💥 How to Profit From the Biggest Disruption in Modern History
Anthropic's Biggest Disruption Yet — IBM's Fall Is Just the Start.
In 1908, horses powered the entire global economy. They moved goods, plowed fields, and carried people across cities. No one questioned them. They were irreplaceable.
Then the gasoline tractor arrived.
Within a single decade, the horse-powered economy collapsed completely. The horses didn’t fail. They didn’t make mistakes. They simply became obsolete — overnight, at a scale nobody predicted, and with a speed nobody was ready for.
Fast forward to 2010. Blockbuster had 9,000 stores and $6 billion in revenue. They charged massive late fees because their customers had no other choice. Then Netflix offered a better way. Blockbuster couldn’t adapt without killing their own profits. By 2010, they were bankrupt.
In 2007, Nokia controlled 40% of the global smartphone market. By 2012, it was nearly worthless.
That’s not a cautionary tale about bad management. Nokia had great engineers and billions in R&D. It’s a cautionary tale about what happens when the ground shifts beneath a company and they cannot move fast enough to stay on top of it.
Right now, IBM is staring at the exact same fate. And if you own tech stocks, you’re standing on the same train tracks.
On Monday, IBM stock collapsed 13.2% in a single day. Its worst single-day drop since October 2000. Over $20 billion in market value vanished in a matter of hours. The trigger? A blog post. One blog post from a startup called Anthropic announcing that their AI tool, Claude Code, can now modernize COBOL — the ancient programming language that powers 95% of all ATM transactions worldwide, and runs almost entirely on IBM mainframes.
During my two decades in finance, I’ve seen market panics come and go. I’ve watched fear drive irrational selling. I’ve seen temporary shocks that recovered in weeks.
This is not a temporary panic. This is a permanent, structural shift.
The era of charging premium prices just because something is complicated is officially dead. And the greatest wealth transfer in modern history has already started.
You need to understand what just happened. Because IBM is only the first domino.
📬 In this issue:
Part I:
1) The 60-Year-Old Trap Running 95% of Your Money.
2) How Anthropic Erased $20 Billion in a Single Afternoon.
3) IBM Is the New Blockbuster. That’s Not a Metaphor.
4) The Real Villain: The Speed of AI.Part II:
5) A Historic Wealth Transfer.
6) Vibe Coding
7) The 3 Phases of AI Disruption.
8) 4 Mental Models To Protect Your Wealth.
9) 3-Step Wealth Protection Plan.
10) What This Means for Your Career.Part III:
11) Important Lessons & Advice to Remember.
12) Final Thoughts
13) Questions From Subscribers.
🤔 But first, how do you feel about AI?
1. The 60-Year-Old Trap Quietly Running 95% of Your Money
Before we talk about AI, we need to talk about a dinosaur called COBOL.
COBOL stands for Common Business-Oriented Language. It was created in 1959. Most people outside of the tech world have never heard of it. But this ancient language quietly controls your entire financial life.
About 95% of all ATM transactions run on COBOL. Every time you swipe your card, check your bank balance, or wire money across the country, COBOL is working behind the scenes. Major banks rely on it. Major airlines rely on it. The federal government relies on it. Over 800 billion lines of this old code run in the background every single day.
And nearly all of it runs on massive IBM mainframe computers.
Here’s why this mattered so much for so long. Updating this old code was an absolute nightmare. It was far too risky and far too expensive for companies to even attempt. You needed an army of specialized consultants. You needed budgets well over $50 million. You needed three to five years just to plan the project — and another few years to actually execute it.
Companies quickly realized it was just cheaper to keep paying IBM massive maintenance fees than to risk touching the system.
This was IBM’s ultimate golden goose. They built a multi-billion dollar empire on a single, elegant fact: fixing the problem was harder than living with it. The extreme complexity was the business model. The confusion was the product. The risk of change was the lock-in.
Then Anthropic flipped the script completely.
2. How Anthropic Erased $20 Billion in a Single Afternoon
On that Monday morning, Anthropic published what seemed like a routine product update. They announced that their Claude Code tool can now instantly understand, document, and update COBOL.
