This transcript is generated with the help of AI and is lightly edited for clarity.
REID
I’m Reid Hoffman.
ARIA
And I’m Aria Finger.
REID
We want to know what happens if, in the future, everything breaks humanity’s way.
REID:
This is Possible.
ARIA:
Hey there, Possible listeners. This week’s episode is a little different. On June 24th, we hosted a live version of Reid Riffs in New York City, in partnership with Village Global. Reid and I did our usual back and forth, but this time in front of a live audience. And then we got into some listener and audience questions. This week, in part one, we’re sharing my live conversation with Reid. And next week we’ll dig into those listener questions. So, without further ado, here is Reid Riffs Live.
ARIA:
So I think we’re going to start off with the hardest question. One of the things Reid always says is, anytime someone says, “Oh, here’s what’s going to happen in 10 years, they’re probably an idiot. It’s really hard to predict things that are 10 years in the future.” But I feel like all of the time on Twitter, people are always saying, “Don’t build for the AI we have today, build for the AI that we have tomorrow. This is moving so quickly. You need to anticipate where AI is going to be, and you need to build for that.” But how do we correctly anticipate what tomorrow is? Is it the AI of next week, the AI of next month, and a year? And I know this is an impossible question because it matters the sector. Is it SaaS? Is it consumer? But just give us a little idea. What AI should we be building for?
REID:
And two hours later we finish the answer to question number one.
ARIA:
Oh, yeah. Reid, 30 second answers, please.
REID:
[Laugh] And so look, as the question, well, sets out part of what you have to look at is the classic startup things, which is, what is the zone in which your competitors are operating in? What is the zone in which the speed at which technology, AI in this case, is changing for this? What is your go-to market strategy? There’s a set of those things. And so the answer will be different not just on sectors, but it’s also a question of making some predictions about which of the different threads, both we see currently that are iterating, improving, and also which ones we see coming in, and then at what speed. So for example, we all know that there is an intense amount of effort around everything that gets to do with large language models—coding, text work, productivity, et cetera.
REID:
And part of what that means is you go, “So I know what it’s going to be in three years.” It’s like, “No, you probably don’t.” And so you really have to be dynamic and really updating quickly for that. If you’re in multimodal and you’re saying—well, for example, one of the things that I learned from Ethan Mollick that I thought was super interesting—and there are many things—but as an example of where Ethan was showing that people are just way under-utilizing these tools that exist today. He said, “Hey, one of the difficult challenges is construction projects and namely, is it on track? What might be running slow?” et cetera, et cetera. And he literally uploaded the construction plan to an AI put, I think it was like, 20 cameras around it and then asked, “How’s it going?”
REID:
Every day, every hour, ask, “How it’s going?” And it gave a pretty accurate construction report of, “This is what’s running slowly, this is how the work really ran today,” et cetera, et cetera. And that already exists in multimodal, but multimodal is probably one of the ones where it’s because error rates and all the rest, and where does that matter? And, like a monitoring thing, you could be wrong and you can go check it and so forth. It’s one of the things. But multimodal is in the middle. And then you’ve got things that are a little bit longer out, which is, even though people are intensely doing science and everything else, it might be the where will the various forms of science acceleration models happen. And so you get all of that vector.
REID:
But a lot of it’s the classic, what’s the speed of the competition? Like a lot of entrepreneurship is about competition. Not usually the mistake of thinking it’s competition with the large players, usually it’s other startups. And how many of them—are they on the same path you are? What is the go-to-market? What is the defensibility of the thing that you establish once you’re there? And all of that plays into it, and the timing on the AI model and projectory of where the hockey puck is moving to depends on that.
ARIA:
Can I just pick up on one thing? We have a lot of founders in the room. You said the mistake that some people make is they think the startups are competing with incumbents, but they’re really competing with other startups. Say a bit more on that.
REID:
Well, so mostly all organizations—small, large—have, you know, call it a small number of priorities. The memory thing is most people can remember seven, plus or minus two things, with a little bit of an emphasis on minus two. And, organization tends to be like three things, plus or minus two things. And so for example, if what you’re trying to do is say, “Hey, I’m going to go build a new office product suite, and I’m going to do it that way, and I’m going to go to market,” you’re going to have a challenge because the giants are actually in fact, really fighting with you on this. It’s like, “I’m going to do desktop search.” Now, by the way, of course, one of the things, if you haven’t started experimenting with putting the ChatGPT plug-in to interrupt your search and do it, you should—you learn interesting things, from it in terms of where it’s ready, where it’s not, and all the rest.
