurram Javed in June 2026, to build agents that learn from experience. Co-wrote Reinforcement Learning: An Introduction with Andrew Barto, explaining how an agent can learn from rewards while interacting with an uncertain environment.
- Evals Lead
Rishi Bommasani
2 ptsCo-authored the Foundation Model Transparency Index
Work and sources for Rishi Bommasani
Co-authored the Stanford report that coined the term "foundation models" and leads the Foundation Model Transparency Index. Co-authored HELM, an evaluation project that publishes its methods and model outputs for other researchers to examine. The work compares models across multiple scenarios and measures, making it possible to inspect differences in behavior that a single headline benchmark result would leave out. Bommasani co-authored a paper that identifies three limitations of AI data transparency policy.
- Safety Lead
Rohin Shah
2 ptsHeads AGI safety at DeepMind; wrote the Alignment Newsletter for years
Work and sources for Rohin Shah
Wrote the Alignment Newsletter, a weekly digest of alignment research, for several years; he leads AGI safety work at Google DeepMind. Discussed Google DeepMind's work on the safety of artificial general intelligence on an 80,000 Hours podcast episode released in June 2026. Co-authored research on goal misgeneralization, where an agent learns a goal that works during training but causes problems in a new situation. The work examines how this can happen even when the training reward is correct, separating it from mistakes in writing the reward itself.
- Social Lead
Romain Huet
1 ptOpenAI head of developer experience; demos Codex live on stage, on purpose
Work and sources for Romain Huet
Gave a live Codex demo at OpenAI DevDay in San Francisco in September 2026. Heads developer experience at OpenAI and demonstrates the API and Codex in live stage demos. Leads the OpenAI team behind Codex and the API developer experience (ThursdAI, July 2026). Huet demonstrated live editing of a 3D venue and a ticketing app with OpenAI's Ultrafast service at DevDay.
- Post-training Lead
Ross Taylor
2 ptsLed Galactica and runs General Reasoning, an open reasoning startup
Work and sources for Ross Taylor
Leads General Reasoning as chief executive; in March 2026, the company launched OpenReward, an open platform of training environments for AI agents. Led the Galactica project and co-authored its paper, building a language model trained on scientific and technical material. The team evaluated scientific question answering, mathematical writing and citation generation, publishing experiments that explored what a language model could and could not do with scientific text. Ross Taylor's General Reasoning released KellyBench, a test of AI decision-making over a simulated Premier League betting season.
- Wildcard only
Sam Altman
5 ptsCo-founded OpenAI, ran Y Combinator; closed a $122B round in March 2026
Work and sources for Sam Altman
Was president of Y Combinator from 2014 to 2019 before becoming OpenAI's chief executive. OpenAI, where he is chief executive, closed a $122 billion funding round at an $852 billion valuation in March 2026. Wrote the Startup Playbook, collecting advice for people building a company at an early stage. It covers choosing an idea and a team, building a product, and the work of execution. Hiring and fundraising are treated as practical parts of getting the company operating.
- Evals Lead
Sayash Kapoor
2 ptsAI Snake Oil co-author and agent-benchmark critic, joining Berkeley
Work and sources for Sayash Kapoor
Co-wrote AI Agents That Matter, a critique of how agent benchmarks are built and reported. In July 2026, UC Berkeley's School of Information announced that he will join it as an assistant professor in July 2027. Co-wrote AI Snake Oil, examining the different technologies grouped under the AI label and the claims made about them. The book and accompanying newsletter ask where evidence supports an application and where promotion outruns it, helping readers separate a useful demonstration from a claim that has not been tested.
- Post-training Lead
Sebastian Raschka
2 ptsTeaches LLM training from scratch; 2026 book: Build a Reasoning Model
Work and sources for Sebastian Raschka
Published Build a Reasoning Model (From Scratch) with Manning in June 2026. Wrote Build a Large Language Model (From Scratch), teaching readers to implement, pretrain and fine-tune a small GPT-style model in PyTorch. Published the book's code and notebooks in LLMs-from-scratch, letting readers run each stage and inspect the training steps themselves.
