Something remarkable is happening. The distance between an idea and a working product has never been shorter.
Empathize, Define, Ideate, Prototype, Test. A design-thinking loop that once occupied a cross-functional team for a quarter can now move at the speed of a small team's decisions. Taste still matters: what to build, what to omit, and why. The change is execution speed. A thoughtful team can turn judgment into working software much faster than before.
The Market
As of February 20, 2026, the iShares Expanded Tech-Software ETF (IGV) was down 24% year to date. Over the prior twelve months, ServiceNow fell 46%, Atlassian 75%, and monday.com 76%.
I mapped roughly twenty SaaS companies across two dimensions: coordination complexity and AI revenue exposure. The spread was wider than I expected:
- AI-Native Infrastructure: averaging just −1% over one year
- Domain Defenders: averaging −20%
- Platform Transformers: averaging −44%
- In Transition: averaging −52%
Across the sample, that is a 51-point gap between the least and most pressured categories. The pattern suggests that AI is a tailwind for some business models and a source of pressure for others.
Where the Reset Lands
In the sample, companies in databases, observability, and streaming are roughly flat. Companies with deep vertical moats in security and compliance are holding up better. Horizontal SaaS built around coordinating work has seen the sharpest reset.
Many systems of record handle administrative coordination: routing, reminders, and status rollups. AI agents and assistants can increasingly handle that work, so tools built around coordination need a clearer source of value. Product design, judgment, and human conversations still matter. AI changes the administration around them.
The Demand-Side Moat Holds
The Bureau of Economic Analysis publishes national accounts data on software investment by type (NIPA Table 5.6.5). The data show that self-built ("own-account") software fell from roughly 30% of total software spending in the 1990s to about 15% today.
Over the same period, companies shifted more spending toward purchased software.
Bank of France research found demand for computer hardware and software to be price-elastic. Lower prices can expand total spending on those products.
Cursor and Claude make domain-specific software easier for enterprises to build. But the product organizations around that software are harder to reproduce. They require companies to hire and retain talent while sustaining a culture that ships. The demand-side moat probably holds.
Where New Companies Gain Leverage
I think the larger source of pressure on SaaS is small AI-native teams, not enterprise customers building internal tools.
AI coding tools automate more boilerplate, integration work, and repetitive refactoring, allowing small teams to ship more software. Peter Steinberger created OpenClaw, an open-source personal AI assistant that quickly attracted a large developer community. He later joined OpenAI to work on agents, while OpenClaw remained independent.
Good engineers can now spend more time on product judgment and less on repetitive implementation work. AI-native startups organize around that leverage from day one through equity incentives, shipping culture, and a clear mission. For incumbents, the question is whether a similar culture can exist inside a 10,000-person company.
What Still Defends Great Software Businesses
Four advantages still defend established software businesses.
Distribution. Selling to Fortune 500 companies still requires long sales cycles, security reviews, compliance, legal work, and trust. Those relationships are real assets.
Data network effects. Products that improve as more customers use them build an advantage that a new entrant cannot replicate quickly.
Workflow integration. Switching costs come from the integrations, automations, and customizations enterprises have built around the software.
Trust and compliance. In security, healthcare, and finance, value also comes from audit trails, certifications, regulatory relationships, and institutional trust built over years. These are difficult to copy.
Features and implementation are easier to replicate with AI. Competition shifts toward distribution, data, integration, and trust.
What to Watch
The reset is real, but the thesis that AI will destroy SaaS is too blunt. Companies with distribution, data gravity, and enterprise trust can redirect engineering effort toward the parts of the product that compound. Companies whose value was concentrated in the coordination layer face more pressure and need a new source of durable value.
Software teams can now attempt more ambitious work with fewer execution constraints. That makes products that once required much larger teams viable for smaller ones.
Sanej Bandgar is the founder of turingly.com.
Market data as of February 20, 2026. Sources: Nasdaq historical data, BEA, and Bank of France. This is analysis, not investment advice.