Charles Hoskinson is the founder of Cardano, co-founder of Ethereum, a mathematician, and a farmer. Please support this podcast by checking out our sponsors:
- Gala Games: https://gala.games/lex
- Allform: https://allform.com/lex to get 20% off
- Indeed: https://indeed.com/lex to get $75 credit
- ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free
- Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savings

EPISODE LINKS:
Charles's Twitter: https://twitter.com/IOHK_Charles
Charles's YouTube: https://www.youtube.com/user/charleshoskinson
Cardano Website: https://cardano.org/
IOHK Website: https://iohk.io/

PODCAST INFO:
Podcast website: https://lexfridman.com/podcast
Apple Podcasts: https://apple.co/2lwqZIr
Spotify: https://spoti.fi/2nEwCF8
RSS: https://lexfridman.com/feed/podcast/
Full episodes playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4
Clips playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOeciFP3CBCIEElOJeitOr41

OUTLINE:
0:00 - Introduction
2:09 - What programming language is the simulation written in?
7:14 - Favorite philosophers
16:15 - Theory vs engineering in cryptocurrency
27:24 - What programming languages should everyone learn
35:39 - Haskell and beyond
39:23 - Plutus: Cardano's smart contract platform based on Haskell
43:50 - What is a blockchain?
48:02 - Hybrid smart contracts
53:53 - Proof of work vs proof of stake
1:02:39 - Cardano's proof of stake consensus algorithm
1:13:11 - What is Cardano?
1:21:52 - Cardano vs Ethereum vs Bitcoin
1:31:47 - The problem with Bitcoin
1:41:21 - Bitcoin Conference
1:45:02 - Ergo and Alex Chepurnoy
1:52:12 - Cardano's Extended UTXO Model
1:59:25 - Chainlink and Oracle Networks
2:06:37 - Cardano and Wolfram Alpha
2:11:32 - The future of video games
2:20:07 - Smart contracts timeline for Cardano
2:27:37 - Decentralized exchanges
2:33:18 - Jack Dorsey and Bitcoin
2:39:30 - Elon Musk and Tesla: Cardano, Ethereum, Bitcoin
2:42:47 - Dogecoin and Elon Musk
2:54:08 - Hydra vs Lightning Network
3:01:42 - Non-Interactive Proofs of Proof-of-Work (NIPoPoWs)
3:05:36 - Cardano failure modes
3:13:57 - Cardano vs Polkadot
3:19:02 - Vitalik Buterin
3:27:13 - Corrupting nature of power
3:37:23 - Satoshi Nakamoto
3:43:34 - Cardano's vision for decentralized governance
3:56:28 - Cardano in Ethiopia
4:00:30 - El Salvador and Bitcoin
4:06:47 - Cryptocurrency will inject capitalism with long-term incentives
4:16:39 - Day in the life of Charles Hoskinson
4:23:56 - Mushrooms
4:30:04 - Joe Rogan
4:34:57 - Video games
4:47:11 - Advice for young people
4:50:20 - Meaning of life

SOCIAL:
- Twitter: https://twitter.com/lexfridman
- LinkedIn: https://www.linkedin.com/in/lexfridman
- Facebook: https://www.facebook.com/lexfridman
- Instagram: https://www.instagram.com/lexfridman
- Medium: https://medium.com/@lexfridman
- Reddit: https://reddit.com/r/lexfridman
- Support on Patreon: https://www.patreon.com/lexfridman

The following is a conversation with Charles Hoskinson, founder of Cardano, co-founder of Ethereum, and a mathematician who's 1 of the most well-read and knowledgeable people on the technical side of cryptocurrency that I've ever spoken to.

Quick mention of our sponsors, Galileo Games, Allform, Indeed, ExpressVPN, and Asleep.

Check them out in the description to support this podcast.

As a side note, let me say that Charles is not just a mathematician or cryptocurrency innovator, but also a Colorado-based farmer of bison and mushrooms, a gamer, a fisherman, and a world traveler.

When I asked him if he has a nice professional picture of himself, he sent me a picture of him in Mongolia with a hawk on his shoulder, meeting the Mongolian president.

That to me pretty much says it all, speaking to the humor and the intelligence of a man who's bold, innovative, and does not shy away from a bit of fun and a bit of controversy, which makes him a fascinating human being to explore ideas with.

I do want to say in terms of ideas that at least to me cryptocurrency is much bigger than just a way for a few Americans to make a quick buck through meme-driven speculation.

