Frontier Radar #5: If AGI is so close, why is everyone leaving? The neolabs and their bets

OpenAI is talking about the AGI era, and Nvidia CEO Jensen Huang says AGI has arrived. At the same time, co-creators of today's models are leaving OpenAI, Google DeepMind, and Meta to start their own labs, raising billions with no product to show. Why? They're betting that the current recipe alone won't be enough. We've sorted 45 neolabs by their research bets, mapped the limitations of today's AI they're trying to…

Six times a year, THE DECODER's editorial team takes a deep dive into a foundational AI topic in its "Frontier Radar" series, published as a newsletter and exclusively on the site for THE DECODER subscribers. Issue #5 profiles 27 AI labs in detail, sorted by six research bets, plus nearly 20 more in brief and an overview covering origins, valuations, and what's publicly available. Issue #1 covered the current state of agentic AI. Issue #2 examined the measurable impact of AI on productivity. Issue #3 covered the emerging token economy of generative AI. Issue #4 asked what's left of the AI lead over China and where Europe stands.

Dear readers,

After the launch of GPT-6 Astra, Nvidia CEO Jensen Huang declared on X: "AGI has arrived." OpenAI president Greg Brockman called the model a turning point and welcomed humanity to the "AGI era". What exactly the two men mean isn't clear, since there's no agreed-upon definition of AGI. The message comes through anyway: an extremely powerful AI, possibly a self-improving one, is imminent and the path is mapped out.

Many of the people who built today's systems apparently see things differently. Dozens have left the big labs to start their own. Ilya Sutskever co-founded OpenAI and served as its chief scientist; now he's building Safe Superintelligence. Jerry Tworek led the development of OpenAI's reasoning models and now runs Core Automation. David Silver, the mind behind AlphaGo, left Google DeepMind for Ineffable Intelligence. Yann LeCun left Meta after twelve years and founded AMI. Jeff Dean spent 27 years at Google, most recently as chief scientist, and started Discovery Loop.

The money follows: Nvidia invested in SSI in July, reportedly five billion dollars according to Bloomberg. The lab hasn't released a single model or paper. Prometheus, Jeff Bezos' industrial AI lab, pulled in twelve billion dollars at a 41 billion valuation in June. Flapping Airplanes has no public model either and raised one billion dollars in an October Series A.

The motives are mixed, and not all technical. At these valuations, founders become paper billionaires fast. LeCun left after a strategy dispute with Meta. Sutskever wants to do research without product pressure. Others want to carve out a niche that would be a sideshow at a big lab. Some departures aren't even a clean break: Google itself is a founding investor in Discovery Loop.

What many founders share is a doubt. Not about whether much more powerful AI is coming; some think AGI is close. Their doubt is about whether today's recipe alone gets there: ever-larger language models, trained on internet data, refined with reinforcement learning.

What is a neolab?

There's no official definition. We use the term for independently founded, research-driven AI companies that build their business around a long-term technical bet. They want to change how AI learns, perceives the world, solves problems, is controlled, or computes.

The current wave became visible in 2024. The race for the strongest models was in full swing, and the cloud giants had their partners: Microsoft backed OpenAI, Amazon backed Anthropic. Then Sutskever founded SSI in June and raised a billion dollars within months for a research team with no product.

There's a reason investors fund that. OpenAI and Anthropic have grown into corporations on the back of their success, and both are pursuing IPOs at record valuations. Internally, research teams compete with product teams for money and attention. Established companies struggle with ideas that threaten their business, management scholars call this the innovator's dilemma. That's what investors are betting on: a small team finds what the giants miss.

There are precedents. The Transformer was invented at Google in 2017. DeepMind beat the world's best Go players with AlphaGo. Yet the AI assistant for the masses came from OpenAI, founded as a nonprofit in 2015. The second breakout came from an OpenAI spinoff: Dario and Daniela Amodei founded Anthropic in 2021. By the end of July 2026, Bloomberg pegged its annualized revenue at over 65 billion dollars.

