The creation cycle is collapsing.
Empathize → Define → Ideate → Prototype → Test. That entire design-thinking loop that used to take cross-functional teams weeks is now one CLI prompt away. A builder with Cursor and Claude can ship a working product in a weekend that would have taken a team of ten three months to build in 2020.
The Market
The iShares Expanded Tech-Software ETF (IGV) is down 24% year-to-date as of February 20, 2026. Forward P/E multiples across the software sector have collapsed from 39x to 21x in twelve months. ServiceNow beat earnings estimates and still plunged 55% in the past year. Atlassian is down 70%. monday.com, 75%.
But not everything is getting crushed equally. I mapped roughly twenty SaaS companies across two dimensions: coordination complexity and AI revenue exposure. A few observations:
- AI-Native Infrastructure (Datadog, MongoDB, Confluent): averaging just −1% over one year
- Domain Defenders (CrowdStrike, Palo Alto, Veeva): averaging −18%
- Platform Transformers (Salesforce, ServiceNow, HubSpot): averaging −48%
- Disruption Zone (Atlassian, monday.com, Asana): averaging −51%
That's a 50-point spread between the best and worst categories. The market is repricing based on how exposed each company is to AI substitution of coordination work.
The Bifurcation
Companies whose products power AI workloads, such as databases, observability, and streaming, are basically flat. Companies with deep vertical moats in security and compliance are holding up. But horizontal SaaS that coordinates human work? That's the impact zone.
The logic is: if AI agents can manage projects, route tickets, and coordinate workflows, then tools whose primary value is orchestrating human collaboration face an existential question. What happens when there's less human work to coordinate?
The Demand-Side Moat Holds
The Bureau of Economic Analysis NIPA Table 5.6.5 tracks software investment by type. It shows that self-built ("own-account") software as a share of total software spending has declined from roughly 30% in the 1990s to about 15% today.
Even as development tools improved dramatically every decade, from mainframes to PCs to cloud to low-code, companies subscribed to software and outsourced development, not less.
The Economist and the Bank of France have documented a complementary finding: a 10% decline in software prices leads to approximately 20% more spending on software. Price elasticity greater than one. Cheaper software means a bigger market.
Enterprises with Cursor and Claude can create domain-specific software fast. But the key is to have the talent flywheel, the retention loops, and the right equity-driven culture, similar to AI-native firms where engineers eat, sleep, and breathe the products. The demand-side moat is likely to hold.
The Real Threat Is Supply-Side
The creation cycle isn't threatening SaaS from the demand side, such as customers building their own tools. It's new entrants competing.
AI-native startups are shipping with two to three people what used to require teams of fifty. Peter Steinberger built OpenClaw, an autonomous AI coding agent, in a short duration. It hit 140,000 GitHub stars in three months. More than React accumulated in eight years. OpenAI acqui-hired him for it.
These AI-native builders have everything the enterprise lacks: equity incentives, shipping culture, mission-driven intensity, and skin in the game. They're not replacing SaaS from the demand side. They're competing with it from the supply side.
What Actually Defends Incumbents
If the threat is supply-side, then what actually defends incumbent SaaS companies? A few areas still hold keys:
Distribution. Selling to Fortune 500 procurement is still hard: sales cycles, security reviews, compliance requirements, legal approvals. Hopefully it gets easier with AI.
Data network effects. Products that get smarter with scale, where more customers make the product better for everyone, have a compounding advantage that's hard to replicate.
Workflow integration. The switching cost isn't the software itself. It's the hundreds of integrations, automations, and customizations that enterprises have built on top of it.
Trust and compliance. In security, healthcare, and finance, the product isn't just the features. It's the audit trail, the certifications, the regulatory relationships, the institutional trust built over years.
Notice what's not on the list: features, UI design, or the code itself. Those are exactly the things AI makes easy to replicate.
What to watch
The repricing is real. But the thesis driving it, that AI will destroy SaaS, is more nuanced. The companies that will thrive are those with distribution advantages, data gravity, and deep enterprise trust. The ones that will struggle are those whose primary value was coordinating human work that AI agents can increasingly handle.
Sanej Bandgar is the founder of turingly.com.