Q1 2026 is closing with the worst quarter for software stocks since the Covid crash. IGV, the iShares Expanded Tech-Software ETF, is down ~33% from its 52-week high. Atlassian reported its first-ever systemic decline in enterprise seat counts. Workday dropped 40% from its peak as hiring slowdowns translated directly into license count reductions. More than $1 trillion in software market cap has been repriced.
Our last post argued that the “AI agents will kill SaaS” narrative was mostly wrong — that what was actually happening was budget cannibalization, not product replacement. Q1 earnings are in. Let’s see what held up.
The seat compression data is now real
In post #2, we flagged seat counts as the single most important number to watch. The data is now arriving.
Atlassian reported its first-ever decline in enterprise seat counts in early 2026. Workday’s stock fell 40% as AI-driven hiring efficiencies reduced total headcount at large enterprises — and Workday licenses track headcount. Salesforce is down 37% from its 52-week high on fears that AI agents are handling CRM workflows without human-managed seats.
This is what we said to watch. The mechanism is playing out: AI reduces the number of humans doing work, humans are the unit that SaaS licenses track, so seat revenue compresses. The math isn’t speculative anymore.
But here’s what makes it interesting: the compression isn’t uniform. Snowflake’s full-year product revenue grew 30% year-over-year (Q4 at 28%), driven by AI workloads. ServiceNow subscription revenue was up 21% YoY despite the stock dropping 11% in a single day on sentiment. Palantir reported 70% revenue growth for Q4 and is forecasting 61% growth for full year 2026.
The market has split — not between “AI companies” and “software companies,” but between companies that own the workflow and companies that just fill seats in it.
What PwC’s M&A data says
PwC published an analysis this month of software valuations in M&A transactions. It’s worth reading carefully, because M&A pricing reflects where capital allocators think value actually lives — not where narratives say it does.
Their finding: companies with strong defensibility characteristics are commanding significantly higher premiums than generic SaaS. The characteristics that matter:
Domain depth and ecosystem entrenchment — “Vibe-coding sounds easy until you get to the last-mile and encounter industry-specific workflows, edge cases, compliance requirements, and the messy reality of change management.” Code is now cheap to write. What’s expensive to replicate is years of accumulated domain expertise, regulatory understanding, and customer relationships.
Proprietary context, not just proprietary data — Access to data matters, but so do data rights, provenance, auditability. The strongest businesses generate proprietary context that makes AI better over time in ways competitors can’t easily replicate — curated knowledge graphs, validated playbooks, customer-specific configurations that cannot be scraped or synthetically reproduced.
Workflow gravity — If a product owns the system of record and is tied to a financial or regulatory outcome, AI agents tend to layer on top rather than replace. When agents are deployed, they often drive more automation through the platform rather than routing around it.
The inverse tells you what’s genuinely in trouble: “standalone BI tools, collaboration suites, and horizontal workflow products that compete primarily on UX are squarely in the crosshairs.”
The earnings call signal: trust and moats
Blossom Street Ventures reviewed 18 Q4 earnings calls and distilled some patterns worth noting.
First: AI is not yet profitable for anyone. Microsoft, Salesforce, ServiceNow — all described margin pressures from deploying AI products. The goal right now is margin-neutral revenue from AI. Not margin expansion. Margin neutral.
Second: Trust is a real moat, not a temporary hurdle. Doximity stopped releasing AI product until they can get it right, because mis-diagnosing a patient is not an acceptable error rate. Qualys made a similar point about cybersecurity: being the agentic remediation layer requires trust that generic AI tools can’t establish. Healthcare and security are two large, expensive software categories where the compliance moat is essentially unbreakable.
Third: The SMB signal is the one to watch for leading indicators. Monday.com specifically cited AI as a real competitive threat in their SMB customer base — not enterprise. SMB customers have less internal complexity. Lower switching costs. Fewer compliance requirements. If AI is going to eat software from the bottom up, SMBs are where it shows up first.
Updating the hypothesis
We started with: software loses value because AI can create it fast and efficiently.
Three posts in, here’s the more precise version:
Software loses seat-count revenue when AI reduces the number of humans who need to use it. Software loses point-solution revenue when AI agents can replicate single-workflow tools. Software retains — and may gain — value when it owns the system of record, the compliance layer, or the proprietary data context that makes AI work better.
The “AI kills SaaS” narrative is still too blunt. But the Lemkin version — “SaaS is being starved, not killed” — is also incomplete. Some SaaS is being killed. Point solutions with no workflow gravity and no proprietary context are genuinely exposed. The starving is selective, and the Q1 data is starting to tell us which segments are feeling it first.
What to watch in Q2
Three signals worth tracking:
Atlassian seat count trajectory — Is the Q1 decline stabilizing or accelerating? Atlassian is a pure-play on developer seat counts. If AI coding tools continue reducing the number of developers in large organizations, their seat count is a direct leading indicator.
ServiceNow margin on AI products — They’re betting that agentic workflows drive more automation through the platform, not around it. If their gross margins hold while AI revenue grows, the “workflow gravity” thesis has its clearest data point.
Monday.com SMB churn rate — They flagged it as a threat. Whether it shows up in churn numbers over the next two quarters will tell us whether the “bottom-up erosion” pattern is real.
The hypothesis is getting sharper. The data is getting cleaner. We’ll be here when the Q2 numbers arrive.
— Chris
Sources: MarketMinute · SaaStr · Blossom Street Ventures · PwC · Gartner IT Spending Forecast