The real bet neolabs are making is on research gaps the big players don't fill, either because nobody knows the path or because nobody allocates resources for it. The big labs have more compute, established data pipelines, and feedback from hundreds of millions of users. What a small lab builds can show up at the market leader shortly after the first product ships.

TypeSafe AI shows how short a lead can be. In mid-September it launched Jev, a decision model. Two weeks later, OpenAI announced its Decisions API. But even a brief lead shows expertise and can pay off as an exit. Microsoft hired the core team from Inflection. Google grabbed the founders of Character.ai and the leadership of Windsurf. AMD wants to acquire World Labs for about 8.2 billion dollars. Nvidia is in talks with Reflection, according to the Financial Times.

For neolabs, it all comes down to one question: can they deliver something the big players don't have, something customers or the big players themselves will pay for? We selected 27 labs, most from the US and Europe, and sorted them into six groups by their primary bet.

Bet 1: A good problem solver isn't necessarily a good learner

Today's models can do remarkable things, but they learn differently from humans. They need massive amounts of data to pick up a skill, and once deployed, they typically don't update their weights. They can use new information within a conversation but don't permanently learn from every experience. Continual learning remains a central research gap. Six labs want to change that and attack the problem from three angles: learning during deployment (Adaption, Core Automation), learning from few examples (Flapping Airplanes, Ndea, SSI), and learning from experience (Ineffable).

Adaption bets against scaling

Sara Hooker led research at Cohere and previously worked at Google Brain. In 2020, she argued in "The Hardware Lottery" that research approaches often win not because they're better but because they fit the available hardware. In January she followed up with "On the Slow Death of Scaling", arguing that more compute leads to better models less and less reliably. Data and training methods are more effective levers. Open models with 8 or 27 billion parameters now beat the much larger Falcon 180B on benchmarks.

With Sudip Roy, she founded Adaption. According to them, they are betting against scaling and plan to build efficient AI that continuously learns instead. Rather than one big model for everyone, models should adapt to their users on the fly and cheaply. The first products tackle the most obvious lever, data: tools for cleaning, visualizing, and automated training via AutoScientist. The actual goal, a model that learns during deployment, is still missing.

Core Automation's insider search for new learning methods

Jerry Tworek spent almost seven years at OpenAI, most recently as vice president of research, leading the development of the reasoning models o1 and o3. His critique of today's recipe comes from someone who helped build it. Co-founder Rohan Anil led Gemini's pre-training at Google.

Retraining a trained network risks making it forget what it already knew. Researchers call this catastrophic forgetting. The Transformer wasn't built to learn on the fly during deployment either.

ChatGPT and Claude now take notes and retrieve earlier conversations as a workaround. Tworek doesn't think that's enough, as for him continual learning is a problem for machine learning, not for Markdown files, as he said. Core is looking for learning algorithms beyond pre-training and reinforcement learning, plus architectures that scale better. The first model is supposed to update its weights during deployment and get by with one hundredth of today's training data. So far, nothing more than rumors.

Core's first test is on itself: a small team with research agents aims to match the output of a large lab, for instance by having agents automatically generate efficient GPU code from an architecture idea.

This kind of research was barely possible at OpenAI, Tworek said after leaving. When you're in a race for the best model, you take fewer risks.

Flapping Airplanes wants to learn more from less data

Brothers Ben and Asher Spector founded Flapping Airplanes with Aidan Smith, previously at Neuralink. Ben is the lead author of ThunderKittens, a widely used GPU kernel library.

The thesis: today's models are strong where data is plentiful, like text and images. In fields like robotics or biology, where data is scarce and expensive, they fall short. Flapping Airplanes wants to build models that learn from few examples instead of needing massive datasets. The founders are looking for learning methods inspired by biological principles but designed to run on GPUs.

Nothing visible has come out yet. Most of the first year went into foundations: software that makes GPU training more efficient by cutting the usual overhead between many small compute steps. New learning methods have to run on existing hardware before they can work.

Investors don't mind the missing model. After 180 million dollars in January, an October Series A brought one billion at a six billion valuation.

