The case for AI optimism

AI doomerism and calls to regulate the emerging technology is at a fever pitch but today’s guest, Reid Hoffman is a vocal AI optimist who views slowing down innovation as anti-humanistic. Reid needs no introduction, he’s the co-founder of PayPal, Linkedin, and most recently Inflection AI which is building empathetic AI companions. He is also a board member at Microsoft and former board member at OpenAI. On this week’s episode, Reid joins Sarah and Elad to talk about the historical case for an optimistic outlook on emerging technology like AI, advice for workers who fear AI may replace them, and why it’s impossible to regulate before you innovate. Plus, some predictions. Aside from his storied experience in technology, Reid is an author, podcaster, and political activist. Most recently, he co-authors a book with GPT 4 called Impromptu: Amplifying Our Humanity Through AI. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @alyssahhenry Show Notes: (0:00) Reid Hoffman’s birdseye view on the state of AI (3:37) AI and human collaboration in workflows (5:23) What’s causing AI doomerism (12:28) Advice for whitecollar workers (16:45) Why Reid isn’t retiring (18:25) How Inflection started (22:06) Surprising ways people are using Inflection (25:34) Western bias and AI ethics (30:58) Structural challenges in governing AI (33:15) Most exciting whitespace in AI (35:00) GPT 5 and Innovations coming in the next two years (44:00) What future should we be building?

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:07Sarah Guo?

  Hi listeners and welcome to another episode of No Priors. This week, we're joined by my longtime friend and partner, Reid Hoffman. He needs no introduction as co-founder of PayPal, LinkedIn, and now Inflection AI, as well as Microsoft board member and former OpenAI founding board member. He's a prolific author, podcaster, and political activist. And he's also one of my favorite technology optimists, big-picture thinkers, and supporter of people and founders. Welcome, Reid. Thanks for doing this.

• 0:31Speaker 01

  Great to be here and love what you guys are doing. And, you know, longtime friends and partners with both of you. So this is awesome.

• 0:38Sarah Guo?

  We will start with a small question, which is, what is your view on the state of AI today? What do we need more of and less of?

• 0:45Speaker 01

  Well, I mean, the obvious thing about AI that everyone probably listening to this podcast already agrees with is that it's somewhere between the largest tech transformation of our lifetime and perhaps the largest tech transformation of human history. And one of the things I use to describe it is like steam engine of the mind. So just like the steam engine gave us physical powers, you know, kind of superpowers of construction and transport and manufacturing and a bunch of other things, this will give us a whole bunch of mental superpowers. It's both the implication of humanity, which is part of what the impromptu book was gesturing towards. And also, there will be some places where we will create, you know,

• 1:28Speaker 01

  kind of substitution, a replacement of work in various ways. And obviously, we'll get into some depth on that. But I think that's the macro picture. And then with that, of course, there's tons of things that are current status and current needs. And, you know, I think everyone tends to a little bit overpredict, like how quickly things, like, everything will change next year. And that's not going to happen. But then they tend to underpredict, you know, 10, 20 years in some ways in terms of how the transitions, although, you know, obviously, because just like all technologies, the doomsayers come out first, whether it's the printing press, electricity, everything else is like, this is the end of the world.

• 2:10Speaker 01

  You can go back and you can find this is the end of the world in each of these things. You know, the printing press was described as, as degrading human capabilities through cognition and spreading misinformation as, as an example. And, but you know what I'd say that probably as an arc, the thing that I would want to see more of in the, and that's part of the reason why I did impromptu the way I did, in the creation, theorization, and the design of what we're doing in artificial intelligence is more in the kind of symbiotic amplification loop. We tend to, as technologists, say, well, I'm going to have autonomous vehicles and

• 2:53Speaker 01

  they're going to drive separately, which I think is a good thing in that case, because I think, you know, you don't need an amplification loop, you just need effective logistics, you know, safety, you know, save the 40,000 deaths that we currently have in, in human driven vehicles and so forth. You can go in depth in that if that's useful. But like, like the fact is, there's going to be a whole bunch of things that are actually going to be better with people plus AI, that plus is a thing to focus on. And I think we haven't nearly as much, and that's of course, part of the reason I wrote impromptu.

• 3:26Sarah Guo?

  Do you have a favorite example of where you already see like the amplification loop you're talking about, or AI and humans working collaboratively together?

• 3:35Speaker 01

  So a friend of mine's kid first started with exposure to GBD4 and was like, ah, I'm going to do sonnets, whatever, whatever, like, I don't care.

• 3:46Sarah Guo?

  Wait, how old is this kid, for context?

• 3:48Speaker 01

  15.

• 3:49Sarah Guo?

  Okay.

• 3:50Speaker 01

  You know, bright kid, you know, really interested in organic chemistry and realized that she could place, you know, scientific papers in GBD4 and say, explain this to me. I'm a 15 year old. Right? And already, like, an entire world opened up to it. And I learned something from that, because I was like, oh, yeah, there's occasionally these really complicated papers that I'm looking at going, I don't have the two hours to try to decode, hey, I could do what she's doing. That's smart, right? And so that exists today. Part of the thing that I try to tell the various concerned pessimists that say, well, you know, is my job, you know,

• 4:32Speaker 01

  to limit what the large tech companies are doing with artificial intelligence? Well, actually, in fact, we have a line of sight to a medical assistant on every smartphone. That's five billion smartphones in the world. And, you know, much less than a billion people have access to doctors. You have a line of sight to a tutor on every subject, for every age group, for everybody. Your actually job is to get that five billion access to all of that, right? This example of being able to use it as a tutor today, if you just apply it a little bit in amazing ways for everybody, right?

