The most brilliant researchers in artificial intelligence are leaving the biggest companies in the world to start their own startups.
And we are not talking about small experimental projects.
We are talking about billions of dollars in investments flowing into companies that, in many cases, are only a few months old.
Names that helped build some of the most powerful AI systems on the planet, inside giants like Meta, Google, and DeepMind, are heading out in search of something big corporations no longer seem able to offer: real freedom to do research.
The race for AI dominance has created a curious side effect. The more Big Tech companies focus on performance, benchmarks, and rapid release cycles, the more they leave gaps wide open across entire areas of research. And it is exactly in those gaps that a new generation of independent labs is setting up shop 🚀
The movement already has impressive numbers to show, well-known names at the helm, and a narrative that could signal a major turning point in the direction of artificial intelligence.
Billion-dollar rounds for companies just months old
What stands out most about this wave of departures is the speed at which the money shows up. Former researchers from labs like Google DeepMind, OpenAI, Anthropic, and xAI are raising hundreds of millions — and even more than a billion dollars — in funding rounds for startups that have barely been around for a year.
David Silver, a former Google DeepMind researcher and one of the most recognized names in reinforcement learning, announced he raised a record seed round of 1.1 billion dollars for his startup Ineffable Intelligence, founded just a few months ago. The company focuses on reinforcement learning, an approach where AI models learn from their own experience instead of relying solely on human-generated data — a fundamental difference from the large language models trained on internet text.
Tim Rocktäschel, another DeepMind alum, is on the same path. According to reports, he is looking to raise up to 1 billion dollars for Recursive Superintelligence, his new company.
And there is more. AMI Labs announced a 1 billion dollar raise in March, just a few months after its founder, Yann LeCun, confirmed he was stepping down from his role as head of AI at Meta. The company is developing artificial intelligence systems capable of learning from continuous real-world data, an approach that aims to overcome known limitations of current models in areas like causality, grounding, and reliable behavior in physical environments.
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According to data from the Dealroom platform, in 2026 a total of 18.8 billion dollars has already been directed toward AI startups founded since the beginning of 2025. That pace puts the sector on track to surpass the 27.9 billion raised the previous year by companies launched since early 2024. These numbers show that investor appetite for this kind of bet is far from slowing down.
When top talent decides to fly solo
Over the past two years, the artificial intelligence sector has seen an unprecedented wave of researchers leaving stable, well-compensated positions at companies like Google, Meta, and DeepMind to found their own labs. It is no exaggeration to say that some of these professionals were among the most valued in the market, with salaries easily surpassing seven figures in dollars and access to computing infrastructure that most universities in the world could never dream of assembling. Even so, the decision to leave came with a frequency that started catching the attention of the entire industry.
What drives this movement goes beyond a simple desire for independence. Inside Big Tech, AI research has become increasingly driven by product metrics, launch deadlines, and commercial pressures that, according to former employees, made it difficult to pursue long-term work or explore research directions without immediate and measurable applications.
Elise Stern, managing director at French VC firm Eurazeo, which invested in AMI Labs, summed up this dynamic well: when you are in a race, your focus narrows. And that narrowing creates a vacuum. Entire areas of research — new architectures, agents, interpretability, vertical models — end up being deprioritized, not because they are unimportant, but because they do not help win the immediate race.
Alexander Joël-Carbonell, a partner at HV Capital, which also invested in AMI Labs, echoed this view. According to him, inside the major foundational labs, the pressure to deliver benchmark performance and maintain rapid release cycles leaves little room for genuinely exploratory research, especially outside the dominant LLM (large language model) paradigm.
For people who spent years contributing real advances to the field, this landscape started to feel suffocating. The practical result is that these researchers enter the market with a rare combination: an established reputation, a top-tier network, and a technical vision that venture capital sees as a safe bet in a sector experiencing rapid expansion.
Finding the gaps Big Tech left behind
These new startups are not simply redoing what the big companies already do. They are aiming precisely at the spaces left uncovered by the commercial AI race.
Ricursive Intelligence, for example, raised 335 million dollars across two rounds between December and January, just a few months after being founded in September. The company is building AI tools focused on chip design. Its founders, Anna Goldie and Azalia Mirhoseini, previously worked at Anthropic and Google DeepMind, where they contributed to the AlphaChip project, which focused on automating semiconductor design.
Goldie made an interesting point about why this kind of work works better outside a Big Tech company: for chip manufacturers to trust a company with their most valuable data and intellectual property, the company needs to be seen as a neutral partner, not a potential competitor. In her words, the company needs to be Switzerland — something impossible to pull off from inside Google.
