AI LEADERS · MOVES & PAPERS

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

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 labs New labs Academia AI hardware Korea China Executives

Field All HBM & memory GPUs & AI accelerators Inference efficiency Training & scaling Agents & coding Robotics & physical AI World models & multimodal Data centers & power AI for science Safety & policy

Career moves · New labs · Inference efficiency

No career moves match these filters yet.

Papers · New labs · Inference efficiency

Paper2026-10-28

Development of High-Efficiency AI Models Capable of Complex Tasks

Nemotron 3.5 Super and Lightning: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Models for Agentic AI

Ashish Vaswani +499 more

What: Researchers have developed Nemotron 3.5 Super and Lightning, open AI models well-suited for reasoning, long-context understanding, and autonomous task execution.
Key result: The Super model achieved 51.78% on software engineering evaluations while using fewer active parameters, outperforming previous models, and demonstrated high throughput performance through various optimizations.
Why it matters: This research demonstrates the possibility of simultaneously increasing the efficiency and performance of large-scale models so they can be practically utilized in diverse environments.AI summary

Inference efficiency Agents & coding

Full summary Read paper ↗ PDF ↗

Paper2026-10-11 · arXiv (Cornell University)

Reducing Prediction Errors in AI World Models by Applying Physical Laws

LeHami: Symplectic Latent Dynamics for World Models

Yann LeCun +2 more

What: Researchers developed LeHami, a new world model training method that combines the geometric properties of physical systems with artificial intelligence.
Key result: Unlike conventional approaches, this method prevents information from compressing and disappearing during long-term prediction and significantly improves velocity prediction performance.
Why it matters: This research can contribute to AI understanding and simulating physical spaces more accurately.AI summary

Inference efficiency World models & multimodal

Full summary Read paper ↗ PDF ↗

Paper2026-10-06

OTel releases AI datasets and models dedicated to the telecommunications sector

OTel: Open Telco AI Datasets, Benchmarks, and Models

Ashish Vaswani +17 more

What: Researchers have released 'Open Telco (OTel)', a collection of resources including datasets, evaluation benchmarks, and models trained with them, necessary for AI training dedicated to the telecommunications sector.
Key result: The models based on the released OTel achieved performances of 93.1%, 0.947, and 87.8% in telecommunications data retrieval, document reranking, and language model accuracy evaluation, respectively.
Why it matters: This release provides a reproducible starting point that promotes artificial intelligence research and applications in the telecommunications field.AI summary

Inference efficiency

Full summary Read paper ↗

Paper2026-09-29 · arXiv (Cornell University)

T2Mem: A Technique for Training Robots to Remember Past Scenes and Perform Tasks

T$^2$Mem: Learning Test-Time Memory for Robotics

Fei-Fei Li +6 more

What: For robot manipulation tasks that require remembering past observation information, we propose a framework (T2Mem) that adds memory capabilities through test-time training to pretrained robot policies that previously lacked memory functions.
Key result: The average success rate across 16 RoboMME tasks increased from 17.93% for the baseline policy without memory to 56.83%, outperforming recurrent memory methods reported in the benchmark.
Why it matters: This is significant in showing that robots' ability to utilize past information not currently visible on screen can be fostered using only demonstration data without external memory devices, though the abstract only covers research-stage results, so commercialization or market effects cannot be confirmed.AI summary

Inference efficiency Robotics & physical AI

Full summary Read paper ↗ PDF ↗

Paper2026-09-28 · arXiv (Cornell University)

JEPA research makes autonomous driving world models 100 times faster

AD-E2E-JEPA: A Joint-Embedding Predictive Architecture For End-to-End Autonomous Driving

Yann LeCun +3 more

What: Evaluated predictive world models (JEPA family) used in autonomous driving and proposed an improved architecture, AD-E2E-JEPA, that significantly reduces computational load.
Key result: The proposed architecture reduced the number of patches used for planning by 1/16 and the embedding dimension by 1/4, increasing inference speed by about 100 times while maintaining planning performance.
Why it matters: This research shows a direction for reducing the computational cost of autonomous driving AI, though the actual in-vehicle application effect and commercialization potential are difficult to judge from the abstract alone.AI summary

Inference efficiency Robotics & physical AI World models & multimodal

Full summary Read paper ↗ PDF ↗

Paper2026-09-12

Cohere Unveils Cost-Effective Large Language-Based Translation Model

North Small Translate: Advanced Cost-Effective Translation (Cohere CAT+)

Aidan N. Gomez +22 more

What: Researchers have introduced 'North Small Translate', a cost-effective large language model-based machine translation model that delivers outstanding performance across 50 languages.
Key result: This model achieved state-of-the-art machine translation performance across 50 languages among model groups with fewer than 1 trillion parameters.
Why it matters: It demonstrates the potential to efficiently perform diverse language translation and post-editing tasks without expensive computations during the inference process.AI summary

Inference efficiency

Full summary Read paper ↗ PDF ↗

Paper2026-08-24

Learning to Act While the Robot Thinks

Learning to Act While Waiting: RL Finetuning of Generalist Robot Policies Under Inference Latency