• 5:14Speaker 01

  Who has access, access being the key thing that I would suggest governments or should be working on, is, you know, stellar.

• 5:23Speaker 02

  I've been, I've been working in technology now for about 20 years. And this is the biggest potential impact to global health and global equity that I've seen. And yet, it's also the biggest immediate doomerism I've seen. And it feels like the foundation for that doomerism was laid many years ago. Why do you think that exists? Or what do you think caused so much almost negative sentiment or pessimism or call for regulation so early by a number of people in the AI community?

• 5:48Speaker 01

  Well, in the AI community, I think a lot of it is.

• 5:57Speaker 01

  Very well meaning, but but conceptually flawed frequently. And there's a couple of different arcs you can go into it. Maybe one of the most common arcs, especially that gets to the so-called X risk people, since we're talking about the people who refer to themselves as doomers, P10 doomers or whatever. One of the things I find most amusing is people say, first sentence, totally agree with this, very strong insight, human beings are very bad at making predictions and instincts off existential curves. Then they go, and then my prediction is, and you're like, well, wait a minute, you should take your first sentence seriously. Right. Because, for example, what they do is they go, well, we have an exponential increase in compute, it's increasing cognitive functions, so then I'm going to hand wave a little bit

• 6:42Speaker 01

  and say that's an increase in IQ, we're going to have superintelligence, and then this is what's going to happen. And you're like, well, it's unclear. Like, for example, if the increase in cognitive functions is actually more like an increase in savants of various ways, then the superintelligence you're describing, by the way, GPT-4 is already superintelligent relative to a number of human capabilities, is actually not that alarming. Go play with GPT-4, it's not alarming, it's actually enhancing and amplifying in various ways. And I think that that kind of thing of going, you know, I I I come to an observation, exponential curve, and I go, oh shit, right? And I'm trying to be helpful. And by the way, of course, the calls for regulation are, hey, I shouldn't solo make

• 7:24Speaker 01

  this decision because I'm a tech creator, I should get, you know, broader sense of society and the representative of society involved in this, not realizing, of course, all you're really doing is calling for panic. For example, when I talked to some of the authors of the six-month pause letter, I was like, well, what did you think was going to happen? And they're like, well, we just hoped everyone was going to pause. I'm like, okay, I thought I was going to need to talk to you about how tech development works, but let's talk about humanity first, because I think you misunderstand humanity. Right? Like, that's not what's going to happen. You don't set up a flare and say, everyone should pause for six months, and oh, look, eight billion people paused. On your theory of the universe, the UN would be a highly functioning organization that we would all use for all a bunch of things. It doesn't work.

• 8:07Sarah Guo?

  Yeah, I, um, one of the things that is sort of surprising to me is how clearly laid out like the variations of Doomer scenario are and how little color there is in the optimistic scenarios, right? And so this is one of the reasons I think you've been a really important, like positive voice on the ways in which humanity will be pushed forward by AI and collaboration. Because as you said, as both of you said, it's entirely predictable that there will be some sort of panic around every new transformational technology, going back to like, because of the advent of the telephone, no one will ever leave their home again.

• 8:49Speaker 01

  Yes, exactly.

• 8:50Sarah Guo?

  And so I think it's very, and this is around news cycles as well. It's very easy to amplify a negative scenario, also because the, I think the set of fiction that actually inspires lots of technologists is much more dystopian in sci-fi than, than utopian because there's no conflict.

• 9:13Speaker 01

  Yeah, well, especially in the video, right? There is some stuff in written that's pretty good. You know, Ian Banks, et cetera, et cetera. But the, but the video is always like person versus machine. And, you know, the machine has to play the conflict evil role. And, and one of the things in 2019, I went and talked to all the CA people saying, look, you're damaging humanity with all these stories. You should put the machine also on the positive category. It could be person plus good machine against bad machine. That's fine. But like, have some understanding that there's a, there's a good role, that there's a potential good role for this. And like, when you look at all these technologies, there's a ton of, this is one of the reasons why I didn't, I didn't sign the 22 word statement of, you know, AI should be treated as an existential risk, along with climate change,

• 9:58Speaker 01

  pandemic, et cetera. Because the mistake, and a bunch of people I treasure, signed this, this, this letter, Sam Altman, you know, Mustafa Suleyman, et cetera. And, and

• 10:09Speaker 01

  This 22 word statement and the reason is is because unlike other things, climate change, pandemic, et cetera, they don't have anything in the positive column. When you get to pandemic, maybe the only way to solve pandemic is AI. Well, a certain help for climate is AI. It's net, I think, strongly in the positive column. It doesn't to say that it might not add some existential risk characteristics to the overall portfolio, but your portfolio is improved within it relative to reduction of it. And, and the reason I think that everybody, that a lot of people, and this is one of the things that I think, you know, given what we're doing here is the critics think they're virtuous because they go, oh, there's this, there's this danger that we should, we should trumpet. And you're like, well, actually, in fact, you may be doing more harm than you're doing

• 10:54Speaker 01

  good in your attempt to be virtuous because by trumpeting the negativity, you're not shaping where we could be in positive. And so my challenge to the critics is say, you have to be articulating where we should be going to and what we should be doing, and then we can navigate around them. Now, I also don't think that the, that the, like the, oh, all technology is just great. We don't need to think about safety at all. That's Bozoville, right? Of course, like, it's like, look, you can clearly do things dumbly with technology. There's a bunch of stuff around viruses that people like by not being careful and all can be really dumb about, or genetic manipulation. You're like, no, no, you have to be, you have to be intelligent and careful about it.