REID:
But, the typical thing is, well, does Google, Microsoft, Amazon, Apple have a small group working on your startup project? Yes, they probably do. And who cares, right? Depending on if it’s not one of the three plus or minus two things that the organization is doing. And matter of fact, it’s almost like a little bit of validation if you say, “Hey, I’m working on this thing and there’s a group at hyperscaler X that’s doing it.” Now, the reason why that’s the general software startup advice, my asterisk is a little bit of, there’s also an intensity to the play here in how AI software is constructed. That involves scale compute, scale data, scale teams. And it doesn’t mean that startups can’t do their own models and can’t be doing things. But if you’re suddenly in the, “I have to be playing scale across those vectors,” then all of a sudden you better have a good strategy of the game there.
ARIA:
So let’s talk about those hyperscalers. Let’s zoom out. In recent weeks, it’s been a little nuts. We had Meta’s investment in Scale AI—at 49%. We had OpenAI pay $6 billion for Jony Ive’s startup. There’s talk of a Nat Friedman and Daniel Gross acquihire to go be the Meta dream team. Is this like a new era in the AI world, where in the past we had acquisitions, and now we care more about talent than we do about product? Is this a new wave, or is it just a blip that we’re seeing?
REID:
Well, it’s a new wave. Partially occasioned by what I have—in a couple different ways—have said is an overly aggressive FTC, where the notion is we’re trying to prevent future issues that look like monopolies versus current ones. And so that kind of thing is actually very anti-business, very anti-venture, very anti-constraint. And that required a bunch of innovation for some new deal types. So its new deal types have now, I think, been definitively added. But, not having been anywhere in the room and having any specific intelligence, but I suspect that if Meta thought that they could have just bought scale, they would’ve just bought scale. It’s much simpler, doesn’t require all these other sorts of things. Now, there are a couple of footnotes in these deals that are interesting. The basic thought is we’re in the largest technological revolution that we’ve had in human history.
REID:
That the outcomes will create, companies that are in the tens, hundreds, and billions—and even now we say trillions—of dollars in value. And so, key talent might actually in fact be worth, not just millions, but tens of millions, hundreds of millions, and maybe even billions, in order to do that. And that still fits within the envelope of how talent is generally valued within companies. Because the framework is usually like, if I pay you ten million dollars, it’s because I’m expecting to make a hundred million dollars off the platform, in terms of doing it. And that’s part of how shareholder value, collective value in the company, and all the rest of the stuff is built. But even with that, because the anticipation of value is so high within some of these AI companies, that’s the bets that are being made. And it’s not crazy.
ARIA:
Should the people in this room do anything different? Obviously, there’s still the path to IPO, and the IPO market is opening up a bit. But do you build any differently when you’re expecting this path versus a pure acquisition?
REID:
I was actually just before this talking to one of my partners who works here, in New York—Seth Rosenberg at Greylock—and I was just talking to him about this. And that part of the thing is to say, you always build for an eternal company. You build for, it’s going to go public, it’s going to be big, it’s going to change an industry, and you change other things based on it. Now the pattern of how you’re building might change. Because the pattern of how you’re building might be like, okay, for example, one of the things that’s a near certainty is within a small N number of years—could be as small as two, could be as great as five—that if you’re a professional and you’re deploying and you’re not deploying with multiple agents in what you’re doing, you’re under-tooled.
REID:
It’s a little bit like saying, “I’m a graphic designer and I don’t use Figma or Photoshop.” Where it’s like, no, not really. You’re not a graphic designer. You might be something else, but you’re not a graphic designer. Or, “I’m a professional, I have neither a computer nor a smartphone.” It’s like, no, no, that doesn’t really work that way. And so that will be intensely there, and that will change certain patterns. Like for example, one of the things that we’ve got classically within management is this notion of there’s individual contributors, and there’s manager plus doers, and there’s managers of doers, and then there’s executives of managers, and so forth. And when you go all the way down to the individual contributors in this universe, here’s an interesting question. I won’t make a prediction as per your earlier thing, but when will MBAs—business schools—start teaching classes on how you manage agents? Because each individual contributor will have to have managing agents as part of their skill set.
ARIA:
I thought you were going to say, “When is the MBA going to become irrelevant?” And perhaps the answer was ten years ago. [Laugh]
REID:
Well, I usually say though my degree in philosophy is more relevant than an MBA.
