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Neolabs: the list, with sources
A neolab is a research-led AI company founded by researchers from frontier labs or academia, working on a long-horizon technical bet and usually funded at scale before it has revenue. This table lists every lab with a verified profile. Funding totals count confirmed rounds only.
Verified profiles7356 core and emerging
Confirmed funding$43.7Bcore and emerging labs
Rounds on record179133 confirmed, 46 not confirmed
Labs valued $1B+42by confirmed valuation
Tier
| Tier | Focus | HQ | Founders | ||||
|---|---|---|---|---|---|---|---|
| PProject Prometheus | Core | Physical AI for engineering and manufacturing ('artificial general engineer') | 2025 | San Francisco, CA, US | $18.2B | $41B | Jeff Bezos, Vik Bajaj |
| MMistral AI | Graduated | Open-weight and proprietary AI models, plus platform and compute for enterprise and sovereign AI | 2023 | Paris, FR | $6.65B | $24.4B | Arthur Mensch, Guillaume Lample, Timothée Lacroix |
| IIsomorphic Labs | Core | AI drug discovery built on AlphaFold-derived models | 2021 | London, GB | $2.7B | —$50B reported | Demis Hassabis |
| RReflection AI | Core | Builds open-weight frontier models (Beam) for coding and agentic work, sold to enterprises and governments | 2024 | New York, NY, US | $2B | $25B | Misha Laskin, Ioannis Antonoglou |
| TThinking Machines Lab | Core | Frontier AI research and product lab: customizable models, Tinker fine-tuning API, open-weights Inkling family | 2025 | San Francisco, CA, US | $2B | $12B$40B reported | Mira Murati, John Schulman, Barret Zoph +2 |
| SSkild AI | Graduated | General-purpose robot foundation model (Skild Brain) for any robot and task | 2023 | CA / PA, US | $1.7B | $14B | Deepak Pathak, Abhinav Gupta |
| MMiniMax | Graduated | Develops multimodal foundation models and AI-native consumer and API products | 2021 | CN | $1.56B | $4.24B | Yan Junjie, Yun Yeyi |
| CCohere | Graduated | Enterprise AI company building language models and the North agent platform | 2019 | Toronto, ON, CA | $1.37B | $6.8B | Aidan Gomez, Nick Frosst, Ivan Zhang |
| MMoonshot AI | Graduated | Large language models (Kimi) with open weights, consumer chatbot and API | 2023 | Beijing, CN | $1.2B | $10B$50B reported | Yang Zhilin, Zhou Xinyu, Wu Yuxin |
| FFlapping Airplanes | Core | Research on more data- and compute-efficient AI training, including a GPU megakernel framework | 2025 | San Francisco, CA, US | $1.18B | $1.5B$6B reported | Ben Spector, Asher Spector, Aidan Smith |
| IIneffable Intelligence | Core | Reinforcement-learning research lab building a 'superlearner' that learns from experience, not human data | 2025 | London, GB | $1.1B | $5.1B | David Silver |
| RRiver AI | Core | API to train, fine-tune and serve custom models with reinforcement learning, toward personal AI | 2026 | Palo Alto, CA, US | $1.1B | — | Igor Babuschkin |
| AAMI Labs | Core | World models (JEPA-style) that learn abstract representations of real-world sensor data | 2026 | Paris, FR | $1.03B | $3.5B | Yann LeCun, Alexandre LeBrun, Saining Xie +2 |
| SSafe Superintelligence | Core | Research lab building a safe superintelligence as its single product | 2024 | Palo Alto, CA, US | $1B | $5B$32B reported | Ilya Sutskever, Daniel Levy, Daniel Gross |
| WWorld Labs | Graduated | Spatial-intelligence models that generate and simulate interactive 3D worlds | 2024 | San Francisco, CA, US | $1B | —$1B reported | Fei-Fei Li, Justin Johnson, Ben Mildenhall +1 |
| XXaira Therapeutics | Core | AI-driven drug discovery and development company | 2024 | South San Francisco, CA, US | $1B | — | Marc Tessier-Lavigne, David Baker, Hetu Kamisetty |
| TTypeSafe AI | Core | AI lab building machine-native models that return typed decisions to software, starting with Jev | 2024 | San Francisco, CA, US | $910M | $7.5B | Diogo Almeida, Erik Gafni, Sasha Sheng |