• 11:37Speaker 01

  But going to a future that is so much better than the present, that's the goal. And that's there. And if you're not articulating that that's possible, that how you think your, your critiques or risks could help navigate to getting there, then actually, in fact, you're being destructive versus constructive. And I think part of the reason why people do this is they go, oh, I know that I'm just being good when I articulate this fear and this risk. And you're like, actually, no, you've got a conceptual mistake. You're actually maybe even being bad, right? So, so how to get to the good future is actually the hard work. So do the hard work.

• 12:16Sarah Guo?

  Consider the opportunity cost of, of all the good that we, we think AI might do in the, in the short and long term. One, one more thought on this in terms of more the short term. What is your advice for the many people increasingly in white collar jobs who are concerned about AI replacing them?

• 12:35Speaker 01

  For various reasons, in the medium and the long term, I'm actually pretty positive and counter a lot of, a lot of very smart AI thinkers that go, oh my God, we may have rampant unemployment and a bunch of other stuff. And, and this is one of the medium term ones that I actually really respect and think is gonna be super, because, you know, steam engine, steam engine of mine, steam engine, you know, helping create capitalism and what else, had huge, uh, human consequences of transformation of society, um, that's important to navigate. That's, uh, as important here, and in part because the speed of transformation will be a lot faster. You know, you have five billion smartphones, um, your ability, you know, and commuter devices and the internet, your ability to have that transformation

• 13:19Speaker 01

  hit is at a much more intense and focused wave. And, you know, call it, you know, mid level white collar jobs, um, including some upper level white collar jobs, are one of the ones that are gonna get transformed first and most, most ferociously. And so I think first, the advice to the, to the folks, which is start playing with AI, use it as amplification, you gotta learn it. I understand that you may say, hey, look, I'm 40, 50, I've got my nice experienced position, I'm comfy, I do not want my society changed. Look, the, the person who was driving the, the horse and buggy carriage felt the same way, the

• 14:00Speaker 01

  Luddite weavers felt the same way. You know, it's like, no, no, this is, you gotta, you gotta re-trot out your learning and your some curiosity. It doesn't have to be perfect. You don't have to be the A student, you just have to be engaged in learning the tools. Some, the same way you were learning Excel for doing your accounting thing and so forth, then just, you know, learn some on it. Now, here is the good news. The good news is, and it's a general arc, and this is one of the things I was trying to do with impromptu, and I'm gonna do some more writing on this next year. Is that anything that AI creates is a challenge. AI can also be part of the solution because you go, well, okay. So it's going to displace a whole bunch of customer service people. Yep. All right. So what can you build for customer service people that can help them figure out what other jobs

• 14:45Speaker 01

  they might be able to do, how they might find those jobs, how they might learn those jobs, how they might do those jobs. And let's make sure those AI tools are built too. So to help with the transition on people. You say, well, okay, there's a whole bunch of paper filing and accountants or form management in marketing groups, you know, kind of doing stuff. And that's all going to be much less human effort relative to the amount of work that's going on. Those people, how do they learn new jobs? Now, part of the thing that I think is the reason I'm more optimistic over time, the transition I pay a lot of attention to, but optimistic about the target. Because of course, the exponential people tend to say, well, no, no. But then they're just going to be better than all humans and humans, you know,

• 15:30Speaker 01

  can't be doing anything. I actually think that if you take that these are kind of these progressing, adding a whole bunch of tasks, we learn and adapt to other things. And so when you say to an individual, let's help with the transitions of the individuals to finding other kinds of things. And by the way, the other thing is it's like, let's use as a parallel, truck driving. So you know, Aurora is obviously trying to do the completely autonomous truck. Well, if every truck manufacturer in the world

• 16:03Speaker 01

  basically started manufacturing AV trucks tomorrow, right? It'll be 10 years before more than half of the trucks on the road are AV trucks. Right? That gives you time to adjust. That gives you time to make this work. And I think that there is more time for adjustment than the usual like, you know, five alarm fire, you know, ringing the bell, both for the individuals and for organizations and for society to do. And so it's like, look, let's navigate into it and be paying attention to and planning and trying to create it. But once again, AI can be part of the solution.

• 16:41Speaker 02

  I guess, I guess, speaking of career transitions, you've had, I think, one of the most impressive careers in Silicon Valley. You know, you were, you started a company that was an early social networking company in the 90s. You were at PayPal as initially a board member, a senior person there. You started LinkedIn, which is one of the most important social products in the world and sort of productivity tools. You ran Greylock, the venture fund. And so you've had this amazing career arc. And usually, once people hit your moment in time, they kind of say, okay, I'm done. And, you know, they move to the, you know, Tahiti, or whatever it is, where people park their boats. Instead, you've decided to start Inflection, which recently releases Chapat Pie, which is, you know, focused on empathetic chatbots and human interaction and everything else. Can

• 17:25Speaker 02

  you tell us more about your decision and not only, like, why start this specific company, but why even do anything at this point?

