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.

For years, updating legacy systems like bank mainframes running COBOL was prohibitively expensive. Modern AI agents are now so proficient at code migration that these projects are finally feasible. One engineer migrated the entire Bun codebase to a new language in just 11 days, a task that previously would have taken a team a year.

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.

What was once a significant moat for SaaS companies—complex data migration—is collapsing. An AI agent, '10k', completed the core lift of a 10-year Marketo data migration, a project quoted at $100k and one year by a human agency, in a single hour for just $14.21 in compute costs.

AI-driven approaches dramatically reduce the time and cost of modernizing legacy systems. What was once a multi-year, multi-million dollar mainframe project can now be completed in as little as 90 days, fundamentally altering the ROI for tackling technology debt.

Migrating ten years of data from a siloed system like Marketo into an environment where a single AI agent could access and act on it end-to-end resulted in a massive productivity leap. The ability for an agent to work with freed data in real-time proved more impactful than years of incremental improvements.

Historically, the difficulty of migrating applications made databases incredibly sticky, creating a moat for incumbents. AI agents can automate this monotonous work because database interfaces are well-specified. This shifts the basis of competition from lock-in to cost, zero-to-infinity scaling, and iteration speed.

Specialized knowledge that takes humans weeks to learn can be codified into compact 'skill files' for AI agents. dbt Labs condensed its training curriculum into a small file that 'teaches' an agent to perform complex tasks, like a data migration, in a fraction of the time and cost.

Technical operations teams can waste up to 70% of their time manually collecting data. Deploying specialized AI agents to autonomously parse unstructured engineering logs, financial databases, and project updates automates this process, eliminating this 'operational tax' and freeing up teams for higher-value strategic work.