TL;DR
Get kitchen gear delivered free with Prime
- Fast, free delivery on millions of items
- Prime Video, Amazon Music and more included
- Member-only deals all year
Sanket Saurav’s essay argues that coding models have changed software economics: building features is cheaper, but running AI-powered products carries ongoing model costs. He says many products still follow pre-AI design patterns and that companies have yet to reorganize around the new economics. The essay is an argument about industry change, not a report of a specific product launch or measured productivity shift.
In an essay titled “The End of Rationing,” writer Sanket Saurav argues that AI coding tools have made software cheaper to build while making AI-powered products more expensive to operate, a shift he says the industry has not yet reflected in most product design. The argument matters to software companies because it challenges familiar approaches such as minimum viable products and established category templates, while emphasizing that model costs per user remain a constraint.
Saurav describes the period before coding agents such as Claude Code as one in which engineering time was the scarce resource. Companies had to hire and retain engineers, coordinate their work, and choose which features to build. Compute, memory and storage, by contrast, became cheaper over time. He says this imbalance shaped products around limited development capacity and helped popularize practices such as the minimum viable product and “do one thing.”
The essay’s central claim is that AI has changed that equation. Coding agents and other models can reduce the effort required to produce software, while products that rely on large language models incur ongoing inference costs. Saurav characterizes the shift as a move from high fixed research and development costs and low marginal costs to leaner development teams paired with substantial marginal costs to serve users. The piece does not provide an industry-wide cost dataset to quantify that change.
To illustrate how old product conventions persist, Saurav points to GitHub pull requests and Salesforce’s lead, opportunity and pipeline model. He argues that later products copied these familiar structures, even when their users or purposes differed. In his view, many current AI products similarly adapt an existing category or repeat the pattern of an AI agent for a particular industry, rather than starting from a different account of what the work should look like.
Why AI Changes Product Economics
The argument bears on both product strategy and business costs. If producing code becomes cheaper but using AI remains costly, companies may have more room to test features while still needing to manage how often and how extensively each user triggers model calls. The old assumption that the cost of serving an additional software user is close to zero does not hold in the same way for products with AI in their core workflow.
Saurav also says the opportunity is not simply to add a chat interface to existing software. He argues that products could be designed around different workflows, including collaboration between people and software agents. That possibility could affect how companies organize work and what they buy, but it is an interpretation of the emerging technology, not evidence that a new design has already displaced established products.
For readers evaluating AI software, the distinction is practical: a product may be easy to prototype yet costly to operate and maintain at scale. Saurav notes that code is not free to maintain and that distribution and trust remain difficult for new companies to acquire. Those constraints mean lower development costs alone do not guarantee a successful or more useful product.
AI coding tools for software development
As an affiliate, we earn on qualifying purchases.
As an affiliate, we earn on qualifying purchases.
From Engineering Scarcity to Model Costs
The essay uses “BC” and “AC” as shorthand for periods before and after Claude Code, rather than as a formal industry-wide timeline. Saurav’s account describes a long-standing software model in which companies invested heavily in development and then served additional users at relatively low cost. He cites a figure of about 75 cents of gross profit per revenue dollar for the median public SaaS company, but the supplied essay excerpt does not specify the data source or measurement period for that figure.
To explain why new technology may not immediately change how products are built, Saurav compares software with the adoption of electricity in US manufacturing. He says factories initially replaced a central steam engine with a central electric motor while retaining their existing systems of shafts and belts. According to the essay, larger productivity gains came when factories reorganized around smaller motors at individual machines. The comparison supports his view that organizational redesign, not just access to a new tool, is needed to change productivity.
He identifies several forces that may preserve existing product patterns: procurement teams often buy within established categories; investors may favor recognizable “X for Y” businesses; and large companies may be organized around headcount and existing processes. These are the essay’s explanations for slow change, not findings from a cited survey in the source material.
““The short answer is rationing. It’s over, and we haven’t caught up.””
— Sanket Saurav, in “The End of Rationing”
large language model inference costs
As an affiliate, we earn on qualifying purchases.
As an affiliate, we earn on qualifying purchases.
Costs and Adoption Remain Unsettled
The essay offers an argument about the direction of software development, not a measurement of how much coding agents have lowered development costs across the industry. It does not provide comparable figures for the cost of building software before and after these tools, or evidence establishing how widely companies have reorganized their teams.
The economics also vary by product. The cost of serving a user depends on model choice, usage and product design, and the source does not quantify those differences. It is also unclear which new software categories will gain adoption, whether buyers will change procurement practices, or how quickly companies can build trust and distribution. Saurav’s claims about clones, investors and incumbents are presented as analysis; the supplied material does not independently verify them.
AI-powered product design software
As an affiliate, we earn on qualifying purchases.
As an affiliate, we earn on qualifying purchases.
Whether Products Are Redesigned
The development to watch is whether companies move beyond adding AI features to established products and instead redesign workflows around what coding agents and other models can do. Saurav points to code collaboration and customer relationship management as areas where he believes a fresh approach could differ from familiar templates. The essay does not name a specific replacement product or give a launch date for one.
For software businesses, the next test is whether lower costs to create and experiment with code can be balanced against model expenses, maintenance, distribution and customer trust. Until companies demonstrate redesigned products and sustainable operating costs, the claim that software will become substantially different remains a thesis rather than a confirmed industry outcome.
As an affiliate, we earn on qualifying purchases.
Key Questions
What does “the end of rationing” mean in the essay?
Saurav uses the phrase for a change in which engineering capacity is less scarce because AI coding tools can help produce software. He argues that the industry has not yet adapted its products and organizations to that change.
Does the essay say AI makes software free to build?
No. It argues that the marginal effort of building a feature is falling, not that development has no cost. The essay also says maintenance remains costly and that AI products carry ongoing model expenses.
Why can AI software cost more to operate?
Products using large language models may incur costs each time users make requests. Saurav argues that this creates marginal costs per user that differ from the low serving costs associated with many traditional software products.
Does the source prove that AI products are not improving software?
No. The essay is an analysis and does not present a systematic comparison of product quality. Saurav’s view is that much current software still follows older design patterns; the supplied source does not establish that as an industry-wide measured result.
Source: hn
Halloween Picks
halloween
As an affiliate, we earn on qualifying purchases.
