My biggest challenge as a venture investor in AI right now is figuring out which of the following 2 camps a particular founding team belongs to:

Opportunists – who are trying to leverage this moment in time when the market has massive curiosity about AI.

vs

Believers – who have high conviction, and are truly mission-driven about AI.

This is a critical evaluation point for these early AI deals. As previous super-cycles have shown us, a bubble-bursting trough in the space is inevitable in a few years (perhaps as soon as 3-5 years?). It will be brutal like previous resets – capital will get reallocated to the winners and dry up for the rest, exits will be on brutal terms, customers will tighten their belts, early-stage talent will flee and the general sentiment will turn from greed to fear.

In my experience, Opportunist founding teams are less likely to survive this trough. It will require grinding out on fumes and focusing on real customer problems vs vanity metrics and perpetual fundraising. It will need gut-wrenching decisions that sacrifice short-term gratification so that the long-term upside can be captured. It will require possibly resurrecting the company many times from the dead.

Being able to do all this requires extremely high conviction deep down in the gut. Founders who are Believers will have this conviction in their DNA, and when the cycle turns negative, this will become their competitive advantage.

Given this is turning out to be a key evaluation point for AI deals, have been thinking through what leading signals can be used to spot Believers with higher probability. Here are some working hypothesis thoughts on this:

[Disclaimer: am just thinking out loud here so please take this with a pinch of salt. This is nowhere near any gospel of truth, nor do I have significant experiential validation around these points given we are literally in the first wave of AI deals].

1/ Pre-ChatGPT AI builders – likely to have been working in AI much before ChatGPT was launched. They were most likely building with ML, NLP, and neural networks in a Big Tech team, a lab, a university, or some sort of R&D/ academic environment.

2/ Pre-AI domain experts – likely to have been working deeply in a specific domain/ industry/ sector/ function from pre-AI days and are now adopting LLMs to carry forward their domain work and solve customer problems that were previously unsolvable or unviable.

3/ Young tinkerers – likely to be fresh grads who started building AI-native products as a hobby during university, maybe as part of a side hustle, or even just out of intellectual curiosity. They would have likely built products and hacked a few early users even without “doing a startup”.

These are only some of the personas I have been thinking through. As I meet more teams, I will keep adding to this list.

If one looks at how the early days of Web 1.0 played out (eg. in eCommerce and Search), most first-movers ended up dying. The 2nd generation companies leveraged both the market that was created by the 1st gen, as well as learnings from their failures, to create new categories and emerge as viable businesses.

History doesn’t repeat exactly but often rhymes, thus requires being even more thoughtful about which companies to back in this 1st generation of AI. In my case, as a US-India corridor investor, there is an additional complexity to think through – how will AI companies being built out of India compete with those in Silicon Valley? Who is most likely to be stronger in which part of the AI stack?

With domestic data being of strategic importance to each country and the rise of country-specific models, is AI going to be an extension of the globally decentralized software product/ SaaS story of recent years? Or will there be opportunities in ring-fenced, domestic AI in each major geography?

These questions and unknowns are what make the present times in AI investing both interesting and challenging at the same time. To manage this context, I am trying to be open-minded, learn fast, and think from first principles as much as possible. But at the same time, balancing this default-optimism stance with being non-trigger-hungry, consciously thoughtful, and taking the time to build personal conviction on each opportunity.

PS: check out the previous post #4 in the AI Musings series – How To Differentiate As An AI Applications Startup?

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