Max Levchin

Levchin rejects treating A/B labels as knowable at hiring: interviews can screen out C and D performance, but the distinction between a good hire and a brilliant one emerges only after joining. Strong opinions and extreme personalities are often worth their quirks; the danger is insecure B players who become managers and hire weaker teams to become “the tallest mushroom.” His non-negotiable is integrity: “Once I can’t trust you, I can’t ever trust you.”

Founders cannot expect employees to inherit their obsession, because the founder’s incentives, passion, and goals are fundamentally different. The leadership task is to discover what each person wants—quality, mastery, or a personal goal—and connect it to the work. Someone motivated by craftsmanship needs help making “every pixel” exceptional, not another demand to work Saturday and Sunday.

Affirm’s postmortem system converts operating failures into an internal library of institutional knowledge. Every blip gets a segregated data stream, a directly responsible author, a written analysis within at most two weeks, and senior review that asks whether it explains the cause rather than merely describing the event. The process must be clinical: “Get rid of all emotion; exactly describe what happened.”

The founding bet behind Affirm was deliberately uncalculated: remove fine print, never build economics around confusing customers, and accept the possibility that the model might fail. Affirm is now public and financially successful. A later infrastructure failure illustrates what “do the calculating” means: although Affirm scaled both front- and back-end infrastructure for a projected 10x launch, the front tier outpaced the back tier, creating too many backend connections and leaving the database in shambles when actual demand was modest.

Speed matters, but an MVP becomes garbage software when a company mistakes evidence of demand for permission to leave the prototype in production. Affirm lets a prototyping team ship rough experiments, then pulls successful features down and rebuilds them for beauty, scalability, and durability. Levchin reserves extra time for “the quality of the invisible parts,” particularly underwriting, infrastructure, and decisions that will persist for years.

Hybrid work is an output problem, not a location ideology, but “enough together time” is non-negotiable. Spending hours on Zoom after an unnecessary commute adds little; never meeting teammates sacrifices trust, shared context, and camaraderie. The same emphasis on presence shaped Levchin’s layoff lesson: leaders must own the responsibility and “be part of the goodbye,” not retreat to their offices.

Levchin said Klarna announced 32% growth last quarter, while Affirm announced 35% growth over the last few quarters; he said Affirm had been taking share based on estimates from those periods. He attributes that partly to serving terms from 45 days through four years and avoids speculating about competitors’ corporate strategies, preferring product and sales teardowns because “someone is making mistakes for us.” As a public CEO, he focuses on Graham’s long-term “weighing machine,” even removing Affirm’s ticker from his desktop to avoid emotional reactions.

An AI narrative without operating substance feeds the market’s voting machine rather than the business. Levchin agrees pure LLM efficiency may be approaching an upper limit, but expects systems built around LLMs—reasoning loops or multiple models debating—to amplify capability. His standard is simply whether the company is using “the most interesting available tools,” not whether it can manufacture a fashionable story.