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Ten31 Timestamp: Bitcoin Fixes the Energy Problem · TFTC: A Bitcoin Podcast

• 0:00
  Welcome back to the 1031 timestamp recap. I'm your host, Marty Bent, joined by the real John Arnold.
• 0:07
  And we spent some time in New York last week in person. It was a great time, John.
• 0:11
  It was. We didn't get to meet the other John Arnold, who was along with me,
• 0:16
  were collectively worth billions of dollars. But we still have not gotten a chance to meet
• 0:21
  in person to compare notes on our wealth, but maybe next time.
• 0:24
  It's going to happen. It's going to happen one day. We have it up here. I think this is episode
• 0:28
  three and we've noticed, hey, we have no calls to action in the show. So I want to highlight why we
• 0:34
  come to do this video every Monday morning. It's because John writes the 1031 timestamp,
• 0:39
  goes out Saturday mornings, great little weekend read. So if you want to subscribe to that,
• 0:44
  go to 1031 timestamp.com. That's T-E-N-31 timestamp.com. Make sure you get all the lists.
• 0:51
  But as we said, we were in person in New York last week for Bitcoin Investor Week. We spent a
• 0:56
  couple days up there, a meeting with founders and investors around the city. And we spent a lot of
• 1:02
  morning time together, particularly at breakfast. Shout out Jack's wife, Frida. We got breakfast
• 1:06
  there two days in a row. The crux of the conversation was about the impact that AI is
• 1:13
  going to have on the job markets and that viral post that went out last week, which after time
• 1:20
  is set in, it seems like it was a bit self-serving. But John, I'll let you do the setup from here
• 1:26
  because it was a very stimulating conversation last week at breakfast.
• 1:29
  Yeah. I mean, there's a lot we could get into,
• 1:32
  but as it relates to that post,
• 1:35
  I think it's an interesting, it was a really interestingly timed post.
• 1:40
  You know, a lot has been said in the last week about,
• 1:43
  I think that got like 50 million views.
• 1:45
  If you don't know what we're referring to, just Google.
• 1:46
  Something big is happening on Twitter or have your agent do it for you better yet.
• 1:51
  But basically a lengthy post that some might call a doom post,
• 1:55
  Others might call it more exciting and optimistic, whatever, but describing all the ways that the acceleration in artificial intelligence capabilities across the frontier models are quickly and exponentially leading to potential disruptions to the way that everyone does work in any knowledge worker does work and eventually any worker at all.
• 2:17
  And so go give that a read if you haven't.
• 2:19
  And there have been a couple, a lot of different chains of thought of reactions around that, a lot of which, you know, call it, well, this is just like an AI slot post or this is a post from a guy promoting his company.
• 2:31
  He's encouraging you to go spend money on a bunch of AI tools that may benefit him in some way, you know, whatever.
• 2:36
  I think the one thing that's interesting about that post, if nothing else, that's worth pulling out is deep in the post, the author talks about these tools, these models, and increasingly these agentic frameworks developing something like taste and judgment, doing increasingly things that you would previously have thought would be the irreducible component of humanity that's necessary to guide these tools and help them.
• 3:06
  them to and force them to make kind of the right decisions and exercise some level of what we would
• 3:11
  call, you know, abstract judgment on what not just like what what is right in the context of any
• 3:16
  micro task, but how all the micro tasks kind of like fit together. And what's what's the right
• 3:21
  thing to be pursuing? What's the right pathway to be pursuing? And what needs to be left aside?
• 3:26
  Whether that's ultimately true or not is, I think, a question mark. I know, Marty, that you
• 3:32
  on our team have been probably the most active in playing with a lot of these things. So maybe I'll
• 3:38
  pause and put a pin in it there and just say like, I'd love, you know, you've talked about this on
• 3:42
  other channels and if anyone's following Marty's Twitter feed, I'm sure they've seen it there,
• 3:46
  but you know, what's your 60 second reaction to that claim? Cause I think that's the most like
• 3:51
  controversial and interesting thing in that post. It's like, we all know, you know, these bots can
• 3:55
  do coding well, they can increasingly read legal docs. Well, they're kind of getting to the point
• 3:59
  where they can maybe do stuff in Excel well.
