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S&P 500
7,569
NDX 100
28,180
VIX
20.82
US 10Y
4.95%
DXY
101.86
WTI
$96.60
Gold
$3,932
USD/KRW
1,344
SOX
10,599
Trump Approve
35.4%
AI LEADERS · MOVES & PAPERS

AI Leaders: Moves & Papers — latest job changes and research

LAST UPDATE 2026-11-03 08:00 KST PEOPLE 77 · PAPERS 76 · MOVES 0
Where the key people at AI labs, big tech and universities move, and what they publish, are early signals of where technology and competitive advantage are heading. Career moves and papers are collected automatically every day, checked by the editor and summarized in plain language with links to the originals.

Highlights

Paper2026-10-30
Development of a Hierarchical Model Surpassing Limitations in Complex Robot Control
Yann LeCun
Paper2026-10-29
A technology for jointly training robot data using diverse controllers
Chelsea Finn
Paper2026-10-29
Proving that artificial intelligence models learn the same world even across different information modalities
Yann LeCun
Paper2026-10-29
Improving Psychiatric Intake AI Performance through Simulated Environment Training
Mark Chen
People All Frontier labsNew labsAcademiaAI hardwareKoreaChinaExecutives
Field All HBM & memoryGPUs & AI acceleratorsInference efficiencyTraining & scalingAgents & codingRobotics & physical AIWorld models & multimodalData centers & powerAI for scienceSafety & policy

Career moves · Frontier labs

Recent career records (from Wikidata; may include unverified information)

Papers · Frontier labs

Paper2026-10-29 · arXiv (Cornell University)

Improving Psychiatric Intake AI Performance through Simulated Environment Training

Training language models in simulation for psychiatric intake
Mark Chen +55 more
What
Researchers demonstrated that language models trained through reinforcement learning in a simulated environment significantly improve structured text-based psychiatric initial intake capabilities.
Key result
In evaluations involving psychiatrists, the trained artificial intelligence outperformed the base model across multiple metrics, including clinical score, patient-centered communication, and differential diagnosis appropriateness.
Why it matters
This shows that simulated environments can serve as a useful testbed for training conversational artificial intelligence in psychiatry and verifying its safety.AI summary
Agents & coding Safety & policy
Full summary Read paper ↗ PDF ↗
Paper2026-10-27 · SuperIntelligence - Robotics - Safety & Alignment

The US White House and AI companies have signed an agreement on superintelligence AI governance.

White House Accord on Super Intelligence
Dario Amodei +6 more
What
The US President and CEOs of major AI companies signed the White House Superintelligence Accord, a four-stage governance commitment for the development of superintelligent artificial intelligence.
Key result
Participating companies agreed to implement strong internal controls, operate independent internal verification teams, undergo external audits, and ensure oversight through an independent board committee.
Why it matters
This serves as a catalyst for advanced artificial intelligence developers to introduce systematic internal controls and external audits for security and safety.AI summary
Safety & policy
Full summary Read paper ↗ PDF ↗
Paper2026-10-22 · Science

Artificial intelligence achieved higher accuracy than doctors in iterative diagnosis processes

Sequential diagnosis with language models
Mustafa Suleyman +14 more
What
Researchers developed a large language model based AI system and an interactive evaluation benchmark that mimic the clinical diagnosis process.
Key result
This AI system achieved accuracies of 83.2 percent and 80.3 percent respectively in medical case evaluations, showing superior performance compared to general practitioners.
Why it matters
Although in a controlled environment, this demonstrates the potential for language models to support more accurate and cost-effective diagnoses.AI summary
AI for science
Full summary Read paper ↗ PDF ↗
Paper2026-10-22 · BMJ digital health & AI

Definition and Evaluation Criteria for Medical Superintelligence in Artificial Intelligence Proposed

Medical superintelligence: foundations and aspirations
Mustafa Suleyman +13 more
What
Discussed how artificial intelligence technology contributes to more effective medical services and introduced six interdependent dimensions of medical intelligence.
Key result
Revealed that improving healthcare quality requires not only outstanding performance in individual tasks, but also systems that collaborate with humans to perform integrated medical reasoning throughout the entire care process.
Why it matters
Proposed a direction to combine these capabilities with existing medical intelligence to enhance the quality and accessibility of medical services.AI summary
AI for science
Full summary Read paper ↗ PDF ↗
Paper2026-10-20 · Zenodo (CERN European Organization for Nuclear Research)

