Bittensor’s (alleged) $10M rug pull (feat. Mark Jeffrey) | E2275

Summary

Podcast Summary: This Week in Startups — “Bittensor’s (alleged) $10M rug pull” (feat. Mark Jeffrey) | Ep. 2275

Date: April 14, 2026
Host: Jason Calacanis
Guests: Mark Jeffrey, Ken Miyachi, Will Squyers, Stefan Cruz, Lon (co-host and producer)

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Episode Overview

This episode dives deep into the recent controversy surrounding Bittensor, a decentralized AI/crypto network, following an alleged $10 million "rug pull" by a prominent subnet operator. Host Jason Calacanis is joined by longtime friend and crypto OG Mark Jeffrey, along with subnet founders Ken Miyachi (Bitmind) and Will Squyers & Stefan Cruz (Macrocosmos). The conversation analyzes the mechanics and implications of Bittensor’s subnet structure, incentive systems, and the vulnerabilities exposed by the alleged rug pull. The episode also showcases exciting subnet projects, debating how decentralized AI and crypto intersect, and wraps up with broader discussions about risk, investment, and even pop culture.

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Key Discussion Points & Insights

1. The Bittensor Rug Pull Incident

• Background & Allegations

  • Subnet operator Sam Dare (“Templar”/Covenant AI) allegedly dumped 37,000 TAO (≈$10M) and walked away from the project, impacting holders (13:37, Lon).
  • Quote:
    • "He dumped 37,000 TAO on his own subnet holders. It's about $10 million in money. And he sort of walked away from the entire project." (13:37, Lon)
  • The abrupt move caused TAO’s price to drop ~25% (15:04, Lon).
  • His explanation centered on “decentralization” concerns, but most of the community sees it as opportunistic and damaging (21:07, Mark Jeffrey).

• Mechanics of a Rug Pull in Bittensor

  • Subnet owners accumulate sizeable subnet and TAO tokens as rewards, which remain liquid and accessible.
  • Quote:
    • “If you have enough of them, and there’s liquidity... you can at any time just sort of flood Uniswap with your tokens and exchange it back out for TAO...and now you’re gone. Which it looks like... is what Sam did.” (16:04, Mark Jeffrey)

• Community Reaction & Root Cause

  • Broad repudiation from Bittensor subnet operators—“I am Spartacus” moment reinforcing community integrity.
  • Mark Jeffrey calls Sam’s justification a post-hoc rationalization rather than legitimate grievance (21:07, Mark Jeffrey).
  • “No, I do not. And you can see this, the evidence, like nobody in the ecosystem believes that.” (21:07, Mark Jeffrey)

• Systemic Vulnerabilities & Proposed Solutions

  • Current flaw is information asymmetry and limited controls—single operators can abscond with funds before anyone notices.
  • Various proposals:
    • Owner lockups: Smart contracts that restrict immediate access to emission rewards.
    • Dynamic “proof-of-conviction” staking: Subnet ownership goes to those who lock up the most tokens for the longest period, thus aligning incentives with long-term value creation (23:32, Mark Jeffrey).

2. Evaluating Bittensor’s Ecosystem and Investment Models

• Uniqueness & Signals

  • Mark Jeffrey praises Bittensor’s early ecosystem as having “more early signal” than any comparable crypto or DePIN (Decentralized Physical Infrastructure Networks) project (03:23, Mark Jeffrey).
  • Subnet projects are producing real, valuable hypotheses with strong founding teams—a contrast to much of DeFi’s “nonsense”.

• Application Layer & Value Creation

  • Jason is interested in crypto moving beyond speculation and money transfer, looking for consumer and enterprise use cases (02:24, Jason Calacanis).
  • Bittensor’s decentralized AI ambitions are compared to the “Linux moment” in OS history—open-source, distributed, permissionless (09:20, Mark Jeffrey).

• Investment Approaches: Token vs Equity

  • Investing in TAO tokens: Broad mutual-fund-like exposure to the protocol and subnets—ideal for those wanting to participate without the bandwidth to pick individual projects (36:29, Jason Calacanis).
  • Investing in subnets: More active, “angel investor” style; potentially higher upside and higher risk (38:15, Mark Jeffrey).
  • Traditional venture/equity investment is still possible (e.g. in Bitmind), but harder for non-accredited investors; tokens level the playing field for the public (39:31, Ken Miyachi).

• Risk & Speculation

  • Clear warnings: “90% of the bets I make go to zero” (36:29, Jason Calacanis).
  • Importance of time horizon, risk appetite, and understanding the mechanics before investing.

3. Showcasing Bittensor Subnets & Operators

a. Bitmind (Ken Miyachi, Subnet 34) — Deepfake Detection & Proof-of-Humanity

• Mission: AI security for deepfake detection; applications in media, social platforms, government, and enterprise.
• Mechanism: Miners compete to build the best detection models; adversarial “red team” miners generate and attempt to fool systems, creating a rapid evolutionary pressure (26:38, Ken Miyachi).
• Quote:
  • “It’s a continuously learning system trying to improve over time.” (26:38, Ken Miyachi)
• Commercial Value: APIs and bots for media/news; business communications security; proving content authenticity is increasingly crucial (29:05, Ken Miyachi).
• Investment & Openness:
  • Raised $3 million from crypto and AI VCs (33:28, Ken Miyachi).
  • Participatory—anyone (not just accredited investors) can buy subnet tokens and support the project.

b. Macrocosmos (Will Squyers & Stefan Cruz) — IOTA & Distributed Training

• IOTA (Subnet 9):
  • Goal: Orchestrate global decentralized compute power (like SETI@Home) to train frontier AI models (44:33, Stefan Cruz).
  • Ambition: Enable cost-efficient, large-scale distributed training rivaling the capabilities of data centers at a fraction of the cost.
  • Innovation:
    • “Train at home”: Install simple software, donate compute, earn tokens passively (46:36, Stefan Cruz; 50:13, Will Squyers).
    • Focus on making “meatloaf” with distributed compute, not just “prime rib” datacenter GPUs—democratizing participation.
  • Mechanics: Handles highly interruptible, variable, and fractional compute—unlike centralized infrastructures (54:22, Stefan Cruz).
  • Quote:
    • “We actually want to be able to make... indistinguishable training compute from this mixture of compute nodes.” (46:36, Stefan Cruz)
• Business Model:
  • Fully bootstrapped, company-based with deep roots in Bittensor.
• Ethics Post-Rug Pull:
  • Strong emphasis on trust and fiduciary duty; owner lockups seen as vital for ecosystem maturity (58:10, Will Squyers).

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Notable Quotes & Insights (with Timestamps)

• On the Rug Pull Allegation:

  • “If you have leadership and you have responsibility, you can mess with it.” – Jason Calacanis (00:06)
  • “…the incentivization alignment engine worked spectacularly well, but then it fell out of alignment when there was sudden success. It created this sort of overwhelming temptation.” – Mark Jeffrey (00:15, echoed at 16:04)
  • “It looks like… this is what Sam did.” (00:32 & 16:04, Mark Jeffrey)
  • “He dumped 37,000 TAO on his own subnet holders. It’s about $10 million in money. And he sort of walked away from the entire project.” – Lon (13:37)
  • “Do I believe that Sam’s epistle about why he left because of decentralization is correct? No, I do not.” – Mark Jeffrey (21:07)
  • “The Bittensor version of ‘I am Spartacus.’” (21:25, Mark Jeffrey)

• On Smart Contract Solutions:

  • "The architecture in crypto is smart contracts. ... The answer is to lock those tokens so he can't spend them. ... If you’re operating the subnet, then your tokens are locked." (23:32, Mark Jeffrey)
  • “The chain will reward capital conviction in a subnet.” (23:32)

• On Investment Models:

  • “You can own the equity in Ken’s corporation, or you can own the tokens. ... It just depends on which you believe in more.” (35:20, Mark Jeffrey)
  • “There are people who want to back others to go play poker tournaments, and those who want to play in the tournaments.” (36:29, Jason Calacanis)
  • “90% of the bets I make go to zero. So you need to have a certain constitution…” (36:29)
  • “If you’re a normal person in the normal world and you’d like to play in this arena, it is open to you; you don’t have to be accredited.” (38:15, Mark Jeffrey)

• On Incentives:

  • “This is the most ferocious form of capitalism ever invented, as far as I can tell.” (42:01, Mark Jeffrey)
  • “[Bittensor’s] incentive alignment engine is its core invention and everything within it is contests within contests within contests.” (42:01, Mark Jeffrey)
  • “It's a way for you to invest in the AI future as an unaccredited investor that you normally don't get.” (38:15, Mark Jeffrey)

• On Distributed Compute (IOTA):

  • "We want to be able to make... indistinguishable training compute from this mixture of compute nodes." (46:36, Stefan Cruz)
  • “Just download this thing, set it up, and now you’re mining Bittensor.” (45:49, Mark Jeffrey)

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Important Timestamps

• 00:00–00:51: Rug pull intro & context
• 04:32–06:17: Uniswap/DeFi explanation and significance
• 13:37–15:08: Details of Templar subnet, Sam Dare’s actions, price drop
• 16:04–18:46: Smart contract loopholes, possible remedies
• 21:07–22:42: Discussion on Sam Dare’s stated grievances vs real motives, proper resignation process
• 23:32–24:39: Smart contract upgrades & new governance proposals
• 25:24–30:53: Bitmind project (deepfake detection), adversarial mining, enterprise/government use cases
• 36:08–40:14: Investment vehicles for public vs accredited investors
• 44:33–54:42: Macrocosmos, IOTA, distributed training, democratizing compute
• 57:19–58:49: Emotional/ethical aftermath of the rug pull; solutions going forward

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Tone & Atmosphere

The conversation is lively, technical but accessible, and often candid—reflecting both the optimism and wariness of veteran entrepreneurs when navigating new, rapidly evolving ecosystems. Frequent analogies (Linux, venture capital, poker tournaments, Airbnb) help frame the concepts. The mood is simultaneously analytical and community-minded, with a strong bent toward practical risk management, trust, and incentive alignment.

