When a startup pivots, it often adapts its existing software instead of rebuilding. This leads to a convoluted codebase built for a problem the company no longer solves. This accumulated technical debt from a series of adaptations can hobble a company's agility and scalability, even after it finds product-market fit.

The rapid pace of development enabled by AI doesn't eliminate technical debt; it accelerates its creation. More code shipped faster means more potential bugs, maintenance overhead, and architectural risk that must be managed proactively, not just reactively.

Fast-growing companies operate with internal chaos ("backstage") as they constantly rebuild systems. The key is to shield customers from this dysfunction, presenting a polished, reliable product experience ("onstage") no matter how turbulent things are internally.

Julie Zhu observes that many of the fastest-growing companies grow so quickly they don't have time to build robust data logging and observability. They succeed on "good instincts and good vibes," only investing heavily in data infrastructure after growth eventually stalls.

Similar to technical debt, "narrative debt" accrues when teams celebrate speed and output while neglecting shared understanding. This gap registers as momentum, not risk, making the system fragile while metrics still look healthy.

Block's CTO argues that engineers mistakenly equate code quality with product success. He uses the example of early YouTube, which had a famously poor architecture but became wildly successful, while the technically superior Google Video failed. The focus should be on solving a user problem, not on perfect code.

The biggest scaling mistake is focusing on running up numbers while ignoring the underlying mindset. During its peak growth, Facebook put every new engineer through a six-week bootcamp not for immediate productivity, but to instill the company's culture. This investment in a shared mindset is what enables sustainable scaling, preventing the chaos that comes from rapid headcount growth.

AI coding tools dramatically accelerate development, but this speed amplifies technical debt creation exponentially. A small team can now generate a massive, fragile codebase with inconsistent patterns and sparse documentation, creating maintenance burdens previously seen only in large, legacy organizations.

The popular tech mantra is incomplete. Moving fast is valuable only when paired with rapid learning from what breaks. Without a structured process for analyzing failures, 'moving fast' devolves into directionless, costly activity that burns out talent and capital without making progress, like a Tasmanian devil.