Solopreneurs, Solow, and the SaaSpocalypse

Four of my favourite charts from Stripe Sessions

Sessions is Stripe’s annual customer conference. This year, John Collison’s keynote covered Stripe’s perspective on the evolving AI and agentic economy. Here are a few charts I found particularly compelling—including some that didn’t make it into the final cut.

The rise of solopreneurs

New business applications are rising worldwide. In the United States, where the Census Bureau tracks filings with enough granularity to distinguish likely employer businesses from other types, a notable divergence has emerged: total applications have accelerated significantly, but “high-propensity” applications—those statistically likely to result in payroll employment—have not kept pace.

Similar wedges have opened between overall and high-propensity filings in the past, such as in the depths of the pandemic and during the implementation of the Paycheck Protection Program (PPP) in the US. The current episode appears to reflect something different. The rapid development of AI tools might be lowering both the barriers to starting a business and the returns to doing so for individual operators—contributing to a rise in AI solopreneurs: individuals using AI to establish sole proprietorships or side businesses without the need or intent to hire. We see some evidence of this in our own Stripe Atlas data, where startups have accelerated since 2023, and especially in the first quarter of 2026. The overwhelming driver of this acceleration comes from solo founders, and we see growth among both AI and non-AI startups (but AI might be enabling easier formations even for firms whose business models are not AI-based).

What the SaaSpocalypse looked like on Stripe

Over 30 days in early 2026, the software sector shed roughly $1 trillion in market capitalization. The sell-off was driven primarily by investor concerns about the long-run revenue prospects of traditional software companies, as agentic AI systems—capable of performing many of the functions that enterprise software has historically been purchased to support—advanced rapidly through 2025.

Payment volumes offer a different vantage point. Unlike equity prices, which reflect market pricing of future scenarios, pay-in volumes capture current economic activity. Looking at weekly transactions for the 100 largest non-AI software-as-a-service (SaaS) companies on Stripe, the episode appears as a brief dip followed by a swift recovery and continued growth—a pattern quite different from what equity markets were pricing. The divergence between current transaction activity and market valuations punctuates how much the SaaSpocalypse was driven by expectations and forecasts, not current ongoing activity.

The Solow paradox and AI

In 1987, economist Robert Solow famously commented that the gains from personal computers were “everywhere except the productivity statistics.” The quip proved premature rather than wrong: most economists date the visible acceleration in aggregate productivity to the mid-1990s—less than a decade after Solow’s observation. (For more reading on how economists thought about the productivity effects of personal computers in real time, before the effects were obvious in the dot-com boom, Paul David—a Stanford economic historian—has a seminal 1990 paper, The Dynamo and the Computer, that’s a classic in this genre.)

The Solow paradox—the lag between a technology’s arrival and its measurable effect on productivity—is, it turns out, historically typical. The electrification of the US economy offers a tangible parallel. For three decades after Thomas Edison began delivering commercial electricity to New York customers in 1882, inflation-adjusted output per worker grew at just 0.5% annually—a sluggish pace relative to what electricity would eventually enable. The gains became evident only as the economy’s capital stock and organizational practices adapted to the new technology: new factories designed around electric motors from the ground up, rather than retrofitted from steam. Some of the impetus for this replatforming came from the weapons production spurred by World War I. In the decade after 1917, productivity growth more than doubled its prior three-decade average.

The implication for AI is that a near-term absence of productivity acceleration in the aggregate data should not be read as evidence that no acceleration is coming. (Stripe Economics has a recent post on how the decline of travel agents might offer some lessons on how technological shocks work their way through the labor market.)

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John’s talk included many more nuggets. Watch it in full below.