Over the weekend, the OpenAI cofounder and former director of AI at Tesla posted a graphic showing how susceptible every occupation is to Al and automation, using Bureau of Labor Statistics data. Different jobs received scores on a scale of 0 to 10, with 10 being most exposed.

While the overall weighted exposure was 4.9, Karpathy’s data also showed that professions earning more than $100,000 a year had the worst average score (6.7), while the those earning less than $35,000 had the lowest exposure (3.4).

His chart quickly drew attention online, with many predicting doom for white-collar workers. But Karpathy soon removed the data.

“This was a saturday morning 2 hour vibe coded project inspired by a book I’m reading,” he explained on X on Sunday morning. “I thought the code/data might be helpful to others to explore the BLS dataset visually, or color it in different ways or with different prompts or add their own visualizations. It’s been wildly misinterpreted (which I should have anticipated even despite the readme docs) so I took it down.”

He didn’t respond to questions about how it’s been misinterpreted and what the correct interpretation should be.

Still, an archived version of the chart may not be much of a shocker as it echoes what others have been saying about how AI could shape the U.S. labor market.

For example, software developers, computer programmers, database administrators, data scientists, mathematicians, financial analysts, paralegals, writers, editors, graphic designers, and market researchers got scores of 9.

That’s as sophisticated AI tools are increasingly being used to crunch numbers and produce content, performing tasks in minutes that used to require knowledge workers hours, days, or even weeks to do.

While AI is seen as a productivity enhancer for experienced employees, evidence is mounting that companies have less need for entry-level workers. More companies are also announcing layoffs and citing AI, though skeptics see it as a scapegoat to correct pandemic-era overhiring.

Meanwhile, Karpathy’s chart showed that construction laborers, roofers, painters, janitors, ironworkers, and grounds maintenance workers got scores of just 1. Similarly, home healthcare aides, nursing assistants, massage therapists, dental hygienists, veterinary assistants, manicurists, barbers, and bartenders got scores of 2.

Earlier this month, AI startup Anthropic issued a report entitled “Labor market impacts of AI: A new measure and early evidence,” that found actual AI adoption is just a fraction of what AI tools are feasibly capable of performing.

Like Karpathy’s data, Anthropic’s paper said AI can theoretically cover most tasks in business and finance, management, computer science, math, legal, and office administration roles. While AI adoption is still lagging, Anthropic said the workers most at risk are older, highly educated and well paid.

And earlier this year, a viral essay by Citrini Research painted a catastrophic picture of an economy destroyed by AI, sparking a stock market selloff.

But Citadel Securities swiftly debunked the doomsday scenario in a blistering report, pointing out that Indeed job posting data shows demand for software engineers is actually up 11% year over year so far in 2026.

Citadel also noted that the daily use of generative AI for work remains “unexpectedly stable” and currently “presents little evidence of any imminent displacement risk.” Instead of a collapsing economy, new business formation in the U.S. is rapidly expanding, and the construction of massive AI data centers is currently driving a localized boom in construction hiring.

Furthermore, if automation expanded at the breakneck pace Citrini fears, demand for compute would inherently rise, pushing up its marginal cost. 

“If the marginal cost of compute rises above the marginal cost of human labor for certain tasks, substitution will not occur, creating a natural economic boundary,” Citadel said.