| HHark | Core | AI lab building its own foundation models and hardware for a personal AI assistant | 2025 | San Jose, CA, US | $700M | $6B | Brett Adcock |
| RRecursive Superintelligence | Core | Lab pursuing recursive self-improvement: automated AI research (Eureka Machine); first model Flint | 2026 | London, GB | $650M | $4.65B | Richard Socher, Tim Rocktäschel, Yuandong Tian +5 |
| CChai Discovery | Core | AI models that predict and design molecular interactions for drug discovery | 2024 | San Francisco, CA, US | $630M | $3.8B | Joshua Meier, Jack Dent, Matthew McPartlon +1 |
| GGeneralist | Core | Embodied foundation models for robots, starting with dexterity | 2024 | San Francisco, CA, US | $600M | $2B$3B reported | Pete Florence, Andy Zeng, Andrew Barry |
| PPhysical Intelligence | Core | General-purpose robot foundation models (VLAs) and cloud API for any robot | 2024 | San Francisco, CA, US | $600M | $5.6B$20B reported | Karol Hausman, Sergey Levine, Chelsea Finn +4 |
| CCuspAI | Core | AI system for discovering new materials, for semiconductors, energy and climate | 2024 | Cambridge, GB | $580M | $2.6B | Chad Edwards, Max Welling |
| LLila Sciences | Core | Builds AI models and automated laboratories for autonomous scientific research ('scientific superintelligence') | 2023 | Cambridge, MA, US | $550M | $1.3B$8.5B reported | Geoffrey von Maltzahn, Molly Gibson |
| GGeneral Intuition | Core | Action and world foundation models trained on Medal gameplay clips, for games and robotics | 2025 | New York, NY, US | $540M | $6.2B | Pim de Witte, Moritz Baier-Lentz |
| AApplied Compute | Graduated | Platform to train, serve and continuously improve open-weight models on a company's own data | 2025 | San Francisco, CA, US | $530M | $3.25B | Yash Patil, Rhythm Garg, Linden Li |
| AAleph Alpha | Graduated | Developer of specialized sovereign language models and AI solutions for public institutions and enterprises | 2019 | Heidelberg, BW, DE | $500M | — | Jonas Andrulis, Samuel Weinbach |
| PPoolside | Core | Foundation models for agentic software development | 2023 | US | $500M | $3B$12B reported | Jason Warner, Eiso Kant |
| hhumans& | Core | Human-centric frontier lab: models for people-to-people collaboration (Ampersand, Boba, Persimmon) | 2025 | San Francisco, CA, US | $480M | $4.48B | Andi Peng, Georges Harik, Eric Zelikman +2 |
| UUnconventional AI | Adjacent | New computing hardware and physics-based substrates for energy-efficient AI | 2025 | Mountain View, CA, US | $475M | $4.5B | Naveen Rao, Michael Carbin, Sara Archour +1 |
| DDecart | Adjacent | Builds real-time video and world models (Lucy, Oasis) and the inference stack that runs them | 2023 | San Francisco, CA, US | $453M | $3.1B | Dean Leitersdorf, Moshe Shalev |
| RRhoda AI | Core | Robot foundation models trained on web-scale video and post-trained on robot data, deployed in industrial settings | 2024 | Palo Alto, CA, US | $450M | $1.7B | Jagdeep Singh, Eric Chan, Gordon Wetzstein +2 |
| CCore Automation | Core | AI research lab pursuing new learning algorithms/architectures and automating its own research | 2026 | San Francisco, CA, US | $432M | —$4B reported | Jerry Tworek, Rohan Anil |
| SSakana AI | Core | AI research company building foundation models based on nature-inspired intelligence, with enterprise AI in Japan | 2023 | Tokyo, JP | $430M | $2.65B | David Ha, Llion Jones, Ren Ito |
| MMagic | Core | Frontier code models to automate software engineering and AI research | 2022 | San Francisco, CA, US | $348M | — | Eric Steinberger, Sebastian De Ro |
| ZZhipu AI | Graduated | Develops GLM large language models and related agent and API products | 2019 | Beijing, CN | $342M | — | Zhang Peng |
| BBlack Forest Labs | Adjacent | Generative image and video models (FLUX) with API and open-weight releases | 2024 | Freiburg, DE | $331M | $3.25B | Robin Rombach, Patrick Esser, Andreas Blattmann |
