In mid-January 2026, Anthropic launched Claude Cowork: an AI agent that autonomously reads files, organizes folders, writes reports, executes workflows and manages integrations with other systems. In the weeks that followed, about $300 billion in stock market value evaporated in the enterprise software sector.
Valuations of SaaS businesses plummeted 30% compared to past years, according to JBR Corporate Finance JBR s Digital & Technology Insights 2026 report. Investors negatively revalued the resilience of existing software business models as they saw how advanced AI models already are. Especially since Cowork was built by Anthropic's AI-powered coding assistant (Claude Code), in just a week and a half.
AI resilience is a structural question
But the valuation effects are not uniformly distributed and have a sharp dichotomy. The strongest corrections are seen in businesses whose core functionality is directly replaceable by AI agents: automating workflows, generating reports or visualizing data - exactly the tasks Claude Cowork already performs. Margins come under pressure and customer turnover increases.
On the other side are businesses with an essentially different value proposition: deeply rooted in a specific industry, built on data that exists nowhere else, indispensable in a critical process that cannot go wrong. So the distinction is in the structure of the competitive advantage:
- Unique data. Businesses with proprietary, long-standing historical or customer-specific data have a strong defensive barrier. After all, AI can only add value based on high-quality data. These include businesses that act as a "single source of truth.
- Impact and (process) integration. Business-critical software that is deeply embedded in a customer's operational processes and facilitates key governance decisions has high switching costs and is not easily replaced by a generic AI agent. The same goes for software that is part of an "ecosystem" with all sorts of links to other software or systems, with sectors with a lot of legacy infrastructure, such as manufacturing and the public sector, being especially resistant to replacement.
- Domain expertise and regulation. Software companies that operate in a specific niche with "deep" offerings are less likely to be affected by generic AI tools, which are more likely to focus on horizontal applications given the larger addressable market. And in regulated industries such as healthcare, financial and legal, human judgment is legally or ethically required.
- Network and platform effects. The more users a platform has, the more valuable it becomes to each individual user. While AI can copy functionality, it cannot duplicate a user network. Businesses that add a lot of value in the physical world, such as through meetings or proprietary hardware, are also less threatened.
The most dangerous gap: what DGAs think versus what buyers see
The link to business value is direct: the stronger and more demonstrably present the aforementioned factors are, the higher the valuation. For example, software companies with low customer turnover (churn) and high net revenue retention (NRR) - a strong indicator of process integration and customer value - realize valuations that are multiples of businesses that score poorly on these. The market no longer pays for potential, but for proven customer retention.
Yet a recent Software Equity Group poll shows a notable perception gap: 80% of professional buyers see AI-driven commoditization as the biggest threat to SaaS businesses, while only a quarter of SaaS DGAs share that concern. This may create a discrepancy in valuation expectations and thus a temporary "deadlock" in the software M&A market.
The crucial question
The lesson of January 2026 is not that AI replaces software. The lesson is that AI replaces generic software. For software DGAs, the question is to what extent their software company has structural competitive advantages in terms of AI resilience. Those who answer that question honestly avoid disappointments about business valuation, feasibility and lead time in a sales process.
