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 199 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
Fox: Starship Can Carry 5 Megawatts of AI Compute Per Launch
“And when you back into the numbers, you get to something like five megawatts of capacity per Starship launch.”
Turley: AI product planning should work backwards from GPU constraints
“It's useful to start with the most zero sum trade off when you do your planning. So I think starting working backwards from GPUs is usually pretty, pretty good idea.”
Ghodsi: Enterprise AI advantage lies in proprietary data and processes
“It really comes down to your company. What data does your company have that's special? That your competitors don't have. Can you leverage that? And can you build AI that really understands that data? Because that's not a commodity. There's not an AI out there …”
Jain: CIOs should sign shorter AI contracts as winners remain unclear
“What we tell people is that I think the winners are yet to be identified and so experiment with more vendors, do shorter term contracts.”
Altman: A 100x drop in compute cost would trigger >100x demand surge
“If the price of compute per like unit of intelligence or whatever, however you want to think about it. Fell by a factor of a hundred tomorrow, you would see usage go up by much more than a hundred. And there'd be a lot of things that people would love to do wi…”
Nadella: AI software improvements are far more exponential than Moore's Law
“You know, we talk about the Moore's law improvement on one end, but the software improvements are much more exponential than that.”
Godement: Enterprise AI Deployments Fail Without Subject Matter Experts on Tiger Teams
“The reality is, like, the standard, like, operating procedures, like the SOPs, are largely in people's heads. And so, unless you have that tiger team, like, mix of, like, technical and, like, you know, subject matter experts, Really hard, like, to get somethin…”
Wu: Enterprise AI Evals Must Be Built Bottom-Up by Operators
“And evals also, oftentimes, need to come up bottom up. Right? Because all of these things are kind of in people's heads, in the actual operator's heads. Like, it's actually very hard to have a top-down mandate of, like, you got, like, this is how the evals sho…”
Wu: Product users often prefer instant substandard AI answers over 10-minute waits
“As a product builder, there's a latency, there's a real latency trade-off that you have to deal with where, you know, your user might not be happy waiting 10 minutes for, like, the best answer in the world. It might be more okay with the substandard answer and…”
Wu: Reinforcement fine-tuning is an order of magnitude more powerful than SFT
“Reinforcement fine tuning introduces like RL or reinforcement learning to this loop. Way more complex, way more finicky, but an order of magnitude more powerful.”
Gerstner: AI models shift from internet compression to tool-using reasoning engines
“So you go back to GPT-IV, you know, and you're compressing, you know, the entire internet. But now we really don't need to do it because we've trained them to use tools like the internet, right? They're true reasoning engines.”
Madra: Meta's open-source stance is its primary recruiting weapon for AI talent
“I think part of the pitch to get everyone there is that it's open. Because everyone that they're pulling over are coming from closed places, and so if you're really passionate about the work you're doing, and you're passionate about where this is going to go, …”
Gurley: AI will shift from proprietary infrastructure to cheaper open-source stacks
“In the internet age, all the startups began with Oracle and Sun. And eventually they all moved to Linux and MySQL. And so there was a, we got to win it all cost phase. And then there was a phase where you started worrying about cost and optimization. And so on…”
Thomas Laffont: Public market institutions avoid assets with 70–80% downside risk
“One of my biggest lessons looking at private market investors versus public market investors is the appetite for institutions in the public market for assets that are perceived to have significant downside, i.e., like, 70 or 80% that are marked to market, I ha…”
Laffont: Profitable companies growing over 25% should prepare for an IPO
“So our advice being to entrepreneurs that if you are growing over 25%, you are profitable. Time to think about whether you should be public. But that doesn't necessarily mean going public. As you well know, there's a difference between being IPO ready and goin…”
Gerstner: Dot-com failed short-term from low bandwidth, but crushed long-term forecasts
“The biggest problem was there just weren't that many people connected to a high speed internet. So all the things we dreamed of occurring were just inefficient to happen at that time. But what's probably even more surprising, Bill, is how dramatically we under…”
Gurley: Macro forecasting is unreliable because complex systems have hidden shifting variables
“First, it's very hard to know which variables are dependent in a complex multi-variable system, and there may be one you don't know about that it flips, and the whole system takes on a different shape, and this makes these things very hard to predict, which is…”
Gurley: Google open-sourced Kubernetes defensively to break AWS cloud lock-in
“Google had a Piece of technology called Kubernetes. It was orchestration that would allow you to move a workload. If that became a standard from one, you know, one large server vendor to another, right? It basically created ease of distribution. So you could r…”
Gerstner: AI memory dramatically increases consumer switching costs
“If you get memory, the switching costs explode.”
Gurley: Hyperscaler CapEx spending is sticky, slow, and irreversible
“The processes for spending that amount of money are sticky and slow. They're not fast. Like, you can't back out. Like, you pre-commit, and you go build.”
Patel: Synthetic training only works in functionally verifiable domains like math
“We can't teach it what good art is. Because we have no way to functionally prove what good art is. We can teach it to write really good software. We can teach it how to do mathematical proofs. We can teach it how to engineer systems, because there are, while t…”
Patel: Expensive reasoning queries unlock pricing power by automating new human tasks
“Yes, the queries are expensive, but they're nothing close to the human, right? And so each level of productivity gain I get each level of capabilities jump is a whole new class of tasks that it can do And therefore I can charge for that. Right. So this is the …”
Gerstner: AI infrastructure capex growth requires matching 30% inference revenue growth
“So, you know, if you think that infrastructure expenses are going to grow at 30% a year, then I think you have to believe that the underlying inference revenues, right, both on the consumer side and the enterprise side are going to grow somewhere in that range…”
Nadella: Companies should never enter markets out of envy
“Sometimes it's okay to fast follow, and it worked out, but you shouldn't do things out of envy. That was one of the hardest lessons I think we've learned. Do it because you have permission to do this, and you can do it better.”
