The game of musical chairs in artificial intelligence is accelerating. Noam Shazeer moved from Character.AI to Google DeepMind and then to OpenAI. John Schulman went from OpenAI to Anthropic and then to Thinking Machines Lab. Barret Zoph left OpenAI to co-found Thinking Machines, only to return to OpenAI. Ruoming Pang moved from Apple to Meta and then to OpenAI after roughly seven months at Meta.
These are not junior employees climbing the career ladder. They are founders, chief scientists and influential researchers moving between companies valued in the tens or hundreds of billions of dollars. The movement raises a serious question for venture investors: what exactly are we buying when we fund an AI lab?
The chart captures selected, publicly reported moves between frontier AI companies. It illustrates an unusual market in which the talent is extraordinarily valuable, but also highly portable.
Billions for a promise
Traditional software companies are valued using some combination of product adoption, revenue, retention, distribution, proprietary data and network effects. Some of today’s most ambitious AI labs are raising billions before proving any of those things.
Thinking Machines Lab raised approximately $2 billion in 2025 before launching its first product. Axios reported that the round valued the company at an $8 billion pre-money valuation. Investors were primarily betting on Mira Murati and the research team she had assembled from OpenAI and other leading labs.
Safe Superintelligence took the thesis even further. Founded by Ilya Sutskever, Daniel Gross and Daniel Levy, SSI raised $1 billion only three months after its formation at a reported $5 billion valuation. The capital was intended for compute and talent, according to Reuters. The company explicitly committed to pursuing safe superintelligence without the distraction of intermediate commercial products.
This represents a significant inversion of the traditional venture model. Startups normally raise relatively small amounts to remove successive layers of technical, product, market and distribution risk. Frontier AI labs increasingly raise enormous sums before those questions have been answered because the cost of attempting the breakthrough is itself so high.
There is a rational explanation. Frontier research requires scarce researchers, expensive infrastructure and repeated large-scale experiments. Epoch AI estimates that the hardware and energy cost of frontier training runs has historically increased by approximately 2.4 times per year. If that trend continues, the largest individual training runs could cost more than $1 billion by 2027.
The requirement for upfront capital is real, but it does not eliminate investment risk. It concentrates that risk in a smaller number of people, technical milestones and assumptions about the future.
When talent is the moat
Axios recently observed that AI valuations are rising while founder loyalty is falling. Lilian Weng’s return from Thinking Machines to OpenAI was the latest example, but it was part of a broader pattern.
Many AI founders are researchers first and entrepreneurs second. They may be deeply committed to the scientific mission while feeling less attached to the specific corporation pursuing it. A researcher moving from one AGI lab to another may not believe they are abandoning the mission. They may see the move as continuing the same work with better compute, different collaborators or a more promising technical approach.
This creates significant key-person risk. If a conventional software startup loses an executive, its products, customers, contracts, data and distribution remain. When a pre-product AI lab loses a celebrated founder or a group of leading researchers, a meaningful part of what investors funded may walk out of the building.
The model weights and compute contracts remain, but the perceived probability of achieving the promised breakthrough may change materially. If the reputation of three researchers supported a multibillion-dollar valuation, the departure of one of those researchers is not merely a human resources issue. It changes the underlying investment case.
Traditional retention tools are also less effective in this market. Leading researchers can choose among several well-capitalised employers offering exceptional compensation, compute and influence. Golden handcuffs become less useful when every competing lab owns a gold mine.
The moat paradox
When it comes to moats in AI, Nikunj, a partner at FPV Ventures put it well:
Many frontier AI companies are valued as if they already possess, or will soon create, an extraordinary moat. The problem is that the main ingredients of that moat may be unusually portable.
Talent can leave. Compute is expensive, but it is generally purchased from the same small group of infrastructure providers. Capital is a barrier, but prominent research teams have repeatedly demonstrated an ability to raise billions before launching a product.
Models can create a capability advantage, but that advantage may decay quickly. A lab can lead the benchmarks in one quarter and be matched by a competitor or an open model in the next. Customers are also building applications that can route tasks between multiple model providers, reducing switching costs and supplier dependence.
Research breakthroughs matter enormously, but knowledge spreads. Papers are published, researchers move and competitors reverse-engineer demonstrated capabilities. Even when the specific methods remain private, showing that a breakthrough is possible gives every competing lab a new target.
The paradox is that frontier AI requires more capital than almost any previous software category, while offering fewer conventional mechanisms for making an advantage durable. Investors may believe they are buying a technological moat when they are financing a temporary lead.
AGI is not a business model
The promise of AGI creates a separate underwriting problem because it is difficult to falsify. A SaaS company either retains customers or it does not. A marketplace either develops liquidity or it does not. A pharmaceutical compound advances through clinical trials or fails.
AGI has no universally accepted definition, technical path or delivery date. The goal can move as systems improve, while capabilities previously considered evidence of general intelligence become ordinary features once achieved.
This makes AGI a powerful fundraising narrative. Almost any technical progress can be presented as movement towards the goal, while delays can be explained by the scale and difficulty of the mission. The problem is not that AGI is impossible. It may arrive sooner than many sceptics expect. The problem is that “we are building AGI” cannot replace measurable milestones.
Investors still need a way to distinguish compounding technical progress from expensive experimentation. A mission without falsifiable milestones can become a blank cheque, particularly when the next experiment requires hundreds of millions of dollars.
The financing model can also become reflexive. A large round increases a lab’s credibility, which helps it recruit stronger researchers. Those researchers support a higher valuation, allowing the company to raise more capital and purchase more compute. The loop can be productive, but it can also reverse if technical progress disappoints or key people leave.
