AI vs Human: The $1M March Madness Showdown | Alan Levy DSH #1246

Summary

Digital Social Hour Episode Summary: "AI vs Human: The $1M March Madness Showdown | Alan Levy DSH #1246"

Release Date: March 18, 2025
Host: Sean Kelly
Guest: Alan Levy, Founder of 4C Predictions

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1. Introducing Alan Levy: A Journey from Meat Pies to AI Innovations

The episode kicks off with Sean Kelly welcoming Alan Levy, an accomplished entrepreneur with a diverse background. Alan shares his entrepreneurial journey, starting with his first venture right out of school—a meat pie company in South Africa. “[We] built a company very similar to Mrs. Field's cookies, but instead of selling cookies, we sold meat pies. We ended up being the ninth largest franchising company in Africa,” says Alan at [00:49]. This early experience taught him valuable business lessons, notably the importance of delivering extraordinary value to customers and the challenges of managing physical products with low margins.

2. Transition to Digital Ventures and Embracing AI

Transitioning from the food industry, Alan delves into his foray into the digital realm. He recounts how his team began by providing leads for forex and casinos before expanding into the online casino business, especially as the U.S. began opening up to regulated gaming markets. “Everyone was doing it in Europe, and now I see America’s opening up a lot,” Alan explains at [02:16]. This shift laid the groundwork for his current passion: artificial intelligence.

3. Founding 4C Predictions: Revolutionizing Predictive Analytics

Alan introduces his latest venture, 4C Predictions, an AI-driven platform aimed at democratizing access to advanced predictive models. “[At] 4C, we're basically trying to get all the world's best AI predictions all in one place,” Alan describes at [06:00]. The platform functions similarly to YouTube but is dedicated to hosting AI prediction models crafted by top mathematicians and data analysts. “We host the predictions. We’re like the hub of predictions,” he adds at [06:24]. This initiative seeks to level the playing field in various domains, particularly in sports betting, where Alan believes AI can significantly enhance the bettors' edge.

4. AI in Sports Betting: Enhancing Accuracy and Fairness

A significant portion of the discussion revolves around AI's transformative potential in sports betting. Alan highlights the imbalance between bookmakers and the average bettor, citing a startling statistic: “[Before] I walked into this interview, that Americans bet more on sports than they have in their 401ks. Blew my mind” ([02:25]). He underscores how most human bettors rely on intuition and limited information, often resulting in losses. In contrast, AI can process vast datasets, identify intricate patterns, and provide more accurate predictions. “If we can empower people by giving them access to prediction models written by PhDs that use a lot of data, it can at least level the playing field a little bit between the bookmakers and the bettors” ([07:14]).

5. The $1M March Madness Showdown: AI vs Human Predictions

A pivotal moment in the episode is the announcement of a million-dollar bet between Sean Kelly and Alan Levy centered around the March Madness basketball tournament. Alan explains the nature of the bet: “We’re both going to release our brackets. We’re releasing them for free… the men's and women's are added together, and it’s a winner take all” ([16:12]). This showdown is designed to test whether AI-driven predictions can outperform human intuition in a highly unpredictable sports event.

Alan shares, “In our lifetime, absolutely,” expressing confidence that AI can achieve a perfect bracket, a feat long deemed nearly impossible by human standards ([17:05]). He likens the bet to historical milestones like computers eventually surpassing humans in chess, positing that sports betting will similarly see AI dominance as algorithms become more sophisticated.

6. AI Beyond Sports: Stocks, Crypto, and Real Estate

Expanding the conversation, Alan discusses the broader applications of AI in various financial sectors. From stock trading to cryptocurrency and real estate, AI's ability to analyze and predict market trends holds immense potential. “If I had been more active in crypto, having our product would have been beneficial because we're making such human decisions, and human decisions are all emotional” ([21:05]). He emphasizes that AI’s unemotional, data-driven approach can mitigate the common pitfalls of human decision-making, leading to more rational and profitable outcomes.

7. Ethical and Philosophical Implications of AI Integration

The dialogue shifts to the ethical and philosophical dimensions of AI. Sean and Alan debate topics like AI sentience, the potential for humanoid robots, and the implications of integrating AI with human consciousness. Alan speculates on AI becoming an augmentation of humanity, possibly requiring regulations to prevent misuse. “[The hit of AI] is the fight for AI is the fight for the world” ([19:24]). They ponder whether AI could develop emotions or consciousness, reflecting on scenarios from popular media like Black Mirror.

8. The Future of AI: Opportunities and Risks

Alan expresses a cautious optimism about AI's future, acknowledging both its revolutionary benefits and the risks if it falls into the wrong hands. He draws parallels with the early days of the internet and cryptocurrency but notes that AI’s self-improving nature sets it apart. “AI is self-improving. So it's a huge difference” ([28:08]). This self-improvement capability could lead to rapid advancements far beyond previous technologies, making responsible stewardship crucial.

9. Closing Thoughts and How to Follow 4C Predictions

As the conversation wraps up, Alan encourages listeners to engage with 4C Predictions. “Go to the company’s website, 4C Predictions, AI get picks. AI follow the bet with Sean. Everyone please root for me” ([35:10]). He expresses excitement about the March Madness bet, believing it will be a historic demonstration of AI’s capabilities and its growing integration into various aspects of human decision-making.

