Have you read the terrifying “2028 economic crisis” scenario yet? The one where AI wipes out entire industries, unemployment skyrockets, and the global economy enters a death spiral within three years?

It’s a compelling read. It’s also probably wrong — or at least, wildly premature.

Johnloeber, an AI analyst who’s been studying these systems for years, argues that Citrini’s research group is painting a picture far too pessimistic. His core thesis: humans and institutions will outlast any technology wave, just like they always have. And the gap between “AI can theoretically do this” and “AI has actually replaced this” is measured in decades, not months.

Let me walk through his arguments. They’re worth hearing, especially if Citrini’s scenario kept you up at night.

Never Underestimate the Inertia of Systems

In 2007, experts declared America was finished — peak oil would destroy the economy. In 2014, analysts wrote off AMD and NVIDIA as dead companies walking. In 2023, pundits predicted Google would be obliterated by ChatGPT within two years.

In every single case, the institutions with strong momentum survived, adapted, and continued growing. AMD is now worth $150B+. Google’s revenue has increased every quarter since ChatGPT launched. America became the world’s largest oil producer.

Why? Because large systems have inertia. They have regulatory capture, installed customer bases, switching costs, brand loyalty, legal frameworks, and thousands of people whose livelihoods depend on maintaining the status quo. These forces don’t evaporate overnight because a new technology appears.

Citrini’s research group uses real estate agents as an example of jobs AI will eliminate. Loeber points out something almost comically true: people have been declaring real estate agents dead for 20 years — since Zillow, Redfin, and Opendoor launched. The entire real estate transaction was supposed to be automated by 2015.

And yet? Loeber and his wife recently bought a house. They were required to use an agent. That agent earned $50,000 for roughly 10 hours of filling out forms and coordinating between parties. In 2026. Two decades after the technology to replace them existed.

The lesson: every change happens eventually, but far slower than predictions suggest. And that time gap gives society the opportunity to prepare and adapt.

Software Is Still Terrible

Here’s an argument that hits close to home for anyone who builds software for a living.

According to Loeber, most existing software doesn’t actually meet real-world needs. Salesforce is bloated and painful to use. Banking apps are riddled with bugs. Most web applications can’t even provide a decent experience on both mobile and desktop simultaneously.

Think about it: we’re in 2026, and most enterprise software still feels like it was designed by committee in 2012. Hospital systems run on interfaces that look like Windows 98. Government websites crash under normal traffic. The average SaaS product has features that 90% of users never touch, and is missing features that 90% of users desperately need.

The paradox is this: AI doesn’t eliminate the need for developers. It creates demand for software that’s hundreds of times better than what currently exists.

This is Jevons’ Paradox in action. When a resource becomes cheaper and more accessible (in this case, the ability to write code), demand for it doesn’t decrease — it explodes. When electricity became cheap, we didn’t use less of it. We invented air conditioning, television, computers, and data centers. Total electricity consumption increased by orders of magnitude.

The same will happen with software. When AI makes it 10x cheaper to build an application, we won’t build the same number of applications at lower cost. We’ll build 100x more applications. Every niche problem that was “too small to justify a software team” suddenly becomes viable. Every clunky enterprise tool gets rebuilt from scratch. Every process that was “good enough on paper” gets a proper digital solution.

The developers who learn to leverage AI tools — like Claude Code — aren’t making themselves obsolete. They’re positioning themselves to ride the biggest wave of software creation in history.

Reindustrialization: The Next Wave of Jobs

This is Loeber’s strongest and most hopeful argument.

America (and most of the West) has a severe deficit in physical manufacturing capacity. Batteries, engines, semiconductors, chemical fertilizers, critical infrastructure — the ability to make physical things has been offshored to an alarming degree.

Consider this: China currently produces approximately 90% of the world’s ammonia supply. Ammonia is essential for fertilizer, which is essential for food. That’s not a technology gap — that’s a strategic vulnerability of existential proportions.

