The difference between early and late AI adopters

0:00We have agents that are capable of replacing, you know, something like 80%, maybe 90 % of the calls that come into a call center. So what is it like when you go out there and you show this technology, particularly to those frontline operatives? I think they feel about it the way that someone who was a blacksmith or a buggy whip maker felt when they saw their first automobiles rolling down the street, right? They could tell something was happening and they don't like it, but what else can they do? I think every person who's developing software knows that getting to the first 70 % is trivially easy.

0:32It's the climb up the last 30%. 70%. You don't just want to turn this thing on and then discover like, oh, overnight it got into some bad loop and pissed off 100 ,000 customers, right? One of the things that I think the general public doesn't understand is like, how much rigorous statistical testing is required to know what are the tail risks associated with a deployment of autonomy? That could be automated customer service. That could be a driverless vehicle. Let's talk about China. There is this tournament-style competition between mayors of cities. They act a little bit like, you know, mini Y Combinator bosses or mini venture capitalists.

1:06Combined with the people, there are just more pro-technology. You say like, oh, we're going to have AI at the hospital. The average Chinese person is not thinking like, oh, what about my privacy? Or what if the AI makes a mistake? The Chinese people are more like, this is awesome. In a future that is full of AI agents, what's something that you might end up doing more of? Again, I'm going to tell you something incredibly crazy. You can break the news here. Today, we are joined by physicist and entrepreneur, Steve Hsu. The most loyal Exponential View listeners may remember. He was on my podcast back in 2019.

1:42Feels like a century ago, so much has happened. We talked about biotechnology and the promise of genomic sequencing. And I actually had to go and buy a maths book after that podcast to make sense of some of the answers. So today we're talking about something else, AI, agents, and what might come next. Steve, it's great to have you here. I want to start with what you're building. You've co-founded a company called Superfocus, which you've described as solving some of the key limitations in today's LLMs. Give us a quick tour. What are you building and what is the deeper shift that it represents?

2:19So I think everyone who's experimented with large language models knows that they hallucinate. So they will sometimes give you a very confident but completely wrong answer and that answer will always be typical of the kinds of things that it saw in its pre-training. So if you ask an LLM, is there a flight that reaches Paris from London that lands around four o 'clock? It will give you an answer, but it might not be real. It will seem like a real answer, like, oh, Air France has one that lands at 412, right? And maybe in the past it did, but maybe right now it doesn't, right? Typical large language model hallucination.

2:56And so our startup realized early on that in order to make practical applications from these AIs, one would have to solve this problem. One would have to control both the behavior of the AI agent, if you want to call it an agent, and also control its fact base, the sort of core knowledge base that it uses to answer questions or conduct operations. And that cannot be drawn from the pre-training data. There's just too much junk in the pre-training data. Or even if it's not junk, it may not be relevant to the specific problem that the AI agent is trying to solve for you. Right. So you're tackling that fundamental limitation in the way that LLMs are built.

3:38I've had that experience. The way I think about this is that they are often accurate at a conceptual level, but not necessarily accurate at the word or token level. So they'll sometimes say of me that I went to Cambridge University, which if you're an alien sitting on Neptune, I went to Oxford. Well, Cambridge is kind of the same thing. But they don't ever say, I went to West Point and I did SEAL training. I mean, I've never had as wild a hallucination as that. And that really is about the way in which they represent the world in this complex, high-dimensional space, isn't it? Yes. So there's something called the embedding space.

4:18The models are actually working in this abstract space, which is literally a space of concepts. Oxford and Cambridge are very, very close in that concept. And maybe there's no other school that's exactly in that space. But, you know, sometimes the details matter. So if I'm a fundraiser for Cambridge, I actually care whether you went to Cambridge or Oxford in deciding whether to contact you, right? Right. Yeah. Yeah. Yeah, so the details matter, but in some sense, they've still got a useful model of the world that perhaps loses some of its resolution if we push really, really hard for it to be precise.

