Unlike traditional SaaS where a bootstrapped company could eventually catch up to funded rivals, the AI landscape is different. The high, ongoing cost of talent and compute means an early capital advantage becomes a permanent, widening moat, making it nearly impossible for capital-light players to compete.

The long-standing 8-12 year path to IPO is being drastically shortened by AI. Companies can now reach IPO-ready milestones like $100M ARR in just 4-5 years. This compression, combined with a backlog of large private companies, suggests a massive liquidity event is imminent for venture capital, ending the recent drought.

AI companies defy old categories. They raise growth-stage capital while pre-revenue (like venture) and serve as both foundational platforms (infrastructure) and direct-to-user products (apps). This blurring of lines demands a new, hybrid approach from investors and founders.

For 50 years, adding engineers didn't speed up software development, giving startups a defensible head start. AI changes this. With proprietary data and massive GPU resources, large incumbents can now 'throw money at the problem' to close gaps quickly, eroding a first-mover advantage.

As long as every dollar spent on compute generates a dollar or more in top-line revenue, it is rational for AI companies to raise and spend limitlessly. This turns capital into a direct and predictable engine for growth, unlike traditional business models.

A unique dynamic in the AI era is that product-led traction can be so explosive that it surpasses a startup's capacity to hire. This creates a situation of forced capital efficiency where companies generate significant revenue before they can even build out large teams to spend it.

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-native companies grow so rapidly that their cost to acquire an incremental dollar of ARR is four times lower than traditional SaaS at the $100M scale. This superior burn multiple makes them more attractive to VCs, even with higher operational costs from tokens.

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.