It is technology that enables freedom from oppression, from suffering in the world, because money is power.

Mongolia, for example, was a reminder of that for me.

Next day, after talking with Charles, I spoke with Wyeonmi Park, who is a North Korean defector and who spent time in Mongolia, as many defectors do, in her and their escape from North Korea.

Her story, the story of North Korea, The story of atrocities throughout the 20th century committed by Hitler, Stalin, and others is a reminder that the world is full of darkness, but it is also full of beauty and love, and it is a world worth fighting for in every way we know how.

This is the Lux Friedman Podcast, and here is my conversation with Charles Hoskinson.

If we're living in a simulation, what programming language do you think it's written in?

And maybe from a software engineering perspective, what do you think are some of the design principles that it operates under?

You know, there are a lot of really lovely papers, like 1 came out of MSR, the Autodidactic Universe.

Oh, you gotta read it, it's like April of 21, it's just brand new.

But basically the idea is that the universe is like some sort of giant self-learning system that can self-evolve almost like Nomek.

And then you have Wolfram running around saying, hey, we can come up with these very simple rules and we can reconstruct all of reality at some arbitrary point.

So I have absolutely no idea what's right.

I look at it kind of like a formal system, and I say, well, if you're stuck within the system, it's hard to actually understand that you're inside the system.

You know, it's kind of like an object language to a meta language.

You know, there's this thing that is outside of it, but because you're constrained and limited by the simulation, you can't really understand the nature of the thing that's outside of it.

It's almost like Minecraft, you can build these redstone computers within Minecraft and simulate and emulate things within it, but you really can't go outside of that environment.

The people outside of it can formally prove that it's correct or whatever, but the creatures inside of the system can't hope to perfectly understand it and prove something about it.

Well, also, the question is, it's a computation question.

Because you'd have to be able to emulate all of these.

And so I have no clue what type of language it would have to look like, but it would probably not be anything we're used to at the moment.

And we'd have to have some more fundamental resolution of the relationship of these formal systems and how they get extended.

So that's like a 22nd century question instead of a 21st century.

Do you like, do you find the Stephen Wolfram idea compelling?

It's a very different way of programming, which is you set some rules, you set some initial conditions, and let it run and see what happens.

I mean, like the Santa Fe Institute's been doing that for a long time, and you can use it for economic modeling, and you can show that in certain cases, there's this concept of simple rules evolving into a complex system is somewhat more predictive than trying to build a complex top-down model for things.

And I guess there's some analogies to these things in AI where you start with some simple things and then somehow it just figures stuff out in its environment over time, and much better than if you actually tried to model it with Prolog or something like that.

So that is exciting because you get to just do a few things, let the thing run, and then see what happens.

In fact, it got so exciting, Wolfram came to us and he said, hey, let's do an NFT marketplace.

And he said, I got all these universes to sell.

So we're gonna set up an auction system or something like that with him and I think maybe end of this month or next month we'll figure out how to do NFTs on Cardano with Wolfram Universes.

So as soon as we can sell 1, I'll give you 1, and then we can all just claim that we're living in some sort of Wolfram simulation.

I don't know how much money I have, but I'm gonna give everything I have to get Rule 30, which is 1 of his universes that he's created.

1 of the first ones where he discovered some interesting complexities.

I think Rule 30 is Turing Complete or is that 45?

Actually, I'm not sure if they proved anything about Rule 30 in terms of whether it's Turing complete or

But there's fun competitions on it, which is trying to predict something about Rule 30, about the way it evolves.

Just like looking at the middle column, try to predict as the system evolves, say anything conclusive about the future of the systems that evolves.

It's fascinating, it's both beautiful that simple rules can create that kind of complexity, and it's also sad that we can't make perfect sense of it, like perfectly predict the future.

Even though it's all simple and deterministic, we can't say something conclusive about like when the thing will end.

When will this little cellular automata like evolve in a certain way and then nuclear weapons will be invented and they blow each other up and then it'll just be this empty 1D cellular automata.

Yeah, it's fun, but when you're trying to create, and we'll talk about something that operates the economy and the way humans transact and cooperate and fall in love and work together, then you'd like to be a little bit more formal and try to say something conclusive.

If this entire universe is just cellular automata, then being conclusive is kind of hopeless, generally speaking.

And the hope is, I guess, that there'll be pockets of which, within the cellular automata, where you could be predictive, where you can formally show that something is true, and then you can rely on that, you'll be resilient and all those kinds of things, even though the rest of the thing is a giant mess that's unpredictable.