ARIA:
Exactly, exactly. The philosopher entrepreneur. Okay, so going to go back to, alright, if we’re building today, we need to think about the AI of tomorrow. Everyone’s talking about what your moats are. It used to be, was it network? Was it product? Today, it’s the magic of AI. You think something’s great, and then next week it’s just table stakes. So in this new era, is it a proprietary feedback loop? Your distribution wedge? What is the moat that these companies should be looking for?
REID:
Well, so first is that most of the classic moats still apply. You get integrated into an enterprise workflow, and part of what happens, that’s a defense. You have a network effect of multiple sorts. And most people, of course, when they think network effects, they think LinkedIn and Facebook.
ARIA:
Never heard of it.
Reid
Yeah. Whatever—this small company. And they think all those things, but there’s also various forms. For example, one of the very original network effects in modern software was in Microsoft’s Office product suite between the data file formats and the application. So there’s lots of different places. And one of the of course key things to look at is, where might there be network effects? Where might there be network effects that are new patterns within the AI thing?
REID:
And most of the pitches that I’ve seen on that so far are more impressionistic—Jackson Pollock-ish—than systematic. But I think there will be some. Obviously some part of the question is as you are solving certain kinds of problems, certain kinds of datasets that really align to that will be very relevant. Because surprisingly large models—GPT-4, Gemini, Copilot, et cetera—can actually do a fair amount of medical stuff because there’s just so much content out there on the internet. But as you begin to get to more specific things like, okay, drug therapy and reactions, the datasets on those things actually in fact really matter. So there’ll be various things on datasets.
REID:
And so there will be some new patterns. And obviously, one of the patterns that the hyperscalers in particular focused on is scale compute. We are still not quite sure where the scale compute really starts delivering sharply asymptotic value. It’s not to say that there won’t be, and all the rest. And that’s actually one of the questions that gets into all the discussions of this. And by the way, sometimes, as you get to different orders—a level of magnitude—getting the models to congeal, come together in the right way, actually has some added difficulty. It’s not just apply 300,000 more H100s and everything is great. There’s ways to try to make that happen effectively. But that’s another one. And that’s actually one of the things that makes the software game a little different because you say, “Well, actually, in fact, I’ve got a self-learning system that creates the real competitive disadvantage, but you actually have to have X hundred thousand H100s to do that,” then that’s less of a startup game and more of a hyperscaler game—when you include like OpenAI and Anthropic and other folks in the hyperscalers.
ARIA:
So speaking of LinkedIn, which does have six times the revenue of Twitter—not that anyone asked, but just for the record—annually. You launched LinkedIn over 20 years ago, and you had your co-founders, you had your first few hires. How does that look different than how AI teams should look today? Is it just less people? Is it mostly engineers? Do you wait a long time for that first business hire because you can do so much with ChatGPT? How was that different from 20 years ago?
REID:
If you’re starting a company from scratch and everyone in the company is not using AI aggressively…I mean, anyone who’s not using AI aggressively—and I call it a seed or a Series A company—I think you probably want to get rid of them. And if you’re the founder, then get rid of yourself. So it’s because it’s where the puck is moving to and all the rest, and understanding it. Doesn’t mean it’ll be perfect for everything. Doesn’t mean it should be used for everything, but that kind of cycle. And that means you’ll have an amplification of productivity across the entire set. And so it allows a few new corner cases. For example, maybe the cost of, as opposed to, “Oh, our minimum viable thing.” Roughly speaking, when I started LinkedIn, call it the minimum viable company to really get stuff going was like in the ten to 15 people.
REID:
When Greylock led the round on Instagram, it was 12 employees, as an instance. And so, that’s the set, and maybe that’s now six people. Or five people, or three people—as how that plays out. Maybe what you can do with one person is now much more amazing. And so there’s some weird corner cases. But on the other hand, this is relatively small. By the way, people who can’t otherwise get seed capital or national, can start early. It’ll mean things for universities and university projects, doing things. And so all of that opens up, and then it’ll be very good. On the other hand, once you get into the kind of venture line, the difference between funding 15 people and seven people is not that big of a difference. Or 15 and 25 people is not that big of a difference.
