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 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
Bose: Customizing model weights is a waste of R&D resources
“Our sort of maximalist thinking is that the frontier labs are going to keep innovating in the level of reasoning and capabilities of their models, and so trying to create these customizations or adding our own token weights Is not a good idea and is a waste of…”
Bose: Non-software companies will fail by building in-house AI coordination tools
“These companies are, you know, are not going to be successful if they end up spending their tokens and their cost on achieving these outcomes that are more about coordination between human beings and AI agents.”
Bose: Forking open-source models leaves software companies 3–6 months behind
“If you create a fork and you're always three to six months behind what your, Competition could be doing. You know, like there'll be other companies, I'm sure, who are thinking about some of the challenges that we are addressing at Asana. I don't want to be thr…”
Bose: Enterprise AI stalls because generic outputs slow down human reviewers
“Today, I don't think customers are getting, like, in general, as people have been embracing AI, they aren't getting that level of exponential output or maybe the right way to say it is they aren't getting the level of exponential outcomes from their investment…”
Bose: Enterprise context grows more valuable as AI reasoning models improve
“The real value we're providing, again, is with the enterprise-weight context and the shared memory. And so that becomes instantly more valuable as the reasoning model gets better.”
Bose: Historical enterprise coordination data is ideal context for AI agents
“Now, the interesting thing that the coordination engine brings to the table is that not only does it define who does what by when, but it also has a track record of how were those projects completed in the past, what happened when that particular project went …”
Bose: Enterprise AI agents require far more work than demos suggest
“The number one thing people misunderstand is the amount of work required to ensure that they provide great output and great outcomes. It's easy to see these demos and be like, oh wow, like, there, there's so many ways in which I could have an AI chief of staff…”
Bose: Multiplayer AI agent transparency reduces team coordination tax
“Because it's running in this multiplayer way, it means the entire marketing team can stay on the same page. Like, no one is confused when they get that document about, hey, what was the prompt? Was the research plan correct? They can go back into the task and …”
Bose: Training AI on company history prevents average, generic outputs
“And so that's step one, which is like, it's learning from this historical set of tasks and creative briefs that your company has created. So again, it's not sort of giving you the average of averages across creative briefs in the world. It's highly trained and…”
Bose: Asana uses OpenAI for chat, but Anthropic for AI Teammates
“We are not using OpenAI right now for AI teammates, but we are using it in other parts of Asana AI where those models have Been proven to be either cost efficient or highly performant for those use cases within our AI studio capabilities or street AI chat capa…”