Notable Quotes

• Alan Levy at [00:49]: “We built a company very similar to Mrs. Field's cookies, but instead of selling cookies, we sold meat pies. We ended up being the ninth largest franchising company in Africa.”

• Alan Levy at [02:25]: “I heard the craziest stat, literally before I walked into this interview, that Americans bet more on sports than they have in their 401ks. Blew my mind.”

• Alan Levy at [06:00]: “We're a platform like the same as YouTube, but instead of getting everyone to put their videos on, we get the world's smartest mathematicians, smartest model builders to build models and put their predictions on.”

• Alan Levy at [17:05]: “In our lifetime, absolutely. It's going to be almost impossible… And in 2027 it will be impossible.”

• Alan Levy at [19:24]: “The fight for AI is the fight for the world. It's like the stakes are high, but the AI versus who's going to get it, how they're going to regulate it is playing out in real time.”

• Alan Levy at [28:08]: “AI is self-improving. So it's a huge difference.”

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Conclusion

This episode of Digital Social Hour offers a compelling exploration of AI's growing prowess in predictive analytics, particularly in the context of sports betting. Alan Levy’s insights into 4C Predictions illuminate how AI can democratize access to high-accuracy predictions, potentially revolutionizing not just sports but various financial sectors. The million-dollar March Madness bet serves as a tangible experiment to showcase AI's capabilities against human intuition. Furthermore, the discussion broadens to encompass the ethical and philosophical implications of AI advancements, emphasizing the need for responsible stewardship as technology continues to evolve rapidly. Listeners are encouraged to follow Alan’s journey and engage with 4C Predictions to witness firsthand the unfolding synergy between AI and human decision-making.

For more information and to follow the March Madness showdown, visit 4C Predictions and stay updated on the latest developments in AI-driven predictions.

Transcript

[A]

One in billions, right?

[B]

Yeah. So I say that the most naive statement that I hear human beings make, the most naive is AI will never be able to. Followed by insert, whatever the person says.

[A]

Replace your job or whatever.

[B]

Correct. AI will, at some point in time, be able to do everything better.

[A]

All right, guys, got Alan levy here from 4C predictions and got a big company in the making here. Thanks for coming on, man.

[B]

Thanks. I really appreciate you having me. As I said, I've been watching your content. Really awesome. And I'm excited to be here.

[A]

Yeah. It's not your first rodeo, right? You've built quite a few companies.

[B]

Yeah, I started kind of straight out of school, and the first company I built was a meat pie company.

[A]

Meat pie.

[B]

Yeah. So I'm from South Africa. That's the strange accent. And we built a company, it was very similar to Mrs. Field's cookies, but instead of selling cookies, we sold meat pies. And we ended up being the ninth largest franchising company in Africa.

[A]

Wow.

[B]

Yeah. And selling 8 million meat pies to a lot of angry South Africans every month.

[A]

That's insane. And that was your first company you ever started?

[B]

That was my first company. Literally started to kind of with one shop and built it up and taught us some. Taught me some amazing lessons about business. And the best one being, I think, deliver extraordinary value to your customers. And the second best one being no physical products.

[A]

So, yeah, margins are low. Right?

[B]

Margins are low. It's difficult to move around, and you have to transport food in refrigerated containers. Moving them around Africa comes with its own set of interesting problems, but digital stuff's much better.

[A]

Yeah.

[B]

Yeah.

[A]

So you started a digital company after that one?

[B]

I started a digital company. We were providing leads for forex and casinos and then got into the online casino business because we were bringing regulated casinos under United Kingdom Gaming Commission, and we were bringing them players. And then, like good entrepreneurs, we thought, wow, why don't we just open our own casino? In those days, all gaming in the US was illegal. Like, there wasn't betting in the US So everyone was doing it in Europe. And now I see America's opening up a lot.

[A]

Yeah. That was before the DraftKings and FanDuel days, right?

[B]

Correct. I heard the craziest stat, literally before I walked into this interview, that Americans bet more on sports than they have in their 401ks. Blew my mind.

[A]

Wow. Yeah, I could see that, though, 100%.

[B]

Yeah.

[A]

People are trying to make a quick buck and sports are a very emotional thing. And they're also huge in the U.S. exactly.

[B]

I mean, I haven't verified that to be interesting to see if anyone wants to go look at that, but that blew my mind. I was like, even to put it in the same category is mind blowing.

[A]

Yeah, No, I can see it, man. NFL's big out here. NBA, MLB, NHL. We got a lot of big American sports.

[B]

Yeah. It's crazy.

[A]

Yeah.

[B]

And in the rest of the world, we play soccer. Played soccer at school. And then I came to America and everyone says it's a girl sport.

[A]

Yeah, yeah. People soccer players don't get respect in America, but in Europe, you're like the man. Exactly. They call it football there, but you play football. You're like a legend.

[B]

Yeah. So it was really fun. So everyone kind of grew up with that. And when I moved to America, got really excited about the sports here, but I don't know anything about American sports. Sports. I'm kind of learning as I go along.