4:54So how do you go about resolving that? Right. So we actually build systems in which we embed the language models in a larger software platform. and the language model itself, we generally are using mainly only for its language and to some extent reasoning abilities, but the knowledge base is stored separately. The fancy way we describe it is as a kind of attached memory for the AI. The AI can rely on that attached memory. And the way we program, we use sort of old style programming in this platform. We sort of force the model to only use that knowledge base in answering the fact part of questions.

5:38And so that extra constraint, it solves the hallucination problem and makes both the behavior and the knowledge base of the AI reliable. How different is that to a traditional RAG, retrieval-vented generation type of approach? So if you look at the architecture, it is actually, a piece of it is RAG. And interestingly, we actually, when we founded the startup, we actually filed a patent, the company filed a patent on our architecture And that was actually before the word RAG was in wide usage. So it is possible, who knows how the USPTO, Patent and Trademark Office, operates, but we might be issued a patent on RAG.

6:17Of course, it's not referred to RAG in the patent filing. It has a lot of similarity. Another thing you might do is you might have multiple models involved in the generation of the response in which some models are just error checking the proposed response of the big model against what the little models can see in the knowledge base. And all that, if you're doing voice, which we do, all that has to happen in a latency time of less than two seconds. So humans, if I stop speaking and I'm waiting for you to respond to me, if it goes more than a couple seconds, it's kind of strange. And so all of the stuff that I just described to you is engineered down so that the latency is between one and two seconds, so it sounds natural.

6:59One of the things that I'm curious about is whether the improvement in models really starts to tackle the hallucination question. So I've been using Gemini Pro 2.5 or Gemini 2.5 Pro for the last couple of weeks, both through the app and via the API and some applications I've built. and I've been really, really impressed with its ability to be factual, be precise and hold that over quite long context conversations. That was often a problem with LLM models, which was, they were very accurate for the first 500 words and by the time you got to 5 ,000 or 10 minutes into your discussion, they're just going all over the place.

7:41And I found that wasn't the case with Gemini 2.5 Pro. So in a sense, isn't a lot of this going to be solved by the investments in bigger and bigger models that can model more precisely this concept space? And that's the direction that the Googles and the open AIs are going? Well, it is improved. The situation is improved if you're using a model that has reasoning capabilities. What's going on in the reasoning is the model has been taught as it sort of talks to itself in trying to solve, generate a good response to your query, it has been taught to double check facts or components of the reasoning.

8:21However, if the model doesn't really have access to the actual ground truth, it can still go off the rails because it can think X is true. It doesn't have a way to independently validate X, and it can then use X in its reasoning pattern. Even though it knows it's supposed to check X, It doesn't really have a way to falsify. The other problem with reasoning models for the application that we're specifically addressing in our market segment is people want for voice, again, fast response. So if you rely on a whole bunch of tokens being generated for reasoning, that's way over the fraction of a second that you have for that generation.

9:01You know, the speech to text and text to speech, all that stuff is wrapped in there. And then the reasoning has to be, you know, even smaller than a second. like a fraction of a second. So you would need a reasoning model that generates all of its reasoning tokens that fast. And then you might be able to use it, yeah. I mean, when we talk at this level of detail, of course, it puts into context what this path to super capable AI really is. Because you're working in a very constrained environment where not only do you have to, you know, take the sound wave, you've got to break it into phonemes, you have to put it into a voice model, you then have to turn that into text.

9:39You then have to reason across it. And you've got a really limited amount of time to do all of that. You know, it's not like O1 Pro going away to think for 10 minutes. And this is sort of the reality of how these products are going to be built over the next few years. With Superfocus, I saw quite an impressive demo where essentially your AI is having like a support conversation with a real person. So where is that now? Where is that technology now? And was that a shiny TED-style demo? Or is that the reality of customers using this? It's interesting. And this gets into a very, I think, important point, even for the broader question of AGI and what's going to happen to human society in the future.

