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 39 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 3 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
Luan: Vertical integration pressure will merge model builders and chip makers
“That's my expectation. To me, like what's interesting about AI from a business side, right, is like it forces the question of like, what Companies or offerings are going to be bundled or integrated and which ones are going to get unbundled. And I actually thin…”
Luan: Leading AI companies are already lobbying for regulatory capture
“I think I think the move to go pull up the ladder behind them is already beginning. And I think that lawmakers don't really understand this technology at all. And so their default instinct is sort of listened to the most credible source. And usually those cred…”
Luan: AI scaling will not suffer diminishing returns on compute
“The second way of improving model performance is just starting to be tapped now, and that's also going to absorb a boatload of compute. So because of that, I actually am not worried about diminishing returns to the compute over time.”
Luan: Base AI model performance requires doubling compute for consistent gains
“So put another way for just scaling up a base language model you need to double the amount of compute for that language model for it to be predictably consistently smarter.”
Luan: AI model progress is pivoting to synthetic data and reinforcement learning
“Now the critical path for model improvement is, is is shifting over to this to this sort of broader sort of simulation slash synthetic data slash like RL loop sort of path. I think it's just a natural consequence of the fact that it's so expensive to just keep…”
Luan: Pre-Trained LLMs Cannot Discover New Knowledge Beyond Human Data
“A model trained that way is only as good as the smartest data in the training set. Like, it cannot discover new knowledge, because its job, the way the models are trained, is to do what a human would do in that situation.”
Luan: Chatbots and AI Agents Are Becoming Distinct Technology Species
“I kind of think that chat bots like chat GPT and stuff and agents are kind of becoming different species of technology a little bit, right? Like I think they'll be useful in very different ways and what they need to be used for super different, right?”
Luan: Hallucinations help chatbots but destroy AI agent reliability
“Having hallucinations in chatbots and in, like, image generators is, like, a really good thing, right? Because it gives you like, a starter tool for, like, getting to, like, solve the blank page problem, right? Like, and, like, gives you, like, little bits of …”
David Luan: Pure model scaling will not solve AI reasoning
“But I think pure model scaling does not deliver solutions to reasoning.”
Luan: There will be only five to seven max-scale LLM providers long-term
“I do think there will not be that many LLM players. I think that there will probably be, my guess is somewhere between five to seven long-term steady state LLM providers at maximum scale, just because of the costs involved.”
David Luan: LLMs alone are not products, software systems are
“The underlying thing that everyone's realizing now is that LLMs themselves are not a product. Like, an actual product is this entire software system that uses LLMs in it.”
Luan: Controlling the AI model layer means controlling all underlying compute
“In the future, when more and more software is just like the logic of software is actually just handled by a, by an LLM, nobody cares anymore about what the base computing primitive is. All you need to do is access these models and compose these models to go so…”
Luan: Apple will dominate private on-device AI running at the edge
“And so I think as a result, I think Apple is just going to completely crush at everything that looks like something that's really private, something that's fine-tuned on your own particular data, but doesn't require massive reasoning capability. And that will …”
David Luan: GPT-4o is significantly underhyped relative to its scientific improvements
“I think the degree to which GPT four O was like, I think relatively under hyped relative to what I think the true scientific improvements have been in that model is, is, is this pretty big gap?”
Luan: Developer-focused model sellers must align with clouds or face commoditization
“Companies that sell models to developers will either need to effectively be the like first party effort of one of these big clouds, or they have a short window between now and commoditization to build such a big economic flywheel that they can afford to stay i…”
Luan: In 5–10 Years, Computer Interaction Will Be Goal-Driven
“I just think in five to 10 years, people are gonna use their computers by giving them high-level goals, right?”
Luan: AI will not take all jobs, humans will drive agentic systems
“Like, that's not, I don't think this is how this is going to play out. I think the way this is going to play out is, Is that what we're going to have as humans fundamentally be the drivers of these agentic systems that like that, like basically give everybody …”
Luan: AI will not plateau like autonomous vehicles due to ongoing model breakthroughs
“What's going to prevent this for what I think has a hope of preventing this from just being a hype cycle that falls flat like AB is that those things are, those shoes are yet to drop. And as they do, the capabilities of these models are going to continue to im…”
Luan: Open-source AI will lag closed models over the next five years
“And I think in the next five years, open will always lag closed. And because open will always lag closed, because open just has fewer resources behind them and fewer incentives for people to go to go make things to be open as these things become more and more …”
David Luan: Iterative prompting is a flawed interface for AI
“You know, when you go work with a coworker, right, you don't just you don't just talk back and forth. You, like, actually, like, share the same canvas. You'll go use a whiteboard. You'll go, like, maybe look at the same thing on the computer together, and then…”
Luan: AI agents in five years will function like brain-computer interfaces
“Agents in five years time, I mean it's kind of gonna be, like, a non-invasive, like, brain-computer interface, basically. I think that's what an agent will be. Like, all of us are gonna be up-leveled. We're going to, you know, it's gonna feel like the same tra…”
Luan: Addressable Work for AI Agents Is 1,000x to 10,000x Greater Than RPA
“What's the percentage of work done today that's addressable by agents? It's like, A thousand X that maybe? 10,000 X that? I don't know. Something in that order of magnitude.”
