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AI may drastically lower the cost of software engineering, threatening the dominant SaaS model by enabling companies to affordably build bespoke in-house software, mirroring the current market dynamics in China.
The rise of agentic coding is creating a "SaaSpocalypse." These agents can migrate data, learn different workflows, and handle integrations, which undermines the core moats of SaaS companies: data switching costs, workflow lock-in, and integration complexity. This makes the high gross margins of SaaS businesses a prime target for disruption.
Ubiquitous local AI agents that can script any service and reverse-engineer APIs fundamentally threaten the SaaS recurring revenue model. If software lock-in becomes impossible, business models may shift back to selling expensive, open hardware as a one-time asset, a return to the "shrink wrap" era.
While AI expands software's capabilities, vendors may not capture the value. Companies could use AI to build solutions in-house more cheaply. Furthermore, traditional "per-seat" pricing models are undermined when AI reduces the number of employees required, potentially shrinking revenue even as the software delivers more value.
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.
Companies are now rejecting expensive SaaS contracts because their internal teams can build equivalent custom solutions in days using AI coding tools. This trend signals a fundamental threat to the traditional SaaS business model, as the 'build vs. buy' calculation has dramatically shifted.
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.
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.
Turing's CEO claims SaaS is dead for two reasons. First, powerful foundation models drastically lower the cost of building custom software internally. Second, existing SaaS products are built for human interaction via GUIs, not for AI agents that will increasingly use APIs and tool-calling functions directly.
The fundamental business model of many SaaS companies is based on per-user pricing. AI agents pose an existential threat to this model by enabling smaller teams to achieve the same output as larger ones. As companies wonder why they should pay for 100 seats when 10 people can do the work, the entire economic foundation of the SaaS industry faces a crisis.
SaaS products like Salesforce won't be easily ripped out. The real danger is that new AI agents will operate across all SaaS tools, becoming the primary user interface and capturing the next wave of value. This relegates existing SaaS platforms to a lower, less valuable infrastructure layer.