Release of Structural Data Used in AI-Based Enzyme Design Paper

Supplementary Data 2. Designed and predicted structures of all designs
Demis Hassabis · Pushmeet Kohli · Rob Fergus +35 more
What
The research team has released all structural data files covered in the paper on AI-driven enzyme design for novel chemical reactions.
Key result
The compressed file contains structures generated by the AI model, redesigned structures, and additional predicted structural data.
Why it matters
The released data allows related researchers to verify the results of AI-based enzyme design and conduct further research.AI summary
AI for science
Full summary Read paper ↗ PDF ↗
Paper2026-10-09 · DigitalCommons-IMSA (Illinois Mathematics and Science Academy)

Introduction to key achievements and career of AI researcher Jared Kaplan

Recipient of the 2026 Alumni Trailblazer Award
Jared Kaplan
What
Introduced the research and career achievements in the field of artificial intelligence of Jared Kaplan, co-founder and Chief Scientist of Anthropic.
Key result
Researched laws predicting the performance of large-scale artificial intelligence systems and pioneered techniques ensuring artificial intelligence aligns with predetermined principles.
Why it matters
Contributed to providing theoretical frameworks and guidelines for the development of safe and beneficial artificial intelligence.AI summary
Safety & policy
Full summary Read paper ↗ PDF ↗
Paper2026-10-08 · Science

Automatically proving unsolved math problems with AI

Advancing mathematics research with AI-driven formal proof search
Pushmeet Kohli +20 more
What
We developed an AI agent that searches for mathematical proofs at scale by utilizing a language where a compiler verifies the proof steps.
Key result
This AI agent successfully and autonomously solved several unsolved math problems and conjectures.
Why it matters
It demonstrated the potential for AI to drive autonomous mathematical discoveries.AI summary
Agents & coding AI for science
Full summary Read paper ↗ PDF ↗
Paper2026-10-06 · arXiv (Cornell University)

A new method for evaluating the self-improvement capabilities of AI agents through experience

Agent Plasticity: Measuring Self-Improvement Through Experience
Rob Fergus +10 more
What
Introduced the concept of agent plasticity and studied evaluation methods to measure the self-improvement efficiency of AI agents that evolve on their own through experience.
Key result
Despite similar learning opportunities, state-of-the-art models showed significant differences in their improvement trajectories, and the agent with the best final performance did not necessarily improve the most efficiently.
Why it matters
Demonstrates that when evaluating the potential of artificial intelligence, it is important to measure not only fixed-point capabilities but also how effectively they improve through experience.AI summary
Agents & coding
Full summary Read paper ↗ PDF ↗
Paper2026-10-04 · arXiv (Cornell University)

Proposal of a method where multiple AI agents stack work in a Git repository to solve problems

GitSwarm: Decentralized Compounding Inference
Rob Fergus +13 more
What
Proposed and evaluated GitSwarm, a 'compounding inference' system where multiple identical AI agents work independently in a shared Git repository, continuously saving, referencing, and combining intermediate results.
Key result
Reported solving all 30 math proof problems in a single run, outperforming the strongest baseline under a fixed budget (65.1 points) with an average of 79.4 points on the programming benchmark ProgramBench, and improving starting architectures through iterative experiments across three neural network structure tasks.
Why it matters
Demonstrates that AI can accumulate intermediate achievements rather than discarding results after each attempt, reusing them for subsequent tasks, thereby suggesting a direction for long-horizon problem solving and AI design for scientific research. However, since these are results at the abstract level, actual industrial impact is difficult to judge from this material alone.AI summary
Agents & coding AI for science
Full summary Read paper ↗ PDF ↗
Paper2026-10-02 · arXiv (Cornell University)

Modular memory design for quantum computer error correction significantly reduces resource consumption

Low-Overhead Quantum Error Correction with Boundary-Connected Planar Modules
Pushmeet Kohli +13 more
What
Researchers divided the quantum processor into planar modules and demonstrated through simulations that utilizing inter-module connections can reduce the number of physical qubits required for quantum error correction by more than 10 times.
Key result
The new modular code design was shown to reduce physical qubit consumption by more than 10 times (and over 30 times in large-scale systems) compared to surface codes.
Why it matters
This study demonstrates the potential for high-density storage devices that maintain the manufacturing advantages of planar modules while leveraging the benefits of highly efficient codes.AI summary
HBM & memory
Full summary Read paper ↗ PDF ↗
Paper2026-10-02 · bioRxiv (Cold Spring Harbor Laboratory)

New enzyme chemistry technology combining artificial intelligence protein design and evolution