• 17:33Speaker 01

  Well, I'm not very good at being bored. I hate boredom, cocktail parties, or waiting in line. And so that's part of it. The other thing, of course, is how do we lead meaningful lives? It's because we leave the world in a much better place than we found it. And we, you can work at any level of scale. I think it's very honorable to say, hey, I'm working at my local senior community. For me, you know, obviously, split scaling and master scale podcasts and all the rest of this stuff, you know, scale is my particular thing. I just have no idea when I'll retire. And I don't really have that much of an interest in yachts. I do have an interest in getting to Tahiti at some point. I've heard about it. It sounds kind of an interesting place to visit.

• 18:16Speaker 02

  It's in the middle of the ocean, in case you don't know much about it.

• 18:18Speaker 01

  I have heard.

• 18:19Speaker 02

  It's very relaxing. Yeah, some have said. So you started Inflection. And can you tell us a little bit about how that came about and the focus of the company?

• 18:28Speaker 01

  So Mustafa Suleiman and Karen Simonian were talking about this amazing transformation that's going to transform all industries, going to affect every different kind of path where language and cognition plays a role in society. Like, everyone, everyone on the planet is going to have a medical assistant if, if we can just get them access to even our friend's smartphone. And there's all this stuff's gonna happen. And you say, well, what exactly is gonna be happening with AI in 10 years from now? And all three of us can essay predictions. And all three of us are kind of probably look foolish in two years on whatever prediction we make today, right? In terms of how this works. That's the nature of the game.

• 19:09Speaker 01

  But go, okay, startups, you're trying to go, well, things that would would live as massive, interesting, independent company is developing a product. And so one of the things we came to was every individual will have a personal intelligence, a pie that's for them, right? That's for Sarah, that's for Elad, that's for Reid, and helps you navigate whatever the particular version of your life is, right? So we were talking about me a little earlier. It's like, well, I try to do these scale things, but you know, other people volunteer at the senior centers. But what's the thing that's useful to them? And we said, well, actually, in fact, like something that is kind of a tool companion that that reflects off whatever thing that you're particularly grappling with.

• 19:51Speaker 01

  It can range from how do I fix my my my flat tire to I had this kind of challenging conversation with a friend and I want to debug it, or I'm trying to think about like what I should do next in in my work or something else and have a have something that that can be there for you. And yes, it's it has elements of a therapist, but it's not a therapist, right? Because it's it's actually deeply knowledgeable in the world and it's not supposed to be just reflecting the, you know, Elad, tell me the thing that you were most troubled about in your relationship with your mother. It's, hey, I'm I'm here to to provide a lens to the world and to help you. And like, for example, unlike the movie Her, where it's like, oh, talk to me, don't go to the world. It's like if you show up and say, oh, you're my only friend, Pi.

• 20:34Speaker 01

  It's like, oh, we should help you get other friends. Let's talk about the importance of friendship and and people you can talk to. And maybe there's some people you could talk to at it because it's helping you with the world. And of course, then when you begin to design it, you think, well, what would be the right thing? For a lot of the people in the world, it may not, it's probably, nothing is probably for everybody, but it's like, well, something that's compassionate and kind, something that has a kind of a point of view. So it doesn't just like reflect, like if you show up and say, I'm a white supremacist and I think race X is evil, it doesn't go, oh, I'm with you. I agree. It goes, oh, well, really, you should think about that. Like, it's much better to be compassionate and to realize that we're all humans, you know,

• 21:17Speaker 01

  and kind of work you through that. And so it has a point of view in how it's, it's, it's operating, but with a view of helping you and amplifying you as a, as a way of doing it. And then bringing, you know, the enormous set of resources that a, that these, that this kind of amazing large scale language models can bring in it. And that we said, okay, that product should exist. It'll be one of the fixed points at, you know, X years in the future. And we see that clearly. So we're going to be building towards it. And as far as I can tell, I mean, you know, you guys are both highly active AI investors. I think, you know, on that path, we're the ones, you know, who are most like of the

• 21:58Speaker 01

  serious teams, we're the ones who are dedicated to that path versus other paths.

• 22:03Speaker 02

  How have you seen people use the product so far in terms of other typical types of interactions? Is it reflecting this original intention? Like, how do you view user behavior relative to the product?

