Early and late-stage hypergrowth.

Last week, a colleague asked why I’d hired an additional new leader onto an important area rather than expanding an existing leader’s scope to incorporate that area as well. The existing leader was a known quantity and doing well, so why not keep expanding them? It’s a good question, and depending on the circumstances I might have done either, but explaining why I specifically brought in a new leader this time depends a bit on a distinction I think of as early versus late-stage hypergrowth.

In Cross the Chasm’s world, early-stage is when you’ve proven product market fit, have won the early adopters, and are just starting to win the early majority. In this phase, there are specific problems, and the most important problem is to solve those specific problems. For example, you might be having scalability issues, and solving that is the company’s almost sole focus for a few weeks. After scalability is fixed, next you’ll need to work on onboarding flows to convert for less technical users, and so on. Not only the executives, but much of the company, serially hunts down solutions to their biggest problem.

When you reach late-stage hypergrowth, you are starting to encounter the late majority and laggards cohorts. This reorients the company and executive teams away from only creating an exceptional product, to also having to solve the numerous concerns and checkboxes that a skeptical audience introduces. Sure, your product might save hours a day for our team, but how does your compliance paperwork look? How stable are you? What contractual commitment will you make regarding customer support resolution? At this point, you’ll still be in an extremely competitive environment to retain the innovators and early majority, while also having to solve the long list of skeptic-driven requirements. Instead of hunting down solutions, the company–and the executive team–now has to solve everything, everywhere, all at once.

Going back to my colleague’s question, in early-stage hypergrowth, it would have absolutely been preferable to expand the existing leader’s scope. In late-stage hypergrowth, expanding their scope would have moved the problem, while reintroducing a previous problem, and that’s a losing strategy in that stage.

It’s been a while since the industry has talked a lot about hypergrowth, but a lot of the lessons of hypergrowth are relevant again as we see the productive chaos of the current AI-era, and this is absolutely one of them. In particular, it’s extremely clear that you can speedrun the early hypergrowth phase with a small, AI-empowered team, but it’s far from clear you can speedrun the late hypergrowth phase with the same approach. Personally, I suspect we will figure that out as an industry, but many of the challenges popping up recently in e.g. Anthropic’s messaging to Claude Code power users, feel to me like they’re rooted in the challenges of making this transition.

Even in the unlikely case that we never solve late-stage hypergrowth using the same AI-staffed mechanisms that support the early-stage case, it’s still an economic miracle, since it’ll allow a smaller amount of capital to culminate into relatively large and derisked companies, which should underpin substantial productivity for the economy.