The artificial intelligence industry is experiencing a significant wave of fragmentation as researchers and executives leave established AI labs to launch competing startups, driven by a combination of personality clashes, scientific disagreements, and abundant venture capital funding. This trend has produced some of the most well-funded AI startups in recent history.
Ilya Sutskever, OpenAI’s former chief scientist, exemplifies this phenomenon after raising $1 billion for his AI safety startup Safe Superintelligence in September 2024, securing backing from venture capital giants Andreessen Horowitz and Sequoia before even launching a product. Similarly, Black Forest Labs, founded by former Stable Diffusion executives, entered talks to raise $100 million at a $1 billion valuation. Paris-based H, launched by former DeepMind staffers, raised an impressive $220 million pre-seed round from Accel and UiPath in May, while SakanaAI, led by StabilityAI’s former COO, secured $200 million from NEA.
The trend arguably began when former OpenAI executives departed to launch Anthropic in 2021, establishing a pattern that continues to reshape the AI landscape. According to PitchBook data, startups building foundational models have raised a record $22.9 billion in VC funding in 2024, up from $18.4 billion in 2023, demonstrating extraordinary investor appetite for these capital-intensive ventures.
An annual state of AI report by Air Street Capital published in October anticipates this fragmentation will continue, driven by “a combination of scientific disagreement, commercial pressures, personality clashes, and availability of capital,” signaling “an ecosystem deepening.” However, the trend isn’t without complications—three of H’s cofounders left just four months after launch due to “operational differences,” highlighting the challenges these new ventures face.
Industry observers attribute the exodus partly to ego dynamics. Noel Hurley, ex-VP at Arm and CEO of Literal Labs, suggests many researchers “have been told they’re brilliant, so they believe it,” with venture capitalists further inflating these perceptions. Nathan Benaich, founder of Air Street Capital, notes that junior researchers promoted to senior levels often want to pursue specific research directions that may not be prioritized at larger organizations.
Looking ahead, experts anticipate a consolidation phase, with these fragmented labs eventually merging under two to three major entities, unless they pivot toward building niche models within specific verticals. The intensive costs of running AI labs may force this consolidation as competition intensifies.
Key Quotes
I think they are also slightly encouraged by the venture capital community. They’re always looking for superstars and so, and once they find a superstar, what they’ll do is they will raise, and they will inflate that superstar because then that helps them pass that investment on.
Noel Hurley, ex-VP at Arm and CEO of Literal Labs, explains how venture capital dynamics contribute to the ego-driven fragmentation of AI labs, suggesting that VCs actively inflate researcher reputations to justify massive investments.
Talent who want to push the envelope often find it easier to branch out and start their own ventures where they have more control and flexibility.
Samir Kumar, general partner at Touring Capital, describes why researchers leave established labs, pointing to bureaucracy and internal constraints that frustrate ambitious researchers seeking to pursue their vision with urgency.
If your competitors are like pseudo vertically integrating to get access to data centers and data and product and users, then I don’t see how you can compete with that by raising a $20 million, $30 million or $100 million round.
Nathan Benaich, founder of Air Street Capital, warns about the structural challenges facing smaller AI startups, suggesting that the intensive capital requirements and vertical integration of major players may force consolidation despite current fragmentation.
A cracked team can get a lot more done with far fewer people these days.
Peter J. Liu, former DeepMind research scientist, announced his departure in October with this observation, suggesting that smaller, focused teams can now achieve what previously required large organizational resources, justifying the startup approach.
Our Take
This fragmentation wave reveals a fundamental tension in AI development: the need for massive resources versus the desire for agility and control. The $22.9 billion in funding for foundational model startups in 2024 represents both extraordinary opportunity and potential excess. What’s particularly striking is how quickly these dynamics are evolving—H’s cofounders departing before launch demonstrates that even the fragmenters are fragmenting.
The predicted “yo-yo effect” consolidation seems inevitable given the economics of AI development. Training frontier models requires hundreds of millions in compute costs, making sustainable independence difficult without either massive revenue or continuous fundraising. The real question isn’t whether consolidation will happen, but which business models will survive: horizontal foundation model providers, vertical specialists, or hybrid approaches. The winners will likely be those who can balance scientific ambition with commercial pragmatism—something that personality-driven splits often struggle to achieve. This moment may represent peak fragmentation before a significant shakeout.
Why This Matters
This fragmentation of AI labs represents a critical inflection point in the artificial intelligence industry’s evolution. The exodus of top talent from established players like OpenAI, DeepMind, and Meta signals both the maturation of AI technology and the growing tensions within organizations racing toward artificial general intelligence (AGI).
The trend has significant implications for innovation and competition. While it could accelerate AI development through diverse approaches and increased competition, it also raises concerns about resource efficiency and safety coordination. The fact that researchers are securing billion-dollar valuations before launching products demonstrates unprecedented investor confidence but also potential market frothiness.
For the broader tech ecosystem, this fragmentation could democratize AI development, breaking the monopolistic tendencies of tech giants. However, the anticipated consolidation phase suggests that only well-capitalized players will survive long-term, potentially recreating the same concentration of power. The phenomenon also highlights critical organizational challenges in managing brilliant, ambitious researchers—a lesson relevant beyond AI to any cutting-edge technology sector. As these new labs compete for talent, compute resources, and market share, their success or failure will shape whether AI remains concentrated among a few players or evolves into a more distributed ecosystem.
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Source: https://www.businessinsider.com/ai-labs-fragmenting-ego-clashes-new-startups-openai-2024-10