As SaaS firms use AI to optimize operations, they feed models data on how their products are built. This creates a deflationary spiral where customers can use the same AI to build cheaper alternatives, threatening the core SaaS business model by accelerating price and profitability compression.

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.

Traditional SaaS companies are trapped by their per-seat pricing model. Their own AI agents, if successful, would reduce the number of human seats needed, cannibalizing their core revenue. AI-native startups exploit this by using value-based pricing (e.g., tasks completed), aligning their success with customer automation goals.

The value in software is shifting from SaaS platforms (like CRMs) to the AI agent layer that automates work on top of them. This will turn established SaaS companies into simple data repositories, or "hooks," diminishing their stickiness and pricing power as agents can easily migrate data.

Users are leveraging AI agents to build their own bespoke software, stripping away unused features from SaaS giants like Notion. This trend toward hyper-personalization threatens the one-size-fits-all SaaS model as users create cheaper, more effective personal tools.

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 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 growth relies on upselling features and adding seats. AI challenges this by enabling customers to build their own integrations that were once expensive upsells. Furthermore, if AI keeps team sizes static, the "expand" motion of selling more seats vanishes.

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.