With AI making code generation cheap, the limiting factors for development velocity are now defining what to build (product) and ensuring its quality (review). Engineers will increasingly focus on high-level systems architecture rather than typing code.
A new engineering role, akin to Developer Experience (DevEx), will focus on building the infrastructure, guardrails, and feedback loops necessary to enable AI agents to autonomously and effectively improve systems, such as optimizing a website's conversion rate.
With AI, building custom internal tools for shallow, high-customization needs (like HR or payroll) is now cheaper and faster than buying and integrating third-party SaaS. This challenges the traditional 'buy vs. build' calculus for standard business functions.
In an era of rapid AI-driven development, competitors can easily replicate core functionality. The defensible advantage lies in mastering the complexities they ignore: unhappy paths, audit logging, RBAC, and other enterprise-grade edge cases.
Even though AI enables PMs to code, it's an inefficient use of their time. Since code creation is cheap and product strategy is the new bottleneck, PMs should focus entirely on product work, not engineering tasks.
Despite AI-driven productivity gains, the increased capacity to build creates more demand for features to stay competitive. The CTO planned for 20 engineers, thinking AI would keep the team lean, but quickly grew to 80 and still felt understaffed.
Investing in a Developer Experience (DevEx) team becomes crucial in the AI era. Making a team of 10x engineers 20% more efficient provides enormous leverage, justifying the investment in custom agents, review tools, and optimized setups.
Simple leaderboards tracking token usage lead to 'token maxing'—engineers burning tokens to look productive. A better approach is to use hack days and demos to reward and showcase high-impact output, which implicitly encourages effective AI use.
For hyper-growth companies, the cost of losing a competitive edge by not adopting powerful AI tools far outweighs the direct token costs. The opportunity cost of inaction makes any efficiency gain worth the price.
Legora's CTO reflects that his biggest hiring mistakes came from doubting his own intuition and deferring to a senior candidate's experience. He sensed something was wrong but convinced himself they knew better, only to be proven right weeks later.