Are Developers and Engineers Obsolete? What Actually Changed

Are developers and engineers obsolete? No. The job moved, and if you are about to pay someone to build software for you, you should know exactly where it moved to.

I recently taught someone with essentially no development background how to use Claude Code to build his own application. Then I helped another person build a platform with a form that generates a PDF on submission. A few years ago, either of those is a hire. Instead I handed over the method and let them run.

Which leaves an uncomfortable question sitting on my desk. Am I training people to replace me?

I don’t think so. But the reasoning is more useful to you than the verdict, because the same shift is reshaping every vendor who will ever quote you for an internal tool or a website.

Are Developers and Engineers Obsolete, or Did the Value Just Move?

For roughly five decades, producing software required knowing a programming language. That was the gate. Without syntax, you hired someone who had it.

Generative AI removed the gate. It did not remove the building.

We have watched this exact substitution before, at smaller scale. WordPress let people publish without HTML. Shopify let people sell without writing a payments engine. In each case the tool absorbed the craft floor and the ceiling kept rising, because the hard part was never the typing.

The number confirms the pattern. GitHub added 36 million new developers in 2025 and passed 180 million accounts, with close to 80% of new signups adopting Copilot in their first week (GitHub Octoverse, November 2025). That is not a profession dying. That is a profession being flooded at the entry level while the definition of the senior end quietly changes underneath it.

The Builder Is a Real Category Now

Call the new arrival a builder. A builder understands the business problem and uses AI and automation platforms to produce something that works. They may not know what a race condition is. They may never learn. I taught two people to be builders on purpose.

Most software does not need an engineer. An internal status dashboard, a lead form, a document generator, a spreadsheet that finally stopped being a spreadsheet: none of that needed a development team in 2019 either, it just cost enough that most companies went without. AI made those affordable, and I refuse to be precious about that.

Engineering Starts Where Consequences Start

Here is the shallow version of the counter-argument, which I want to reject out loud: “engineers still matter because AI writes bad code.” That is not it. AI writes fine code, often better than the median human first draft.

Engineering has never been typing. Engineering is the ownership of consequences.

It begins the moment someone has to answer questions the generated code cannot answer for itself. What happens when this runs on ten thousand records instead of ten? Where do your customers’ files actually live, and who at the hosting company can read them? What happens when two submissions hit the same record in the same second? How do we roll back at 4pm on a Friday? Does the backup restore, or do we merely have backups? How much does this architecture cost in month eighteen?

And the question I get paid the most for: should this be built at all?

The generated code is maybe 20% of the difficulty. The rest is architecture, integration, security, monitoring and the judgment call about what not to automate. When we at Carlos Arias run a build-buy-automate decision with an owner, the code is almost never the deciding variable.

The “Almost Right” Problem

You already know this failure mode, even if you have never read a line of code. The output looks finished and reads with total confidence. One detail inside it is invented.

The courts wrote it down first

The public AI Hallucination Cases database maintained by researcher Damien Charlotin catalogues court decisions in which a party was found to have relied on fabricated AI output. As of its June 9, 2026 snapshot it listed 1,598 matters, up from roughly 200 a year earlier, and the maintainer notes the real figure is higher because only explicit judicial findings are counted (AI Hallucination Cases). Sanctions have climbed from a $5,000 fine in 2023 into five figures with suspensions, and they now issue from federal appellate panels rather than trial courts alone (Norton Rose Fulbright, 2026).

Courts are simply the field that records its errors in public. Finance and journalism are running the same experiment with a thinner paper trail.

No document in that database looked wrong. Every one of them looked excellent.

Developers report the identical pathology

In Stack Overflow’s 2025 survey, the single largest frustration, named by 66% of respondents, was AI output that is “almost right, but not quite,” with 45% citing longer debugging of AI-generated code. Meanwhile 46% actively distrust AI accuracy against 33% who trust it, even as 84% use or plan to use the tools (Stack Overflow, 2025).