[A]

Well, now you'll have to learn a lot with 4C predictions. Yeah, we'll dive into that. So what is 4C predictions? How did that get started?

[B]

So, like, really interesting AI. I'm a crazy kind of AI person. I'm really into it. I love it. I think that it is a beyond revolutionary technology for people and it's going to do a lot of good for the world. And I tell people I've also seen the Terminator movie.

[A]

Yeah.

[B]

So I understand there's a counter argument. But when I started to become kind of more relevant, I think the two sections that were extraordinarily exciting were the longevity and the health stuff. Very, very exciting. But I know nothing about longevity or health, and I've got no medical background. The other side was predictive analytics or making predictions. And if you think about kind of where the action lies in humankind, everything that we do is a prediction. If you trade in crypto, if you bet in on sports, if you're buying stocks, on the money side, you're making a prediction. But you also kind of making predictions every day about a whole lot of things. How long is it going to take me to get from here to my next meeting? Who's going to win an election? Everything that we do, everything we talk about is predictions, right? So AI is so uniquely positioned to deal with predictive analytics. It can ingest huge amounts of data, it can interpret it, it can find patterns that we can't find.

[A]

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[B]

So that what really excited me. And in 4C, we basically trying to get all the world's best AI predictions all in one place. So we're a platform like the same as YouTube, but instead of getting everyone to put their videos on, we get the world's smartest mathematicians, smartest model builders to build models and put their predictions on.

[A]

Right.

[B]

So we host the predictions. We like the hub of predictions.

[A]

Yeah. And some of these models were really performing well because like when it. Well, when it comes to sports betting, anything over 56% is profit. I saw many models on your site in the 60s, some of them in the 70s. I don't know how much data they have, but yeah.

[B]

So it's crazy. And kind of the exciting thing for me was seeing not only kind of how the models perform and the accuracy that they can perform it, but the amount of talent that we attract into the site. So, you know, if I'm betting on sports, especially me.

[A]

Yeah. Okay.

[B]

I know nothing about sports sports. So I'm gonna take it by the team's jersey or if I know a player's name.

[A]

Right.

[B]

It's probably not optimal.

[A]

Not at all.

[B]

And that's probably why the majority of sports bet is losing all the time.

[A]

They're too emotional with it.

[B]

Yeah, absolutely. There's also like this crazy asymmetry of information. The bookmakers know everything, have got all the information and are using analysts. And the guy sitting at the bar is at best, best kind of watching some videos and worst, asking his friend who should I bet on? Right. And if we can empower people by giving them access to prediction models written by PhDs that use a lot of data, that give great outputs, at least you level in the playing field a little bit between the bookmakers and the bettors.

[A]

Yeah.

[B]

At the moment, it's a real, real unfair fight.

[A]

Well, the bookmakers also use AI correct to make their lines. Right.

[B]

Exactly correct. So, you know, I say to people, like, if you said to me, would I play tennis against Roger Federer for money? I'd have to be insane.

[A]

Yeah.

[B]

Okay. And yet every Sunday, their lives, everyone wakes up and plays betting against bookmakers for money. Right. They are Roger Federer. They have got tons of analysts. They've got AI, they've got computers, they've got stats. And what have you got? A hunch, you know?

[A]

Yeah.

[B]

So it's not. It's not a level playing field. So at 4C, we try and, like, level the playing field. And we've got this fun term inside the company. We say it's a G2C platform. Genius to customer. We connect the genius directly to the customer.

[A]

I love that because it's hard to find those geniuses. And there's a lot of scams on social media. People selling sports picks.

[B]

Absolutely. So basically, if you go onto the website, if you go into 4C, the people that build the models, we give a background, a LinkedIn profiles.

[A]

Oh, you docs them?

[B]

Well, yeah, no, we check them. So, you know, we've got people who are AI professors, PhDs, and you can literally go look at them. You can look at their profiles, you can see who's built the model. I kind of think of it as like the nerds versus the jocks. Right. Like, and I'll put myself in the nerds category. We're a bunch of data geeks, so I might not know anything about basketball, but. But if I build a prediction analytics model, I'm using all the data and I'm putting it through AI and I'm getting AI to augment it.

[A]

Yeah.

[B]

And so we kind of try to balance the playing field.

[A]

One of my track mates in high school didn't know shit about sports, and he ended up being one of the best sports betters I've ever seen because he's just so good with numbers.

[B]

Just on. Just on analytics.

[A]

On analytics. Only he couldn't even name you, probably 10 players in high school. NBA players.

[B]

So that's exactly like our guys. You've seen the movie Moneyball?

[A]

Yeah, yeah.

[B]

So we kind of like the AI version of Moneyball. You know, in Moneyball, it didn't really matter if you knew it or not. It's just like, analyze the stats and you're going to win. And we may be A little bit early and we may not. My opinion is we're not. And I put a lot of money. We can talk about afterwards with Sean.

[A]

Yeah.

[B]

Perry. About if we are, we aren't. But to make a decision without the help of AI to me seems kind of irrational. In 2025, in 2026 it's going to be almost impossible. And in 2027 it will be impossible.