• 4:01
  So you can kind of see the broad outlines
• 4:03
  of how it's like starting to trickle
• 4:04
  into white collar work.
• 4:05
  But like this idea of these things developing
• 4:08
  like taste and judgment independently,
• 4:10
  like what have you seen with that?
• 4:12
  How did that strike you?
• 4:13
  I haven't seen it as much as what Matt,
• 4:17
  I forget his last name,
• 4:18
  who wrote the post as he described in the article.
• 4:22
  But I think definitely if you're working
• 4:24
  at an AI company and specifically the hyperscalers
• 4:28
  top tier models, OpenAI, Anthropic.
• 4:32
  You're probably seeing that internally
• 4:33
  as somebody using OpenClaw specifically.
• 4:36
  And we've been using Clawed code
• 4:39
  for as long as it's been out.
• 4:41
  We've been using, we decided to use Clawed internally
• 4:45
  at TFTC over a year ago and stuck to it.
• 4:48
  So we've been staying up to date with the latest models.
• 4:51
  And now with OpenClaw, it's really sort of extended
• 4:54
  what we can do.
• 4:55
  And it's been honestly shocking over the last month implementing it and seeing how it can help us, TFTC, automate stuff on the back end so I can focus on a lot of the 1031 stuff more as well when we've been leaning into it.
• 5:10
  But when it comes to developing taste and recognizing that development via my interactions with OpenClaw, I'll be honest, haven't seen it.
• 5:19
  It's a lot of sort of prodding and iterative conversation that I have with my OpenClaw agent to get it to do exactly what I want to do.
• 5:27
  And it still has memory issues.
• 5:29
  And this could be a product of my setup.
• 5:31
  Point being, I would not be surprised if engineers inside of Anthropic OpenAI are beginning to see this taste develop.
• 5:39
  I don't think it's hit mainstream yet.
• 5:41
  And to your point about the job disruption, in terms of implementing these tools at companies,
• 5:47
  I think that's a lot of what we discussed last week.
• 5:49
  There is still a massive learning curve, even if somebody has been playing with the top
• 5:55
  of the line tools for the last two years and a team that has been doing it.
• 5:58
  I guess we're a good example of a company these large language model providers are targeting.
• 6:06
  We're a media company.
• 6:07
  We're trying to stay lean.
• 6:08
  I guess we're one archetype of one company that they could be targeting.
• 6:12
  We've been experimenting with it for three years.
• 6:14
  Now we're using OpenClaw.
• 6:15
  It certainly helped us.
• 6:16
  The taste isn't there.
• 6:17
  Point being, if you're going down the list of every company
• 6:19
  that is going to need to incorporate this,
• 6:22
  they're going to have to go through that learning curve too.
• 6:24
  Until that taste factor hits these mainstream applications,
• 6:29
  I don't think everybody's going to see it
• 6:33
  as people inside Anthropic or OpenAI
• 6:36
  maybe seeing it up close and personal. Does that make sense?
• 6:39
  Yeah, for sure. No, I mean, I think it's one of the things that we talked about last week,
• 6:43
  you hit on this point was this idea of like James Burnham's framework for like the, you know,
• 6:49
  he wrote in like the 40s, this book, The Managerial Revolution about the emergence of like the
• 6:53
  managerial class in the modern kind of Western, you call it capitalist, but he might even kind of
• 6:59
  dispute that term But what we think of as like the modern you know advanced Western economy is made up of these organizations that are themselves dominated by and controlled by like managerial elites of all these different types And they have different functions
• 7:14
  things, different things that they're, you know, they excel at or don't excel at.