Design-Evolution Synergy Enables New-to-Nature Enzyme Chemistry
Pushmeet Kohli +16 more
What
Developed an enzyme that performs carbon-hydrogen amination reactions not found in nature by combining artificial intelligence-based protein design and evolution techniques.
Key result
Succeeded in synthesizing a new enzyme with programmable positional and enantioselectivity, high catalytic efficiency, and superior enzyme stability.
Why it matters
Demonstrates that protein design technology can be utilized to create enzymes that promote demanding chemical reactions.AI summary
AI for science
Full summary Read paper ↗ PDF ↗
Paper2026-09-30 · Nature

Development of Technology to Invisibly Mark the Origin of AI-Generated Proteins

Function-preserving watermarking of AI-generated proteins.
Demis Hassabis +20 more
What
Researchers have developed 'SynthIDBio', a technology that leaves marks on AI-generated protein sequences and structures without loss of function.
Key result
It has been demonstrated that watermarks can be detected with near-perfect accuracy while maintaining protein functions similar to existing ones.
Why it matters
It demonstrates the potential of provenance tracking tools in the field of AI-driven bioengineering to address biosecurity and information authenticity issues.AI summary
AI for science Safety & policy
Full summary Read paper ↗
Paper2026-09-29 · arXiv (Cornell University)

Proposed meta-reasoning technique for AI agents to determine 'how to proceed' before performing tasks

Thinking Before Thinking: Scaling Agentic Inference Through Meta-Reasoning
Rob Fergus +11 more
What
Proposed an 'agentic meta-reasoning' approach that systematically treats execution control decisions—such as how much to reuse previous task results, whether to start fresh, and when to stop—for AI agents performing long-running tasks.
Key result
On the ProgramBench program reconstruction benchmark, the GPT-5.5-based version recorded 71.5%, which is higher than Codex's 58.0%, and the Opus 4.8-based version recorded 67.2%, higher than Claude Code's 65.5%.
Why it matters
According to the abstract, this suggests that as agents perform longer tasks, allocating computational resources to structured execution control may become more important. However, since the results in the abstract were obtained under specific benchmarks and experimental conditions, it is difficult to conclude that they directly translate to real-world product performance.AI summary
Inference efficiency Agents & coding
Full summary Read paper ↗ PDF ↗
Paper2026-09-28 · arXiv (Cornell University)

A Framework for Evaluating AI Consciousness Across Five Levels Proposed

From cacophony to hierarchy: a principled framework for assessing AI consciousness
Shane Legg +13 more
What
The authors organized various theories dealing with AI consciousness into a five-tier hierarchical framework and proposed a method to evaluate current Large Language Models (LLMs) using indicators for each level.
Key result
Current LLM consciousness assessments varied widely from under 0.01 to about 0.8, depending not only on how evidence is interpreted but also on how much weight is placed on which theory.
Why it matters
Given that consciousness-related indicators heavily overlap with structures necessary for general intelligence, this implies that as AI becomes more capable, it may become a stronger candidate for consciousness. However, this is not a definitive conclusion, and the abstract makes no direct mention of investment.AI summary
Safety & policy
Full summary Read paper ↗ PDF ↗
Paper2026-09-28

Analysis of the Impact and Policy Responses Triggered by Automating AI R&D

What if automating AI R&D triggers an intelligence explosion?
Jakub Pachocki · Geoffrey Hinton · Yoshua Bengio +19 more
What
Researchers evaluated the possibility of AI R&D automation triggering an intelligence explosion, along with its potential impacts and policy response measures.
Key result
AI systems are expected to automate most AI R&D tasks within a few years, which could lead to rapid capability growth at a level society struggle to handle.
Why it matters
It warns of the extreme risks such as loss of control alongside the benefits that AI R&D automation may bring, urging policy preparedness.AI summary
Safety & policy
Full summary Read paper ↗
Paper2026-08-10 · AI and Ethics

Analysis of risks arising when AI appears to be conscious

Seemingly conscious AI risks
Mustafa Suleyman +3 more
What
Researchers systematically classified and evaluated the characteristics of AI systems that make users feel they are conscious, and the various risks arising from this.
Key result
Risks to individuals, such as emotional dependence and infringement of autonomy, are already observed and have a high probability of occurrence, whereas social risks have a low probability of occurrence but high severity.
Why it matters
This helps understand the risks that occur when AI appears conscious and sets future research directions.AI summary
Safety & policy
Full summary Read paper ↗ PDF ↗
Paper2026-07-02 · arXiv.org

Google Releases Open-Source AI 'Gemma 4' with Improved Performance and Efficiency

