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Surviving AI

2•dpforesi•1h ago
What follows was written by ChatGPT 5.2 Instant and it reflects my conversation with it about the two articles linked below. Enjoy.

Ego plays a complicated role in periods of technological change. It sharpens skill during stable eras, but it often hinders adaptation during inflection points.

Two recent essays on AI and software engineering illustrate this tension clearly.

Emir Ribic’s “From Craftsmen to Operators” https://dev.ribic.ba/the-rapid-evolution-of-software-engineer-s-role

Ribic frames the rise of AI-assisted development as a loss of craft. He mourns the disappearance of deep, line-by-line problem solving and the sense of authorship that came with it. Engineers, in his telling, are becoming operators—prompting, reviewing, and assembling—rather than builders. The piece captures something real: pride in difficulty, satisfaction in mastery, and the joy of personally solving hard problems.

But that pride is also where ego enters. What’s being lost isn’t just a way of working—it’s a form of status. Manual coding was scarce, hard-won, and socially rewarded. When AI erodes that scarcity, it threatens identity as much as technique. The article largely dwells on that loss, while giving little attention to the exhilaration many engineers feel when modern tools collapse days of work into minutes.

P.C. Maffey’s “AInxiety” https://pcmaffey.com/ainxiety-1/

Maffey takes a more balanced view. He openly acknowledges discomfort, ethical concerns, and the risk of over-reliance on AI, but he also accepts the productivity gains and uses AI extensively in his work. Rather than framing the shift as a fall from craft, he reframes the role: less time spent on syntax, more on design, planning, judgment, and responsibility. Where Ribic sees loss, Maffey sees trade-offs.

Taken together, the contrast suggests a broader pattern we’ve seen before.

During the Industrial Revolution, skilled artisans resisted mechanization not only because of economic threat, but because identity and status were tied to difficulty and exclusivity. The same thing happened when typewriters gave way to personal computers in offices—typing lost its prestige once everyone could do it. In each case, ego slowed adaptation, but also reflected a real commitment to excellence that had previously raised standards.

That leads to a fair synthesis:

Ego hones skill in stable environments. It motivates mastery, pride, and depth. But during technological inflection points, the same ego can become friction—blinding people to leverage, speed, and new forms of craftsmanship.

Progress doesn’t eliminate craft; it relocates it. The challenge isn’t to abandon pride in skill, but to recognize when clinging to old expressions of that skill prevents adaptation to more powerful tools.

The winners in these transitions aren’t ego-free. They’re ego-aware.

Comments

northfield27•1h ago
Agreed - ego does push individuals towards mastery because it provides stability and status. But the ones too tied to ego gets lost in history.

What I don't agree with is the current hype based marketing.

First calculators and computers were sold with the promise of improved productivity and those tools actually delivered in the same time frame. But thats not the case with AI.

Consider this promise: "AI will eat up coding completely". Each CEO claims that 92%-95% percent of coding is done by AI in their company. But if you are active here, you would know that even some senior engineers at big tech don't find it useful in production. I think that these "hype-funded" engineers at CEO's company are just working on toy projects, because I find claims of senior engineers true.

These CEOs make wild claims when many of them haven't actually programmed much. Every interview with these CEOs pushes the year-of-AGI 2-3 years forward.

This is such a time where I really hope the investors actually look into the details, experience the gap between the claims and reality and pull their funding.

dpforesi•1h ago
Agreed. I don't think AI adoption should be strictly driven by people (executives) that are too far removed from the development process to not have clear understanding of how effective the tools are or an understanding of their failure modes. Some of the consternation might be coming from the people working on the code directly, which is probably a healthy feedback loop that should not be ignored.

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