# The 2028 Global Intelligence Crisis Explained - What Happens When AI Breaks The Economy?

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- **Канал:** TheAIGRID
- **YouTube:** https://www.youtube.com/watch?v=khWnSVIfMQM
- **Дата:** 25.02.2026
- **Длительность:** 32:43
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## Содержание

### [0:00](https://www.youtube.com/watch?v=khWnSVIfMQM) Segment 1 (00:00 - 05:00)

So, the 2028 intelligence crisis is blowing up right now and we have to talk about it. So, there is a report on Twitter from some of the most respected macro investors on the internet right now and it's gone absolutely viral on Twitter this week. It's called the 2028 global intelligence crisis and it's written as if we're already in 2028 looking back at what occurred. And I'm going to be honest with you, when I read this, it was quite surprising because the argument here isn't that AI fails. The argument is one that's completely different. It's AI succeeds so fast that the economy can't handle it. So, I'm going to walk you through the entire thing section by section in plain English. No finance degree needed. And by the end of this video, you'll understand why the smartest money in the world is genuinely worried and what you should be thinking about. So, let me give you guys the big picture. This research report is written by a research firm called Citrini Research. It's written as a thought exercise and they're imagining it to June 2028 and they're writing a memo explaining how everything went wrong over the previous 2 years. You know, they're very clear up front that this is not a prediction. This is a scenario and they're modeling something that hasn't been talked about enough. And in their words, what if our AI bullishness continues to be right? And what if that is actually bearish for the markets? I mean, think about that for a second. What if AI is even better than we thought and that is what causes the crash? And they say, hopefully reading this leaves you more prepared for potential left tail risks as AI makes the economy increasingly weird. And left tail risk is basically just finance speak for the bad thing. that probably won't happen, but if it does, it's catastrophic. So, remember in this scenario, it's June 2028, and here's how they open. The unemployment rate printed 10. 2% this morning, a 0. 3 upside surprise. The market sold off 2% on the number, bringing the cumulative draw down in the S& P to 38% from its October 2026 highs. For context, a 38% in the stock market drop is worse than the COVID crash. We're talking a serious financial event. And then they say the line which is incredibly chilling. 2 years. That's all it took to get from contained and sector specific to an economy that no longer resembles the one we grew up in. 2 years. And let's trace exactly how they think it happens. So the report starts in late 2025. And the trigger isn't some crazy sci-fi event. It's something pretty mundane. Coding tools just got really good. That sounds pretty familiar. And it says that in late 2025, Aentic coding tools took a step function in capability. And what does that mean in plain English? Well, it means the AI could now help one decent developer, basically clone a piece of software that used to cost hundreds of thousands of dollars per year. They state verbatim, "A competent developer working with a clawed code or codeex can now replicate the core functionality of a mid-market SAS product in weeks. Not perfectly or with every edge case handled, but well enough that the CIO reviewing a 500k annual renewal started asking the question, what if we just built this ourselves? " Now, here's why this matters. SAS stands for software as a service. Think tools like Slack, Asana, Monday. com, Salesforce. Companies pay subscriptions for these tools. Some of them are incredibly expensive and we're talking hundreds of thousands, even millions of dollars a year for big companies. And for years, these companies just paid because building their own software version would cost them even more. It took entire engineering teams and years. But now, one developer with an AI tool could potentially knock out a basic version in weeks. The report gives the perfect example. A procurement manager, that's the person who negotiates software deals for a big company, is sitting across from a salesperson who's expecting to raise prices by 5% like every year. And the procurement manager basically says he'd been having conversations with OpenAI about their forward deployed engineers using AI tools to replace the vendor entirely and they renewed at a 30% discount and that was a good outcome. He said the long tale of SAS like Monday. com's AP and Asana had it much worse. So even if companies didn't actually use these models to build their own version, just that threat alone that they could enough was to kill that pricing power completely. And then came the moment where it stopped being small software companies getting hit. It hit even the big players. Service Now Net's new ACV growth decelerates from 14% to 23% announced it 15% workforce reduction and structural efficiency program shares fall 18%. Now Service Now is one of the