Yes, well I wonder what the demon's up to these days.

Okay, Thank you for entertaining me with that.

But sticking on philosophy, you've also mentioned among others that Bertrand Russell and Saul Kripke are 2 of your favorite philosophers.

Maybe you can comment on what ideas of theirs you find insightful and also what to use the difference, because you're both an engineer and a thinker, so what to use the difference between philosophy and computer science?

Yeah, so yeah, there's both a deeply human element to both Russell and Saul, and then there's this, of course, amazing work that they did in the late 19th and 20th century.

And you can't really talk about Russell or Saul without also mentioning Wittgenstein and Tarski.

Because when you actually look at these guys and you put them together, what they were attempting to do was increase the level of precision we had in analyzing both formal languages and also language in general.

And so Wittgenstein makes no sense at all to me.

So there's amazing people out there that somehow can parse that, but.

However, Kripke has Krippkenstein, you know.

He has his whole building on that, right?

And at least there I have modal logic, and I have the little boxes, and I have the diamonds, and I can do a computation, and I can kind of reason about things that people are saying.

But really it was all just about precision and the nature of truth, precision, the nature of necessity and possibility.

And the magic of these statements is that you can then start getting a better understanding of basically how far formal language can take you.

You know, David Hilbert also did the same thing.

And Russell, he got his career started working with Alfred North Whitehead.

He was a logician And there was this whole desire in late 19th century mathematics to formalize mathematics in a completely new and better way.

And they started with geometry, and Hilbert's geometry was like a complete system, although recently we've discovered there's a few holes in that.

But for the most part, it's complete, and the axioms are independent and they're consistent and they said, oh, well now we can do this for all of mathematics.

And Russell and Whitehead wrote this huge set of books, like 2 big books, Principia Mathematica, a thousand pages and the conclusion's 1 plus 1 equals 2.

So they linked set theory and arithmetic and logic all in these beautiful ways.

Then, little by little, as we entered the 20th century, logicians started chipping away at this idea that you could actually construct a complete system of mathematics.

First with Gödel, and then later with the work of Turing and Church and others, they said, oh, you're not complete, you're not decidable.

And so suddenly, Russell was left in this really bad position where his early life's work was basically forgotten.

And so he went into ethics and he went into different fields of philosophy, and he became this titan in analytic philosophy, and he was also a great pacifist, and he was just a phenomenal writer.

You know, if you read Why I'm Not a Christian, or any of his other attacks on metaphysics, You know, he said, look, I can only deal with the world I'm in and the senses that I have, and if I can deduce it, I believe it.

If it's outside of that, I really can't make meaningful statements about it.

And he said it in a lovely English prose that you would expect of a man of his stature.

Now, Saul Kripke, it was like the complete opposite.

This guy's really down to earth dude, not aristocratic at all.

And he was 1 of those guys that just could have done anything because he's so brilliant.

But I mean, literally, when he was in high school, he wrote these papers in Logic, and Harvard contacted him and said, hey, could you teach graduate courses at Harvard?

And he said, no, I really would like to finish high school first before I go and teach grad school at Harvard.

And so this is just 1 of those guys that you can see through his work that he can think deeply about anything.

And he chose to try to clean up a lot of the messes that Tarski and others couldn't resolve.

He said, let's really get serious about the nature of truth.

Let's really try to build things in such a way that the work that we leave behind can actually be built upon, and it's not thrown away every 50 years or 100 years.

And it was the same for Tarski, he comes in, he says, we mathematicians love this concept of truth, but yet we've never really created a nice rigorous definition that doesn't have paradoxes embedded inside of it.

So we had to invent meta languages and object languages, all these notions and so forth.

So I really like those 4 if you think about them and there's a lot of great lessons.

And where it's relevant today is you have human beings and you have computers and they're trying to understand each other.

And computers live in the formal world and human beings live in the natural language world and those bridges between those 2 are still not completely clear.

And so a lot of the work that these guys were doing in the 20th century, 19th century had nothing at all to do with that, but gives you hope that perhaps a bridge can exist between those 2 worlds.

And maybe there are some nice tools for that bridge to be built upon.

And maybe that in some way will allow computers to better understand us.

I mean, they've even created languages, natural spoken languages that are completely ambiguity free, like Lojban and things like that.

It's based on a language called Loglan, and it's a spoken language that's equivalent to first order predicate calculus.