Nadella: Hubris is the only thing that brings down companies and civilizations
“Because I always, as I always say, from sort of ancient sort of Greece to modern Silicon Valley, there's only one thing that brings civilizations, countries, and companies down, which is hubris.”
Gurley: Anthropic Open-Sourcing MCP Matches Google's Kubernetes Strategy
“I think it is one of the most savvy competitive Weapons you can bring to the field. I'm sure they were worried that open AI would create proprietary data connectors and they want everyone to use this one very similar to what Google did with Kubernetes.”
Gurley: Scaling variables in fitting algorithms eventually stops adding value
“Historically, Mathematical algorithms that do some type of fit, and this is a very sophisticated form of that, but when you take the variables up to a certain level, they, it stops adding value. You just get too close to the fit.”
Ball: Venture capital shifted from cottage industry to institutional asset class
“The venture markets have transitioned from a high margin cottage industry to a institutionalized lower margin industry, and that has a lot of implications.”
Gurley: NVIDIA's competitive advantage is strongest at massive system scale
“NVIDIA's competitive advantage is strongest where the size of the system is largest, which is another way of saying what Renee said. It's flipping it on its head. It's not to say it's weak on the edge, but it's super powerful when you put a whole bunch of them…”
Madra: Real-time AI inference cannot run across geographically distributed data centers
“You can train a model across a distributed site and it may just take you a, you know, a month longer because you have to move traffic around. And so instead of taking three months, it takes you four months, but you can't really run a model across a distributed…”
Jensen Huang: Curating data to train AI requires AI itself
“A lot of people don't even realize that it takes AI to curate data to teach an AI. And that AI alone is pretty complicated.”
Huang: Models built on NVIDIA run on NVIDIA without modification
“If you built it on this architecture, Without any consideration, it will run on this architecture, ok? You could still go and optimize it for other architectures, but at the very minimum, since it's already been architect, you know, built on NVIDIA, it will ru…”
Jensen Huang: Foundation models are necessary but not sufficient for AI
“A model is an essential ingredient for artificial intelligence. It's necessary, but not sufficient.”
Huang: AI should embrace both open and closed source models
“It is, I believe wrong-minded to be closed versus open. It should be closed and open.”
Gerstner: Downturns in travel and entertainment spending signal severe consumer strain
“Airbnb and Disney and the cruise lines and the airlines start seeing downticks, now you know that the consumer is really getting pressured. So there is no doubt.”
Gerstner: Rate cuts preceding recession fears historically cause market selloffs
“But remember, historically, rate cuts that are preceding a recession or fears about a recession are oftentimes sold, right? It doesn't cause the market to go up. It actually causes the market to go down.”
Gerstner: When Asset Prices Are High, Investors Should Reduce Risk Rather Than Exit
“When prices are high in venture, you know, you don't just stop doing venture. Right? But you do less. And you wait for the really great stuff. And when prices are high in the public markets, you don't necessarily step out of the markets altogether, but you jus…”
Gurley: Crypto and defense investments force venture capitalists into politics
“As you move into things that they've been moving into, crypto defense tech, like, you're getting into heavily regulated industries, so you're gonna bump up against this more, and you're gonna think about policy more”
Gurley: Companies consistently blowing out estimates signal real secular shifts
“He pointed out that if you go to the past and start to notice one to three companies just materially blowing out numbers above everyone's estimates over several quarters. There may be something happening that you should pay attention to.”
Gurley: AI Models Succeed as Tutors Due to Infinite Patience
“One thing that seems blaringly obvious is this teacher element, and Saul Kahn, who's present in some of the demos this week has been leaning into this heavily. He's someone we should definitely pay attention to but they look real, and we talked about the model…”
Gurley: Pure voice interfaces are too tedious for comparative visual tasks
“If you want to, you know, see four different hotel choices in Taiwan, I don't think you want that read to you.
I think that will be tedious versus looking at it on the screen.”
Gerstner: Serving AI models is far lower margin than traditional software
“In fact, I was talking to a large data company this week who's in the business of serving models, and they were lamenting the fact that the pricing umbrella was set by open AI and there is no price, right? So that it's a much lower margin business than the tra…”
Gurley: No individual venture capitalist can force a company to IPO
“No single VC's gonna stand up and make a company go public. That's not gonna happen.”
Gerstner: Behavioral resistance to valuation markdowns blocks tech IPOs
“And I think this is one of the big psychic or behavioral hurdles to these companies getting out and getting into the public markets.”
Gerstner: LLM Compute Intensity Fundamentally Alters Startup Risk-Reward Dynamics
“The capital intensity of that undertaking is very different than starting a software company, right? And so the risk reward to both the founders and the risk reward to the investors changes a lot.”
Gerstner: US Cannot Resolve AI Leadership Without Addressing Energy Needs
“And so, if Washington wants to get serious, it needs to be an integrated, right, national policy. You can't fix AI without also taking on our future energy needs.”
Mylod: Give employees cash instead of equity if they undervalue stock
“If we can't convince somebody that a dollar worth of stock is worth a dollar, Then you actually shouldn't grant it. You should just give a dollar of cash because they'll actually value it, you know, greater.”
Gurley: Startups Switch to Multi-Model Setups in Production for Cost
“When I look at what I see going on in the startup world, they might start with one of these, you know, really well-known service models that's proprietary, but the minute they start thinking about production, they become very cost focused and on the inference …”