When Code Becomes Cheaper Than Debugging: The Rise of Ephemeral Software

More than 90% of new TiDB Cloud clusters are now created by AI agents, not humans. These databases manage millions of schemas and execution environments that exist for mere minutes before vanishing. This isn’t happening at some research lab—it’s production infrastructure at a major cloud database provider, and it points to something bigger changing in how we think about software.

The economics tell the story: AI agents’ capabilities are doubling every 4-7 months. Current models achieve almost 100% success rates on tasks that take humans less than 4 minutes, according to recent METR research. If you extrapolate this, we’re looking at AI agents capable of automating a month of human software development before 2032—maybe as early as 2027 if the pace holds.

What happens when rebuilding software from scratch becomes cheaper than debugging it? This compression from human-months to agent-minutes does more than speed up development. It enables something different: ephemeral software.

1. Disposable Code

Sean Grove has been working on this with his Conjure project, building UIs that “build themselves as ephemeral, on-demand, iterable programs that appear out of nowhere to be used, then disappear when they’re no longer needed.” He thinks the future of UI development breaks down roughly like this: 10% fixed, hand-built UIs, 40% conversational interfaces (think ChatGPT), and 50% long-tail, on-the-fly UIs generated for one-time use.

But it goes beyond UIs. Satoshi Nakajima calls this “disposable software”—using AI to “instantly generate software with sufficient completeness, skip maintenance, and discard it once it has served its purpose.” Code gets created in real-time, used once, deleted. Developers talk about “just-in-time programs that serve the purpose on the spot,” where you don’t search for existing solutions, you just generate what you need and swap it out when requirements change.

The evidence is already showing up in production. That TiDB stat I mentioned earlier—90% of new clusters created by AI agents—means databases are managing millions of schemas and execution environments that last minutes. AI agents are ephemeral by design, spun up for specific tasks, then torn down.

2. How This Changes Development

Traditional software development assumes you’re building for permanence. Months to build means it needs to last years. High switching costs mean you plan carefully upfront. Long maintenance cycles mean technical debt piles up. Ephemeral software inverts this. Minutes to build means single-use is fine. Near-zero rebuild cost means you optimize for now. No maintenance—just regenerate when things change.

The whole Software Development Life Cycle is getting rewritten. AI agents are moving from copilots to autonomous builders, and development is converging into multi-agent platforms where specialized agents handle design, code, test, and deployment in integrated workflows. Planning that used to take months now takes minutes. Design used to optimize for 5-year lifespans—now it optimizes for current needs and you rebuild when requirements shift. Implementation that needed teams coordinating for weeks now happens through multi-agent ecosystems with specialized roles: Frontend Specialists, Backend Architects, Security Guardians, Performance Optimizers. Testing cycles give way to instant validation. Releases become continuous deployment of micro-changes. And here’s the weird part—years of bug fixes get replaced by a cost calculation: is it cheaper to rebuild than debug?

The market’s betting this is real. The generative AI in SDLC market is projected to jump from $0.25 billion in 2025 to $75.3 billion by 2035—a 300x increase. 68% of organizations are already using or planning to use generative and agentic AI in development.

3. What This Means

Some uncomfortable questions come up. If users can generate custom apps on-demand in minutes, why pay monthly fees for generic SaaS? Ephemeral apps and instant-generated interfaces might outnumber traditional SaaS by orders of magnitude.

Right now 76% of developers refactor at least half of AI-generated code before using it. But when rebuild cost hits zero, does “good enough for now” become the standard? Does disposability mean lower quality, or does rapid iteration improve things? Human engineers will probably shift toward high-level strategy, creative problem-solving, and managing AI agent ecosystems instead of writing code directly. The skill set moves from implementation to specification, orchestration, validation.

There are security implications too. AI agents running with delegated authority and cross-domain access create new challenges around transparency, interpretability, and ethical use—especially when the software itself keeps regenerating.

4. The Real Shift

When rebuild cost approaches zero, the specification becomes the constant while implementations become fluid and disposable. This is getting formalized as Specification-Driven Development (SDD)—executable specifications (requirements, tests, behavioral descriptions) become the primary artifacts, with AI agents generating implementations on demand. Requirements and tests become what lasts in a world where code is ephemeral. The challenge moves from “how do we write maintainable code?” to “how do we write maintainable specifications that generate correct code?”

We’re not just automating software development. Software itself is becoming ephemeral, disposable, generated just-in-time for specific needs. The implications for how we build, sell, and consume software go pretty deep. The question isn’t whether this happens—it’s whether organizations are ready for a world where software gets measured in regenerations, not releases.