The SaaS Warning To The World

For two decades, the beauty of the Software-as-a-Service (SaaS) model rested on simple arithmetic: develop a product once, distribute it infinitely at near-zero marginal cost, and collect recurring revenue streams protected by prohibitive switching costs supporting pricing power.

The business model was an implicit bet on organizational scale: more employees equated to more user licenses. This era of ‘seat-based’ growth is now visibly colliding with a new reality: an AI-driven decoupling of productivity from headcount, leading to fewer seats and displacing white-collar labor (the impact of which is hard to anticipate from a social, political, technological, and economic perspective).

Software providers are now being punished for the effectiveness of their own products. Marc Andreesen famously stated that software is eating the world. It is now eating its own economics.

Furthermore, AI lowers the barriers to entry. Internal corporate teams can now assemble bespoke workflow tools that once required a specialist vendor, while new entrants can develop and efficiently scale up new products faster than ever to replace legacy systems, creating a risk of churn for SaaS firms.

To make matters worse, the disruption is not only about slowing demand but also about cost structure. Each AI-driven interaction incurs expensive computing and third-party model costs, adding variable costs to AI-supported features that structurally weigh on SaaS firms’ profitability.

If SaaS is not being killed, it is being starved. Investors have been rotating away from traditional SaaS for months, with a scare episode last week (accentuated by a macro-data related rotation into cyclicals).

The exposure to this microeconomic shift is not uniform. Horizontal tools that automate generic knowledge work (document creation, basic project tracking, or customer relationship management) are most vulnerable because their functionality overlaps with the native capabilities of foundation models.

Conversely, vertical, industry-specific software that governs regulated processes, physical infrastructure, or complex logistics remains more resilient. These systems derive value from decades of embedded domain expertise and proprietary data unavailable to generic intelligence.

To fight back, SaaS companies must overcome the innovator’s dilemma, accelerate their investments in AI, adopt value-based pricing, and be ready to compromise on profitability to support growth.

What is happening to the SaaS world is relevant to all industries. Like SaaS companies, participants across economic sectors must strategically, operationally, and culturally pivot to transform their firms into ‘transcorporates’ with integrated AI capabilities, totally redefined contracts with stakeholders (employees, suppliers, customers), and new corporate discourses.

Firms that fail to reposition themselves will be overtaken by competitors, including new entrants, and risk being taken over, possibly by large tech companies with advanced AI implementation expertise executing a ‘reverse AI’ strategy: the acquisition of a physical platform (e.g., robotics, logistics, route-based services) to create value by accelerating the acquiree’s digital transformation.

Enterprise AI is in its infancy. SaaS is just a warning to the world. Everything is set to change.