While many fear an AI bubble, Ben Horowitz argues that current valuations are supported by fundamentals. Unlike past cycles, the customer adoption and revenue growth rates for AI companies are unparalleled. This historic demand justifies the rapid value creation, suggesting it's more than just speculative inflation.

In the current AI boom, companies are raising subsequent funding rounds at the same high revenue multiples as previous ones, months apart. This is because growth rates aren't decelerating as expected, challenging the wisdom that valuation multiples must compress as revenue scales.

While AI models and coding agents scale to $100M+ revenues quickly, the truly exponential growth is in the hardware ecosystem. Companies in optical interconnects, cooling, and power are scaling from zero to billions in revenue in under two years, driven by massive demand from hyperscalers building AI infrastructure.

Cerebras faced skepticism for heavily optimizing its chips for the transformer architecture. Its successful, oversubscribed IPO demonstrates this bet paid off. The failure of alternative AI architectures to emerge has solidified demand for their specialized hardware, silencing critics and proving their strategic foresight.

For a proven, hyper-growth AI company, traditional business risks (market, operational, tech) are minimal. The sole risk for a late-stage investor is overpaying for several years of future growth that may decelerate faster than anticipated.

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.

While training has been the focus, user experience and revenue happen at inference. OpenAI's massive deal with chip startup Cerebrus is for faster inference, showing that response time is a critical competitive vector that determines if AI becomes utility infrastructure or remains a novelty.

For the first time, investors can trace a direct line from dollars to outcomes. Capital invested in compute predictably enhances model capabilities due to scaling laws. This creates a powerful feedback loop where improved capabilities drive demand, justifying further investment.

AI chip company Cerebras saw its IPO massively oversubscribed, with $100 billion in demand for a $4.8 billion offering. This intense institutional interest reflects strong confidence in their wafer-scale chip technology, even though it doesn't guarantee a huge initial stock price surge.