Luan: 2012-2018 AI breakthroughs came from bottom-up curiosity without corporate objectives
“During that twenty-twelve, twenty-eighteen era, the way people made progress what I mean by bottom-up basic research is you hire the most brilliant scientists, they come to work every day with, like, No near-term objective they're being held accountable to. An…”
Luan: ChatGPT was just GPT-3 with instruction tuning released a year later
“ChatGPT was really just GPT-III with instruction. It was basically like more chat tuning, but GPT-III API came out, I think, over a year before ChatGPT did, but only developers could play with it.”
Luan: Scaling GPT-2 without architectural changes unlocked three-digit arithmetic
“When we were training GPT-II, we trained GPT-II in various different sizes. And at the smallest size, the model was just, like, unable to do three-digit arithmetic. But as the models got bigger and bigger and bigger, we didn't change anything else. We just had…”
David Luan: AI reasoning must be solved at the model provider level
“I think the general capability of reasoning will need to be solved at the model provider level. And that's because What you're actually doing is you're not just using the model to reason. You are trying to improve the model's ability to reason, which means the…”
Luan: Every major cloud and LLM provider is developing custom chips
“Every one of the major clouds is working on And every major LLM provider is working on a strategy to have their in-house chips because that way they have better margins.”
Luan: Google's TPU team had under 500 people on a shoestring budget
“I think the TPU team when I was at Google was, like, sub-five hundred people and their budget was a shoestring budget, and yet somehow every generation they taped out quite good chips that were then used to train Gemini and Palm and are used by third parties n…”
Luan: Reliable AI agents require full integration from UI to model
“I do think that in the agent space, it's extremely important that you own the entire stack from what is the end user interface. I think like we were talking about the Apple example earlier, owning the interface gives you tremendous leverage in this era of AI t…”
Adept avoids signing enterprise AI deals funded by experimental budgets
“One of the things we do, for example, is we really try to not sign deals that are coming out of experiment budget because we want quality revenue basically.”
David Luan: Chat interfaces are not the right paradigm for AI
“And so that's why the HCI problem, like people just aren't spending enough time thinking about like chat is obviously not it.”
Luan: OpenAI and DeepMind pivoted AI research strategy before competitors
“What OpenAI realized before basically everybody but DeepMind was that the next phase of AI after Transformer was not going to be about research paper writing. It was going to be about let's choose a major unsolved scientific problem and just try to solve it.”
Luan: AI success is existential for tier-one cloud providers
“I think every tier one cloud provider existentially needs to win here.”
Luan: Google Brain was the Bell Labs of the 2012–2018 AI era
“That, like, 2012 to 20 18 or so era, Google Brain was just, like, incredibly dominant. They did an amazing job picking talent. Like, the people who invented Transformer, the people who invented the diffusion model, people who did all of these new optimization …”
Luan: AI development feels more like gardening than traditional software engineering
“And I know exactly the behavior of the system that I've built will be. But the cool thing about AI is that every day you come to work and you make some tweaks to the model. And what you get on the other end is actually somewhat unpredictable. Like you kind of …”
Luan: Every enterprise workflow is an edge case
“I was talking to Parag, who used to be the CEO of Twitter, we were just hanging out the other day, and he's like, dude, every enterprise workflow is an edge case, and he's absolutely right, and that's why you need to control the same thing.”
Luan: Co-pilots are a great strategy for incumbents adapting to AI
“Like, I think co-pilots are a great incumbent strategy because it lets them morph their existing software business model to something that kind of looks the same while getting in on the AI thing.”
Luan: AI will make workers generalists supervising specialized AI co-pilots
“So I think what it's gonna do is it's gonna make humans at work much more like generalists, and it's gonna have, like, causes to create larger and larger, oh, sorry, like or giving people sort of, like, larger and larger scope over various different, like, are…”