This new tool maps out thousands of lines of old code in minutes. It automatically documents entire corporate workflows. It identifies hidden risks that would take a team of senior consultants months to discover. It translates decades of undocumented business logic into something modern engineers can actually work with.
In short: Anthropic just made fixing the problem cheaper than living with it.
The grueling work that used to require a 50-person consulting team can now be done by a small team using AI. It takes a fraction of the time. It costs a tiny fraction of the price.
When I worked at JP Morgan, we spent enormous amounts of money on legacy system maintenance. It was a constant, never-ending cost. If we’d had a tool like Claude Code back then, we would have saved millions of dollars almost immediately — and so would every other major bank on Wall Street.
Wall Street investors did the math fast. If banks can modernize their old systems cheaply, they don’t need IBM anymore. They don’t need expensive consultants. They don’t need premium maintenance contracts at rates that have never been competitive.
The customer lock-in that IBM built its entire empire on? Gone.
Overnight.
The market panic that followed wasn’t irrational. It was completely justified.
3. IBM Is the New Blockbuster. Here’s Why That’s Not a Metaphor.
I want to give you a harsh truth right now.
Based on my analysis, IBM is completely trapped — and most analysts on Wall Street are dangerously underestimating how bad it really is.
Many analysts look at IBM’s history of reinvention and say, “They’ve adapted before. They’ll figure it out.” They point to IBM’s pivot from hardware to services. They point to their acquisition of Red Hat. They call it “resilience.”
I see a vastly different story. I see a giant company with one foot chained to a dying business model.
IBM is the new Blockbuster. Anthropic is Netflix. And the parallel is almost too precise to ignore.
Blockbuster made a fortune on late fees. They actively punished their customers because those customers had absolutely no other choice. IBM makes a fortune on maintenance fees. They charge premium prices because their customers are stuck with old technology that’s too scary and too expensive to replace.
When Netflix offered a genuinely better way, Blockbuster faced an impossible choice: kill their late fees and lose their most profitable revenue stream, or keep charging them and lose their customers slowly. They couldn’t adapt without destroying themselves. They chose to keep the fees. They died.
IBM faces the exact same deadly dilemma right now.
To survive, IBM needs to help customers move off their expensive mainframes using AI. But if IBM helps customers leave, IBM destroys its own highest-margin business. If IBM doesn’t help customers leave, those customers will use Anthropic, Microsoft, or Google to do it without them.
There is no good path. They’re caught in a fatal trap.
This is exactly why the 13% single-day drop isn’t a blip. It isn’t panic selling by uninformed retail investors. It’s the market pricing in the beginning of a long, permanent decline for legacy tech giants.
The same drop happened to Blockbuster’s stock in 2005. Then again in 2007. Then again in 2009. Each time, analysts said “it’s priced in.” Each time, they were wrong.
4. The Real Villain: The Speed of AI
Here’s the part that should genuinely worry every investor, regardless of whether you own IBM or not.
The threat isn’t just that AI can do this work. The real threat is the speed at which these capabilities keep arriving.
In the past, technology shifts took decades. The internet took 10 years to fully mature. Cloud computing took 15 years to dominate enterprise software. Companies had time to adapt. They had time to pivot, retrain their teams, and gradually shift their business models.
AI is moving at a completely different velocity.
Look at what Anthropic alone has released in recent weeks:
Week 1: Legal plugins that directly attacked the legal tech market
Week 2: Security tools that hammered cybersecurity stocks
Week 3: COBOL modernization that wiped out IBM’s core competitive advantage
This isn’t a slow evolution. It’s a series of precision strikes. Every few weeks, a new industry gets targeted by a single software update.
The villain in this story is the speed of AI adaptation. Companies like IBM built their entire business on one core assumption: that technology moves slowly enough for them to stay ahead of it. They assumed their customers would tolerate expensive, slow, complicated services because the alternative was too risky and too costly to pursue.
Now, the alternative is a software subscription costing a few hundred dollars a month that works around the clock, never takes vacation, and gets smarter every week.
Legacy companies simply cannot reinvent themselves in weeks. They have too many employees, too many contracts, too many legacy systems of their own. But AI can reinvent their entire industry in days.
This speed asymmetry is what makes this disruption genuinely dangerous — and genuinely historic.