### [5:00](https://www.youtube.com/watch?v=khWnSVIfMQM&t=300s) Segment 2 (05:00 - 10:00)

most valuable software companies in the world worth hundreds of billions and they just got hit hard. But here's the twist that the report highlights and this is the thing that makes the crisis different from every crisis before it. The companies being disrupted just became the most aggressive adopters of AI. the companies most threatened by AI becomes AI's most aggressive adopters. And why is that? Well, because what else are they going to do? Sit still and die. And so, Service Now is being disrupted by AI. So, they cut their workforce to use and they use those savings to buy more AI. And that does help them survive in the short term, but it also means that thousands more people lose their jobs. And those people go out and spend less money. And the companies they use to buy things make less money. and those companies now invest more in AI to protect their margins. The report calls this the human intelligence displacement spiral and it had quote no natural break. Okay, so by early 2027 in this scenario AI has gone from something tech people talk about to something that everyone is using without even knowing. It says by early 2027 LLM usage had become default. People were using AI agents who didn't even know what an AI agent was. And the same people who never knew what cloud computing was use streaming services. And that's a pretty good analogy. Most people don't know what a server farm is. They just use Netflix. The same thing will exist in this concept. AI agents running in the background of your phone, your apps, your devices, and then making decisions for you. The big one was shopping. Commerce stopped being a series of discrete human decisions and became a continuous optimization process running 24/7 on behalf of every connected consumer. Think about what that means. Instead of you choosing to buy something, your AI agent is scanning every website, every deal, every option, and just getting you the best price automatically without you doing anything. Now, this sounds amazing, and for you personally, it is. But for the entire economy that's built around making money off your laziness and habits, that's actually a disaster. The report basically says it's like this. Over the past 50 years, the US economy built a giant rent extraction layer on top of human limitations. Things take time, patience runs out, brand familiarity substitutes for due diligence, and most people are willing to accept a bad price to avoid more clicks. Trillions of dollars of enterprise value depended on those constraints persisting. And if you read that again slowly, it says trillions of dollars of value existed because humans are lazy and impatient. Insurance companies make a fortune because you don't shop around every year. Subscription services make money because you forget to cancel. Travel booking platforms exist because comparing flights across 15 different sites is annoying. An AI agent doesn't actually find any of that annoying. It does it in milliseconds. And the report gives an example of Door Dash. And this is one of the most interesting examples in the whole piece. It says that the Door Dash mode was literally you're hungry, you're lazy, this app is on your home screen. An agent doesn't have a home screen. It checks Door Dash, Uber Eats, the restaurant ownite, and 20 new vibecoded alternatives, so it can pick the lowest fee and fastest delivery time. Now, they do state here, and this is just what they state in the article, that the entire business model of Door Dash is that you open up the app you already have. You don't comparison shop, you're hungry, you click done. But AI agents do comparison shop every single time automatically. And there's another layer that's absolutely wild here. It says that agents started routing around credit card fees. Mastercard and Visa make money by charging around 2 to 3% on every single purchase that you make. And that's how the whole rewards point system is funded. The merchant pays that fee to the card network. When AI agents are doing all the transactions, they notice, hey, if we use crypto stable coins instead of a credit card, this transaction costs a fraction of a penny instead of 2 to 3%. And so you've got titles in this article talking about Mastercard Q1 2027 net revenues drop. And apparently in this scenario, Mastercard drops 9% in a single day on that report. and Visa drops too and their moat thinking that they had a business that was good based on the friction. Now, here's where it gets really important to understand. Up until this point, late 2026, in this scenario, most people, most investors, most analysis would think that this is just a sector problem. Like, sure, software companies are getting hammered. Sure, travel booking is getting disrupted, but the broad economy still will be fine. The employment rate is still okay. things are still growing. The report says this was the wrong way to think about it. The US economy is a white collar services economy. White collar workers represented 50% of employment and drove roughly 75% of discretionary