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Memorable Moments

• Metaphor for crypto investing:
  • Backing a fund (buying TAO) vs. being an active angel (buying specific subnets).
  • “There are people who want to back others to go play poker tournaments, and those who want to play in the tournaments.” (36:29)
• On Bittensor’s capitalist Darwinism:
  • “Contests within contests within contests.”
• Train-at-home analogy:
  • “Ribeye steak” (prime GPU) vs “meatloaf” (fractal distributed compute). (46:36, Stefan Cruz)
• Community's “I am Spartacus” moment in the wake of the rug pull — subnet operators rallying to reaffirm trust.
• Meta discussion:
  • Comparison of spectacle vs. authenticity in art/entertainment during "off-duty" segment (Bieber vs. Sabrina Carpenter at Coachella; 65:10+).

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Conclusion & Takeaways

• The Bittensor rug pull exposed important architectural and governance vulnerabilities common to decentralized systems—information asymmetry, liquidity, and incentive misalignment.
• The problem is not unique to crypto, echoing early failures in venture/startup history (“shutting down and taking the money to Vegas”).
• The remedy is likely institutionalized via smart contracts — requiring “skin in the game” and minimizing single-actor risk.
• Despite setbacks, the Bittensor ecosystem is showing robust early signals and cultivating a unique, meritocratic, and incentive-driven environment for decentralized AI.
• Individuals can participate in multiple ways — from passive mutual-fund-like vehicles (TAO) to directly engaging with/operating subnets or funding corporations when possible.
• The episode underscores the importance of trust, community, and aligning long-term incentives in all forms of decentralized, collaborative innovation.

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For listeners:

If you’re interested in the intersection of AI, crypto, and decentralized governance—or just want to understand how these rapidly evolving models work—this episode provides both a cautionary tale and a showcase of bleeding-edge innovation.

Transcript

[Mark Jeffrey]

Do I believe that Sam's epistle about why he left because of decentralization is correct? No, I do not.

[Jason Calacanis]

If somebody runs a subnet, they do get a little bit of power and they can do what's called a rug pull. If you have leadership and you have responsibility, you can mess with it.

[Mark Jeffrey]

The reason why this all happened, the incentivization alignment engine worked spectacularly well, but then it fell out of alignment when there was sudden success. It created this sort of overwhelming temptation. It looks like an. And this is sort of allegedly. This is what it looks like on chain. It looks like this is what Sam did.

[Jason Calacanis]

And this is all allegedly and a lot of ifs here. But if the trail is true, one might speculate that a large amount of money was at stake and it would be like somebody raising venture capital and saying, wow, there's millions of dollars in the bank. I should deposit in my personal account and shut the company down.

[Mark Jeffrey]

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[Jason Calacanis]

All right, everybody, welcome back to Twist. It is Monday, April 13, 2026. AO 77 something. There was a lot of hand wringing in the Tao Bittensor community over the last week or so and there's been a lot of progress made. So to break it all down with us, Lon brought on my friend Mark Jeffrey, known for 30 years, was an early OG in Bitcoin, created one of the first online virtual communities, the palace, back in the 90s when many of you were not born. And is one of the OGs of the Internet online space. And also now crypto and Tao Distributed computing with welcome back to this week in Startups, Mark Jeffrey.

[Mark Jeffrey]

Thank you for having me, sir. Great to be back.

[Jason Calacanis]

Mark started telling me Lon about Bittensor and Tao because I was like, hey, what's going on? Because I think crypto's turning a corner here. It's becoming legal, it's becoming regulated in a very intelligent way. And I'm curious, is there anybody doing anything where a Consumer or an enterprise gets value other than store of value and money transfer. Very well established uses of crypto, of course, between Bitcoin and, you know, Stablecoins. Great to transfer money around and great to, you know, speculate and all that stuff. But I care about the application layer and actually some value being created. He said, yeah, let me tell you about this thing, Bittensor, Tao and subnets. And I was like, that's legit. And then I watched the people getting involved in and said, these are legit people as opposed to crypto, Mark, which I think you agree you had the early cyberpunks, crypto heads, and then it kind of got hijacked a bit along the way.

[Mark Jeffrey]

But I would say that within the Ethereum community, when that all booted up, when decentralized finance booted up, you had the invention of Uniswap and the decentralized exchange and the invention of decentralized lending, and that was valuable. These are financial products, so they're not really, you know, products like the way you and I are used to seeing belts, but they do have value and they're not like sort of nonsense. But I'd say 80 to 90% of it is nonsense. When I encountered Bittensor and started digging around the subnets, you know, having been a participant in many early ecosystems, including the 90s, as you, you know, talked about earlier, as well as early crypto, what I saw in Bittensor was quite, quite a lot of early signal. I would say, you know, half the teams were doing something of value and were decent hypotheses. You don't know whether these hypotheses are going to pan out and become very large companies or phenomenons, but they are pretty good. So I've never seen an ecosystem where there is more early signal than the Bittensor ecosystem. So that's one of the reasons why I got very excited about it.

[Jason Calacanis]

Maybe explain Uniswap. You mentioned that. I'm sure people in the audience are going, oh, that is real. Uniswap is kind of an automated market maker of some type where people can swap value.

[Ken Miyachi]

Yeah, yeah.

[Mark Jeffrey]

I mean, it's really a decentralized coinbase, right? Yes, you're correct. Everything you said is correct. But a lot of people don't know what an automated market maker is. It is technically called that, an amm. But the thing that was really, that was happening before Uniswap. If you wanted to get your coin listed on a, you had to first of all get it on a centralized exchange. There was no other option, which meant you had to pay Binance or Coinbase or Kraken or somebody, usually like a million bucks to get the listing right. And then you had to provide your own market making, which meant that you had to provide a bunch of other coins to trade against your coin when it listed on the exchange. The exchange would not provide that liquidity. Right. So along came Uniswap and they said, why don't we do something like a Coinbase or a Kraken or a Binance? But the entire thing is just powered by smart contracts and lives on the chain. Nobody owns it, nobody controls it, except for the Smart contract. And we attract liquidity by promising some portion of the trading fees to anyone who provides the liquidity to the smart contracts. So anyone could become a fractional owner through providing liquidity of an exchange, of this decentralized exchange. So you had two real big advantages when you had permissionless listing. Anybody could list any coin so long as they were able to define the swap pairs on Uniswap. And two, the community provided the liquidity. And this worked extraordinarily well and is basically the backbone of all defi today.

[Jason Calacanis]

Lon, we're off to the races here. So much interesting information, hard to keep track of. And I've got my notepad here and I've got my favorite pen. This is my Zebra G750. But as fast as we're talking, I'm not going to get it all down right. I might write down two or three words. It's not going to be structured. I'm going to have to go back. I can't keep up and I got to be present here. So what do I want to do? I have my plot pen, I press the button. Boom. I get a haptic. Now it's being recorded and it's going to be summarized. Red light on it. So I'm not like covertly recording anybody. I'm just keeping my notes. So when you're in a meeting, if you don't have your phone handy and you don't want to flip through, you know, your different apps to try to record something. The plot stores it all. When I put it in the cradle at night to charge the battery, and the battery lasts forever. It seems like I charge this thing every two weeks and I use it constantly. It syncs everything and then it automatically runs it against an LLM does a summary. It's one of the more brilliant products and I'm addicted to it. I hear you got one for your mom.

[Lon]

I did. My mom watches our show and she's like, you know, they should be advertising these to seniors, not executives, because I'm forgetting stuff constantly. So she's hitting it like all during the day, remind me to do this, update my calendar for that. And it's just keeping her more like in the flow and helping her sort of dodge her forgetfulness. And it's the smartness of it. It's not just taking notes for you. It's filing them, it's arranging them, it's coordinating them, it's keeping you organized, all with just hitting your Plaud pin. So if your work relies on conversations, you too need a Plaud note pin, just like my mom. Check it out at Plaud AI Twist. And if you use the code twist, they're going to give you 10% off.

[Jason Calacanis]

Okay? So thanks, Plod. Let's get to Tao and Bittensor. What attracted you? Yeah, what attracted you to this specific project and why?

[Mark Jeffrey]

Mark, I remember talking to you at a conference, one of your conferences, where you said, you know, pivot from crypto to AI, right? And thought about that because you're a smart guy and, you know, you have advice like that, I'm going to listen. And my conclusion was, well, you can't really pivot to AI because the only people who are going to own AI are the big players, right? There's like five companies, right? You have to have giant iron to compete. And anything other than that is you're just, you know, an API layer on top of one of those five companies. And you're probably, you're probably just running into a buzz saw. That was my initial conclusion. And then with Bittensor, I saw a way to sort of fuse the crypto stuff with the, with a decentralized version of what the centralized people were doing. And, and the more I understood what was going on there, the more I realized that this was sort of the Linux approach to, you know, it sort of reminded me of the OS wars. When you had OS 2, you had Microsoft NT, you had Sun, Solaris, but who won? It was Linux, right? Linux powers 90% of our world today. And what I saw with Bittensor was sort of the early moments of what looked like an AI Linux OpenAI. But like what OpenAI should have been truly OpenAI.