A builder reads output and sees that it works. An engineer reads the same output and asks under what conditions it stops working.

Vibe Coding Is Fine Until the Stakes Change

I am not going to attack vibe coding. I use AI to write code every working day, and pretending otherwise would be theater.

Describe what you want, then iterate by feel. For prototypes, internal tools, experiments and testing whether a business idea is even real, it is the fastest method that has ever existed. Ship it.

The calculus inverts when the system touches money, authentication, health information, customer PII, privileged material or anything a regulator would want to hear about. Then “it works on my machine” becomes a liability posture rather than a status report.

Vibe coding is not the problem. Treating a prototype as a production system is the problem, and that mistake is made by the buyer at least as often as by the builder.

AI Probably Increases the Need for Senior Engineers

The counterintuitive part. More software produced means more software to review, secure, integrate, host, patch and eventually delete.

AI amplifies the discipline you already have

Google’s DORA program put AI adoption among software professionals at 90% in its 2025 report and found that higher adoption correlates with both greater delivery throughput and greater delivery instability. Its central conclusion is that AI functions as an amplifier: it magnifies whatever engineering discipline an organization already has, including the absence of any (DORA, 2025).

Read that as a buying signal. Handing agentic tooling to a weak team does not make it a strong team, it makes it a faster weak team.

Nobody has a clean productivity number

I distrust anyone quoting a multiplier. METR’s randomized trial of 16 experienced open-source developers across 246 real tasks found them 19% slower with early-2025 AI tools, while those same developers estimated they had been 20% faster (METR, July 2025). METR’s February 2026 update, with 57 developers and 800-plus tasks, moved the estimate to roughly a 4% slowdown with a confidence interval spanning -15% to +9%, and the team flagged serious selection effects because many participants declined to submit tasks they didn’t want to do without AI (METR, February 2026).

Sit with the gap between the 19% measured and the 20% believed. Thirty-nine points, in professionals, about their own working day. Self-report is not evidence. That applies to whoever is writing your quote.

Labor demand has not read the doom coverage either. The World Economic Forum’s Future of Jobs Report 2025 still ranks software and applications developers among the fastest-growing roles through 2030, at 57% projected growth, inside a forecast of 170 million jobs created against 92 million displaced (WEF, January 2025).

What I Actually Do Now

My work looks less like writing an application and more like designing the system that produces the application.

I specify architecture. I write the repository rules and the agent instructions. I define acceptance criteria, testing requirements, security policy, deployment procedure and observability. Then agents do the implementation labor and I supervise the output, which is where the real bottleneck now sits: not generating the diff, proving it is safe to ship.

One rule governs all of it, and I apply it to agentic systems the same way I apply it to a merge queue. The layer that produced an artifact never clears it. That is the core of how we contain autonomous agents in production, and it does not weaken because the artifact is a pull request rather than a customer email.

I have been reading pattern shifts like this one since 1988, on a Commodore 64. I founded the practice in 1999. In 2022 I moved it entirely to agentic AI, and the twenty-five years before that is precisely how I know what to automate and what to leave alone.

What This Means When You Hire

You are going to get quotes from people who can generate a very impressive demo in an afternoon. Some of them are builders and some are engineers, and the price will not tell you which.

Ask a different set of questions. Who owns it when it breaks at 11pm the night before your busiest day of the year? Where does customer data sit, and under whose terms? What is the rollback? What did you refuse to build, and why?

An engineer will answer those flatly, including the parts that make the proposal look less shiny. A builder will change the subject to features. Neither answer is disqualifying on its own, but only one of them belongs near a system your revenue depends on.

The future engineer will write less code than any generation before them. They will be responsible for far more software than any generation before them. That is not the death of software engineering, it is the start of a version of it that is harder to fake and easier to check.

If you are staring at a quote and cannot tell which one you’re buying, write me at [email protected]. I’ll tell you what I’d ask.