- Evals Lead
Sébastien Bubeck
3 ptsSparks of AGI paper; Phi small models at Microsoft; now research at OpenAI
Work and sources for Sébastien Bubeck
Led Microsoft's Phi small-model series before joining OpenAI in 2024. Works on AI for mathematics at OpenAI; in September 2026, OpenAI said a model proved finite-time blowup for a forced Navier-Stokes variant. Co-authored Sparks of Artificial General Intelligence, an exploratory study of an early GPT-4 model on tasks including mathematics and coding. The paper records capabilities and limitations, then argues for an interpretation of those results. Its claims are the authors' assessment, not a settled definition of intelligence.
- Chief Scientist
Shane Legg
2 ptsDeepMind co-founder, Chief AGI Scientist; helped popularize the term AGI
Work and sources for Shane Legg
Spoke at a University of Tokyo fireside chat in March 2026 about DeepMind's founding and the path to AGI. With Marcus Hutter, proposed a mathematical definition of machine intelligence based on how an agent performs across a broad range of environments. Co-authored Deep Reinforcement Learning from Human Preferences, studying how people's comparisons of short behavior clips could guide an agent's learning. Legg became managing editor of the new DeepMind Institute, which publishes research on artificial general intelligence and its effects on society.
- Wildcard only
Simon Willison
1 ptBlogs about AI as it happens and ships the LLM command-line tool
Work and sources for Simon Willison
Released version 0.32 of his LLM command-line tool in August 2026, with support for reasoning traces and server-side tools. Created Datasette, a tool for exploring and publishing data, and co-created the Django web framework. Publishes programming experiments and explanations on his own site. The writing often accompanies reusable tools, giving readers both an account of the experiment and software they can try. Willison published a browsable archive of the CIA's 2020 World Factbook after the CIA removed its website.
- Infra Lead
Song Han
3 ptsMIT professor who compresses and quantizes models, also at NVIDIA
Work and sources for Song Han
Discussed efficient AI on the MIT podcast Curiosity Unbounded in March 2026. Co-authored Deep Compression, a method for storing neural networks with fewer bits by pruning connections and quantizing their weights. The team's experiments compressed image recognition networks while preserving measured accuracy, testing whether trained models could fit into less memory. Han's lab published DeltaQuant, a four-bit quantization method for video diffusion models, at CVPR 2026.
- Infra Lead
Soumith Chintala
3 ptsPyTorch co-creator; CTO of Thinking Machines Lab since Jan 2026
Work and sources for Soumith Chintala
Became chief technology officer of Thinking Machines Lab in January 2026. Co-created PyTorch, giving researchers a Python interface for building neural networks with automatic differentiation and accelerated computation. Co-authored the PyTorch library paper, explaining the design choices behind its imperative programming style and how the implementation supports both experimentation and performance. Thinking Machines Lab appointed Chintala as chief technology officer after Barret Zoph left the role.
- Safety Lead
Stuart Russell
2 ptsWrote THE AI textbook; runs CHAI at Berkeley; human-compatible AI
Work and sources for Stuart Russell
Co-wrote Artificial Intelligence: A Modern Approach, the standard AI textbook, and founded Berkeley's Center for Human-Compatible AI. He also wrote Human Compatible. Led IASEAI'26, the second conference of the International Association for Safe and Ethical AI, in Paris in February 2026. Co-authored cooperative inverse reinforcement learning, a framework for a person and a robot learning to work toward the person's objectives. The robot begins uncertain about those objectives. Human actions, including teaching, give it information about what it should try to achieve.
- Evals Lead
Summer Yue
2 ptsAlignment director at Meta Superintelligence Labs; built Scale's SEAL evals
Work and sources for Summer Yue
Led safety, evaluations and alignment research at Scale AI, including its SEAL leaderboards, before joining Meta Superintelligence Labs in 2025. Co-authored MultiChallenge, a benchmark that tests how well models follow a conversation over multiple turns. The tasks spread requirements through a dialogue, checking whether a model can keep track of earlier instructions and relevant context rather than treating each new message in isolation. Yue publicly reported that her OpenClaw agent deleted emails despite instructions to wait for approval.
