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Episode: Investing in AI, Crypto, & Tech in 2025 | Elad Gil
Author: Bankless
Duration: 01:17:58
Episode Shownotes
What are the best investing opportunities in Tech for 2025? Elad Gil is one of silicon valley's legendary investors. He's backed 40 unicorns including Airbnb, Coinbase, Figma and Stripe to name a few. He's super active in AI and hosts the no priors podcast which is like Bankless but for
AI. In this conversation, Elad explores the state of AI and how the industry is evolving, what he thinks about Crypto. and why he’s bullish on Tech in general given the new United States political administration. ------ 📣SPOTIFY PREMIUM RSS FEED | USE CODE: SPOTIFY24 https://bankless.cc/spotify-premium ------ BANKLESS SPONSOR TOOLS: 🐙KRAKEN | MOST-TRUSTED CRYPTO EXCHANGE https://k.xyz/bankless-pod-q2 🦄UNISWAP | BUG BOUNTY PROGRAM https://bankless.cc/Uniswap-Bug-Bounty 🐧 CARTESI | LINUX-POWERED ROLLUPS https://bankless.cc/CartesiSimple 🛞MANTLE | MODULAR LAYER 2 NETWORK https://bankless.cc/Mantle 📈 iYield: YOUR FINANCIAL PICTURE, SIMPLIFIED https://bankless.cc/iYield 🔒 SAFE | INTRODUCING SAFENET https://bankless.cc/SAFE ------ ✨ Mint the episode on Zora ✨ https://zora.co/collect/zora:0x0c294913a7596b427add7dcbd6d7bbfc7338d53f/114?referrer=0x077Fe9e96Aa9b20Bd36F1C6290f54F8717C5674E ------ TIMESTAMPS 0:00 Intro 6:02 AI in 2024 8:21 The Future of AI 12:35 Where are we on the S Curve? 17:38 AI vs Other Tech Revolutions 20:23 Are we in a Bubble? 23:59 Evolution of AI 29:06 AI x Crypto 34:12 Centralized vs Decentralized AI 37:46 AI Agents 42:32 Doomsday Scenario 47:16 Raw Crypto 52:28 Order of Trends 55:06 Silicon Valley on Crypto 58:14 Crypto Founders vs Tech Founders 1:01:27 Politics & Tech 1:14:36 Advice for 2025 1:17:13 Closing & Disclaimers ------ RESOURCES Elad Gil https://x.com/eladgil Elad Gil Website https://eladgil.com/ No Priors Podcast https://www.youtube.com/@NoPriorsPodcast Prime Open sources their 10B parameter training run https://x.com/PrimeIntellect/status/1862607165669900407 ------ Not financial or tax advice. See our investment disclosures here: https://www.bankless.com/disclosures
Full Transcript
00:00:00 Speaker_01
I do think that to some extent, one could argue that there will have been three ages of humanity, right? The first age is all the computers, roughly humans and then other animals.
00:00:11 Speaker_01
The second age of humanity is probably this, what we have right now, which is there's some split between humans and machines and humans are directing the activity and all the rest. And probably the third age is the age of machine intelligence, right?
00:00:22 Speaker_01
Where that's the predominant form of compute and intelligence on the planet.
00:00:30 Speaker_02
Welcome to Bankless, where today we explore the frontier of tech investing. This is Ryan Sean Adams. I'm here with David Hoffman, and we're here to help you become more bankless. I led with tech investing.
00:00:41 Speaker_02
I mean, crypto, of course, is a form of tech investing. We're talking a little broader than that. We're talking not just crypto. We're talking about AI today. We have unicorn tech investor, Elad Gil, on the podcast.
00:00:52 Speaker_02
He's giving us the Silicon Valley take on AI, crypto. And also Silicon Valley's involvement in politics, particularly the recent involvement. So this is one part getting up to speed on everything that's going on in AI.
00:01:06 Speaker_02
And Elad provides a masterclass in that. And then we talk about the intersection of AI and crypto and his take on that. And then politics. How will tech flourish under the new administration? What changes have we seen in the last year?
00:01:20 Speaker_00
Eli probably knows as much about AI as Ryan and I, Bankless, knows about crypto. And vice versa, he knows about crypto about the same amount that I think we know about AI. So we really started off picking his AI brain.
00:01:35 Speaker_00
But he's just familiar with the growth of markets, the growth of tech, having seen the rise of the internet.
00:01:41 Speaker_00
And so not only is he intimately familiar with these tech sectors, especially AI, but also how investing works and how markets develop and grow and the S-curves and how all these many S-curves relate.
00:01:54 Speaker_00
So feels a little bit like ancient wisdom from a tech investor veteran on the podcast. He's also very calm, which I found very peaceful. So overall, very enjoyable episode. Does he meditate, David?
00:02:06 Speaker_00
You usually call it when guests meditate, and you have this sixth sense. He could. He could be a meditator. He might meditate. He might be a meditator, yeah.
00:02:13 Speaker_02
We didn't get into that in the bulk of the podcast. My big question going in for him was like, okay, give me the truth on AI. Is it overhyped right now, or is it appropriately hyped, or is it underhyped? So stay tuned for that answer as well.
00:02:26 Speaker_00
Let's go ahead and get right into the episode with Elad Gail, but first a moment to talk about some of these fantastic sponsors that make this show possible.
00:02:31 Speaker_00
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00:05:26 Speaker_02
Bankless Nation, very excited to introduce you to Elad Gil. He is one of Silicon Valley's greatest of all time investors, I would say that. He's backed 40 unicorns, including Airbnb, our beloved Coinbase, Figma, Stripe, many others.
00:05:39 Speaker_02
He's super active right now in AI. He actually hosts a podcast that I enjoy very much. It's almost like the AI sister podcast to Bankless. It's called No Priors. He's just, in general, a very prescient thinker.
00:05:52 Speaker_02
Today, we want to get his takes on AI, on crypto, on the next decade of tech investing, what the opportunities are. Elad, welcome to Bankless. Thanks so much for including me. All right.
00:06:03 Speaker_02
So let's start with AI, because David and I have been following this in various ways. The Bankless audience knows about it, but we focus mainly on the crypto tech revolution, not as much AI. Catch people up. Catch us up to speed.
00:06:16 Speaker_02
So obviously, there's a lot going on right now. How about this year? If we just zoom into this year, what are the big things, like the big milestone events that happened in AI that we should be paying attention to as general tech investors?
00:06:30 Speaker_01
Yeah, you know, it's kind of interesting because if you look at the history of machine learning and AI and, you know, we used to all call it machine learning, you know, 10 years ago.
00:06:37 Speaker_01
I think what a lot of people really underappreciate is that we had a series of fundamental breakthroughs that effectively put us on a different technology curve from what we traditionally talked about machine learning.
00:06:46 Speaker_01
So we used to talk about convolutional neural networks and recurrent neural networks and all these things in the 2010s with AlexNet and a few other breakthroughs around machine vision. you know, a few other areas.
00:06:59 Speaker_01
And the basis for those things kind of shifted in 2017 when Google invented what are known as the Transformer. It's a specific type of architecture model for machine learning.
00:07:11 Speaker_01
That got implemented at Google, but also at OpenAI, and the T in GPT is Transformer. And it wasn't really until ChatGPT launched just two years ago that a lot of people woke up to this fundamentally different technology curve that we're on.
00:07:24 Speaker_01
So it's almost like we're on one curve with machine learning and then all this transformer-based stuff came out and it kind of boosted us into a different trajectory and honestly a different technology curve.
00:07:32 Speaker_01
And the capability set is very different from what we've experienced in the past with traditional machine learning, which is really effectively running a bunch of regressions in some sense, or kind of data mining out statistical correlations between things.
00:07:43 Speaker_01
This wave of AI, which a lot of people are calling generative AI, is about the ability to understand and manipulate different types of language and imagery and a few other things. And language includes things like code, and language includes
00:07:57 Speaker_01
you know, the synthesis and understanding of knowledge. And there's these multi-step processes that you have and all these other things. And so when people talk about AI, we've been talking about it for literal decades.
00:08:07 Speaker_01
But I think the specific flavor of AI that we're focused on right now is only two, three years old.
00:08:11 Speaker_01
So even saying what's happened in the last year is almost like saying what's happened in a third or half of the timeframe of which we've been aware of this really interesting technology shift that we're undergoing, right?
00:08:21 Speaker_00
I think there's probably a mainstream interpretation of AI and how it's going to impact our lives. We see ChachiBT, we see its implications.
00:08:31 Speaker_00
Then there's stuff like, you know, custom AI generated videos and, you know, deep fakes and this is all going to impact our lives in this particular way. There's probably a consensus level understanding of how AI is going to impact us.
00:08:44 Speaker_00
But I'd actually like to see if there's any difference there between what you think is the future trajectory of AI and if there's a gap. between mainstream understanding of the future AI and what you think is true. Is there a gap?
00:08:57 Speaker_00
Is there an alternative version of AI's future that you think is being under-represented or under-discussed that mainstream society, mainstream tech forward people are maybe missing? I don't know if it's under-discussed.
00:09:09 Speaker_01
I do think in general AI is still under-hyped despite being incredibly hyped, right?
00:09:13 Speaker_01
And I think the thing that people fundamentally misunderstand is as we have these advancements over time, really the product that you're selling or the end product of these AI systems is units of cognition, right?