### [10:00](https://www.youtube.com/watch?v=khWnSVIfMQM&t=600s) Segment 3 (10:00 - 15:00)

consumer spending. The businesses and jobs that AI was chewing up were not tangential to the US economy. They were the US economy. This is the key insight. We're not talking about a niche being disrupted. We're talking about the engine of the American economy being disrupted. And everyone kept saying, "But technology always creates new jobs. ATMs didn't kill bank tellers. The internet didn't really destroy employment. It created more jobs. " And they're right. And this has been true for 200 years. But the report makes one argument that I think is genuinely important. Every new job, however, required a human to perform it. AI is now a general intelligence that improves at the very tasks a humans would be redeployed to displaced coders simply cannot move to AI management because AI is already capable of that and every previous wave of automation created new jobs that needed humans specifically and the new jobs that AI creates or AI can also do those and meanwhile it says that the jobs data is getting brutal and it talks about job openings being down 15% year-over-year and those aren't factory jobs disappearing These are middle layers of the economy. And there's one more thing to talk about why this is not like a normal recession and why it doesn't self-correct. It talks about the fact that this wasn't hyperscala style capex. It was OPEX substitution. If you have a company that has been spending $100 million a year on employees and 5 million on AI now spent $70 million on employees and 20 million on AI. AI investment increased by multiples but it occurred as a reduction in total operating cost. In layman's terms, even in a downturn, companies keep buying more AI because they were buying AI instead of employees, not in addition to employees. So there was no slowdown in AI investment because the entire point was to replace those costs. Now, by 2027, it talks about this intelligence displacement spiral. And it talks about the fact that you don't need to read economic data to see it. Just go to a dinner party. And they're talking about 2027 here. So, they say that our friend of ours was a senior product manager at Salesforce in 2025. They had a title, health insurance 41k, $180,000 a year. However, their friend lost the job in a third round of layoffs. After 6 months of searching, she started driving for Uber. Her earnings dropped to $45,000. Now, multiply that across hundreds of thousands of workers in every major city. All those former 180k a year people are now driving for Uber. And what happens when the Uber driver market suddenly gets flooded with overqualified workers desperate for income? Wages for existing Uber drivers collapse, too. Sector specific disruption metastasized into economywide wage compression. And it gets worse. The displaced workers who were still employed, they were terrified they were next. So, they stopped spending. And by February 2027, it was clear that still employed professionals were spending like they might be next. They were working as twice as hard, mostly with the help of AI, just not to get fired in the hopes of getting a promotion. That's because the hopes of promotion and raises were gone and saving rates ticked higher and spending softened. And then the claims confirmed what people already knew was happening. US initial jobless claims surged to 487,000, the highest since April 2020. And the overwhelming majority of new filings were from white collar professionals. And here's the thing about why this is so dangerous compared to a normal recession. In a normal downturn, job losses are spread out across all income levels. But in this scenario, the losses are concentrated at the top of the income ladder. The top 10% of earners account for more than 50% of all consumer spending in the United States. The top 20% account for roughly 65%. These are the people who buy the houses, the cars, the vacations, the restaurant meals, the private tuition, the home renovations. So, a relatively small percentage of high earners lose their jobs or take massive pay cuts. The spending impact on the economy is enormous, way more than the same number of lower wage workers lost jobs. A 2% decline in white collar employment translated to something like a 3 to 4% hit to discretionary consumer spending. And by Q2 of 2027 in this scenario, official recession is confirmed. Two consecutive quarters of negative growth. Next is where we get into where the financial system starts to crack. And they talk about a daisy chain of correlated bets. So over the last decade, there's been a massive growth in something called private credit. You've probably heard about some of the big names, Blackstone, KKR, Apollo, and these are called private equity firms.

