The most dangerous words in investing might be 'this time is different,' but the second most dangerous might be 'this time is the same.' Almost every major technology transition has seen people underestimate the ultimate scale of the winning technology while simultaneously overestimating the near-term pace of adoption and monetization.
Joshua also explains why most people have quietly adapted to capabilities that would have seemed unimaginable just a few years ago, and why the biggest changes from AI may arrive gradually rather than all at once.
most people have quietly adapted to capabilities that would have seemed unimaginable just a few years ago, and why the biggest changes from AI may arrive gradually rather than all at once
The real insight is that the S-curve is not just a technology adoption curve — it's also a valuation curve. The multiples that investors are willing to pay expand dramatically as a technology moves from the early adopters to the early majority, and then compress just as dramatically as growth slows and the technology matures.
The danger with the AV transition is that it's going to be pretty sudden. It's not like, 'Hey, we're going to have 10% more AVs every year.' The technology is going to get approved, it's going to be proven, and I think you'll see a rapid acceleration of AVs into the market.
The best tech investors understand that the S-curve is not just a description of what happened — it's a prediction tool. When you're early on the S-curve, the technology is improving faster than the market appreciates, and when you're late, the technology is maturing faster than the market appreciates.
The biggest mistake investors make is that they think about technology adoption as linear, when in reality it follows an S-curve. The early part of the S-curve looks like nothing is happening, and then suddenly everything happens at once, and then it plateaus. Most investors are either too early and give up, or they extrapolate the steep part of the curve forever.
I mean, you know, if I reverse engineering my career, it's really been about normalizing disruptive technologies to make them more understood and to drive that mass adoption.
if I reverse engineering my career, it's really been about normalizing disruptive technologies to make them more understood and to drive that mass adoption.
UL's been around for more than 100 years; it started as a way for insurance companies to standardize fire and safety testing as electricity was the new technology spreading into homes. But now it's everywhere, and "safety" in tech doesn't just mean the hardware.
Actual data showing that young children sometimes feel more comfortable being read to or reading to a robot than a human being. It's less stressful for them.
Compute is still growing exponentially. But AI adoption curves often look like S-curves... there's going to be deceleration. So OpenAI shot up to basically a billion MAU, monthly active users... that curve is slowing down. There's just no way you can be like, yeah, actually, we're expecting 10 billion ChatGPT users next year because there aren't 10 billion people.
5mo ago
Underscored — save the words that stop you in your tracks.
