AI coding tools are stripping cost and friction out of software development, turning tasks that once took teams weeks into prompts that run in seconds. That shift is already pushing prices down for some digital products and services, and it is tempting to assume that software itself is on its way to becoming a dirt‑cheap commodity. The reality is more complicated: code is getting cheaper, but the systems that matter most are becoming more strategically valuable, not less.
As AI automates routine expertise, the economic center of gravity in software is moving from writing lines of code to owning data, workflows, and distribution. The question is not whether AI will flood the world with code, it already is, but who will capture the value when software is abundant and almost anyone can build an app.
Software was already deflationary, AI just stepped on the gas
Long before generative models started writing functions on command, software had a built‑in tendency toward lower prices. Once a product is built, it can be copied and distributed at virtually zero marginal cost, so each additional customer makes the economics better without requiring more factories or trucks. Several structural factors, including Scalability and Low, mean that once a product is working, every new user is almost pure profit. That is why classic enterprise licenses have steadily given way to cheaper, usage‑based subscriptions and freemium tiers.
AI intensifies this deflationary pull by attacking the most expensive part of building software, the labor that goes into research and development. For vendors, automated coding and testing promise higher margins by cutting R&D expense, but they also increase competitive pressure, since rivals can ship similar features faster and at lower cost. Analyses of the sector describe software as a that makes technology cheaper and more powerful over time, and AI is now accelerating that curve.
Code is becoming a commodity, but software is not
At the level of raw material, code is clearly on its way to commodity status. Generative tools can now produce boilerplate APIs, CRUD backends, and integration glue that used to justify entire consulting projects. Commentators describe how Abundance of code unlocks new uses, making projects that once looked uneconomic suddenly viable. In that world, the value of any single snippet drops, just as the value of a barrel of oil is constrained by global supply.
Yet building reliable products still requires far more than dumping AI‑generated functions into a repository. As one practitioner put it, Software Is Still because the hard work sits in architecture, security, support, and reliable data sync. That is why some investors now talk about code as Raw Material Driving Value Creation, a commodity input that powers higher‑order products rather than the end product itself.
AI is crushing basic coding premiums, not all engineering work
The first group to feel this shift is not software buyers, it is developers whose work is easiest to automate. Commentators on the labor market describe The Commoditization Threat as entry‑level jobs are increasingly automated, with basic coding skills losing pricing power. Another analysis breaks down how Reduced Value in Basic Coding Skills pushes developers toward higher‑leverage work in infrastructure and product thinking.
At the same time, there is evidence that AI coding does not simply erase jobs, it reshapes them. Research on Gen AI CIO 250 technology leaders finds that code development through generative tools can cut effort by 20 to 30 percent, which frees teams to focus on design and integration. Another assessment of How AI Coding argues that as AI transforms software development, the industry is likely to see job growth, not loss, with more emphasis on code review and testing.
Where the new scarcity lives: data, workflows, and integration
As models and coding assistants become widely available, the competitive frontier is shifting to the assets and capabilities that cannot be easily copied. Analysts of AI product strategy argue that commoditization has moved the battleground from model development to data, workflow integration, and industry‑specific solutions. In other words, the scarce resource is no longer the ability to train a model or write code, it is the ability to wire AI into messy real‑world processes in healthcare, logistics, or finance.
That pattern mirrors earlier waves of IT commoditization. The rise of cloud and software subscriptions revolutionized the software, making technology more accessible and cost‑effective while shifting value to providers that controlled platforms and data. Today, AI is doing something similar at the application layer, pushing generic features toward low prices while rewarding companies that own proprietary datasets or deeply embedded workflows.
Deflation does not always mean lower spending
Even if AI makes individual features cheaper, overall spending on software can still rise as usage explodes. Economists describe this dynamic as Jevons paradox, where efficiency gains lead to more consumption rather than less. Commentators on AI commoditization warn that, As AI gets more efficient and accessible, we may end up using far more of it, embedding models into every workflow and device instead of a handful of flagship products.
Practitioners on the ground are already seeing this play out. One engineering leader notes that AI is making, but Everyone assumes the same thing and then discovers that lower unit costs do not reduce total spending. Instead, organizations commission more experiments, more internal tools, and more automation, which keeps budgets high even as the price per feature falls.
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