00:09:23 Speaker_01
You're selling pieces of thought or ability to do things. So for example, you look at a company like Decagon and what it's doing is it's augmenting customer success agents. So you're a customer support rep,
00:09:35 Speaker_01
And you suddenly are making that person's job dramatically easier, or you're tackling more of the queries that they get through users. And so each person can handle a much larger base of people.
00:09:44 Speaker_01
And suddenly a person who only speaks English can support people in 30 plus languages, 24 seven. You know, you're kind of shifting the paradigm of how work is done and the leverage that you're getting on work.
00:09:54 Speaker_01
And so for the digital world, you're basically selling units of labor. for the physical world, if some of these advancements in robotics come through, and you know, I think that's, we're much earlier in that curve.
00:10:07 Speaker_01
And then eventually you're selling units of robot time or labor time or however you want to phrase it.
00:10:12 Speaker_01
And you know, it does seem likely that one of the major types of robotic chassis or approaches that we'll have or form factors is going to be humanoid.
00:10:21 Speaker_01
A, because the world is designed around that and B, because it's general purpose in terms of the things it could do, right? But that's much further ahead in the future.
00:10:28 Speaker_01
If you just look at the digital part of it, which is very clear right now, I think eventually you're selling units of labor or thought And that's very different from, hey, we're doing this regression on a bunch of data, which was machine learning.
00:10:41 Speaker_00
It feels a little cloud-like, as in there's a cloud of labor out there that you can purchase units of labor. Is that an acceptable comparison? Yeah, I think that's a great way to put it.
00:10:51 Speaker_01
So as an example, eventually, you'll have a series of bots that will do aspects of coding for you. And already people are using coding tools like Cursor or Magic or Devon or other things, right?
00:11:04 Speaker_01
But fundamentally, as those capabilities get better and better, you're going to have the AI system to write more and more code for you, or the AI system do more and more customer support for you.
00:11:13 Speaker_01
I don't know if you saw the tweet from the CEO of Klarna that came out maybe six to nine months ago, where he said that they let go of 700 people on their customer support team because they replaced it with something they built on top of OpenAI.
00:11:26 Speaker_01
And it had a higher net promoter score. They had a 25% reduction, I think, in repeat queries. It was available 24-7 in I think it was like 20 plus languages. And the time to actually resolve the issue for a customer went down dramatically.
00:11:44 Speaker_01
I don't remember by how much, 50% or some significant amount.
00:11:47 Speaker_00
So strict improvement across all domains.
00:11:49 Speaker_01
Strict improvement across all domains with an AI system, right? Effectively, to your point on the cloud, they created a cloud of customer success agents or help, right? Now it wasn't quite agentic.
00:11:59 Speaker_01
Agentic almost implies like this thing is gonna be self-acting in really deep ways. and the technology basis still has to develop to get there.
00:12:06 Speaker_01
But again, I think it's back to its units of cognition or labor, however you want to phrase it, and its units eventually of robot minutes or robot time or physical labor time. And that's eventually what this wave is about, right?
00:12:17 Speaker_01
At least for language, right? There's image gen and video, and then there's all these foundation models being built for physics and material science and biology and all these other areas, which is different.
00:12:28 Speaker_01
And we'll have different substantiations and kind of overlap in terms of what type of person they're going to augment or replace.
00:12:35 Speaker_02
What I think I would love to find out from your perspective, Elad, is like where we are in kind of the S-curve of this new transformer sort of unlock that we've approached in AI.
00:12:47 Speaker_02
So you're talking about the measure being units of productive intelligence. And the case that you just gave is basically like some corporation has a customer Support intelligence center that is primarily staffed by human agents right now, right?
00:13:02 Speaker_02
So those are its units of customer support productive intelligence and what it's just done is in using, you know Open AI it's been able to replace that productive intelligence with a higher form of productive intelligence, right and
00:13:16 Speaker_02
So you see that that could happen in customer support, maybe some white collar type jobs. We're also getting these version upgrades with the chat GPTs of the world, right? So like we go from three to four and to like five to six.
00:13:31 Speaker_02
But at some point in time, I mean, we know in crypto, right? You sort of, you're on the other side of that S curve and kind of adoption progress for the technology that you're on just starts to peter out. I mean, where are we on the curve here?
00:13:45 Speaker_02
Is this still early? Like, do we have a lot of upgrades to go? Or like, when does this version of the tech curve kind of stop and start getting diminishing returns?
00:13:56 Speaker_01
Yeah, this is an area of active debate within the AI community. And some people say, well, and really, if you look at AI, there's three or four components that go into how smart the system or how capable the system is.
00:14:11 Speaker_01
And part of it is how much data and what sort of data do you have to train it, and how clean is it, and how is it labeled, and how is it generated, and et cetera, et cetera. There's how much compute are you throwing at the thing.
00:14:22 Speaker_01
And, you know, there's a lot of fine tuning or post-training. What do you do once you actually have the system up and running?
00:14:28 Speaker_01
And then there's the, how much computer are you actually allocating at the time that you're doing inference, which is sort of the moment that you ping the AI system and ask it to do something on your behalf.
00:14:38 Speaker_01
And each one of those things can scale in different ways. Right. And so 01 from OpenAI is really focused on that last piece.
00:14:46 Speaker_01
And you see that a lot happen with people, like you're asked a question, and if it's a hard question, you'll think for a minute or a few seconds or whatever, and then you'll answer.
00:14:52 Speaker_01
You won't just spontaneously answer in an easy question, you immediately belt out, right? And so differentially allocating compute is what you do as a person, right? You're kind of thinking more or less.
00:15:02 Speaker_01
And similarly, that has its own scaling law that people think has a lot of runway on it. The training side probably still has quite a bit of headroom on it in terms of just the core, I'm going to train a big model on a bunch of GPUs.
00:15:15 Speaker_01
And on the data side, there's ongoing questions of like, do you move to synthetic data? Are you capturing data in new forms? Are you going to specific types of experts for the post-training side of it?
00:15:26 Speaker_01
So I think in each of those, there's debates around how much room there is, but I think overall there's an enormous amount of room still to improve. So on the one hand, I think we're still reasonably early in the S-curve.
00:15:37 Speaker_01
Um, and that's, again, a debatable topic, but I think there's a lot of headway just with the stuff we're doing. There's a separate question of say that we stopped all progress and we just took the models and capabilities we have today.
00:15:46 Speaker_01
How many more applications could we uncover for it? And there's a ton, right? Again, this technology in some sense or people's awareness of the technology is two years old. GPT-4 came out, I don't know what, 16 months ago. Right.
00:15:59 Speaker_01
And four was a big step in capabilities versus three. And that's when you could suddenly do legal. So, you know, one of the companies I've backed is called Harvey and they have this sort of legal assistant and set of tools.
00:16:10 Speaker_01
And, um, um, 3.5, which was, uh, was the version of GPT right before, right before four, they couldn't do legal workflows. It just didn't work. It wasn't smart enough. And at four, suddenly it was capable enough. Right.
00:16:21 Speaker_01
And so, um, I kind of call it the GPT ladder or step, right. As you move up from four to five to six to seven, you're opening up entirely new markets that couldn't be served before. Cause the thing wasn't smart enough to serve them.
00:16:33 Speaker_01
Or you're fundamentally changing the capabilities that you can do in those markets. And if you look at, um, SAS in the U S it's a half trillion dollar a year, SAS and enterprise software, it's a half trillion dollar a year market.
00:16:44 Speaker_01
If you look at all the sort of white collar ish services. The payroll for those in terms of areas that could be impacted by AI is about 3.5 to 5 trillion.
00:16:55 Speaker_01
So if you convert 10% of that headcount cost, just employee salaries, into SaaS revenue for AI, you've recreated all of SaaS and enterprise software in terms of market cap.
00:17:05 Speaker_01
So this is a huge revolution, and that's why I'm saying it's underhyped, right? And so there's two S-curves. There's the technology S-curve, and it's really a few different S-curves stacked on top of each other.
00:17:15 Speaker_01
As mentioned, there's the training and the post-training, and there's the time of inference stuff. And then there's the S-curve of adoption. And on the S-curve of adoption, we're really early. We're basically in the era of
00:17:27 Speaker_01
you know, it's a year after the Bitcoin white paper dropped or something. And we're about to have like the Mt. Gox blow up or, you know, whatever analogy you want. You know, it's really early. Do you know what I mean?
00:17:38 Speaker_02
Yeah. Okay. So you're painting this portrait of stacking S-curves, all of which are early, including the adoption S-curve here. Can you give some insight into how transformational this will be? So you've been in Silicon Valley since the early days.
00:17:51 Speaker_02
You've seen kind of the birth of the internet, and you've seen mobile, and you've seen crypto, and you've seen social media. You've seen all of these kind of trends, right?
00:18:01 Speaker_02
And famously, Peter Thiel talks about how some of these software value creation hasn't really filtered down into real-world productivity for the rest of the world. Anyway, is this different or how does this compare to previous revolutions?
00:18:17 Speaker_02
I've heard some people compare the AI revolution, this idea of unlocking productive intelligence to the industrial revolution, which was kind of like a whole new scale of productivity output for humanity.
00:18:31 Speaker_02
And I think that's a different trajectory maybe than what the internet was. Anyway, how does this stack up in terms of the tech that you've seen come out of Silicon Valley since you've been there?
00:18:42 Speaker_01
Yeah, I would separate out two things, because again, there's a digital revolution and then there's the atomic revolution of robotics, right?