### [15:00](https://www.youtube.com/watch?v=khWnSVIfMQM&t=900s) Segment 4 (15:00 - 20:00)

Private credit grew from under 1 trillion in 2015 to over 2. 5 trillion by 2026 and a big chunk of that money was lent to software companies. SAS businesses evaluations that assumed that those companies would keep growing forever. A meaningful share of that capital had been deployed into software and technology deals. Many of them leveraged buyouts of SAS companies at valuations that assumed mid- teens revenue growth at in perpetuity. Now, basically what they're saying here is that they were betting that those companies would keep growing 13 to 15% every single year forever. And then the AI happened. Those assumptions were dead. But here's the thing. The official valuations didn't reflect that. The firms were slowly marking them down. 100 cents on the dollar, 92, then 85. All while public comps said 50. Public companies in the same space were trading at half the value, but private funds were pretending their stuff was almost full price. Now, the smoking gun was apparently a company called Zenesk. Now, Zenesk is a popular customer service company. It was taken private in 2022 for $10 billion. To fund that purchase, they took on $5 billion in debt, the biggest loan of its kind at the time, led by Blackstone with Apollo, Blue Owl, and other names all involved. The whole loan was structured and around the idea that their revenue would stay strong and recurring. But by mid 2027, AI agents were handling customer service automatically. No tickets needed, and Zenesk's entire reason for existing was being replaced. The ARR was the loan that was being underwritten against was no longer recurring. It was just revenue that hadn't left yet. And this is one of the most brutal lines in the whole piece that the revenue was still coming in, but only because customers hadn't cancelled yet. It was a slow bleed. The largest ARRbacks loan in history became the largest private credit software default in history. Now, everyone starts asking the same question at once. Who else has been pretending everything is fine? The report says that this should have been survivable. Private credit is designed exactly for this situation. It's locked up. Investors can't withdraw. There's no bank run. But then here comes the twist. Over the prior decade, the largest alternative asset managers had acquired life insurance companies and turned them into funding vehicles. Apollo bought Athen, Brookfield bought American equity, and KKR took Global Atlantic. And these private equity firms weren't just managing money for rich investors. who are managing the money inside life insurance companies which means your savings and annuities which means normals people retirement money and when regulators started forcing these insurance companies to raise more capital to cover their losses the entire thing essentially unraveled now this is the section that I think is pretty interesting because it talks about another scenario which is the mortgage question so it talks about US residential mortgage market is $13 trillion and the entire system is built on one assumption. The person who just took out the mortgage will stay employed at roughly the same income for the life of the loan, 30 years in most cases. White collar employment crisis has threatened this assumption with a sustained shift in income expectations and the report asked the question that would have seemed insane 3 years ago. Are prime money mortgages money good? And prime mortgages are essentially the good ones, the safe ones, the people with 780 credit scores, 20% down payment, clean history, great jobs. These aren't the sketchy loans from 2008. These are the best borrowers in the system. The report is asking, what if even these people cannot pay? Every previous mortgage crisis was driven by lending to people who couldn't afford it. That was the 2008 subcratin crisis. Rising interest rates killing adjustable loans. the early 1980s, a single industry collapsing in a single region. For example, the oil in Texas, auto in Michigan. This is none of those things. In 2008, the loans were bad on day one. In 2028, the loans were good on day one, but the world just changed after the loans were written. People borrowed against a future they could no longer afford to believe in. That sentence is pretty haunting. You can take out a mortgage assuming you'd earn 180K a year for 30 years, but now you're driving Uber making 45K a year. The mortgage was fine. Your life changed. And the report shows how people tried to hide the stress before it came obvious. Heliloc draws, 401k withdrawals, and credit card debt spiking while mortgage payments remained current. People were draining every other source of money just to keep making their mortgage payments technically current. One more shock away from default. The cities started showing stress first. San Francisco, Seattle, Austin, Manhattan, the places with the highest concentration of tech and finance workers, the people who thought

