The Death of Software Economics
For thirty years, software had a deal with capitalism.
Build it once. Sell it forever. Watch the margins compound. The economics were almost embarrassingly good, high fixed costs at the start, near-zero marginal costs after that, and digital distribution that let you reach the whole planet without a single truck. That’s the machine that built Salesforce, Microsoft, ServiceNow, and a hundred smaller empires. That’s the machine every VC underwrote, every operator optimized, and every founder tried to replicate.
That deal is now being renegotiated. And the new terms are worse.
Not for everyone. Not in every category. But the logic, the underlying economic logic that made software uniquely attractive as a business, is cracking at the foundation. Generative AI is doing three things simultaneously that the old model never had to survive: it’s making code cheaper to produce, it’s making features easier to copy, and it’s injecting variable costs into products that used to scale on rails.
The outcome isn’t the death of software. It’s the death of software economics as a default. The new stack can still produce extraordinary businesses. But they’ll be built on different inputs, different moats, and a fundamentally different theory of where rents come from.
Here’s what’s actually happening.
The Old Machine
Classical software economics depended on three things being true at the same time.
First: the first copy was expensive. Building required real engineering talent over real time. Second: every copy after that cost almost nothing. Distribution was digital, marginal cost was close to zero, and gross margins in the 70-80% range were not unusual, they were expected. Third: once a customer was in, they stayed in. Switching costs, integrations, workflow dependencies, and just the sheer inertia of enterprise IT made churn structurally low.
Stack those three together and you get the SaaS flywheel: spend to acquire, retain through stickiness, harvest through expansion. The gross margin funds the go-to-market. The go-to-market funds growth. Growth compounds the valuation.
That model works until one of the three assumptions breaks.
Generative AI is breaking all three simultaneously.
What’s Actually Changing
Cost of production is collapsing. Code generation tools don’t eliminate engineering, they compress the accidental complexity while leaving the essential complexity intact. The grunt work gets automated. What remains is architecture, system design, judgment about what to build, and validation that what got built actually works. That’s real value, but it’s not the same as being protected by a production moat. If a team of five can now do what fifty used to do, the barriers to entry across entire software categories collapse.
Feature parity is arriving faster. Historically, a lead product had a runway. Competitors needed years to catch up. Now they need months, sometimes weeks. AI-assisted development compresses the cycle from idea to functional product so aggressively that “we have more features” is no longer a durable answer to “why should we pay for you?” The competitive dynamics shift from product depth to distribution, trust, and workflow gravity. Who’s already embedded? Who do you already trust? Those questions matter more than feature counts.
Delivery now has real variable costs. This is the one most people underweight. Classical SaaS had nearly zero marginal serving costs. The database query was cheap. The API response was cheap. The incremental cost of the millionth customer was trivially close to zero.
AI-native products don’t work that way. Every inference call has a price. Every retrieval operation has a price. Every agent that browses the web, executes a tool, runs a workflow, that’s a metered cost, billed per token or per action. OpenAI charges by token. Anthropic charges by token. Google charges by token. Salesforce is now pricing Agentforce by conversation or by action credit. That’s not a cosmetic pricing change. It’s a structural signal that a large and growing category of software now operates more like a metered service than a replicable artifact.
The downstream consequence: gross margins in AI-native products are often structurally lower than what the SaaS model trained investors to expect. Not temporarily lower while you scale, lower as a function of the product’s architecture. Fast-growing AI startups are scaling with gross margins well below the software norm. Microsoft’s own filings acknowledge that expanding AI infrastructure raises operating costs and compresses gross margin percentage even as demand accelerates.
The economics still work. They just work differently.
Where the Rents Are Moving
Here’s the principle that makes sense of everything else: when one constraint weakens, value migrates to the next binding constraint.
For the last thirty years, code was scarce. Good engineers were expensive and slow. That scarcity was the moat. Build something, defend it with complexity, hire more engineers, compound the advantage.
Cognition is becoming cheaper. Code is becoming abundant. So the scarcity moves.
It moves to compute. To energy. To proprietary data that models haven’t seen. To distribution infrastructure, the relationships, channels, and defaults that determine where software gets discovered and adopted. To trust, compliance posture, regulatory standing, security credibility. To workflow gravity, the deep embedding in processes that makes switching genuinely costly.
None of those are new moats. They were always present. What’s changed is their relative importance. When code is abundant and features are replicable, the firm that wins is not the one with the best codebase. It’s the firm that sits in the flow of work. That controls the interface. That owns the data exhaust from its customers’ operations. That has earned enough institutional trust to be the default.