5. The AI Scare Trade and a Historic Wealth Transfer
IBM’s collapse didn’t happen in a vacuum.
A perfect storm of fear hit the market at the same time, creating what I’m calling the AI Scare Trade — a new pattern where AI announcements trigger mass, irrational selling across entire sectors simultaneously.
First, a report from Citrini Research circulated widely. It painted a picture of 2028 where AI agents fully replace food delivery platforms, credit card processors, and vast categories of white-collar work. It described a wave of white-collar unemployment affecting millions of people.
Then Nassim Taleb, the author of The Black Swan and one of the most respected risk thinkers in the world, chimed in publicly. He warned investors to brace for sudden, unexpected bankruptcies in the software sector — the kind of collapses that happen faster than anyone anticipates.
Fear took over. Retail investors panicked. DoorDash dropped 6%. American Express fell 6%. Cybersecurity stocks tanked hard. People sold first and asked questions second.
My decade on Wall Street taught me one vital lesson about market panic: panic destroys wealth for the fearful, and creates wealth for the prepared.
Here’s how to think about what’s actually happening beneath the surface noise:
Every dollar leaving a legacy tech company has to go somewhere else. The money doesn’t disappear. It rotates. It flows from the companies getting disrupted into the companies doing the disrupting.
This is a historic wealth transfer — arguably the largest since the early days of the internet. The old tech guard will get slaughtered. But the money leaving those companies is flowing directly into AI infrastructure. Into cloud providers. Into chip makers. Into energy companies powering the data centers that run these systems.
Your only job is to make sure you’re standing on the receiving end of that money flow — not the losing end.
6. Vibe Coding: Why the Entire Software Industry Just Got a Wake-Up Call
There’s a brand new concept you need to understand if you want to protect your portfolio. It’s called vibe coding.
Vibe coding means you describe what you want in plain English, and AI builds it for you. No coding degree required. No expensive development team. No six-month project timeline. You just tell the machine what you need, and it appears.
If anyone can build software just by talking, traditional software companies are in serious, serious trouble.
Think about what this actually means in practice. A local bakery owner wants a custom inventory tracking app. Three years ago, she’d have to pay a developer $10,000 upfront, wait months for delivery, and then pay ongoing maintenance fees. Today, she can describe what she needs to an AI, get a working app in minutes, and pay nothing.
Now scale that up to every small business, every mid-sized company, every enterprise buying overpriced software subscriptions for tools they’re using at 20% of their capacity. The math becomes terrifying fast.
The middlemen who charge high fees for connecting people are going to starve. The middlemen who charge premium prices for analyzing data are going to starve. If a company’s entire value proposition is being the expensive bridge between two points, AI is about to burn that bridge to the ground.
This is what the broader market is starting to price in. And it’s only in the early stages.
7. The Three Phases of AI Disruption (And Where We Are Right Now)
Based on patterns I’ve observed across multiple technology cycles, I believe we’re now entering Phase Two of a three-phase disruption of enterprise software. Understanding which phase we’re in right now tells you exactly where to put your money.
Phase One: The Tool Phase (2023 to 2024)
AI companies built general-purpose chatbots and broad coding assistants. Businesses experimented cautiously. Productivity improved, but incrementally. Markets were skeptical, calling it “overhyped.” Most executives treated it like they treated cloud computing in 2005 — interesting, but not urgent.
We just finished this phase.
Phase Two: The Application Layer Phase (2025 to 2027)
AI companies stop building general tools and start building specific weapons for specific industries. Legal plugins. Security scanners. COBOL modernizers. Each one targets a specific revenue model with surgical precision.
This is where we are right now. This is the phase where legacy tech stocks get hammered, because investors are finally realizing the “complexity moat” — the thing that made these companies untouchable — is completely gone. In Phase Two, the money is flowing into AI infrastructure (chip makers, data centers, cloud providers) and into the specific AI tools attacking each vertical.
Phase Three: The Agent Phase (2028 to 2030)
AI stops assisting humans and starts replacing entire workflows. Vibe coding becomes the standard way to build software. Business users describe what they want, AI builds and maintains it. Traditional software vendors — the ones charging big annual fees for tools nobody fully uses — become irrelevant at scale.
