Developers are not disappearing. They are moving up a floor.
For years, companies overvalued one thing in developers: visible output.
Lines of code.
Execution speed.
The ability to ship a feature faster than the person next to them.
The reassuring presence of someone who keeps the sprint moving.
Then generative AI entered the room, and the shift turned out to be far deeper than a tooling upgrade.
It writes.
It rewrites.
It completes.
It documents.
It suggests tests.
It fixes bugs.
It can even prepare pull requests and handle certain tasks in the background as a software agent (GitHub Docs, OpenAI).
Many saw that as a replacement story.
That is the lazy reading.
Developers are not leaving the game. They are changing altitude.
Their center of gravity is moving away from raw code production toward something far rarer: judgment.
That is where people decide what should be launched, what should be rejected, what must be secured, what deserves to be maintained, and what should be thrown away before it poisons the entire system.
That is where the topic becomes strategic.
The most dangerous illusion: confusing speed with value
AI is accelerating code production. On that point, the evidence is already too strong to dismiss. GitHub published research showing faster task completion and positive developer sentiment with Copilot. McKinsey also documented major gains on selected software tasks. The real debate is no longer whether AI speeds things up. It does (GitHub, McKinsey).
The trap begins right after that.
Once the marginal cost of producing code drops, companies instinctively think they have found a productivity engine. They think “more features.” They think “less time.” They think “fewer senior developers.” They think “we will ship faster.”
And they forget what this acceleration creates in parallel:
more software surface area,
more dependencies,
more review work,
more technical debt risk,
more security exposure,
more long-term maintenance complexity.
In other words, producing code faster without governance means industrializing problems with a smile.
DORA is explicit on the underlying point: AI behaves as an amplifier. It strengthens already healthy organizations and exposes the weaknesses of poorly structured ones even more brutally. The issue is not the tool. The issue is the system around it, or the lack of one (DORA 2025, Google Research).
The useful developer tomorrow will not be the fastest one
Tomorrow, almost everyone will be able to produce more code than yesterday.
That single fact changes everything.
When a capability becomes widely distributed, it stops being the main differentiator. Scarcity moves elsewhere.
It moves toward people who know how to:
frame a problem,
separate a prototype from a product,
evaluate trade-offs,
choose a maintainable architecture,
spot hidden risks,
set limits,
say no to a technically seductive but strategically bad idea.
The important developer of tomorrow will not be the one who impresses everyone with visible speed.
It will be the one who protects the company from its own intoxication.
Because AI creates an immediate sense of power. It makes everything look easier. In software, what destroys value is not only what fails. It is also what gets accepted too quickly.
From keyboard to governance
This is the shift.
Before, a large part of a developer’s value sat in execution.
Now, an increasing share of that value sits in technical governance.
Someone still has to decide what stays.
Someone still has to review what the machine proposes.
Someone still has to validate what deserves entry into the codebase.
Someone still has to secure the flow.
Someone still has to document what must survive the next turnover.
Someone still has to preserve coherence when multiple humans and multiple AI agents are producing in parallel.
GitHub already documents workflows in which the agent creates branches, pushes code, opens pull requests, and iterates inside GitHub. OpenAI also describes Codex as a software engineering agent that can work on many tasks in parallel. That means one very concrete thing: the center of gravity of the profession is shifting from writing, to supervision, and then from supervision to responsibility (GitHub Docs, GitHub, OpenAI).
Code is becoming easier to produce.
Coherence is becoming harder to preserve.
Companies using the wrong metrics will pay for it
Many organizations still evaluate developers with outdated markers:
tickets closed,
volume of code shipped,
raw speed,
visible activity.
These metrics feel reassuring because they are easy to count.
They are also becoming increasingly misleading.
