Scaling AI Without Burning Cash

When I founded my company in 2005, tech upheavals were a constant, but the current climate demands a different kind of discipline. We set a target of 100 million dollars in free cash flow within three years, forcing us to scrutinize every AI commitment. Unlike the industry giants, we cannot afford failed experiments. We bypassed expensive, locked-in frontier subscriptions in favor of open-source, model-agnostic tools like OpenCode. This allows us to match specific models to specific tasks, avoiding the trap of ballooning costs seen at firms that burned through their entire AI budgets in months.

Strategy must dictate technology, not the other way around. We rejected the pressure to switch to token-based pricing, choosing instead to maintain a model aligned with merchant success. By focusing on how AI can simplify complex merchant workflows—such as data analysis and inventory management—we ensure our investments create measurable value rather than just technical noise. We also use internal AI deployments as a proving ground for external products. By using machine learning to untangle our own siloed data and fragmented spreadsheets, we build solutions that directly address the resource constraints our customers face. Technology evolves, but the fundamentals of fiscal responsibility and customer-centric value remain the only reliable metrics for success.