• 22:13Speaker 01

  Um, I think we're getting that. I mean, you know, just like the surprise I, I kind of shared with a G P T four, which like, wow, that's an amazing use case that is great. Like, um, actually, one of the, uh, one of the people at Greylock is a new parent. And one of the things she came up to tell me about was, oh my God, it's given me great, like, how do I navigate, you know, all the things? It's, I'm a first time parent and like, what are the things I should do? What should I pay attention to? You know, which, which, which things did I like read more about? Which things should I really obsess about? Which things do I not need to worry about? And it's just like, it's, it's there. Like when I go, oh, I encountered this and I can ask right now and it helps you right

• 22:55Speaker 01

  now. That's awesome. It's the thing that's useful to you. And so There's just a whole stack of them. And and part of the reason I was using like the flat tire example is because I had personally conceptualized pie entirely conceptually. It's like, you know, how do I help navigate my my my path through human society? Whether it's work and the people I'm talking to or friends, well, someone went up to pie and said, okay, how do I fix my flat tire? Right? And helped. It was interactions with pie that got me to to update my my recommendation everyone to experiment with AI. Because it's like, look, don't just go try to do something like, well, okay, I'm sitting in front of GBD for I'm gonna write a sonnet.

• 23:37Speaker 01

  Right? Like, because I haven't written a sonnet and writes on it. And so I've seen them and blah, great, don't go ahead and do that. There's nothing to say and don't do that. But my recommendation to people is, and this gets to the the white collar work thing that that you raised earlier, Sarah, as well. It's like, no, no, try it with something that matters to you. Right? Like, like that you may not expect it to get a good answer. And by the way, sometimes you won't. These things are not perfect in all kinds of ways. Sometimes you go, well, that was kind of lame and useless. Like when I first got access to GBD four, you know, months before it was publicly accessed, because I was on the OpenAI board. I sat down and said, how can I read Hoffman make money through investing in artificial intelligence?

• 24:19Speaker 01

  Because I just wanted to try it. And it was useless. It was it was the classic MBA. Like, I don't understand anything about investing, and I'm gonna write something that sounds really smart. Like, you're gonna study markets and address large TAMs, and you're gonna know which technological transformation. And then you're gonna go find teams that are doing that. And you're like, no, that's not the way in this technology investing thing works. I understand how you might teach it in, you know, if you're not knowledgeable at seemingly smart MBA course, but it's not how it works. You'll find some of the answers are not useful to you, but you'll find other of them are.

• 24:52Sarah Guo?

  Reid, I don't know. We're just following the steps. It seems to be going pretty well so far.

• 24:57Speaker 01

  Well, for example, it can be useful when you say, have an associate. And you go, what's all of this stuff? Where should I focus my due diligence? Actually, in fact, giving you a summary on that stuff for an associate can actually be very useful. It's which things is it useful for is the key thing to start experimenting on. Because some of the stuff it's great, and some of the stuff it's like. Not so much.

• 25:22Speaker 02

  And I think you have a really key embedded point here, which you mentioned earlier, which is it's who's it relevant for? And that's very personalized in terms of the specific context of the individual. One thing related to that that you mentioned was that you wanted it to have an opinionated perspective. You know, you wanted to come with some pre-existing framework or pre-thought-out perspective on the world. And I think the racism one is actually a very cogent one, given a lot of what's happening in the world today relative to universities and the perception of them in terms of, you know, are they doing the right thing or not relative to antisemitism or race or other things. Many of the people who actually work on AI ethics come out of these institutions that are now being viewed increasingly as potentially biased. How do you think about where that perspective should

• 26:05Speaker 02

  come from? And who should actually decide what the right perspective is? Because you look at, for example, Falcon and the UAE is an open source LLM. And I think one of the reasons they're doing it is because they don't necessarily want the Western perspective to be, you know, thrust on every single AI model. And it's a very specific Western perspective. So I'm just curious how you think about the ethical and moral frameworks that should be applied to AI and who should actually determine it, which perhaps is an even more important question.

• 26:30Speaker 01

  Well, the thing that I think is very much baseline is I think you should be the developers, and we'll get to the full answers to your question, the developers should be honest, open and transparent about what they're designing to. And one of the things that frequently a lot of Silicon Valley people say, which I think is Bozoville, is that that technology is value neutral. Right? I actually think values are embedded in it in various ways. And I don't think that's a bad thing. I just think it's it's like one of the reasons why I love The Economist is one of my is one of my favorite magazines or The Atlantic, is because they don't go, we're value neutral. They go, here are values, right? And here's what we're trying to do and hold us accountable to the way that we

• 27:15Speaker 01

  are articulating what we're trying to say. And it gives you a much more intelligent perspective. And I think that that's what technology companies should be doing. I think that's what, you know, AI companies should be doing. And I think the, you know, kind of AI agents and whatnot is a way of doing it. And so I think that's what's most important. Now, ultimately, You start with, like, when you're doing startups or initial products and there's a field of at least some choice, I think it's the developers of the products. I think it's the companies being transparent, open about things. We're doing X for this reason. This is why we're doing it. And I think one of the things that is technology companies, one of the challenges, of course, is they become more ubiquitous and important across

• 27:58Speaker 01

  all of society, e.g. shaping our collective mindsets, whether it's search or, you know, social networks or, you know, kind of, you know, video networks or other kinds of things as ways of doing this. This does have society-level impact. And so there's responsibility to not just the individual as a participant in customer, but society as a customer. And how do you navigate that? And I think that's important across all these things. And I think that's important as we begin to get the AI stuff to scale. And you could say it's important to have a certain amount of diversity and participation in that for set of options and perhaps limitations. Because if you say, hey, I'm going to create