• 7:17
  But one of his big kind of takeaways is like, this is like, it kind of takes that structure,
• 7:22
  that infrastructure takes on a life of its own. And it's really good at perpetuating itself and
• 7:26
  keeping itself alive and like finding new ways to add to its fiefdom. You know, this idea of like,
• 7:32
  if you've ever been a manager or talked to managers in kind of corporate America, you know,
• 7:36
  the idea of getting like budget allocations or headcount allocations for your department is like
• 7:41
  a, you know, a badge of honor, a badge of pride. It's kind of what you're optimizing for,
• 7:44
  even in like less functional organizations, like you can end up optimizing for that more than like
• 7:49
  actual, you know, relevant KPIs that are theoretically most valuable to shareholders.
• 7:53
  And certainly that's even more of the case in government institutions, right? So this,
• 7:57
  I think there's this idea that maybe certain like incredibly hyper intelligent, hyper forward
• 8:05
  thinking like AI maxis have not fully grappled with that. Just like it takes a lot longer than
• 8:11
  like a rational economic kind of model would suggest for disruptive transformative technologies
• 8:18
  to actually break into these big organizations just because there's so much inertia from the
• 8:24
  the way that the managerial class is kind of set up and structured and the incentives are like not
• 8:28
  there for anyone to go disrupt their own headcount. You know, the incentives are not there for people
• 8:33
  to like train their replacements basically, right? Like whether that's a human replacement or like a
• 8:37
  robot replacement. And, you know, there are a lot of jobs that could have been basically like
• 8:43
  optimized away in both certainly the government, but also like corporate America over the last 20
• 8:47
  years through a bunch of different, you know, advancements in just basic technology, SAS products,
• 8:52
  you know, VBA and Excel. But like, we still have tens of thousands, hundreds of thousands of jobs
• 8:58
  that, you know, have continued to persist like through that. And I think it's just a testament to
• 9:01
  how much friction there is and inertia there is in organizations to actually disrupt themselves
• 9:08
  and adopt new technologies that are going to, that would potentially have a threat of leading to like
• 9:13
  a shrinking headcount or whatever. Right. So I think it's like very much worth considering
• 9:18
  the sand and the gears that will dominate a lot of these organizations that theoretically
• 9:22
  should benefit the most from like totally gutting their existing SaaS stacks and implementing
• 9:28
  something else that's based on AI or like agentic frameworks. But all that said, you know,
• 9:33
  these things move, we're not well prepared to think in exponents and like the way how quickly
• 9:38
  these things can move. And so like, yeah, it's probably not the case that six months from now,
• 9:43
  like you're gonna have mass unemployment because every organization suddenly realizes
• 9:47
  like they can cut a bunch of heads and they can, you know, get rid of their existing software stack
• 9:53
  and totally replace it and overhaul it. But it's also, I think, not a great bet to bet against,
• 9:59
  like over time, the destruction of, or at least the reduction of like excess margin in a cost
• 10:05
  structure. You know, like the managerial elite dynamic is like one big thing that pushes against
• 10:12
  that, but it's like always in competition with this countervailing force of, you know, what
• 10:15
  Bezos said about your margins by opportunity, like the nature just abhors excess cost. And like,
• 10:21
  eventually it will like extract its pound of flesh from excess cost in some way, even if it can't
• 10:25
  like fully get it all. And so I think it's very much worth reading that piece with the mindset of
• 10:31
  like, even if this is sensationalist and like a little too, you know, too on the doomer side of
• 10:36
  things, and it assumes things are going to happen faster than they are. You know, I think it's worth
• 10:40
  thinking about, well, what if it's like 20% right over the next like two to three years, right?
