Software stocks have had a difficult year. Investors moved away from traditional application software as AI agents started to raise uncomfortable questions about automation, seat growth, pricing power, and the long-term durability of some subscription models.
The price action shows how broad the pressure has become. Since July 2025, is down about 22%, is down about 40%, and is down almost 48%. The rebound from recent lows has not resolved the debate. It only shows that investors are still trying to separate an oversold bounce from a deeper structural repricing.
New AI coding tools from Anthropic and OpenAI helped trigger the recent software-stock selloff. The market reaction points to a larger issue for the sector. Investors are trying to identify where software still has real pricing power, and where AI makes rebuilding the product more credible. For years, many software companies were valued as durable platforms with recurring revenue, high gross margins, and strong customer retention. AI is forcing investors to separate true platforms from application layers that may be easier to automate, rebuild, or replace.
What does “software as a commodity” really mean?
“Software as a commodity” sounds dramatic, so it needs to be defined carefully. It means parts of the application layer may lose pricing power if customers can recreate enough of the value with cheaper AI-assisted tools, internal workflows, or agent-based automation.
The vulnerable category is not the whole software stack. It is the thinner layer of products where the main value comes from a user interface, a narrow workflow, and seat-based access. These tools can still be useful. The issue is that usefulness alone does not protect pricing power. If a customer can build or automate most of the workflow internally, full platform pricing becomes harder to defend.
The stronger category looks different. These products sit closer to the enterprise control layer. They hold the data, manage access, enforce rules, or run processes that cannot easily break. AI agents still need those systems to act safely. They need trusted data, permissions, security, and governance. That is why the control layer can become more valuable as AI adoption grows.
The new divide in software is clear. Platforms and systems sit on one side, thin workflows and replaceable interfaces sit on the other.
AI changes the buy-or-build decision
The old enterprise software model worked because internal software was expensive, slow, and risky to build. Most companies chose to buy mature products and let vendors carry the operational burden. That was often cheaper and safer than building, securing, updating, and scaling everything internally.
AI changes that calculation in selected categories. It will not push companies to rebuild their core enterprise systems from scratch. Those systems are too embedded, too sensitive, and too hard to govern.
The pressure shows up closer to the edge of the enterprise. Dashboards, simple automations, reporting layers, support tools, and department-level apps are easier to recreate. They are useful, but they are less protected. If a company can build 70% of the functionality internally with more customization and lower recurring cost, the external vendor loses some pricing power even if the customer never fully churns.
That is why the “software is dead” narrative is too simplistic. AI does not flatten the entire software stack. It attacks the parts where the product was mostly a packaged workflow.
The old pricing model is under pressure
Traditional application software was often built around seats. More users meant more subscription revenue. More modules meant higher expansion revenue. That model worked extremely well when software adoption expanded alongside headcount and when each additional employee needed access to the platform.
AI agents create a different pattern. A company may need fewer human users to complete the same workflow. Some tasks can move from human seats to automated agents. Some customers may expect AI functionality to increase output without accepting a proportional increase in software spend.
That creates tension in seat-based pricing. If AI lets one employee do the work of several, “per user, per month” pricing becomes harder to defend in certain categories. Vendors may need to move toward usage-based, outcome-based, hybrid, or AI-consumption pricing. That transition can work, but it also creates uncertainty around margins, revenue visibility, and customer willingness to pay.
ServiceNow is already trying to distance itself from the pure seat-based concern. Reuters reported that more than 50% of the company’s new business comes from non-seat-based pricing models tied to platform usage rather than user licenses. The same report noted that management said it had not seen pressure from customers to cut prices on core products, even as customers increased spending on AI solutions.
Platforms survive while workflows get repriced
The strongest companies own something deeper than the workflow interface. Systems of record, identity layers, security platforms, compliance infrastructure, data platforms, observability tools, developer ecosystems, and cloud infrastructure are harder to replace. AI agents may change how work gets done, but they still need trusted systems to access data, enforce permissions, execute tasks, and leave an audit trail.
Microsoft is the clearest example of this logic. It controls large parts of the enterprise operating layer: productivity, cloud, identity, security, developer tools, collaboration, business applications, and AI distribution. If enterprise AI adoption grows, Microsoft has multiple ways to participate across the stack.
Salesforce sits in the middle of the debate. It owns valuable CRM data and remains a major system of record for customer relationships, which gives the business real protection. However, investors are still asking whether AI agents will reduce the value of traditional sales, service, and marketing workflows. Agentforce is the company’s answer, with $800 million of annual recurring revenue and 169% year-over-year growth, but the stock reaction shows that investors want proof that AI can expand Salesforce’s economics without weakening the core model.
ServiceNow is the cleaner platform case. Its workflow platform is deeply embedded in enterprise processes, and the company is already showing AI monetization. It raised its 2026 subscription revenue outlook to $15.74–$15.78 billion, while Q1 subscription revenue rose 22% to $3.67 billion. Now Assist had already exceeded $750 million in annual contract value and was expected to top $1.5 billion by year-end, which shows how AI can become a new revenue layer when the software already controls critical workflows.
The vulnerable group is simpler. These are products that sit at the edge of the enterprise rather than inside its core systems. They help teams coordinate, report, track tasks, or build simple workflows. That can be valuable, but it is easier to recreate. When a product has little proprietary data and limited operational importance, AI-assisted internal tools become a real alternative.
The matrix frames the software divide through two variables: how deeply a product is embedded in the enterprise, and how easily its workflow can be rebuilt with AI. Microsoft sits closest to the platform core because it controls cloud, identity, productivity, security, and developer infrastructure. ServiceNow also remains well protected through deep workflow integration, while Salesforce sits in a more contested zone: it owns valuable customer data, but parts of its sales, service, and marketing workflows face greater agent-driven pricing pressure.
My base case
My base case is that the software selloff is a moat test. Some of the punishment is probably too broad. The market often sells first and separates later. Strong software companies with real data ownership, deep enterprise integration, security, compliance, identity, infrastructure exposure, and mission-critical workflows can still deserve premium valuations. AI may even increase their strategic value because customers will need trusted systems for agents to operate safely.
The weaker part of the sector faces a structural problem. A software company built mainly on seat expansion, simple workflows, and limited data ownership has to prove that its product still deserves the old software premium. Recurring revenue alone is no longer enough if the workflow can be rebuilt, automated, or replaced with a cheaper AI-assisted alternative.
That is why I would not frame the software selloff as a simple buying opportunity. The real opportunity is selective. The market is repricing software based on what the product actually controls. From here, the dividing line is switching cost. Software that controls records, permissions, and daily execution remains hard to displace. Software that mainly packages a repeatable task behind a clean interface becomes easier to rebuild. In an AI-native world, that distinction matters more than ever.
