CEO Tony Schneider categorizes decisions by reversibility. Protocol-level changes are treated as long-term, irreversible commitments, while app-level features are seen as reversible experiments that can be launched and changed quickly. This dual-speed approach balances stability with agility.

Instead of viewing velocity and dependability as a trade-off, engineer systems where the easiest, most automated path is also the safest. This "pit of success" makes the right choice the default for developers, aligning speed with reliability.

Eliminate one-size-fits-all reviews. Small, self-contained features ship rapidly on a fast track with only lightweight checks. Major, systemic changes require a separate, rigorous product strategy review to ensure alignment before development begins.

Instead of creating a massive risk register, identify the core assumptions your product relies on. Prioritize testing the one that, if proven wrong, would cause your product to fail the fastest. This focuses effort on existential threats over minor issues.

The 'move fast and break things' mantra is counterproductive for complex AI development. Tools and philosophies prioritizing correctness and thoughtful architecture over raw speed are better suited for building meaningful, non-trivial AI features that don't become overwhelming to manage.

Pushing for extreme speed does more than just accelerate timelines; it forces teams to triage ruthlessly. It creates a filter that naturally eliminates work on problems that don't absolutely need to be solved, preventing over-engineering and wasted effort. It's not about more hours, but higher focus.

When building AI for high-stakes domains like payroll, you must balance rapid innovation ('gas') with unwavering reliability ('brakes'). While teams can move fast on prototyping, the core promise of compliance and trust is non-negotiable, requiring safeguards, deep expertise, and risk-based rollouts.

To inject responsibility into a speed-obsessed culture, frame the conversation around specific risks. Create documented assumptions about what might break and, crucially, identify who bears the impact if things go wrong. This forces a deliberate consideration of consequences.

At the $100M ARR mark, complexity rises. "Slowing down" means intentionally focusing on quality and planning to prevent rework and tech debt. This allows teams to ship faster in the long run, like taking a shorter, well-planned hiking trail instead of running a longer one.