[Jason Calacanis]

I wasn't saying crypto's bad in any way, just saying if you're in crypto, I would pivot to AI because there's such a much more. There's so much application for AI because intelligence touches everything. Whereas with crypto, finance doesn't touch everything. Finance people might say yeah, finance touches everything. Finance doesn't touch me writing a poem. Finance doesn't touch me going and, you know, cooking a meal or something like, it doesn't touch everything, but intelligence does. Right? And that was very similar to, I think what the Tao people were saying was, hey, AI is so complex and there's such a need for compute, there's such a need for transport, moving packets around. Hey, maybe there's something here. You're running essentially a fund, like a venture fund, but for Tao. I'm a partner in it, I'm an LP in it. I'm deep down the rabbit hole on this. But you're picking subnets in which to buy those tokens and invest in. So what are some of the ones you've invested in and why? Debugging sucks and it takes up time. Your team could be spending on awesome new features and products, but now there's a better way. Sentry's AI powered debugging agent, Seer, isn't just guessing about what might have gone wrong with your system. It's analyzing your actual data. And because it has context, it spots buggy code before that code ruins your entire day. Plus, Sentry works alongside coding agents like Cursor, passing along an idea for a fix that can then be applied to your code base and sent to a human team member for review. End to end automation, or all the way from bug detection to pull request. So join the millions of devs and companies like Claude and Disney who use Sentry to move faster. Check them out at S E N T R Y IO twist and use the code twist for $240 in Sentry credits.

[Mark Jeffrey]

Yeah, so we've invested in about eight so far. And we, you know, sort of the big ones are ridges, shoots, Hippeis, Vidio score and basically all of these what we look for. We're sort of conservative investors so far as one exists in the Bit Tensor ecosystem in that we look for subnets that have product market fit and revenue primarily. Right. So it's pretty much the same criteria that you use probably to judge startups. Right. We look for a mature team, we look for mature products. And there's a class of product inside of Bittensor that I call the research subnets. And these are people that are trying to do extraordinary things that don't necessarily have an obvious product market fit right out of the gate, but if they work, they could be absolutely enormous. Right, but you don't. Yeah. So, you know, so great example of that. Well, Nova is a great example of that. Right, so Nova is hunting pharmaceutical molecules and it's going to take a while for them to probably find one. But if they do find one, it could be a billion dollar subnet kind of overnight. Right, but it's very speculative. We're mining for gold basically. We might not find it.

[Jason Calacanis]

Now Lon, it's important. Incentives matter. And so I want to sort of tee up this story here. If somebody runs a subnet, they do get a little bit of power. They have the subnet, they recruit miners, they have validators making sure the work was done properly. But they become like a small business owner and they can do what's called a rug pull. So they could either be virtuous and they can provide people with this incredible opportunity. Tips give and give knowledge through a large language model or an algorithm. They could give compute, they could give storage. They can contribute something in order to win the prizes of Tao or the subnet token, which is essentially one and the same in some ways. So they can earn money, but they can also, if you have leadership and you have responsibility, you can mess with it. So we had last week somebody running one of the subnets decided they wanted to take their marbles and leave the game. And that's the core of what happened.

[Lon]

Yeah, Lon, we actually had spoken with Templar, which was the subnet in question. Sam Dare is the sort of creator of it. And so, yeah, what's up? Sam dare, he dumped 37,000 tow on his own subnet holders. It's about $10 million in money. And he sort of walked away from the entire project. It was Covenant AI, the designers of that 70 billion parameter model. You had spoken about it with Jensen Huang on, on all in. We had discussed it with Sam here on the show. So you know, he sort of walked away from coveted AI. They had been operating three subnets on BitTensor, Templar, Basilica and Grail. Templar was sort of like the, you know, the big one, the high profile one was the reason that Tao had sort of been taking off, getting a lot of more mainstream attention. The coin had sort of gone up in money. He and he and Const had some sort of. We're still getting sort of all of the details. They had had some sort of back and forth. He felt that constant const, Jacob Steves, one of the co founders of Bittensor, was sort of overstepping his authority. There was some sort of conflict between the two of them. And then as you said, Sam sort of took his marbles, walked away, sold 1% of the Alpha holdings on three covenant subnets that were near 100%, you know, and so, yeah, so that, that. That is sort of the situation. 37,000 tow. That was kind of the rug pull, that. That $10 million. And now we're sort of in the.

[Mark Jeffrey]

In the.

[Lon]

In the aftermath. The price of town dropped about 25% in the immediate aftermath.

[Jason Calacanis]

I don't know if you were on the episode when this gentleman was on, but I found him a little squirrely.

[Lon]

Yeah, we discussed this after the show. I mean, big personality, a funny guy. Like, he was a great guest on a podcast because he had very big, outspoken opinions. And he was a big personality guy. I really enjoyed talking to him. But we did discuss this afterwards, and there was also an edge to him. It was a little bit hard to put your finger on the. There was something a little unpredictable about his demeanor and I guess.

[Jason Calacanis]

Yeah. And how. What was coming next? I kind of pushed him on, hey, what are you doing next? And how does this work? And the answers, Mark, weren't as crisp or as tight as I might have expected from somebody running something significant, let's just say. Yeah, so what is the back channel here? And maybe you could explain Constant, the founder of Bittensor and his role in all this.

[Mark Jeffrey]

So CONST stands for Constantine, just FYI.

[Lon]

I did not know that. I did not know that.

[Mark Jeffrey]

Yes, I know. A lot of people don't know that. So CONST actually built the initial subnet version of Templar 3. And Sam used to work directly for Const at the Open Tensor Foundation. And so Const basically bought the subnet for him, wrote the initial version of the decentralized training environment Templar and handed it off to Sam. Right. So he gave Sam his start. And then basically what we discovered here is. Let's just stop for a second, because what happened here, the reason why this all happened, is because something in Bittensor worked extraordinarily well. There's several things that are working pretty well, but it's because something worked. The incentivization alignment engine worked spectacularly well, but then it fell out of alignment when there was sudden success. And it created this sort of overwhelming temptation for a subnet owner who, when you're a subnet owner, you acquire a large number of your subnet tokens. They're emitted into your wallet. Right? Because you get paid effectively by the Chain for being a subnet owner. And if you have enough of them, and there's the liquidity pool in the on chain uniswap that I described earlier, you can at any time just sort of flood that uniswap with your tokens and exchange it back out for Tao. And if you have the majority of the tokens, you can get the majority of the liquidity pool. And now you've got Tao. Now you can run off the binance, sell the Tao for dollars, and now you're gone. Which it looks like. And this is sort of allegedly. This is what it looks like on chain. It looks like this is what Sam did. And it also looks like that his developers on his team were caught, blindsided by this also.

[Jason Calacanis]

And this is all allegedly. And a lot of ifs here. But if the trail is true, then essentially one might speculate that a large amount of money was at stake. And it would be like somebody raising venture capital and saying, wow, there's millions of dollars in the bank. I should deposit in my personal account and shut the company down. Which is actually one of the big fears in venture capital. We run on a trust based system. And it has happened. You can go look at the history of venture capital. Every year or two, somebody takes the money and goes to Vegas or absconds to another country.

[Lon]

There was just that story about the director, the Netflix director, Carl Renk. Do you remember this? Netflix paid him to make like an eight episode sci fi series for them. And he just took the money, bought crypto, bought a bunch of luxury mattresses, bought some cars. He's doing a little jail time right now for that. But yeah, it happens in every industry. People get paid up front and then they walk away with the bag.

[Jason Calacanis]

Yeah. And important to say, we don't know if that's the case here, but that is part of any capitalist system. So there's no way to stop this unless you have a board of directors, if you have a controller, a cfo, and then even if you do, people, you know, the board meets four times a year and then there's 361 days in between those board meetings where shenanigans can happen.

[Lon]

I guess I had, I had sort of a two part question. I'd love to throw both of you about this. One is, do you guys believe that Sam had legit grievances against Const or the system? He's been saying this is activism, like Bittensor is overreaching. It's not truly decentralized. And I had no choice but to do this. So one, do you think, is there anything behind that at all? Or do you suspect that this was just. That's how he's explaining himself after the fact that he just wanted the money. And then two, how would you have preferred a subnet owner react if they do feel like I want out of the bit Tensor community, I'm not happy with the way it's being run. What should Sam have done to avoid rug pulling all of the people who believed in his project? Allegedly, but allegedly, but still separating himself from Bittensor more generally.

[Jason Calacanis]

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[Mark Jeffrey]

Sam's epistle about why he left because of decentralization is correct? No, I do not. And you can see this, the evidence, like nobody in the ecosystem believes that. Okay? And you can see this from all the posts from all the subnets. Everybody's saying my subnet is Bittensor and Bittensor is my subnet. There's a ton of these posts.

[Lon]

Yes, I've seen those.

[Mark Jeffrey]

Basically the Bittensor version of I am Spartacus, right? And everyone is standing up and saying, no, none of this stuff is true. And Const gave a very I thoughtful reply to the letter that Sam posted as his rage quit letter. Basically. Right? Const replied to it. It was a very legitimate response. So, yeah, so I think that I don't, you know, I don't think there's anything to his claim of decentralization. Nobody else feels that way.

[Lon]

So purely hypothetically, Sam had a legitimate grievance. You're running a subnet. You're like, I don't like the way Bittensor.

[Jason Calacanis]

How should he have dealt with It.

[Lon]

How should he have dealt with it?

[Mark Jeffrey]

He should have gone to Const and said, you know, look, I'm not happy. You know, let's find a way to amicably part ways. Let me hand off, let's figure out how many subnet 3 tokens I should be allowed to keep. Then I should hand over the subnet private key to the foundation to be redistributed to somebody else to run. And Conch could figure out who that was, or the foundation could figure out who that was. Basically negotiate an exit which he gets something for his contribution to date. But then the subnet and the token holders are are allowed to carry on without him.