REID:
And so I think it’ll be less like, “Oh, we’re not just now doing a lot more with the people.” And I think that will be a plan. And I think it’ll probably be getting to the blitzscaling phenomena. The speed of motion is now in many cases—especially when you have small teams—going to be absolutely important. The question of will people be fast following your particular go-to-market motion—that’s going to be relevant. So there’s going to be a whole stack of new patterns here. And by the way, in this regard, while there’s new patterns, it’s not like, “Oh, AI is the only time that new patterns have happened.” I mean, part of the thing that between my first startup, SocialNet, and then PayPal, and then LinkedIn is like, how much do you have to have Sun equipment? And UNIX boxes that you’re doing on-prem, is just going down through the floor. Probably a lot of people here are like, “What’s a Sun box?” Anyway, it’s a new set of tools, a new set of play. But that’s not unheard of to change the game and the software game.
ARIA:
So it sounds like you’re saying we’re still going to need these—or perhaps not need—but once we get into the venture space, yeah, you’re still going to have 15 employees. You’ll start at six, you’ll get to 15, you’re just going to move more quickly. And so maybe that means that you’re going to get to revenue more quickly, or more revenue more quickly. So from the venture side, I mean, we’ve seen some eye-popping seed rounds lately. But forget the Mira Murati’s and the one-offs—is venture just going to be investing earlier? Or they’re going to be investing with more revenue and at the same stage? How does that change in the age of AI, or the age of quicker work? And just the speed is going up so fast.
REID:
Well, I think there will be cry-me-a-river for VCs, even though we’re here with Village and Seth’s in the audience, and all the rest. [Laugh] It’ll be harder because you can possibly blink and miss it, more quickly. And so speed of decisioning, speed of making an offer, taking risks before there’s super traction, et cetera. I mean, you tend to get this barbell approach where either you have to go seed or you have to go growth. And the intermediate becomes to some degree harder. Now seed’s always somewhat hard because you’re like, “Okay, will they figure out product market fit? Will they figure out something that will get to scale product market fit?” And then in growth, a lot of it’s like, “Okay, well how big does this get, and is it going to work?” And then, one of the things that frequently, once upon a time, Silicon Valley was very derided from—and I think now is no longer is—you’re not just taking product market fit risk or entrepreneurial risk or competitive risk, but actually sometimes you take business model risk. And I suspect that one of the things we’re going to see a lot of, almost like the parallel from the early internet to now with AI, is we’ll see a lot of business model risk.
ARIA:
So we’re going to switch gears. My coworker Thanasi put this question in the question set because he knows what a huge fan of crypto I am. So here we are, we’re going to talk crypto resurgence. Regulators have a changed posture towards crypto with the new administration. We saw Circle IPO—it’s up 620%, I think, as of today. And crypto prices are still wildly swinging. Perhaps we’re just looking at memes to see what’s going to happen. But there’s also the GENIUS Act, which passed the Senate and is waiting to pass the House. And they’re going to insert some regulation into the crypto market, so this can be a stable financial game—or not. Or are meme coins going to continue to run the show?
REID:
Well, as you know, usually when you say A or B, I say mix, both, et cetera. Matter of fact, I think it was Ben Casnocha from Village who said, “Your tagline should be nuanced.” It was a fun observation. So one, look, GENIUS Act—great. Because actually, in fact, getting to more frameworks in which there’s clarity of regulatory space for entrepreneurs to act in, for the financial system to interact with, to be clear between those, is actually a really good thing. I think that’s a very strong set of progress. Generally speaking, the pattern should be, look, what’s the way that we should bring it into the financial system? What are the regulatory controls for whatever your particular concern is?
REID:
And what should you be doing relative to that regular regulatory concern is the thing to be generally doing. Versus the just “hit with stick” which was one of the failure points of the previous administration. I think that’s a very, very good thing. And I do think that, when you get to, for example, stablecoins with the GENIUS Act and the balancing of the dollars, it’s actually good for the overall dollar ecosystem. It’s good for the world. Because a lot of different places can use stablecoins now for financial trading systems, whether it’s Venezuela or any other place. And I think that’s a very good thing. But there’s still a long way to go in terms of what all the use cases that people are describing, whether or not that’s digital identity, digital digitization of assets, scarce digital commodities, all the rest. And of course, we will have lots of memes. [Laugh] And they may even be named after a president, who knows?
ARIA:
Who knows, who knows.
REID:
So, cherish that thought. It’s a science fiction universe.