A developer augmented by AI can produce much more. That says almost nothing about the quality of decisions made, the cleanliness of the architecture, or the future resilience of the system. Stack Overflow’s 2024 survey also shows developers remain divided on the accuracy of AI output. More importantly, professional developers are less trusting than people learning to code. Experience does not make people naive. It makes them more careful (Stack Overflow).
That caution is a high-value capability.
It matters enormously in a world where the machine proposes a lot, quickly, and with enough confidence to mislead entire teams.
Security and maintenance are executive topics again
Another classic mistake is treating AI coding as a simple individual productivity booster.
That is already outdated.
As soon as agents are writing, modifying, documenting, or proposing code at scale, security, compliance, traceability, and maintainability stop being purely technical topics. They become governance topics.
Snyk notes that AI-generated code can contain vulnerabilities just like human-written code, and speed never removes the need for solid DevSecOps controls. DORA, for its part, stresses the importance of a clear AI policy, a healthy data ecosystem, and strong organizational practices if companies want real value from adoption (Snyk, DORA 2025).
Put simply: once AI enters the SDLC, the senior developer does not become optional.
That person becomes a guardrail.
And in some companies, a governance role.
Tomorrow’s developer will have to know how to say no
This may be the most underestimated shift of all.
For a long time, coding was tied to a form of productive obedience:
you are asked for a feature,
you build it,
you ship it.
With AI, developer value moves upward because production is no longer the rarest part.
The rarest part becomes the ability to say:
this need is poorly framed,
this prompt produces unstable output,
this solution adds hidden risk,
this component will not hold,
this shortcut will become expensive in six months,
this agent should not have that level of autonomy,
this code may work today and damage everything else tomorrow.
In my book, I argue that technology does not create value on its own: it needs vision, strategy, clear communication, and an aligned team. Chapter 14 is specifically devoted to the application of artificial intelligence.
That is exactly the point here.
AI does not remove the need for mastery.
It makes mastery more urgent.
What leaders should already be changing
Companies that want to handle this shift properly should immediately rethink how they view software teams.
Not in six months.
Not after an incident.
Not after flooding the platform with quickly generated and poorly owned code.
They should already move evaluation toward five much more serious dimensions:
quality of technical judgment,
ability to arbitrate,
architectural robustness,
quality of review and security thinking,
ability to preserve long-term coherence.
They should also accept an uncomfortable truth: a developer who writes less code but eliminates ten bad decisions may create far more value than the one feeding the machine nonstop.
The new prestige will not be about quantity.
It will be about control.
Move up a floor or fade into the background
The developer who will resist this shift best is not the one who denies AI.
It is not the one who worships it either.
It is the one who uses it without surrendering judgment to it.
The one who understands that value is leaving the gesture and moving upward into decision-making.
The one who can collaborate with agents without dissolving into their output.
The one who becomes visible to leadership not as a production resource, but as a force for coherence, security, and sound decisions.
AI is not removing developers from the picture.
It is stripping value away from an outdated definition of the role.
And that is very good news for those who intend to matter more.
👉 In your company, are developers still evaluated on what they produce… or already on what they prevent from breaking?
References
(DORA) = https://dora.dev/research/2024/dora-report/
(DORA) = https://dora.dev/dora-report-2025/
(GitHub) = https://github.blog/news-insights/research/research-quantifying-github-copilots-impact-on-developer-productivity-and-happiness/
(GitHub Docs) = https://docs.github.com/copilot/concepts/agents/coding-agent/about-coding-agent
(GitHub) = https://github.blog/news-insights/product-news/github-copilot-meet-the-new-coding-agent/
(Stack Overflow) = https://survey.stackoverflow.co/2024/ai
(McKinsey) = https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/unleashing-developer-productivity-with-generative-ai
(Snyk) = https://snyk.io/articles/leveraging-generative-ai-with-devsecops-for-enhanced-security/
(OpenAI) = https://openai.com/index/introducing-codex/
(Google Research) = https://research.google/pubs/dora-2025-state-of-ai-assisted-software-development-report/