Gemma 4 Technical Report
Demis Hassabis · Koray Kavukcuoglu +298 more
What
Google has developed 'Gemma 4', a next-generation open-source multimodal language model with enhanced computational efficiency and reasoning capabilities.
Key result
Performance has significantly improved across various evaluation benchmarks, including Science, Technology, Engineering, and Mathematics (STEM) and multimodality.
Why it matters
This indicates a broadened range of open-source AI model options capable of solving complex problems with fewer resources.AI summary
Inference efficiency Training & scaling
Full summary Read paper ↗ PDF ↗
Paper2026-06-10 · arXiv.org

Development of an Evaluation Method to Pinpoint Errors in AI Agents

Layer-Isolated Evaluation: Gating the Deterministic Scaffold of a Production LLM Agent with a No-LLM, Regression-Locked Test Harness
Alexandr Wang +2 more
What
Researchers have developed an evaluation system that divides the structure of AI agents into multiple layers to independently and rapidly inspect errors in each layer.
Key result
They confirmed that even when the overall success rate barely changes, the score of a specific troubled layer drops significantly, enabling accurate identification of the root cause.
Why it matters
This can contribute to enhancing the stability of AI systems and quickly finding the cause when problems occur.AI summary
Agents & coding Safety & policy
Full summary Read paper ↗ PDF ↗
Paper2026-06-09 · arXiv.org

Analysis of Limitations in Conversational Agent Evaluation by LLM-as-Judge

Catching One in Five: LLM-as-Judge Blind Spots in Production Multi-Turn Transaction Agents
Alexandr Wang +2 more
What
Using production food-ordering conversational agents, we measured how effectively LLM-based judges detect actual defects compared to comprehensive human inspection.
Key result
LLM judges failed to catch even a quarter of the systematic issues identified by humans, and in certain batches recorded zero detections.
Why it matters
This suggests that automated evaluation methods serve merely as a minimum baseline and cannot completely replace human review.AI summary
Agents & coding
Full summary Read paper ↗ PDF ↗
Paper2026-05-28 · arXiv.org

Extracting easy-to-understand operational units from inside large artificial intelligence

Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet
Chris Olah +25 more
What
Researchers utilized sparse autoencoders to extract interpretable features from the intermediate layers of the commercial AI model Claude 3 Sonnet.
Key result
The extracted features exhibited multilingual and multimodal characteristics, ranging from famous figures and abstract concepts to potential risks, confirming they can be used to control model behavior.
Why it matters
This demonstrates a technological advancement that can contribute to interpreting the internal workings of AI models and identifying potential risks.AI summary
Safety & policy
Full summary Read paper ↗ PDF ↗

