The Wisdom Wall

“editing a video is like, is like, you know, going to be actually in many cases, a harder task than coding. Because the, because again, the code right now is like, it has this great property of in the eval process, in the training process rather, you can instantly evaluate, did the code run?”

“unless there's some so kind of closed proprietary research event and, and breakthrough that happens that just simply nobody else knows about, and we have no evidence that we've ever had one of those in AI, like, like, you know, these things just eventually sort of emerge across the ecosystem. Unless that happens, I…”

“open source is insanely valuable, but not in the sense where a law firm should go off and build their own AI project using an open source model. Like, That, that is just a recipe for disaster if, if, you know, we think that, that every single company on the planet is going to go build their own technology to go…”

“We will be the reviewers of the AI agents work. We will be the editors. We will be the managers. We'll be the orchestrators. And that's actually how you then get the productivity gains.”

“For the first time ever with AI, we can bring automation to effectively all of that work. And that automation can kind of be tuned based on just how much compute we throw at the problem.”

“And the losses are a choice, to be clear. Like, that's very, that's very obvious. Like, they're choosing to lose that money. They're doing it, ah, for a strategic reason. You know, that, that's at least their decision. The strategic reason is, is that this is such a valuable market to own, and to dominate in, that,…”

“Apple's ultimately advantage is, is they have a distribution model that nobody else has, and they have a form factor of where AI could show up that nobody has, so they don't need, they don't necessarily need to have the best model relative to, you know, one or two months being ahead of anybody else. They just need to…”

“And, um, and the equation goes from, okay, I got to call the HR team. I've got to make sure we have budget. We have to go hire a lot of people to now it's like, well, do you want like, like a hundred leads or a thousand leads or 10,000 leads? Not, and that's not going to be driven by how many people I hire. It's going…”

“So this is kind of the big prize because it goes from the TAM The total addressable market being, you know, all of engineers to now the total addressable market is every knowledge worker, and that's probably about a 30 to 50 x larger market in terms of, you know, humans on the planet and, and, and their use cases.”

“You're not in an IDE. You're not coding alongside it. You're just setting off a task, and it's going to go and do a bunch of work for you. And now, obviously, it's very clear that that's the dominant paradigm that we're going to be in. Uh, Codex has proven it. Um, uh, you know, Cloud Code has proven it. Devon and…”

“You need to, you need to have purpose-built solutions that, that solve sort of tailored use cases. Those can be very big use cases, like all of AI coding, but you probably don't want to be in a position where you have to kind of bootstrap this or, or build it all out yourselves.”

“This is not a panacea type of, of, of solution where you could take an existing workflow, drop AI directly into it, and then all of a sudden that workflow will be, you know, three X better. You usually do have to re-engineer the work to take advantage of AI.”

“As long as you are really good about what context you're giving the AI and, and how you are, are effectively grounding the, the AI in Uh, trustworthy data with the right kinds of prompts and a, and a high enough quality model. Um, you can nearly eradicate, uh, the, you know, all of, if not the vast majority of, of…”

“I thought it was obviously a ridiculous You know, ridiculously bad situation, uh, deeply, obviously, you know, offensive and dangerous, but also not really that much of a meta story about AI simply because you can get these models to do anything you want.”

“Which is, if you're watching this curve, you probably should be building software for what is going to exist in three years from now, as opposed to today, because you don't want to be building software that sort of assumes that the tokens are expensive, and they're not that high quality, and they're kind of slow.”

“Um, you know, I think it's, to some extent, it was sort of an inevitable, um, outcome because if you think about it, like if you have this AI model that is super intelligence packed into a model, it eventually has to converge on On, you know, all of this, all the same use cases will be represented by that. And so then…”

“in general, what you, what you really want is, The, as much as possible, you want the agents to do things like, generate me a table of the pros and cons of this thing. Right. And, and make sure that you make arguments for both sides, and then you want to be really in the position of interpreting that, and making a…”

“the, the, the common practice and, and sort of state of the art is, is effectively, uh, don't give open claw or, or something access to your inbox. Create a separate inbox for the agent. And really treat that agent as a, another colleague that you're working with. And so it has its own set of resources. It has its own…”

“there is one thing in AI that, that is, um, is just like, like there's just no free lunch, uh, which is, which is that you can have something fast, like insanely fast, but like moderately accurate or pretty accurate and insanely slow. Right. And like, you just get to choose.”

“They don't, they don't need to own a percentage of, of open AI. Like that's like, they need to sell chips to open AI. And so, and so really they, they just need to ensure that they've got a very strong, you know, relationship that is, is sort of very sturdy and, and supporting the broad tailwinds of AI. And I don't…”

“you used to have to spend like two weeks on like a, like a, like a library, you know, change that you wanted to make in your code base. And that's now a 10 minute activity. And, but, but are we spending any less time building software? No, it's because we're just now doing the things that we didn't get to because we…”

“what happens is the early adopters, the people that, you know, we all hang out with and talk to all day, they're going to try everything. We're going to try these crazy goggles, and we're going to put, you know, magnets on our head, and we're going to do the craziest things. We're going to wear Google Glass, and that…”

“basically the new form of engineering work out there is to come up with incredibly precise prompts that are super tuned for his use case. And then kick off all these agents in the background that, that are going off and doing work. And then he goes and reviews their code and he integrates that code into the broader…”

“If I could get an engineer within Box to, you know, write 20% more code, and let's just imagine it's all perfect, you know, code, or a sales rep to be 20% more productive, i.e., for the same dollars, they can sell 20% more in, in revenue. In both of those examples, The benefit I'm going to, the, the, the, uh,…”

“ultimately, like if you're a hyperscaler in the cloud, what you really want is all of our workloads. You just want us to build our full application on your, your tech stack. So You're not really trying to make that much margin on the AI itself. You actually want, like, the data. You want the compute. You want the…”

“if you go and ask like 10 AI startups that are doing anything with agents, you'll probably get, you know, more or less 10 different architectures, um, of, of kind of how the agent actually functions. But what is at least similar to all of them is the LLM or the, the, the, uh, the model is really acting as the reasoning…”