• 28:41Speaker 01

  an AI that's going to enable terrorists around the world, we say, well, we don't think that's a good idea, right? And we're going to do something about that. Or, for example, you know, a stunning failure on a question, we're going to create an AI that helps people articulate and advocate for genocide. Like, no, that is clearly bad. Genocide against any human category, any, is terrible and evil. Full stop. And so I think, you know, there's a dialogue within society about that. I do think that one of the things that, you know, is an uncomfortable truth is that, you know, we go, oh, AI is

• 29:25Speaker 01

  shaping everything, and so everyone wants to put their hands on it in order to shape it. And yet, technology is built by small groups of people doing things. And you just can't have AI built by UN committee. It just doesn't exist. And it's one of the things that academics mostly don't understand because they've never, most of them, not all, of course, but most have never built anything, don't really understand how these organizations work, don't understand how technology development works. They think that if you just kind of write an essay, then a technology piece will come out of it. And you're like, that's not how it works. You have to understand that and understand how technology is built in various ways in order to make that happen. And then you have to try to shape it. That's part of the reason why that productive dialogue about like, not, you guys are evil

• 30:08Speaker 01

  because you fucked up on this bias case for this, blah, blah, blah, blah, blah. Lead the, lead, lead, lead the, lead the witch hunt. That's not useful. It's like, well, actually, in fact, your stuff on race is not good. Here's some ways you could make it better and here's some benchmarks that you could do in order to, to avoid it. And I'm gonna, and by the way, if after I say that, you're not listening to me and you're not making it better in some way, then fine, I'll, I'll, I'll go to the streets with it. Right? Because we should be better on bias and race and all the rest.

• 30:37Sarah Guo?

  One of the things that you said that really resonates with me is the idea that you're going to, like, technology products, they take a point of view. They're built in a certain way by a relatively small group of people. And the way you govern them, if they have impact on society, is you interact with those people. Right? And then you hold that group accountable. I think one of the challenges is, I feel like a big driver of the current narrative is, well, like, because we didn't regulate and control social media companies that ended up being publishers that surfaced or drove certain points of view, we need to get that right with AI very early. I think the challenge is, like, that's true in many parts of society. Right?

• 31:20Sarah Guo?

  Maybe it is uncomfortable because it is a set of people that are going to have outsized influence on society. By the basic nature of building the thing, like, there's not really a way around that. Right? All you can do is interact with them and govern the thing. And I think we should also expect to see that more around academia, or I would ask for it.

• 31:40Speaker 01

  Yeah. And I think it's a good thought in academia. I mean, look, one of the things that people don't understand is the only way you make progress in technology and get to it is you deploy, you learn, you iterate. And so you're going to have errors. There's no way to not have any errors. I mean, I would love it to have zero bias errors. And in terms of the AI regulation, yeah, I've heard the same thing. It's like, oh, my God, we made this total mistake because we we allowed the social networks to go without regulation. And, you know, I think that the the problem is that you don't really know the shape that you need to navigate it in until you begin to see it. And so, like, I went to the UK AI summit, this AI Safety and Innovation Summit at the

• 32:25Speaker 01

  beginning of November. It was a very good summit. The the the British government, I think, you know, triggered a whole bunch of stuff to to kind of go in the right way. But one of the dumbass things that I heard at the summit was, and this time, we will not allow innovation before we regulate. Like, well, that's dumb on several levels. One, we've already innovated. Two, there's no way to do that. None of us know how to do that. And what's more, generally speaking, regulation is enshrining the past against the future. And if you look at every industry that goes really intense in regulation, it's it's it slows down intensely on innovation. And if you say, well, that's what we should be doing in AI, it's like, look, I think you are categorically wrong and harming humanity. Think about it.

• 33:10Speaker 01

  Let's try to get to the medical system for everybody.

• 33:12Speaker 02

  I guess, speaking of benefits, how do you think about the areas of AI where there's a biggest sort of startup available opportunities? Because often, when you look at these technology waves, there's a set of value that goes to incumbents. You know, it's somebody who already owns a workflow for a SaaS tool or whatever, they just layer on AI, versus things that are greenfield, where suddenly you can do something new and exciting. Maybe that'd be something like Harvey for legal or other areas. Are there specific areas that you are most excited about or keenly looking for companies to exist in or, you know, alternatively think could be big areas for startup innovation?

• 33:48Speaker 01

  There's areas that I think are underdone that I would really like to see. Cybersecurity with AI. You know, I think it would be very good to have that relevant to society. I think that the notion of

• 34:03Speaker 01

  You know, how do we make these transitions for the white collar workers is I think something that, you know, I would like to see more of. I think, you know, the reason we don't is because it's not the best economic opportunity possibly. And so people are all focused on the best economic opportunity. And by the way, as an entrepreneur and as a investor who resembles that remark, right, I'm sympathetic. But the, but you know, like, how do we get those things as well? But I think there's just, just, just tons. Like, I literally come up with a new AI thing that I think about, oh, I could, I could help get that co-created, like, every week. And I just had

• 34:44Speaker 01

  the, the kind of the, the resources to do it. It would be like spawning new things.

• 34:49Speaker 02

  Yeah. And then I guess related to that, I think you were really forward looking in terms of AI as a very important area of technology. And I remember going to an event you organized, I think it was like eight years ago or something, where it was like a small group discussion of AI in the future and things like that. As you look forward in the next generation of AI, so say we go from GPT four to GPT five, are there specific technological leaps that you anticipate happening even with that single increment? Or how do you think about the, the pace of innovation, but also the big shifts that are about to happen from a technology perspective over the next, you know, 12, 24 months?