10:24So the technology for, you could call them customer support agents, is very advanced now. So we have agents that are capable of replacing, you know, something like 80%, maybe 90 % of the calls that come into a call center. So if you're ordering a pizza or you're changing a delivery address for a package, you're wondering what happened to your package, you know, all of those things, AIs can actually handle pretty well now. However, in terms of what fraction of labor that used to be done by humans, entirely by humans in these call centers, has been replaced thus far by AI agents, it's still minuscule.

11:03It's very tiny. And a lot of it's held up by human decision-making. A lot of it's held up by sunk costs in old systems and old ways of doing things. And so one of the lessons that people don't understand, like the real pain that startups have to go through isn't just inventing the technology, it's actually diffusing the technology and getting customers to understand how it works, to adapt their workflow to it, to buy it, make a bet because, you know, you're taking some risk to let some guy come in and say, hey, my black box AI is going to replace those hundred people over there in the field that are in the call center.

11:39So it's just slower than what people think. I was going to ask about that because you, I do remember you were going off to the Philippines quite regularly a couple of years ago where BPO business process outsourcing is 2 million people in the country employed there, about 10 % of GDP. It's obviously bigger in India, 6 million roughly working BPO and support by the end of this year. Those are big numbers and they tend to be above average in terms of their education, in human capital and above average in their income. So what is it like when you go out there and you show this technology, particularly to those frontline operatives who, in a sense, you are producing technology that does quite a lot of their work?

12:28I've been in many, many meetings now where in the room are usually it's managers, more senior people at a company, maybe a company that does BPO work or a company that hires outsourced BPO providers, but people who are familiar with the customer service problem and have maybe even worked, some of these people have worked their way up. So they have worked in a call center themselves or they spend time in call centers all the time. And what's amazing is how big their eyes get when, you know, we might show them a video demo, which is a recording of maybe me or somebody else talking to one of the AIs.

13:04And okay, that's impressive enough. But then like some of them are a little more suspicious. So like, well, let me talk to the AI. Can I talk to your AI? And I'm like, sure. And I turn my laptop around and, you know, hit a button and they can talk to the AI. And when they realize that the AI is actually under, you know, quote, understands what they want and can make changes in the system, generate a return label, you know, can do lots of things that we've, you know, functionality that we've equipped it with. It's kind of shocking because you see the wheels turning in their head and they say, wait, I employ a thousand people who do mostly this.

13:38Like surely there are some corner cases where you really want the AI to pass it off to a human. But 90 % of the labor that those thousand people do is being done by this AI. And if I choose to power it with DeepSeek or something now, I can do it for, you know, we used to think in terms of one-tenth the cost per hour of labor versus the human, but you could go down another order of magnitude. I mean, that sounds insane, but it's actually true. It's crazy. And at a one one hundredth of the cost, you could run this across five agents at the same time. And if four of them agree, you just move to the next step, right?

14:15You can construct a sort of a voting mechanism to reduce the number of edge cases you can't handle. That, of course, though, let's talk about what that means in those employment circumstances in those companies in that country. You said that deployment is slower than you expect because buying and decision making is slow. So that is from the bosses of the companies over in, say, the Philippines. But what's your sense for the extent to which people have understood what this might mean once the technology is being bought and deployed and ultimately copied, the deployments being copied by everyone's competitors in the various suburbs where this gets done?

14:58So we are right in the middle of one of the greatest examples of the future is already here. It's just not evenly distributed. Typically, like if you're, let's suppose you're a growth stage startup. So you found a particular niche and you're growing like gangbusters, but you're still very functional. You're a small company, relatively small, and you've got very good startup people, agentic people, etc. etc. Those companies are going to or are deploying AI very fast. And so some of those companies, when you deal with customer service there, or you have to do something in their system, you literally are dealing with an AI and they've embraced the technology.

