By raising billions for Safe Super Intelligence without any product plans, Ilya Sutskever is employing an extreme version of the "pre-revenue" strategy. This approach avoids scrutiny of business metrics, allowing the company's valuation to be driven entirely by narrative, talent, and the promise of future breakthroughs, mirroring tactics from previous tech bubbles.
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The memo details how investors rationalize enormous funding rounds for pre-product startups. By focusing on a colossal potential outcome (e.g., a $1 trillion valuation) and assuming even a minuscule probability (e.g., 0.1%), the calculated expected value can justify the investment, compelling participation despite the overwhelming odds of failure.
The investment thesis for new AI research labs isn't solely about building a standalone business. It's a calculated bet that the elite talent will be acquired by a hyperscaler, who views a billion-dollar acquisition as leverage on their multi-billion-dollar compute spend.
The massive capital expenditure in AI is largely confined to the "superintelligence quest" camp, which bets on godlike AI transforming the economy. Companies focused on applying current AI to create immediate economic value are not necessarily in a bubble.
Founders in deep tech and space are moving beyond traditional TAM analysis. They justify high valuations by pitching narratives of creating entirely new markets, like interplanetary humanity or space-based data centers. This shifts the conversation from 'what is the market?' to 'what could the market become?'.
The startup landscape now operates under two different sets of rules. Non-AI companies face intense scrutiny on traditional business fundamentals like profitability. In contrast, AI companies exist in a parallel reality of 'irrational exuberance,' where compelling narratives justify sky-high valuations.
Ilya Sutskever's new company, focused on fundamental AI research, is attracting growth-stage capital for a high-risk, venture-style bet. This model—allocating massive funds to exploratory research with paradigm-shifting potential—blurs the lines between traditional venture and growth equity investing.
Products like Sora and current LLMs are not yet sustainable businesses. They function as temporary narratives, or "shims," to attract immense capital for building compute infrastructure. This high-risk game bets on a religious belief in a future breakthrough, not on the viability of current products.
The current AI investment climate feels as 'risk-free' as the 2021 bubble. Venture firms are likely using flawed loss-ratio models, underestimating how many AI 'unicorns' will fail to generate returns, just as they did with the B2B SaaS unicorns from the previous cycle.
Contrary to common belief, the earliest AI startups often command higher relative valuations than established growth-stage AI companies, whose revenue multiples are becoming more rational and comparable to public market comps.
In the current AI hype cycle, a common mistake is valuing startups as if they've already achieved massive growth, rather than basing valuation on actual, demonstrated traction. This "paying ahead of growth" leads to inflated valuations and high risk, a lesson from previous tech booms and busts.
