Note: This article is a developer-focused adaptation of my original blog post in Italian. You can read the full, in-depth personal reflection here: La storia si ripete? on Nospace.
Every time we transition through a major technological shift—from the steam engine to personal computers—we witness the exact same arena. One side screams about imminent miracles, while the other predicts the end of human work. Today, with the artificial intelligence hype cycle at its absolute peak, we are watching history play out on fast-forward.
If we look back, the script never changes. From the taming of Fire that altered our biology, to the Agricultural Revolution that anchored us to the land, through the Age of Metals, the industrial muscle-multipliers of the Steam Engine, and the information storage of the Personal Computer—each leap has split humanity into fierce believers and doomsday prophets. Now, as we enter the Noetic Revolution (or Cognitive Era)—where tech no longer just replicates our muscles but directly targets our cognitive processes—the cycle begins anew.
Let me be clear: these are my personal opinions, born from daily battles in the software engineering trenches, looking at messy real-world systems, and refusing to drink the Silicon Valley Kool-Aid. This isn't doom-and-gloom pessimism. I build systems for a living; I know technology is a fantastic utility. But as engineers, our job is to apply healthy skepticism and debug the hype.
If you were around during the PC boom of the '80s and '90s, you’ll probably recognize the tune the industry is playing right now. From the "Wintel" Spiral to the GPU Loop
If you programmed or worked in IT during the '80s and '90s, you surely remember the Wintel (Windows + Intel) alliance. It was a perfect, self-serving commercial spiral: Intel manufactured increasingly powerful and expensive microprocessors; Microsoft and other software giants responded by releasing heavier, more resource-hungry operating systems and programs (the classic bloatware).
At the end of this chain sat the customer—often a confused business owner—forced into a perpetual hardware upgrade cycle just to run software that ultimately did the exact same tasks as before, like writing documents and filling basic spreadsheets. It felt like an endless loop, but it eventually hit a physical and economic ceiling.
Today, the players have changed, but the playbook is identical: Nvidia acts as Intel, holding a chokehold on high-end GPU hardware (and charging eye-watering prices for the privilege). OpenAI, Anthropic, Google, and Microsoft are the new Microsoft, shipping increasingly resource-hungry models that require massive server infrastructure just to generate text.
They tell us we are on the verge of AGI, but the business model is exactly the same: push more expensive silicon to run heavier, heavily subsidized software that often struggles to deliver a proportional jump in day-to-day utility compared to the previous release. It is the Wintel bloatware cycle, retrofitted for probabilistic token prediction. The Illusion of the Plateau and "Cooked Benchmarks"
With every new model launch—whether OpenAI releases GPT Astra, Anthropic drops Fable 5.1, or Google demoes a new version of Gemini—we get the same coordinated PR blitz. Look at this benchmark! Look at the leaderboard!
But anyone writing production code daily knows that real-world performance is hitting a visible plateau. The jump from one model version to the next is no longer a paradigm shift. It is a steady refinement of details: slightly larger context windows, a few milliseconds shaved off latency, or cleaner JSON outputs.
These academic benchmarks are increasingly saturated or, worse, "cooked" to look good in marketing slides. In a clean, sterile lab environment, the scores go up. But when these models hit the messy, real-world production trenches—with dirty legacy data, undocumented business logic, and a complete lack of determinism—the magic evaporates.
As tech critic Ed Zitron pointed out in his explosive interview on Steven Bartlett’s The Diary Of A CEO (check it out here), the underlying economics are a house of cards. We are seeing massive, circular cross-funding agreements (hyperscalers funding startups so they can spend that money back on their own cloud infrastructure) to keep valuations high, while actual enterprise adoption yields tiny, unprofitable returns. It's a bubble, and the foam is about to blow off. The "Democratization of Error" and the Clean-up Crew
There is a loud, naive narrative—pushed by tech VCs and occasionally echoed even by brilliant creators like Salvatore Sanfilippo (antirez) in his latest video, I smell AGI: Chollet, GPT Astra and Fable 5.1—that software engineering is a dying career. The theory goes that because AI can write code, non-technical business users will soon prompt their way into building fully functional enterprise software all by themselves.
History, however, has already debugged this delusion: The '80s Spreadsheet Boom: Lotus 1-2-3 and VisiCalc were supposed to kill the IT (EDP) department. Managers would build their own financial tracking systems! Instead, it created an epidemic of corrupt formulas, silent calculation errors, and terrible business decisions. The 2000s WYSIWYG Web Builders: FrontPage and Flash promised that anyone could build a professional website in an afternoon. Developers spent the subsequent decade rebuilding those slow, unoptimized, and unmaintainable sites from scratch.
Lowering the technical barrier of a tool does not lower the cognitive barrier of systems analysis. When non-technical users prompt an LLM to generate code, they produce what I call the Democratization of Error: a chaotic web of unmonitored scripts, silent data corruption, security holes, and massive technical debt generated by people who don't understand system state, database normalization, or security.
We aren't going anywhere. Our role is simply shifting from typing syntax to system architecture, verification, and reverse engineering. Senior developers will become the highly-paid "Architectural Clean-up Crew," hired to untangle the spaghetti messes generated by AI-enabled business departments. Formula 1 Cars vs. Trucks in Medieval Traffic
Why is there such a massive gap between tech visionaries and on-the-ground engineering? I believe it is a matter of scale and perspective.