• 35:21Speaker 01

  So I think there's two things at least, and I think there's going to be much more. So like always, like, part of the delight, the reason the three of us do this is we learn things that we just hadn't thought about. And those really bright entrepreneurs come to us and we're like, oh, that's really great. And because, you know, the thousands of people innovating through the network is great. One is, we're going to get a lot more robust and capable on all the language model transformations. So, you know, whether it's a coding assistant, a legal assistant, a medical assistant, a meeting note taker, a, you know, an amplification of, of, of slideshows through Tome or, or workflow with

• 36:00Speaker 01

  Coda or any of this other stuff, that's all going to get just better. Right. The second thing is, is the, the part of the superpowers of these things, because it's a scale computing, is breadth. So like, how does the protein folding lead to drug discovery? Or, you know, or other things like that, like things that are very broad space in this. We'll also get special purpose tools that will be, I think, magnificent. I don't know if the result for those special purpose tools will be in a year or two. But the intensity of the work and the beginning of it, there will be, there will be, you know,

• 36:42Speaker 01

  gold and platinum from that kind of over time. And I think that's in over a short time, like small end years, but we'll begin to see it over the next year or two. And I think those, the, the one level scale, you know, because that's, you know, the 10x GPT-4, GPT-5, the one level scale will unlock some of that stuff. And that's part of it. Now, I'm certain that there's other things that two years from now will look back and say, oh, yeah, that was, that was maybe even now, in terms of like obvious, but something I missed in that answer. By the way, I'd be curious, your guys' answer to that. What would, what would, what would you guys say to that two-year GPT-4 to GPT-5?

• 37:19Speaker 02

  Yeah, I mean, I think there's going to be three steps in, or three areas of capability improvement. I think one is going to be, to your point on baseline models, both in terms of broadening the knowledge base, as well as the increase in the ability around chain of logic or the ability to think or, you know, do simple thinking. So I think there's, there's one thing all around that and how much better these capabilities get. And you see that, for example, between GPT-3 or 3.5 and four, where you had big steps up in, in medical knowledge and understanding, legal understanding, et cetera. And you can do things on GPT-4 that you just can't on GPT-3.5 or other models. And I think we'll somewhat see a step up in other functionality for other fields with that, as well as sort of that chain of logic. I think a second is just augmentation of these things.

• 38:00Speaker 02

  Augmentation may include forms of memory, so you can actually loop back in a more reasonable way to sort of chain, chain logic or chain actions. Augmentation may be things like RAG or the ability to augment other types of information or data sets in. And so I think we're going to see a lot of capabilities around that. And obviously, there's a big debate right now in terms of fine tuning versus just increased context windows and prompt engineering and how other things play off of each other and how that affects generalizability. But I think we'll make a lot of progress on those sorts of things. And then third is, I think we'll make a lot of progress in bespoke models for specific application areas.

• 38:36Speaker 02

  And that may be biotech, to your point, or specific protein folding, or it could be materials or robotics, or it could be all sorts of things. But I think everybody's moving more to sort of end to end, both reinforcement learning, but more like sort of deep learning approaches. And some places where they hadn't applied them as deeply before, and they're using more heuristics, like self-driving would be an example of that. And it feels like the whole world is flipping to this more modern-based approach instead of architectures. And then maybe I'd actually throw in a last thing, which is I think there will be some experimentation with new architectures. And the question is, besides transformers, and the question is, will they matter? And I, you know, I don't know. So those would be kind of my four 12 to 24-month predictions. But they may be incorrect, to your point on predicting this stuff.

• 39:19Speaker 01

  I think the new architectures, there's a bunch of stuff that I've been trying to work with, but I don't think new architectures will be one to two years. I'd be surprised if it was.

• 39:28Speaker 02

  Yeah, because you need to scale them, right?

• 39:29Speaker 01

  So, yeah.

• 39:31Sarah Guo?

  Yeah, I think the, all of this is clearly going to be wrong for all of us. It's not a judgment on both of you. It's three, given what's happened.

• 39:38Speaker 02

  I think the two of us are going to get it right. So I don't know.

• 39:41Sarah Guo?

  Okay, you two are going to get it right? Fine, fine. Just me, I'll be.

• 39:46Speaker 01

  Have conviction, Sarah.

• 39:48Sarah Guo?

  Yeah, well, here, conviction, conviction, conviction is making, making decisions based on those beliefs and plowing ahead, and always seeking the truth, but not knowing you're going to be totally sure. So it depends on what you describe as a new architecture, right? I think that there's a lot of experimentation around new attention mechanisms right now. And that, you know, credit to Ashish and Nicky and Noam and the whole Transformers team originally, and everybody who's worked on scaling it up. But that actually hasn't been that interesting of an area for a while. And I think there's much more interest in that.

• 40:27Sarah Guo?

  I think the biggest labs are enthusiastic about code and validation for code and some sort of, you know, self-reinforcing feedback loop of improvement, which I think like there are obvious reasons to be optimistic about.

• 40:48Sarah Guo?