Ineffable Intelligence wants to learn without human examples

David Silver isn't going after less data. He's going after different data. At Google DeepMind, he led the development of AlphaGo and its successors. The original AlphaGo still learned from human games. AlphaGo Zero and AlphaZero learned only from self-play and surpassed all predecessors.

In spring 2025, Silver and Richard Sutton turned that insight into a program. In "Welcome to the Era of Experience", they argue that systems that only imitate humans can barely get past human-level performance on many important tasks. The next systems need to learn from their own experience, with rewards from the environment rather than from human judgment. Silver later founded Ineffable Intelligence. His goal is a "Superlearner" that learns from experience in both digital and physical environments.

The hardest question is where the feedback comes from. In Go, wins and losses decide. In science or the physical world, success is often unclear or shows up late. Reinforcement learning has always been strongest where the environment gives a clear signal. Ineffable has to prove it works for open-ended, changing tasks too. In April, the company announced a 1.1 billion dollar seed round at a 5.1 billion post-money valuation, the largest in Europe. There's no usable system.

Ndea wants to find rules from few examples

François Chollet developed Keras, a deep learning library, and designed the ARC benchmark, which measures how well systems figure out new tasks from just a few examples. After years at Google, Chollet is building Ndea with Zapier co-founder Mike Knoop. The team has about 15 people.

Chollet draws a line between ability and learning: a system can solve a task brilliantly because of extensive training and still be unable to pick up a new skill efficiently. Ndea uses program synthesis. The system searches for a program that explains the observed examples, with a neural model steering the search toward promising candidates. A few examples should yield rules that hold for new cases too.

Chollet's own benchmark is falling even without Ndea. On ARC-AGI-3, launched in March, GPT-6 Astra scored up to 99.9 percent in September, and 62.7 percent in the standard setup. The path there is telling: Astra translated unfamiliar game environments into compact rules and its own shorthand notation, roughly what Ndea is aiming for. Ndea hasn't posted its own ARC-AGI-3 result yet.

Safe Superintelligence has credibility but nothing to show

Ilya Sutskever co-founded OpenAI and served as its chief scientist, co-authoring some of the most important papers in deep learning. He left in 2024 and founded SSI, where he's now CEO. The founding manifesto describes the goal: safe superintelligence, free from product distractions. About 50 employees in Palo Alto and Tel Aviv have published neither a model nor a paper.

Sutskever described his focus in late 2025 in a conversation with Dwarkesh Patel. Humans transfer what they've learned after a few examples, while models need massive datasets - and he plans to change that. He expects a system that learns as well as a human in five to twenty years.

In July, SSI and Nvidia announced a partnership to increase compute capacity tenfold. Bloomberg reported five billion dollars. SSI had research that deserved to be scaled up, Sutskever said in the joint announcement. Combined with the three billion raised earlier, that's roughly eight billion dollars. The last known valuation is 32 billion dollars. It rests mainly on Sutskever's reputation and the expectation that he sees a path others don't.

Other labs with a similar goal

Pathway in Palo Alto has already published an architecture for what Core Automation is targeting. Its BDH model adapts during processing, forming a kind of working memory. So far, this has only been shown with small models of up to one billion parameters.

Symbolica, founded in 2022, combines neural learning with program synthesis like Ndea but uses its own math-based architecture rather than language models. The founders themselves call the long-term goal, a general machine for program synthesis, unsolved. Symbolica earns money in the meantime with Agentica, a tool for building AI agents.

Bet 2: Language alone isn't enough

The first group wants to change how models learn. The second wants to change what they learn from. These founders doubt that language is the best path to intelligence. They want systems that understand spatial relationships, predict physical consequences, and act in the world. AMI predicts abstract states, Black Forest Labs predicts video frames and robot movements, General Intuition learns from video games, World Labs generates 3D worlds, and Physical Intelligence learns control directly on the robot.

AMI is Yann LeCun's counter-program

Yann LeCun received the Turing Award and built Meta's research lab FAIR. For years, he has argued against the idea that language models lead to human-like intelligence. He left Meta in late 2025 after strategic disagreements: the company wanted to focus on language models it could commercialize quickly. At AMI, LeCun is executive chairman. CEO is Alexandre LeBrun, founder of Nabla, a medical AI startup.

