AI Adoption Rates Starting to Flatten Out

The number of use cases for which I use AI is actually rapidly decreasing. I don't use it anymore for coding, I don't use it anymore for writing, I don't use it anymore for talking about philosophy, etc. And I use 0 agents. even though I am (was) the author of multiple MCP servers. It's just all too brittle and too annoying. I feel exhausted when talking to much to those "things".... I am also so bored of all those crap papers being published about LLM. Sometimes, there are some gems but its all so low-effort. LLM papers bore the hell out of me...

Anyway, By cutting out AI for most of my stuff, I really improved my well-being. I found the joy back in manual programming, because I am one of the few soon that will actually understand stuff :-). I found the joy in writing with a fountain pen in a notebook and since then, I retain so much more information. Also a great opportunity for the future, when the majority will be dumbed down even more. And for philosophical interaction. I joined an online University and just read the actual books of the great thinkers and discuss them with people and knowledgable teachers.

For what I use AI still is to correct my sentences (sometimes) :-).

It's kinda the same than when I cut all(!) Social Media a while ago. It was such a great feeling to finally get rid ot all those mind-screwing algorithms.

I don't blame anyone if they use AI. Do what you like.

Adoption = number of users

Adoption rate = first derivative

Flattening adoption rate = the second derivative is negative

Starting to flatten = the third derivative is negative

I don't think anyone cares what the third derivative of something is when the first derivative could easily change by a macroscopic amount overnight.

Apollo published a similar chart in September 2025: https://www.apolloacademy.com/ai-adoption-rate-trending-down... - their headline for that one was "AI Adoption Rate Trending Down for Large Companies".

I had fun with that one getting GPT-5 and ChatGPT Code Interpreter to recreate it from a screenshot of the chart and some uploaded census data: https://simonwillison.net/2025/Sep/9/apollo-ai-adoption/

Then I repeated the same experiment with Claude Sonnet 4.5 after Anthropic released their own code interpreter style tool later on that same day: https://simonwillison.net/2025/Sep/9/claude-code-interpreter...

They show two different surveys that are supposed to show the same underlying truth but differ by a factor of 3x? For the Ramp survey: why the sudden jump from 30% to 50% in March? For the Census one: How could it possibly be that only 12% of companies with more than 250 people „adopted“ (whatever that means) AI? It would be interesting if it were true but these charts don’t make any sense at all to me

My guess is AI will find niches where it provides productivity boosts, but won’t be as useful in the majority of fields. Right now, AI works pretty well for coding, and doesn’t really excel anywhere else. It’s not looking like it will get good enough to disrupt the economy at large.

What is their definition of adoption? A company where every employee has some level of access to AI is the bare minimum of “full adoption” for a given company but a threadbare one.

A company that has implemented most current AI technologies in their applicable areas in known-functionally capabilities? That is a vastly larger definition of Full Adoption.

It's the different between access and full utilization. The gulf is massive. And I'm not aware of any major company, or really any, that have said, "yep, we're done, we're doing everything we think we can with AI and we're not going to try to improve upon it."

Implementation of acquired capabilities, implementations... Very early days. And it appears this study's definition is more like user access, not completed implementations. Somewhat annoyingly, I receive 3 or 4 calls a day, sometimes on weekends, from contracting firms looking for leads, TPMs, ML/Data scientists with genai / workflow experience. 3 months ago, without having done anything to put my name out any more that however it had been found before that, I was only getting 1 ever day or two.

I don't think this study is using a useful definition for what they intend to measure. It is certainly not capturing more than a fraction of activity.

Given the charts, that’s a ridiculous claim. Just compare early 2024 in the first chart, for example.

It’s way too early to decide whether it’s flattening out.

If I was openAI or whatever I would be investing in circular partnerships with claude or whatever, claim agentic use should be considered the same as real users, then have each other's LLM systems use each other and finally achieve infinite, uncapped user growth

From the chart, the percentage of companies using AI has been going down over the past couple of months

That's a massive deal because the AI companies today are valued on the assumption that they'll 10x their revenue over the next couple of years. If their revenue growth starts to slow down, their valuations will change to reflect that

The least volatile dataset, employee count 1-4 businesses, is steadily climbing in adoption. I feel like as long as the smallest businesses (so the most agile, non-enterprise software ones) increase in adoption, other sizes will follow.

No no, we just need to put even more money in.

Not to be lost, but the first chart is actually a 3-month moving average. Surprised they buried that in the notes and didn't simply include it in the chart title. "Note: Data is six-survey moving average. The survey is conducted bi-weekly. Sources: US Census Bureau, Macrobond, Apollo Chief Economist"

Without weighing in on the accuracy of this claim, this would be an expected part of the maturity cycle.

Compare to databases. You could probably have plotted a chart of database adoption rates in the '90s as small companies started running e.g. Lotus Notes, FoxPro and SQL server everywhere to build in-house CRMs and back-office apps. Those companies still operate those functions, but now most small businesses do not run databases themselves. Why manage SQL Server when you can just pay for Salesforce and Notion with predictable monthly spend?

(All of this is more complex, but analogous at larger companies.)

My take is the big rise in AI adoption, if it arrives, will similarly be embedded inside application functions.

I think what is happening is that people are realizing AI is not just plug and play. It can do amazing things but needs engineering around it.

I think what will happen is in parallel more products will be built that address the engineering challenges and the models will keep getting better. I don't know though if that will lead to another hockey stick or just slow and steady.

What a shitty plot. Here are the sins I count:

1. No y axis label.

2. It supposedly plots a “rate”, but the time interval is unspecified. Per second? Per month? Per year? Intuitively my best guess is that the rate is per-year. However that would imply the second plot believes we are very near to 100% adoption, which I think we know is false. So what is this? Some esoteric time interval like bi-yearly?

3. More likely, it is not a rate at all, but instead a plot of total adoption. In this case, the title is chosen _very_ poorly. The author of the plot probably doesn’t know what they’re looking at.

4. Without grid lines, it’s very hard to read the data in the middle of the plot.

I'm more interested in what the implications are for the economy and what this next AI winter looks like.

What happens to all the debt? Was all this just for chatbots that are finally barely good enough for satnav and image gen that does slightly better photoshop that the layperson can use?

The average person has no idea what to use AI for to get substantial value out of what it can now do.

It's the switch between: know which service to use, consider capabilities, try to get AI to do a thing, if you even have a thing that needs done that it can do; versus: AI just does a thing for you, requiring little to no thought. Very active vs very passive. Use will go up in direct relation to that changeover. The super users are already at peak, they're fully engaged. A software developer wants a very active relationship with their AI; Joe Average does not.

The complexity has to vanish entirely. It's the difference between hiding the extraordinary engineering that is Google search behind a simple input box, and making users select a hundred settings before firing off a search. Imagine if the average search user needed to know something meaningful about the capabilities of Google search or search in general, before using it. Prime Google search (~1998-2016) obliterated the competition (including the portals) with that one simple search box, by shifting all the complexity to the back-end; they made it so simple the user really couldn't screw anything up. That's also why ChatGPT got so far so fast: input box, type something, complexity mostly hidden.

so no expot. growth? who would have guess?

/s