Similar to the dot-com era, the current AI investment cycle is expected to produce a high number of company failures alongside a few generational winners that create more value than ever before in venture capital history.

The drama at Thinking Machines, where co-founders were fired and immediately rejoined OpenAI, shows the extreme volatility of AI startups. Top talent holds immense leverage, and personal disputes can quickly unravel a company as key players have guaranteed soft landings back at established labs, making retention incredibly difficult.

The current AI boom isn't just another tech bubble; it's a "bubble with bigger variance." The potential for massive upswings is matched by the risk of equally significant downswings. Investors and founders must have an unusually high tolerance for risk and volatility to succeed.

Top AI labs face a difficult talent problem: if they restrict employee equity liquidity, top talent leaves for higher salaries. If they provide too much liquidity, newly-wealthy researchers leave to found their own competing startups, creating a constant churn that seeds the ecosystem with new rivals.

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.

The dot-com era saw ~2,000 companies go public, but only a dozen survived meaningfully. The current AI wave will likely follow a similar pattern, with most companies failing or being acquired despite the hype. Founders should prepare for this reality by considering their exit strategy early.

Thinking Machines Lab, founded by ex-OpenAI leaders, raised $2B pre-product. Its current struggles, including executive departures and inability to raise more funds, suggest investors are shifting focus from founder hype ('vibe founding') to concrete products and business strategies.

The current AI funding climate is characterized by massive seed rounds raised on long-term vision alone, with no concrete near-term plan. The process has become highly transactional, forcing investors to make decisions in under a week, preventing deep diligence or the formation of a true partnership with founders.

In the AI era, technology moats are shrinking as tools become commoditized. Consequently, early-stage investors increasingly prioritize the founding team itself, specifically their execution velocity and ability to leverage AI, over any specific technical advantage.