[Lon]

Right?

[Mark Jeffrey]

That's what he should have done.

[Jason Calacanis]

Mark, before we get to our guests, how can we avoid this in the future? And who owns a subnet and who should own a subnet? Should it be the miners themselves or should there be governance here? Should there be a 51% rule where the people contributing to it own it? What's the best practice here? Because hey, as an investor, if I wanted to back Lon to do, you know, an algorithm on and do a subnet, I would be like, okay, I want to own it. I want Lon to own it. I want the community own it. How does that get codified Outside of the Delaware C Corporation, the Texas Nevada Corporation, where we set up a board of directors, where we set up shares in a company, we have preferred shares, and that whole architecture, which is old

[Mark Jeffrey]

but trusted, the architecture in crypto is smart contracts. So the answer to this is to implement a new smart contract or change at the chain level. In Bittensofter's case, the problem is that Sam had access to all of his tokens and could spend them in one gulp before anyone knew what was happening. Right? He had information asymmetry and he controlled the subnet. So he could just dump this giant amount of tokens on the market. So the answer is to lock those tokens to basically make it so he can't spend them. And if he's operating the subnet, you can't both operate the subnet and have unlocked tokens like there has to be. If you're operating the subnet, then your tokens are locked. And there's a couple of different proposals underway as to exactly how to do this. Const released a new one this morning where basically the ownership of the subnet is always up for grabs based on who locks the most tokens for the longest period of time. That's effectively the mechanism. So you might lose your Subnet to someone who has more conviction. So the idea is that the chain will reward capital conviction in a subnet. So it is possible you'll lose your subnet to somebody that believes in it more than you.

[Jason Calacanis]

We have some guests, lon.

[Lon]

We do.

[Jason Calacanis]

Let's bring some of our guests on and we'll have a talk about some of the other subnets out there.

[Lon]

Yes, speaking of great subnets, as we often are, we have three more subnet founders joining us on the show today. First up, from Macro Cosmos, they also run three individual subnets. We're going to welcome Will Squyers, the CEO and co founder and Stefan Cruz, the CTO and co founder. Gentlemen, thanks for being here. Also joining us, he is the co founder of Subnet 34, Bitmind. They're a system for detecting deep fakes and other AI generated content. Give it up for Ken Miyachi. Ken, thanks for being here.

[Jason Calacanis]

Okay. All right, so maybe Ken, you can explain a bit about your subnet, who you are and why you created it.

[Ken Miyachi]

Yeah, thanks for having me on. Hey everybody, I'm Ken. I'm the co founder and CEO of bitmind. So we are an AI security company and we're very focused on deep fake detection. We have a variety of consumer applications where if you're scrolling X or you're on the Internet, you can put in a video image, piece of audio, et cetera, it will tell you if this is real or fake. And then we also have a bunch of enterprise services where we create custom models for essentially businesses trying to secure their communications, their data, et cetera. And what we're really focused on right now is going towards proof of human. There's a lot of grifters, there's a lot of North Koreans, fake people trying to infiltrate your systems and just AI slop in general throughout the Internet, bots trying to sign up and use your services. And we are creating services to essentially detect and stop that type of activity from happening on the Internet.

[Jason Calacanis]

And how is this done? Is there an algorithm that analyzes a piece of data and says, hey, this is human. This is likely an LLM. This is a deep fake, this is a real photo. And what do the miners contribute to this? They have their own algorithms or their computer? Both, correct?

[Ken Miyachi]

Yeah. So essentially the miners are competing on developing the models themselves, these detection models that essentially in different modalities, whether the image, video or audio, that essentially they win the competition. They earn our subnet token if they produce the highest accuracy, most high fidelity models. And then on top of that, we Also have another set of miners that are actually incentivized to generate data and try to fool them. So it's a continuously learning system trying to improve over time.

[Jason Calacanis]

So you have a white hat and a black hat. You got the black hats coming in and saying, hey. Or a red team. I guess that might be a way to say it. Even better. Here is some content. And we're going to make a generative AI picture of Lon, but we're going to make them, you know, 10% more robust, or we're going to GLP them and make them 10% thinner. And then they try to trick

[Stefan Cruz]

the

[Jason Calacanis]

other miners, and that makes their algorithm sharper. So steel making steel on steel, making it stronger.

[Ken Miyachi]

Exactly. Trying to give the chisel jaw.

[Stefan Cruz]

I don't know.

[Ken Miyachi]

Whatever you want, right?

[Jason Calacanis]

Sure.

[Ken Miyachi]

Why this is so important is because generative AI and AI in general is the velocity in which these new models coming out is so fast. And so if you did this in a traditional way, what you have to do, wait for cdance to come out, wait for nano nanopro 2 to come out, generate a bunch of that data, retrain your detection model. And what we have is an open, decentralized, permissionless system where people are generating data for us all the time. And people are also coming up with new detection algorithms, retraining quickly. And so we can actually update our system on a daily, weekly at a very fast cadence to always stay ahead of the newest generation models.

[Jason Calacanis]

So, okay, this seems like an incredible game you've constructed. One team is truth maxing, one is fake maxing. They battle it out, their algorithms get sharper. Fantastic. But is there a commercial product that is created for enterprises or consumers? Can the New York Times or could axe Twitter say, hey, we want to put these trending posts in here. And then maybe in the community notes say, hey, it has this score, you know, from your subnet, you know, and It's a certified 98.7% truthy or truth maxed.

[Ken Miyachi]

Yeah, definitely. So I think the two enterprise use cases you touched upon were essentially social media and news outlets. So with news outlets.

[Jason Calacanis]

Right.

[Ken Miyachi]

That's a really important use case for us. If you are in traditional media and media at all, you want to make sure what you're reporting on is actually real and you lose trust and credibility, which they're already struggling with. And so as a reporter, you're going to want to verify whether this image, whether this data that you're receiving is real or not. And so there's definitely use cases like that where you could either drag and drop an image or video, or you can use our API however way you want to use it to determine that. And then on social media, it's a really big use case. We have something called bitmindbot which is very similar to Grok, but it has a specific focus on is this real or not? The other use cases I think on the enterprise side are really about securing essentially business communication. There's been a lot of recent drama around interviews. Whether you're like, I think there's two main use cases. There's one where it's like just this person is completely fake. Yeah. And like.

[Jason Calacanis]

Right.

[Ken Miyachi]

The person can't put their hand in front of their face or something like that and they're trying to infiltrate your organization. But then there's another use case where it's just like actually grifting people, trying to get multiple jobs and putting on a facade of themselves. And so we're trying to stop both of that. And then the other actually really big use case that is pretty shocking, but not a lot of people are thinking about is actually in government. So more and more there is evidence being shown in cases in a wide variety of contexts that is AI generated. And you need to be able to prove this. That this is AI generated is the new my dog ate my homework. Right?

[Jason Calacanis]

Yeah.

[Ken Miyachi]

About you. You just say I generated.

[Jason Calacanis]

We say it on every episode. These days the AI revolution is here and sitting on the sidelines is no longer an option. No more waiting outside. With Netsuite by Oracle, you can start putting AI to work for you today. NetSuite is the number one AI cloud ERP and it's trusted by over 43,000 businesses. It's not just an AI add on or, or a chatbot that sits in your browser. No, this is a unified source of truth that brings together all the data you need to run your business. From software and IT services to healthcare equipment manufacturing, financial services and many other great American industries, NetSuite delivers a customized solution for your business if your revenues are at least in the seven figures. Get the Daily Business Guide Demystifying AI at netsuite.com/ the guide is free to you at netsuite.com/ what's interesting to me, Mark, is just putting on my angel investor hat. Go read the angel the book and it's in 13 or 14 languages. It was really nice. I was at a major Hollywood party last night, just very famous. Studio head Lon came up to me and said, you're Jason Calacanis. I said, yes, I am. And this is in like a room full of like celebrities or whatever. He said, I read your book. It's just like movies. You would have been a great studio head. Anyway, enough promotion of my book. It was one of the great moments of my life that in a room where I assume nobody knew who I was, a studio head, a literal studio head had read the book.

[Lon]

David Zaslav. This is what he's doing with his time now.

[Jason Calacanis]

It wasn't big, but it was on that. Even bigger than that. Anyway, it's as crazy as it seems. I won't say who, but I'm looking at this through my angel lens, Mark, and saying, wow, there is a business here. I don't know exactly what it is, but you can see Ken is doing product market fit. He's doing a go to market function where, hey, we're trying to find customers. And he's also raised $3 million for this project as well. Correct, Ken and who put $3 million into the company? And then Mark, as an investor, after Ken tells us who put the 3 million in, I want you to explain to me how I should think about this. As a partner in Stillcore at an LP with, you know, hundreds of thousands of dollars of my own personal money at stake versus, hey, should I invest in Ken's corporation? So, Ken, maybe you could tell us who invested the 3 million. Is it crypto people giving you tokens or is it like a venture capital firm?

[Ken Miyachi]

So my background, I come from both AI and crypto. I worked at Amazon for a couple of years doing recommendation systems. And then prior to that I was at a blockchain protocol called NEAR. And so I had relationships with a variety of VCs in both of the spaces. Let's see Arch Capital, Canonical Crypto Mechanism Capital Blockchain Builders Fund. There's a wide variety. When we raised our seed fund about a year ago now.

[Jason Calacanis]

And how did they think about. Well, let me have Mark answer the question of where is value going to be created and how should I think about this? Because now I'm getting pulled in both directions. I've got my venture firm here and then I'm a partner in Stillcore. Okay, I place my bet here, but should I also be like, maybe launch or my syndicate should talk to Ken about investing in his corporation?