| PPeriodic Labs | Core | AI scientists and autonomous labs for the physical sciences (materials, superconductors) | 2025 | San Francisco, CA, US | $300M | $1.3B$7.5B reported | Liam Fedus, Ekin Dogus Cubuk |
| RRicursive Intelligence | Core | AI lab building self-improving systems, starting with AI-driven chip design | 2025 | Palo Alto, CA, US | $300M | $4B | Anna Goldie, Azalia Mirhoseini |
| SSimile | Core | Builds behavioral simulation models that simulate how people respond to decisions | 2025 | Palo Alto, CA, US | $300M | $2B | Joon Sung Park, Michael Bernstein, Percy Liang +1 |
| HHarmonic | Core | Develops Aristotle, an AI system that formally verifies mathematical proofs, software and hardware | 2023 | Palo Alto, CA, US | $295M | $1.45B | Tudor Achim, Vlad Tenev |
| LLiquid AI | Core | Builds efficient foundation models (Liquid Foundation Models) designed to run on devices | 2023 | Cambridge, MA, US | $288M | $2B | Ramin Hasani, Mathias Lechner, Alexander Amini +1 |
| SSarvam AI | Core | Full-stack sovereign AI platform for India: Indian-language models, speech and agents | 2023 | Bengaluru, Karnataka, IN | $275M | $1.5B$3.5B reported | Vivek Raghavan, Pratyush Kumar |
| AAxiom Math | Core | Builds AI systems for mathematical reasoning and formal proof verification in Lean, extended to verifying code | 2025 | Palo Alto, CA, US | $264M | $1.6B | Carina Hong, Shubho Sengupta |
| FFundamental | Core | Large Tabular Model (NEXUS) for predictions on enterprise structured data | 2024 | San Francisco, CA, US | $255M | $1.4B | Jeremy Fraenkel, Gabriel Suissa |
| GGoodfire | Core | AI interpretability research lab building tools to understand, monitor and design AI models | 2024 | San Francisco, CA, US | $207M | $1.25B | Eric Ho, Dan Balsam, Tom McGrath |
| ddoubleAI | Core | Frontier AI lab for science and engineering, building expert-level reasoning with verification | 2023 | Ramat Gan, IL | $200M | $1B | Amnon Shashua, Shai Shalev-Shwartz, Gal Beniamini +3 |
| IImbue | Adjacent | AI agents and software tools users own and control (Studio, Sculptor) | 2021 | San Francisco, CA, US | $200M | $1B | Kanjun Qiu, Josh Albrecht |
| MMirendil | Core | Frontier lab building AI systems for AI R&D, aimed at automating the research loop | 2026 | San Francisco, CA, US | $200M | $1B$5B reported | Behnam Neyshabur, Harsh Mehta, Shayan Salehian +1 |
| RReka | Core | Multimodal and omni models and infrastructure for physical AI | 2022 | Sunnyvale, CA, US | $170M | — | Dani Yogatama, Cyprien de Masson d'Autume, Mikel Artetxe +2 |
| EEvolutionaryScale | Graduated | AI research lab and public benefit company building protein language models (ESM3, ESM Cambrian) for the life sciences | 2023 | New York, NY, US | $142M | — | Alexander Rives |
| NNous Research | Core | Open-source AI models, the Hermes Agent and decentralised training infrastructure | 2023 | New York, NY, US | $140M | $1.5B | Jeffrey Quesnelle, Karan Malhotra, Ryan Teknium +1 |
| DDyna Robotics | Core | Builds robots and robot foundation models that complete end-to-end workflows in hospitality, logistics and factory operations | 2024 | Redwood City, CA, US | $120M | — | Lindon Gao, York Yang, Jason Ma |
| GGenesis AI | Core | Physical AI research lab building a universal robotics foundation model and a robotics platform | 2024 | Palo Alto, CA, US | $105M | —$3B reported | Zhou Xian, Théophile Gervet |
| EEngram | Emerging | Trains models on an organisation's own context (memory / continual learning) to cut token costs | 2025 | San Francisco, CA, US | $98M | $600M | Dan Biderman, Sabri Eyuboglu |
| SStandard Intelligence | Emerging | Trains computer-action models on video pretraining to build agents that explore and learn like humans | 2024 | San Francisco, CA, US | $75M | — | Galen Mead, Devansh Pandey |
| EEdison Scientific | Emerging | AI scientist platform (Kosmos) for scientific R&D and drug development, commercial spinout of FutureHouse | 2025 | San Francisco, CA, US | $70M | — | Sam Rodriques, Andrew White |