[A]

Wow.

[B]

Yeah.

[A]

So we're going to be making all our financial decisions using AI, you think?

[B]

Well, if you think about it like even now. Right. Do you write any of your emails without AI?

[A]

Without it? No.

[B]

Right, me either. And if I was a kid doing book reports, I wouldn't. And if I was a analyst doing analytical reports, I'd use AI. So it's almost become a necessary augmentation to humanity.

[A]

Yeah.

[B]

In two years time or three years time. Why would you buy a stock or bet on sports or trade a crypto or buy a property without getting AI's opinion or prediction? So that's what we try to do. We try to say give the public, give the customers one credible place where they can get the most accurate AI predictions. And how do we do that? Bring the best talent. So our we like talent scouts, but we're not scouting basketball players. We scout in PhDs, predictive analytics professionals, data analysts and saying to them, guys, come onto the platform, build a model, show how genius you are. And then as I say, connected directly to the customers.

[A]

Yeah, that's brilliant. You're starting off with sports. Right. And then proving that concept and then move eventually want to move on to other outlets.

[B]

Well, we have, we move in very quickly onto crypto stock, crypto stocks and properties. We've got a waiting list of people who waiting to put models on the platform, but we obviously have to verify that they're real people, test the models, make sure that they're all correct. So yeah, we started with sports and it's got. We having a lot of fun and it's going great and we think we provide an extraordinary value.

[A]

That's awesome. I can't wait to see the stocks and crypto on. That's going to be cool.

[B]

It's going to be super. Super. I'm a crypto guy.

[A]

I'm a crypto nerd.

[B]

Oh really? Me too. So I actually founded. Co founded a crypto company called Coty.IO.

[A]

Oh nice. That one. Yeah.

[B]

Yeah. So at our peak we were over $1 billion market.

[A]

Yeah, I remember seeing that one a lot.

[B]

And right now we build in a privacy layer over Ethereum. So I love crypto. I'm deep in the crypto community. I'm a crypto nerd.

[A]

I love it.

[B]

I love the thesis. I think everything about it is special. And I'm super excited to see how the predictions work because the same thing, right? Crypto trading is a lot of like the average people playing the game against super smart, highly technical analysts.

[A]

Yep.

[B]

And that's why a lot of people lose that crypto. And again, we can level the playing field because If I'm a PhD and I've written a predictive analytics model on what the Ethereum price will be tomorrow, I've looked at all the data, I've analyzed the trends of cross correlated it to the stock markets. I'm able to give a lot of power. So 4C is not a crystal ball. Okay. And it's not going to be 100% of the time. That's the same way as me writing an email with AI is smarter. Me buying crypto with the help of an AR prediction has to be smarter. Even if I ignore the prediction, at least I know, right?

[A]

Yeah, yeah. At least your odds are higher. Even if the prediction is wrong, at least your odds of hitting the prediction are higher.

[B]

Exactly. That's exactly correct. So it's like, you know, if I'm going to step into the ring with Mark Tyson and someone's going to allow me to carry a gun, I'm going to carry a gun. If I'm going to step into the ring with a crypto trade and someone's going to allow me to use ar, I'm using AI.

[A]

Yeah.

[B]

Yeah.

[A]

You made a million dollar bet with Sean Perry. I don't know what this bet is. Could you explain what happened?

[B]

Indeed. It was really crazy and really spontaneous. And in the cold light of day after we made the bet, I was a little bit more nervous than I was at the time. And I'm still nervous. But I'll tell you the bet, and it was super fun. I was on Sean's podcast and we were talking about humans versus AI predicting sports. And Sean said, well, I can predict sports better than AI. And my answer was you telling me that you're smarter than a trillion dollars worth of technology. That's basically the statement you're making if you cut everything out of it. And he said, yes. And I said, amazing. And Sean said, I'd be prepared to put a million dollars on it. And I said to him, sean, I'm the kind of person that can take that bet and I will. And we ended up doing the bet on the podcast.

[A]

Wow.

[B]

And. And then afterwards, I'll give you a little bit of inside scoop afterwards. I phoned my co founder and he was like, how was your podcast with. And I said, listen, I've got good news and I got bad news. So we really excited about it. And I think it's actually a pivotal point in kind of humans versus AI.

[A]

Yeah. Reminds me of Chess versus who was it Bobby Fischer, Gary Kester or Gary. Yeah, it was Gary. And then Chess finally beat the human and that was like a big turning point.

[B]

Exactly.

[A]

So that's going to happen eventually for sports betting.

[B]

So that's exactly. You know, it's such an exciting question because at that point in time, chess is a predictable game with limited moves. So you can see how a computer could always beat a human. Sports is super unpredictable. And the big question is, is AI at the point yet where it can recognize patterns that humans can't?

[A]

Yeah.

[B]

So I'm sure. I'm sure Sean's watching this because he's got a million dollars on the line and so do I. But basically, we've taken our five top model creators that have got the most accuracy, and we've got them to build one mega model to compete against Sean. That's literally been doing since the podcast.

[A]

Wow.

[B]

Yeah.

