Worth noting that the “evidence from the nascent AI industry” link has bits of evidence pointing in both directions. For example:
Training a single AI model can cost hundreds of thousands of dollars (or more) in compute resources. While it’s tempting to treat this as a one-time cost, retraining is increasingly recognized as an ongoing cost, since the data that feeds AI models tends to change over time (a phenomenon known as “data drift”).
Doesn’t this kind of cost make AI services harder to commodify? And also:
We’ve seen a massive difference in COGS between startups that train a unique model per customer versus those that are able to share a single model (or set of models) among all customers....
That sounds rather monopoly-ish doesn’t it? Although the blogger’s takeaway is
Machine learning startups generally have no moat or meaningful special sauce
I’ll be somewhat surprised if language modeling gets commodified down to 0 profits even if Google and Facebook release competing models. I’d expect it to look more like cloud infrastructure industry, “designed to extract maximum blood” as the author of your blog post puts it. See e.g. https://www.investopedia.com/terms/o/oligopoly.asp
Worth noting that the “evidence from the nascent AI industry” link has bits of evidence pointing in both directions. For example:
Doesn’t this kind of cost make AI services harder to commodify? And also:
That sounds rather monopoly-ish doesn’t it? Although the blogger’s takeaway is
I’ll be somewhat surprised if language modeling gets commodified down to 0 profits even if Google and Facebook release competing models. I’d expect it to look more like cloud infrastructure industry, “designed to extract maximum blood” as the author of your blog post puts it. See e.g. https://www.investopedia.com/terms/o/oligopoly.asp