AI Startup Trends: Insights from Y Combinator’s Latest Batch

First, AI agents are likely to lead to new go-to-market strategies for SaaS companies. With potentially fewer humans in the loop, there is likely to be a further decoupling of seats/users and contract value. To capture value, LLM applications will need a new scaling factor for pricing: variable value-based pricing is a plausible solution.

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In each technology wave the value, revenue, market cap, profits and great people captured by startups versus incumbents differs. In some waves it all goes to startups, while in others it goes to incumbents or is split between them. Unexpectedly, the prior wave of value from AI roughly all went to incumbents over startups, despite a lot of startup a... See more

The moats are in the customers, not the data. We predicted that the best generative AI companies could generate a sustainable competitive advantage through a data flywheel: more usage → more data → better model → more usage. While this is still somewhat true, especially in domains with very specialized and hard-to-get data, the “data moats” are on

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However, we have noticed in many cases that AI companies simply don’t have the same economic construction as software businesses. At times, they can even look more like traditional services companies. In particular, many AI companies have:

Two ways for an AI company to protect itself from competition: (a) depend not just on AI but also deep domain knowledge about a particular field, (b) have a very close relationship with the end users.