Unlike past platform shifts that caught many off-guard, the AI wave is universally anticipated. This 'consensus innovation' intensifies all existing competitive pressures, as every investor—from mega-funds to accelerators—is aggressively pursuing the same perceived opportunities, pushing factors like Power Law belief to an extreme.

Mega-funds can justify paying "stupid prices" at the seed stage because they aren't underwriting a seed-stage return. Instead, they are buying an option on the next, much larger round where they'll deploy real capital. This allows them to outbid smaller funds who need to generate returns from the initial investment itself.

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.

Y Combinator's model pushes companies to raise at high valuations, often bypassing traditional seed rounds. Simultaneously, mega-funds cherry-pick the most proven founders at prices seed funds cannot compete with. This leaves traditional seed funds fighting for a narrowing and less attractive middle ground.

The recent surge in demo days and YC-style incubators from major VCs is a delayed reaction to the valuation boom of two years ago. These programs are a strategic play to get cheap, early-stage access to a wide portfolio of AI companies, de-risking entry into a hyped and uncertain market where good ideas are hard to differentiate.

The rapid evolution of AI means traditional private equity M&A timelines are too slow. PE firms and their portfolio companies must now behave more like venture capitalists, acquiring earlier-stage, riskier AI companies to secure necessary technology before it becomes unaffordable or obsolete.

The AI fundraising environment is fueled by investors' personal use of the products. Unlike B2B SaaS where VCs rely on customer interviews, they directly experience the value of tools like Perplexity. This firsthand intuition creates strong conviction, contributing to a highly competitive investment landscape.

The ideal period for venture investment—after a company is known but before its success becomes obvious—has compressed drastically. VCs are now forced to choose between investing in acute uncertainty or paying massive, near-public valuations.

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.