Software used to be expensive to build and slow to evolve. It required specialized talent, long timelines, and real capital. That scarcity gave it value, and for years the fact that something worked at all was enough to make it defensible.
That era is ending; AI-assisted development has collapsed the cost of creation. Gartner projects that 75 percent of enterprise software engineers will be using AI code assistants by 2028, up from less than 10 percent in early 2023, and I estimate much sooner. What used to take teams weeks now takes individuals hours. Software is not disappearing, it’s becoming abundant, and abundance changes what matters.
When anyone can build a working tool from a prompt, the question for technology leaders is no longer whether a piece of software functions. It is whether the software has any reason to exist the day after it ships. A growing share of what enterprises build and buy in 2026 will fail that test, and the risk is treating disposable software and enduring systems as the same category of investment.
Defining disposability
Disposable software is not defined by quality. It is defined by replaceability. If a tool can be rebuilt in a day, recreated from a prompt, or swapped out without organizational consequence, it belongs in this category regardless of how polished it looks. Internal dashboards, workflow automations, small SaaS tools, thin wrappers around APIs or models, and a growing universe of bespoke utilities now fit the description.
In all of these cases, the logic is no longer scarce and the implementation is close to free. Competition rises, differentiation fades, durability declines. None of that makes disposable software bad. It makes it a different kind of asset, one that should be treated as an experiment rather than an investment.
Three layers where software creates value
Software creates value at three distinct layers. Code is abundant and replaceable. Products can be differentiated but remain fragile when their underlying logic can be reproduced by a competitor in a week. Systems are embedded, continuously evolving, and defensible by design.
AI collapses the value of the first layer and pushes advantage upward to the third.
Most AI-generated tools live at the code layer. They solve a specific problem adequately. What they do not do is compound. They do not become more valuable with use, and they are not harder to replace tomorrow than they were when they were written. Enduring value lives at the systems layer.
What makes systems endure
- Models are increasingly commoditized, so the differentiator is what they learn from. Systems that collect proprietary data, observe real-world usage, and improve outcomes over time create a compounding loop. More usage produces more data, more data produces better outcomes, and better outcomes drive more usage. Code does not compound. Learning does.
- Software becomes durable when it becomes part of how work actually gets done. Systems that integrate into daily workflows, shape decisions and behavior, and become trusted for critical operations cross a threshold where replacing them stops being a technical question and becomes an organizational one.
- AI makes it easier to write code. It does not eliminate the complexity of coordinating across teams, handling edge cases at scale, or integrating across fragmented environments. Over time, the systems that manage this complexity expand outward into APIs, integrations, and developer ecosystems. What started as software becomes infrastructure that others build on. At that point, it is no longer a tool. It is a platform.
The enterprise paradox
On the surface, AI looks like a threat to enterprise software; if anyone can build tools quickly, the logic goes, why should large platforms matter? The assumption misreads what enterprises actually buy. Enterprises do not buy software. They buy predictable outcomes, integrated workflows, scalable systems, and measurable impact. AI concentrates the market; players with large proprietary datasets, embedded workflows, established trust, and proven performance are precisely the ones AI amplifies. New entrants can replicate features quickly. They struggle to replicate systems at all.
A better question for 2026
The old question in software investment was whether something could be built. That question is now trivial. The better question, for anyone evaluating a tool, a vendor, or an internal project, is what about this gets better the more it is used and harder to replace over time. If the answer is nothing, the work is disposable, and it should be scoped and funded accordingly. If the answer involves data, workflows, trust, and compounding improvement, the work has a chance to endure.
AI has clarified the importance of software, not diminished it. Code is no longer scarce, systems are. As creation becomes easier, value shifts to what cannot be easily recreated: accumulated data, embedded workflows, trusted outcomes, and continuous learning. In a world where anyone can build software, advantage belongs to those who build what cannot be replaced.
