eladgil

Hmm, which verticals did I do this in?

The majority of companies listed I did not invest in, as well as in some cases areas I have not invested in at all

I am actively involved in AI and have been for a few years, so this both gives me insights and obviously conflicts. My hope is to write things that are useful vs just shilling as I would lose credibility otherwise

I agree this is a potential outcome. One big question is generalizability versus niche models. For example, is the best legal model a frontier model + a giant context window + RAG? Or is it a niche model trained or fine tuned for law?

Right now at least people seem to decouple some measures of how smart the model is from knowledge base, and at least for now the really big models seem smartest. So part of the question is well is how insightful / synthesis centric the model needs to be versus effectively doing regressions....

I think this is already true for things based on diffusion models - e.g. Midjourney, Pika etc

For LLMs, Character and ChatGPT are arguably two vertically integrated consumer apps (with some B2B applications for ChatGPT as well)

Would love to get any views / counterpoints on these topics.

Eg which AI infra approach thrives relative to the big clouds?

What new architectures are needed for agents vs transformers?

Etc...

Lots of stuff to figure out....

I forgot to list Bitcoin as another one that launched with no VC money :)

It is remarkable how many of the tech companies that have lasted the longest and got the biggest started off so lean... (although BTC of course is not a company)

Braintrust also provides eval, so nice tie in across both...

100% agree the theory on AI is old and actually dates back to the early days of "cybernetics". But the real difference is at what point do we considered it sufficiently reduced to practice? I chose GPT-3 but undoubtedly people can point to earlier examples as glimpses of what was coming for sure.

The bar for what is "AI" keeps moving. For example plane autopilots would be "AI" in the 1980s, the ability for a machine to win at chess, go, and other games etc.

Definitely not my intention to forgot or denigrate the past. Obviously all this exists due to deep learning and prior architectures. What I have been running into is many people and companies are interpreting this as "just more of the same" for prior ML waves, when really this is an entirely new capability set.

To the (bad) analogy on cars versus planes - both have wheels and can drive on the ground, but planes open up an entirely new dimension / capability set that can transform transportation, logistics, defense and other areas that cars were important, but different enough in.

I agree that most startups fail. Only a small handful of companies at a given moment are the "breakouts" that are clearly working, and those are the most derisked to join (although not risk free for certain).

I think the entire industry forgot about startup risk during COVID, and unfortunately it is coming rushing back now with the changing environment....