People

Frontier labs
Noam ShazeerGoogle Gemini Co-LeadDario AmodeiAnthropic CEOLast activity 2026-10-27Demis HassabisGoogle DeepMind CEOLast activity 2026-10-20Mark ChenOpenAI Chief Research OfficerLast activity 2026-10-29Alexandr WangMeta Superintelligence Labs LeaderLast activity 2026-06-10Jakub PachockiOpenAI Chief ScientistLast activity 2026-09-28Jared KaplanAnthropic Co-founderLast activity 2026-10-09Jeff DeanGoogle Chief ScientistNat FriedmanMeta Superintelligence LabsNoam BrownOpenAIRob FergusHead of Meta FAIRLast activity 2026-10-20Mustafa SuleymanCEO of Microsoft AILast activity 2026-10-22Shane LeggGoogle DeepMind Co-founderLast activity 2026-09-28Jan LeikeAnthropicOriol VinyalsGoogle DeepMindShengjia ZhaoChief Scientist, Meta Superintelligence LabsJason WeiMeta Superintelligence LabsKoray KavukcuogluGoogle Chief AI ArchitectLast activity 2026-07-02Chris OlahAnthropicLast activity 2026-05-28Pushmeet KohliGoogle DeepMind Science AILast activity 2026-10-20
New labs
Mira MuratiCEO of Thinking Machines LabAndrej KarpathyFounder of Eureka LabsYann LeCunFounder of AMI LabsLast activity 2026-10-30Ilya SutskeverCEO of SSILlion JonesSakana AILast activity 2026-10-06Lilian WengCo-founder of Thinking Machines LabBarret ZophFrequent moves (reports of returning to OpenAI in 2026 and moving again)Sergey LevinePhysical Intelligence and UC BerkeleyLast activity 2026-10-06Aravind SrinivasPerplexity CEOArthur MenschMistral AI CEOAshish VaswaniEssential AILast activity 2026-10-28Aidan N. GomezAidan GomezCohere CEOLast activity 2026-09-12John SchulmanChief Scientist, Thinking Machines LabChelsea FinnPhysical Intelligence and Stanford UniversityLast activity 2026-10-29Fei-Fei LiCEO of World Labs and Stanford UniversityLast activity 2026-10-07
Academia
Richard SuttonUniversity of AlbertaStuart RussellUC BerkeleyAndrew Y. NgAndrew NgDeepLearning.AI and AI FundYoshua BengioUniversité de Montréal and LawZeroLast activity 2026-10-12Geoffrey HintonProfessor Emeritus, University of TorontoLast activity 2026-09-28Christopher D. ManningChristopher ManningStanford UniversityLast activity 2026-07-20Percy LiangStanford University and Together AILast activity 2026-10-22Pieter AbbeelUC BerkeleyLast activity 2026-10-06Kaiming HeMITLast activity 2026-10-01
AI hardware
Bill DallyNVIDIA Chief ScientistTri DaoTogether AI and PrincetonLast activity 2026-08-12Song HanMIT and NVIDIALast activity 2026-10-07Andrew L FeldmanAndrew FeldmanCerebras CEOJonathan RossGroqJim KellerTenstorrent CEOJim FanNVIDIA Robotics
Korea
Ha Jung-wooVice Chairman of the Presidential Committee on National AI Strategy (Resigned as Senior Presidential Secretary for AI in 2026)Last activity 2026-04-22Kim Sung-hoonKim Sung HunCEO of UpstageKyunghoon BaeBae KyunghoonMinister of Science and ICT (Former Head of LG AI Research)Alice H. OhOh Alice HaeyunKAISTLee Hae-minSenior Presidential Secretary for AI Future Planning (Appointed in 2026)Kyunghyun ChoCho KyunghyunNYU and GenentechLast activity 2026-10-04
China
Liang WenfengDeepSeek FounderLast activity 2026-10-12Yang ZhilinMoonshot AI FounderLast activity 2026-07-27Kai-Fu Lee01.AI
Executives
Kwak Noh-jungSK hynix CEOMark ZuckerbergMeta CEOSam AltmanOpenAI CEOYoung-Hyun JunJun Young-hyunSamsung Electronics Head of DS DivisionJensen HuangNVIDIA CEOC.C. WeiTSMC Chairman and CEOGreg BrockmanOpenAI PresidentLisa SuAMD CEOLip-Bu TanIntel CEOSatya NadellaMicrosoft CEOSanjay MehrotraMicron CEOMasayoshi SoneMasayoshi SonSoftBank ChairmanSundar PichaiAlphabet CEOAndy JassyAmazon CEOElon MuskxAI and Tesla CEOKevin ScottMicrosoft CTOTan Hock EngHock TanBroadcom CEO

Why follow AI leaders' moves and papers?

In AI, people are the technology. Which company a key researcher joins, or which lab they found, often turns into products, funding rounds and acquisitions months later. This page tracks job changes across frontier labs, new labs, academia, AI chips, and people in Korea and China.

Papers show what a person is focused on right now. We sort papers into 10 investable fields such as HBM & memory, GPUs & accelerators, inference efficiency, agents and robotics, and give a three-line summary — what, key result, why it matters — based on the abstract, with links to the original and PDF. This is information for finding ideas, not a recommendation to buy or sell any security.

Frequently Asked Questions

Q. Who do you track?

Key researchers and executives at frontier labs (OpenAI, Google DeepMind, Anthropic, Meta and others), new labs, academia, AI chip companies, and in Korea and China. Executives who rarely publish are tracked for career moves only.

Q. Where does the data come from, and how often is it updated?

Papers come from Semantic Scholar and arXiv, and careers from Wikidata (CC0) plus official announcements and press reports, collected automatically every day. Only items checked and approved by the editor are published.

Q. Who writes the paper summaries?

An AI (Google Gemini) drafts each summary from the paper's abstract, and the editor checks it before publishing. Other languages are AI translations of the checked Korean summary. Always confirm exact figures and context in the original paper.

This page compiles published papers, official announcements and press reports for information only. Paper summaries are written by AI from the abstract and reviewed by the editor; they may differ from the original, so check the paper for anything important. Nothing here is a recommendation to buy or sell any security or investment advice. Career moves cite official announcements or major press reports; some career records come from Wikidata (CC0). Summaries on this page were translated by AI from the editor-reviewed Korean version.

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