What is Really Happening to Jobs? Separating AI Hype from Reality

A challenge with this kind of study is that coding agents (Claude Code, OpenAI Codex) only started working really well in late November, which for most people meant early January due to the December break.

General agents (OpenClaw, Anthropic Copilot, ChatGPT "Work") started working even later than that.

This category of software may have a much more meaningful impact on work than the mostly-chat systems we were using from 2022-2025.

Studies that mainly focus on 2022 to end of 2025 might be missing out on a material uptick in capabilities.

I find that productivity follows a Pareto distribution (80:20 rule) and that AI is a sharper tool in that it enables the already productive to be proportionally more productive and this effect increases as the intelligence of AI increases. So 80:20 becomes a 90:10, 95:5, 99:1 etc. I also think that corporate bureaucratic change is laggy so change there will still be rather slow. I think where change will be rapid is when the most productive employees leave the company to create a new smaller company to compete with it, so there will be a displacement of medium to large companies by much smaller ones. On one hand this greater competition of more efficient companies will result in an increased in consumer surplus, on the other hand it will result in mass economic displacement and a collapse of the tax base. I think AI has only recently been good enough to do this and it takes time to spin up competing companies so I wouldn’t expect to see this effect in any lagging indicators just yet. From personal experience, I’m well down the path of commoditizing my niche industry where I can practically give away a better version of the top tier software and still personally make a lot of money. Additionally it would be counter productive to alert my competitors to this new reality. I know I’m not the only person doing this, so this multiplied by a bunch of industries would be absolutely world changing.

Why does the unemployment chart show a slow rise in unemployment for a year or more before covid started? This does not agree with the data from the BLS, which showed unemployment spiked very suddenly in March 2020. https://www.bls.gov/charts/employment-situation/civilian-une...

Also, what is “AI exposure” in 2015? LLMs hadn’t been invented yet. I did just read some of the cited paper. Essentially the quintiles boil down to use of computers, not really use of AI as we know it today. I know LLMs aren’t all AI, but the thing that’s missing is the distinction between “AI” that can play chess and LLMs that can actually do your job, which have only existed for maybe a year.

Recently poked around the job market to see what I qualify for in this day and age. Working as a solo builder in my org I would say that I have done enough in the last 18 months to consider myself “with it”.

What I found was pretty brutal. Companies asking for 4 years of agentic AI experience… pardon?

Then it hit me.

Oh they are all making shit up now and have no bar that anyone can hit because they are believing in the hype without understanding the fundamentals.

GREAT. Even as I climb the AI-Native ranks, I apparently am unqualified for any AI-Native job.

Big caveat to the productivity claim is that it’s concentrated in lesser experienced engineers and vanishes or goes negative with highly experienced engineers.

This intuitively makes sense and generally agrees with my experience. LLMs move your baseline towards the mean. If you are below average it improves and if you are above it hinders.

But also in my experience, my memory retention of the work done with an LLM is worse than doing it myself self. This leads me to believe that the lesser experienced engineers are not gaining experience!

My tinfoil hat says that this is what tech CEOs want. They want workers that are low skilled and can be paid less.

A couple of important points to note:

1. Most of the productivity studies in Figure 3 about are from the 2023-2024 era. (Which is why as some comments note, Copilot is actually way up there in the numbers. Note that this was from the era of spicy autocomplete and long before coding agents exploded on the scene.)

2. AI adoption at work is actually very low: even though 50%+ of Americans currently self-report (major caveat) using AI at least weekly, they use it for only 6% of work hours. (You can play with the charts here to see this [0]) This is what the recent Google study [1] called "broad but shallow use."

Notably, the same surveys find time savings of 2% of working hours, so a whopping 30%+ productivity boost per hour of AI used. And this is across industries. Despite being self-reported, it does line up with many of the other controlled studies (see TFA and [2, 3]). Some economists suggest that even with this low level of adoption, we may already be seeing the impact on labor productivity at national-level aggregate statistics! [2, 3]

As I said in another thread, my concern is that the impact on jobs is only beginning because 6% of work hours is a very low number. However given how useful people are finding it based on self-reported, micro- and macro-level numbers, adoption is only going to up, both in breadth (more people) and depth (more tasks). I fear the impact will happen gradually, as adoption inches up... and then suddenly.

[0] https://www.genaiadoptiontracker.com/ […]

The biggest impact of AI at my job are LLM code generators for software engineers. That's only scratching the surface of what AI can do for enterprises. My company doesn't yet have the orientation, inclination or technical expertise to unlock the true power of AI for our business goals. We're stuck buying packaged solutions from third parties and aren't integrating layers of intelligence in our environment.

I doubt we're unique. Chat bots are useful. But it will take years, possibly decades for work to transform to due to AI. Probably longer for everyday life. The diffusion of new technology, even something as profound as AI, has to fight the friction and realities of the real world. Always has.

> Dario Amodei, CEO of Anthropic, has predicted that AI could wipe out half of white-collar jobs and push unemployment to 20 percent.

And then he also says that a certain model is too dangerous to release.

Organizational inertia is a real thing. There are still fortune 500 companies with internal bans on AI. A lot of the answer to "how much impact has AI had" comes down to "how much have we even attempted?"

In my workplace, we're going to decline to renew some software subscriptions because a non-programmer vibe-coded their replacement in a week.

The impacts are here, they're just not evenly distributed yet.