Claude Code purportedly has over a billion dollars in revenue.

In terms of economic value, coding agents are definitely one of the top-line uses of LLMs.

My mom uses the Google app instead of just going to Google.com on Safari. She’s probably going to use Gemini because she’s locked into that ecosystem. I suspect most people are going to stick with what they use because like you said, to consumers, they can’t really tell the difference between each model. They might get a 5% better answer, but is that worth the switching costs? Probably not.

That’s why you see people here mention Claude Code or other CLI where Gemini has always fallen short. Because to us, we see more than a 5% difference between these models and switching between these models is easy if you’re using a CLI.

It’s also why this article is generated hype. If Gemini was really giving the average consumer better answers, people would be switching from ChatGPT to Gemini but that’s not happening.

Will your mom pay for chatgpt or just stop when they will try to start converting more users ?

Coding agents seem the most likely to become general purpose agents that everyone uses eventually for daily work. They have the most mature and comprehensive capability around tool use, especially on the filesystem, but also in opening browsers, searching the web, running programs (via command line), etc. Their current limitation for widespread usage is UX and security, but at least for the latter, that's being worked on

I just helped a non-technical friend install one of these coding agents, because its the best way to use an AI model today that can do more than give him answers to questions

The other issue with this is that AI is still unprofitable and a money hole.

If consumers refuse to pay for it, let alone more than $20 for it, coding agent costs could explode. Agent revenue isn’t nearly enough to keep the system running while simultaneously being very demanding.

AI coding has massive factors that should make it the easiest to drive adoption and monetize.

The biggest is FOMO. So many orgs have a principle-agent problem where execs are buying AI for their whole org, regardless of value. This is easier revenue than nickle-and-diming individuals.

The second factor is the circular tech economy. Everyone knows everyone, everyone is buying from everyone, it's the same dollar changing hands back and forth.

Finally, AI coding should be able to produce concrete value. If an AI makes code that compiles and solves a problem it should have some value. By comparison, if your product is _writing_, AI writing is kind of bullshit.

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