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Which Jobs Will AI Disrupt Most?

The AI Daily Brief: Artificial Intelligence News and Analysis

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  • AI Usage Data
    • Anthropic’s Economic Index analyzes actual AI usage logs instead of relying on surveys.
    • This offers valuable data on AI adoption across various professions. Transcript: Nathaniel Whittemore This is a research paper which sought to determine which professions were using AI, how they’re using it, and how much they’re using it. It drew on anonymized interactions with Claude gathered over the past two months, meaning it’s extremely up-to Importantly, it’s the first paper of its kind based on analyzing actual Usage logs rather than just self-reporting through surveys, which makes it a really valuable addition to this canon. On a high level, Anthropic found that over one-third of occupations are using AI across at least a quarter of their workplace tasks. However, only 4% of occupations are using AI in three quarters of their tasks. AI usage also leans towards augmentation of workers rather than full automation of tasks, but not by that much. 57% of AI use was augmenting a human worker, while 43% was automation of individual tasks. This number I think actually should be a dramatic wake-up call. If Anthropic is already saying 43% of this usage is AI directly performing tasks, and that’s in the pre-agentic era, how much more of this behavior is going to shift in that direction? Work. (Time 0:01:33)
  • Leading AI Adoption
    • Programming and development lead AI usage, representing 37.2% of all logged activity.
    • Creative and editorial tasks follow at 10.3%, with other fields showing lower adoption rates. Transcript: Nathaniel Whittemore And way, way out ahead in the lead, the number one category for work was programming and development. That represented 37.2% of all use. The second highest category of work was creative and editorial, or as they call it, arts and media, with 10.3%. There were also a wide range of tasks here, mostly in writing. Producing and performing in film, TV, theater, and music was actually fairly high with 1.8%. But there were also things like marketing use cases. Following that in categories was educational tasks at around 9%, office and administrative at around 8%, science, including life, physical, and social science at a little over 6%, And business and financial at 5.9%. And again, in this study, they also get down to the task level, so you can have a sense of the use cases that are actually driving the industry right now. Now, before we move on, it’s really worth digging into the developer use case. As we were discussing in an earlier show this week, at this point, Anthropic has established themselves as the go-to model provider for coding assistance. They generate around 85% of their revenue from API usage compared to 28% for OpenAI. The point of that is not to say that this is not useful, but that it may not be representative of LLM and AI use in general, given that it’s using Claude chat logs and programming is so far And away their biggest use. (Time 0:02:38)
  • Augmentation vs. Automation
    • AI currently augments human work more than it automates tasks, but the gap is closing.
    • This suggests a potential shift towards greater automation as AI develops. Transcript: Nathaniel Whittemore Now, looking at job replacement or augmentation again, Anthropic wrote, as we predicted, there wasn’t evidence in this dataset of jobs being entirely automated. Instead, AI was diffused across the many tasks in the economy, having stronger impacts for some groups of tasks than others. The conclusion for them is that AI adoption is about automating or augmenting a subset of routine tasks rather than wholesale worker replacement. (Time 0:06:35)
  • AI and Salary
    • AI use correlates with mid-range salaries ($50,000-$100,000).
    • High-paying jobs like obstetricians and low-paying jobs like shampooers see less AI use. Transcript: Nathaniel Whittemore But I also just tend to think that this data still represents very nascent usage. There was also some interesting insight to be had around salary analysis. Almost all of the job categories that Anthropic chose represented less than 1% of Claude use. There were numerous roles in the $50,000 to $100,000 salary range that showed up in the data between 0.5% and 1.5% of overall usage. And all of the heavy use roles, like software developers and copywriters, had median incomes close to this range. The example Anthropic used for an ultra-high profession that isn’t using Claude very much was obstetricians and gynecologists. And down on the low end of the salary range, the example job that isn’t getting much use out of AI was shampooers. As I mentioned a couple times, Anthropic goes to pains to provide the caveats to help you contextualize this information. The one additional that I haven’t mentioned yet that is really important to note is that this was an analysis only of the free and pro tiers of chat and excluded enterprise team and API Users. I would be particularly interested to see a version of this where enterprise and team were in place, as it feels like it would change the results fairly dramatically. The other big thing that I keep coming back to is that this is still a pre-agent survey of AI usage. (Time 0:07:00)
  • Software Engineering Job Postings Decline
    • Software engineering job postings saw a 70% decline between 2022 and 2025.
