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Easier Bets to Get Early Customer Validation and VC Attention

The article argues that achieving scale in Enterprise AI requires significant resources, making early customer validation difficult for most startups. It suggests that VCs prioritize user adoption, which is easier to obtain with focused ideas like personalized AI agents or domain-specific GPTs rather than large platforms. The author notes that VCs outside the US are particularly risk-averse and require proven revenue before investing.

read1 min views28 publishedMay 24, 2026

There is not much of scale to be achieved in the Enterprise AI space unless you have a big team, a solid funding pipeline and a large multi-capability platform. Most AI work on the B2B large organization side is going to be building services, data products, APIs and integrating AI agents. From my experience, what VCs look for is user adoption/ customer validation. Now, that typically takes a year or so depending how strong your network is or whether you have a dedicated sales and marketing org within. Most startups do no have that kind of money or resource, so getting customer validation early is difficult. Personalized AI agents, recruitment AI, domain specific GPTs, smaller SaaS etc. are better ideas to get both, some early recurring revenue and the consecutive VC support. VCs outside of the US ( especially San Francisco) are risk averse and need to see the money before they can potentially invest. I am not trying to dissuade people from entering the enterprise space, rather I have listed the things that I have observed being in this space for the past few months or so. Would love to know yall's views.

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