The significant barrier of messy, legacy data is being overcome by AI. Snowflake is developing "agent-driven migrations" that automate the process of moving data from old systems onto modern platforms. This drastically reduces project timelines from multiple years to just a few weeks.

The ability of AI agents to automate complex data migrations between platforms will significantly weaken "switching costs" as a competitive advantage for software companies. Businesses will need to rely more on other moats like network effects.

Traditional SaaS switching costs were based on painful data migrations, which LLMs may now automate. The new moat for AI companies is creating deep, customized integrations into a customer's unique operational workflows. This is achieved through long, hands-on pilot periods that make the AI solution indispensable and hard to replace.

Confusing credit-based AI pricing models will likely be replaced by a straightforward value proposition: selling AI agents at a fixed price equivalent to the cost of one human worker who can perform the work of ten. This simplifies budgeting and clearly communicates ROI to CFOs.

Migrating from legacy enterprise systems was once a multi-year ordeal, creating powerful vendor lock-in. AI now automates the process by analyzing environments, converting code, and validating results. This has reduced migration timelines to as little as 30 days, dramatically lowering switching costs for large companies.

The primary moat for many SaaS companies was the complexity and high cost of migrating away from their product. AI agents can now automate this process, eroding that advantage, increasing competition, and giving buyers significant leverage to renegotiate contracts.

The true threat to SaaS isn't just cheap software creation, but AI agents that automate data migration between platforms. This destroys the lock-in effect of proprietary data models, turning SaaS into a low-multiple utility business where switching costs approach zero.

Even for complex, multi-hour tasks requiring millions of tokens, current AI agents are at least an order of magnitude cheaper than paying a human with relevant expertise. This significant cost advantage suggests that economic viability will not be a near-term bottleneck for deploying AI on increasingly sophisticated tasks.

The business model for AI agents fundamentally shifts the value proposition from selling a tool (license) to selling an outcome (automated work). This allows vendors to tap into operational or labor budgets, not just IT budgets, unlocking a new price-for-value equation and exponentially larger contract sizes.