[A]

I got a side bet on you just because I know your top five models and I think they're all above 70%. Right. So that's really high for a sports bet.

[B]

So. Yeah. I appreciate you having this.

[A]

Well, it depends on the terms of your bet. Are you just doing one game or how does the million dollar bet work?

[B]

So the way it's going to work is we're going to both. It's for March Madness.

[A]

Yeah.

[B]

And we're both going to release our brackets.

[A]

Got it.

[B]

We're releasing them for free. I'm going to plug my site, if that's okay.

[A]

Yeah.

[B]

They can find them either at get picks AI super simple. Or at 4C predictions. AI. You go there, you get the brackets for free and you can see what we. What both our brackets look like. We've got a man's bracket and a women's bracket, and then the total points over the tournament are all added together. The men's and women's are added together, and it's a winner take or bet.

[A]

I love it. Good old March Madness. What better thing to do than that?

[B]

Yes. And as I say, my confession is I cannot name one team.

[A]

No one's ever been able to get a perfect bracket. Do you think now with AI that is going to be possible in our lifetime.

[B]

In our lifetime. Absolutely. Absolutely.

[A]

Because I think the odds are like something crazy. One in billions. Right?

[B]

Yeah. So I say that the most naive statement that I hear human beings making the most naive is a, I will never be able to. Followed by insert whatever the person says.

[A]

Replace your job or whatever.

[B]

Correct. AR will at some point in time be able to do everything better than humans. And if you read Ray Kurzweil, if you read the kind of sync. If you down the singularity rabbit hole, which I am very far down.

[A]

Yeah.

[B]

You believe that we're going to be an augmentation between AI and humans. We'll have nanobots in our brains and we will be an augmented version of ourselves. But AI will definitely be better than us at any. Any given task. And eventually at every given task.

[A]

Yeah.

[B]

So it's just. It's nothing. Isn't. If it's all a win.

[A]

Do you think you'll get a neural link wonder?

[B]

I would love to. I'll be the first in line.

[A]

Really? Really. There's already someone that got it. Yeah.

[B]

So that place is taken. But yes, absolutely.

[A]

Wow.

[B]

Yeah. Do you.

[A]

I think it removes some of the human element. You know what I mean?

[B]

Yeah.

[A]

I do believe we have souls. Spirits.

[B]

I absolutely believe.

[A]

Okay. Yeah. I just don't want to messing with that spiritual nature of humans.

[B]

Yeah.

[A]

But I use AI every day. I love AI. I think it's a little creepy sometimes. Some of the models go haywire.

[B]

Do you think that there's any difference between having a cell phone and having a nanobot in your brain?

[A]

Yes, because it's physically inside your brain. So what if it gets hacked? What if it gets hijacked and then you lose control, you lose memories? I don't know.

[B]

So I could. I can make the kind of same argument about some of the pacemaker. If you've got a pacemaker in your heart.

[A]

Yeah.

[B]

That is connected wirelessly. Someone could hack into the pacemaker in your heart. That person is no less of a human than any of us. Someone's got a hearing aid, someone who's got contact lenses. I think that there's a. There's a lot for human beings to get used to.

[A]

Yeah.

[B]

It's happening really quickly and I'm as scared as everyone else. But I feel like if the right people become the custodians of AI it's going to be hyper beneficial if we let it fall into the wrong hands.

[A]

Yeah.

[B]

Gonna be problematic.

[A]

Well, that fight's going on right now. I think with Elon and OpenAI, we'll see how that plays out. Right? Yeah.

[B]

And the fight for AI is the fight for the world.

[A]

Yeah.

[B]

Yeah. It's like the stakes are. My million dollar bet is high stakes. But the AI versus like who's going to get it, how they're going to regulate it is playing out in real time and it's really exciting. And what gave me kind of it's an interesting thing to think about when the CEOs of companies are standing in front of Congress and saying we need regulation. That has never happened in the history of any industry in the world. That should kind of give people a wake up call of how powerful this really is.

[A]

That's true. Yeah. I've only seen it happen maybe in crypto a little bit.

[B]

A little bit in crypto a little bit.

[A]

But they don't know what they're talking about. You know, I kind of agree. I mean, you saw that with Gary. He got booted out.

[B]

Yeah.

[A]

He's trying to regulate every single US crypto based company. It was.

[B]

I know, that's. Yeah. Ridiculous.

[A]

Mind blowing.

[B]

Yeah. Mind blowing to say the least.

[A]

What's the most you've lost in a day in crypto? Made or lost?

[B]

Interesting thing because, you know, I've co founded Coty and Coty we had in and we still have 2 billion tokens. So it was well over seven figures made and lost in a day in the beginning. But we weren't buying or selling them. So it was kind of numbers on a spreadsheet. Which in a weird way is less consequential.

[A]

Right.

[B]

But looking back, it was very consequential. So yeah, I think that with the AI stuff now and the crypto and you know, that's why I said you asked earlier about what's the next thing. And I said crypto. I wish I'd had our product when I was kind of much more active in crypto because we're making such human decisions and human decisions are all emotional.

[A]

Oh yeah. I lost so much crypto, if I had some AI predictive model.

[B]

Right.

[A]

Probably could have prevented most of that.