• 10:45
  what would that mean for every investable asset class and the economy? And I think maybe that's
• 10:51
  like a good time to transition to the chart of the week here in the timestamp, because it was
• 10:56
  poetic timing that everyone, the internet was freaking out about, you know, this piece and
• 11:01
  these ideas and all, you know, not just software stocks anymore this week. It was everything from
• 11:06
  insurance to ratings agencies, to trucking companies were all going through like these
• 11:10
  kind of like darkly funny, like rolling sell-offs every day of like, you know, today, like all the
• 11:15
  trucking companies are down 20% because like somebody vibe coded like a freight forwarding,
• 11:18
  you know, software stack from, you know, from quad code or whatever. And you've kind of seeing
• 11:23
  all that. So it's funny that all of that was happening and coinciding with the release of
• 11:28
  the data underlying this chart. So this is the CBO just released their annual, annually,
• 11:34
  they refresh their projections for the next decade ahead on the federal budget revenues and spending
• 11:40
  and deficits. And you saw they revised this decade's cumulative deficit up by 1.4 trillion
• 11:47
  over the next decade. And it was interesting that they would put this out this week, because if you
• 11:52
  go into the piece that they released, kind of giving voiceover to the data, and you control
• 11:59
  artificial intelligence or AI, I think you get like one hit back. And it's like some suggestion
• 12:05
  that ai could lead to like increased productivity um but there's no there's no section in there
• 12:10
  that's like well here's how we sensitize the scenario of if this guy you know if this thesis
• 12:14
  is right or even if this thesis is like 10 right here's what that does to like automatic stabilizer
• 12:19
  payments or incremental unemployment payments or like new like federal reskilling programs we're
• 12:24
  gonna have to roll up to make sure that we don't have like 20 unemployment or whatever right it
• 12:28
  doesn't really think through any of that stuff and so i think it's just it's very interesting like
• 12:32
  Like we're in like a, the government is in a really tenuous position in many ways, but I think it's funny that, you know, again, darkly funny that we're looking at these budget deficit projections, which are already, you know, if you look at their forward projections out even beyond this, kind of just assuming that federal debt to GDP held by the public is just up and to the right forever to like 200% in like, you know, 15 years or whatever it is.
• 12:55
  We're already in that position. And this isn't even accounting for what happens if like some small version of this massive disruption takes place. And so, yeah, I think it all just like, it's an interesting like set of synchronicities this week to all these things to kind of happen at the same time. Yeah. And here's the, here's the really the money shot chart for the next 20 years.
• 13:15
  and it just tells you how non-consensus this kind of still is outside of like schizopo thing,
• 13:23
  Twitter, like all of us, you know, there are literally dozens of us or literally, you know,
• 13:27
  tens of thousands of us who are thinking about this every day and kind of going down these rabbit
• 13:30
  holes, but it's not in the math yet, right? The math is already like tenuous and not great.
• 13:36
  And this massive disruptive force is still like not even close to like baked into all these numbers.
• 13:41
  Well, that's one thing I've been trying to discern over the last week, particularly after our conversations at breakfast in New York, is what percentage of the population do you think is aware of this stuff?
• 13:53
  Like we're very online, particularly on X.
• 13:56
  And I think since we're experimenting with this and reading as much content
• 14:00
  about Bitcoin, AI, the intersection of both, we're definitely getting fed
• 14:05
  a bunch of sort of positive news about the proliferation of this technology just because of the algo and just picked up on us liking this content and consuming it rather voraciously And that what I trying to discern is like what percentage of the overall
• 14:22
  population have these ideas actually penetrated to? Like, is it 0.1%? Is it 1%? Is it 5%?
• 14:30
  My gut tells me it's less than 1%. Yeah, no, I think it's absolutely right.
• 14:35
  You know, I think most people that I talk to anecdotally, this isn't very like useful data,
• 14:40
  but like most people that I talk to, you know, really have their, their exposure to any of this
• 14:46
  is like, they've used chat GPT a couple of times. Right. And it's, you know, something that the,
• 14:50
  the author of that piece kind of wrote about that you use it a couple of times it, you know,
• 14:54
  maybe you're on the free version and you're at like a, you know, using a severely degraded model
• 14:57
  relative to kind of what's available on the frontier or even just with like a $20 plan.