S&P Global describes frontier AI labs as the most speculative part of the AI ecosystem because of the gap between their funding requirements and the uncertain pace of monetisation. The critical transition is from capital-intensive training to revenue-generating inference, supported by enough differentiation to sustain pricing and margins.
The Israeli laboratory
Israel offers an interesting view of these dynamics. The country combines academic talent, semiconductor expertise, military technology experience and a startup culture built around global markets. It is becoming both a source of frontier AI companies and a strategic talent base for international labs.
SSI is an Israeli-American company operating from Palo Alto and Tel Aviv. The company explicitly cites its Israeli roots and access to local technical talent on the SSI website. Its rapid valuation increase, despite having no commercial product, represents one of the clearest examples of investors placing enormous value on a small group of researchers and the probability of a future breakthrough.
That concentration creates clear key-person risk. Co-founder and CEO Daniel Gross left SSI to join Meta’s superintelligence effort, leaving Sutskever to take over as CEO. SSI subsequently announced a strategic partnership with Nvidia intended to increase its compute capacity tenfold within 12 months.
AI21 Labs represents a different path. Founded by Amnon Shashua, Yoav Shoham and Ori Goshen, the company developed its own models while building products including Wordtune, Jamba and Maestro. AI21 raised $155 million at a $1.4 billion valuation in 2023, bringing its total capital at the time to $283 million, according to Reuters.
AI21’s focus on enterprise reliability and reducing hallucinations is an attempt to turn model research into a clearer commercial position. However, its reported acquisition discussions with Nvidia reveal how the market may value it. Reuters reported that Nvidia was considering paying between $2 billion and $3 billion, with much of the strategic interest attributed to AI21’s approximately 200 specialised employees.
The reported Anthropic acquisition of Israeli startup Decart adds another dimension. Anthropic is in talks to acquire Decart for approximately $6 billion, although the transaction has not been finalised and may not proceed, according to Reuters.
Decart is not simply another foundation-model company. Its work spans real-time generative video, world models and software designed to improve the efficiency of training and serving AI systems. If completed, the transaction would be Anthropic’s largest acquisition and a strategic bet on the economics of intelligence rather than benchmark performance alone.
As Calcalist noted, the winner may not be the company that produces the best model in isolation. It may be the company that can train and serve strong models at the lowest cost, with the lowest latency and infrastructure burden. Efficiency can become a more durable moat than a temporary capability lead.
OpenAI is also increasing its commercial attention on Israel. The company has begun making local hires and engaging with the Israeli startup ecosystem, although the operation is expected to remain part of its broader EMEA organisation rather than become a fully independent regional office. Globes reported that former AWS executive Tricia Troth is leading startup go-to-market activity across EMEA, including Israel.
Israel therefore sits on both sides of the AI musical chairs market. It is producing independent labs and strategic AI assets while becoming a recruiting, acquisition and distribution battleground for OpenAI, Anthropic, Nvidia and Meta.
What investors should ask
None of this means frontier research should not receive significant funding. The technical progress is real, and commercial adoption is accelerating. Menlo Ventures estimates that enterprise spending on generative AI reached $37 billion in 2025, more than three times the amount spent in 2024.
The distribution of that spending is instructive. Menlo estimates that $19 billion went to the application layer, where companies turn model capabilities into products and workflows. Much of the durable value may therefore accrue outside the labs training the underlying models.
Strong AI moats are likely to combine proprietary data, workflow integration, distribution, customer trust, superior inference economics and products that improve through usage. A frontier model can be an important ingredient, but it is not necessarily sufficient. The most defensible company may not be the one that briefly produces the smartest model. It may be the one that converts intelligence into a product customers cannot easily replace.
Investors considering frontier labs should ask five questions. Is the team committed to the company or only to the research problem? What happens if one or two key researchers leave? Which technical and commercial milestones can be independently evaluated? How much additional capital will be required? What creates a durable moat after the next model release?
These questions should also inform deal structure. Vesting, secondary liquidity, succession planning and knowledge concentration are not administrative details when the talent is a central part of the valuation.
When the music stops
The AI talent market currently resembles a game of musical chairs in which new chairs keep appearing. Researchers leave one multibillion-dollar lab and immediately find seats at another. New labs launch, raise enormous rounds and recruit from the incumbents.
As long as capital remains plentiful and valuations keep rising, the system can sustain itself. Venture returns, however, are not created by the amount of capital a company raises. They are created by the durable value it builds.
The uncomfortable possibility is that some investors believe they are funding companies when they are actually funding temporary collections of highly mobile researchers, expensive compute contracts and a difficult-to-measure promise of AGI. Some of those teams may produce breakthroughs that transform the world. Others may discover that talent was their only moat, and that the moat could walk out of the door.
When the music eventually stops, the winners will not necessarily be the labs that raised the most money, hired the most famous researchers or made the boldest claims about superintelligence. They will be the companies that converted intelligence into a product, the product into a business and the business into a durable advantage.
He is a former General Partner at Google Ventures (GV) in Europe, former head of Google for Entrepreneurs in Europe, and founding head of Campus London, Google's first startup hub. Eze writes on Israeli tech, venture capital, artificial intelligence, and founder strategy.
He is also the founder of Techbikers, a nonprofit that brings together the startup ecosystem on cycling challenges in support of Room to Read.
- AI Musical Chairs - August 18, 2026
- Your next cyber threat may apply for a job - August 17, 2026
- AI Watermarks and the Trust Layer - August 16, 2026