This is what Citrini Research was imagining. Whether the exact timeline is right or off by two years doesn’t matter. The direction matters. And the direction is clear.
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8. 4 Mental Models That Will Protect Your Wealth From Here Forward
I want to give you four specific mental models you can apply to every stock in your portfolio starting today. These are the cognitive frameworks I use personally. Use them as your filter for navigating the rest of this disruption.
The Complexity Razor
Look at any company and ask yourself one simple question: do they make money because their work is genuinely innovative, or just because it’s complicated?
If their only competitive advantage is doing something that used to be hard, AI will destroy them. Complexity is no longer a business model. It’s a liability. COBOL was complicated. IBM profited from that complication. Now Claude Code handles it in minutes. Run every company you own through this razor.
The Technical Debt Fallacy
For years, investors assumed that old, entrenched computer systems were totally safe because they were too difficult and too expensive to replace. This assumption powered IBM’s valuation for decades.
That assumption is now dead. AI just made replacing legacy systems cheap, fast, and low-risk. Entrenched systems are no longer protective moats. They’re ticking time bombs. If a company’s safety net is “our system is too old and complicated for anyone to bother replacing it,” sell the stock.
The Pick-and-Shovel Mandate
During the 1849 Gold Rush, the men who went digging for gold mostly went broke. The men selling picks and shovels made fortunes — consistently, regardless of who struck gold and who didn’t.
In the AI revolution, you need to own the picks and shovels. Stop gambling on which software company will win or lose the AI race. Instead, invest in the massive data centers, the energy grid infrastructure, and the semiconductor chips that every AI company needs to run on — regardless of who wins.
NVIDIA doesn’t care if Anthropic beats OpenAI. Data centers don’t care if IBM survives or collapses. Own the infrastructure and you win no matter what.
The Margin vs. Revenue Test
When AI targets a company, you need to assess the exact threat level with precision. There are two very different outcomes:
AI makes a company’s service cheaper to deliver internally (good for the company — higher margins, same revenue)
AI eliminates the need for the company’s service entirely (existential threat)
If AI just lowers a company’s internal operating costs, the stock might actually be a hidden bargain — the market might be incorrectly panicking. But if AI eliminates the reason customers need that company at all, sell the stock quickly.
IBM falls into Category 2. So do many traditional consulting firms, basic data analytics companies, and intermediary software vendors. Run this test on every holding you own.
9. The 3-Step Wealth Protection Plan You Need to Execute
Information without action is just entertainment. You need a specific plan before the next wave of disruption hits — because it will hit, and it’s coming faster than most people think.
I’ve been investing for over 20 years. I know for a fact that waiting for perfect clarity is the most reliable way to miss every major wealth-building opportunity in the market. The clarity always arrives after the best prices are gone. You need a system. Here’s mine.
Step 1: Audit Your Portfolio for Complexity Moats
Open your brokerage account today — not next week, today. Look at every single stock you own.
Ask yourself for each holding:
Does this company charge premium prices because customers are stuck, or because customers love the product?
Is this company’s advantage genuine innovation, or just the friction of switching away?
Does this company rely on processes that AI can now replicate in minutes?
Any company that profits primarily from customer confusion, switching friction, or process complexity goes on your sell watchlist. This isn’t panic selling. This is strategic positioning before the rest of the market catches up.
Step 2: Build Your Disruptor Buy List
Every time a legacy tech stock crashes during an AI scare trade, the money doesn’t disappear — it rotates. Build a specific buy list of the companies positioned to receive that money flow.
Focus on:
Cloud infrastructure providers (the backbone that AI runs on)
Semiconductor and chip companies (every AI model needs massive computing power)
Energy infrastructure (data centers consume enormous amounts of electricity — this is a wildly underestimated opportunity)
AI-native companies attacking specific high-margin industries
These are your modern picks and shovels. Every panic in legacy tech is a buying opportunity in these names. Be ready.
Step 3: Set Aggressive Price Alerts Now
The AI scare trade creates wild, irrational price swings. Great companies will get dragged down with the bad ones during moments of blind panic — because retail investors sell everything when they’re scared.