[Mark Jeffrey]

Yeah, I think it's very different in Bittensor than it was in the old Ethereum and Solana days where you had this token or equity kind of bifurcation. And they both were sort of. They appeared to be ownership of the company. The ownership of the token in Ken's subnet, the subnet that he operates and runs, is actually the protocol product department of a much larger organization, the company, right? So the business and the intellectual property, so much as it exists, the revenue stream of his company, that the equity is how you own a piece of that, the product department and the engine that makes this product possible. Being a. You're basically a part owner in a Bitcoin like fashion of that piece of it. And you're always liquid, right? With his corporate owning equity in this corporation, you're not liquid, right? You're basically tied up for whatever period of time, but you can sell anytime you want, as we've seen in Bittensor, right. Recently, you can in fact sell those tokens. Right? So it just depends on which one you believe in more and what optionality you want to have.

[Jason Calacanis]

So interesting, Lon, one way to think about this is if you owned equity and Airbnb, the corporation, Lonn, you could also be an Airbnb host and make money in the marketplace, but you could also own Airbnb. And I do know people who were Airbnb investors who are like, I want to start an Airbnb, or there are people who are Tesla investors who want to have a fleet of robo taxis and manage those. Or it could be you are running a corporation at scale and the corporation has debt and you can buy corporate paper, that is the loan that people pay 12 or 15% on. And so you could own Tesla and you could own Tesla's debt, or you could own Uber and Uber's debt, or Amazon and Amazon's debt. So there's multiple ways to get exposure, I think.

[Lon]

What do you guys think? Just in general, if I'm a regular person watching this who's like, I like the cut of this project's jib, I want to get in on it. Would it just make sense to go to a place like Kraken or Coinbase and buy the Tao token? Is that a valid way to get exposure here? Or should you really be thinking about specific subnets and sort of digging in deeper?

[Jason Calacanis]

This is an important question. I think. I think the easiest thing to do is buy a little bit of Tao. You get the mutual fund effect, which would be like, if you ask me, should I be an angel investor or should I put money into a VC firm? If you put money into a VC firm, you're saying, I trust the partners there to make 20 bets. And I don't make those 20 bets. But in 10 years, I hopefully get back More than double my money and I beat the public markets. But if you want to be an angel investor and you read my book and you join Angellist and the syndicate and you join other SPVs, you get to pick and choose which ones you bet on and you get to make your own decision. And some people like that style. In other words, you can back somebody to go play a series of poker tournaments. There are people who do that and there are people who want to play in the poker tournaments. Depends on your time, I would say. It's basically a time commitment thing. I don't have the time to evaluate every subnet. So I'm a partner and investor in Stillcore Capital, which Mark is doing. All of these things are incredibly speculative. 90% of the bets I make go to zero. So you need to have a certain constitution I think is the way to say it. Yeah, Lon, Yes.

[Lon]

I could not afford to have 90% of my bets go to zero. That's definitely not well.

[Jason Calacanis]

But you could afford to have 90% of 10% of your bets go to zero, which would be 9% of your overall net worth. Yeah. So that's another way to think about it. Right. If you're going to put 5 or 10% of your net worth into speculative things. Mark, any thoughts here of how an individual, without giving financial advice, but just how you think about this, of where the value will be created? Because Stillcore has probably, I don't know if it's half or two thirds of its money in Tao and half or a third of its money into subnets at this point. What's the blend you think you should have?

[Mark Jeffrey]

Yeah, I mean it's about 80% subnets right now, but I want to back that off a little bit to maybe 30, 70. But I think, because I think we're in a unique period right now where if you make the right early bets it's going to be extraordinarily rewarding. And you have to know it's basically evaluating these subnets and the companies around them and the products like you would a regular startup. Knowing when to get in, also knowing when to get out. Right. And this is a highly, highly, highly dynamic environment as we've seen recently. So sort of scrambling around and getting to know everyone and everything and knowing as much information as possible is, is more than a full time job. And, and that's, you know, you can do that if you want. There are people who are doing it, you know, but we, we take over that function for accredited investors if they'd like to park their money with us and not worry about all that stuff. Right. So however, if, if you're a normal person in the normal world and you'd like to play in this arena, it is open to you that you don't have to be accredited. Right. So that's one advantage to betting on subnets as opposed to trying to get into equity. Most people can't get in equity. Right. So, so this is, this is a way for you to invest in the AI future as an unaccredited investor that you normally don't get.

[Ken Miyachi]

I kind of want to touch on that too. I feel like that's one of the most important things. I think something that's so interesting that specifically in the US AI is more unpopular than Trump. And I think a lot of the reason there is because no one can participate in the economic system that it's building. And what's so exciting about Bittensor is these are all extremely early stage companies that you wouldn't ideally have the ability to participate in in any other way. And this is, it's kind of a double edged sword. Right. Your early stage startup is getting marketed, is getting valued by the public market, but the public can participate in it. And I think that's what you need to do to keep AI beneficial to all humans. Have everyone that's participating have economic upside

[Jason Calacanis]

here, which I think everybody in America. It's just really leveling up the discussion. But it's a great point, Ken. There is Invest America accounts now called Trump accounts, where they're giving Every young person 500 or $1,000 and companies are going to start to contribute. There's some other wells coming into the system. I understand from Brad, who told me there's going to be some other Michael Dell type people who will probably contribute. Brad Gerstner of Altimeter Capital, Brad Garner, Val, Tim, who, you know, championed this idea, give him a lot of credit for that. If people could, more people can participate. And then I interviewed this ce, the, the new head of the sec and he's. This is his second stint as SEC chair. All Atkins.

[Stefan Cruz]

Yeah.

[Jason Calacanis]

He's all in on creating a test at the SEC where you can become accredited and participate in private companies. So participating in private companies where the value is could be a major unlock for people. Also incredibly risky, which is why rich people, when they go to dinner, talk about whatever private deals they're doing and what they have access to because they understand if it's a 1 in 10 chance of going 100x, you probably take that bet every day. So incentives matter. I think that's the key thing we've learned here, Mark. There are incentives all over the place. And the game of Tao, the brilliance of it, dare I say, is that it's very focused on incentives, validators, miners, competing, just. It's a elegant and beautiful system. When you look at the first subnet we looked at, what comes to mind in terms of where that could wind up in two or three years.

[Mark Jeffrey]

I think you're right. The brilliance of it. The incentive alignment engine of Bittensor is its core invention and everything within it is contests within contests within contests. Everybody's competing with everybody and only the most excellent get anything. So in companies, even the best run startup, there's a lot of inefficiencies. You have to hire people, right? Some of them hide in the company successfully without doing very much and they're sort of mediocre. There is no middle management when there's contests within contests within contests. This is the most ferocious form of capitalism ever invented, as far as I can tell. So yeah, I love it.

[Jason Calacanis]

Let's meet our next subnet. And I think at the pace we're going with two or three subnets, every couple of episodes, we're going to get through all 128.

[Lon]

We are, that's our, that's become our goal. And actually Will and Stefan from Macrocosmos, they run three different subnets, Jason. So we're knocking out, we're knocking out three. We've got APEX subnet one, IOTA subnet nine and then data universe, gravity, that's subnet 13. So thanks for Will.

[Jason Calacanis]

So greedy. So greedy. Taking up all these subnets. He owns, he owns 2.7% of the subnets. Okay.

[Will Squyers]

We actually used to run five subnets. We decided that we would, we would like to live long enough to actually see these things reach the market. So we had to, we had to downsize a little bit. Running a subnet is no joke, as Mark can attest and certainly can. But I think Will and I would love to speak a little bit about our most ambitious work on BitTensor, which is IOTA. So IOTA is Subnet 9. And perhaps I'm being hyperbolic, but I firmly believe it's the most ambitious thing that is being built within Bittensor right now. And I think from a very high level, what we want to be able to do is orchestrate the world's compute in the way that Bitcoin did, but to train models that rival chatgpt, frontier scale models. And there is actually a growing consensus in the industry that Training the next generation of very large models is becoming prohibitively expensive. As I'm sure you guys are aware. We're talking about data centers the size of downtown Manhattan and the next generation is going to dwarf even this. And I think what becomes inevitable at some point is we need to think about cost efficiency, we need to think about capex and something. In fact one of the core ideas that inspired Bittensor from Const himself was the idea of doing what's called distributed training. So distributed training is taking compute from pockets all around the world and coordinating that compute and making that equivalent to a large data center, yet it's just one that is dispersed all around the world. Now there's an enormous amount of work

[Jason Calacanis]

you have to do.

[Will Squyers]

As you can imagine, a lot of networking, a lot of algorithm and a lot of research that needs to be done, but it's a very high ceiling for the project. So we've been working on IOTA for about nine months. Will, my co founder and CEO, myself the cto, we're really, really excited and motivated about it. And yeah, the dream of training in Bittense is very much alive and kicking.

[Jason Calacanis]

And to put this in context, Lon and Mark, to create a frontier model now is, and you do a run basically to create the next ChatGPT, next Claude, next Grok, whatever the estimates are, $200 million in compute costs. This isn't the staff, this isn't the company testing or anything, just the raw compute cost is 200 million. Now that's a big number. But if you had, well, if you had let's say 10 million people participating in contributing some compute, well you know, then you, it would be a couple of $20 per person. So you start. Or if it's a million, it'd be $200 worth of compute. Each was 100,000, it'd be 2,000. If it was 10,000, it'd be 20,000 in compute. So maybe you get down to 10,000 people providing 20,000 in compute and hey, it's conceivable in the same way SETI at home was.