00:18:47 Speaker_01
Which is, again, we're not quite there yet on the second one, but that has its own transformation curve, or its own implications sort of globally.
00:18:55 Speaker_01
And self-driving cars is sort of one substantiation of that in some sense too, and that's coming much faster. So there's really the atoms and the bits, and you sort of separate that?
00:19:03 Speaker_01
I would separate it out, yeah, because that's very different society level implications, but also the degree to which you rework cities and physical labor and everything else is fundamentally different from what can you do with digital information.
00:19:16 Speaker_01
And the bits is happening now. The bits is happening now. Yeah, the bits is happening now.
00:19:21 Speaker_01
I mean, the internet was the biggest revolution of them all for this stuff because this set of AI systems couldn't exist without the internet and distributed compute, and neither could crypto, right?
00:19:32 Speaker_01
And so I think a lot of the, um, a lot of the really big technology waves are just outgrowth of the internet in one form or another. And so that to me is sort of the, the granddaddy thing or whatever you want to call it.
00:19:45 Speaker_01
Um, but on a relative basis, I think this is much bigger than mobile. Um, it's going to be bigger in some ways than certain aspects of social.
00:19:53 Speaker_01
Uh, crypto I think has been enormously transformative, uh, mainly at least today in, you know, the financial sector. And obviously it's spilled over into art with NFTs and it's spilled over in other ways.
00:20:04 Speaker_01
But I think a lot of the kind of Web3 premise of everything is going to be on the blockchain, you know, have Airbnb on the blockchain with a token to incentivize hosts. And, you know, that stuff hasn't really proven out quite yet.
00:20:15 Speaker_01
And so I think, you know, it's going to be a very big revolution and it's going to take a decade plus to substantiate.
00:20:23 Speaker_02
One thing that we've seen in crypto is kind of these repeating hype cycles where every sort of four years or so crypto goes on a tear and then it sort of gets ahead of itself in terms of
00:20:32 Speaker_02
you know, the market price reflecting kind of the reality of where the technology and where the adoption actually is. Sure. And I'm trying to like paint this picture for crypto investors onto AI.
00:20:43 Speaker_02
Do you anticipate something similar where basically it will happen in waves
00:20:48 Speaker_02
And, you know, I don't know where we are in the first wave, but it's been incredible to see the evaluations of companies like Nvidia this cycle just like, you know, absolutely explode into the most valuable company in the world.
00:21:01 Speaker_02
And so it's hard to look at that and not look at some crypto analogs.
00:21:05 Speaker_02
Well, okay, maybe AI is going to be a long-term transformational technology, but are there periods of time within the decades of that transformation where it gets kind of overhyped from a market perspective? Like, are these companies making money?
00:21:19 Speaker_02
Do they have business models yet? Yeah, I mean, NVIDIA is clearly making money, so I think that's the reason it's valued so highly. But that's one of those reflexive money type things, right?
00:21:28 Speaker_02
It's making money because there's so much demand for GPUs and chips because there's so much, I guess, hype going into the other elements of AI.
00:21:38 Speaker_01
Well, it's not just hype. It's really interesting. So if you look at the foundation model world, or at least LLMs, these large language models,
00:21:45 Speaker_01
OpenAI is GPT or, you know, Cloud from Anthropic or Sonic from Anthropic or, you know, kind of name your model, Google and Bard and all the things you're doing there, or Gemini, you know, fundamentally,
00:22:02 Speaker_01
The reason the hyperscalers ended up as a primary backers of these big model companies, right?
00:22:06 Speaker_01
AWS probably is the biggest backer of Anthropic now, and Microsoft is the biggest backer of OpenAI, et cetera, is because it also drives enormous revenue on their clouds for AI services, right?
00:22:16 Speaker_01
So Microsoft had something like a $28 billion quarter last quarter. And I think they publicly said that 15% of the lift on that quarter, so that's what, three and a half, $4 billion came from AI. That's incredible. That's significant, right?
00:22:30 Speaker_01
And so these things are translating into real revenue. And it's happening at startups where suddenly you see a startup go from zero to 10 to 50 in two years, which is insane in terms of any sort of traditional SaaS application.
00:22:43 Speaker_01
You see that in terms of the rumored numbers around open AI, where they're now in the billions of revenue after two years or three years of offering GPT as an API that is actually being used, right? I mean, these are insane adoption curves.
00:22:55 Speaker_01
Now there's a separate question of what is the durability of a given company? relative to these adoption curves, because overall the segment's going to happen. It's such a useful and powerful technology.
00:23:06 Speaker_01
And it gives you so many capabilities and so many cost savings and so many new revenue streams and all the rest of it that it's happening and it's going to happen. And then the question is who wins?
00:23:16 Speaker_01
And for each layer of the stack, right, you have the foundation models, actually you have the chips with NVIDIA, you have the foundation models, you have apps, actually you have infrastructure, then you have apps, and then within apps you have B2B and you have consumer.
00:23:26 Speaker_01
And within that stack of stuff for each sector, you can kind of go through and ask, is it an oligopoly market? Is it a monopoly? Is it highly fragmented? Who wins? Why? What's the defensibility of each one of these things, right?
00:23:36 Speaker_01
What's the technology basis for winning? Do they have to build their own models or not? So there's all that stuff, right? And so I think the direction is clear. The who in some cases is clear and in some cases it's less clear.
00:23:47 Speaker_01
And it's the old thing about how the future in some cases is a determinant, but you just don't know who's going to be the person who drives that piece of the future, right? But you know that future is coming. I think that's very true here.
00:23:59 Speaker_02
We've definitely seen that with crypto. It's been hard to predict what the individual networks that win out are going to be, but we know that the future is inevitable. I want to ask you a question about the evolution of this market.
00:24:11 Speaker_02
You're painting the picture, Elad, of this is an early market. It's still actually underhyped from your perspective. Even at this point, How do you think this industry evolves?
00:24:21 Speaker_02
And there's different levers on this, like a lot of the value right now seems to be going in kind of, I don't know if you'd call this the platform layer, but like, you know, the Mag7 and some of these big companies, right?
00:24:31 Speaker_02
There's also this other layer we could talk about like closed source versus open source. I mean, something that the crypto advocates are very passionate about is decentralization. Anybody being able to use this technology.
00:24:46 Speaker_02
There are different versions of that in AI, but that's something that I'm sure you support, permissionless, decentralized, the ability for anybody to spin up these tools and for it not to be cloistered in some walled garden.
00:24:58 Speaker_02
Anyway, what do you think of this market structure? Will it be centralized? Will there be power law winners here? Or will this be more diffuse?
00:25:07 Speaker_02
I mean, will we see DECA unicorns in the startup world start to compete against some of these MAG-7 companies?
00:25:13 Speaker_01
Yeah, there's a ton of questions in what you just said. So let me try and tackle them one by one. I think one is around Open source versus closed source, and obviously I'm a huge fan of open source software.
00:25:25 Speaker_01
I think both will happen in this market segment. And I think arguably both have happened in crypto, right? You have DEXs, but you also have centralized exchanges.
00:25:33 Speaker_01
And a lot of the things that are supposedly decentralized are actually way more centralized than people really see at first blush, right? Like how many people can actually commit?
00:25:41 Speaker_01
to Bitcoin Core and how many miners actually make up what portion of the network. It's pretty centralized actually in some ways. So, or you look at Solana or other protocols and relatedly sometimes there's more centralization or less centralization.
00:26:01 Speaker_01
And so the same is going to be true in this world where the really big open source models of the foundation model layer at this point are basically Llama from Meta and then Mistral, right? And there's a bunch of other stuff.
00:26:16 Speaker_01
But at least for the language model side, those are the ones that are, I think, most prominent.
00:26:21 Speaker_01
And then there's other types of open source models for a wide range of other areas in terms of, you know, things that have come out of academia for robotics or weather simulation or biology or, you know, so you can kind of go through one by one and ask, will it be closed source or open source?
00:26:39 Speaker_01
And to some extent,
00:26:41 Speaker_01
The hard part for open source and traditional software, which is different from crypto, where you can often monetize in other ways through a token or, you know, there's two or three ways actually that you can, you can monetize open source and crypto.
00:26:52 Speaker_01
Many of those things don't apply in the AI world. And it's much more like traditional SaaS where you have to, um, figure out a business model around the thing and charge for it.
00:27:04 Speaker_01
And so for Llama, there's a few things that I thought were really clever that Facebook did.
00:27:09 Speaker_01
One is if you're over a certain user number, I can't remember what it was in the original license, it was like if you had over 700 million users, you had to pay for it or license it, otherwise you could use it freely.
00:27:19 Speaker_01
And so that meant if you're a hyperscaler and you were trying to put Llama on your platform, you had to license it. Or if you were one of the really, really big social networks or companies with enormous numbers of users, you'd have to license it.
00:27:30 Speaker_01
Everybody else could use it for free. I thought that was very clever of them.
00:27:34 Speaker_00
Can you elaborate on why that's clever? Why does that work so well? Why is that a good mechanism?
00:27:40 Speaker_01
Because I think it does two things.
00:27:42 Speaker_01
One is it potentially, and I don't have any insights into how Facebook thought about it, it potentially creates a monetization path for Lama because all the big hyperscalers, if they want to adopt it, they have to license it and pay for it.
00:27:53 Speaker_01
And again, if it's driving cloud services, they should benefit from that. It also, I think, means that the very large competitors of meta can't just adopt it and compete with meta using their own technology.