• 15:01
  Right. And it kind of maybe hallucinates once, or it doesn't fully like understand what you're
• 15:05
  asking for. And it's like, Oh, well, this is basically just kind of like a different version
• 15:08
  of a Google search, I'll just go back to Google. And I think that's, there's a, there are a lot of
• 15:11
  people out there that for whom that is like the case. Um, and so, yeah, I don't, in my anecdotal
• 15:17
  conversations, like there's very little understanding of how quickly a lot of this is
• 15:21
  moving on the frontier. None of that is necessarily to say that you can totally extrapolate forward and
• 15:26
  think that in two years, all the white collar work will necessarily be fully automated,
• 15:30
  but I can guarantee you that like that isn't even in like the, as you think about like the,
• 15:36
  decision tree or like the scenario tree for the next year, two years, five years, the vast majority
• 15:41
  of people don't have any scenario on that table that involves something like that happening.
• 15:46
  Right. And the probability may, that may only be like 10% and AI maxes think it's like a hundred
• 15:51
  percent, right? Maybe it's only 10%, but most people, you know, for most people, it's 0%,
• 15:55
  right. That in the way that they're thinking about it. So all that is to say, yeah, like I,
• 15:59
  I think all of this is quite non-consensus still. And if it's even partially true, if it even,
• 16:06
  And if some version of it turns out to be true, you know, I think it's going to have meaningful implications for what the government is going to have to do in a bunch of different domains over the next five years.
• 16:17
  And that's going to have implications for how you want to have your assets allocated and also what you might want to just be kind of getting started on, you know, getting experience with with with some of these tools over the next year or two.
• 16:28
  Yeah, it's it's a while. So that's another thing I've been thinking through is how like what shocks the public awake?
• 16:35
  and maybe not even the public, but large companies, large corporations,
• 16:40
  particularly the publicly traded ones.
• 16:42
  And I'm sure, as on all the radars, but you have to imagine that,
• 16:46
  again, going back to the sort of managerial framework that you described earlier,
• 16:51
  there's a lot of inertia and people are probably not in a rush to replace themselves.
• 16:56
  And so I think the shock that is really going to wake people up is just you have something like a startup
• 17:02
  or one-man startup vibe coding something
• 17:04
  that completely takes out one of these incumbents,
• 17:08
  undercuts the cost, has a user experience
• 17:10
  that's at parity or superior
• 17:12
  and is widely adopted very quickly.
• 17:15
  And I think we have maybe a glimpse of that.
• 17:18
  It's not really disrupting incumbents,
• 17:21
  but it's disrupted a ton of the VC capital
• 17:24
  that's been deployed into AI agent wrappers
• 17:27
  over the last couple of years, which is OpenClaw,
• 17:28
  this open source AI wrapper that adds an incredible memory engine to your cloud code or GPT models that you're using and makes them incredibly more useful.
• 17:44
  And so Peter Steinberger, for those who are unaware, got picked up by OpenAI.
• 17:48
  Yesterday, OpenClaw is going to be an open source foundation, at least for now.
• 17:52
  We've seen the story with OpenAI before, but point being is you had this guy vibe coding in his apartment for three months and basically got taken out by OpenAI over the weekend.
• 18:05
  And there's been hundreds of millions, billions of dollars of capital allocated towards companies that are supposed to do what he did by himself.
• 18:11
  Well, eventually it wasn't by himself.
• 18:13
  It became a very popular open source project rather quickly.
• 18:16
  But I think that's an example of this disruption we're trying to highlight here.
• 18:21
  Yeah.