[Mark Jeffrey]

Yeah Mark, that's absolutely correct. And the thing that I love the most about what these guys have done is, you know, as someone who has watched mining occur within Bittensor but been unable to participate until I got my claw and I started vibe mining with that. But you know, minus the claw and all that, really these guys were the first people to provide a ready packaged mining application for their sub that nobody else had done that. Right. So, and you at home could now be a bit Tensor miner for the very first time and not have to know anything about anything. Just download this thing, set it up, and now you're mining Bittensor. It was fantastic. I loved it.

[Jason Calacanis]

So talk to me about how the IOTA install works. Is it as simple as, like downloading Slack or downloading a Chrome browser and then it just runs in the background?

[Stefan Cruz]

I think a lot of the principle of this, and, you know, I'll talk about the differences in our training methods later, but train at home, as we call it, the application, or iota, is like two clicks to install. And again, Mark is. Mark is one of our miners, so I don't actually have to pitch for myself. You install the application, it's a simple binary. Stefan has apparently gone to the wrong place. But you basically just install the app, put in a key to be paid out with, and we use your computer when you're not using it. And one of the reasons training models is so expensive is not just that you need immense amounts of compute. It's also that you train with the prime rib or the ribeye steak. You take the best compute for the longest period of time. It's the filet mignon cut, and that makes it even more expensive because it's the filet mignon cut. But the cow has to be rested for 50 days and you've got to leave it there and you've got to look at it. And what we want to do is fundamentally make you let models out of ground beef. So we take bits of compute from here or there, we perform some magic in the background and we make meatloaf and everybody loves ribeye steak, but actually meatloaf's what you want when you're comfortable. And there's some sort of joking in the metaphor there, but materially, you have two major markets for AI compute and GPU use. One is inference. Everybody here knows that marks running clause. You guys are using ChatGPT or your preferred model. And it's actually inference that creates the money. It's where the revenue is associated with when you track OpenAI or anthropic budgets. It's all from inference tokens. But the reality is you have these incredible training workloads, huge data center size training workloads that the main labs are spending immense amounts of money to satisfy. And these are taking up city blocks of compute. And the sort of lost opportunity there is, you can't use that compute to make inference. So the way IOTA works is the sort of minimum compute unit we can use is at the moment the size of a MacBook and for about 30 minutes, and we can actually extract value from that. So the sort of ultimate goal of this is we actually want to be able to make, we call it indistinguishable training compute from this mixture of compute nodes. And this is sort of one of the philosophies Steph and I have had when we first founded macrocosmos. There's a lot of people, when they approach any project in web 3 or crypto is they say, oh, how can I do what people do in a centralized way, but in a crypto way? So, you know, we were talking about Templar. Templar. Does training Templar effectively allows you to train in a centralized way using crypto Rails. I won't go into the technical details, but the nodes they used were Blackwell. So B200 nodes. It's a quarter of a million bucks for each compute unit. So it's prime rip, maybe it's prime rib, and you run it around your houses doing that. But it's still incredibly expensive. We think the interesting thing about this sort of merger between crypto and AI is how can you use fundamentally fractal compute, distributed compute, 10 minutes here, 20 minutes there. Mark's Mac Mini when it's sat idling in the evenings and sum that together to something that creates economic value in a very, very constrained market. So we actually released a research paper today that validates a lot of our work, but we've been working on this problem for a year and it's hard but compelling work.

[Jason Calacanis]

Are people calling opencloth Clause you are? Okay, fine. I just want to make sure there's not a new piece of software that I'm not aware of. So explain how I get the package on my Mac.

[Will Squyers]

To join iota, you can download it through terminal, through the command line interface if you're more coding proficient. Or you can go to the website iota mecosmos AI. Scroll down and you're gonna see a download button. As you mentioned before, Jason, it's just like downloading Slack or any other application. Exactly. You download this, it will generate a wallet for you. And then what will happen is when you open the application, you click Start and your machine will connect to our globally distributed training cluster. You will earn passive income while you sleep. Between 10pm and 6am your machine can be quietly being used as a training node. So the benefit to you is you get to accumulate iota, you get to be part of the big mission. And the benefit for us is we basically have, we have an always on super cluster of compute. And that's really the value.

[Lon]

I have a question. If I start doing this overnight on my work laptop, is active track gonna look like I was up all night working? Because this could really work.

[Jason Calacanis]

For sure. Yeah. We have security on our laptop.

[Stefan Cruz]

We will let you do your work for you.

[Lon]

Yeah. This is going to be amazing.

[Jason Calacanis]

I'm going to look like it would show your. Well, no, it would show massive CPU usage. So we have for background there's a piece of software called ActiveTrack just to lock down software and there's 20 of these. So we have confidential data on our network. But it shows like what the computers at our offices are doing. If somebody were to download everything, you would see that in ActiveTrack if you were exporting stuff, look how much GPU

[Lon]

Lon was using all night. Lon was using so much compute, he must have been crunching numbers all night. What a genius.

[Jason Calacanis]

There's multiple ways to participate in iota. One is as a user and you would download, you would have a wallet and then you would earn TAO by contributing your compute. But there's also becoming a node. So maybe you could explain what being a full node is versus that.

[Stefan Cruz]

I suppose that there's a couple of expressions of iota 1 is this very like participatory. Everybody shares, everybody co trains a model version at which we sort of nicknamed train at home because that's kind of what it is. The second version of this is the benefit of this tech is it has applications at sort of any scale within the AI stack. Now one of the ways we expect this to materialize in some of the earlier runs is it will use frontier compute. So H1 hundreds, B2 hundreds, those quarter of a million dollar machines. But what we will have availability for is anybody can register their nodes on a sort of rotating basis. So we're working on this in the background where you can post compute for a fixed interval, 20 minutes, half an hour. There's some fine tuning on the machine learning side for us to work out the sort of sweet spot there where we get value from it. But we've spent a lot of time talking to GPU providers and otherwise. And the sort of thing we found is that particularly if you can make an instance interruptible is what they call it. So you know, Jason, you've got 10 computers, you sell them to Mark most of the time for five bucks an hour. But you know, Mark sort of on a Thursday is like I don't need the compute last minute, you know that he might sort of pick it up Friday. And you don't want to you don't want to go sell it to somebody else. People in that instance are just looking to recover capital returns and operational costs. So you can get compute at 10 cents on the dollar. So we want to work with major providers to basically harness all that computer. 10 cents on the dollar, because if we can get our training efficiency within a factor of 10 at that point.

[Jason Calacanis]

Okay, this is fascinating. So somebody who has extra compute, like, let's say it was even coreweave, I interviewed the founder of that company. Let's say they had old H1 hundreds and they hadn't sold them yet, and they're five years old and they're still in racks, and they could say, you know what? Just we haven't sold them yet. We don't have a new customer for them. We might as well put them to use. It would almost like being. If you had a house and you hadn't sold it yet, you put it into the Airbnb inventory just to cover your cost basis or something at a discount, just to make sure you pay your taxes, your. Your real estate taxes, and your, you know, operating costs for the home.

[Stefan Cruz]

It's like that. But it's. This is sort of one of the foundations of Deepin and decentralized AI is people are like, oh, you know, I can use my compute for a day here. What's unique about IOTA is it's like an Airbnb, but you can subnet for 20 minutes with no notice and get paid for it. That's a better analogy there, because it's.

[Jason Calacanis]

You could use the home for one hour.

[Stefan Cruz]

Yeah, an hour. And that's where we think the real value is. Because if you're, if you're renting out your house for a day or a week, then you want. You want a reasonable bit of money for it. You want to account for all these wear and tear if you're trying to get 20 minutes, or you're just trying to make a quick buck of something that's already in production. And the economics change really dramatically. And we validated this with seven or eight different data center clients who all say, basically, the key thing is they want to be able to take the compute off you. And there's no training method in the world that allows you to have the level of interruptability we're designing for. Because typically training in a data center is something called synchronous training. So all of the nodes move in lockstep, and it's actually the slowest node that sort of drives the speed of the whole system. We're trying to design for this world where we assume that Mark will slam his laptop shut. We assume that coreweave will take their rack back and sell it to a premium customer. We actually assume that in sort of, even the case of large labs, you might just have an inference spike and you want to reallocate training compute to revenue workloads and then pull it back when you come down. Like, my father was an energy engineer and you always designed for the sort of peak load in the sinusoidal. And we're trying to make training compute liquid so that we can sort of achieve ultimate economic value, really.

[Jason Calacanis]

Any questions there, Mark, and your thoughts on the project, just generally speaking and how disruptive it is?

[Mark Jeffrey]

Yeah, I mean, I think it could be one of the bigger projects in Bittensor. I think these guys are, you know, they've been around for a while and they know the system better than most of the other subnet owners. And so I look, I'm very bullish on them. I love both of what. I love what both of them are doing.

[Jason Calacanis]

And is this a corporation, Will, and you have venture capital backing it, or is it just a straight anachronistic, crypto weirdo project? How is this running?

[Stefan Cruz]

We are actually. We are a company, but we are fully bootstrapped. We haven't raised venture capital today. We've been. We're fortunate to build. Actually Stefan built the first subnet on Bittensor for his sins.

[Jason Calacanis]

I saw that.

[Stefan Cruz]

So we've been around for a while and yeah, we have no outside franchise except for our relationship with Bittensor. So we're very fortunate to that. Very fortunate to the people who've sort of invested over time.