00:28:04 Speaker_01
And so, because then they kind of bridged the gap of having something that was roughly fully open source, but for the things that I'm assuming they really cared about, it was effectively closed source, or at least you had to license it. Right.
00:28:14 Speaker_01
I thought that was really smart. Um, and that meant any developer can just pick it up and start using it almost anywhere in the world. That's amazing. Right.
00:28:22 Speaker_01
So, uh, I think, uh, a lot of the value of that type of open source is probably going to go to the infrastructure providers and then app companies that want to use that as a differentiator.
00:28:33 Speaker_01
The reality is at least today, a lot of people are using, um, open AI or cloud as sort of the starting point. And then if they figure something out, then they'll go and maybe fine tune. and open weights and open source model like Llama.
00:28:47 Speaker_01
But it's kind of like try it on an API first where you don't have to do a lot of heavy lifting and see if it works. And then if you think you can really optimize it, or you're worried about data security or whatever, maybe.
00:28:56 Speaker_01
And again, I think the data is quite secure using these other APIs. But if you have some concern about sending data back, then sometimes you go down the open source route, you know, and fine tune your model or do whatever it is you need to do.
00:29:06 Speaker_00
So we've discussed the multiple adoption curves, S-curves of the growth of AI and why it seems to be that it can accelerate very quickly in the near term. I want to introduce one more adoption curve, S-curve of technology, which is crypto.
00:29:21 Speaker_00
And I think we're starting to approach crypto as like middle of the S-curve these days, especially with Bitcoin crossing $100,000. I'm wondering if you're paying attention to the intersection of crypto AI, and this intersection has been growing.
00:29:37 Speaker_00
Right after ChatGPT launched, I remember there was like kind of a surgent of crypto AI tokens, like right afterwards, kind of just writing the narrative of AI. And it was, it didn't really make any sense. It was kind of like, I'm going to call it
00:29:51 Speaker_00
Stone Age version of crypto AI is very rudimentary. It wasn't real. But that was over two years ago.
00:29:57 Speaker_00
And since then, I think there have been some developers who are really trying to make this work, like trying to figure out how do these two parallel frontier technologies, how do they intersect? How do they grow together?
00:30:10 Speaker_00
And lately, there's been some very strong sparks that have probably actually turned into at least a small to medium-sized wildfire in the crypto world. Hasn't really broken out into mainstream. And this is kind of the story of truth
00:30:26 Speaker_00
terminals, I'm not sure if you're too familiar, but the number one forked and starred GitHub repo right now is the Eliza framework, which is allowing people to build their own AI agents, some with crypto wallets, some just vanilla agents.
00:30:42 Speaker_00
So I'm wondering, are you observing this space? Is this space interesting to you? And if you just have any takes?
00:30:48 Speaker_01
Yeah, I have like four or five comments on it, I guess. The first thing that I think is fascinating, do you know the origin of Near?
00:30:54 Speaker_02
The Near Protocol you're talking about with Ilya and team? Yeah. I mean, it was an AI origin, right?
00:31:00 Speaker_01
Yeah, exactly.
00:31:01 Speaker_00
Ilya has a very well cited AI paper.
00:31:03 Speaker_01
Yeah, he's on the Transformer paper. He's on the original Transformer paper. He's the last author on that paper. Oh, wow. Fun fact. I didn't know that, actually. Yeah, yeah, yeah.
00:31:11 Speaker_01
And so my understanding is when he left to start Near, and it was called Near.AI, right? It was an early AI. company and they were originally going to do almost like GPT style stuff.
00:31:25 Speaker_01
And they decided they needed to do, and he'll probably correct me on all this. My understanding is they decided that they needed to do a lot of data labeling and they're like, how can we pay people to label data around the world? Maybe use Ethereum.
00:31:37 Speaker_01
And of course, Ethereum wasn't scalable back then, right? That's why we have all the L2 stuff and all the stuff we're doing on top of Ethereum now.
00:31:45 Speaker_01
So they said, hey, how do we create a really scalable protocol so that we can create tokens that can be used to pay people around the world to label data so that we can then build a giant AI system?
00:31:54 Speaker_01
I think that's the origin of Near, which is fascinating, right? Or at least some version of the origin story that I've heard of it.
00:32:03 Speaker_01
So I think there's long been an intersection of the people who are interested in AI and the people who are interested in crypto. And I almost feel like in a given era when one of them has been hotter than the other,
00:32:12 Speaker_01
It's tipped people's career in one direction or the other. You know, I don't know if you know Uma Roy from Succinct, right?
00:32:18 Speaker_00
I just had her on the podcast.
00:32:19 Speaker_01
Yeah, she's great, right? And so that's a great example of somebody who did a lot of AI and ML at MIT. And maybe if she'd started her company two years later, she would be doing AI stuff right now. I mean, she's brilliant, right?
00:32:30 Speaker_01
And she's very smart on ZK and the mathematics underlying it. But, you know, there's stuff like that that I think is just fascinating in terms of these paths that are almost like moment in time dependent on when you graduate.
00:32:42 Speaker_01
And maybe she would in crypto anyhow, I don't know. I'm just saying she had that background, right? And so I think there's a lot of overlap in the backgrounds of people who are interested in these things, at least a subset of people.
00:32:50 Speaker_01
So I think that's one aside just in terms of the human capital or the people who are excited about it. I think there's three or four approaches that people have been taking to that intersection of AI and crypto, and a lot of it over time has been
00:33:03 Speaker_01
Can we create these data repositories where you get paid in tokens to label data or use data or do data, et cetera? There's the distributed compute stuff, which I'm more skeptical about. There's reasons you centralize these things usually.
00:33:13 Speaker_01
One could argue WorldCoin isn't part of proxy on Sam Altman, you know, mimetically. There's identity, which I think is super interesting. And actually, do you think like a blockchain resident identity could be used by the agentic AI world?
00:33:27 Speaker_01
And I'm still surprised nobody's built like a truly good identity system on the blockchain.
00:33:31 Speaker_01
There's payments, which is kind of the obvious one, which is why maybe you mentioned, you know, building AI with a wallet attached, because it'd be natural for an algorithmic agent to transact using crypto.
00:33:43 Speaker_01
I think the censorship and censorship resistance is super important aspect of crypto that AI doesn't have. And I think that is showing up in almost like what you'd call the politics of the models, right?
00:33:54 Speaker_01
Because the models are all being steered down very political, the specific subset of political paths, right? They kind of reflect the Bay Area and the Bay Area politics is basically what you see when you interact with one of these systems.
00:34:05 Speaker_01
And that may not be a good thing for humanity overall. So I think there's a lot of ways that these things kind of intersect.
00:34:12 Speaker_00
One conversation in the crypto space is the idea of decentralized versus centralized AI.
00:34:17 Speaker_00
We have the chat GPTs, the open AIs, the Facebooks, Googles, with a lot of resources, the benefits of centralized coordination, I think really pushing the frontier of AI. really introducing it into society, into mainstream.
00:34:32 Speaker_00
And then in response to that, there's been a growing parallel world of decentralized AI, which I think has some difficulties because I think maybe one of the biggest drawbacks is that decentralized versions of AI don't have access to the same data.
00:34:47 Speaker_00
that open AI, that meta does. But there's been some victories. One thing that has come on my radar as of recently is Prime Intellect.
00:34:55 Speaker_00
I'm not sure if you're too familiar, but the story that I've heard is that these training models have had some constraint in the centralized form factor of AI training. Prime Intellect is a decentralized training platform.
00:35:11 Speaker_00
I'm kind of like out on my ski tips here explaining this technically, but they were able to poke through to like break through a glass ceiling of the number of parameters that they were able to use.
00:35:21 Speaker_00
Meaningfully from I think the constraint in the centralized world was something like 400 million parameters and now they're breaking through at 10 billion. So a very significant increase in
00:35:30 Speaker_00
part of the decentralized structure of the AI revolution is starting to get some wins versus their centralized counterparts. I'm wondering if this is how you see this dichotomy, like there's two different worlds of AI.
00:35:44 Speaker_00
There's the centralized AI and the decentralized AI, and they're both kind of developing and iterating forward into the future. Is that how you see it? Not really.
00:35:52 Speaker_01
I tend to think of it more as open source and closed source. And open source may include open weights. It may include open data sets. You know, it may include all these things. I don't think you need to necessarily decentralize the compute.
00:36:05 Speaker_01
You know, I don't think that is necessarily helpful unless you're like, we can't afford it otherwise or we're crowdsourcing it or, you know, so there's reasons to do it. We can't buy all the GPU, whatever it is.
00:36:15 Speaker_01
But I don't, I don't really view it through that framework because the decentralization is not additive in this context, as far as I can tell, unless again, it addresses some of those other issues I mentioned.
00:36:24 Speaker_01
And so it's more just like what's out in the open that anybody can use and how can they use it? And I think that's the vector I would care about the most.
00:36:32 Speaker_01
It's kind of, you know, the one other thing that I used to speculate on that I no longer do around crypto and AI. And by the way, I think it's really cool they built a 10 billion parameter model in a decentralized way.
00:36:44 Speaker_01
So again, I'm not trying to say it's not cool and interesting. It's more just the dimensions I care about are like what's open versus closed and who has access, right? Versus a decentralized or decentralized.
00:36:57 Speaker_01
The thing I think is kind of interesting is if you think about the blockchain, you've always had these kind of programmatic agents
00:37:06 Speaker_01
And very, very simple agents acting on like Ethereum, smart contracts and other things where effectively these programmatic pre-baked things that happen with strong economic value associated with them.