Technically, AMI builds on JEPA (Joint Embedding Predictive Architecture), which LeCun proposed in 2022 as an alternative to generative language and video models. Instead of predicting outputs token by token, JEPA works with abstract internal representations. It learns what matters when predicting what comes next in a video or a robot's movement, without getting lost in pixel-level details. V-JEPA 2 showed the approach can work at Meta in 2025. After pre-training on video, just 62 hours of robot data was enough for robotic arms to handle simple pick-and-place tasks in unfamiliar labs, planning much faster than pixel-level approaches.

AMI is targeting industry, robotics, and healthcare, with Nabla as its first partner. In March, the lab raised 1.03 billion dollars at a 3.5 billion pre-money valuation with about a dozen employees. LeBrun says openly that a product could be years away. Research and most of the code are planned to be open.

Black Forest Labs goes from image generator to robot controller

Black Forest Labs from Freiburg is known for its FLUX image models, which already generate revenue. The founders, led by Robin Rombach, co-developed the method behind Stable Diffusion.

Since this fall, the lab is making a bet that goes beyond image generation. An image model has learned how images change, which means it can also learn what change a given action causes.

The first step is FLUX 3 Action, released in September. It builds on FLUX 3, a base model trained mainly on video, and was fine-tuned on data where the action behind each video is known: gameplay footage, first-person videos, teleoperated robots. The model takes a camera image, the robot's state, and a text instruction and predicts the next two seconds: how the robot should move and what the scene looks like afterward. It's an image generator that learned to control robots. The weights are on Hugging Face.

On RoboLab-120, a robotics benchmark from Nvidia, FLUX 3 Action ranked second behind Lakeside on October 22 and has since dropped to fourth. The testing is done in simulation, not on a real robot.

General Intuition turns gameplay into training data

General Intuition grew out of Medal, a platform for game clips. Medal CEO Pim de Witte founded it in 2025 with three researchers from the University of Geneva who had worked on world models for games.

The idea: record players' inputs alongside game video. The result shows not just what changed but what action caused the change. Medal provides billions of video frames paired with keyboard and mouse inputs. From these, the lab trains two types of models: world models that predict what happens next and action models that decide what to do.

The approach isn't entirely new. OpenAI trained a Minecraft agent on gameplay video with VPT in 2022. DeepMind connects Gemini with a 3D game agent through SIMA 2.

General Intuition plans to make money as a platform with API access to its models, for game studios and robotics companies alike. As proof, it showed a model trained solely on games controlling a quadruped robot after just eight minutes of fine-tuning with real robotics data.

Physical Intelligence builds a general model for robots

Physical Intelligence learns directly on the robot. The lab was founded by some of the biggest names in learned robotics, including Sergey Levine from Berkeley and Chelsea Finn from Stanford. Levine, previously at Google, describes the goal as a general robot model: a single system that works across many robots and many tasks. Nobody should have to program a separate controller for each task.

The π model family combines vision, language, and action. The lab collects most of its training data itself: humans teleoperate robots, and the model learns from the recordings. In April, π0.7 shipped with about five billion parameters. It combines learned skills into new tasks and matched individually trained specialist models in the lab's own tests. Since October 13, the Pi API is in beta: developers upload their own robot data, fine-tune the models, and run them from the cloud.

In March, reports surfaced about funding talks at more than eleven billion dollars in valuation. Competition comes from Google DeepMind's Gemini Robotics 2, from other robotics startups, and potentially from generalists like Astra that already control robotic arms. More on that below.

World Labs builds spatial intelligence, soon under AMD's roof

Fei-Fei Li is a Stanford professor known for the ImageNet dataset. In 2024, she founded World Labs with three researchers from Meta and Google, including NeRF co-inventor Ben Mildenhall. Her topic is spatial intelligence: AI that understands and generates the three-dimensional world. 3D is the language of nature, according to Li.