The reindustrialization thesis goes like this: as AI gradually replaces white-collar office work, the most viable path for displaced workers isn’t “learn to code” (ironic, given the context). It’s mega-projects in physical infrastructure:

  • Building water treatment plants
  • Developing clean energy facilities
  • Expanding high-speed rail networks
  • Constructing semiconductor fabrication plants
  • Rebuilding chemical manufacturing capacity

These are jobs that machines cannot fully replace — not because the physical labor can’t be automated (much of it can), but because the planning, coordination, local adaptation, and human judgment required are extraordinarily complex. Building a water treatment plant in Phoenix is fundamentally different from building one in Jakarta. The engineering is similar; everything else is different.

More importantly, these are jobs where people find genuine meaning. There’s a psychological difference between pushing pixels on a screen and watching a bridge take shape with your own eyes. The reindustrialization wave offers something that the knowledge economy has been slowly eroding: tangible proof that your work matters.

The Developer’s Perspective

As someone who builds software every day with AI tools, here’s what I actually see happening on the ground:

AI is making good developers more productive, not replacing them. I use Claude Code daily. It handles the mechanical parts of coding — boilerplate, repetitive refactoring, pattern matching. This frees me to focus on architecture, user experience, and business logic — the parts that require human judgment.

The demand for software is infinite. Every time I ship a feature faster with AI assistance, the product team has three more features waiting. The backlog doesn’t shrink. If anything, the ability to build faster makes stakeholders dream bigger.

The skills that matter are shifting. Raw coding speed matters less. Understanding problems, designing systems, communicating with stakeholders, and knowing when the AI is wrong — these matter more. The developers who thrive aren’t the fastest typists. They’re the best thinkers.

Nobody is getting replaced tomorrow. In my network of hundreds of developers across Vietnam and Southeast Asia, zero have lost their jobs to AI. Some have changed roles. Many have become more productive. A few have launched AI-powered side projects. But the feared mass layoff? Not happening. Not yet, anyway.

The Timeline Matters More Than the Destination

Loeber doesn’t deny that AI will change everything. He even admits that at some point, he himself will become obsolete. His argument isn’t “AI won’t be transformative.” It’s “the transformation will take longer than doomsayers predict, and that time gap is everything.”

“It will happen” is fundamentally different from “it will happen tomorrow.”

The distance between those two statements is measured in years — possibly decades. And that gap is the space where:

  • Governments can craft intelligent policy
  • Educational systems can adapt curricula
  • Workers can reskill and transition
  • New industries can emerge to absorb displaced labor
  • Society can build safety nets

The history of technology transitions supports this view. The automobile didn’t kill the horse industry overnight. It took 30+ years. During that time, an entire ecosystem of new jobs emerged — mechanics, gas station attendants, highway engineers, traffic police, insurance adjusters, driving instructors. The total number of jobs created by the automobile exceeded the number destroyed by it.

AI will follow a similar pattern. The jobs of 2035 will look different from the jobs of 2025. But they’ll exist. And the transition, while painful for some, will be measured in decades, not quarters.

So What Should You Do?

If you’re a developer reading this, here’s the pragmatic takeaway:

  1. Learn AI tools now. Not because you’ll be replaced if you don’t, but because you’ll be dramatically more productive if you do. Claude Code, GitHub Copilot, Cursor — pick one and go deep.

  2. Invest in judgment, not just skill. The ability to evaluate AI output, catch its mistakes, and make architectural decisions is more valuable than the ability to write code from scratch.

  3. Don’t panic. The Citrini scenario is a thought experiment, not a prophecy. The people who thrived through every previous technology transition were the ones who adapted calmly, not the ones who panicked.

  4. Build things that matter. The demand for good software is about to explode. Position yourself to build it.

  5. Stay curious. The developers who get left behind are never the ones who lack talent. They’re the ones who stop learning.

The world isn’t ending. It’s changing — like it always has. The question isn’t whether AI will transform everything. It’s whether you’ll be one of the people shaping what comes next, or one of the people watching from the sidelines.

I know which side I’m choosing.

Want to stay ahead of the AI curve? The Claude Code Mastery course teaches you to work with AI, not against it — 16 phases from foundation to full-auto workflows. Phases 1-3 are free.