  I think this isn't quite like just advancement of GPT-4 to 5, but I'm an investor in Mistral and the efficiency that you get of being able to do the same reasoning at much smaller scales begets more applications, right? And I think that also, like, you just get much more experimentation in that case. And I'm pretty excited about, like, when you, when you take away that barrier to entry for application developers, you're going to get so much more experimentation because they can go take that, the cost of integration and workflow and domain understanding and put all the energy there, right? So kind of what Reid said about like, all of these workflows are going to get a lot better and they're going to get a lot broader.

• 41:33Sarah Guo?

  Like, companies like Harvey, like, you need to go collect very specific data if you want to increase the sophistication of the legal tasks you're doing. You may or may not believe that this falls in the path of core reasoning, but one of the things I'm really excited about is the democratization of, like, content creation and creativity in general. That has been so dramatic this year, and I'm constantly surprised by what, like, HeyGen and Pika are doing in terms of, oh, okay, we can get avatars to walk around and take actions now, or we can do, we have much better controllability around video. And I think for anybody who's worked on a social network, as both of you have, like, if you can create those new content types, like, you get so much more expression.

• 42:16Sarah Guo?

  Reid mentioned, like, and a lot, you and I have talked about this, but like, the categories that make me most excited about impact on society, like, education and healthcare are two of the areas that have been most resistant to, like, societies forays to get it to be better for cheaper. But I think the one other that I would mention here, if we talk about code generation, is, all right, today, you know, software is built by very small groups of people, they're often in Silicon Valley, sometimes they're in Paris. But if you can enable more people to build software that is useful, I think that will dramatically change society. So I'm excited about that piece. But, like, we're just gonna be wrong. So I have conviction that, like, none of these predictions are exactly right.

• 43:02Speaker 02

  Yeah, I think Sarah sparked one other thing, which is in the mention of Mistral or Mistral. I never know how to say it because I can't do the French accent. Mistral is that...

• 43:12Sarah Guo?

  Oh my god, Arthur, I'm so sorry.

• 43:16Speaker 02

  I feel good about it. Is I do think that there's a lot of questions right now about inference versus cost and what infrastructure to use. And there's all these different folks doing everything from sort of like Stripe for open source APIs for these different models on through to different hosting solutions and everything else. And I think in two years, there'll be sort of a clear fallout of what are the set of approaches and how do you do it? And what's the cheapest inference platform? And, you know, I think there'll be a lot of work done in terms of just basic ability to use these models at high scalability and low cost at, you know, across the board. And so I think that's another big shift where people are still kind of figuring things out right now, but I think it'll be pretty solved in two years.

• 44:00Speaker 01

  Like what Sarah was bringing up in terms of creativity, I think that part of it is we're going to have a number of superpowers that we don't currently envision. And part of it is, like, for example, one of the, you know, slogans that I've, I've borrowed from Kevin Scott at Microsoft is the, the most significant programming language in the next few years is going to turn into English. And then, of course, rapidly followed by Chinese because the broad use of the language and being able to create things, computational artifacts, code, et cetera. I mean, you know, even today, someone can go to these AI agents and code up a website where they wouldn't have been able to code up a website before.

• 44:40Speaker 01

  And that's part of the reason I'm optimistic about, about there being a symbiotic relationship, you know, people plus the AIs, because I think there's that sort of, of, of direction. You know, part of the thing to do is to say, don't try to say, no, no, I want to stay, I want to keep the present exactly as it is. It's what future should we be making? And, you know, you can say, hey, there's a danger over here. Look, as we go to this, let's try to avoid that. That's totally good. But like, it's where, it's where should we be going? What should we be doing is, is the most important context for all of that.

• 45:19Sarah Guo?

  Is there anything we didn't cover that you wanted to talk about?

• 45:23Speaker 01

  Well, obviously, we'll probably do this again. And I think that the there's just a ton. But the way the technology is created is a small group that that does something bold, takes a risk and makes it happen. And most people outside of the tech industry don't really fully understand that. And so we need to help them understand that's going on, but also to have the dialogue about like, look, raise your considerations and so forth. But but frankly, as as the cars get steered, only the people in the car really have their hand on the steering wheel. So you have to have a dialogue, just like you're driving down the road and navigating

• 46:04Speaker 01

  with other cars and so forth, about what you're doing as opposed to, you know, we will all decide this is what's going to happen because, you know, you know, maybe this is top of mind from the EU Act stuff, which, you know, always makes me think that they're trying to hold on to the past so ferociously that they're just completely willing to sacrifice the future anyway.

• 46:27Sarah Guo?

  And they have such an opportunity to, given like they actually have, you know, great talent in Europe working on AI now.

• 46:33Speaker 01

  Yeah, no, exactly.

• 46:36Sarah Guo?

  Okay, well, we take no paid sponsors for this program, but legitimately, you should read impromptu, drink Y3K AI Coke and listen to No Priors. Reid, thank you so much for doing this and until next time.

• 46:49Speaker 01

  Thanks for joining us. Always great to see you guys.

• 46:51Sarah Guo?

  Find us on Twitter at NoPriorsPod. Subscribe to our YouTube channel if you want to see our faces. Follow the show on Apple Podcasts, Spotify, or wherever you listen. That way you get a new episode every week. And sign up for emails or find transcripts for every episode at no-priors.com.