To The Moon

Andrej Karpathy had a really interesting post on X over the weekend:

Here’s the tweet in a visual funnel format

I think this diffusion question is a really important one. In particular, how quickly does AI diffuse? What Karpathy’s X post shows (in his opinion) is that diffusion is happening slower. PNC research shows that only ~2% of US households are paying for some sort of AI service.

What is the diffusion gap? It’s the delta between what you can accomplish with models (ie how powerful they are) and how the average person uses models.

Here’s why I think it matters. A big question in the market today is “will the revenues match the promised CapEx commitments.” It’s a big reason why all the semi-cap stocks got clobbered this week, and questions like “Will $CoreWeave, Inc.(CRWV)$ go bankrupt?!” have been repeated over and over again online.

The heavy early adopters clearly drive a ton of spend. And for this early adopter group, their spend seems to jump meaningfully as model progress continues. Check out the graph below posted from OpenAI. You can see usage inflect for the power users and median users (OpenAI users will clearly be categorized as overall power users relative to rest of the world)

But the “average” user is lagging behind. So to answer the question of “will the revenues match the capex obligations” you either believe that the power users will continue to spend more and more, or the “average” spender will start to catch up. Or both. I think for the market to really gain confidence, we need the latter to happen (the average user starts using / spending more on AI).

So I see two paths on diffusion. Either it takes a bit longer (the blue dotted line below), or diffusion / spend follows a typical S-Curve. And this applies both to business and consumer users.

So which path do I think we take? Probably something in the middle, BUT something that looks a lot more like the orange line than the blue line. Why do I think this? Typically big diffusion gaps in tech have all closed in a similar fashion - the end user doesn’t get more sophisticated, but the tools get easier and easier to use and abstract away the complexity.

I think I talked about this a few weeks ago with Muse / Instinct vs OpenClaw. OpenClaw was super popular among power users. But it didn’t really diffuse. Then Muse / Instinct and other personal agents exploded! Really these were “OpenClaw wrappers” (so much more than that, but for the sake of the argument let’s call them that). Muse / Instinct were technology that let averages users stay “unsophisticated” but start to adopt the technology.

Kind of similar to the internet. People didn’t learn how to use the internet in a terminal - they got a browser and THEN internet usage exploded. People didn’t learn how to use GPS, they got $Alphabet(GOOG)$ Maps. I think we’re starting to see the technology layers around the models really start to inflect which will allow the average users usage to inflect (and thus help close the diffusion gap).

Markets are always moving - and sometimes, the best move is knowing what works for you.

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