Why I'm Paying Closer Attention Now

I’ve dismissed a lot of AI job-displacement talk as hype. Tech bros want attention online. Companies need investment dollars. Everyone has an incentive to overstate things.

But three recent developments have me reconsidering. Not panicking—just paying closer attention.

First item: Eroding entry-level jobs

In late January, Anthropic (which runs the AI model Claude) CEO Dario Amodei published an essay titled “The Adolescence of Technology.” In that essay, he wrote about labor market disruption:

This is a topic that I warned about very publicly in 2025, where I predicted that AI could displace half of all entry-level white collar jobs in the next 1–5 years, even as it accelerates economic growth and scientific progress.

For example: I spent my early career doing entry-level marketing writing—brochures, web copy, email campaigns. Last week I ran an experiment with Claude (not even their best model) to see if it could do that work. With good prompts, it could. Instantly. And as well as I did it twenty years ago.

The Second Item: Compounding progress

Progress in AI has been steady. But now, the models have gotten so good at writing code that they are writing their own next versions. And much faster than human engineers could.

We’ve seen this recently with OpenAI’s latest model:

Crucially, OpenAI says that the new GPT-5.3-Codex model is its "first model that was instrumental in creating itself."

And with Anthropic’s Claude Cowork model:

Anthropic’s newest productivity experiment, Cowork, is notable not just for what it does, but for how it was made. Cowork is essentially a version of the AI coding tool Claude Code for non-developers. And according to the company, much of Cowork was built by Claude Code itself, turning the AI into both the product and a key part of the development process.

Humans aren’t good at understanding compound growth. We think in linear terms—moving from A to B to C—not exponential terms.

With the automation of code writing, the pace of innovation is exploding. We’re already seeing it.

In his excellent article, Matt Shumer shares why this happened:

The AI labs made a deliberate choice. They focused on making AI great at writing code first... because building AI requires a lot of code. If AI can write that code, it can help build the next version of itself. A smarter version, which writes better code, which builds an even smarter version. Making AI great at coding was the strategy that unlocks everything else. That’s why they did it first. My job started changing before yours not because they were targeting software engineers... it was just a side effect of where they chose to aim first.

They’ve now done it. And they’re moving on to everything else.

The cheapest and fastest way for AI companies to innovate was for the AI to innovate—to be great at coding. We’re there. The rest of modern knowledge work is next.

(I highly recommend reading Matt’s full article.)

Third: the falling value of knowledge work

This blew my mind:

KPMG, one of the world’s largest auditors of public and private companies, negotiated lower fees from its own accountant by arguing that AI will make it cheaper to do the work, according to people familiar with the matter.

KPMG—the world’s fourth largest accounting firm—just told the world its own line of work is worth a little less now.

Because of AI.

The devaluation of knowledge work has begun.

OK, so should we freak out?

No. But we should pay attention.

Many smart people point out that technology regularly renders certain jobs obsolete. And then the technology creates all kinds of new jobs. Software has been eating jobs for decades, for example, and creating lots of new needs.

Maybe that happens again.

But the scale here is different. This isn’t one industry getting disrupted. It’s knowledge work broadly. And it’s happening faster than previous technological shifts.

I don’t know what happens next.

What I do know: the next few years will be bumpy. Some jobs will disappear, new ones will emerge. The transition will be messy.

So what do we do?

Two things:

First: Don’t try to keep up with everything—that’s impossible now. Pick one or two AI tools relevant to your work (or hobbies!) and learn them thoroughly. Become the person in your circle who knows, rather than speculates.

Second: Taste, discernment, relationships—these things are tougher to automate. Cultivate those skills in your areas of expertise.

None of us knows exactly where this all going. So let’s be ready, and not surprised.

Next up: Back to the AI lessons.