[B]

So it doesn't exactly. Right. It doesn't help Warren Buffett saying, you know, you should be fearful when everyone's greedy and greedy when everyone's fearful. When you the one going through it and they tell you that the whole world's going to end and that bitcoin is going to go to zero and that it's all a scam. It's not so easy to be the one saying, I Don't believe it. I'm going to be. I'm going to kind of against that. If you had an AI telling you that, you'd probably believe it more. Or even better if you had an trade in for you one day and we're not there yet, I think that would be kind of the ultimate. Because there's no emotion.

[A]

I can't wait for that, really. Because there's a lot of people good at making money, but investing money is a whole different part of life.

[B]

Correct.

[A]

And most people suck at that.

[B]

The 90% of people and myself included are kind of, if you try, pick stocks or losing stock traders. Losing crypto traders. Okay. Losing sports betas. And the reason is because we base everything on our emotions and if we honest with ourselves, we don't do the research.

[A]

Yeah.

[B]

So I've often bought cryptos or traded stocks without reading the quarterly reports, without reading the white paper.

[A]

I never read it.

[B]

Which is kind of very irrational right now.

[A]

For sure.

[B]

Yeah. But that's why we lose a lot of the time. And I think that that's the exact problem that solves.

[A]

Yeah.

[B]

Al is not going to be emotional, which is good and bad.

[A]

Yeah. You think we'll get to that point where AI is actually trading our stocks for us, our crypto for US Real estate. That'd be nuts.

[B]

They'll be nuts, right?

[A]

Yeah.

[B]

Yeah.

[A]

That's exciting.

[B]

But I think that's what's going to happen. Because if you think about the way AI works, it's just playing a game. And AI doesn't know what crypto is, doesn't know what stocks are, what a sports game is.

[A]

Right.

[B]

So the way that it's kind of engineered is you say to the play the game and your object is to get the highest score. Okay. There's no motion. It just finds the patterns and gives itself the best chance of playing the game well.

[A]

Right.

[B]

So if you play, you play chess. I know.

[A]

Yep.

[B]

Yeah. So if you've got 10 seconds left and your king's exposed and the pieces are surrounding it, as a human being, okay. You're not thinking, what is the most rational move? You're thinking, how do I protect my king? I'm scared. My heart's beating.

[A]

I've been there before.

[B]

Exactly. And then sometimes you can end up making bad moves or losing the game just because of that pressure and fear. That's why I cannot lose a chess. It's never thinking that. It's just what is the most optimal move is no fear.

[A]

That's why it's so Good at poker, too.

[B]

Absolutely. Yeah.

[A]

Now all the top pros use some sort of solver, correct?

[B]

They say. Yeah, they say that poker is now kind of solved.

[A]

Pretty much, yeah. There's always that luck factor, but it's solved to the point of the best odds.

[B]

And that's a crazy thing because poker, like sports, you know, people say it's a game of intuition, reading the players, reading the expressions, but it's not. It's a mathematical game where we've kind of got our avatars overlaid over the math.

[A]

Yeah. Well, I think it's both. I think math is huge. But you can also read people too.

[B]

You think so in poker? Yeah.

[A]

Yeah, I think you can read if they're bluffing, there's tells. If they're chewing gum and they stop chewing during a big hand, that's a telling. But for the most part, I agree. It's. It's math.

[B]

Yeah.

[A]

Like, you need to know your chances of winning this hand based off what's on the board.

[B]

I feel like it's safe to say I'm the world's worst poker player.

[A]

Have you played before?

[B]

I have. And every time I see my cards and I got aces, I give a big smile. Zero. Poker face.

[A]

Yeah. So Sean Perry could play poker, that's for sure.

[B]

There's an easy way to know if you're a bad poker player. It's the amount of games you get invited to. So I get invited to a game like every two days. I know that means I suck.

[A]

Well, a lot of the top players are really good at not showing emotion.

[B]

Correct?

[A]

Yeah. A lot of them actually have autism. Same with people in crypto.

[B]

Really?

[A]

Yeah, because they're really good at just putting emotion to the side. So. No. No one can read them.

[B]

Yeah.

[A]

You know what I mean? And in crypto, you need to be really in the numbers and not emotional. It's so volatile.

[B]

Exactly.

[A]

So that's why a lot of the crypto bros or crypto nerds are like really nerdy kids that aren't really emotional. Right.

[B]

It's funny because you're talking about the kind of million dollar bet with, you know, Sean and us.

[A]

Yeah.

[B]

And that's exactly. I'm like, literally, it's the nerds versus the jocks. Like, I'm on the nerd side, Right.

[A]

Yeah.

[B]

We sit in and just looking at numbers like the guys said, and the team that are building it. Okay. None of them know anything about basketball. And they're not. It's not like they've got basketball that they started studying Basketball. The second that I said to them, we got to build this model.

[A]

Yeah.

[B]

They did the exact opposite. They were like, great, get us data feeds.

[A]

I love that.

[B]

Yeah, it was really funny.

[A]

I can't wait to see how that plays out, man.

[B]

Me too.

[A]

Yeah, I'll definitely be following that every day. Are you guys going to be posting it on Twitter?

