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 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)
I think the difference this time is the types of capabilities provided by transformers vs prior waves of AI are sufficiently different to allow many more types of startups to emerge, as well as big changes in some types of enterprise software by incumbents - in ways that were not enabled by pre-existing ML approaches.
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....
I definitely agree motivations for starting a company are mixed, and vary a lot from person to person. For the most successful companies I have seen, the motivations have largely collapsed to the ones I listed - with different mix of motivation by founder.
Finding the right word was definitely a struggle, but I think you need to lack alternatives in order to start a company. So "desperation" seemed to fit.
Hmm, I did not mean to abuse the term "VR". The original usage is much broader than just "put on a headset" immersive VR. For example, text-only MUDs & MUSHes
However, given how strongly people feel about the term, happy to use something different in the future to avoid ambiguity. E.g. "Virtual spaces" or "Spatial video".
Really the intention was to simulate aspects of the real world and in particular to virtualize human interactions in an online space.