Patrick Collison: "What If You Succeed?"

Patrick Collison opens by reflecting on his 20-year relationship with Harj Taggar and his early work on Chroma, a Lisp dialect. When asked whether a modern teenager should still write code dialects given AI capabilities, he argues that despite AI's power, retaining knowledge in what he calls 'cognitive L1 cache' remains valuable because internal knowledge retrieval is faster than querying external agents. He notes that companies still place enormous premiums on cognitive ability and that he personally avoids using AI-generated writing suggestions, finding LLM output deficient at reasoning about multidimensional aspects of reality.

Collison discusses his decision to drop out of college twice—once after freshman year to start a company with Taggar, and again to found Stripe. He explains that while he initially felt urgency to pursue opportunities before they disappeared, in hindsight this concern was unfounded. He advises college students not to fear missing out on a supposed 'last window' to start companies, comparing contemporary doomism to historical millenarian predictions like those surrounding aviation's invention.

Regarding Stripe's founding, Collison explains that while the idea seemed simultaneously obvious and ridiculous—two young founders starting a financial services company when 'fintech' didn't even exist as a sector. Banks were skeptical, but the venture succeeded because it was grounded in a concrete, viscerally-felt customer problem: the genuine difficulty of processing payments online.

On Stripe's lengthy two-year private beta before public launch (September 2011), Collison explains this wasn't recklessness but necessity. Financial services required extensive security, partner relationships, and infrastructure work before scaling. Critically, they had production users almost immediately—their first customer in January 2010, just two months after the first lines of code. This provided constant grounding in reality rather than hypothetical assumptions, allowing just-in-time development driven by actual customer requests.

When asked about whether AI-enabled cheap development should encourage more ambitious product launches, Collison suggests the traditional lean startup doctrine may need updating. He notes that the internet is far more competitive than 20 years ago, so aggressive de-correlation through ambitious starting points might now be necessary. Many recent successful companies (labs, Anduril) took this anti-lean-startup approach, suggesting that cheaper development capabilities make this more feasible today.

Collison addresses the intellectual rewards and losses in building Stripe. While financial services involves tedious tasks like payroll setup, he's fortunate because Stripe works with the world's most innovative companies—25% of new Delaware corporations start with Stripe's Atlas service. He argues that asking 'what if you succeed?' before raising money is crucial; he needed to genuinely want to work on payments for decades, which he does because every business represents an applied theory worth understanding.

On fears that big AI labs will crush startups, Collison argues the historical record doesn't support this concern. Google appeared omnipotent 20 years ago but didn't accomplish everything despite material capabilities to do so, because organizations struggle to prosecute hundreds of priorities simultaneously. While specific tasks may be obviated by AI, the broader trend Stripe data reveals is unprecedented: more businesses are starting than ever before, with 2x year-over-year growth—the largest relative jump they've seen. Crucially, median businesses are performing better year-over-year, and probability of reaching revenue milestones ($1M, $5M, $10M) is increasing across the board.

Collison attributes this growth to enterprises being 'spring-loaded' to adopt new approaches because they fear being left behind by archaic operations more than they fear startup risk. The risk of status quo now appears as dangerous as the risk of the new, creating an unprecedented window for startup adoption. Combined with Stripe data showing more AI adoption than ever, Collison argues the future appears to be heading toward decentralization with thousands of winners rather than concentration among a few companies.