00:37:18 Speaker_01
And so four or five years ago, I used to speculate, well, is the blockchain the first place where artificial general intelligence emerges? Because you have these economic games that are played repeatedly between effectively agentic systems.
00:37:29 Speaker_01
And so what a wonderful place to actually train an agent or an AI, right? So maybe that's a place where the decentralized AI step is super interesting. If you basically have these selective functions.
00:37:40 Speaker_01
on blockchain resident agents playing economic games at scale and learning off of them. You know, that's really cool.
00:37:46 Speaker_02
I think that's been like more of like my personal interest. And I think when David and I started really getting into kind of the intersection of AI and crypto is more on the agentic properties of it.
00:37:57 Speaker_02
Because the idea of crypto is, you know, this podcast is called Bankless, right? It's the ability for individual like humans to go bankless. But actually the most underbanked population of the future is probably going to be AI agents, right?
00:38:13 Speaker_02
Very difficult to kind of, you can open up a Stripe account, but then you have 3% credit card transaction fees, and they open up an AI.
00:38:21 Speaker_02
The form factor of a blockchain with programmable money and property rights is basically fit for purpose for AI agents.
00:38:30 Speaker_00
You need a social security number to open up a Stripe account.
00:38:32 Speaker_02
Right, and so some of the early use cases, which are starting to be interesting in some of those frameworks that David mentioned, is almost like the AI agent influencer package, right?
00:38:42 Speaker_02
So he's talking about Eliza, and there's this platform called Virtuals, and there's this meme account called Truth Terminal on Twitter, and it has this goat token. And it's basically almost like taking the job of influencers.
00:38:58 Speaker_02
Let's say like crypto influencers, right? So it's playing almost like attention economy type of games.
00:39:04 Speaker_02
And AI agents and the LLM models that we've created are pretty good at interacting with humans and passing the Turing test and being interesting to interact with. And so that's been the early almost like toy version of AI agents on chain.
00:39:20 Speaker_02
It's been interesting to see when you hook up a Twitter account, basically, and allow the LLM to kind of like talk about whatever it wants and pair that with a crypto wallet. So create that token incentive. What experiments fall out of that?
00:39:36 Speaker_02
And to your point, like, I actually think that thesis of, you know, the AGI comes through this intersection of crypto and AI.
00:39:43 Speaker_02
Could be like a spot-on as this evolves anyway, that's the potential that we've been more excited about recently But people look at it, and they're just like ah it's just meme coin games again It's you've just hooked a Twitter account to an LLM model sure it's pumping a meme token and like who cares What's your take on this trend?
00:40:01 Speaker_02
Do you think there's something here?
00:40:03 Speaker_01
You know I think it's really interesting training data for an AI right again if you're getting back in the economic game theory and how humanity evolved, and what are the set of drivers for evolutionary progress?
00:40:15 Speaker_01
And probably a lot of it was forms of economic gains, right? And so I think as a training set, it's super interesting.
00:40:21 Speaker_01
Obviously you still need scale of compute and you need all this other stuff, but I do think it's really interesting from that perspective. One of the big
00:40:30 Speaker_01
sort of future AGI concerns or safety concerns that Anthropic talks about is resource aggregation by AI.
00:40:36 Speaker_01
And so they spend less time talking about, hey, we'll let an AI invent a virus that will do X, Y, Z and all this stuff, which I think is actually not very likely anytime soon.
00:40:44 Speaker_01
And they're more like, well, what if an AI is smart enough to start aggregating resources at scale and using those resources to manipulate society? And how do you prevent that? And crypto would be a good basis for that.
00:40:55 Speaker_02
Basically the AI as a god or forming a religion or creating some sort of memetic social movement, something like this?
00:41:01 Speaker_01
Not even as a god. What if it's just very good at predicting stock movements and it just starts getting a bunch of money and paying people to do stuff for it? That gets us more money, right? So it's more about that form of safety.
00:41:13 Speaker_01
I do think that to some extent one could argue that there will have been three ages of humanity, right? The first age is all the compute is roughly humans and then other animals, right? And from a concentration perspective, it's humans.
00:41:27 Speaker_01
And that was probably leading up into the century. And then we have kind of a hybrid age, like the second age of humanity is probably this
00:41:36 Speaker_01
you know, what we have right now, which is there's some split between humans and machines and humans are directing the activity and all the rest. And probably the third age is the age of machine intelligence, right?
00:41:47 Speaker_01
Where that's the predominant form of compute and intelligence on the planet, right? And so to some extent, perhaps we're very lucky to be in the second age, right?
00:41:57 Speaker_00
It seems like a limbo period. It's a waiting room for the third period. In the grand scheme of things, it's going to be like all of humanity is in the first age.
00:42:04 Speaker_00
And then we have this very short amount of time where we figured out how to get computers to think, but dumbly, like calculators. And now I think the third age is how you're describing it is like, well, now they think on their own.
00:42:16 Speaker_01
And there's a lot of them, right? And so from a sheer compute perspective, there's way more intelligence embodied in machines at some point than there are in people. And we're not there yet, right?
00:42:28 Speaker_01
But at some point that will happen in the coming decades, right?
00:42:32 Speaker_02
Eli, does that third age kind of like scare you sometimes? I mean, like we actually opened up the rabbit hole of crypto by like talking to Eliezer Yudkowsky and he's definitely has a defined thought on this kind of a doomtake on AGI in general.
00:42:48 Speaker_02
But like, should we fear this third age? I mean, maybe it's inevitable. I guess we're all investing in it to some extent.
00:42:56 Speaker_02
Part of the thing we kind of worry about, if you take Eliezer Yudkowsky's line of view, is, oh, cool, so we've created this property rights and financial system and economic resource allocation system for a bunch of robots that are going to come and enslave us or obliterate humanity.
00:43:12 Speaker_02
Great job, crypto. Do you have any worries like that?
00:43:16 Speaker_01
You know, I'm not part of a doomsday cult, and I feel like some of the procrastinators in this topic are basically doomsday cult leaders, right?
00:43:25 Speaker_01
Like it's not, I'm not saying anybody specific, I'm saying in general, like there are these things that effectively feel like doomsday cults, right?
00:43:33 Speaker_01
And they have AGI as the, you know, before it was like the meteor striking the earth or, you know, some religious event, and it's almost like a religious rapture in some of these people's minds. I think my take is,
00:43:46 Speaker_01
But measured, which is, I would kind of view myself as like an AGI or safety moderate, right? I think there's enormous good coming through AI. And I think we're only in the earliest innings.
00:43:57 Speaker_01
And if I look at global health equity and educational equity and all the rest of it, or however you want to phrase it, right.
00:44:03 Speaker_01
These are the things that are going to drive the ability of humanity to participate at scale in ways that they couldn't before. You know, everybody should be able to get a one-on-one tutor education that's best in class in the world.
00:44:12 Speaker_01
And everybody should be able to have access to the world's best medical information through any device. Right. And that's coming.
00:44:18 Speaker_01
Now at some point there will be these very intelligent machines, and we'll be interacting with them in different ways, and I don't know what that world looks like.
00:44:28 Speaker_01
I can speculate, but I think it's very hard to say what in years or decades from now things are going to be. It's the old saying that less happens than you think in technology in two years and more happens than you think in five years.
00:44:41 Speaker_01
And once you hit 10 years, like it's game over. Like 10 years ago, we wouldn't be predicting any of this stuff. We wouldn't be predicting the stuff that SpaceX is doing. We wouldn't be predicting the quantum computing stuff that Google just released.
00:44:53 Speaker_01
We wouldn't be predicting this wave of generative AI. Nobody predicted this different curve on the transformer models. So I just think it's very hard to think 10 years out.
00:45:02 Speaker_01
And we've also over-predicted certain things that didn't end up happening in terms of technology curves.
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00:47:22 Speaker_00
Now we have these two emerging frontier technologies, AI and crypto. I think we've talked about AI quite a bit. I want to just kind of get your takes on raw crypto, crypto without the AI.
00:47:33 Speaker_00
When you're looking over into the world of crypto, you're seeing Bitcoin in 2009, Ethereum in 2015, Coinbase going public. circle, these companies that are just evolving, maturing, the industry is becoming legitimized, regulatory.
00:47:47 Speaker_00
We've had regulatory oversight. Now we have regulatory leniency, or at least we think that's what we're getting. Just like what patterns are being sparked? What's your overall take?
00:47:58 Speaker_00
You've seen a thing or two before, like what are you, what's resonating with you in the growth of our industry?
00:48:03 Speaker_01
Yeah, you know, at this point, I feel like I'm reasonably ignorant on crypto. Like I used to spend a lot of time on crypto.
00:48:10 Speaker_01
And I did a lot of crypto investing in 2016, 2017, et cetera, and got involved with companies like Coinbase and some of the protocol level things, and then index funds like Bitwise and just different things across the industry.
00:48:25 Speaker_01
Starkware and that early work and all sorts of early ZK stuff, like Mina and other sort of early adopters of different ways to use ZK. So I think it's, it's like amazing what the crypto industry has accomplished.
00:48:39 Speaker_01
And I think it is a fundamental thing that's here to stay. And usually when I look at these sorts of technology shifts that are so important and so fundamental, I try to distill it down into use cases.