• 18:21
  Yeah. No, I mean, I think it's a good dynamic to think about because, you know, I put in the newsletter this week as well that I think it was UBS highlighted that, you know, they're estimating something like $100 billion of write-offs or other credit losses in the broader private credit complex, which is like trillions of dollars.
• 18:43
  So right now it's kind of a drop in the bucket, but attributing those losses to different software companies or other companies that have that are seeing disruption on the margin from how quickly AI is moving.
• 18:55
  And they highlighted in their report as well that, you know, they're saying it's a hundred billion dollars, but there are tail risks where it could be like quite a lot higher.
• 19:01
  And if you go look at I believe it was Bruce Richards from Marathon was on CNBC this past week as well, kind of giving an interview about kind of the same dynamic, noting he's increasingly expecting something similar.
• 19:11
  You've had Orlando Bravo from Toma Bravo come on CNBC a couple of times in the last couple of weeks to talk about, you know, it's a private equity firm focused famously on software companies and SaaS, among other things.
• 19:21
  But, you know, talking about how, you know, he doesn't see that being the case and he's kind of defending the thesis.
• 19:25
  And, you know, I have no dog in a fighter or a view on necessarily where it's going to go with software in the next year.
• 19:32
  But there is definitely like a there's a lot of leverage tied to that sector, like the legacy software sector.
• 19:39
  And, you know, there used to be this meme that like software contracts are better than first lien debt. Like you're just you always pay to keep the business running in the lights on like you're always going to pay the, you know, the software contracts, the enterprise software contracts. They're very, you know, sticky and low, low churn, good pricing power because they're so kind of locked into the workflows and everything. And I think a lot of that's still true. But I think a lot of credit has been issued on that kind of framework. Right. And so, you know, it doesn't really take that much given how levered the whole system is broadly, not just not just like
• 20:09
  private credit, but like the water that we swim in, it doesn't take that much for a few funds to
• 20:15
  on the margin, you know, the laggard funds that are exposed to the worst opportunities. Like if
• 20:21
  you start seeing those losses pile up, you know, for reasons that most people aren't, don't even
• 20:26
  have reason to be aware of yet, you could start to trigger some like, you know, meaningful financial
• 20:31
  instability that the authorities would need to respond to quickly. And so I wonder if something
• 20:35
  like that could also be a kind of a wake-up call that like oops we kind of like borked the financial
• 20:39
  system because like a few you know the the bottom quartile like software companies are getting
• 20:44
  disrupted and that means that a bunch of private credit funds are getting disrupted and that's
• 20:48
  going to have daisy chain impacts on the broader credit complex in the us and well we need a new
• 20:52
  fed facility to respond to that right so i do wonder if something like that ends up actually
• 20:56
  being the thing that shocks a lot of people and companies awake to kind of what's going on yeah
• 21:00
  Yeah, great time to own Bitcoin.
• 21:03
  And speaking of it, I mean, it's just, it's probably going to be a recurring theme because
• 21:07
  AI is evolving at a rapid pace.
• 21:09
  We'll probably be talking a lot about this.
• 21:11
  So it will be important to highlight the intersection of Bitcoin and AI as it proliferates and particularly
• 21:16
  the agentic economy.
• 21:17
  And so this week to highlight that intersection we going to talk about Lightning Labs What they released last week which is basically a revamp of their L402 protocol
• 21:29
  and making it so it's very easy for agents to down,
• 21:33
  to get set up with Lightning wallets
• 21:34
  and interact with Lightning invoices.
• 21:37
  And so they announced, they released a set of tools
• 21:40
  that give agents native access to Lightning Network,
• 21:42
  and GET for automatic L402 payments,
• 21:44
  MCP for node operations, remote signing for key isolation,
• 21:48
  And scope credentials for spending control.
• 21:50
  Machine payable web starts now, and Bitcoin makes it possible.
• 21:54
  And for anybody who is unaware of this machine payable web idea,
• 21:58
  it has been around in Bitcoin since 2015.