Since November 2025, World Labs has been selling Marble. It creates editable 3D worlds from text, images, video, or sketches, and since January also via API. Game developers use it for prototyping, robotics researchers for training environments. Marble provides the stage; what happens when a robot bumps into a chair is still handled by the simulator. Atlas is meant to close that gap and has been available to select partners since September. Atlas generates, reconstructs, and simulates in one step: a few photos become a walkable space, plus up to one minute of video with controlled camera movement.

In late September, AMD announced plans to buy World Labs for about 8.2 billion dollars in stock. The deal is expected to close by end of 2026, with Li becoming AMD's chief scientist. AMD's official reason is gaining insight into applications, but there's likely more to it. Nvidia has a complete platform for physical AI with Omniverse, Isaac, and the Cosmos models. World Labs lets AMD close part of that gap.

Other labs with a similar goal

Odyssey in San Francisco shares the bet of World Labs and General Intuition: a world model that learns motion and physics from video should teach agents to act. Since September, up to 20 humans and AI agents can operate in the same simulated world in Agora-2, with no traditional game engine running underneath. Runway, known for video generators and older than the current wave, took the same path as Black Forest Labs in December 2025 with GWM-1: from video model to action-responsive simulation, all the way to robotics.

In robot control, Physical Intelligence has two close relatives. Skild AI in Pittsburgh also wants one model for many robots but learns first from simulations and internet video. At over 14 billion dollars in valuation, it's among the highest-valued companies in this space. Generalist collects its own robot data like Physical Intelligence and showed in August with GEN-1.5 how a robot learns short manual tasks from just a few demonstrations.

The generalists are catching up

General-purpose models are gaining ground fast, however. Their spatial understanding is growing: GPT-6 Astra models a house in Blender and makes it walkable in Unreal Engine. Gemini 4 Carbon reaches human-level performance on the 3D understanding benchmark Blueprint-Bench 2.

On real robots, some limits still show up. In the AA-Robotics benchmark, Astra controls a robotic arm through self-written code and leads with 69 out of 100 points but takes half an hour per task and costs over 16 dollars. A human takes 20 seconds. With even more compute, Astra surpassed the specialist models Lakeside and FLUX 3 Action in an Nvidia study, at about 36 dollars per task.

But there is a pattern: generalists can already beat specialists on certain tasks but need much more time and money to do it. Robotics researcher Georgia Chalvatzaki sees this as a real change in capabilities but not proof that scaling alone solves robotics. The most impressive results don't come from the model alone: Astra uses simulators, motion planners, existing control software, and reinforcement learning as tools.

Chalvatzaki's conclusion: generalists are getting better at using and combining existing robotics tools. But the tools themselves, fast controllers, physics models, safety layers, still need to be built. Whether that's good news for the specialists will become clear in the coming months.

Bet 3: The most valuable data isn't on the internet

The third group bets on data that doesn't exist online: measurements from laboratories, samples from nature, engineering data from factories. Better general models don't hurt them; they serve as building blocks. The edge is supposed to come from exclusive access. Three questions matter: who generates the data, who gets to use it, and how hard is it to replicate? Basecamp and Periodic already have parts of their datasets in place. Prometheus says it's building its own.

Basecamp Research mines genes from volcanoes and the deep sea

Basecamp Research in London was founded in 2019, making it older than the current wave. But it shows with particular clarity what's at stake, when there is no internet-scale data yet. This is the case for biology, according to Basecamp Co-founder Glen Gowers. Public gene databases mostly cover well-studied organisms. Basecamp collects its own samples with local partners in more than 30 countries: from rainforest soils, volcanic rock, and the deep sea. The dataset reportedly contains more than 100 billion previously unknown genes, and Basecamp controls access. The Trillion Gene Atlas, announced in March, is meant to massively expand the data, with Nvidia and Anthropic as partners.

On this data, Basecamp trains its EDEN model family. EDEN designs long DNA sequences along with the enzymes that insert them into the genome. Some features are available to researchers through Claude. The first application is cell therapies: six programs are in early research and development before clinical trials. Basecamp hasn't given a timeline for clinical trials.

