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 7 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
Cham: The best venture investment decisions are always highly controversial
“The best investment decisions were always very, very controversial.”
James Cham: Doomsday and labor replacement debates miss real AI opportunities
“The danger, though, is that a lot of the conversation focuses on what I would call either Theological questions about the end of the world, which is interesting, but I think oftentimes not relevant, or questions around straightforward labor replacement, and I …”
James Cham: Groups are uniquely bad at making interesting decisions
“Groups are really good at Getting new diverse points of view and new bits of information. And groups are actually also really good at doing complex coordinated tasks. Groups are uniquely bad at making interesting decisions.”
James Cham: Standard SaaS pricing metrics fail for machine learning software
“The more data you get, the more valuable it is actually for the SaaS company, right? And so all the normal units of measure around how much you should charge are, don't really apply and don't entirely make sense.”
Cham: Smartphone adoption turns all workers into knowledge workers
“I think we live now in a world where everyone is a knowledge worker, and so everyone from the construction worker to the chef, the line chef, will have a Android phone and will be able to digitize their work.”
James Cham: Machine learning should be viewed as smart statistics, not magic
“One way to think about machine learning is that it is basically Really, really smart statistics that, that ends, that end up constantly improving. And I think that as a frame for thinking about machine learning rather than machine learning as magic is, is, is …”