[Jason Calacanis]

But what do you think of the Stefan, what do you think of the kerfluffle with one of the subnets taking their tokens and marbles and leaving all of a sudden? And the impact that has on the ecosystem. As one of the early adopters of

[Will Squyers]

subnets, I think it's very disappointing. I come from an AI background, not a crypto background, so being honorable and being trustworthy is a really important thing to me and something that I am extremely sensitive to when I operate in a crypto world. It's disappointing. It's disappointing that someone did that to a lot of people. I won't dive too deep into it, but I think it's a shared emotion that we have right now. I think the response of Jake is. Is on point, which is to say this is a great opportunity for us to reflect on a part of Bittensor that was too Immature and needed to be iterated on and refined. And I think that's the right pragmatic lens to see the experience. So yes, this will be, I'm sure, just a bump in the road in the long term.

[Jason Calacanis]

So you're frustrated, disappointed. And what do you think the solution is to avoiding it in the future?

[Will Squyers]

I think what Mark described earlier, which is this idea of owner lockup, so you should have more of a fiduciary duty to the people that believe in you, to put it simply. And I think there's a way that you can translate that into things like smart contracts or similar, which shows the commitment of founders and servant operators to their community and that can actually be used to, for price discovery for the project itself. Right. It's a measure of intention, it's a measure of conviction. I think that's quite an elegant, an elegant solution and I'm sure, I'm sure we'll see a few different prototypes of that and we might not get it right the first time, but it feels like the roughly the right direction of travel to not have this experience again.

[Jason Calacanis]

Athena I'm the first investor. They are the greatest assistants in the world. For a great price, $3,000 a month, you get an awesome full time assistant to work with you on your scheduling, on research projects and you say, well, oh, we guys are talking about all this AI. These assistants are AI powered and so they will do all kinds of jobs for you and help you have a human in the loop as you're working. I was the first investor in the company. They're now a partner of all in. They're a partner of ours here and it just makes me super, super productive. So you can basically turn your EA into your Chief Health Officer.

[Lon]

That's this month's productivity hack. Thanks to Athena, we do a productivity hack once a month. This month's productivity hack, you should turn your EA into your Chief Health Officer. You know that for founders, their health can often take a backseat. Jason so one of the ideas we came up with were all the different ways that you could sort of delegate some of these tests to your ea. Keeping up with your regular checkups, dental cleanings, handling your referrals and follow ups, taking care of your insurance paperwork and your reimbursements, helping you plan healthy meals and what groceries to buy, worrying about your nutrition. All of these things can be things that you pass off to your EA to help save time if you want to learn more. Athena.com jcal J C A L There you go. That's where to Go.

[Jason Calacanis]

And as an example, you know, I lost weight, shout out to my guys at ro Co with GLPs. But now I want to add more muscle and I've been adding some, but I need to get a trainer. So I said hey to my Athena Assistant within a 20 minute drive of my house of the ranch. I want a trainer who has their own facility to do one on one training with me, you know, one to three times a week. Let them know I travel a ton, my schedule is variable and here's how I'd like to pay because a lot of times they're like, you have to pick the same slot every week or whatever. Doesn't work for me. So I said, hey, I want to buy 20 sessions up front and then I want to be able to just drop in, you know, and it could change dynamically. I might need to cancel within, you know, the same day. Right, etc. So they explain the rules of the road and some people are like, yeah, we don't work that way. You have to do Tuesday at 4pm every week for the year, whatever. And it's like that's not going to work. So they pre vetted and did all those discussions because I don't want to have that awkward discussion with the person. And then I'm negotiating with a trainer. Then I said, and then find me three people to come to the house and I would do the same thing. I'll pay them for 20 sessions up front so they get the value of that money up front. But they have to be willing to let me cancel same day within two or three hours if you have to get on a plane. And it worked. They went through 20 different people and found me three of each. And I just give them a go like that. Just keep calling people until you get to three.

[Lon]

Yeah. And they can take a day to do that. You can't do that. You have too much other stuff.

[Jason Calacanis]

I don't have the time for that. The other thing that I had them do was I had them go on to different people's LinkedIn mark. And I said, I'm not happy with the flow of people coming in for these three positions. So here's what I want you to do. I want you to go on Lon's LinkedIn, my LinkedIn or Heidi's LinkedIn and just send InMail through LinkedIn to anybody with this title in these three cities. Austin, Houston, San Antonio. Hey, we're looking for this position in Austin in an office. If you know anybody, let us know or if you might be Interested, Please let us know. We get, you know, a 2% response rate to that, but we fill two positions doing that. Yeah, but that means that you have to get to 500 outbound. So I just said, well, I can't do 500 outbound. Long. Can't do five. It's a full time job. I said, okay, for four hours a day. This is all I want you to do. 10 people an hour, 4 hours a day, 40 people a day for 10 days. Go. It worked. But I would never do that. That's the job of a full time recruiter. So shout out to our friends, Athena.com Shakeout. You get a couple of weeks off.

[Lon]

There you go. Check it.

[Jason Calacanis]

All right. All right. What do we got next here on the doc?

[Lon]

We have a little bit of ready to wrap. We have a little bit of off duty. I have some viral stuff we could react to or we could just do some recommendations. It's up to you.

[Jason Calacanis]

Okay. I think I'd love to go off duty. So let's go off duty. All right, Jake out and Mark's going to join us. You got to give us an idea. I don't know if they told you what off duty is, but it's basically. We're not doing crypto.

[Lon]

We can talk about whatever.

[Jason Calacanis]

What are you.

[Lon]

Whatever we want. Product about, whatever.

[Jason Calacanis]

You bought a gadget, you bought a TV show, you're watching music, an album.

[Lon]

I want to get your your thoughts on one viral story before we get into recommendation. So Coachella, the Coachella Valley music and art festival for weekend one was this past weekend. There's been. There were two headliners, very different performances. People are really debating, you know, and I think it speaks a lot.

[Jason Calacanis]

Bieber was one.

[Lon]

Bieber was one. Sabrina Carpenter was the other. And I think it really speaks to what. What are we looking for in a performance? What is the value of authenticity versus showmanship? I think it gets to some really interesting questions. So first up, Sabrina Carpenter, she headline. It was massive cars on stage, huge sets, dancers. We can take a look at a quick clip here that I pulled up. Yeah, so let's do that.

[Jason Calacanis]

She's extremely. Yeah. Sabrina Carpenter, extremely entertaining. I saw her at Austin City limits. Acl.

[Lon]

So here's Sabrina's performance. You could see they built this massive set. She's got tons of dancers. It's a huge thing that's going on.

[Jason Calacanis]

This was production.

[Lon]

We're not playing the song. This is Espresso, her huge hit there.

[Jason Calacanis]

Of course. Thinking about me.

[Lon]

Yeah, think about that. That's. That Me, Espresso. Oh, how do I do this? Okay, there we go. And then I want to show this. This one other part too. She actually drove a car off the set. Like she drove through the crowd in Coachella. Through the crowd, you know, like, this was how she closed it out. If you can see this bigger shot.

[Mark Jeffrey]

Wow.

[Jason Calacanis]

Literally driving a car. That car's not her driving. It must be on a track.

[Lon]

No, it is. She's just driving. They cleared out this aisle here, and you can see she's just driving out of Coachella in this car. So that's cool. It's very big. It's spectacle.

[Jason Calacanis]

A lot of showmanship.

[Lon]

It was like a party. It was like showmanship. And then there was Justin Bieber. He was the headliner on Sunday night. And he went on stage alone, just him and a laptop and a screen behind him that was screen sharing. And he played YouTube clips, a lot of them of himself. And then he sang over his old music videos, sort of treating them like a backup singer. I'll show you a little clip. So he. You can see.

[Jason Calacanis]

Yeah, this was weird.

[Lon]

Here he is, he's on stage. He's pulling up YouTube. You can see his screen. He's typing in baby, which of course, that was his old school song with him as a kid. And then he stands up and he's watching the video. He's not even facing the audience. He's watching the video and he's like singing along with himself from when he was a kid. And then here's. He wasn't even just singing the whole time. He also just watches YouTube for some of it. And here he is watching the goteem meme. And then you'll see he flips over to Double Rainbow and he's just. He's hanging out, playing on YouTube. So a lot of people were praising this. It's so authentic. It's so real. And he started his career as a YouTuber. Justin Bieber, he was arguably the first YouTuber to get super, super famous. Yeah. Here he's just watching.

[Jason Calacanis]

I can give my judgment.

[Lon]

So there's a lot of back and forth, of course, between the Sabrina and the Bieber stands. But I think everybody, I think it's an interesting debate. Which performance would you be more interested in seeing? And if you pay all this money to go to coachella, it's like $1,000 plus to get to Coachella. Do the artists owe you a Sabrina Carpenter level spectacle?

[Jason Calacanis]

Okay. So it really is in the execution of these things. It on its face, when you describe what Justin Bieber did. You would say that was phoning it in a little. Just played his video and he talked over or whatever. Okay. Sure enough, it's. That would be a surface level interpretation of it. However, he kind of did some storytelling in there that was interactive with the audience. And at the end of the day, was the audience engaged in this? They were. And I would say they were equally engaged in Bieber's surfing YouTube as a performance art. So it's very meta commentary, and it was done so well that I think he hit the right note, which is, this is a meta commentary on my career and I'm going to relive how you experienced me. Exactly. And there was some brilliance to that. Now, if you were, you know, any other artist who was not a native YouTube artist, they might not have pulled off that meta commentary on.

[Lon]

Right.

[Jason Calacanis]

There is another analogy to this. I. I had dinner the other night and I. I sat next to Jimmy Iovine.

[Lon]

Wow, you had a big weekend.

[Jason Calacanis]

So anyway, I start talking to Jimmy. He's also a kid from Brooklyn. He's from Red Hook. I'm from Bay Ridge. You know, just right on the gown canal there. We had a talk and I told him, hey, you know, I'm a big Dire Straits fan. I know you did the album Making Movies. And we talked for an hour about that.

