AI will democratize software development to the point where building your own custom apps becomes commonplace. Instead of settling for one-size-fits-all solutions, people will create "personal software" perfectly tailored to their specific workflows, like a custom workout tracker.

Just as YouTube lowered media distribution costs, AI is lowering software development costs. This could shift the SaaS market away from large, one-size-fits-all platforms toward a model where small, elite teams deliver highly customized software solutions directly to enterprise clients.

The barrier to creating software is collapsing. Non-coders can now build sophisticated, personalized applications for specific workflows in under an hour. This points to a future where individuals and teams create their own disposable, custom tools, replacing subscriptions to numerous niche SaaS products.

AI tools enable users to create bespoke applications tailored to their needs. This shift towards personalized software challenges the one-size-fits-all SaaS model, potentially rendering many subscription products obsolete and causing market underperformance, as seen in the Morgan Stanley SAS index.

AI is becoming the new UI, allowing users to generate bespoke interfaces for specific workflows on the fly. This fundamentally threatens the core value proposition of many SaaS companies, which is essentially selling a complex UX built on a database. The entire ecosystem will need to adapt.

For decades, buying generalized SaaS was more efficient than building custom software. AI coding agents reverse this. Now, companies can build hyper-specific, more effective tools internally for less cost than a bloated SaaS subscription, because they only need to solve their unique problem.

The dominant per-user-per-month SaaS business model is becoming obsolete for AI-native companies. The new standard is consumption or outcome-based pricing. Customers will pay for the specific task an AI completes or the value it generates, not for a seat license, fundamentally changing how software is sold.

Software has long commanded premium valuations due to near-zero marginal distribution costs. AI breaks this model. The significant, variable cost of inference means expenses scale with usage, fundamentally altering software's economic profile and forcing valuations down toward those of traditional industries.

AI is moving beyond enhancing worker productivity to completing entire projects, like drug discovery or engineering designs. This shift means software will be priced like a services business, based on the value of the outcome delivered, not the number of users with access.