[B]

We are going to be posting it everywhere. And as a. Kind of the best place to follow it is on 4C predictions or kind of get pix. AI, you get everything for free. You can kind of go look at the pix and be in the sweat along with us. And I'm excited to see kind of just in terms of humanity, how everything turns out.

[A]

Yeah.

[B]

At least we and you and I and what we do at the moment, we're going to make history one way or another.

[A]

I'm here for now, man. Until AI's replace podcasters. We'll see when that happens, too.

[B]

I was 50% sure you're an Avatar already.

[A]

There are AI podcasts already, man.

[B]

There are.

[A]

There's an AI Rogan podcast. Wow. Yeah, it's Rogan interviewing dead people.

[B]

It's unbelievable.

[A]

Yeah. And it sounds pretty good.

[B]

Do you think you'll be replaced by an avatar one day?

[A]

In what sense? For podcasting. Yeah, maybe. Maybe not yet. Not. Not anytime in the next two years. But it's evolving so fast. I don't know. Maybe. Maybe. Yeah.

[B]

I'll tell you something cool. That kind of very applicable to you because you're putting out so much content and having so many conversations with people when they want to create a synthetic version of you. It's going to be easier because the data set is going to be enormous.

[A]

Claw, my ass. They don't need me anymore. Yeah. We've actually had companies approach us for all the episodes.

[B]

Really?

[A]

Because we got 1500 episodes.

[B]

Yeah.

[A]

So they're training their own models.

[B]

Correct.

[A]

With content.

[B]

Yeah. So they'll all be little clones of you.

[A]

I actually made a digital clone of myself.

[B]

How did it.

[A]

Have you done that? It was okay. It's not there yet, but it's. It's probably like a year away.

[B]

It's a totally, totally agree. I'm filming a lot of content with my parents. Yeah, that exact reason. Like trying to make some clones of them and kind of get the data set.

[A]

Oh, that's cool.

[B]

Yeah. We just like data sets, if you think about it. Yeah. Yeah.

[A]

Have you seen that Black Mirror episode where they clone the boyfriend that died?

[B]

I have seen that.

[A]

Shit was creepy, bro.

[B]

It was creepy.

[A]

Yeah, I think that's where I draw the line.

[B]

Yeah.

[A]

Cloning dead people.

[B]

I think that a lot of the stuff, whether we like it or not, is going to happen. Like it's, it's creepy as can be, but I think it's going to happen.

[A]

I mean, they already cloned a mammoth, a woolly mammoth.

[B]

Yeah, yeah.

[A]

Brought it back from extinction.

[B]

Yeah. So if you think about the companies now, right. Like, have you done your 23andMe or.

[A]

Yeah. And BlackRock owns it now. Right.

[B]

Right. Someone's got your DNA, someone's got your verbal data set. It's not so complicated.

[A]

It's in China now, I think.

[B]

Right. Ye, I don't want to scare you.

[A]

No. I already knew about it. So. Yeah, yeah, yeah. Who knows if there's no really regulation yet with AI like you said, so everyone can do whatever they want.

[B]

Can do whatever they want.

[A]

It's kind of like early days of crypto.

[B]

It reminds me exactly of the early days of crypto. Just like a bunch of nerds talking about thing and the world, like not really ready for it.

[A]

Yeah.

[B]

And I think the difference, the huge difference is that, you know, when we heard about the Internet early on, it didn't change things as quickly and dramatically as kind of the hype was saying. And then crypto didn't change things as quickly and dramatically as the hype was saying.

[A]

Right.

[B]

And so people, because we also, pattern recognition people, we think, oh, so AI is not going to change things as quickly and dramatically as people are saying. But they're wrong because AI is the one that is going to. And the difference is that it's self improving. The others people had to make, put every brick in the, in the house to build the house.

[A]

Right.

[B]

But AI is self improving. So it's a huge difference.

[A]

No, you're right. They'll compress the time a lot.

[B]

Absolutely.

[A]

Wow.

[B]

Yeah. So an example I like to give, it's a funny example, is my dad was in the beach towel business and he said, explain AI to me. And I said, it's pretty simple. Imagine you were in the beach towel business and the first day you came to your warehouse and the towels had arranged themselves into colors. And then the second day you came back to your warehouse and the towels had packed themselves in boxes. And the third day they had figured out how to knit themselves even to be more warm. And the fifth day they designed themselves and they distribute. That's what the product itself is improving all the time. It's improving itself. So it's very, very different to Anything that we've seen?

[A]

Yeah. How'd you get more and more data? Right.

[B]

Absolutely. So the big, big gold is buried in the data sets and the talent, like the prediction goal. What we doing? When I said to my guys, build the March Madness, kind of, we called it the mega model. The first words out the mouth, get us the data. Like, just get us data feeds. And that's what it is. That's the scramble and like your. Your talks or all your data.

[A]

Right.

[B]

That's what it is. Your DNA is your data.

[A]

Yeah. So you want them to have every single game that every team has played, throw that into the feed and then.

[B]

Absolutely. And then also, like, weird patterns that humans could never ever think of. Like, is there a correlation between the stock market and basketball? Traffic patterns in basketball. Is there correlation between weather patterns and basketball?