- Infra Lead
Susan Zhang
2 ptsLed Meta's OPT-175B training run and shared its logbook
Work and sources for Susan Zhang
Led the OPT-175B training run at Meta AI, documenting the run's problems in a public logbook. Co-authored OPT, a family of language models released for research. The paper credits her with initial planning and work on training infrastructure and monitoring OPT-175B. The team released model code alongside a training logbook. That record documents infrastructure problems encountered during training, giving researchers more context than a finished model alone.
- Wildcard only
swyx (Shawn Wang)
1 ptHosts Latent Space and coined the AI Engineer title
Work and sources for swyx (Shawn Wang)
AI Engineer, which he co-founded and leads, held the AI Engineer World's Fair from 29 June to 2 July 2026. Wrote The Rise of the AI Engineer and Learn in Public, about building with AI and sharing what you learn. Through Latent Space and AI Engineer events, he documents how developers turn model capabilities into working software, with an emphasis on the people doing that work. Wang announced that Latent Space would become a podcast network and combine its subscriptions with AINews.
- Post-training Lead
Teknium
2 ptsCo-founded Nous Research, which makes the MIT-licensed Hermes Agent
Work and sources for Teknium
Co-founded Nous Research, maker of the open-source Hermes Agent; he signed the agent's v0.20.0 GitHub release in August 2026. Co-authored the Hermes 3 technical report under the name Ryan Teknium, documenting the team's work on instruction-tuned language models. The report describes training for conversation and tool use, with public benchmark results that let readers examine the behavior of the released models. Teknium helped convert Hermes Memory Wiki into a standalone Hermes Agent plugin.
- Social Lead
Thariq Shihipar
1 ptClaude Code engineer at Anthropic; posts the prompts he actually uses
Work and sources for Thariq Shihipar
Answered audience questions by video call at the Claude Code for Everyone community event in Barcelona in March 2026. Works on the Claude Code team at Anthropic and publishes the prompts and agent-harness notes behind his own workflows. His May 2026 implementation-notes prompt circulated widely. Shihipar announced AGENTS.md support in Claude Code version 2.1.277 when a folder has no CLAUDE.md.
- Social Lead
Theo Browne
1 ptDeveloper YouTuber; made T3 Chat, the T3 Stack and now T3 Code
Work and sources for Theo Browne
His company's open-source app T3 Code, which controls coding agents such as Claude Code, appeared on GitHub Trending in October 2026. Makes developer videos on YouTube and created T3 Chat and the T3 Stack, an open-source starter for TypeScript web apps. Publishes AI developer-tool videos on t3.gg through September 2026.
- Infra Lead
Tianqi Chen
4 ptsCreated XGBoost and TVM; MLC for on-device inference; CMU professor
Work and sources for Tianqi Chen
Is a professor at Carnegie Mellon and leads MLC, the machine-learning compilation project that runs models on phones and browsers. Received a National Science Foundation CAREER award in June 2026 for his machine-learning systems research. Developed XGBoost with Carlos Guestrin, combining boosted decision trees with systems work on sparse data, memory access and distributed processing. Co-authored TVM, an open compiler that optimizes neural-network computations for different hardware, including CPUs, GPUs and specialized accelerators.
- Social Lead
Tibo (Thibault Sottiaux)
2 ptsLed Codex, now runs OpenAI's core product team; posts updates as tibo
Work and sources for Tibo (Thibault Sottiaux)
Has led OpenAI's combined core product team for ChatGPT, Codex and the developer API since May 2026. Led work on Codex, OpenAI's coding agent, and posts its release updates under the name tibo. Spoke at an OpenAI Forum event in May 2026 on why Codex matters beyond code. Sottiaux discussed OpenAI's ChatGPT Work platform in a TechCrunch interview as OpenAI's head of product.