This is why the hyperscalers are not worried about AI disrupting their business, they are the business. The compute, the energy contracts, the data-center footprint, the enterprise relationships, that infrastructure is not reproducible at startup speed. Microsoft, Google, and Amazon are not fighting the AI transition; they’re extracting rent from it. Every token processed by every AI-native app that runs on their clouds is a margin point flowing upward to the infrastructure layer.
The new rents are upstream.
The Labor Question Is More Complicated Than the Discourse Suggests
The “AI kills jobs” framing and the “AI augments workers” framing are both partially right and equally incomplete.
The field evidence is real: call-center agents resolve issues significantly faster with AI assistance, with the biggest gains among junior workers. Writing tasks accelerate materially. Consultants working within their competency zone improve both speed and quality. These are not hypothetical or theoretical gains, they’re measured outcomes from production deployments.
But two things are also happening simultaneously. Online labor demand has fallen in AI-exposed freelance categories. Entry-level employment in AI-exposed occupations is contracting. The demand for the structured, repetitive cognitive work that used to be how junior talent learned the game is softening.
The uncomfortable synthesis: AI-assisted productivity and entry-level compression are not contradictory. They’re the same phenomenon from different vantage points. When you automate the routine and structured work, you increase the output of the people above that layer while reducing demand for the people who used to do it. The aggregate labor market looks fine in the short run, firms absorb efficiency through hiring freezes and backlog reduction before laying people off. But the pipeline is being starved. If junior work is automated, the supply of senior talent in five years becomes a problem that no one is currently accounting for.
That’s a third-order effect. It doesn’t show up in this quarter’s earnings. It shows up in the decade’s talent market.
The Uncomfortable Implication for Operators
If you’re running software-adjacent businesses, building products, scaling teams, allocating capital, the strategic frame has to change.
Feature advantages don’t compound the way they used to. The right question is no longer “what can we build that they can’t?” It’s “where can we sit that they can’t easily displace us?” That means distribution, not features. Workflow embedding, not functionality. Proprietary data that lives inside your customer relationship, not generic capabilities running on borrowed cognition.
Gross margin needs to be engineered, not assumed. Caching, routing, model selection, product design that minimizes unnecessary inference calls, these are now core economic levers, not infrastructure niceties. The firms that optimize AI delivery costs structurally will have durable advantages over the ones that treat model spend as a cost of doing business and hope margins recover when prices fall.
And the classic build-to-exit playbook gets harder. If feature moats erode quickly and distribution moats require years of trust accumulation, the window for a fast build-and-sell narrative shortens. The businesses that win in this environment are the ones that get embedded early and hold position, not the ones that sprint to feature parity.
What This Means by Stakeholder
Founders: The product is the wedge, not the moat. Use AI to get to market faster, but know that the durable advantage is the workflow you capture after the sale, the data, the integrations, the daily habit. Build distribution like it’s your primary product.
Operators: Gross margin is now an engineering problem. Every percentage point you can recover through architecture decisions, caching, model routing, task decomposition, is a percentage point your competitors have to pay for. The AI era rewards operators who understand the cost structure of their own stack.
Investors: The SaaS-margin assumption needs an audit. AI-native products with legitimate distribution advantages and proprietary context can justify lower gross margins if the revenue quality is right. But applying SaaS multiples to AI-native products with borrowed cognition and no distribution moat is a category error. Ask about workflow gravity, data ownership, and switching costs before you ask about ARR.
Governments: The binding constraints in AI are compute, energy, and proprietary data. Those are infrastructure questions, not software questions. Nations that control or cultivate those inputs will have durable competitive positions. Nations that treat AI as a software procurement problem will find themselves paying rent to whoever controls the stack above.
The Clean Conclusion
Software economics isn’t dead. Code-first software economics is.
The replicable artifact still matters, but less. What matters more is everything that sits around the artifact: context, workflow position, distribution gravity, compute access, trust, and governance standing. Those are the scarce assets now. Those are where the new rents will live. Those are where the fights will happen.
The firms that built their advantage on production speed are going to find that speed is no longer the constraint. The firms that built their advantage on distribution, trust, and irreplaceable workflow position are going to find that advantage getting more valuable as the rest of the stack commoditizes.
The bargain is dead. The software isn’t.
But the economics are being written from scratch, and most of the old intuitions are wrong.