    • Some attribute this to increased developer productivity due to AI coding tools. Transcript: Nathaniel Whittemore This draws specifically from government employment data and shows a 70% collapse of software engineering jobs between 2022 and 2025. Greg Eisenberg writes, this chart is nuts. Software developer jobs down 70% from peak. People will blame the end of free money, but something way more interesting is happening. The middle-class engineer is dying, and it’s dying because they’re not needed anymore. One good dev with GitHub co-pilot ships what entire teams did five years ago. Microsoft just reported the highest revenue per employee in history. The entry-level engineer doesn’t exist anymore. Instead, we have product builders who happen to code. Armed with AI, they ship entire products in days. Meanwhile, the truly elite engineers are making more money than ever. They’ve shifted to working mostly on frontier tech. I mean the stuff that’s really hard. AGI at OpenAI, designing rockets at SpaceX, self-driving car tech at Tesla. Product builders are becoming solopreneurs and creators while frontier engineers are making hedge fund money. In 2025, software engineer doesn’t mean what it meant in 2020, and that’s what this chart really shows. (Time 0:15:19)
  • Altman’s Prediction
    • Sam Altman predicts a significant shift in software engineering due to advanced AI agents.
    • He believes agents will drastically increase efficiency, similar to the impact of Deep Research. Transcript: Nathaniel Whittemore Even if software engineering isn’t being replaced by AI right now, there is a visceral concern that that day is coming. Earlier this week in Paris, Sam Altman discussed how advanced agents will change software engineering. He likened the change to the launch of Deep Research, claiming that companies could use 50 cents of compute to complete $500 to $5,000 worth of work. He said, companies are implementing that just to be way more efficient. I think you’ll see this in a big way with the software engineering agent. Still, many think that we’re getting out over our skis. DevList founder Alex Charbono writes, I think if someone is claiming how software engineering jobs are becoming obsolete, it’s just a signal they haven’t used the software engineering AI tools themselves to see how much they suck. They help with boilerplate and how do I format this date, but try building anything more complex than a template with just AI. Entrepreneur Adam Small wrote, AI is not going to replace software engineers, developers, or whatever title you call it. AI is going to make SWEs more productive and accelerate the pace of development. If you aren’t using AI as a dev tool, then you will be replaced by someone who is. That is what you have to worry about as a software engineer. (Time 0:18:08)
  • Adapt or Be Replaced
    • Software engineers should embrace AI tools for increased productivity.
    • Those who don’t adapt risk being replaced by those who do. Transcript: Nathaniel Whittemore Now, I tend to think that arguments around capabilities right now are the worst arguments for understanding where AI is actually going to be disruptive in the future. It just changes so fast. And while all these critiques of what is available right now from coding agents may be true, it’s not going to be the same in six months to say nothing of 12 and 24 months. At the same time, and what might be really important about software engineers, is that there is probably no other role that more directly has the ability to translate productivity Gains into building way more rather than just building the same amount with less effort and less money. When engineers are taken off the leash to be able to build as though they had a team of 10 people helping them, they’re not just going to build the same stuff. They’re going to build wildly more complex stuff or customized stuff. And I think that to the extent that we see this as the model where people translate the productivity gains they get from agents into building more, better, cooler, more interesting Stuff. The more that that becomes the normal expectation. And the less we just see agents as ripping out every job that exists right now. Anyways, that is going to do it for today’s episode. Lots and lots of interesting things to chew on. (Time 0:19:08)