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.
why aren't all 10 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
Taylor: Community building provides more startup growth leverage than marginal technical improvements
“Sometimes you can over index on the technical and getting maybe, you know, your platform to be five percent more fast or supported on one more browser might not 10 X you the way that spending that time building a community would.”
Taylor: Data science and AI startups should prioritize LinkedIn over Twitter
“Twitter might get you more impressions, I think. And you might get your message out wider. So I don't think it has to be an either or, but if I was going to spend a lot of my time, especially if I was a data science or AI startup, I would learn where my audien…”
Taylor: Model observability is the most important focus for data scientists
“I harp on observability a lot because I think it's, like, probably the most important thing a data scientist can focus on”
Taylor: Healthcare lags eight years in tech, creating massive upside for ML
“I think that health care is historically like eight years behind everyone else, but there's also a very massive upside there right now for people who are going to be using machine learning and data science to solve problems.”
Taylor: Security machine learning must hyper-focus on outliers instead of discarding them
“There's a tenant of machine learning where like you just throw out the outliers because they're going to mess up your distribution and you kind of don't want to deal with them. For security, what you do is you find the outliers and you hyper focus on them beca…”
Taylor: Deploying machine learning models fundamentally alters the targeted adversarial problems
“And it's something that I think traditional machine learning hasn't really been agile enough to deal with. Right. Like the act of doing machine learning is fundamentally changing the problem you're trying to solve.”
Taylor: GenAI in games introduces new capabilities rather than replacing human jobs
“It's no one's Full-time job to make an NPC, like say your name. It's just a functionality we've never had. So you're not really replacing anything people were doing. You're just making something that didn't exist exist.”
Carly Taylor: Centralized data teams lose domain depth, embedded teams lose standards
“As soon as you centralize something, you will inevitably lose the deep expertise you can get from embedding, but as soon as you embed everyone, you lose that, like, you know, Center of excellence where everyone comes together and you set standards for your dat…”
Carly Taylor: Gaming data teams have better diversity than core programming roles
“I see more representation in, in data teams than I do for something, let's say like the, I don't know, hardware level programming. You know, which has just historically been, like, a lot of these, like, deep nitty-gritty computer science fields have been, like…”
Taylor: Startups without ready data must prioritize hiring a cloud data engineer
“If you don't have your data ready yet, you can't skip the data engineering piece of this, and I'd say you probably need someone who's going to be like your cloud data engineer. Like you just have to have those basics covered”