The New Software Engineer: How LLMs and AI Agents Are Changing the Way We Build Startups

Open any tech feed and you’ll see someone predicting the Last Human Pull Request. In 2023, it was expected that coding would be obsolete by 2024. Then the date slid to 2025. Now the forecast hovers somewhere between 2026 and 2027 — always imminent, never arriving. 

Yet when I’m actually at the workbench — writing code, running tests, building things — it doesn’t feel like the end of anything. It feels more like someone quietly upgraded the tools around me. The work remains hands-on and familiar, but the pace at which everything moves has changed dramatically. 

How Local Development Has Changed 

From autocomplete to agents 

Not long ago, AI in development felt like autocomplete on steroids. Tools like ChatGPT or early Copilot can generate snippets or entire functions, but you still drive the process. It was essentially a faster version of the old Stack Overflow workflow: you asked for something useful, copied it back into your editor, and manually adjusted it. 

Agentic AI changed that dynamic. They plan steps, run code, observe outcomes, and try again. If a human can compile and execute a program, an agent can too. This creates a fundamentally new development loop. 

Instead of laboriously implementing every detail yourself, you describe the outcome, the AI builds toward it, and you step in occasionally to guide the direction. It shifts your role from hands-on builder to collaborator and reviewer. 

A new rhythm: many threads at once 

This naturally leads to parallelism. I often run multiple agents at once, each tackling a different feature or experiment. While I’m fixing one issue, several others are already being worked on. 

The odd discovery is that the bottleneck is no longer the computer — it’s me. For the first time in my career, I’m struggling to keep up with the volume of output being produced. It feels like managing a small team whose pull requests never stop coming. 

That shift in workflow sets the stage for an even more significant change when applied to startup environments. 

Why This Matters for Startups 

In a startup, speed is oxygen. When you’re working with a small team, the ability to convert ideas into experiments in days instead of months is the difference between moving forward and falling behind. Every hour you save on boilerplate or infrastructure is an hour you can spend making real product decisions. 

Faster loops mean better intuition 

Startups succeed by iterating quickly. You don’t debate for weeks whether a design tweak is safe — you ship it, watch what happens, and learn. But historically, even simple prototypes came with friction: you had to set up environments, wire up boilerplate, and fight through the early “just-make-it-work” steps before you could test the idea itself. 

AI agents wipe out most of that friction. You can spin up a working prototype in minutes, poke at it, discard it, and try a different direction. And because each loop is so small, your product intuition sharpens naturally. 

Working with AI also changes how engineers learn. I’ve always been the kind of developer who learns by doing — breaking things, seeing what happens, understanding why. AI accelerates this process as well by eliminating tedious setup and dropping you directly into the interesting parts. 

Developer experience becomes easy to improve 

There’s also a quieter benefit: all the engineering improvements you always put off suddenly cost almost nothing. I use AI to automate linters, parallelize tests, generate DevX scripts, and clean up workflows. These were the tasks that used to get deprioritized because product deadlines came first. When they take minutes instead of days, you just build them. The team becomes faster almost by accident. And when the team becomes faster, product delivery improves naturally. All of this makes startups uniquely well-positioned to benefit from AI-driven development. But speed alone isn’t enough — at least not without guardrails. 

The Parts You Can’t Automate 

The danger of limitless output is that it can quickly turn into noise. Without discipline, AI becomes a dopamine machine: endless ideas, endless snippets, endless clever one-offs. A month later, your codebase looks like a junkyard of half-polished experiments. 

Technical debt doesn’t evaporate just because an AI wrote part of the code. You still need judgment. You still need taste. You still need the willingness to refactor, simplify, and delete. 

Culture is what keeps this under control. Teams need shared standards, clear definitions of “done,” and the confidence to remove things that don’t belong. Reviewers should behave like gardeners — shaping, pruning, and improving — not just gatekeepers who approve or reject. 

AI agents are best understood as extremely fast, extremely eager interns: capable of great work, but never accountable for it. They need direction. And they need someone who knows what “good” looks like. 

Startups exist to explore new ground. LLMs, by design, operate on patterns they’ve already seen. They can handle the solved problems — CRUD, auth flows, API glue, infrastructure scaffolding — the things that have been built a thousand times. That gives humans more time to work on the unsolved aspects: insight, product sense, and originality. 

Your code used to have one audience: humans. Now it has two. AI requires the same things humans do: readable code, reliable tools, and clear patterns. The standards don’t change. If anything, they matter more.