Interesting perspective from the author. Insightful to note that VCs perhaps are not as smart as they would like investors and the public to believe (which I totally agree with, they only human...at least for now) or simply leveraging hype with these recent high profile AI investments.<p>But I think there are some assumptions within the article worth looking into to better explain why these ventures raised as much as they did.<p>First of all there is a difference between a "deep-tech" startup trying to develop a better foundation model vs startups trying to build applications (usually a thin wrapper application). The former are the ones raising tens to hundred of millions (apparently as billion dollar valuation or more).<p>No thin wrapper startups, afaik have raised at the same amount and valuation. These startups on the other hand are actually raising at pretty typical amounts and valuation.<p>An explanation for this dichotomy is perhaps because there is actually a need for a large upfront investment to get the business of building foundation models off the ground. This by itself of course does not justify giving these startups such high upfront valuation. What may in part justify the valuation is perhaps their moat. The moat which funny enough comes in the form of the high upfront investment required for infrastructure, and perhaps the providing the financial incentives to attract the limited number of star researchers every VC perceive as essential to the success of creating new foundation models. (I assume they are well compensated for taking the leap from top companies already known for top of the market compensation). There are only so many of these star researchers that can raise such monies, which imposes a ceiling on the number of such startups in the near future.<p>Of course a moat alone is little reason to assign value to a venture. There may be a moat, but is the moat protecting gold or coal? And how much gold justifies a 10x, 20x or even higher multiples? I believe the 10x multiple is simply a rule of thumb based on perceived growth potential and comparison against mature peers in the same segment. Rules of thumb can and should be ignored sometimes, and this may be one of those times. Growth potential looks unprecedentedly good given how fast ChatGPT gained users, and their rumoured revenue growth from 28m in 2022, to a projection of 200m in 2023 and 1B by end of 2024. For reference, a 40% yoy growth counts as hyper-growth and have in the past been used to justify a greater than 20x multiple. The lack of any commonly accepted mature peers based around LLMs, GAI probably only served in these companies' favour given the above results. I am also on the fence as to whether the LLM/model as a service market will be a winner take all or perhaps some kind of competitive oligopoly like we see with cloud computing.