Set price alerts 15% below current market prices for the high-quality AI infrastructure names on your buy list. When fearful investors panic sell and the alerts trigger, you won’t need to make an emotional decision in real time. The system makes the decision for you. You buy quality assets at a significant discount while everyone else is fleeing.
This is exactly how wealth gets built during disruption cycles. Not by being smarter than everyone — by being more prepared.
10. What This Actually Means for Your Career (Read This Part Carefully)
I’ve spent most of this piece focused on investment positioning. But there’s something equally important that I’d be doing you a disservice to skip over.
If you work in technology, finance, law, consulting, or any knowledge industry, you’re probably reading all of this and quietly asking yourself one question: Is AI coming for my job?
Here’s my assessment after watching automation reshape industries for two decades.
AI isn’t coming for your job. It’s coming for the most repetitive, most tedious, most time-consuming parts of your job.
The COBOL programmers who will thrive in this new era won’t be the ones who try to compete with Claude Code. They’ll be the ones who learn to leverage it. The ones who understand both the legacy systems and the new AI tools. The ones who can bridge the gap between what AI produces and what the business actually needs done.
The same pattern is already playing out across every knowledge industry:
Lawyers who use AI for document review will replace lawyers who don’t
Financial analysts who use AI for data processing will replace analysts who do it manually
Consultants who use AI to accelerate their work will replace consultants who still bill for hours of repetitive manual tasks
The most important skill in this new era isn’t technical knowledge. It’s judgment. Knowing what questions to actually ask. Understanding the full business context behind a problem. Building genuine trust with clients and stakeholders. Making strategic decisions under uncertainty.
AI can analyze thousands of lines of COBOL code in minutes. It can’t understand why that code matters to the business. It can’t negotiate with a CFO who’s terrified of the migration risk. It can’t build a two-year trust relationship with a client. It can’t make the call when the data is ambiguous.
Those skills? They become more valuable, not less, as AI takes over the mechanical work.
My honest advice: If you work in an industry that AI is targeting, spend five hours this week learning how AI tools actually work in your specific field. Not theoretically — hands on. The professionals who thrive through this shift will be the ones who make AI multiply their output, not the ones who wait and hope they’re somehow immune.
The learning curve is small. The competitive advantage is enormous.
11. Important Lessons & Advice to Remember
The greatest wealth transfer in modern history has begun. Money does not disappear. It moves. Every time a legacy company loses a dollar an AI innovator gains a dollar. This is a historic wealth transfer.
Own the picks and shovels of AI. Stop guessing which software company will win. Invest in semiconductor chips, data centers, and energy grid companies. These power the AI.
Leverage AI to multiply your productivity. Do not compete against it. Ride it. The professionals who thrive will make AI work for them.
Spend five hours learning AI tools this week. Do not wait for your company to train you. Take ownership. The learning curve is worth it.
“Vibe Coding” changes everything. You can now describe software in plain English and have AI build it. This ruthlessly cuts out the middleman. If a company’s value proposition is being a bridge between two points, AI is about to burn that bridge down.
Master the "Intent Layer" (Vibe Coding). Spend 5 hours this week using Claude or ChatGPT to build a tool that automates one part of your job. If you can’t direct the machine, you will be replaced by someone who can.
12. Final Thoughts — The Only Question Is Which Side You’re On
In 1876, Western Union was offered the chance to buy Alexander Graham Bell’s telephone patent for $100,000.
They passed. Their internal memo described the telephone as “an electrical toy” with no serious business use. Within a decade, Western Union was watching helplessly as the telephone made their entire telegraph network obsolete.
Western Union never recovered from dismissing the telephone.
Not because they were stupid. Not because they didn’t work hard. But because they were optimizing for a world that was disappearing, while a better world was forming right in front of them.
I think about that memo every time I see a major company dismiss a new technology as a temporary threat.
I started my career in finance when cloud computing was still called “utility computing.” Most senior bankers I worked with in those early years thought it was a fad that would never touch their business. They’d heard the same thing about the internet. They ignored it until they couldn’t.
I’ve now watched multiple complete technology cycles reshape markets from the inside. And every single time, the pattern runs the same way:
Dismissal: “This technology is overhyped. It won’t affect our business.”
Panic: “This will destroy everything. Sell everything.”
Rationalization: “The technology is real, but the transition will be slow. We have time.”
