The Exchanges

Q But before you move on, where would you say the US and China are in terms of competitiveness on AI technology and especially not, not even broader, but like, especially about the way that they apply it in war?

A So if you look at just the raw technology, the US is ahead, but China is, is, is moving, is fast following. You know, and we like to break it down across three dimensions. So AI really boils down to three pillars. It boils down to, um, algorithms, computational power, and data. Um, so algorithms are the kinds that, you know, folks at OpenAI or Google or other companies build. Um, computational power comes down to chips and GPUs. Um, you know, the, the kind that Nvidia, uh, produces out of TSMC's factories or TSMC's fabs, um, in Taiwan. And then lastly is data, which, uh, is maybe the, the least focused on of the three pillars, but certainly just as important for the performance of these AI systems. If we were to rack and stack versus China, we're ahead on algorithms. We're ahead on compute computational power, thankfully due to a lot of the export controls that the commerce department has put in place. Um, and then on data, it's a little bit of a jump ball. You know, the, the conventional wisdom is that China's actually probably going to be ahead on data in the long run because they don't care as much about sort of personal liberties and, and, um, you know, protecting personal data in the same way that we do in the West. And so, um, so right now the US is ahead. That being said, the sort of deployment of AI to military You know, it's hard to track exactly. The PLA doesn't tell …

AI assessment note: “if you look at just the raw technology, the US is ahead, but China is”

Q that. And then you think about, well, where else could this Be of use if it's not going to be for regular people, right? Like we're not, we don't have an AI phone right now, but we have like plenty of companies working in AI software and the military is just like the perfect example of where it could apply because of all of the information and the logistics issues.

A Yeah, exactly. And, and I think that this is, you're hitting on the core point, which I think is some, is, is often glossed over. I think when people think about, uh, the military and think about a war, they often think about the, the literal battlefield and the sort of actions on top of the battlefield. But, um, you know, 80% of the, of the effort that goes into any war fighting effort or any military is all of the logistical coordination that goes into, you know, The manufacturing of weapons or the manufacturing of various supplies, um, the logistics and sort of delivery of all the supplies to, to a battlefield, um, the decision-making process, um, the, the sort of data processing of all the information that's coming in. And so, um, uh, most of what happens actually looks to your point, a lot like an enterprise, the stakes are just dramatically higher.

AI assessment note: “80% of the effort that goes into any war fighting effort is all logistical coordination”

Q Okay, so just walk me through, like, what that experience might look like. You don't, you know, we don't have to, um, stick with this, like, it doesn't have to necessarily be the use case, but since you've imagined, uh, the idea that AI agents could end up helping us in 2025, like, what are some experiences that are in the realm of feasible for someone?

A So let's, first let's, let's walk through what, what's an ideal AI agent. An ideal AI agent is one that, that I think is, um, observing and, and naturally in all the sort of like core flows of information and core flows of, of, of context that you are in digitally. So, you know, it's, it's in all your Slack threads. It's only your email threads that like, you know, it's see, it reads your, your JIRA or all of your tools to understand everything that's going on in your, in your work life. And then it helps to sort of organize all that information to start taking certain actions. And so like one agent that I think, um, will, would be super beneficial and one that I think is in the realm of feasible is, you know, something that starts to, um, uh, take a hand at responding to a lot of your emails, um, you know, flagging when it needs you for like Additional context or information to, to be able to, to address your emails, um, can sort of summarize a lot of your emails for you naturally. And so something that just turns the, the experience of doing email from, Hey, I'm like having to respond piece by piece to every single email to leveling you up to being, Hey, this is like all of the overall work streams and workflows. And how do you want to engage Um, at a high level on top of those workflows.

AI assessment note: “one that I think is in the realm of feasible is, you know, something that starts to”

Q in some way it's combating these guardrails that companies and institutions have set up for us. On the other hand, it could end up wasting a lot of people's time. Uh, like I'm, I really, uh, anticipating like no agent policies from like certain schools or institutions being like, if you're going to reach out to us, it has to be a person versus an agent. What do you think?