### [20:00](https://www.youtube.com/watch?v=khWnSVIfMQM&t=1200s) Segment 5 (20:00 - 25:00)

they were the most protected. So, at this point in the scenario, it's mid 2028, and you've got workers losing jobs, spending collapsing, private credit defaulting, insurance companies under pressure, home prices falling, and a government that is running out of money and ideas. The government's whole tax system is built on humans earning money through and paying taxes on that income. But remember, the productivity thanks to AI is still there. The output is still happening. It's just that the gains are going to the companies and the AI infrastructure, not to the workers. Labor's share of GDP declined from 64% in 1974 to 56% in 2024. a four decade grind lower driven by globalization, automation, and the steady erosion of worker bargaining power. In the four years since AI began its exponential improvement, that has dropped to 46%, the sharpest decline on record. So GDP looks fine, output is actually growing, but the workers, the people who actually vote, the people who spend, the people who pay taxes are getting a smaller and smaller slice of that pie. Remember, the output is still there, but it's no longer routing through households on the way back to the firms, which means it's no longer routing back to the IRS either. And the government spending is going up. At the same time, the receipts are going down. The politicians are fighting about what to do. There are proposals on the table, a transition economy act with direct payments to displaced workers, an AI shared prosperity act that would basically tax AI generated output and give it back to citizens. But nothing is getting done. The right is calling for transfers and redistribution Marxism and warns that taxing compute hands the lead to China. The left warns that attacks drafted with the help of incumbents becomes regulatory capture by another name. Meanwhile, people are furious. The Occupy Silicon Valley movement has been emblematic of wider dissatisfaction. And last month, demonstrators blockaded the entrances to Enthropic and Open Eye San Francisco offices for 3 weeks straight. And the anger makes sense. The founders and early investors have accumulated wealth at a pace that makes the guilded age look tame. The guilded age is when Rockefeller, Carnegie, and Vanderbilt were accumulating unprecedented wealth while workers lived in poverty. And the report is essentially saying that the AI wealth concentration makes that look modest. By the way, if you actually want to future proof yourself and not just worry about the impending AI doom narrative that's going around, I would say just check out my free guide. It's basically going to future proof you. It's the step-by-step guide to securing a job and income before a hit to talk about skills, strategies, income streams. And this is actually based on a ton of different research papers that I read, individuals I spoke to in the community, and many professionals. So, this isn't something that I just, you know, whipped up a quick prompt. So, I've been developing this for a very, very long time. It's completely free. All you need to do is just click the first link in the description if you want to download it. Otherwise, we'll get back to the video. Now, one of the last things it talks about here is the intelligence premium unwind. It talks about the fact that human intelligence derived its inherent premium from its scarcity. Every institution in our economy from the labor market to the mortgage market to the tax code was designed with that in held. And I think that is probably, you know, pretty true. It talks about the fact that, you know, you can replicate almost everything at scale. But the person who could think, analyze, create, persuade, manage, that was pretty rare. That was valuable. And that was what the entire economy was built around rewarding. Now you know when we think about machine intelligence is now basically a competent and rapidly improving substitute for human intelligence across a rapidly growing range of skills. The financial system optimized over decades for a world of scarce human minds is repricing. And this is what the report calls the intelligence premium unwind. The extra value that humans got paid just for being smart that is going to be going away. The most chilling line in the entire piece is that it won't change the fact that a Claude agent can do the work of an $180,000 product manager for $200 a month. They actually know the name Claude. It's the AI I'm using my workflow every day. And they're basically saying can replace you. And so yeah, the report basically ends by reminding you that this is fiction and we're not in 2028 and that we're actually reading this in February 2026. So we still have time to act. Okay, so let's actually dive into some of the key issues with this because as someone in the AI space who actually knows a lot about AI, I think there are some key issues that these guys are missing. I'm not just saying that to say that they're completely wrong that it is certainly a well-written post. It's just that when you actually dive into some of the raw details, it just doesn't hold up. So one of the things they talk about is the fact that you know the scenario hinges on companies building their own software with AI instead of buying SAS and the procurement manager who got a 30% discount by trying to build internally. Yes, that is real leverage but the piece then extrapolates to SAS is fundamentally broken. Building and maintaining enterprise software is not