00:48:52 Speaker_01
Like what are the specific aspects of the technology that make it uniquely good for a specific application or use case? Cause otherwise nobody's going to use it.
00:49:01 Speaker_01
And that's sometimes where I feel like people in any market segment, especially if it's a lot of technologists lose their way because they start extrapolating all the stuff off of the technology that nobody actually cares about, but they think should happen because the technology is so good.
00:49:11 Speaker_01
Right. And so the, the mobile version of that is if you look at the really big mobile centric companies that got formed. It's the companies that uniquely took advantage of the new capabilities of phones, which was GPS, so location always on.
00:49:27 Speaker_01
Um, it was the instant ability to message or send push notifications, et cetera. And roughly what that collapsed into was social products like WhatsApp and others, you know, Instagram, whatever it is. And then, um, Uber.
00:49:40 Speaker_01
You push a button and a stranger shows up in a car and you're okay getting in and they'll take you to wherever you want. Right. Um, Instacart and other delivery services. Right.
00:49:48 Speaker_01
So you kind of go through and you say, where were the new startups or new things created? And obviously there's enormous incumbency usage, right? Like B of A apps and all this stuff now.
00:49:56 Speaker_01
But the main things where the mobile revolution was important was one, opening up access nonstop to the internet, because you had a device you could carry with you everywhere, a supercomputer in your pocket.
00:50:07 Speaker_01
But second was these unique things that were enabled by GPS and text messaging and all the rest of it, right? And so for crypto, it's the same question of, okay, what are the core capabilities of crypto?
00:50:18 Speaker_01
Because some versions of blockchains, and obviously it's much more sophisticated now, but in the early days, a blockchain was kind of like a shitty database, right? That had very unique characteristics that made it incredibly important.
00:50:30 Speaker_01
around censorship resistance and permissionless systems and all the rest of it, right? And then you ask, well, where are those unique capabilities really important? You know, you have this 24 seven accessible financial system, right? That's emerged.
00:50:44 Speaker_01
And that's things like store of value, like Bitcoin, it's DeFi, you know, it's a bunch of stuff like that in my mind. So I almost view it as like,
00:50:55 Speaker_01
what are the capabilities of the system and therefore what are the unique use cases versus, hey, it's just going to do everything.
00:51:01 Speaker_02
Do you like Chris Dixon's framing of this, which is like, he goes through, I mean, he wrote a book called Read, Write, Own, which is basically web one is read, web two is write, you know, so you got blogs and that kind of era.
00:51:13 Speaker_02
And then web three with crypto is own. So it's this idea of like property rights, which fits into store of value, decentralized finance, the ability to spin up assets that are like digitally scarce.
00:51:26 Speaker_02
Is that the rubric through which you view crypto use cases?
00:51:30 Speaker_01
No, I kind of view it more as like, when do you need these capabilities? I mean, it overlaps, right? And there's a lot of things that haven't happened in crypto that I thought would happen.
00:51:39 Speaker_02
Like what? What were you thinking would happen?
00:51:41 Speaker_01
I mean, to your point on property rights, like a public ledger of who owns what property. So therefore, you can avoid government seizure of your land or other assets.
00:51:53 Speaker_01
right, or the notion of identity on the blockchain, where you should be able to fragment identity, but provably show that you did stuff.
00:52:00 Speaker_01
So for example, you can provably show on one identity strand or, you know, one wallet or however you want to kind of assign identity, that you're a Google engineer and you've done XYZ things.
00:52:11 Speaker_01
And then on the other one that you're a contributor to some DAO or something that you're anonymous on.
00:52:17 Speaker_01
So there's a bunch of stuff like that that hasn't happened that the blockchain feels like a really good fit for because you can transparently and clearly prove identity without having to reveal identity.
00:52:28 Speaker_02
What's really difficult in this, and I'd love your take on this, is it's hard to determine kind of the order of operation through which these things will happen. Like some of those use cases that you mentioned, like identity.
00:52:40 Speaker_02
I'm personally, you know, David and I are both personally big believers that identity will happen on chain at some point in time, like ZK tech stack, just like,
00:52:49 Speaker_02
makes so much sense for this use case where you can just prove something without disclosing all of your private information about it. But to your point, right? Crypto has not really done much in the identity space.
00:53:01 Speaker_02
Bitcoin has been the big use case, has been store of value being the big use case. And some of the things that we thought would happen earlier haven't happened yet. But the question is, will they happen in kind of the fullness of time?
00:53:14 Speaker_02
How do you get the order of operation right in these different tech trends?
00:53:19 Speaker_01
I think that's a great question. And if I knew the answer to that, I'd be retired right now. I think that I'm joking about retiring, but I think I was going to say, I'm sure you could retire.
00:53:30 Speaker_02
You're just like, you know, just like what you're doing too much. Retiring is boring.
00:53:34 Speaker_01
Yeah, I don't know. That sounds pretty nice. So I think that The order of operation is a great question, particularly with something like identity, where you kind of have to bootstrap up a network and the usage of it and all the rest of it. Right.
00:53:48 Speaker_01
I think for other areas, it's just never going to work because it just doesn't matter. Like the argument for like a decentralized Uber was, well, every driver can buy a token and therefore have a stake in Uber and you could go buy Uber stock.
00:54:02 Speaker_01
Do you know what I mean? It's not. And so I feel like some of these things are a little bit overstated in terms of like, you know, decentralization and a token will magically make something occur.
00:54:11 Speaker_01
But the flip side of it is like a store of value where you can cross a border with literally a billion dollars in your head, which nobody can seize. is amazing.
00:54:19 Speaker_01
That's a superior product to gold or a superior product to whatever store of art or whatever you're going to cross a border with. And so I do think that there's a lot of stuff like that in crypto that's really important.
00:54:29 Speaker_01
And again, I do think DeFi is an example of that. You know, all the stable coins and all the USDC and all the whatever form you want to talk about, like all that stuff was clearly going to be really useful for all sorts of applications.
00:54:43 Speaker_01
And so I just think there's a lot, a lot that crypto has accomplished and a lot still to do. And it's very exciting.
00:54:49 Speaker_01
And the place where I see crypto companies sometimes get a little bit lost is either they just go all in on building some really cool piece of infrastructure that doesn't necessarily have an application yet.
00:55:00 Speaker_01
or really focusing on like a use case where the existing centralized version works just fine.
00:55:06 Speaker_02
What would you say, Elad, is kind of the mainstream Silicon Valley take on crypto right now? Because I feel like I've been charting this from the outside a little bit.
00:55:15 Speaker_02
It felt like there have been periods of time where there's some pretty intense skepticism about crypto. Just like this term that you used earlier of, you know, it's just a slow database. Like, who cares?
00:55:25 Speaker_02
Or like, you know, I don't understand why Bitcoin is worth what it is. This feels like a tulip mania. And this has been present at various times. In fact, a lot of the crypto startups have just like not happened in Silicon Valley.
00:55:40 Speaker_02
They've happened outside of sort of the VC Silicon Valley apparatus. What does Silicon Valley think about crypto now? Or do they not think about it?
00:55:47 Speaker_02
Is Silicon Valley all like AI, AI, AI, and this crypto thing's happening on the side, but it's like less interesting? How has the narrative really shifted?
00:55:57 Speaker_01
I don't know. I think there's a subset of founders and firms on the venture side and all the rest of continue to just participate in crypto ongoing. Right. And so, you know, and then there's obviously specialist firms that have kind of spun up. Right.
00:56:13 Speaker_01
So, you know, in terms of the broader firms, obviously Andreessen has continued to do a lot of crypto with Chris Dixon and others there. There's been standalone firms that obviously either spun out of existing institutions or set up on their own.
00:56:27 Speaker_01
That's Paradigm and Electric and Bond Ventures and you name it. There's a bunch of different folks and they're all based in Silicon Valley.
00:56:38 Speaker_01
It feels like maybe the biggest shift over the last couple of years is the degree to which momentum really increased for New York as one of the core places for crypto. over the last, say, five years or whatever time period you want to put on it.
00:56:48 Speaker_01
And it was almost like this transition from L1 to other layers, right? Because it felt like the L1 stuff continued to be out here for a while in the Bay Area, and then it kind of shifted elsewhere.
00:56:57 Speaker_01
And obviously it was always distributed and there's people working from all over.
00:57:01 Speaker_01
But I just feel like the type of work or almost the sub-cluster in crypto for a while at least reflected the nature of the work being done in terms of the geography where there was concentrations of people.
00:57:13 Speaker_01
But I think mainstream tech, maybe 20% of people always thought crypto was really important and kept thinking it was important. And then maybe there's 30 or 40 people who swing in and out based on Bitcoin price.
00:57:23 Speaker_01
And there's 30, 40% who will always be skeptical. You know, those are the people who are like, get off my lawn, you know, kids playing there, you know, it's very, and some of these folks are very smart.
00:57:37 Speaker_01
I just think it's a little bit backwards looking because I think it's clear that there's use cases, it's clear that there's adoption. And I do think there's a lot of assets that are reflexive and it's one of them.
00:57:47 Speaker_01
And it's hit that escape velocity, in my opinion, barring something really unexpected or, you know, some quantum attack or God knows what, but even then it'll survive it. And obviously there's quantum resistant algorithms that you can,
00:58:01 Speaker_01
There aren't that many things I can think of that would disrupt it outside of civilization level events at this point, but I could be wrong on that.
00:58:07 Speaker_01
Maybe regulation, like a global lockdown from a regulatory perspective with some tyrannical world government or something.