Sergey Levine +19 more

What: Researchers have developed ARLI, a latency-aware reinforcement learning framework that solves problems caused by the thinking time (inference latency) of robot models.
Key result: This new method enables robots to successfully continue learning even in environments with latency, showing performance equal to or better than an ideal latency-free environment.
Why it matters: It opens up the possibility for complex and massive robot AI to autonomously improve performance in real-world fields without freezing or stuttering.AI summary

Inference efficiency Robotics & physical AI

Full summary Read paper ↗ PDF ↗

People

Frontier labs

Noam ShazeerGoogle Gemini Co-Lead Dario AmodeiAnthropic CEOLast activity 2026-10-27 Demis HassabisGoogle DeepMind CEOLast activity 2026-10-20 Mark ChenOpenAI Chief Research OfficerLast activity 2026-10-29 Alexandr WangMeta Superintelligence Labs LeaderLast activity 2026-06-10 Jakub PachockiOpenAI Chief ScientistLast activity 2026-09-28 Jared KaplanAnthropic Co-founderLast activity 2026-10-09 Jeff DeanGoogle Chief Scientist Nat FriedmanMeta Superintelligence Labs Noam BrownOpenAI Rob FergusHead of Meta FAIRLast activity 2026-10-20 Mustafa SuleymanCEO of Microsoft AILast activity 2026-10-22 Shane LeggGoogle DeepMind Co-founderLast activity 2026-09-28 Jan LeikeAnthropic Oriol VinyalsGoogle DeepMind Shengjia ZhaoChief Scientist, Meta Superintelligence Labs Jason WeiMeta Superintelligence Labs Koray KavukcuogluGoogle Chief AI ArchitectLast activity 2026-07-02 Chris OlahAnthropicLast activity 2026-05-28 Pushmeet KohliGoogle DeepMind Science AILast activity 2026-10-20

New labs

Mira MuratiCEO of Thinking Machines Lab Andrej KarpathyFounder of Eureka Labs Yann LeCunFounder of AMI LabsLast activity 2026-10-30 Ilya SutskeverCEO of SSI Llion JonesSakana AILast activity 2026-10-06 Lilian WengCo-founder of Thinking Machines Lab Barret ZophFrequent moves (reports of returning to OpenAI in 2026 and moving again)Sergey LevinePhysical Intelligence and UC BerkeleyLast activity 2026-10-06 Aravind SrinivasPerplexity CEO Arthur MenschMistral AI CEO Ashish VaswaniEssential AILast activity 2026-10-28 Aidan N. GomezAidan GomezCohere CEOLast activity 2026-09-12 John SchulmanChief Scientist, Thinking Machines Lab Chelsea FinnPhysical Intelligence and Stanford UniversityLast activity 2026-10-29 Fei-Fei LiCEO of World Labs and Stanford UniversityLast activity 2026-10-07

Academia

Richard SuttonUniversity of Alberta Stuart RussellUC Berkeley Andrew Y. NgAndrew NgDeepLearning.AI and AI Fund Yoshua BengioUniversité de Montréal and LawZeroLast activity 2026-10-12 Geoffrey HintonProfessor Emeritus, University of TorontoLast activity 2026-09-28 Christopher D. ManningChristopher ManningStanford UniversityLast activity 2026-07-20 Percy LiangStanford University and Together AILast activity 2026-10-22 Pieter AbbeelUC BerkeleyLast activity 2026-10-06 Kaiming HeMITLast activity 2026-10-01

AI hardware

Bill DallyNVIDIA Chief Scientist Tri DaoTogether AI and PrincetonLast activity 2026-08-12 Song HanMIT and NVIDIALast activity 2026-10-07 Andrew L FeldmanAndrew FeldmanCerebras CEO Jonathan RossGroq Jim KellerTenstorrent CEO Jim 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 2026-04-22 Kim Sung-hoonKim Sung HunCEO of Upstage Kyunghoon BaeBae KyunghoonMinister of Science and ICT (Former Head of LG AI Research)Alice H. OhOh Alice HaeyunKAIST Lee 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 2026-10-12 Yang ZhilinMoonshot AI FounderLast activity 2026-07-27 Kai-Fu Lee01.AI

Executives

Kwak Noh-jungSK hynix CEO Mark ZuckerbergMeta CEO Sam AltmanOpenAI CEO Young-Hyun JunJun Young-hyunSamsung Electronics Head of DS Division Jensen HuangNVIDIA CEO C.C. WeiTSMC Chairman and CEO Greg BrockmanOpenAI President Lisa SuAMD CEO Lip-Bu TanIntel CEO Satya NadellaMicrosoft CEO Sanjay MehrotraMicron CEO Masayoshi SoneMasayoshi SonSoftBank Chairman Sundar PichaiAlphabet CEO Andy JassyAmazon CEO Elon MuskxAI and Tesla CEO Kevin ScottMicrosoft CTO Tan 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.