### [25:00](https://www.youtube.com/watch?v=khWnSVIfMQM&t=1500s) Segment 6 (25:00 - 30:00)

the same as building a prototype in weeks. The piece does acknowledge the ED's cases, but brushes past the ongoing cost of security and compliance, integration with legacy systems, bug fixes, updates, support, and documentation. The more accurate version of this is SAS pricing power is impaired, and margins compressed. And yes, that might be bearish a little bit for the multiples, but it's not the same as SAS revenue collapsing 60 to 80% the way the price implies. Now, here's the thing, okay? Yes, vibe coding can get you an app, okay? AI vibe coding is good and maybe guys in two years, yes, it is going to be tremendously better than it is now. But this is the thing, okay? And they made the Door Dash argument, but I think it actually does fall apart. And if you use Door Dash, you use Uber Eatats, whatever delivery service you use, you'll know that what they're saying isn't exactly true. So the Door Dash argument falls apart because the app isn't the moat. Okay, the piece is assuming that door get door dash's competitive advantage is the habit and home screen placement. Okay, when you think about it, that's just surface level reading because the actual modes are network density distribution and Door Dash has spent billions, you know, over years building a network where there are enough drivers in enough places that when you order, someone picks it up in 8 minutes. And drivers go where those orders are, and orders go where the drivers are. This is a classic two-sided network that, you know, doesn't dissolve just because someone wrote a competing app. Okay? It took Door Dash years and billions in subsidies to crack this tech in every new city. A vibecoded competitor can't replicate with that, no matter how good that app is. It's going to need real humans. cars, real geography, density, and money to subsidize both of those sides until it reaches critical mass. Trust me, guys. I don't know why I did this, but previously I did a lot of research on how delivery companies operate. it costs a lot of money and you lose very quickly. Okay, having the funding to be able to actually get that off. Now remember guys, this is just one example. So don't take this all the way, but I think you can understand what I'm trying to say here. Okay, when you think about the fact that Door Dash has negotiated commission rates, integrations, and exclusivity arrangements with hundreds and thousands of restaurants, a new app can't just list restaurants without their corporation or tablet integration. Many restaurants actually have contractual obligations or simply won't on board 20 new platforms. Then you've got to think about insurance, compliance, legal infrastructure. Operating a gig delivery platform means dealing with worker classification law, food safety, liability, city bycity regulatory licensing, commercial insurance. This is just, you know, glossing over the complete vibe coding problem where you've even got edge cases on that. And this is why there's only a serious handful of players despite, you know, food delivery being a large market for the last 15 years. So when you think about that, and I mean I can even get into the fact that the driver incentive math doesn't even work. The piece says competitors pass 90 to 95% of the driver fee to the, you know, uh, driver and this pulls the drivers away. But think about what that means. If the platform that you vibe coded only takes 5 to 10% commission instead of Door Dash's 15 20%. then you've got no revenue to fund operations, marketing, customers. Sure, insurance or driver incentives. That's a charity, not a business. And this is what I'm saying across Vibe Coding. Sure, this doesn't really add up. Okay? And that's why I'm trying to say that like some of the assumptions that it's making, it just doesn't hold up. So, some of the arguments start to fall apart because the piece basically confuses low software barrier to entry with low barrier to entry. Building an app, yes, that is easy. Building a delivery network is not a software problem. It's logistics, capital, regulatory, coordination problem the software can assist with but not substitute for. And the moat was never the app. The moat was the operational infrastructure underneath it. And as time goes on, one of the things I really want to reiterate to you guys is that, you know, when it comes to business in the future, I think distribution is going to be one of the most important things. Now, of course, there is also the fixed amount of work trap. So, when they're talking about, you know, employment and stuff, the article is basically assuming that there is a finite set amount of white collar work in the world. It's based on this logic is that if an AI makes an employee 50% faster, the company will fire 50% of its employees. When in reality, economies don't work like that. When a product or service gets massively cheaper, people just consume way more of it. If software development becomes dirt cheap because AI agents are writing the code, we won't fire all the developers. Instead, every small business, a local bashery, niche hobby group will suddenly want their own custom software. The demand for the tech explodes. Then human managers, editors, and visionaries will be needed to direct all that cheap AI labor. And in fact, I'm pretty sure I was browsing Twitter and I actually saw the fact that software job postings went up or there was some articles or some fact, it's probably going to be on screen right now, but it actually does support the fact that this is actually happening because of how crazy software development is. And I know it myself actually tried to vibe code a few apps and I actually reached out and actually paid certain developers because I really didn't have any experience in software development. Now, of course, there is also liability. You know, the article here once again claims that a company

