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.

Low-cost AI tools create a new paradigm for entrepreneurship. Instead of the traditional "supervised learning" model where VCs provide a playbook, we see a "reinforcement learning" approach. Countless solo founders act as "agents," rapidly testing ideas without capital, allowing the market to reward what works and disrupting the VC value proposition.

Sam Lessin predicts massive losses for seed VCs backing companies branded as "AI businesses." These ventures are too capital-intensive and commoditizable to generate traditional venture returns, even if they become massive. AI should be a tool, not the business model itself.

AI enables tiny teams to build products that achieve massive traction before needing capital. This means successful founders will bypass seed and Series A rounds, raising their first institutional money at a half-billion dollar valuation or more, decimating early-stage funds.

AI companies raise subsequent rounds so quickly that little is de-risked between seed and Series B, yet valuations skyrocket. This dynamic forces large funds, which traditionally wait for traction, to compete at the earliest inception stage to secure a stake before prices become untenable for the risk involved.

AI drastically accelerates the ability of incumbents and competitors to clone new products, making early traction and features less defensible. For seed investors, this means the traditional "first-mover advantage" is fragile, shifting the investment thesis heavily towards the quality and adaptability of the founding team.

As AI enables founders to build products in a week for under $500, the need for traditional seed capital for engineering will diminish. The bottleneck—and therefore the need for capital—will shift to winning the intense battle for user attention. VCs will fund marketing war chests instead of just development.

While AI enables startups to reach $1-2M ARR with almost no hires, post-PMF companies are raising larger rounds than ever. Capital is still a weapon for scaling faster, and the surface area for AI products is so large that teams feel constrained even with enhanced productivity.

Unlike traditional software, AI model companies can convert capital directly into a better product via compute. This creates a rapid fundraising-to-growth cycle, where money produces a superior model with a small team, generating immediate demand and fueling the next, larger round.