The Ledger

Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.

clear all ✕

why aren't all 11 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 0 checkable ones are still open, waiting for their date. predictions held up or didn't; assertions are supported or contradicted. on every card: ▮▮▮▮▮ certainty · ▮▮▮▮▮ debate potential. speakers are clickable

Prediction Not checkable as stated
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.”
Bradford Cross Sep 28, 2017 ▶ 15:01 AI Startup Predictions // Bradford Cross - A fireside chat with Matt Turck (FirstMark's Data Driven)
Prediction Not checkable as stated
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.”
Bradford Cross Sep 28, 2017 ▶ 17:44 AI Startup Predictions // Bradford Cross - A fireside chat with Matt Turck (FirstMark's Data Driven)
Prediction Not checkable as stated
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.”
Bradford Cross Sep 28, 2017 ▶ 8:27 AI Startup Predictions // Bradford Cross - A fireside chat with Matt Turck (FirstMark's Data Driven)
Prediction Not checkable as stated
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.”
Bradford Cross Sep 28, 2017 ▶ 12:35 AI Startup Predictions // Bradford Cross - A fireside chat with Matt Turck (FirstMark's Data Driven)
Assertion Not checkable as stated
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.”
Bradford Cross Sep 28, 2017 ▶ 19:21 AI Startup Predictions // Bradford Cross - A fireside chat with Matt Turck (FirstMark's Data Driven)
Opinion
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…”
Bradford Cross Sep 28, 2017 ▶ 11:32 AI Startup Predictions // Bradford Cross - A fireside chat with Matt Turck (FirstMark's Data Driven)
Insight
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.”
Bradford Cross Sep 28, 2017 ▶ 16:16 AI Startup Predictions // Bradford Cross - A fireside chat with Matt Turck (FirstMark's Data Driven)
Insight
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.”
Bradford Cross Sep 28, 2017 ▶ 18:36 AI Startup Predictions // Bradford Cross - A fireside chat with Matt Turck (FirstMark's Data Driven)
Insight
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.”
Bradford Cross Sep 28, 2017 ▶ 23:57 AI Startup Predictions // Bradford Cross - A fireside chat with Matt Turck (FirstMark's Data Driven)
Assertion Partly supported
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.”
Bradford Cross Sep 28, 2017 ▶ 3:10 AI Startup Predictions // Bradford Cross - A fireside chat with Matt Turck (FirstMark's Data Driven)
Prediction Not checkable as stated
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.”
Bradford Cross Sep 28, 2017 ▶ 9:08 AI Startup Predictions // Bradford Cross - A fireside chat with Matt Turck (FirstMark's Data Driven)
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