A You know, I, I saw this, uh, this thing on Reddit, there was this post of, Of how, um, a, a, uh, an admissions officer, she sort of, um, created all these, uh, all these ways in which they could, they could track whether or not an essay was AI generated or not. And there were very detailed things. It was very specific. There were a list of maybe 20 or so criteria that, um, that they, that they looked for. And, um, and I think that, you know, to your point, it's, it's, uh, It was kind of heartbreaking to see because that means that, you know, let, yeah, for, if a student used an AI to generate an essay, you know, they have to spend way more time just figuring out whether or not it was AI generated to, like, sift through all the noise. Um, and so, yeah, I think, I think you're totally right. I think we're going to need, there will, um, almost in the same way that there will be, like, an internet for humans and an internet for agents. There will be processes for humans. Processes for agents and, and a lot of, um, uh, a lot of things that are high intent or very expensive or otherwise special in some way are going to be reserved for humans, um, only. And, and it'll sort of be the, the sort of like more transactional stuff that, uh, that can be handed off to agents in, in mass.

AI assessment note: “I think you're totally right. I think we're going to need... processes for humans.”

Q say we have all these specialized fields input their knowledge, does that eventually make like AI complete? If it just kind of knows everything about every subject, or does it have to hit like a new benchmark to really show that it's has this like next level intelligence? Like, does it have to start making discoveries of its own? What are you, what do you think the benchmark should be?

A Yeah, I think I, well, um, to me, I think there's like clearly many more levels of improvement. So now it's, it's sort of testing, okay, can it, can it do each of these things right once or, um, or how, there's sort of the first track was just reliability. So getting these models from doing something, um, right once in five times to write in 99.99% of the time. And that requires a lot of development just to get to that, you know, Increase the level of reliability of the systems. And then to your point, it's really about how can the model start taking more and more actions in a row? You know, one of the things that really, um, is true in all the models today is that they're not that good at, you know, uh, at taking multi-step actions. Whenever it has to take a few hops, whenever it has to take, chain a few things together, it'll invariably make mistakes along the way. And so, Um, the, the next level of improving reliability is really enabling the models to do more and more multi-turn, more and more multi-step, um, uh, reasoning to be able to enable them to sort of do more and more complex tasks. And then the last piece as we go is to, and, and this is the, the key to where you're going is like eventually It'll be able to start, be able to start making its own hypotheses, running those tests on its own, and sort of ultimately making its own sort of, uh, discoveries or realization…

AI assessment note: “eventually It'll be able to start, be able to start making its own hypotheses”

Q So why did they, why did they do that before you move on? Why did they do that? I know they also somewhat disappeared Jack Ma, right? Like they had Chinese tech icons that have sort of gone away. Uh, was it that the tech industry was growing so large it threatened the government or what it could be the possible logic there.

A Yeah, I do think that was, that's the sort of fundamental risk. I mean, I think that, um, if, uh, if the government, if the CCP has a desire to ensure that they consolidate all the power, either they have to nationalize the tech firms, or they have to ensure that they stay weak. And so, um, and there were some other, yeah, there were some other- Totally. And I think it's, I, a lot of this hinges on, I think, they do really see the world differently from the way that we do. I think we, you know, um, in the West it seems totally insane, but I think in, uh, in certain doctrines or in certain, with certain ideals, I think it make, can make total sense, right? Um, but, but there is a death of the Chinese innovation ecosystem. So, um, so a lot of what they have to do, uh, in AI is, is just catch up and copy what, what we've been up to. Um, uh, which they have been pretty successful at. So, for example, the, the, um, you know, OpenAI released O-one and released the O-one preview a number of months ago.

AI assessment note: “if the CCP has a desire to ensure that they consolidate all the power”