The astronauts who circled the moon in 1968 could still see Earth. They were farther from home than anyone had ever been, but when they looked back, there it was - blue and white and impossibly fragile against the black. They knew the math. They knew the trajectory would bring them home. But they’d never actually been that far before. Beyond low Earth orbit, past the point where you could fix things if they went wrong.

That’s where we are with AI and software development. We can see what’s coming. We know the trajectory. But we’ve never actually been there before.

The fear is real

Right now, the primary job of every developer is to automate what they’re doing - to figure out how to build systems that are verifiable, safe, and reliable. There’s still enormous work to do there. But given the pace, we’re probably six months from prototypes of fully automated AI development teams. Maybe twelve months from it being the norm.

The anxiety: what happens to developers after that?

The assumption is simple: we automate ourselves out of a job. We keep talking about AI replacing developers, but the pace has gotten so fast that the question isn’t “if” anymore - it’s “when.” And probably “soon.”

But here’s the thing. We don’t actually know what happens next. Because we’ve never been here before.

What automation actually does

Every time we’ve automated something fundamental, we’ve been terrified. And every time, we’ve been wrong about what would happen.

ATMs and bank tellers. When ATMs arrived in the 1970s, everyone knew what would happen: no more tellers. Why would you need humans to dispense cash when a machine could do it 24/7? Except that’s not what happened. Teller headcount increased. Banks could open more branches cheaply because they didn’t need as many tellers per branch. And tellers moved from dispensing cash to relationship banking - helping people with loans, mortgages, financial planning. Their job got better.

Spreadsheets and accountants. VisiCalc launched in 1979. It was supposed to eliminate accountants. Why would you need people to add numbers when a computer could do it instantly? Instead, the number of accountants grew. Cheap calculation created more demand for financial thinking. The job became analysis instead of arithmetic. The tedious part got automated, and suddenly everyone needed someone who could actually understand the numbers.

Compilers and assembly programmers. High-level languages were going to replace low-level coders. Why write in assembly when COBOL or C could do it for you? Instead, they expanded who could program. The whole industry exploded. And the people who understood the fundamentals - who knew what was actually happening under the hood - became more valuable than ever.

Earthrise from Apollo 8, December 1968. Photo: NASA/Bill Anders, Public Domain

The pattern is consistent: automation doesn’t eliminate jobs. It elevates them. The tedious part gets automated. The interesting part expands. The problem is we can’t see what “the interesting part” is yet. We’re still in orbit, looking at the trajectory, trying to imagine what’s on the other side.

Three things developers might actually do

If AI truly owns implementation - if code generation becomes commoditized and reliable - what becomes valuable? Here are three possibilities. Not predictions. Just hypotheses for what’s beyond orbit.

1. Outcome architects

Right now we spend most of our time on implementation. Writing code, debugging, refactoring, getting tests to pass. Once AI owns that, the valuable skill becomes knowing what to build.

Not “what features should this have?” - AI can help with that. But precise problem definition. Deep understanding of users and domains. Specifying outcomes well enough that AI can execute them correctly. Knowing why you’re building something and what success actually looks like.

This is harder than coding. Most developers are bad at it today because they could always hide in the implementation. “I don’t know exactly what we need, but I’ll figure it out as I build” worked when you were the one building. It doesn’t work when you need to specify upfront.

The people who can clearly articulate problems, understand domains deeply, and communicate precisely with AI systems - those people become more valuable, not less.

2. AI system auditors

Someone has to verify that AI-generated systems are correct, safe, not subtly broken at scale. This is a new discipline. Not QA in the old sense - more like understanding emergent behaviors in complex systems.

When AI writes a million lines of code in a day, you can’t audit it the way you’d review a pull request. You need to understand system-level properties. You need to know what “correct” even means in contexts where requirements are ambiguous. You need to spot the kinds of mistakes that pass tests but break in production.

The more we automate, the more critical this becomes. Right now we catch bugs by writing code carefully. When AI writes code instantly, verification becomes the bottleneck. The people who can do that well - who can look at a system and know whether it’s trustworthy - that’s a skill that doesn’t exist yet but will absolutely be necessary.

3. The impossible backlog

There are enormous problems humanity hasn’t solved because they were too expensive to tackle. Not because we didn’t have ideas - because we didn’t have implementation capacity.

When software cost approaches zero, developers go after problems that seemed absurdly ambitious. Personalized healthcare coordination for everyone. Real-time climate system monitoring and intervention. Scientific discovery pipelines that actually scale. Tools so customized to specific workflows that nobody would have bothered building them before.

We’ve been bottlenecked by the cost of building. Remove that bottleneck and suddenly there’s work that couldn’t exist before. Not replacing what developers did - unlocking what was always impossible.

We don’t know the ceiling

The Apollo 8 astronauts were afraid. Of course they were. They were going farther than anyone had gone. They couldn’t see what was on the other side of the moon until they got there.

But they went. And they found out there was another side.

We’re afraid of what automation means for developers. The fear is rational. The transition will be hard. Some jobs won’t survive - that’s true of every major shift. But we’ve consistently been wrong about this before. We thought ATMs would eliminate tellers. We thought spreadsheets would eliminate accountants. We thought compilers would eliminate programmers.

What actually happened was the job changed. The tedious part got automated. The ceiling got higher. The work got more interesting.

Maybe this time is different. Maybe AI really does eliminate the need for human developers entirely. But we don’t actually know that. We’ve never been here before.

We’re in orbit, looking at the trajectory, trying to imagine what’s beyond. We can see home from here. We know the math. But out there?

We’ve never left orbit.

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This post was dictated by a human and edited by Claude Sonnet 4.6