The Economics

Why Cheaper Software Means More Demand

The cost of implementing software has collapsed. What used to take a team of five two weeks can now be done by one developer with AI agents in two days. But this doesn't mean you need fewer engineers. It means you need different ones. Programming is still hard, even with these tools. It still takes a lot of effort. Anyone telling you otherwise is selling something.

Implementation Time Collapsed. Thinking Time Didn't.

AI agents eliminated the tedious part of coding: the typing, the boilerplate, the repetitive patterns. What they didn’t change is the hard part. Understanding the problem, choosing the right architecture, making tradeoff decisions, and knowing what NOT to build. The ratio of thinking to typing shifted from 30/70 to 70/30.

The Jevons Paradox: Cheaper Software Means MORE Demand

In economics, when a resource gets cheaper, demand for it increases. When coal became cheaper to burn, the world didn’t use less coal. It used dramatically more. The same applies to software. When building features costs 80% less, companies don’t build the same features cheaper. They build 5x more features. Your backlog won’t shrink. It’ll explode with things that were previously “not worth building.”

Fewer Developers, Higher Caliber

A senior developer with agentic AI tools can produce the output of 3-5 junior developers. But you still need that senior developer’s judgment, architectural knowledge, and ability to direct the AI effectively. The uncomfortable part: juniors working unsupervised with these tools will ship bugs they can’t catch. They don’t yet have the experience to spot when the agent is confidently wrong. That doesn’t mean you ban juniors from the tools. It means you structure their access. Pair them with seniors for the first few weeks. Limit them to AI-ASSIST tasks where a senior reviews every PR. Have them write the prompts and review plans before the agent executes. They’ll learn faster this way than they ever did reading Stack Overflow. The winning formula: give your best engineers AI superpowers, ramp juniors with guardrails, and add technical leadership to wire it all together.

What Works Best, and Where It Falls Short

AI agents perform best with statically-typed languages (TypeScript, Go, Rust) and established codebases with clear patterns. They’re excellent for CRUD apps, web applications, and SaaS products. I’ll be honest about the limits: for very large codebases with delicate, security-critical code (think the Linux kernel, low-level C++ systems), I’d be skeptical of the gains. Agentic coding shines brightest on the kind of software most companies actually build: web apps, APIs, internal tools. Greenfield projects with no conventions give the agent nothing to learn from, unless you front-load the spec work. Open-ended features and new architectures absolutely work with agentic coding, but the investment shifts. Instead of spending time on implementation, you spend it on super detailed PRDs, specs, and architecture docs. The agent’s output quality is directly proportional to the spec quality. Vague prompt, vague code. Detailed spec with clear acceptance criteria, constraints, and edge cases? The agent nails it. Legacy codebases with good test coverage are surprisingly productive because the tests guide the agent.