15:39And it's what all the other companies are going to look like five or 10 years from now. On the other hand, I have to go to these like trade shows and expos, founder led sales, right? So I'm often at a meeting where Everybody at the meeting is somebody who owns like, I own the customer service function for, you know, Hilton Hotels. I own the customer service function for XYZ Bank. I own, you know, those are the people there. And those people want to deploy this. They're being told by their CEO and CFO that they should deploy it. But if you look at their incentives, okay, imagine you're someone who came up, you're 50 years old and you came up for, you know, you've been working for 25 years in a call center environment.

16:21you worked your way up, you know how to manage people, you know how to deal with, you know, contractors in the Philippines or in India. There's a bunch of like specialized knowledge that you've built up over the years. Okay. Some crazy professors, startup founder guy comes to you and says, Oh, you see this little black box. This is going to replace this little black box, which isn't even really here. It's in the cloud is going to replace all of those people that your core competency is managing and provisioning those people. My little box is going to handle that for you. My little cloud thing.

16:53Let me just give you a cryptographic key and then you can hook your system. Most of these people are incredibly threatened by that because they think ahead one year and they say, what do they need me for? They need me to manage the now reduced by an order of magnitude set of people who just handle the edge cases. And this young whippersnapper guy, his thing is now the whole thing I used to manage. One guy who's a friend of mine who owns the entire CX function for a very well-known entity, sort of similar to DoorDash or Uber Eats or what's the one you guys have, Deliveroo? We have Deliveroo, yeah.

17:31So this guy owns the whole CX function for a company like the ones I just mentioned. And he told me most of these guys you're going to deal with in my space are going to pretend they're interested in installing your system. they're going to try to learn from you as much as possible, but they're dragging their heels. They actually don't want to install it for the reasons I just, the incentives, because of the incentives that I just described to you. If you're trying to get the factory to switch from using steam to electricity, all the people who build steam and fit steam pipes are like against you.

18:06They're actually against you at some level and you have to fight through that. So, I mean, I think that that's very reasonable assessment of the incentives and the pressures. It is the difference between the long term and the short term that we see with new technologies. And in a way, it goes to answer the question of what happens to the people who are working in the call centers. But I'm quite curious just to come back to that idea, which is when you meet the frontline workers who have access to Google and YouTube and ChatGPT, I mean, they know what the trajectory of the technology is and they know what the promise is.

18:44and virtually every discussion about AI and the future of work puts BPO and call center workers right at the tip of the spear. How do they feel? How are they thinking about it? Is there a sense of, I don't know, what is the sense that is amongst those people in the conversations you've had? I think they feel about it the way that someone who was a blacksmith or a buggy whip maker felt when they saw their first, you know, their first automobiles rolling down the street, right? They They could tell something was happening and they don't like it. They're vaguely worried about the future. But what else can they do?

19:21Now, I will tell you that in some context, again, some of this is proprietary information, so I can't say it. But in some context, companies that we are working with have said to us, we cannot say anything publicly about this because it's very bad optics for us to be shown to be replacing all these humans with robots. And furthermore, we're actually negotiating a labor agreement. Like our whole industry maybe is negotiating a labor agreement. And the unions are kind of like, they're kind of aware of this. They're putting in little clauses. Like if you're displaced by technology, then you're entitled to this severance payment or there's a pool of displaced workers and the employers must hire from that pool first.

20:09if they fill any new job in the, they must hire first from that pool. So these are all standard things that a union would negotiate with the corporate employer. But the moment that like, they become more aware that this threat is imminent, then they're going to negotiate even harder, right? So all of this is very sensitive. What do you think is a workable zone of agreement to bridge this chasm that's going to emerge? I think the workable thing will be you install a super focused agent you pay us one-tenth or one-fifth of what you're paying the human and everybody's happy i'm not sure what you're asking me is you know just no what i know i mean i mean in terms of the relationship with the workers and i think in terms of you know 10 percent of philippines gdp right what what is where is there a a sort of balanced approach where or is it or is it just the law of the market and and you know we hope the labor market is flexible enough and there's enough enterprise in these countries to spring up new jobs in all of the promising areas that will emerge?