ARIA:
So,as we said, you are a philosopher-entrepreneur. You got your master’s in philosophy, not an MBA. And so, switching to a more philosophical question, one of the things that people worry about in the age of AI is losing meaning, losing purpose. Whether it’s because people think there won’t be jobs, so people won’t have careers to give them purpose. I don’t know—if we don’t do the dishes every day, how are we going to find our purpose with AI creating video, and content, and creativity. And so, what do you think about the direction that AI is heading in as it relates to human creativity, flourishing purpose? Could we go down the wrong path? Or you’re convinced that we’re still on the right track? A or B, please.
REID:
Well, if it was a B, as you know, from Superagency—which I published earlier this year—I would say we’re on the right track. Now, your set up of the question actually led me to think you were going to do one of these tweets that I really love from last year—or year before—which is, “I wanted AI to do the dishes so I could do poetry, not AI that did poetry, so I can do the dishes more.”
ARIA:
I literally was thinking about that.
REID:
[Laugh] Yes, yes. It sounded like that as you were asking the question. I am ultimately very positive on human beings’ ability to figure out how to have a, we operate as a tribe, and have a meaningfulness for society. Transitions can be super painful. Super difficult. And we might really fuck up the transitions. And we can really fuck them up. I mean, for example, take in China the Cultural Revolution, but many other circumstances where you can actually just deliberately really, really screw stuff up. And, like Venezuela today—lots of good oil and totally dysfunctional society. So it’s totally doable. And that’s one of the reasons why to put a lot of energy into steer and all the rest. But that being said, we’ve kind of run, if you look at a bunch of the medieval countries—including Europe—if you get to the point where, for example, AI robots are doing all the work, that’s essentially what the nobility we’re living like when they had all the peasants and surfs and all the rest. And so, you have dinner parties, and you have poetry recitals, and is Bob talking to Sarah, and is Sarah still friends with Michelle? And does Michelle regard Bob?
ARIA:
So Reid thinks gossip is really going to increase in the age of AI. I like that take.
REID:
Well, it’s social—it’s an interesting kind of social thing. And so I’m convinced we find meaning in how we interact with each other, what our positions in society are. Sure, sometimes it can be, does this person get promoted, gets a higher salary—classic work stuff—but it’s also is this person invited to the party, et cetera. And so I think all of that. For example, part of the reason why people find a crisis of meaning is they go, “Well, this thing that I was really good at now gets outmoded.” And so, for example, “This thing I was really good at, which is I was a human calculator, keeping the accounting books, and then spreadsheets come along, and they’re not relevant anymore.” But like, accounting didn’t go away.
REID:
Right? Accounting now became other things—became scenario planning and strategic analysis. He said, “Well, AI can do that.” It’s like, well, okay, but maybe it’s cross-checking it, or training it, or managing it, or running scenarios. For example, because this is like with agents, I think one of the things you’ll see more of is, “Well run five different plans and compare them with each other.” Which are outside of our bandwidth now, but like if you said, “Okay, I’m going to have this group of agents doing that, this group of agents doing that, this group of agents…”
ARIA:
Everyone is a manager.
REID:
Yes. As kind of instance doing. And so I’m both bullish—ala stay the course, Superagency—on, even if we get to the Star Trek kind of science fiction. But I also think people tend to overstate how quickly work’s going away. I teased Dario a little bit about white collar blood baths.
ARIA:
It did get a lot of headlines.
REID:
[Laugh] Because I actually think that the transitions will be real. And that’s the thing that I think Dario was trying to emphasize. But I think human organizations and human institutions, we move at the speed—we might be accelerated, might be pushed into it—but we move at the speed that we need to competitively. Because we’re kind of happy with the environment we’re in now. Challengers, entrepreneurs are always doing the new thing, the different thing, and seeing if they can stick, and can seeing if that can work. And that will ultimately also pick up the pace on this stuff.
ARIA:
So a related question that we get a lot is—of course your answer’s going to be—if you’re in the workplace, you have to use AI. Everyone is going to have an agent, a co-pilot, doesn’t matter what profession you’re in. But what are the more human skills that are going to be important in the age of AI?
REID:
Well, I think there’s a stack of human skills that will obviously persist. For example, as a classic, there’s an interesting hypothesis of, I’m certain that we will have some AI artists that we will care about in some version, but we might very well have a few, and mostly still just care about human artists. And you say, “Well, well, wait, this song, this painting, the sculpture was produced so much more with precision, and fine, and craft by the eye.” It’s like, “Oh, but I’m interested in what other human beings are doing.” And so that, what other human beings are doing, Aristotle said, we’re citizens of the polis, political animals. But it really means we’re citizens of the village, the city, et cetera—the tribe.
