The AI frenzy has divided the VC market, as the gap between top and bottom performers has more than doubled for 2024 funds compared to funds from 2017 to 2021 (Bloomberg)

The data shows a structural shift in venture capital, not a temporary cycle. Funds that missed the AI wave are now underperforming by margins not seen in the previous five-year window. This divergence is not just about returns; it changes the risk calculus for engineers building products. A startup’s ability to secure Series B funding now depends more on its AI narrative than on traditional metrics like customer acquisition cost or gross margin.

Adopting AI is no longer optional for venture-backed companies. The cost is twofold: immediate investment in AI talent and infrastructure, and the opportunity cost of delaying other product roadmap items. For teams without AI expertise, the gap becomes self-reinforcing, lower valuations lead to less capital, which limits hiring, which in turn makes it harder to catch up. The market is effectively pricing AI as a must-have, not a nice-to-have.

Where this dynamic stops working is at the edges of AI’s applicability. Companies in regulated industries or hardware-centric sectors may find that AI hype does not translate into tangible product improvements. Similarly, startups with strong unit economics but no AI story risk being overlooked by VCs chasing the next AI unicorn. The polarization also creates a feedback loop: top funds attract the best AI talent, which attracts the best startups, which widens the performance gap further.

The divergence is not just financial; it is operational. Engineering teams in AI-driven startups will face pressure to ship faster, iterate on models, and integrate AI into every feature. This can lead to technical debt if AI is bolted onto existing systems without proper architecture. Conversely, teams in non-AI startups may struggle to justify R&D spend, leading to stagnation. The VC market’s split is thus a forcing function for engineering priorities across the industry.