21:12The problem is particularly acute in the Philippines because it's about 10 % of their GDP. It's 2 million workers. And those workers, that's one of the best paths into the middle class. If that reduces by an order of magnitude and size, I think there are just really strong social repercussions in that country. I don't know how to fix it. I do think it's going to take some years before all of this is done. I was just giving reasons a moment ago why technology diffusion is a bit slower than you think it will be. Like even though the tech is ready, it takes some time before it's widely adopted, right?

21:45So we do have some time to react, but I just can't imagine like these jobs that can be done at one-tenth the cost using AI, they are going to be replaced. Yeah, no, I mean, it's the logic is there, the economic logic, the historical analogies are there. And what we'll also start to see is the firms who get there first will be able to compete better than the firms that don't. and I'm not sure if this will hold for AI, but it certainly held for automation in factories in the 70s and 80s and 90s, which was that while there were job losses, it tended to be in the firms that didn't automate because they got outcompeted by the companies who did.

22:28I want to pivot to the other area that I think you've spent a lot of time thinking about. You're such a polymath. You cover so many areas, but let's talk about China. I think we've all seen that There's innovation across so many sectors in China, electric vehicles, semiconductors, solar, maybe even photolithography and so much more going on. And one of the things I think that has allowed that has been that from the top down direction, there is this tournament style competition between mayors of cities. And Chinese cities are essentially nation sized. They had 20 million people, 30 million people with great universities, extreme educational competition, attainment focused to get those top schools.

23:15and the mayors themselves they act a little bit like you know mini y-combinator bosses or mini venture capitalists pushing key technologies fostering ecosystems to support research and development and competing with each other as to who can be you know the self-driving car capital the smart city capital of china and and of the world to what extent does that dynamic drive the rate of adoption and development of these types of technologies at the moment? Yeah, I think the way you described it is perfect. So I think for people who are not familiar with China, on the one hand, you might think, based on mostly Western propaganda, that China is closer to North Korea as a polity or an economic system.

24:07On the other hand, you could say, no, it's actually closer to Singapore. because Singapore also is not the freest political system, right? It's a little bit more authoritarian than maybe we're used to in the West. So is it more like North Korea or is it more like Singapore? And I would say it's much more like Singapore. So you have this economy in which the people who run a particular province or run a particular city, as you said, which could be 20 million people or even 100 million people, their incentive to rise in the party is economic and technological development. And in a way, their job is more like that of a CEO than it is like a Western politician who has to get reelected in two years.

24:53And if you think about what pressures is a CEO under, someone's gonna measure their performance, like the market or investors, or maybe the Communist Party Central Committee is gonna look at the numbers from Guangzhou from 2025. And they're going to ask you like, well, what happened to your GDP? What happened to your average level of education? What happened to the number of patents that were produced? You know, all these things they're going to get graded on. And of course, they still have a little bit of sensitivity. Like if the local labor union protests or people are very unhappy about some policy, that also gets registered, right?

25:27And they're graded on that. So they're kind of balancing the way a CEO of a company has to balance public opinion, performance of the company, internal R &D of the companies. That's kind of more like... It sounds a bit like working at Bridgewater, frankly, with all this grading going on, it was popping up in my head. So that seems to be why we often misunderstand if you're sitting in the West, that dynamism within China. Do you think that that sets up the conditions for more rapid AI deployment than we might see, particularly in the US. I mean, the US has always led on these types of technology deployments as they expand out the economy because it is a leader in developing them because there's a lot of capital.

26:14The market is big. It's a single market. So it's a great place for the entrepreneurs to sell. And of course, American bosses have an incredibly positive attitude towards technology by and large. So they're willing to move forward. Just many more of them are early adopters than you might find in the UK or Belgium or Germany. But is there anything that you've seen that suggests that maybe that calculus changes over the coming few years with AI? I think that all of those factors that you mentioned, and I'll add one more, which is that as a fraction of the total student population in the country, in China, they're about two times more likely to do really hardcore STEM majors.