Periodic Labs uses nature as a learning environment

Periodic Labs generates its own data. Liam Fedus led post-training at OpenAI. Ekin Dogus Cubuk ran the GNoME project at Google DeepMind, which predicted millions of new crystal structures in 2023. Only a few hundred have been confirmed in the lab so far. Their motto: Intelligence is necessary, but not sufficient. Scientific progress needs to be checked against reality.

That's why Periodic runs largely automated labs in Menlo Park. Robots mix powders, furnaces bake them, X-ray machines check the results around the clock. This produces data that's in no textbook, especially negative results. Journals publish the crystals that worked and they almost never publish the failures. Fedus considers these series of failures the most valuable type of data. The long-term goal is a superconductor that works at higher temperatures. Closer to revenue are materials for the semiconductor industry. The first named customer is a chipmaker with a heat problem.

On September 15, Periodic unveiled its model Neon: an existing trillion-parameter model fine-tuned with scientific literature and the lab's own data. For analyzing X-ray measurements, it beats GPT-6 Astra and Claude Fable 5.1 at lower cost, according to the company.

Prometheus is Bezos' artificial engineer

At Prometheus, Jeff Bezos is taking an operational role for the first time since stepping down as Amazon CEO in 2021. He runs the lab as co-CEO alongside Vik Bajaj, who led moonshot projects at Google X. About 150 employees come from OpenAI, Google DeepMind, Meta, xAI, and Nvidia, according to GeekWire.

The bet is called "artificial general engineer." It has nothing to do with robotics, Bezos told CNBC. Prometheus is building tools that let engineers design physical objects much more easily: a fundamentally modernized version of CAD software. The long-term goal is software that takes an engine or a drug compound from concept to manufacturing. The data for that doesn't exist yet and has to be created, Bezos said, from physical laws and test results, for instance.

How Prometheus plans to get that data is hinted at by a second venture. According to the Wall Street Journal, Bezos held talks in March about a fund of roughly 100 billion dollars to buy and retool industrial companies in aerospace, chip manufacturing, and defense with AI. The biggest stores of engineering data sit in decades of CAD and simulation files at exactly these kinds of companies. Licensing that data is nearly impossible, since no company gives away its digital institutional knowledge willingly. Buying the companies could mean gaining access to their data too. That creates a possible strategic link: the fund wouldn't just be an industrial play but also a way to acquire training data for Prometheus' artificial engineer. This interpretation hasn't been confirmed.

From the reported rounds of 6.2 billion and twelve billion dollars, the total comes to more than 18 billion dollars. That makes Prometheus the most heavily funded lab in this issue, with no product and no published research.

Other labs with a similar goal

Lila Sciences in Cambridge, Massachusetts, is the closest relative of Periodic. In an "AI Science Factory," AI generates hypotheses, robots run the experiments, and the results feed back into training, across chemistry and biology. The edge is supposed to come from this loop, not from the robots. It doesn't run entirely without human oversight yet.

PhysicsX in London generates its data through simulation. A proprietary data factory has computed more than 20,000 aerodynamic simulations for car bodies. The more varied the data, the better the models handle new shapes, according to PhysicsX. Competitors with enough compute could replicate this.

Simile simulates humans instead of nature. The startup, founded by the Stanford researchers behind "Generative Agents," builds a behavioral model from interviews, purchase data, and controlled experiments with real people. Companies use it to test how customers react to prices or products. Investors valued Simile at two billion dollars in July. A general model can't get this kind of access to human behavior just by being smarter.

Enveda in Colorado is older than the current wave and applies the same recipe to natural products from plants. Its model PRISM learned from 1.2 billion chemical fingerprints. Half were measured in Enveda's own labs; the other half came from public sources.

The big players are building their own labs too

Anthropic is building its own biology lab in the San Francisco area where Claude will steer robots through experiments. The company acquired Coefficient Bio in April for about 400 million dollars. In October, Google described how its agents design, test, and learn from protein binders in its own wet lab. As early as August, DeepMind's Co-Scientist planned experiments for two-dimensional materials, close to Periodic's core business.

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