Oh, interesting, so no ambiguities, it's logically consistent.

Yeah, there's people who actually write poetry in Lojban.

I'm gonna switch to that and start tweeting in it.

So, you know, there's a lot there, and they're just fun to study and think about, and unfortunately, if you go down that rabbit hole, you'll spend way, way too much time, and there's diminishing returns.

Now, the second question you asked was 1 on theoretical computer science to, I guess, engineering.

So first step, you said humans and computers.

So theoretical computer science is theory of the computer and philosophy is the theory of the human.

And then we can dissect different stuff about the computer, but in terms of these 2 worlds of the theory of the human, which is philosophy and the theory of the computer, which is computer science, what do you think is the difference?

Like, as we try to bridge that gap, as you mentioned, what is going to be the biggest challenge?

Can we formalize music, art, poetry, all that kind of stuff?

Or is that a human nonsense that we need to get rid of?

No, I don't think it's human nonsense at all.

I mean, there's even attempts to create algorithmically generated music.

And the question is, is love just strictly a chemical phenomena?

Is there something like metaphysical about it or transcendent of some sort of formal system?

I mean, computer science is just saying, hey, we have this notion of computing.

We have this brain that we've constructed, this formal system that we've built, and given that we have it, what can we do with it?

And so some people worry about the roots of the tree of knowledge, the great Yggdrasil of computer science.

They worry about the roots and say, how far can we grow them and let's keep adding these new models of computation.

And other people worry about the trunk of the tree and some people worry about the leaves of the tree.

And the more advanced the field gets, the closer and closer it gets to the people who constructed it, us.

You know, we have better image processing, we have better ways of handling speech to text, and we have better ways of computers kind of understanding the intent of what a human being is saying.

And then the question is, well, how will a computer understand love or poetry or music?

Well, it'll understand it the same way we understand it.

You have to get a computer to grow up to a point where it can learn the way we learn or as close to it as possible, then you just expose it to the things that we were exposed with, and then at some point the computer will start creating things.

So the question is, well, how do you quantify creativity?

make it into an NFT and see how much it sells for.

But basically, yeah, there's so much of it is subjective.

And that's a fascinating whole area of, that I'm fascinated with is human-robot interaction is, how

Whether it's art or just 2 humans talking or 2 humans interacting in some kind of way to maximize the richness of the subjective experience.

And I think that could be an optimization problem that could be solved.

Human civilization is constantly trying to, we're constantly trying to impress each other.

Whatever, fall in love, impress your boss at work by all the awesome stuff you do.

I mean, we're trying to optimize that problem that's purely, for the most part, is subjective.

Right, Did you ever watch Blade Runner 2049?

Yeah, did you remember the whole relationship between Joy and Kay?

Did she really love him, the hologram or not?

Fake it till you make it is my view on love.

So let's go to the difference between theoretical computer science and software engineering.

Or I don't know if you draw a distinction, but if we look into this computer world now, is there a difference between theory, things you can say formally, and the pragmatic implementation of that theory into actual systems that people use.

Which I guess we'll call software engineering.

So, you know, the engineer, they're obsessed with the domain of, well, what do you want to accomplish and who are you accomplishing it for?

So they live in the world of people, if they're good engineers.

And they say, okay, what's the experience?

And you collect all these business requirements.

And once you've done all of that, the better job you do, the more self-evident it is of how do we apply the toys and tools of computer science and other such things to actually resolve that.

And the point of theoretical computer science from the software engineering domain is it can tell you kind of where your guardrails are.

It won't make perfect programs, and there's no such thing as that, but rather it can give you a good notion and sense that your program has some desirable properties.

Like maybe you can prove that it can terminate if you're dealing with total programs, or maybe you can prove you'll never have a buffer overflow, or it won't divide by 0 somewhere, or something like that.

Some event won't occur that'll cause a catastrophic failure in your system, But there's always this combinatorial explosion between what you can test and think about and what you can actually code.

So the stuff on the left hand side lives in a different cardinality, a different universe.

There's something significantly larger there.

And the tools on the right hand side, we have property based testing and these SAT solvers, we have all this great stuff here in formal methods land and computer science theory land, but there's only a small subset of things that they actually give you good answers about.

So the balance of the 2 things is basically saying, well, what do you care about and what are you okay throwing away?

That's the art of engineering and building these types of things.

So, you know, in cryptocurrencies, we deal with these complex distributed systems that have cryptography and game theory and Byzantine actors.