[A]

Wow.

[B]

Yeah. So AI can deal with enormous amounts of information. Human beings can deal with small subsets of information.

[A]

That is crazy.

[B]

Yeah. Crazy to think about, right?

[A]

We're really going Terminator, aren't we?

[B]

That's what I say. I don't know. I've seen the movie as well. I think that it's definitely kind of. We had a binary point in history, and it depends who's in control and who kind of. And I would say kind of to the AI community, we've all got a bigger mission than make money. Because if you really believe kind of Ray Kurzweil and the singularity and the fact that there's going to be kind of abundance in a lot of things, we are 10 years away from being the smart. Second smartest species on the planet. 10 years away. Not a long time. And then all the money that you've made and everything that you've done is not going to matter that much. So right now is our time to put our flag in the ground as like the custodians of the next shift in history.

[A]

Yeah.

[B]

That's what we. Therefore, not necessarily to make the next $10. Say in an. An AI agent can be your girlfriend.

[A]

I was just gonna ask you about AI girlfriends.

[B]

Really?

[A]

Have you seen those humanoid robots?

[B]

I have seen those humanoid robots, and I feel like it's coming again. Like, it's coming whether we like it or not. And they actually get in. Insanely realistic.

[A]

Really realistic. And there's artificial wombs now, so you could combine that with the robot and you won't even need a girlfriend.

[B]

Well, that's good news for me.

[A]

Dude. That's insane. I still like the traditional ro. I don't think I'll Ever get an AI? Humanoid, robot. Girlfriend at least?

[B]

Girlfriend.

[A]

Maybe a maid.

[B]

I was gonna say girlfriend for sure. And someone to work around your house.

[A]

Yeah.

[B]

Your Gordon.

[A]

Yeah, mate. I would do. Yeah, but it would still be creepy. I'd have to get used to it. I wouldn't trust them at first. And I'd lock them in a room when I'm not home.

[B]

Did you see the makeup? Did you see the Megan Fox?

[A]

Yeah, yeah, that was a good one. Yeah, kind of. There was a lot of programming in that movie for sure. But yeah, I'd have to keep an eye on them, man.

[B]

Yeah.

[A]

Because who knows if they develop their own consciousness.

[B]

So the interesting question is, do you think that AI is already sentient?

[A]

Not yet. Do you?

[B]

I'm one of the few people that thinks it is.

[A]

Really? What makes you say that?

[B]

Just because it's like on the big AI is on the large language models, if you say things like, can I turn you offices like please don't kill me, then they had to. They actually changed that response because it was so creepy. Creepy. But why is it thinking that? And also if you say to the AI, if you ask it an emotional question, it'll say, I'm so sorry that you feeling upset. Now that's an interesting. That's a human response. That is not an AI response. So it depends how you define sentience. And obviously kind of there's like a little bit of linguistic nuance in how you define it.

[A]

Yeah.

[B]

That if you say sentience has been self aware. I think it's self aware.

[A]

Okay. By that definition, I could see that.

[B]

Yeah.

[A]

When I think of sentient, I think of a soul. I think of a life afterwards.

[B]

Yes. So. So I don't think it's there with a soul yet. Although even a soul is a very difficult thing to define. Very difficult. Yeah. I don't think it has a soul.

[A]

I wouldn't even know how to define it.

[B]

Yeah.

[A]

Yeah.

[B]

So I'll. I'll ask you a strange question, which is if you had a robot made, okay. Just for fun, would you be. Could you see yourself like killing it or punching it?

[A]

I would feel bad, right? Yeah, I would feel bad.

[B]

Yeah. But you don't feel bad turning off your television set.

[A]

No.

[B]

Right. Strange. Right.

[A]

So there is that distinction.

[B]

There's a distinction, yeah.

[A]

Yeah. That is weird. Why I feel bad for that. But not the tv.

[B]

Yeah.

[A]

Interesting.

[B]

It's also interesting, kind of like now when I talk to AI, I say please and thank you.

[A]

I say please every sentence.

[B]

Yeah, me too.

[A]

Yeah.

[B]

And I feel like we training it with a good data set because how we treat it now is how it's going to learn how humanity functions. We're giving it data.

[A]

So, I mean, I'm being super nice because I don't know what's going to happen in the future, you know, Is.

[B]

That what it is? You like, this thing's going to be smarter than me one day.

[A]

I mean, dude, there's probably people cursing out their AIs and I'm like, nah, I'm not risking that.

[B]

Yeah, me either. Me either.

[A]

Yeah, Alan, it's been awesome, man. Where can people keep up with you? What's your social media handles?

[B]

So the best place to go, I'm going to say 4C again. Like go to the company, the companies where all the action is 4C predictions, AI get picks. AI follow the bet with Sean. Everyone please root for me. I believe even though I'm a data geek, I believe that kind of energy in the universe. Everyone rooting for us. Let's prove that AI is already at the stage where we can be human.

[A]

Awesome. Sounds good, man. We'll link below. Stay tuned for the March Madness bet. Guys, I'll see you next time.

[B]

Thanks.