• 22:01
  I spent one whole Bitcoin in 2015 on a 21Co computer.
• 22:05
  And for those who are unaware, Balaji, Srinivansam, he was the CEO of 21Co,
• 22:11
  and they revolved the company around this idea of the machine payable web.
• 22:14
  And you got this computer.
• 22:15
  It was a very small computer, but it was hashing and quote-unquote mining Bitcoin.
• 22:20
  You were getting SATs payouts.
• 22:22
  But the idea was that you would use these SATs payouts on-chain to enable this machine payable web.
• 22:29
  And like many ideas in Bitcoin, it was a great idea.
• 22:32
  It was just too early.
• 22:33
  Fast forward 11 years, and now we're at the point where the machine payable web makes sense on top of Bitcoin,
• 22:39
  specifically because we have payments networks,
• 22:43
  sub-protocols on Bitcoin that actually make this feasible.
• 22:47
  It didn't make sense in 2015 because agents receiving
• 22:50
  and sending on-chain payments to get tasks done in milliseconds
• 22:54
  just doesn't make sense.
• 22:55
  With the Lightning Network, it's now incredibly possible.
• 22:57
  So this has also been a big topic of discussion
• 23:00
  within the Bitcoin sphere over the last two months is,
• 23:03
  okay, agents are here.
• 23:04
  They're going to need to spend money.
• 23:06
  What do we need to do to make sure
• 23:07
  that they're able to spend Bitcoin easily.
• 23:09
  We've seen many people rush to get developer kits
• 23:12
  and MCP protocols to market Lightning Labs
• 23:15
  who created the L402 protocol being one of the latest.
• 23:19
  I believe Breeze has one out there.
• 23:21
  Money DevKit, Nick Slaney is building that out.
• 23:25
  That's existed for a few months.
• 23:26
  They implemented with Replit at the end of last year.
• 23:29
  And so we are seeing machine payable web emerge
• 23:33
  and the sort of value prop of Lightning
• 23:36
  emerging alongside it
• 23:37
  or being reconfirmed alongside it.
• 23:40
  Yep.
• 23:41
  And I think like one of the most exciting things
• 23:43
  about this moment too,
• 23:44
  is like we've been talking about,
• 23:45
  as you referenced,
• 23:46
  like L402 for a while,
• 23:49
  we wrote some pieces back in the day,
• 23:51
  a few years ago,
• 23:51
  on kind of the eventual use of that
• 23:54
  and the intersection of Bitcoin and AI
• 23:56
  that we thought would eventually happen.
• 23:58
  I think the really interesting element
• 24:01
  of what's going on right now though
• 24:02
  was Matt Corralo, longtime Bitcoin developer, had a post last week about how we're now in a position
• 24:07
  where a few years ago, if you had, I mean, the L4-2 concept has existed for years and it was
• 24:12
  highlighted a few years ago as AI really started to ramp up, but only now are we at a point where
• 24:18
  the barrier to entry to like do something with that has collapsed to like essentially zero.
• 24:23
  Like a few years ago, you know, if you had seen that post from Lightning Labs, you'd basically
• 24:27
  say like, okay, that's great. That's cool. Now let's wait for like someone to go get like VC
• 24:31
  capital and with a few million dollars, you know, hire like a few engineers and spend something up
• 24:36
  and then, you know, wait to get product market fit and try to find traction before they could
• 24:40
  really kind of start to scale it. Or let's wait for some big corporate to like have somebody on
• 24:44
  the inside who wants to push for directing some internal resources to this and, you know, whatever.
• 24:48
  It's like this kind of abstract primitive that doesn't necessarily have like an immediate path
• 24:53
  to realization. Like, well, Matt's point was now we're at a place where like you or I with no,
• 24:58
  no technical experience to speak of other than like some vibe coding or like, you know,
• 25:02
  taking like a rep one-on-one course, right? Like we, we can just sit down and like,
• 25:06
  potentially if we have an idea, like we can just start using that and leveraging that.
