TL;DR
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A report by Sanket Saurav argues that AI coding tools have changed software economics: building features is cheaper, but running AI-powered products brings ongoing model costs. The report says many companies still reproduce familiar software categories rather than redesigning products around the new constraints; how to make that shift—and its business results—remains unsettled.
Sanket Saurav argues in an essay titled “The End of Rationing” that AI coding tools have made software development cheaper while making AI-powered products more expensive to operate. The report says the industry has not yet turned that shift into substantially different products, leaving many new offerings modeled on established software categories.
Saurav describes the earlier software model as one in which engineering time was scarce, while computing, storage and memory became cheaper. Companies managed that constraint by limiting what they built, using approaches such as the minimum viable product and “do one thing.” The essay says product conventions created under those conditions spread across the industry: GitHub’s pull-request workflow and Salesforce’s lead, opportunity and pipeline model became templates that later products often inherited.
The essay characterizes the economics after AI coding tools as an inversion. Smaller teams and coding agents can reduce the cost of producing features, Saurav writes, but products that rely on large language models incur ongoing model costs with each use. That means the expense is no longer concentrated mainly in research and development; operating the product can carry a meaningful marginal cost per user.
Despite cheaper development, Saurav says many AI products look like existing software with a chat interface or are variations on familiar agent products. He identifies several reasons for the lag: established product-building beliefs are hard to dislodge, buyers often procure within known categories, investors continue to back recognizable startup formulas, and large companies are organized around existing staffing models.
Why Cheaper Coding Changes Product Economics
The argument matters because lower development costs do not automatically produce better or more original products. If businesses continue to build around old assumptions, AI may accelerate the production of familiar features rather than change how software works. Saurav’s distinction also highlights a new operating question: as model use becomes part of a product’s core functionality, each additional user may bring additional costs, unlike the near-zero marginal costs associated with many traditional software services.
For companies, the proposed shift affects product design, budgeting and organization. A business may be able to test and discard features more cheaply, but it still needs to account for ongoing inference costs, software maintenance and the challenge of earning user trust. The essay’s claim is not that these pressures have disappeared; it is that the old practice of rationing engineering effort may no longer be the main limit on what teams can build.
For users, the distinction could shape whether AI products offer genuinely new workflows or simply add an assistant to a familiar interface. Saurav points to code collaboration and customer relationship management as areas where a rethinking of the underlying product—not just an AI layer—could produce a different result. Those examples are proposals about where change may occur, not evidence that a replacement has already emerged.
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From SaaS Constraints to AI Costs
Saurav frames the shift as a change from a software model with high fixed development costs and relatively low costs to serve another user, toward one with leaner development teams but significant costs tied to frontier-model use. The essay says the median public SaaS company historically retained about 75 cents of each revenue dollar as gross profit, presenting that figure as context for the earlier model. It does not provide a date range or a source for the statistic in the supplied text.
The report compares the current moment with the adoption of electricity in U.S. manufacturing. It says factories initially replaced steam power with a central electric motor while keeping much of the existing machinery layout; larger productivity gains came later, when factories redesigned their floors around individual motors. Saurav uses that analogy to argue that software companies may need to reorganize how they build products, rather than simply add AI to existing designs.
The essay also notes that some older constraints remain. Established companies retain advantages in distribution, while trust can take years to earn. Code may be cheaper to produce, Saurav writes, but maintaining a product as it grows still requires sustained work. The source presents these as continuing limits alongside the new cost of AI usage.
“The short answer is rationing. It’s over, and we haven’t caught up.”
— Sanket Saurav, in “The End of Rationing”
Evidence Behind the Product Shift
The supplied report is an argument, not a measured industry survey. It does not quantify how much coding costs have fallen, how AI operating costs vary by product, or how many companies have redesigned their software in response. Its observations about agent products, investor preferences and procurement practices are not accompanied in the supplied material by named examples or data.
It is also unclear whether the proposed first-principles alternatives to established products will attract buyers, perform better in practice or sustain lower overall costs. The essay says code can be cheap to write but harder to maintain, and that every user brings token costs; it does not set out a cost model or report results from a specific company. Its reference to a second form of change ends mid-sentence in the supplied source, so that part of the argument cannot be assessed here.
Whether Products Are Rebuilt
The next test is whether software companies move beyond adding AI features to familiar products and build workflows around the capabilities and costs of AI systems. Saurav points to code collaboration designed for both people and agents, and customer relationship management rethought without its traditional data model, as examples of the kind of change he expects.
The essay does not name a launch, company or timeline for those proposed shifts. Readers will need to watch for products that show how their new designs work, what they cost to operate at scale and whether customers adopt them outside existing software categories. For now, the report’s conclusion is a forecast: companies that reorganize product development before competitors may gain an advantage, but the source provides no results confirming that outcome.
Key Questions
What does “the end of rationing” mean in the essay?
Saurav uses the phrase to describe a decline in the scarcity of engineering capacity. AI coding tools, he argues, make it cheaper for teams to produce software features, though they do not remove costs such as maintenance, distribution or operating AI models.
Why might AI software cost more to operate?
Products that use large language models incur model-related costs when users make requests. The essay argues that this creates a recurring marginal cost per user, unlike the comparatively low additional serving costs associated with many traditional software products.
Does the report say software products have already been transformed?
No. Saurav argues that many offerings still resemble older products or add chat interfaces to familiar designs. The essay describes deeper product redesign as a possibility that has not yet become the norm.
Which software categories does Saurav cite?
The essay discusses code collaboration, using GitHub’s pull-request workflow as an example, and customer relationship management, referring to Salesforce’s lead, opportunity and pipeline model. These are examples of inherited conventions, not announcements of competing products.
What evidence does the supplied report provide?
It offers an analysis and historical analogy, including the author’s account of changing software economics and a comparison with factory electrification. The supplied text does not provide a dataset, a quantified estimate of cost changes or named examples demonstrating that a redesigned product has succeeded.
Source: hn
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