[Lon]

Okay.

[Jason Calacanis]

And he also had. Bruce Springsteen was, you know, the big artist that he had produced in the day. He did Born in the usa, one of the great seminal works of all time.

[Lon]

On the Edge of Town. He did. Yeah, yeah.

[Jason Calacanis]

So this is like a, you know, a legend I got. It was real privileged to talk to him for all this time. And Bruce Springsteen did Springsteen on Broadway. And when he did Springsteen on Broadway, he did a meta commentary of him. He did sing some of his songs.

[Lon]

Right.

[Jason Calacanis]

Like Bieber did, but he also told the story behind the songs. It was so engaging, it was so rich that as a fan, you can get away with that. Now, I've also seen Bruce Springsteen no less than 20 times, probably over 30 years in concert. You know, like, I've seen him two or three nights in a row. My mom's a big Bruce fan. I actually took my mom to Bruce on Broadway. You have to execute at a very high level. You have to be a true star. There has to be a lot behind it in order to pull that off. So I give Justin Bieber credit for actually pulling it off, which is not easy. Sabrina Carpenter couldn't pull that off right now. Sabrina Carpenter, if she has 10 more espressos if she lasts.

[Lon]

Right.

[Jason Calacanis]

I don't know how long Bieber's been in pop culture now, but I think it's 15 years.

[Lon]

Yeah, 20 years or so.

[Jason Calacanis]

Close to 20 years. When Sabrina Carpenter, 20 years into her career, she is. She will be able to pull it off, but not in year three of her career.

[Lon]

Right.

[Jason Calacanis]

So I think both of these were fantastic. Sabrina Carpenter has to build the foundation brick by brick in order to do the meta commentary, which only comes after you have 50 hits, 25 hits, you know, in a certain size audience. So I give them both credit. Mark, any thoughts here? I know you're a big rock and roll fan.

[Mark Jeffrey]

Yeah, you guys should know better and ask me about anything other than classic rock, but. Okay, I'll. I'll dive in a little bit here. So, I mean, I'm sort of split because I feel like, I do feel like Bieber. I've seen like little clips of it. It looked fun, right? Like, it didn't look, it didn't look bad. Right. It didn't look like it was crashing out on stage. It looked fun. Right. So I, you know, I, I think probably the audience enjoyed it. That said, I love a spectacle. I love a big rock show. You know, JKL and I went to go see the Wall when Roger Waters was turning that around.

[Lon]

Yeah, that's a lot of fun

[Jason Calacanis]

changing.

[Mark Jeffrey]

Yeah, I totally agree. And it was. That was like a very big production of tunes that you've listened to your entire life and just love. Right. So that, that's what I want when I go to a show. That's probably the, the apotheosis of that. So, you know, I guess I'm sort of split.

[Lon]

I think I probably would have preferred the B. I'm not a huge fan of. I, you know, I'm not like a Die hard Sabrina or Bieber fan, but I think just. I would have preferred the Bieber set. It's something real. You're seeing him really think about and process this whole career, this trip down memory lane, this sort of nostalgic look back with him. I think that would be a lot more engaging as a viewer than, you know, big dance routines in the big. I kind of feel in general, and this is old man of me, but I'm going to say it anyway. When I used to go to Coachella, like the early and mid aughts, it was just bands playing there. Wasn't this like, it wasn't this like you're going to see a pop star, like fly out of something and parachute into the arena. And it was just like bands playing songs you liked or rappers performing songs you liked or whatever, DJs. And I feel like I like that more authentic sort of feel.

[Jason Calacanis]

Yeah. I mean, the kids love the vibes. There's a meta commentary going on and it's vibe culture and that, you know, if the vibes are either immaculate or the vibes are inauthentic and, you know, you're taking a little bit of risk. I have been one time to Coachella, and it was quite enjoyable to go from set to set to set. I think I saw Lorde.

[Lon]

Oh, nice.

[Jason Calacanis]

And mgmt. And like other incredible artists, like, back to back to back to back. And it was like, wow, this is pretty impressive.

[Lon]

I went in 04. It's like a classic one. Radiohead, Pixies, Flaming Lips, MF Doom.

[Jason Calacanis]

Wow.

[Lon]

Air. Really good year that Beck was there that year.

[Jason Calacanis]

Anyway, thanks Unk.

[Lon]

See ya.

[Jason Calacanis]

Like a 3unk special.

[Lon]

Oh, man. Yeah. We're old to be talking about this stuff, but I did. I thought it was an interesting conversation that's going on between this sort of.

[Jason Calacanis]

I'll tell you, there's an analogy there.

[Lon]

Yeah.

[Jason Calacanis]

I went one night to see Chappelle. He happened to be in town. A friend of mine's friends with him. I texted my friend and I was like, hey, your friend Chappelle is playing. He's like, oh, let me text him and we'll go see the show. Last minute, we go see the show. After the show, Chappelle says, hey, you guys want to roll with me? We're going to go do this after show. And he goes to this, you know, small comedy place in San Francisco that hosts maybe 100 people, 150 people. We go there and we got a table set up for us. And then he does. Just him interacting with the audience.

[Lon]

Sure.

[Jason Calacanis]

He's had a couple of drinks or whatever and smoking a cigarette.

[Lon]

Crowd work.

[Jason Calacanis]

What do they. He's doing crowd work.

[Lon]

Crowd work is what they call. Yeah.

[Jason Calacanis]

Telling stories. And it's not a set. It's not a funny set like he had just done at the Chase Center. It is just him bantering and, you know, some jokes in it. And he was not working on new material exactly, but it was just a different type of performance. So to see both back to back and then compare them again back to this meta commentary. There was him talking about the show he just did, then him talking about his life. And it was really like to see those two bookends together was actually really nice. Just like going and seeing a Bruce Springsteen show and then seeing Springsteen on Broadway. Talk about Bruce Springsteen and his life. Also super valid sometimes. What a great episode.

[Lon]

Intimate evenings. Yeah.

[Jason Calacanis]

You got anything else here on?

[Lon]

I did. There was one thing I want to recommend. There's a movie. I saw it at the Austin Film Society here in Austin, one of my favorite local theaters. Shout out to them. But it is now on Hulu. It's a Spanish film called Surat. It's about a father and a son. The father's older daughter has gone missing. She was a big raver who attended a lot of these raves in the desert in North Africa. So the father and son are following these moving raves around the desert looking for this missing young woman. And then it leads into this whole separate adventure story. I don't want to give anything else away about this movie because it takes some real turns, but wow. It is.

[Jason Calacanis]

Don't give it up.

[Lon]

It is incredible film. It is a real experience. You don't want to learn too much about it. You just want to watch it and take the ride with it. Yeah, I loved it. Real intense.

[Jason Calacanis]

All right, I'll give one recommendation here. People have heard me reference my favorite pen.

[Lon]

Yes.

[Jason Calacanis]

I'm not like a pen snob. I don't like the idea of buying 100 or $500 pen. I don't collect pens, but I like to write and I like a great pen. But I know I'm going to lose a pen. So somewhere between, you know, a commodity pen for, you know, a dollar or two dollars and these hundred dollars or pens that cost hundreds of dollars, I found one of the nicest writing pens ever. The G750 retractable pen. And I think it goes for 10, 15 bucks.

[Lon]

Yeah, there it is, like 13.94 here. Yeah.

[Jason Calacanis]

Yes. And you can get it on Amazon. And it's just an incredible pen. When you write with this pen, you will learn why this is such an amazing pen. Now, the reason I know about this pen is because Unk is basically Unk, that's me, was on TikTok, and I stumbled upon the Staples baddie. Kaden Roland works at Staples.

[Lon]

Sure.

[Jason Calacanis]

And if you type in her name and you type in zebra. 750. G 750. She's the one who recommended it to me. And she is this incredible young person who works at Staples. She might be trans or, you know, I'm not sure exactly.

[Lon]

Are you sure?

[Jason Calacanis]

But she has an incredible performance where she goes and talks with extreme passion about Staples and photocopies and pens and journals and she literally clanks her nails and snaps up her favorite office products and she's probably done more for the brand of Staples with young people and all the way up to Gen X myself than Staples could ever do with $100 million ad campaign. I don't know what they're paying her. Are they paying her?

[Lon]

I don't, she's a, she's embracing her,

[Jason Calacanis]

she's an, a store employee, Lon.

[Lon]

Oh but yeah, but they're not paying her to make these videos. She, they're just paying her to work

[Jason Calacanis]

the red I think now they are paying her. I, I, they must be paying her now. If not there's a hundred people who would hire her to be the Walmart baddie or the Target baddie. I mean she should be getting paid a million dollars a year. Minimum, minimum of a million dollars a year. Any if one of my startups came to me and said we want to hire this person to run our social media for a, for, you know, $100,000 a year plus $900,000 a year in stock options, I would say do it, do it immediately because the ability to authentically engage and then get me to buy the pen and know who the Staples baddie is is just infinitely valuable.

[Lon]

Yeah, I found her. TikTok. She has 582.3 thousand followers on TikTok. That's crazy.

[Jason Calacanis]

I mean somebody's got to poach her

[Lon]

just for talking about Staples. There's a Papa John's guy too, right? I've seen him a bunch on TikTok where he's just a guy.

[Jason Calacanis]

I think now this is going to become a thing.

[Lon]

He's just a guy who works at Papa John's but he talks about how they make great pizza, which I don't.

[Jason Calacanis]

Another amazing episode of Twist is in the can for Monday, April 13th. Thanks Mark. Jeffrey.