Predictions by Anthropic Researchers

/log/2025/06/04

Dwarkesh Patel has recently interviewed Sholto Douglas and Trenton Bricken for a second time, and the podcast is very enlightening in terms of how the big AI labs think in terms of their economic strategy:

(Clicking will start the video around the 1hr mark, the part that is relevant to this post.)

According to Sholto and Trenton, the following have been largely “solved” by now:

  • Advanced math/programming:
    • “Math and competitive programming fell first.” (Sholto)
  • Routine online interactions:
    • “Flight booking is totally solved.” (Sholto)
    • Successfully “planning a camping trip,” navigating complicated websites. (Trenton)

And below are their predictions for what will be solved by next year, around May 2026:

  • Reliable web/software automation:
    • Photoshop edits with sequential effects: “Totally.” (Sholto)
    • Handling complex site interactions (e.g., managing cookies, navigating tricky interfaces): “If you gave it one person-month of effort, then it would be solved.” (Sholto)

And below are what they predict will probably not be solved by next year:

  • Fully autonomous, high-trust tasks:
    • “I don’t think it’ll be able to autonomously do your taxes with a high degree of trust.” (Sholto)
  • Generalized tax preparation:
    • “It will get the taxes wrong… If I went to you and I was like, ‘I want you to do everyone’s taxes in America,’ what percentage of them are you going to fuck up?” (Sholto)
  • Models’ self-awareness of its own reliability and confidence:
    • “The unreliability and confidence stuff will be somewhat tricky, to do this all the time.” (Sholto)

I interpret this and the rest of the interview as follows:

The labs can now “solve”1 any white-collar task or job segment if they put their resources into it. From now on, it is a question of how much it would pay off.

In other words, if the labs think it will make more money to automate accounting (or any other task), then they will create benchmarks for that and start optimizing. Until now, they have mostly been optimizing for software engineering2, because of high immediate payoff.

Below are some job segments that I predict to be affected first (not Sholto or Trenton):

  • Marketing & copywriting: actually the first segment that already fell. Many AI companies (including TextCortex) was initially focused on this segment. Automation in this sector will increase even more in the upcoming years.
  • Customer service & support: many countries where this is outsourced to, like India, will be affected.
  • Data entry, bookkeeping & accounting tasks: while it is a dream to automate bookkeeping, accounting, taxes, etc. it will most likely fall last due to regulations and low margin for fuckups.
  • Paralegal & contract-review tasks: Many companies popped up to target the legal system. Current law forbids automated lawyering in the US and most of the world. It will eventually fall as well, starting first with paralegal tasks, advisory services, etc.
  • Internal IT & systems administration: will be automated the fastest, because it is being optimized for under the software engineering umbrella.
  • Real estate & insurance processing: related companies will see that they are able to save a lot of money with AI. There will be a lot of competitive pressure in every country once the first few players are successfully automate their processes. These will most likely be smaller players, who will disrupt incumbents.
  • Product/project management (routine parts): cue recent Microsoft layoffs3, ending 600k comp. product manager positions. It is already happening, and will only accelerate.

  1. Automate a considerable part of it, so that the work will turn into mainly managing AI agents. ↩

  2. E.g. the SWE-Lancer benchmark by OpenAI. ↩

  3. See this article. The company’s chief financial officer, Amy Hood, said on an April earnings call that the company was focused on “building high-performing teams and increasing our agility by reducing layers with fewer managers”. She also said the headcount in March was 2% higher than a year earlier, and down slightly compared with the end of last year. ↩