dominating the AI debate immediately: that AI goes to interchange all of us, that jobs will disappear inside 18 months, that the collapse of the labor market is inevitable. Some say it with alarm, others, with enthusiasm. But almost nobody stops to take a look at the true data.
This primary episode within the series isn’t a blind defense of technological optimism, nor a rejection of pessimism. It’s an try to read reality because it is with its frictions, its limits, and its opportunities.
There’s a line from Friedrich Hayek that captures the spirit of this evaluation:
The identical applies today to anyone who looks at AI through just one lens. To know what AI is definitely doing to our reality, you could have to cross technology, economics, history, and philosophy.
Reality as Competitive Advantage
David Beyer (@dbeyer123) published an evaluation that completely captures the central tension of this moment. Imagine two medical corporations. The primary processes tens of millions of radiology images. The second handles tens of millions of medical insurance claims.
The primary has an issue AI can solve brilliantly. The photographs don’t change; knowledge converges through data. With enough compute, anyone can reach the identical level of precision. It’s a static problem.
The second faces something entirely different: a coupled system in constant flux. Regulations, policies, billing codes that get updated, disputes that evolve. The operational knowledge there can’t be studied or simulated from the skin; it’s earned by receiving rejections from the system, adjusting, and trying again. Beyer calls this “scar tissue”: the knowledge that only the true world can offer you, through friction, in real time.
AI can speed up learning when the foundations are fixed. Nevertheless it cannot generate the surprises of the true world. It cannot force regulators to vary their rules faster, or competitors to attack before you’re ready. The educational speed in these systems is restricted by the speed of reality, not the speed of compute.
Reality itself is your hardest-to-replicate competitive advantage.
The Adoption Crisis: Recursive Technology ≠ Recursive Adoption
AI models improve recursively; models training higher models. That’s real and extraordinary. But many individuals extrapolate that recursiveness into the economy and assume that mass alternative of labor is equally imminent and exponential.
An evaluation by Citadel Securities (@citsecurities) on the “Global Intelligence Crisis of 2026” dismantles that logic clearly: recursive technology isn’t the identical as recursive adoption.
Real-world adoption is strongly constrained by aspects that don’t scale at software speed:
- Physical capital and infrastructure construction
- Energy grid availability and capability
- Regulatory approvals
- Organizational change, the slowest of all
To see these physical limits in motion, take a look at manufacturing construction spending in the USA. The promise of AI requires monumental physical backing: semiconductor fabs, data centers, and energy networks.
Spending jumped from roughly $75 billion to greater than $240 billion between 2021 and 2024, the most important recorded jump. And that physical backing takes years, not months.
Furthermore, AI-driven productivity shocks are, historically, positive supply shocks: they reduce marginal costs, expand production, and increase real income. Keynes predicted (wrongly as usual) in 1930 that, due to productivity gains, by the twenty first century we’d be working 15 hours per week. He was incorrect because he underestimated the elasticity of human desire. As technology drives down costs, we don’t stop working; we simply expand our consumption frontier, demand higher quality, latest services, and construct industries that were previously unimaginable.
The actual data bears this out: there was an unprecedented jump in latest business formation in the USA since 2020, at levels which have remained historically high lately. Removed from contracting, humanity’s creative activity expands when the foundations of the sport change.
And contrary to the mass-displacement narrative, the demand for technical jobs like software engineering has found solid footing, stabilizing to 2019 levels despite the post-pandemic correction. This underlines how technology acts as a complement to our labor: restructuring work quite than eliminating it outright.
Will AI Replace Us? The Mistaken Query
In the event you’ve been following the newest AI news and podcasts, you’ve probably read something like this. A few of it’s sensationalist exaggeration; a few of it has been said by CEOs, founders, and distinguished figures at major corporations and startups. However the query we want to ask isn’t whether AI replaces us; it’s how we remain precious in what we do.
I don’t imagine all jobs shall be automated, nor that there won’t be room for developers, accountants, lawyers, and so many others. Not anytime soon. What I do imagine is that we’ll enter a mode of labor assisted by AI systems and agents, making our work potentially way more efficient. But that demands a special type of effort from us.
The questions we needs to be asking are:
- How can we remain precious in what we do?
- How can we keep improving and learning?
- How do I keep my mind lively and my critical considering sharp?
- In a world where my job is constructing prompts and guiding autonomous agents, how do I exploit AI in the most effective possible way? Being more efficient, without losing the thread of what I’m doing.
Our primary work on this latest world shall be:
- Systems design and solution architectures
- Strategy creation that agents can execute
- Business understanding and translation into concrete plans
- Skill-building alongside AI
- Critical considering to steer AI-assisted work in the correct direction
- Deep research alongside agents to unravel real problems
- Metrics, orchestration, monitoring, and governance of systems and agents (and subagents).