REID:
And so we really care about what other people are doing and saying, and what our relationship with them is. That’s part of the earlier finding meaning and whatnot. And so all the skills to go with that persist. And you say, “Well, do we care about thinking scientifically, mathematically, et cetera?” And I think the answer will be yes. And not just because even if you say, “Hey, look, this curve’s not going to asymptote. Ultimately these devices will be much, much better than us overall.” But it still actually in fact can be useful, because it’s like cross-checking understanding. Being able to participate and know where to be in what’s happening with a world of AI. In part because like for example, today we all know that part of the creative force of these things also creates hallucination and being able to be aware of, “Well, did it matter in this circumstance?” And so anyway, yes, all these classic human skills—but I don’t think the other skills, I think they change shape. I don’t think they go away. It’s similar, like the, “Hey, what I’m really good at is you get, what is 112 times 276?” And some people go, “I got you an answer right now.” And other people go, “I pulled out my smartphone.” And that’s fine. So it changes in terms of how those play.
ARIA:
If no one’s taking computer science right now, who’s going to train and tune the models in 20 years? We absolutely still need expertise
REID:
And part of computer science—here’s a more interesting depth point of view, which is every profession will evolve to the point where part of that agents that you’re managing, one or more of them will be software construction agents. So the way that you operate as a professional—anything from accountant, lawyer, doctor, teacher, small business owner, et cetera—you’ll actually be thinking in software patterns in part because that will be the way that you elaborate with your businesses. And maybe a lot of those software patterns will be scripting, et cetera, but that’s an inevitable truth. That’s a computer science way of thinking. And by the way, that doesn’t mean that that’s the only computer science thinking, because there’s all kinds. For example, today you’ve got vibe coding. It’s very cool. If someone came to me and said, “I’m vibe coding the efficiency of my hyperscaling server stack,” I’d be like, “Oh, have someone else invest in you.” And then let me know how it goes.
ARIA:
You’ll pass. Wishing you the best.
REID:
Yes. And that’ll change.
ARIA:
So we have a lot of founders in here who are focused on AI and healthcare. And I’ve been lucky enough to have a front row seat to the company you co-founded called Manas AI.
REID:
Which is based here in New York.
ARIA:
Which is based here in New York, the capital of AI, I would say. [Laugh]I haven’t really heard of any other cities who do it better. So based here in New York…
REID:
Maybe five. But yes.
ARIA:
[Laugh] So, one of the things I’m most excited about with AI is just the enormous propensity to save human life, stop human suffering, get rid of terrible paperwork as it relates to when you go to the doctor’s office. Tell us what you’re most excited about as it relates to AI and healthcare. Feel free to talk about what you’re doing at Manas. It’s been really exciting to watch.
REID:
Well, I’m going to only say three things. I think it’s one of the things that really matters. The first is—and I won’t say the name because I haven’t gotten permission for this entrepreneur—but this entrepreneur was hiking with a cousin of his, they went to a local hospital because the cousin was having some problems. The local hospital said, “Oh, you’re fine. Take a couple Advil.” My friend checked ChatGPT, in this case, and the ChatGPT said, “Take him to another hospital,” and took him to another hospital. And the prognosis was, “Had you gotten here two hours later, he’d be dead.” So, second opinion, right? It’s a very good thing to use as a second opinion, even today. First opinion, when and how, and so that doesn’t mean it’s always wrong in a first opinion.
REID:
But as a second opinion, killer. Because the second opinion, one part will tell you, should I get a third? The second thing is, I think that the notion of part of the thing, yes, we’re going to have all of these transition issues, and workforce, and so forth. And people are like, “Why should we have to do that? I’m very happy with where I am. Why should the technologist be allowed to do this?” Common discussion thread, et cetera, et cetera. And it’s like, well, look, actually, we have line-of-sight to a medical assistant that’s better than today’s average. Today’s GP that’s available 24-7, that can run for less than five dollars an hour—legal assistant, education, tutor, et cetera. And the human elevation across all of that is simply worth a massive amount of transition. And we should be getting that. And so, part of what you can imagine, I think this is a trivial part of how healthcare will evolve is very rarely will you go up and say, “Hey, Aria your my doctor, start talking to me about this.” It’ll be when I show up, it’ll be the, “Okay, give permission for your agent to talk to my agent, download all that. Now let’s go into that because I’ve already been interacting with my agent.” And by the way, when I’m in single-payer systems, like NHS or others, that can be part of the whole triage. Medical care always has to be rationed. You can’t afford infinite everything for everyone. That’s part of having an economy. But today it’s kind of blunt force. It’s like you have some concern and you’re not going in the emergency room.