Adaptation: Winners emerge. Losers fade. New business models prove far more durable than the old ones.
Right now, we’re moving from Step 2 into Step 3. Most of the market is currently rationalizing — telling themselves that the IBM drop was overdone, that these companies will figure it out, that the transition will be gradual enough to manage.
Some of them are right. Most of them are wrong.
Now let’s circle back to where we started.
I keep thinking about those horses.
Not because I’m sentimental about them. Because they represent something haunting about technological change. They didn’t fail. They didn’t make mistakes. They didn’t suddenly become lazy or incompetent. They just became obsolete—through no fault of their own.
The horses didn’t see the tractor coming. They just woke up one morning and found that their hard labor was suddenly worthless. The shift was sudden. It was brutal. And it was completely permanent.
The horses didn’t lose because they tried less. They lost because the world stopped needing them.
IBM isn’t losing because it’s managed poorly. It’s losing because the world is about to stop needing it.
IBM and the legacy software giants are staring at the exact same fate. Anthropic didn’t just release a new coding tool. They released the digital tractor. They permanently changed the core economics of enterprise technology in a single blog post.
The era of charging premium prices for basic complexity is over.
But here’s what makes this moment genuinely different from the story of the horses: you have a choice they didn’t. You can see this shift happening. You can read the data. You can act before the crowd figures it out.
The same AI destroying legacy tech is simultaneously creating unprecedented opportunities in cloud infrastructure, semiconductor manufacturing, energy grid expansion, and AI-native companies attacking every major industry simultaneously.
This is the gold rush. And you get to own the picks and shovels.
Stop worrying about whether AI will eventually take your job. Start figuring out how to own a meaningful piece of the infrastructure that’s running the entire AI revolution.
Warren Buffett has said the best investment you can ever make is in your own understanding. That advice has never been more relevant than it is right now.
The AI extinction event for legacy tech isn’t approaching. It’s already here. The ground is shifting beneath the entire tech sector. The wealth transfer has already started.
The pattern never changes. Only the speed accelerates.
The only question left is whether you’ll be buried by it — or build on top of it.
The choice is yours.
13. Questions From Subscribers
Q: What stocks or sectors actually benefit from this AI disruption?
The consistent winners are infrastructure companies that every AI model depends on, regardless of which AI company wins. This includes semiconductor manufacturers (every AI model needs chips to run), cloud infrastructure providers (the platforms hosting AI services), energy companies supplying power to data centers (a wildly underappreciated opportunity), and AI-native software companies attacking high-margin legacy industries. The pick-and-shovel principle applies: rather than betting on which AI company wins, own the resources every AI company must purchase to compete.
Q: Is my 401(k) safe if it holds tech stocks?
Your 401(k) is likely diversified enough to survive, but you should check your holdings. Most target-date funds and index funds have broad exposure to companies that could face AI disruption. Look at your largest holdings and run them through the Complexity Razor. If you see companies that profit primarily from customer lock-in or complexity, consider adjusting your allocation. The good news is that most 401(k)s also hold the picks-and-shovel companies (Microsoft, Amazon, NVIDIA) that benefit from AI. You may already be positioned better than you think.
Q: Will AI really take my job?
Almost certainly not in the way you’re picturing. AI is not coming to replace you. It’s coming to replace the most repetitive, mechanical, time-consuming parts of your work. The professionals who lose out won’t be the ones who tried to keep up — they’ll be the ones who refused to adapt. The lawyers, analysts, and consultants who learn to leverage AI will be dramatically more productive than those who don’t, and they’ll price the non-adapters out of the market. The advice is simple: learn your field’s AI tools now, while you still have time to build an advantage rather than just catch up.
Q: Is it too late to pivot my career toward AI?
Absolutely not. We are only in Phase Two (The Application Layer). The "Agent Phase" (where everything changes) isn't until 2028. You have a two-year window to become an "AI Architect" before the real shift hits.
Q: What is “vibe coding” and why should I care?
Vibe coding means describing what you want in plain English and having AI build it instantly—no coding degree required. If anyone can build software by talking, traditional software companies are in serious trouble. The middlemen charging high fees to connect people or analyze data are about to starve.
👋My Final Words:
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