So, the balance there is saying, okay, what can't fail in that system?

And that's the kind of stuff that you want to apply as heavy a tool set as you can, because when that stuff fails, you either have a loss of billions of dollars, privacy, or potentially even life, depending on how these systems get adopted.

Is it okay if the block doesn't get made every now and then?

Is it okay if your latency goes up or your network suddenly becomes asynchronous and you disconnect from it, you have to restart the computer or something like that.

That's probably okay, it's an inconvenience and burden to the user.

And if you actually try to chase that tail, you'll end up spending 10 years chasing phantoms and ghosts.

So it's really figuring out those balance of the 2 and what's really beautiful is that the formal methods tools have gotten so much better over the last 20 years in particular, mostly because of incredibly high investments from Microsoft and Google and big universities because these guys are building these gargantuan systems.

If you look at the Googleplex or what Amazon has or others, and they have so much value, so many users, so many things going on, and no person can keep that in their head.

And So you're talking about systems may have 10 million lines of code, 15 million lines of code, millions of nodes connecting, faulty processes happening all the time, hackers breaking in on a regular basis.

So when you're trying to model all of that, try to ask yourself, what formal guarantees and properties can I get to simplify this system as much as possible?

So instead of the applications of formal methods slowing you down, in many cases, it actually massively reduces your debugging time and your ability to find where errors occur.

In some cases, you can't find where errors occur inside these massive concurrent systems.

And you say, well, where are cryptocurrencies going?

But we're talking about a much more hostile operating environment, where instead of it running in a pristine data center in California somewhere, it's running on your cell phone.

It's running on some computer in Mongolia that may have good internet on Tuesday but not any other day.

So when you live in that kind of environment, you really do need to think carefully about a whole new class of protocols, And then you need to think carefully about a whole new class of tools and techniques to test the reliability of those systems.

And you need to separate the world and say, what is high assurance and cannot fail?

The other thing I'll mention is there are perverse financial incentives in our industry because the reality is when something blows up, the people who built those things that blow up usually get paid up front.

So what they're focusing on is time to market, speed to market, and getting tokens out and getting them liquid.

I mean, then people come in, they buy it, but if there's a nascent bug in some DeFi protocol, it'll probably be discovered 6 months later or something like that.

They already got, the people that created that already got paid.

Exactly, that's why you pay the guy who makes the brake software for your train last.

And you make sure he rides the train every day.

So that's, you're basically describing the complexity of a distributed system that's fundamentally game theoretic and like if we think about turtles all the way down, it's humans all the way down.

At the very bottom is still human nature.

Is there something you can say formally about human nature?

To try, you said there's certain parts of the system that can't fail.

You know, some people talk about nuclear war in that same kind of way, that there's this game theoretic construction of mutually assured destruction, oh, that rhymes, see, I'm a poet.

That system can't fail because you're gonna blow everyone up, but you can't formally say for sure it's not going to fail.

So like you're basically trying to chase, like statistically reduce the probability that these particular critical aspects will fail.

And then you test, I guess, by deploying in the real world at small scale to see where things go wrong.

And you know, the problem with game theory and mechanism design is that you can develop this concept of a rational actor.

And I don't think in my life I've ever met a rational actor.

You know, There's a rational actor on Tuesday, but any other day of the week, who the hell knows?

And there's even, I think there was a book, Freakonomics, and there's a few of these things where it just shows again and again where people behave in ways that are against their best interest.

So then you have these protocol designers, and they say, well, we need an honest majority for this thing to work.

And they say, oh, okay, well, we'll create this incentive model, and rational actors will behave with that incentive model.

And they say, well, the individual won't do that, but the firm, the government, the entity will.

The problem with that is we have a lot of counter examples where the system was actually behaving in weird ways.

Like we almost completely eradicated the human population twice in the 20th century.

Once during the Cuban Missile Crisis, and again in the 1980s, there was a Russian colonel and they installed a new satellite system and it said, hey, the Americans are launching missiles at us.

You need to turn the key and launch all the missiles in the silo.

He was reprimanded for not launching the missiles.

So in both cases, a single person stood against the systems of superpowers between us and nuclear annihilation.

So in general, we're really bad at building these types of things.

So what you look for instead is say, can the system be self-correcting?

It's more about can the problem be resolved?

See all Lex Fridman transcripts on Youtube

Charles Hoskinson: Cardano | Lex Fridman Podcast #192