REID:
“Fine, we’ll schedule you for six weeks because you’re in the queue.” Well, some people should be never in the queue whatsoever. Some people should be showing up today at the emergency room, this minute. And then everything in between.
ARIA:
The efficiency gains are enormous.
REID:
The efficiency gains, allocation of resources, all the rest—helping you with the other things. So it’s not just a, “Hey, is this rash something?” Or, “Can I take ibuprofen when I’m on this drug?” Or other kinds of things, which you can actually already do as part of the 101 medical system stuff. But like that kind of thing. And then, you of course get to Manas. I was having a discussion with a bunch of my partners at Greylock, including Seth, and I was saying, “Look, I think there’s going to be a whole bunch of work productivity stuff that’s going to be really interesting. I think there’ll be some consumer stuff that will surprise all of us that we need to look for. Think there’ll be coding agents, et cetera, et cetera.” All of which is great. I’m happy to help. But I think one of the places that we have as a blindspot is when it’s not just pure CS, and it’s CS plus other things. Now, obviously, sometimes it gets done into robotics and when will the robotics happen—and there’s a whole thread we can talk about there, But for example, drug discovery. It’s when you go atoms and bits—biological—are kind of in the middle and it’s closest to the zone of bits, and what you’re doing in software. I think there’ll be a ton of stuff here. But the problem is you can’t just do it—like a lot of CS people think, “Oh, you do it all in simulation.” They think, “Oh, you just create an AI drug researcher and press play, plus a hundred play.”
ARIA:
And you need a wet lab. You need things to work in the real world.
REID:
Yeah. And you need to understand what this dataset is, and how this works, and configure. But these new tools are simply stunning. Not obviously just the recent Nobel Prizes, but in terms of how this plays. And so I think drug discovery. And so part of when I start thinking about these things, I go start talking to the smartest people I know. Siddhartha Mukherjee is one of them. And I was actually having dinner with him here in New York, and I ran him through this, and he said, “Oh, that’s a really good thesis.” And I was like, great. Because I had known Sid, not just as the brother-in-law of one of our partners, David Sze. Not just as the celebrated author—Pulitzer Prize-winning The Emperor of All Maladies, a bunch of other books.
REID:
Not just as one of the most famous, smart cancer doctors on the planet. Or Professor of Oncology at Columbia. But those are the things I knew. And as a friend, I knew him as. And he says, “Well, you know, I’ve actually brought drugs to market.” And I’m like, “What?” He’s like, “Yeah, I’ve helped start up a number of different companies. This sounds great, let’s talk about this.” And so I got a, literally, crash course of hours and hours and hours of what the drug discovery process looks like. And part of what we were doing is bouncing back and forth between, “Okay, this is what AI today looks like. This is what AI probably looks like in one year and three years. Here’s some of the tools that everyone knows about. Here’s some of the tools that very few people know about. Where can they make a difference in each of these places, and what kinds of things to do.” And that’s part of how Manas got started.
ARIA:
Well, and the mission of Manas is to use AI to cure cancers.
REID:
Yeah. And this was Sid’s fundamental thing. Because part of the thing is, look, cancer kills everybody, all age groups, et cetera. And it’s fatal. And our current attempts at therapy are brutal. And if we could do it this way—massive unlock of value for everybody.
REID:
Possible is produced by Wonder Media Network. It’s hosted by Aria Finger and me, Reid Hoffman. Our showrunner is Shaun Young. Possible is produced by Katie Sanders, Edie Allard, Thanasi Dilos, Sara Schleede, Vanessa Handy, Alyia Yates, Paloma Moreno Jimenez, and Melia Agudelo. Jenny Kaplan is our executive producer and editor.
ARIA:
Special thanks to Surya Yalamanchili, Saida Sapieva, Ian Alas, Greg Beato, Parth Patil, and Ben Relles. And a big thanks to Jennifer Whiting, Sheila Goodman, Ben Casnocha and the Village Global team, Robert Kingsley, Geri Madlambaya, Samuel Henriques, the Ritz-Carlton team, and, of course, Vincent Lucero.