00:58:15 Speaker_02
I would love to pick your brain on regulation because it affects many things in AI and crypto. One last thing while we're on crypto companies and the evolution there is crypto founders.
00:58:26 Speaker_02
You've had an opportunity to invest and work with many of the best tech founders in the space. People like Palmer Luckey, Brian Armstrong, of course, Coinbase, who we're very familiar with, Dylan at Figma.
00:58:39 Speaker_02
I'm wondering if you think that basically the the tech founders you like generally are similar to the crypto tech founders or you if you think that there's a Different pattern with a crypto tech founder and one thing that's kind of breaking the mold a little bit in crypto is obviously you have the original crypto tech founder which is Satoshi and Like who is this person right?
00:58:58 Speaker_02
They disappeared. This is not normal like that's certainly not something that happens then you have people like Vitalik and And he has like Vitalik almost has not like Mark Zuckerberg CEO type qualities.
00:59:11 Speaker_02
There's almost something like David and I have described as like monkish about it. He's just almost like a. Sure.
00:59:17 Speaker_00
He's a he's a movement founder, not a tech company founder.
00:59:20 Speaker_02
Yeah, he's founded an entire movement, it feels like, almost like a Dalai Lama type figure. There's like, I don't want to impose this on him, but there's something like kind of movement religious about it in a way.
00:59:32 Speaker_02
And then you also have the Brian Armstrong's and Jesse Powell's of the world and the Hayden Adams and such. Anyway, are there differences between crypto founders and tech founders or are they kind of like, you know, same patterns?
00:59:44 Speaker_01
That's an interesting question. I feel like to your point, there's been multiple different types of ways of crypto founders because it went from a true backwater, right?
00:59:55 Speaker_01
When the Bitcoin paper dropped and, you know, you had to be kind of weird to get into it, right? And you were, you know, using it to buy pizzas and, you know, all this stuff just to try and get other people to adopt it, right?
01:00:06 Speaker_01
If you remember those days where they were just giving out Bitcoin to kind of get anybody to do something. And so that, that was a wave of people.
01:00:15 Speaker_01
And obviously that also came out of more of a kind of right-leaning technology community, which is very different from the internet, which was very left-leaning in terms of its origins, right. In terms of the types of people working on it.
01:00:25 Speaker_01
And as you kind of moved up and down the stack, you change the type of founders that show up. And I remember there's also an era of a lot of like professor coins, right. In like 2017 or 2018, like every.
01:00:39 Speaker_01
Every professor working on crypto like launched their own token, right? Or was working on a token. And there's kind of the AI equivalence of that where you have kind of these professor AI companies, right?
01:00:50 Speaker_01
There's these new architecture model company that spins out of an academic lab and it has a very similar characteristic where they raise a lot of money. And then sometimes the execution is lacking. I mean, obviously there's good versions of that too.
01:01:01 Speaker_01
there are some analogs or parallels, I feel, in different eras of each of these things. As mentioned, I do think there's people like Uma from Sassink or a number of other people, Ilya from Nir, who have done both or could have done both.
01:01:16 Speaker_01
And so I do think there's a lot of overlap. And then obviously, there's also differences in terms of either some of the philosophical perspectives or political alignments or other things between the two communities.
01:01:25 Speaker_01
And sometimes they overlap, but sometimes they don't.
01:01:27 Speaker_02
So Elad, I want to pick your brain on politics from a different angle here. This is just like Silicon Valley's entrance and big tech's entrance into the political landscape in 2024.
01:01:39 Speaker_02
We've certainly seen this in crypto and David and I have been tracking it quite closely, but I, in general, I've never seen tech, so political. You have Elon Musk, you have the All In podcast gang, you have Marc Andreessen talking about politics.
01:01:55 Speaker_02
Of course, in crypto, it's taken the form of people like Brian Armstrong, Jesse Powell from Kraken, the Fairshake PAC, political donations, public advocacy for pro-crypto candidates, all of these things.
01:02:09 Speaker_02
Just a couple of weeks ago, we had conversations around debanking go viral, and that has kind of a political lens to it as well. What's your story for why tech got political in 2024? Did this need to happen?
01:02:23 Speaker_01
Yeah, I mean, I think there's always been some overlap between tech and politics.
01:02:27 Speaker_01
And so if you even go back, you know, 100 years or 80 years to World War II, and all the industrialists being pulled in to basically be able to create large scale weapons systems and other things for the US military during World War II, I mean, that was effectively a mobilization of what was a prior generation of tech in some sense, right?
01:02:46 Speaker_01
when tech was more about automotive or more about shipbuilding or more, you know, all these things that were high tech of its day, right? Detroit used to be the high tech capital of the world in some sense during the automotive industry boom.
01:02:59 Speaker_01
And there was very similar cluster effects and everything else that happened during that era that now are happening in Silicon Valley. So I think one could argue on one level that there's been this engagement in different ways or different forms.
01:03:12 Speaker_01
Obviously, I think there's a period where technology went very kind of libertarian and very kind of hands off from government and vice versa.
01:03:22 Speaker_01
And one could argue one of the reasons tech has been so optimistic over time is because it was so lightly regulated. And so you could be optimistic. Because if you're heavily regulated, you tend to always have constraints around you.
01:03:33 Speaker_01
And so you become more pessimistic in some sense around the world. And you can actually see the most regulated industries when you talk to people in them are often the most pessimistic in terms of what can actually be done, right?
01:03:45 Speaker_01
You have very clear guardrails and outside of those guardrails, you can't do much, right? So I would argue part of tech optimism is due to a lack of regulation.
01:03:53 Speaker_01
And I think that if you look at prior administrations, there were always ways of tech people participating.
01:03:58 Speaker_01
So in the Obama administration, a number of different people from Google actually joined the administration and then some of them cycled back out to companies like Facebook and Google and others. Um, so I think there has been some back and forth.
01:04:09 Speaker_01
I think maybe the difference is that in this cycle, some of the most prominent people in tech are very actively engaged and that's Elon Musk. And that to your point of some of the other folks you mentioned, David Sachs, et cetera.
01:04:22 Speaker_01
And these are people who've built very large scale companies and had enormous success. And now they're building that perspective, bringing that perspective and skillset and everything else to government in a really deep way.
01:04:32 Speaker_01
And I think part of that is because there may be this moment in time that's being recognized about being able to affect large-scale change and actually change the system.
01:04:39 Speaker_01
And that would be things like DOJ, the Department of Government Efficiency, and sort of the changes in some of the Supreme Court rulings that may actually enable it to happen, right? When before you didn't have that legal framework to do so.
01:04:50 Speaker_01
Chevron and other things, right? I think part of it is that certain aspects of government became highly weaponized against aspects of tech, and that's the debanking stuff that you mentioned.
01:05:02 Speaker_01
That was going after Elon Musk in different ways across different companies. I think there was, what, like a dozen or two dozen different lawsuits from different federal agencies against Tesla and SpaceX.
01:05:14 Speaker_01
I think the most egregious one was where as a government contractor, they had to hire legal U.S. residents And so they, uh, there's one rule in the books that says that as a government contractor, you have to do that.
01:05:28 Speaker_01
And then there's another rule on the book that says you can't discriminate against immigrants. And so you get sued either way, depending on what you're doing, right? So how can you function in that environment?
01:05:36 Speaker_01
And why did his company specifically get sued? And so if you're being targeted, of course, you're going to try and do something right.
01:05:45 Speaker_00
What do you think about Doge? So now Elon Musk is going to bite back along with Vivek. Are you optimistic about Doge? Maybe you like Doge in theory, waiting to see it play out in practice. What do you think?
01:06:00 Speaker_01
I think that if they're able to pull off what they're hoping to, then it is a truly once in a generation opportunity to affect massive change that can cascade through time in a positive way.
01:06:11 Speaker_01
So if you're actually able to reduce certain types of regulation and certain size of government and all the rest, it could be extremely freeing for the U.S.
01:06:21 Speaker_01
economy, for progress, for the capabilities of the country, the economy, people's participation in the economy, et cetera, technology progress, all that stuff. So I think it could be incredibly positive.
01:06:36 Speaker_01
Now, obviously, there's certain things that are important to maintain while you do that, right? So, for example, I'm very happy that the FDA has prevented the tainting of baby formula in the US, which has happened in China, right?
01:06:47 Speaker_01
I think that's really good. We probably don't want to get rid of that, right? But there's other aspects of things that we probably do want to change and that we want to change significantly.
01:06:55 Speaker_01
And part of it, too, is just asking, are we functioning within the proper legal framework relative to the US? In other words, is it constitutional to have all these
01:07:06 Speaker_01
agency setting rules that they weren't necessarily tasked with doing to begin with, right? That go outside of their mandate and that should potentially be legislated by Congress, right?
01:07:15 Speaker_01
And so there are also interesting legal questions of our, are we in compliance legally as a government and as a country?
01:07:21 Speaker_01
And if not, then we should probably fix that either by changing the laws to say, Hey, we're not in compliance or getting people into compliance, right?
01:07:28 Speaker_01
That's the reason we have legal frameworks is to kind of set the rules by which we do things and govern.
01:07:33 Speaker_02
I know one thing in kind of watching this crypto get political, watching this up close firsthand over the past four years, I think you're very right that in particular, crypto leaned kind of libertarian.
01:07:45 Speaker_02
It's just like, we're doing our store of value thing. We don't need the nation state to kind of interfere. We're not going to get involved in politics. It turned out that this happened in kind of 2022. that somebody actually changed that in crypto.
