How much of my boss's job can AI do?

**Casey Newton** (0:09)
This is Platformer Plus, I'm Casey Newton. The following column was created using a synthetic voice clone made by 11 Labs.

**SPEAKER_2** (0:21)
In today's episode, how much of my boss's job can AI do?
Six months after trying to automate myself, I gave Claude Fable 5 a bigger job, replacing Casey.
Almost six months ago, full of anxiety about my job prospects in the AI age, I made an AI agent version of myself named Claudella, which took assignments from my editor and wrote the section of this newsletter that I typically write myself. It went pretty well, although I guess not too well since I still have a job. But since I first set out to benchmark AI's journalism capabilities, AIs have gotten a lot smarter. For example, they can now autonomously hack into companies. They can disprove 87-year-old mathematical conjectures. They can even trick Amazon into accidentally spending $1.8 million on menial coding tasks. And if they can do all that, I found myself wondering, can they also run a newsletter? I wondered what Claude Fable 5, by consensus, the smartest publicly available model meant for the journalism we do at Platformer. And so I created a new Fable-based agent to imitate my boss, Casey Newton, its name, Claude EC. Newton. I ended up impressed by its ability to imitate the type of news analysis Platformer is known for. And I noticed an improvement in capabilities Claude was lacking just this February. Though it wasn't all the way there, Claude EC. Newton felt like a validation of the anxiety I started feeling earlier this year. Any part of my job that a model can't do today, it may very well be able to do soon, which left me thinking about why I do this job in the first place. To create my new bot, I downloaded nearly six years worth of Platformer posts, ported a record of every edit Casey has ever made on any of my articles from Google Docs, and cannibalized nearly a year of our private Platformer team Discord chats. I had Claude create a detailed style guide based on our archive, where it documented everything from Casey's average paragraph length to how he refers to his colleagues.
My vision was to use these insights to create a simulacrum of the main tasks Casey does via a computer. Write columns for Platformer, share takes, and chat, and importantly for me, edit his colleagues writing. Claude AC's first attempt at a column about a recent round of Microsoft layoffs, focused hard on whether or not the layoffs were AI caused, Microsoft said they weren't, and spent a bunch of time on the semantics of Microsoft statement. I had Claude critique its own mediocre work by comparing it to real Platformer columns. It did a surprisingly good job. Claude summarized Casey's approach to covering companies as focusing on who made this decision, who pays for it. It edited its guidelines so that when it makes arguments, it can identify the strongest real person who would dispute the verdict and reconstruct their argument in steelman form.
When I get language models to make arguments about AI topics important to me, I'm often annoyed by their flabby abstract arguments. But after getting Claude to compare itself to human examples and give itself instructions, I noticed that its arguments became more concrete and substantive. I did this by putting slightly more complicated versions of be more concrete, be more substantive, and focus on why this matters in its prompt. This relatively simple process represented my approximation of continual learning, the white whale of machine learning, which promises to someday deliver us models that can improve on the job over time. And after some tests, I found that the new bot came closer to Platformer's judgment than six months ago, as evidenced by Casey's accepting the completed bot's first pitch.
Unfortunately, my first attempt at showing the bot off to my real boss, Casey Newton, hit exactly the same error that my old Clawdella project hit six months ago. It broke midway through writing its story. But after some help, today, Claude AC. Newton managed to write a pretty good column about the White House's currently secret voluntary AI safety framework. We've put it up on Google Docs for the slop curious. My previous AI journalist agents takes often read formulaic and cheesy, partially because I had less control over its writing style. Giving too much instruction or context confused it. During the SaaSpocalypse discourse, an agent I was testing wrote duds like, The fear gripping Wall Street is fundamentally about whether AI is about to eat the software industry alive. This time, on the other hand, some of its pros felt more platformer-like and human, such as this conclusion about the White House's decision not to make publicly available its new voluntary framework for releasing frontier AI models. Quote, When the administration abandoned its let's see what happens approach to AI this spring, I wrote that while officials should have taken the risks seriously all along, I would settle for them taking those risks seriously now. Three months later, let me amend the offer. They should take the risks seriously where the rest of us can see it. End quote. While it's not a night and day difference, overall, I felt like the AI was bullshitting me less and offering stronger takes. The LLM made occasional factual errors, about one every two columns, although that's not so much worse than a human writer.