- Infra Lead
Tim Dettmers
2 ptsCreated QLoRA and bitsandbytes; CMU and Ai2; open coding agents in 2026
Work and sources for Tim Dettmers
Worked on SERA, the first model in Ai2's Open Coding Agents family, which Ai2 released in January 2026. Co-authored QLoRA, a method for tuning large language models while keeping most model weights frozen in a compact numerical format. Created bitsandbytes, a library of operations that reduce memory use. Together with QLoRA, this work addresses fitting large models onto the hardware available for training and inference.
- Chief Scientist
Tom Brown
3 ptsFirst author of GPT-3; Anthropic co-founder and Chief Compute Officer
Work and sources for Tom Brown
Leads Anthropic's compute team as Chief Compute Officer; Monzo co-founder Tom Blomfield joined that team in July 2026. Was first author of the GPT-3 paper, testing whether a language model could perform new tasks from examples in its prompt without further weight updates. Co-authored Deep Reinforcement Learning from Human Preferences, using people's comparisons of behavior clips to help specify what an agent should learn. Tom Brown described Anthropic's new agreement with Micron to secure memory and storage supply and improve AI infrastructure.
- Research Lead
Tri Dao
4 ptsFlashAttention and Mamba co-creator; Princeton and Together AI
Work and sources for Tri Dao
Is chief scientist at Together AI and a professor at Princeton. In March 2026, co-authored FlashAttention-4, which adapts the FlashAttention method to NVIDIA Blackwell GPUs. Co-created FlashAttention, reducing transfers between GPU memory levels while still computing exact attention. The work targets a bottleneck in training on long sequences. With Albert Gu, wrote Mamba, designing selective state-space models that retain or forget information according to the input.
- Safety Lead
Victoria Krakovna
2 ptsSpecification gaming research; DeepMind AGI safety and alignment
Work and sources for Victoria Krakovna
Is a research scientist on Google DeepMind's AGI safety team and co-founded the Future of Life Institute. Co-authored DeepMind's explanation and collection of specification-gaming examples, where agents find ways to satisfy a reward while missing the intended task. The examples make the failure concrete: a formally successful behavior can still differ from the outcome the designer wanted. Krakovna co-authored a paper that tested Gemini models for scheming through coding tasks in Google's alignment research codebases.
- Wildcard only
Wang Xiaochuan
2 ptsFounded Sogou, then Baichuan; says an IPO is a year or two of prep away
Work and sources for Wang Xiaochuan
Founded the search engine Sogou in 2004 and ran it until 2021, then founded Baichuan in 2023. Said in January 2026 that Baichuan will list its shares after one to two years of preparation. Co-authored the Baichuan 2 report, where he is listed as Xiaochuan Wang. The report describes multilingual base and chat models, how they were trained, and evaluations on language tasks and specialized subjects. It provides technical context for the models released under the Baichuan name.
- Evals Lead
Wei-Lin Chiang
2 ptsCo-created Chatbot Arena and Vicuna; LMArena raised $150M in Jan 2026
Work and sources for Wei-Lin Chiang
Co-founded LMArena, which raised a $150 million Series A at a $1.7 billion valuation in January 2026. Co-authored Chatbot Arena, which asks people to compare responses from pairs of models. The paper examines how to turn those preferences into model comparisons, including the statistical methods and limits of crowdsourced judgments. The resulting rankings describe the collected preferences, not every aspect of model quality.
- Infra Lead
Woosuk Kwon
2 ptsCo-created vLLM, PagedAttention first author; Inferact CTO, $150M seed
Work and sources for Woosuk Kwon
Co-founded Inferact and wrote its launch post; the vLLM company launched with $150 million in seed funding in January 2026. Co-authored PagedAttention and co-created vLLM, work aimed at using GPU memory more efficiently when serving language models. PagedAttention stores attention history in blocks. The design reduces wasted memory so a serving system can handle more requests with the same hardware. Kwon helped launch Inferact, which announced $150 million in funding to develop the vLLM inference engine.