| AAdaption Labs | Emerging | Adaptive AI: continual-learning models, adaptive data and AutoScientist automated model training | 2025 | San Francisco, CA, US | $50M | — | Sara Hooker, Sudip Roy |
| IInherent | Emerging | AI-native science: Faraday agent for open-ended scientific discovery with human-AI teaming | 2026 | London, GB | $50M | — | Edward Hughes, Tantum Collins, Louis Kirsch +1 |
| PPoetiq | Emerging | Meta-system that recursively self-improves reasoning agents on top of existing frontier LLMs | 2025 | Mountain View, CA, US | $45.8M | — | Shumeet Baluja, Ian Fischer |
| DDeep Cogito | Emerging | AI startup building open-weight language models through post-training and iterative self-improvement | 2024 | San Francisco, CA, US | $43M | — | Drishan Arora, Dhruv Malhotra |
| LLatent Labs | Emerging | Builds generative AI models for designing proteins such as antibodies and enzymes | 2023 | London, GB | $40M | — | Simon Kohl |
| NNeoCognition | Emerging | Builds self-learning AI agents that specialize in a domain, sold mainly to enterprises | 2025 | Palo Alto, CA, US | $40M | — | Yu Su, Xiang Deng, Yu Gu |
| EEnact Intelligence | Emerging | AI models to control robots, with actuators that double as force and touch sensors | 2026 | CH | $30M | — | Manuel Yves Galliker |
| DDeepSeek | Graduated | AI research lab building open-weight large language models, with a commercial API | 2023 | Hangzhou, Zhejiang, CN | — | —$51.5B reported | Liang Wenfeng |
| DDiscovery Loop | Emerging | Builds AI systems that automate the experimental loops of science and engineering, starting with ML research | 2026 | Palo Alto, CA, US | — | —$50B reported | Jeff Dean, Sanjay Ghemawat, Quoc Le +1 |
| EEssential AI | Core | Open-source models and tools for frontier AI, with a focus on STEM and code | 2023 | San Francisco, CA, US | — | — | Ashish Vaswani |
| ffal | Adjacent | Inference platform for generative image, video and audio models | 2021 | San Francisco, CA, US | — | —$16B reported | Burkay Gur, Gorkem Yurtseven |
| IInception | Emerging | Builds diffusion-based large language models (Mercury) for production applications | 2024 | Redwood City, CA, US | — | — | Stefano Ermon, Aditya Grover, Volodymyr Kuleshov |
| IIsara | Emerging | Software that coordinates thousands of AI agents on complex analytical tasks | 2025 | San Francisco, CA, US | — | —$650M reported | Eddie Zhang, Henry Gasztowtt |
| PPotential Sciences | Emerging | Physics-grounded training data and AI for chemistry and drug discovery | 2025 | London, GB | — | — | Hatem Helal |
| SStepFun | Graduated | Develops Step series large language and multimodal models | 2023 | Shanghai, CN | — | — | Jiang Daxin |
| ZZyphra | Core | Open models, multisilicon (AMD-first) inference cloud and an agent product | 2021 | San Francisco, CA, US | — | —$5B reported | Krithik Puthalath, Beren Millidge |
Raised = sum of rounds with Confirmed status. Valuation = latest confirmed figure; a reported figure from talks is shown below it and is not confirmed. Sources for every figure are on each lab's page.
How to read this list
Tiers. Core labs have a research thesis at the centre, research founders, and a disclosed round of $100M or more or a valuation of $1B or more. Emerging labs fit the same profile at an earlier stage. Graduated labs have reached material revenue, listed, or been acquired. Adjacent companies are often named alongside neolabs but are infrastructure or application businesses; they are listed for reference and left out of the totals.
Why some well-known figures are missing. A valuation quoted from talks that have not closed is shown as reported, not as the lab's valuation. If a lab has never disclosed a round, its profile says so.
What is not here. Labs without a verified profile are not listed. A profile is published only when name, focus, founders, headquarters and funding status each have a public source.
Missing a lab, or see a wrong figure? Suggest an entry or submit a correction.