But at the identical time, we want to take care of a continuing effort to read, learn, analyze, query, and validate what we’re doing. The answers that agents give us have to be complemented by time, effort, and the lively use of our own minds, our critical considering, and the power to make non-obvious cross-references that no model could make by itself.
Much may occur in the approaching years. The narrative concerning the disappearance of labor will keep intensifying. But don’t lose sight of the incontrovertible fact that the trail to success stays what it has at all times been: preparation, study, research, and significant considering toward all the pieces we read and listen to.
What If the World Doesn’t End? The Scenario No person Is Pricing In
There’s an evaluation from @KobeissiLetter) that I feel is important to finish this picture: The core argument is powerful: when a narrative becomes too obvious, the market has already priced it in, and reality tends to surprise from the opposite direction.
The market has already absorbed the apocalyptic scenario: IBM suffers its worst day since 2000 when Claude automates COBOL code; Adobe falls 30% as AI compresses creative workflows; CrowdStrike loses $20 billion in market cap in two trading days when Anthropic launches an automatic security tool, even Nvidia has struggled. These moves are real and so they make sense: markets are repricing the price of cognitive labor in real time.
However the catastrophist reasoning accommodates a fundamental logical trap: it assumes demand is fixed. The bearish loop goes: AI replaces staff → wages fall → consumption contracts → corporations automate further to defend margins → the cycle feeds itself. It’s a totally static model of the economy.
Technological history systematically contradicts that logic. When the price of manufacturing something collapses, demand doesn’t stay flat, it expands. When computing became low-cost, we didn’t devour the identical amount of computation at a lower cost: we built entire industries on top of that foundation. The worth of private computers has fallen 99.7% between 1980 and 2025:
The result? No collapse. There was the web, mobile phones, e-commerce, streaming, social networks, cloud computing and a complete digital economy that today employs a whole bunch of tens of millions of individuals in categories that simply didn’t exist in 1980.
Kobeissi introduces two concepts price holding onto: “Ghost GDP”: output that appears in the info but doesn’t profit households — versus “Abundance GDP”: growth combined with an actual fall in the price of living. The optimistic AI scenario doesn’t require nominal wages to rise; it requires service prices to fall faster than income. If AI reduces the price of healthcare administration, legal services, accounting, education, and technical support, households gain real purchasing power even when their salary doesn’t move a single dollar.
And an important signal is that that is already happening. U.S. labor productivity has accelerated to its fastest pace in 20 years:
The shaded zone marks the generative AI era. The index isn’t just still rising, it’s rising faster. This is strictly what we’d expect to see from a positive supply shock: more output per hour worked, which historically translates into greater aggregate well-being.
The query Kobeissi raises: WThat is the correct query. Not because abundance is guaranteed, but because markets and public opinion have over-indexed the collapse narrative, leaving the expansion scenario dramatically underrepresented in the general public debate.
Probably the most underpriced scenario today isn’t dystopia. It’s abundance
What Does All This Mean?
We’ve checked out three distinct perspectives on the identical query: what’s AI doing to our reality?
Beyer tells us that reality has frictions AI cannot simulate: the operational knowledge earned through friction in complex systems is the hardest-to-replicate competitive advantage.
Citadel Securities reminds us that technological speed isn’t equal to adoption speed. The physical, regulatory, and organizational world sets its own speed limit, no matter how briskly models improve.
Kobeissi proposes that essentially the most underpriced scenario is abundance, not collapse. That when cognitive costs fall, humanity doesn’t stand still, it creates.
These three points don’t contradict one another, they complement one another. Together they form a coherent picture: AI is an actual and powerful transformative force, nevertheless it is embedded in a reality with its own rules, timelines, and frictions. The simulation isn’t reality. And in that gap, between what AI can calculate and what the true world demands, lives the chance for those willing to continue learning, considering, and constructing.
AI will democratize access to capabilities that previously required years of technical training. What it cannot democratize is judgment, discernment, the experience earned through friction in the true world, and the willingness to do the work that nobody else desires to do.
That’s the “scar tissue” that nobody can take from us.
This is just the start. In the approaching episodes we’ll keep unraveling these dynamics connecting technology, science, economics, history, and our own human nature.
Follow me for more updates https://www.linkedin.com/in/faviovazquez/
Sources and References
- Beyer, David. — Evaluation on AI’s limitations against complex real-world systems and the concept of operational scar tissue.
- Citadel Securities. — Macroeconomic evaluation on recursive technology vs. recursive adoption and the physical limits of AI.
- The Kobeissi Letter. (2026) — x.com/KobeissiLetter
- Penrose, Roger. Knopf, 2005.
- Hayek, Friedrich. Quote from and related writings on interdisciplinary economics.
Data and statistical series
All five charts in this text were created by the writer using data retrieved from the Federal Reserve Bank of St. Louis (FRED) database.
