Unlike traditional software that optimizes for time-in-app, the most successful AI products will be measured by their ability to save users time. The new benchmark for value will be how much cognitive load or manual work is automated "behind the scenes," fundamentally changing the definition of a successful product.

Historically, software did ~10% of the work (tracking, organizing). AI will invert this, with software actively performing 70-80% of tasks. This fundamental shift means customers will refuse to buy legacy software that doesn't do the majority of the work for them, massively expanding the total addressable market.

Alex Karp argues that the future of enterprise software is not about forcing companies into standardized SaaS workflows. Instead, AI's true power lies in creating custom systems that amplify a company's unique "tribal knowledge" and operational data, turning their specific processes into a competitive advantage that no other enterprise can replicate.

Contrary to fears of AI making SaaS obsolete, the reality is that most enterprise software is deeply flawed. A contrarian view is that AI will provide the tools to finally rebuild these systems better, creating a massive new wave of demand for software development and product design.

In SaaS, value was delivered through visible UI. With AI, this is inverted. The most critical, differentiating work happens in the invisible infrastructure—complex RAG systems and custom models. The UI becomes the smaller, easier part of the product, flipping the traditional value proposition.

A critique from a SaaS entrepreneur outside the AI hype bubble suggests that current tools often just accelerate the creation of corporate fluff, like generating a 50-slide deck for a five-minute meeting. This raises questions about whether AI is creating true productivity gains or just more unnecessary work.

The market is rejecting 'lame co-pilots' that provide minor workflow improvements for an extra fee. Successful AI products create entirely new, powerful use cases and deliver substantial, tangible value on day one, justifying their place in the budget.

Adopting AI acts as a powerful diagnostic tool, exposing an organization's "ugly underbelly." It highlights pre-existing weaknesses in company culture, inter-departmental collaboration, data quality, and the tech stack. Success requires fixing these fundamentals first.

With AI commoditizing code creation, the sustainable value for software companies shifts. Customers pay for reliability, support, compliance, and security patches—the 'never ending maintenance commitment'—which becomes the key differentiator when anyone can build an initial app quickly.