Focusing on AI for cost savings yields incremental gains. The transformative value comes from rethinking entire workflows to drive top-line growth. This is achieved by either delivering a service much faster or by expanding a high-touch service to a vastly larger audience ("do more").

Consumer price sensitivity adapts slowly. If a service traditionally costs $2,000 due to labor, you can use AI to deliver it for a fraction of the cost while charging the legacy price. This creates a huge, temporary window for margin expansion and operational leverage.

AI makes it cheaper to build new features. Instead of passing these savings on through lower prices, companies should use this efficiency to expand their product's scope to solve adjacent customer problems. This bundling strategy increases the overall value proposition, allowing you to charge more and become more integral.

Focusing AI efforts on efficiency and cost reduction offers limited, short-term benefits. The truly transformative approach is to invest in AI to create new revenue streams, enhance product offerings, and grow the business exponentially.

AI is making core software functionality nearly free, creating an existential crisis for traditional SaaS companies. The old model of 90%+ gross margins is disappearing. The future will be dominated by a few large AI players with lower margins, alongside a strategic shift towards monetizing high-value services.

Simply making your team more productive with AI (e.g., doubling PRs) won't increase revenue unless you redesign your business model to leverage that new capacity. The goal isn't to do old things faster, but to find entirely new things that are now possible, like letting customers order cars via email in 1995.

As AI dramatically lowers the cost of building software, competitive advantage shifts. Value now accrues to leaders who can best identify real user problems (product) and effectively scale distribution in a crowded market (go-to-market), rather than just the ability to build.

As AI agents perform tasks autonomously, the per-seat SaaS model becomes obsolete. The market is shifting to outcome-based pricing (e.g., pay per resolved ticket). There is a massive opportunity for startups to either build new outcome-based solutions or create services that help large, legacy SaaS companies make this difficult transition.

In a world where AI makes software cheap or free, the primary value shifts to specialized human expertise. Companies can monetize by using their software as a low-cost distribution channel to sell high-margin, high-ticket services that customers cannot easily replicate, like specialized security analysis.