Everything Bradford Cross said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Investors funding AI startups without scientific competency will lose money
“So I think as, insofar as that continues to happen, Those people are gonna lose a lot of money.”
Big tech companies will lose massive amounts of money on ML APIs
“I do not think that these numbers are going to be good for these machine learning APIs. And I think those bigger companies are going to lose a massive amount of money over the next several years.”
Silicon Valley will see few consumer tech wins before a major correction
“I don't expect to see very many consumer success stories coming from Silicon Valley right now, until we have a major correction.”
Most companies cannot adopt machine learning due to limited talent pools
“I think actually most companies will not be able to use, ah, machine learning. Even though deep learning may be commodity within the machine learning community is still way too small to be commodity overall.”
Top machine learning talent is heavily concentrated at Google and Facebook
“Almost all of the really smart machine learning people are in less than 10 companies in the world. They're actually in less than five, right? In fact, they're mostly all at Google and Facebook.”
Twitter's Magic Pony acquisition marked the peak of AI talent premiums
“But I think that's, you know, that was the sort of tailing off of the, for me, the Twitter Magic Pony deal was kind of the, ah, the tail end of this absurd, extraordinary premium on, on deep learning, and then now, more and more, it's becoming just part of the…”
Capable engineering teams prefer open-source tools over buying machine learning APIs
“If a team knows what they're doing, then they tend to use open source and cobble it together themselves.”
Startups building low-level tagging APIs lack defensibility and ability to scale
“If you're doing a low-level tagging API, I really, really worry about your defensibility and the ability to scale that business.”
AI startups targeting traditional industries must build full-stack applications for adoption
“So you've got to kind of come all the way up to spoon feeding them the solution, or else I worry a lot about getting adoption for machine learning applications.”
JPMorgan Chase and Citi each spend $2B to $4B annually on compliance
“The big ones, like JPMorgan Chase, Citi, et cetera, have 10,000 plus analysts working on this every year. They're spending two to four billion dollars a year on compliance.”
Standalone chatbots will eventually be absorbed into broader software platforms
“What'll happen is they end up getting folded into systems that really meet the more basic human needs.”
the MAD Podcast 1