15 May 2026

Garry Tan shipping 11,417 lines of code per day with a toolkit of AI agents is the kind of statistic that gets forwarded to CTOs by CEOs who want a conversation. Before that conversation goes badly, it's worth understanding what's actually happening here.

Lines of code is a notoriously poor productivity metric. It always was. AI-generated code inflates it further because the models are verbose, they write tests, they add comments, they scaffold. The number tells you something about velocity of generation, almost nothing about quality of decision-making. In consumer credit, where your loan origination logic sits inside FCA regulatory perimeter and your affordability assessments have to survive CONC scrutiny, the hard work was never writing the code.

The interesting signal in gstack is the orchestration model. Tan isn't prompting a single AI. He's built a structured team of specialised agents with defined roles, handoffs, and workflow automation. That architecture is transferable and it maps reasonably well onto how a credit broker's technology function actually works. You have people who own decisioning logic, people who own customer journey, people who own integrations with lenders. Agents that mirror those boundaries, with a human in the loop at the points of regulatory consequence, is a credible operating model.

The risk for UK fintech leaders is the wrong takeaway in both directions. Dismissing this as a founder showing off misses genuine capability shift. Treating it as a blueprint for cutting your engineering headcount by 80% before you understand where AI agents fail quietly and confidently is how you get a mis-pricing incident you explain to the FCA.

The productivity gains are real. The judgement about where to apply them still sits with humans who understand the regulatory stakes. The question worth asking your team is which parts of your build cycle are genuinely constrained by code generation speed, and which are constrained by everything else.