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The current AI investment cycle will likely result in a power-law distribution of returns. A small group of roughly 25 "magnificent" private companies will generate most of the value, while the vast majority of other AI startups will lead to significant capital deprecation for their investors.

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The venture market is bifurcated, with a small group of high-profile AI companies—a 'Private Mag 7'—commanding massive valuations based on narrative strength. This elite tier operates in a different reality from the rest of the startup market, which still functions under more normative conditions.

The VC landscape has split into two extremes. A few elite firms and sovereign wealth funds are funding mega-rounds for about 20-30 top AI companies, while the broader ecosystem of seed funds, Series A specialists, and new managers is getting crushed by a lack of capital and liquidity.

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 frenzy in AI investment mirrors past technological revolutions like railways. Following Schumpeter's theory, overinvestment occurs as many firms race for dominance. This leads to a bust where most fail, but the infrastructure they built remains, benefiting society in the long run.

Aggregate venture capital investment figures are misleading. The market is becoming bimodal: a handful of elite AI companies absorb a disproportionate share of capital, while the vast majority of other startups, including 900+ unicorns, face a tougher fundraising and exit environment.

The venture capital landscape is experiencing extreme concentration, with a handful of AI labs like OpenAI and Anthropic raising sums that rival half of the entire annual VC deployment. This capital sink into a few mega-private companies is a new phenomenon, unlike previous tech booms.

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.

Unlike previous tech eras, today's top AI companies (e.g., OpenAI, SpaceX) are achieving valuations in the hundreds of billions to over a trillion dollars while still private. This unprecedented scale places them among the world's largest companies before they even enter public markets.

A strong power law effect is at play across markets. In the private sphere, the top 10 unicorns now account for almost 40% of all unicorn value, doubling their share since 2020. This concentration mirrors the public markets, highlighting an increasing 'winner-take-all' dynamic.

AI startups' explosive growth ($1M to $100M ARR in 2 years) will make venture's power law even more extreme. LPs may need a new evaluation model, underwriting VCs across "bundles of three funds" where they expect two modest performers (e.g., 1.5x) and one massive outlier (10x) to drive overall returns.