Why Token Maxing is Failing Enterprise Startups | Legora CTO

AI generates majority of Legora's codebase

Claude and Cursor are the top contributors to Legora's codebase, collectively generating well over 50% of code and far outpacing any individual human engineer.

Unlimited budget for developer AI tooling

Lorettson views AI tooling spend as having infinite ROI due to the extreme opportunity cost of slower development in competitive markets.

Productivity gains reshape team structures

AI tools have compressed the coding phase—the traditional 100-year bottleneck—forcing organizations to restructure around faster iteration cycles.

Engineering shifts to architecture and guardrails

As code writing becomes commoditized, engineers must focus on systems design, strategic trade-offs, and establishing mechanistic guardrails for autonomous AI agents.

AI code review remains nascent and inadequate

Current AI review tools are insufficient for evaluating systems architecture and security boundaries, creating a critical need for specialized review agents and better tooling.

Rise of meta-engineering for agent optimization

A new engineering function is emerging focused on optimizing agent effectiveness, creating data feedback loops, and enabling AI to independently self-improve systems.

Mandatory human review for security despite AI speed

Legora maintains human review of all PRs to mitigate novel security vulnerabilities introduced by AI-generated code, as threat actors become more efficient.

Automated incident response and postmortems

AI agents now analyze logs and telemetry to diagnose incidents and draft postmortems, significantly reducing the need for engineers to wake up for on-call emergencies.

PMs prototype independently before engineering handoff

Product managers can now use AI to build functional prototypes for user testing immediately upon conceiving ideas, frontloading validation work.

Taste becomes the critical moat against AI slop

"Taste"—an opinionated stance on product identity—prevents convergence to generic "AI grayness" and maintains differentiation when copying is instant.

The final 10% separates viable products from demos

While competitors can "vibe code" to 90% feature parity quickly, enterprise value lies in handling edge cases, audit trails, RBAC, and unhappy paths that require deliberate engineering.