Enterprises are increasingly investing copious amounts of cash in AI without a lot to show for it. This could be, in part, because the wrong people are leading the change.

As I’ve argued before⁠, AI isn’t likely to eliminate developers so much as change what we need from them. For example, we keep asking whether junior developers are needed in a world where large language models can write code faster and cheaper. What this overlooks is the reality that these younger developers and their relative inexperience may be exactly what we need to rewrite the rules of software development.

This thought hit me while reading James Governor’s riff on something Ben Griffiths⁠ wrote about our industry’s habit of confusing age with authority. Griffiths remembered sitting through a conference talk in which a speaker tried to shame a young audience for not recognizing some of the older men who had shaped computing. The irony, Ben noted, was that many of those “old men” had done their world-changing work when they were younger than the people being lectured. Bill Joy wrote vi when he was 22, John Carmack created Doom at 23, Linus Torvalds launched Linux at 22, etc. Many of our industry’s titans made their biggest contributions before they had decades of experience.

The point isn’t that young people are smarter. They’re not. The point isn’t that the key to AI success is to ignore more experienced developers. That’s dumb. Rather, it’s a suggestion that Griffiths’ larger point is right: At the beginning of big shifts, experience can be a mixed blessing. It can help you see risk, but it can also make you overconfident in old ways. The most successful enterprises will find ways to balance youthful innovation with experienced guardrails.

The factory doesn’t redesign itself

Zara Zhang recently pointed to Paul David’s classic 1990 paper, “The Dynamo and the Computer,”⁠ as a way to understand why so many companies have “adopted” AI without much to show for it. David’s argument, simplified, is that electricity didn’t immediately transform factories. For a long time, factories simply swapped out the central steam engine for an electric motor while keeping the same layout, the same workflows, and the same assumptions.

Electricity was new, but we largely stifled its potential by force-fitting it into old factory systems.

The big productivity gains came later, when factories stopped treating electricity as a cleaner steam engine and started redesigning work around smaller motors distributed throughout the factory. Once each machine could have its own motor, the factory no longer had to organize itself around a single driveshaft. Work could instead be reorganized around the flow of production.

That’s a decent description of where many enterprises are with AI. Enterprises today are buying copilot licenses by the thousands, wiring agents into existing applications, etc., and then wondering why the results are so uneven, as I’ve written⁠. This is the equivalent of swapping the steam engine for an electric one and declaring that the AI modernization work is done. It’s not. Not even close.

The real payoff won’t come from asking AI to write the same tickets a bit faster. It will instead come from changing how teams define work and how (and what) developers build. The “factory” has to change.

So here’s the uncomfortable question: Who is most likely to build the new factory?

Experience cuts both ways

There’s an obvious danger in romanticizing youth. Plenty of bad software has been written by people with unlimited confidence and limited context. Enterprises need software that works, yes, but “works” also means it complies, scales, respects security boundaries, and more.

This is where experienced developers matter. A lot.

As I pointed out recently, the agent era makes engineering judgment more important than ever. After all, AI makes it easier to generate code, but easier code generation can become easier technical debt generation. Hence, the limiting factor becomes less of “Can we create something?” and more of “Can we create the right thing, in the right place, with the right constraints?” Taste is required, in other words.

Senior engineers are often better at seeing those constraints because their experience gives them “taste.” They know why the weird validation rule exists, and they remember the customer who depended on the undocumented behavior. They understand why a simple schema change can turn into a multi-week migration.

But experience also has a shadow side, because it can make the current process feel inevitable. A senior engineer may see an AI assistant as a faster autocomplete because that’s the easiest way to fit AI into their existing mental model. A junior developer, less invested in the old workflow, may ask the more interesting questions: Why are we doing this ticket at all? Why isn’t the spec executable? Why can’t the agent generate the test harness first?

It’s not that the more experienced developers don’t know these questions. Rather, they may simply not have the energy to rage against the machine, as it were.

The value of inexperience

The worst way to use junior developers in the AI era is to treat them as cheaper versions of senior developers. That was always a bad idea, but AI makes it worse. If the job is “take this ticket, generate some code, and send it to a senior person for review,” the junior developer becomes a human wrapper around a coding assistant. That helps no one. The junior doesn’t learn much, the senior gets buried in review, and the enterprise ends up with more code, which, as I’ve said, is hardly a good thing.

Instead, junior developers should be given room to explore new workflows, with just enough oversight from experienced colleagues. That might mean giving these newer developers interesting questions to answer, such as:

• How would we redesign onboarding if every internal API had an AI-readable contract and examples that actually worked?
• How would we change code review if the agent produced a change summary, test evidence, dependency risk, and rollback plan with every pull request?
• How would we build features if product requirements were written as executable acceptance tests rather than vague prose?
• How would we reduce toil if agents could safely perform routine migrations, dependency updates, or incident triage within clearly defined boundaries?

These are not toy problems. They’re not “junior work.” They’re exactly the sort of process redesign that enterprises need but generally avoid because everyone is too busy running on the existing hamster wheel.

Finding the balance

So what should engineering leaders do? First, stop treating AI adoption as an individual productivity contest. We seem to be moving quickly away from the idea that “lots of tokens” equals “great engineer,” but the fact that we even flirted with it is damning. I love how Santiago Valdarrama eviscerates this vanity metric: “Measuring AI productivity in number of lines written is a stupid mistake. One day, everyone will have always been against this.” Instead we should be asking questions like, “What part of our software delivery process no longer makes sense?” AI’s biggest gains will come when we change how we specify, test, review, and ship software.

Second, mix up your AI workflow teams. No, not committees or PowerPoint-producing centers of excellence. I’m talking about combining two or three newer developers who are already fluent in AI-native tools with two or three senior engineers who understand production, security, architecture, and organizational constraints. Then give them a real workflow to redesign, such as dependency upgrades or test creation.

Third, make the senior engineer’s job less about saying no and more about defining the guardrails within which others can say yes. I’ve argued that golden paths are key to using AI effectively. Good senior engineers should define the paved roads: approved patterns, test requirements, observability standards, etc. Then let junior developers and agents move quickly inside those boundaries.

Fourth, reward deletion. This may be the most important point. Going back to the factory electricity metaphor, we’ll fail with AI modernization if we simply add AI without removing outdated processes.

Bring everyone to the table

The future of software development won’t belong to the young. It won’t belong to the old, either. It will belong to teams that combine the talents of both.

Newer developers often bring impatience. They’re less likely to accept the existing workflow as sacred. They’re more likely to try weird tools, compose them in unexpected ways, and wonder why enterprise software development feels like a ritualized exercise in waiting for permission.

Experienced developers bring judgment. They know that software has users, auditors, attackers, budgets, latency, history, and consequences. They know that the right answer is often boring, and boring is good.

Enterprises need both. They need the developer who asks why the factory is still organized around the old drive shaft, and they need the developer who knows which machines will kill someone if moved casually. In sum, every development team needs people who know why the old system exists… as well as those who don’t.