### [30:00](https://www.youtube.com/watch?v=khWnSVIfMQM&t=1800s) Segment 7 (30:00 - 32:00)

will just have an AI agent replicate a 500k software platform in a few weeks to save money. But this overlooks the liability. Companies don't just pay big software vendors like Microsoft or Service Now for the code. They actually pay for the security, the compliance, the legal shield. If a company uses an AI to build its own payroll system, and if that gets hacked and deletes everyone's financial records, that company is entirely on the hook for that. Okay? Big corporations are going to happily pay a software vendor just to have someone to sue if things go wrong. Privacy issues. You've all seen it happen before. You don't want to be that big company who vibe coded your own thing and then of course things go wrong. One thing it's also forgetting is that corporate realities are super slow. Big companies move like molasses. The timeline in this article going from normal to a total economic collapse between 2026 to 2028. It's kind of like a fantasy. Okay. Large corporations are incredibly slow. It can take a new Fortune 500 company 18 months just to migrate to a new email server or approve a new logo. The idea that giant banks and healthcare conglomerates are going to rip out their entire software infrastructure and trust it to AI bots in less than 2 years ignores corporate bureaucracy. Now, of course, this is one of the craziest things that I'm I don't know why they didn't consider this. Um, and this is the regulation war. The article assumes that AI bots will just bypass credit card fees like Visa and Mastercard by doing transactions on crypto networks in fractions of a penny. This overlooks completely the reality of lawyers, lobbyists, and government regulators. The banking industry and the government are not going to just roll over and let unregulated AI bots move trillions of dollars off the grid just to save 2% on merchant fees. Consumer protection laws, anti-money laundering rules, fraud prevention, all of these require heavy human-driven friction. So, look, I kind of want to summarize this by saying that is this going to happen? Not necessarily, but the authors themselves do pose a scenario. It's not a prediction, but I think of course this was worth your time. It's one of the most coherent explanations of how things could go badly wrong, even if AI succeeds. Not because the technology fails, but because it works too well, too fast for the institutions built around human labor to adapt. Remember, the argument isn't that AI is bad. The argument is that we built our entire system on something that is going to change. And I think that is a true thing. Okay? You have to think about what is it in your life that you've built on the assumption that might not survive in the next 5 to 10 years. Is that your career, your investments, your savings, the company you work for, the products you're buying stock in. I'm not saying to sell everything and panic. That doesn't make sense. But of course, you do have to think about this and you have to stay ahead of what's coming. That is basically the entire point of the article. It's to foresee the unseen. Of course, like I said, I've got the PDF there. It's completely free if you guys want to check it out. But let me know what you guys think.

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*Источник: https://ekstraktznaniy.ru/video/11914*