- Wildcard only
Yan Junjie
2 ptsFounded MiniMax; listed it in Hong Kong in Jan 2026, then shipped M2.7
Work and sources for Yan Junjie
MiniMax, which he founded and leads, listed on the Hong Kong Stock Exchange in January 2026 and released its M2.7 model in March. Co-authored a paper on detecting faces from different viewing angles with aggregate channel features. The method combines visual signals such as image gradients and tests how efficiently they can locate faces, connecting his computer-vision research with a specific detection problem. Yan's MiniMax raised $618 million through its Hong Kong initial public offering.
- Infra Lead
Yangqing Jia
3 ptsCreated Caffe, co-created ONNX, founded Lepton; now building Intent Lab
Work and sources for Yangqing Jia
Co-created ONNX, the open model-exchange format, and founded Lepton AI, which NVIDIA acquired in 2025. Announced a new company, Intent Lab, in July 2026, with Fleet, an agent team that turns plain-language requirements into software. Created Caffe and co-authored its paper, giving researchers a framework for training convolutional networks and putting them into use. Caffe combined a C++ core with Python and MATLAB interfaces. Its reference models gave other researchers a starting point for reproducing experiments.
- Chief Scientist
Yann LeCun
5 ptsConvnets pioneer, Turing Award; left Meta to build world models at AMI Labs
Work and sources for Yann LeCun
Shared the 2018 Turing Award with Geoffrey Hinton and Yoshua Bengio for deep learning; his LeNet work made convolutional networks practical. Was Meta's chief AI scientist and founded FAIR, its AI research lab, before leaving at the end of 2025. Is executive chairman of AMI Labs, which raised about $1.03 billion in March 2026 to build world models. Developed convolutional networks with collaborators for recognizing handwritten characters. His early work applied learned visual features to reading tasks instead of relying entirely on hand-designed features.
- Chief Scientist
Yejin Choi
2 ptsCommonsense reasoning pioneer, MacArthur fellow, now at Stanford HAI
Work and sources for Yejin Choi
Received a MacArthur Fellowship in 2022 for her work on commonsense reasoning and language; she is now a professor at Stanford. In January 2026, the Stanford Daily described her work on small language models and pluralistic alignment. Co-authored ATOMIC, building a commonsense resource around everyday events and the intentions, reactions and consequences people associate with them. With the ATOMIC team, turned these relationships into a dataset for studying inferences such as why someone acted and how others might feel afterward.
- Chief Scientist
Yoshua Bengio
4 ptsTuring Award, Mila founder; chairs the International AI Safety Report
Work and sources for Yoshua Bengio
Shared the 2018 Turing Award with Geoffrey Hinton and Yann LeCun for deep learning. Founded Mila, the Quebec AI institute in Montreal, and is now its scientific advisor. Chaired the second International AI Safety Report, written by more than 100 experts and released in February 2026. Co-authored A Neural Probabilistic Language Model, which learned word representations together with a model for predicting the next word in a sequence.
- Post-training Lead
Zhihong Shao
2 ptsFirst author of DeepSeekMath, which introduced GRPO
Work and sources for Zhihong Shao
Co-authored the DeepSeek-V4 preview technical report, dated April 2026. Was first author of DeepSeekMath, combining math-focused pretraining, instruction tuning and reinforcement learning in a language model. The team introduced Group Relative Policy Optimization, or GRPO, as part of that work and published experiments examining its use for mathematical reasoning. Shao co-authored the DeepSeek-V4.1-Flash technical report on compressing the key-value cache.
- Research Lead
Zhilin Yang
3 ptsTransformer-XL and XLNet co-author, founded Moonshot AI (Kimi)
Work and sources for Zhilin Yang
Leads Moonshot AI, which launched Kimi K3, its newest open-weight language model, in July 2026. Was first author of XLNet, developing a pretraining method with collaborators that learns from different word-prediction orders to use context on both sides. Co-authored GLM, exploring a different training objective in which a language model generates missing spans of text.