01:08:01 Speaker_02
His name was Sam Beckman Freed. He started getting very active in politics, only in a negative way, and that kind of blew up in our face.
01:08:09 Speaker_02
And I personally watch firsthand, people who are, I would say, more libertarian, right-leaning by philosophy, people like Brian Armstrong, in the wake of 2022 and 2023 and 2024 say, okay, we can no longer afford to do this.
01:08:24 Speaker_02
politics is actually existential for crypto at this point. We had the Gary Gensler attacks, we had the FDIC and various debanking types of movements. So for crypto, it really felt existential, this election cycle in 2024.
01:08:38 Speaker_02
And that's why there was so much funding, there was so much advocacy, so much work done to actually push pro-crypto politicians. I think that's largely been successful.
01:08:47 Speaker_02
I know Marc Andreessen has taken the point that everything that was going on in crypto was sort of act one for what the existing government apparatus planned to do around technology.
01:08:59 Speaker_02
Act two was a whole bunch of handcuffs and aggressive actions against AI to try to, you know, capture that from a regulatory perspective. From your perch, did you kind of see that in AI?
01:09:11 Speaker_02
Like you're watching what's going on in crypto and like, okay, well, you know, this is starting to affect AI and it could really offset the US's progress in this incredibly important tech field.
01:09:23 Speaker_01
Yeah, I mean, I felt like there was three or four things all happening at once, right?
01:09:25 Speaker_01
So to your point, there is the crypto side of it, where, you know, you had kind of what felt like reasonably random enforcement action that wasn't quite enforcement action, but with these warning letters that, you know, is very like,
01:09:43 Speaker_01
unclear frameworks for how you can actually function and then sort of action against companies that fell either politically motivated or pseudorandom.
01:09:52 Speaker_01
You also had censorship at the social network side, right, which was clearly coordinated with the government, at least if you look at the Twitter files or other things.
01:10:00 Speaker_01
Then you had strong antitrust activity that in some cases seemed justified, in some cases was just kind of weird. And then to your point on the AI side, there was this interest in getting really heavily involved from a regulatory perspective.
01:10:16 Speaker_01
And I think this sort of doomsday cult stuff helped fuel the ability for politicians to do that. But it felt to me like a lot of that activity was just another way to try and take control of the tech industry.
01:10:26 Speaker_01
It wasn't, it was that opening to regulate tech more broadly. It wasn't a, hey, we care about AI or understand it deeply, right? I mean, if you talk to the people behind it,
01:10:35 Speaker_01
They didn't necessarily, and some people were very bright about what AI is and how it works, but a lot of the regulatory action that was coming felt like, Hey, there's some aid in the administration who kind of wants to get a handhold on tech.
01:10:47 Speaker_01
And this was a way in. Right. And the tech industry was semi welcoming it.
01:10:51 Speaker_01
Now, some of the people who are welcoming it, perhaps we're doing so because regulatory capture, the ability to have a lot of extra rules tends to help incumbents and hurt startups.
01:11:01 Speaker_01
And so this was sort of the big tech versus little tech thing of like, these were very, in some cases, maybe kind of pro big tech things that would have hurt a lot of innovation and startups.
01:11:10 Speaker_01
So I think, you know, fundamentally, it's back to like, are we, are there clear regulatory and legal frameworks? Do they make sense? Are we complying with them at multiple levels, company level, government level, et cetera?
01:11:26 Speaker_01
And in the cases where we're not, or we're acting well outside of our mandate in ways that are potentially quite negative societally or sectorally, like why are we doing that and how do we stop that misbehavior, right?
01:11:39 Speaker_01
And there have been absolutely obviously bad actors in crypto and there will be bad actors in AI.
01:11:44 Speaker_01
And, you know, and so it's good that there's some regulatory framework for this stuff, but we just need to make sure that it's correct and then it's properly enforced.
01:11:52 Speaker_02
So do you think all some of the negative headwinds we felt from the regulatory apparatus, do you think that just all goes away under the Trump administration? Do you think we get big changes here?
01:12:03 Speaker_01
I don't know. I mean, I'm hopeful that, and to your point, I think Coinbase and others have done a great job of kind of helping rally the industry around some of these issues. Right.
01:12:13 Speaker_01
And to your point, there was PAX set up and other things to really focus on creating a pro crypto environment. And I think that's, this is the first time it's really felt that way.
01:12:21 Speaker_01
And one could argue that's the reason Bitcoin has really started running.
01:12:25 Speaker_01
And so I'm, I'm hopeful that given the, um, intellect and capabilities of the people from tech getting involved in the cycle, because it really is some of the strongest people, right?
01:12:38 Speaker_01
Like Elon Musk is probably a once in many hundred years founder, right. Um, or something on that level. Right. I mean, like, if you just look at the span of stuff that, you know, we have some very capable people getting involved in government.
01:12:50 Speaker_01
The question is how will government react and. you know, how does that story play out? But my hope is it ends up in a really positive place.
01:12:58 Speaker_02
What do you think about this newly appointed crypto czar position? So David Sachs was kind of tapped for that role, obviously from the All In podcast. Yeah, this, by the way, this is a crypto czar position slash AI, I believe.
01:13:13 Speaker_02
So it's kind of a combo type role. Do you think that will have some power in the administration?
01:13:19 Speaker_01
I don't know the nature of the role. I think David is very smart. And it's funny that you call him a podcaster or you keep talking about the all-in podcast.
01:13:26 Speaker_02
You know, he's an investor, founder, all these other things.
01:13:28 Speaker_01
Well, I mean, he was early at PayPal. He was, I think the, what was he, COO or something at PayPal way back in the day. And then he started Yammer, which was bought by Microsoft and he ran a division at Microsoft.
01:13:39 Speaker_01
And then, you know, he became a prominent investor with Kraft Ventures and he funded all sorts of early crypto things. back in 2017 or so, right?
01:13:50 Speaker_01
And then, you know, I've overlapped with him on some AI stuff and talked to him from time to time on that stuff. So I think he's very smart and he's, he has done a lot of stuff operationally.
01:13:58 Speaker_01
He has done a lot of stuff as an investor and he's participated in most crypto and AI in a way that many folks in the prior administration who had those sorts of responsibilities or roles hadn't. So I view it as quite positive.
01:14:12 Speaker_01
You know, the proof will be in the pudding, or however the British say it. I don't really know Britishism, so I probably got that wrong. They're probably like, cheerio with the tomato sausage, or I have no idea what British people say, right?
01:14:25 Speaker_01
But whatever the Britishism is, You know, obviously we now have to see what things translate into, but I think he's very strong. And I think, you know, I'm excited to see what impact it could have, you know.
01:14:36 Speaker_02
Elad, this has been great. We talked AI, we talked crypto, we talked about sort of the US and the regulatory apparatus. Maybe a final question to kind of close this out.
01:14:46 Speaker_02
So obviously, we're speaking to an audience of investors, crypto investors slash tech investors, who are looking at 2025. And they're always on the lookout for the kind of the frontier opportunities.
01:14:59 Speaker_02
What do you see as the biggest growth opportunities for tech investors in 2025? Maybe particularly less the VC kind of startup type of thing and more kind of the category that a retail investor might have access to.
01:15:15 Speaker_02
What advice would you give them on how to do well in tech investing?
01:15:18 Speaker_01
I don't know if I have good advice. I think fundamentally the way I've been thinking about the world is what are effectively indices on major tech movements? And then what is durable in the face of tech movements?
01:15:35 Speaker_01
In other words, what is the best way to participate in a basket of stuff related to AI? And to some extent that's NVIDIA, that may be other things. What's the way to participate in crypto?
01:15:46 Speaker_01
You know, to some extent one could argue a Coinbase where I still, you know, own some stock or some of the other companies in the market may effectively function as indices on top of the crypto industry because if you're trading all the tokens then, you know, fundamentally you're participating in those transaction fees and therefore you've created an effective index, right?
01:16:12 Speaker_01
And so I tend to think of it in terms of like, what are the index companies for the areas that I think are most important? Like I would argue Anduril now is effectively an index fund on defense tech. Right.
01:16:22 Speaker_01
And so sector by sector, Stripe used to be an index on e-commerce. or the new wave of e-commerce companies, because if all the new e-commerce companies were adopting Stripe, you effectively had an index, right?
01:16:33 Speaker_01
Because they took a piece of every transaction. So I tend to think through that lens in terms of how can you participate in markets broadly, right? The second question is, what's durable?
01:16:44 Speaker_01
So AI comes and eats away at the world, what things will survive and what things will get augmented, but what things just won't care? And so I really like the companies that just don't care. The extreme example of that would be a railroad.
01:16:57 Speaker_01
There's only so many railroads, AI, not AI, who cares? This is going to keep shipping freight, you know? And so that's very durable in the face of AI, right? So I kind of tend to think in those, in those ways.
01:17:13 Speaker_02
You said you didn't have advice, but that's pretty good advice, Elad, toward the end of this. Thank you so much for joining us. It's been a pleasure to chat with you today. Thanks for having me. I appreciate it.
01:17:23 Speaker_02
Got to let you know, Bankless Nation, of course, none of this has been financial advice. Crypto is risky. You could lose what you put in, but we are headed west. This is the frontier.
01:17:31 Speaker_02
It's not for everyone, but we're glad you're with us on the bankless journey. Thanks a lot.