• 25:09
  And I know you've already been doing it right to some extent with your, with your agents. So
• 25:12
  yeah, it's like, it's a really interesting time for the compounding, like flywheel of not just
• 25:17
  like Bitcoin and AI have this intersection, but also as AI gets more and more powerful, like
• 25:21
  random Bitcoiners, people who are just interested can like sit down and like do something with that
• 25:25
  potentially really like really useful. Um, just the only limit, right. Is, is effectively like
• 25:30
  their creativity and like their patience with like playing with the tools, but it doesn't require
• 25:34
  like, you know, a multi-year software bootcamp or time spent, you know, working at like a massive
• 25:38
  tech company to kind of like get their, get their feet under them. Like they can just start and like,
• 25:42
  you know, they can just do things right. As the meme goes. So really, really interesting and
• 25:46
  exciting time, crazy and somewhat scary, but also a lot of, a lot of opportunities.
• 25:50
  Yeah. And just to build on that point and wrap it up here, the possibilities here, like you said, I've been implementing Bitcoin payments with my agents for the last month. It works. It's very easy. There's many ways to go about it. There's many ways to skin the cat of doing it.
• 26:08
  But Matt Corallo and Callie quote tweeted that Matt Corallo message.
• 26:13
  And Callie is the founder of the creator of the Cachew Protocol, the Chami Amin Protocol.
• 26:18
  The point they were both making was like, hey, not only is it possible, but the onus is on us to actually go build these things.
• 26:24
  Because I think many Bitcoiners naively believe like, oh, the agents are just going to pick Bitcoin.
• 26:28
  It's the best digital native internet currency that's ever existed.
• 26:32
  Of course they're going to pick it.
• 26:33
  It's not true.
• 26:33
  Like I said earlier, it's not at that point, at least on the consumer apps, where it has that taste component.
• 26:39
  Maybe it will in the future, but right now it doesn't.
• 26:41
  And the agents are going to do what you tell them to do.
• 26:44
  And if you tell them to implement a stablecoin protocol, maybe it's Coinbase.
• 26:49
  I think they have a base MCP that makes it easy to send and receive stablecoins.
• 26:55
  Visa came out and announced that they're going to create a framework for agents to use their network to send payments in a secure way.
• 27:02
  obviously Stripe's going to be working on this. And so there's gonna be a ton of competition for
• 27:06
  agentic payments outside of Bitcoin. And if we're being frank, they definitely have more firepower
• 27:11
  in terms of capital behind them to go after these implementations. And so not only was what Matt's
• 27:18
  saying like a message of, hey, this is possible now, but it was also the onus is on us to make
• 27:22
  sure that Bitcoin payments become prominent in the agentic world. It's not just going to happen.
• 27:28
  There's competition out there.
• 27:30
  And I fully agree with that.
• 27:32
  I actually had a pretty long conversation with Justin Moon.
• 27:37
  And we talked about this at length.
• 27:39
  It was released on Saturday on the TFTC feed.
• 27:42
  If you guys want to go watch that, I highly recommend that you do, actually.
• 27:45
  This is the point.
• 27:46
  It's like, we can't be complacent.
• 27:47
  It's not going to happen just because Bitcoin is the best money.
• 27:50
  We truly believe that.
• 27:51
  I think it's objectively true.
• 27:52
  But work needs to be done to make that a reality in the agentech world.
• 27:56
  Once again, co-sign.
• 27:58
  Crazy time to be alive.
• 27:59
  Great time to be in Bitcoin.
• 28:01
  No doubt.
• 28:02
  In a world with seismic shifts in the global economy, geopolitics, domestic politics, a lot of uncertainty, being able to find certainty in a monetary protocol that is very simple, very easy to audit, and very hard to change, has a lot of value.
• 28:20
  We'll be back next week.
• 28:26
  Thank you.