Ricursive Intelligence also brought back former colleagues of the founders. The core AlphaChip team was reassembled, with hires from their previous collaborators. Other team members came from Google, Anthropic, Nvidia, Apple, and xAI.
Periodic Labs, founded by former OpenAI and DeepMind employees, raised 300 million dollars in September, just months after launching. The company focuses on developing autonomous laboratories.
Humans&, based in San Francisco, was launched in October by former Anthropic and xAI employees. The company raised 480 million dollars in January and is also betting on reinforcement learning, the same approach that David Silver’s Ineffable Intelligence is pursuing.
This pattern shows that the new labs are not competing directly with each other for a single niche. Each one is exploring a different vector of innovation, from hardware design to autonomous learning and systems capable of operating in the physical world 🧩
The paradigm question: are LLMs enough?
A theme connecting many of these new ventures is the growing doubt about the limits of the current approach based on large language models. According to Joël-Carbonell from HV Capital, a growing number of AI researchers are questioning whether simply scaling up current LLMs will be enough to reach the next level of artificial intelligence capability.
This is a question that has been gaining momentum in recent months, as incremental performance gains in language models start looking smaller and more expensive to achieve. Current models, as impressive as they are at generating text, code, and even images, still face serious difficulties with tasks that require causal reasoning, understanding of the physical world, and truly reliable behavior in real-world scenarios.
AMI Labs, founded by Yann LeCun, is perhaps the most emblematic example of this thesis. The company stated that this was the right time to emerge because, despite major AI advances in content generation, current systems still struggle with grounding, causality, and reliable behavior in real environments. And as AI moves beyond screens — into industry, robotics, healthcare, and other physical settings — these limitations become increasingly relevant.
For anyone following the sector, this represents a potential fork in the road for AI research. On one side, Big Tech companies continue investing heavily in scaling LLMs. On the other, these new labs are betting on alternative paths that could lead to more robust and versatile systems in the long run.
The domino effect inside Big Tech
This redistribution of talent has consequences that go far beyond the tech job market. When top-level researchers leave Big Tech and take with them not just technical knowledge but also a different vision of how artificial intelligence should be developed, the field as a whole tends to diversify. That means more approaches being explored in parallel, more models with distinct architectures being tested, and potentially more chances that a significant breakthrough emerges from an unexpected place.
Many of these new companies have been hiring extensively from their founders’ former employers and from other AI giants. Investors have provided the resources these startups need to attract top-tier researchers away from Big Tech, creating a self-reinforcing cycle. The more a startup establishes itself, the easier it becomes to attract the next big name.
For Big Tech companies themselves, the phenomenon represents a challenge that goes beyond the loss of individual talents. When a former researcher founds a startup that quickly becomes a reference in the field, it creates a new point of comparison for other professionals still inside those companies. The domino effect may be slow, but it tends to intensify as more success stories emerge in the market.
Companies like Google and Meta have already responded with compensation increases, more autonomy for internal research, and dedicated programs to retain strategic talent. But the underlying issue, which is structural and cultural, cannot be solved with salary adjustments alone 💡
Governance, ethics, and a new kind of competition
There is also an important dimension related to governance and ethics in AI development. Several of the labs founded by these former researchers have placed topics like safety, model alignment, and transparency at the center of their proposals — something that, according to the founders themselves, was harder to prioritize inside corporate structures under constant pressure for commercial results.
If this talk translates into real development practices, the movement could help raise the bar for the entire sector, creating a kind of healthy competition around technical responsibility as well. When smaller, more agile labs demonstrate that it is possible to do cutting-edge research with safety principles built in from day one, it pressures larger companies to do the same.
The founders who worked in frontier labs have, in the words of Elise Stern from Eurazeo, a unique perspective. They know what works at scale, and they know exactly what is being left on the table internally. That is where the opportunity shows up.
What all of this means for the future of AI
What is happening right now in the artificial intelligence ecosystem can be seen as one of the largest reorganizations of technical talent in recent tech history. The concentration that Big Tech built over the years is being challenged not by regulation or traditional competitors, but by the very professionals who helped build it.
With robust investments continuing to flow into these new ventures and heavyweight names like David Silver, Yann LeCun, Anna Goldie, and Azalia Mirhoseini leading these companies, startups founded by former researchers have everything they need to redefine who actually leads the advancement of AI in the coming years.
Google, Meta, Anthropic, and OpenAI did not respond to requests for comment on the matter. But the silence from these companies speaks for itself — while they try to adjust their internal processes, the talent that got them this far is already building the next chapter of artificial intelligence somewhere else.
