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Happy Wednesday,
Last week’s big debate in the AI circles was this tweet (is it still called a tweet?) from Jacob Coxon. Discussions have ranged from the idea that this is solely hype from Anthropic (pre-IPO) to the idea that he is an activist doing this.
I don’t know the exact truth behind this, but what matters to me is that people I’ve followed for a long time and respect didn’t dismiss him outright. Like Ethan Perez (Alignment team lead at Anthropic)
And yes, we can argue that this comes at a time when Anthropic seems to be falling slightly behind OpenAI, is preparing to go public, and might want to make it harder for competitors to enter the space. Or they sincerely want to regulate it.
On top of this, Dario Amodei published a long article (worth the read) titled Pacing the frontier, where he argues that we need to slow things down and put regulations in place. This has also sparked debate about why they keep building it if they see these risks.
I like parts of Ben Thompson’s perspective (I usually do). He wants AI policy to focus on concrete risks without assuming uncontrollable superintelligence is inevitable. He argues that society should preserve human freedom and continue developing AI while addressing problems as they arise.
I find this pragmatic and grounded. Let’s not over-regulate and get caught up in the labs' hype cycles too much.
But. Even if we decide to slow further development, organizations still have to deal with what these systems can already do. What we have right now is good enough to make a real economic impact once organizations start scooping up compute.
As an example, last week I needed to change DNS settings for whatever reason. I know how to change DNS settings; it’s not that hard, but it requires a lot of clicks. So I figured I’d let Astra do it through computer use.
And it did, in roughly the same amount of time it would have taken me. I could do other stuff instead, and my part was to hand it over and check the result.
Across a team, handing over recurring tasks like that could mean taking on more work with the same people. How much you gain depends on what the tools cost, how reliably they work, and how much time you spend checking the result. But if those numbers add up, you have more room to decide how to run the business.
A couple of days ago, I spoke to an organization that had barely started with AI. They were stuck and didn’t know how to move forward, their CEO doesn’t yet see value etc etc. The usual objections. Interestingly, I’ve worked a lot with an organization in the same industry. They are investing heavily in AI, not only by having me train their lead team and managers, but also in raw compute. I would put them at one of the organizations in Europe that have invested the most in AI models outside the tech companies. And that is starting to pay off; we're already seeing better margins as an early effect.
That would give them choices the other organization doesn’t yet have. They could keep more of what they earn, invest more, or compete on price. If they can deliver comparable work at a lower cost, they can put pressure on competitors long before anyone announces an AI-driven restructuring.
That pressure could eventually affect jobs through fairly ordinary decisions. The next hire might no longer be necessary. Someone leaves and isn’t replaced. One company wins more business and hires people while another loses work. You won’t necessarily see an instant dramatic wave of AI-related layoffs, even as companies change how many people they need.
Organizations are, as I wrote last week, slow and messy. I’m not seeing widespread AI-driven job losses among my clients today, as I said, including those furthest ahead. That friction makes the timing hard to judge, but I struggle with treating it as reassurance about what comes next.
I would still be extremely surprised if we don’t see job losses grow in the next 10–18 months. My bet is that we’ll see this happen in the US first, since we have more regulations to abide by in the EU (for better or worse). I could be wrong about the pace, and some uses may turn out to cost too much or need too much supervision to make economic sense. Those are useful objections to work through.
But what frustrates me is hearing influential voices use the lack of widespread job losses as a reason to relax, especially when they spend very little time using these tools themselves. If you’re advising an entire profession or organizations on the limits of AI, you should probably have some direct understanding of where those limits are today.
(I’m trying, very, very, very, very hard usually not to dunk on other people and instead show what good looks like, but it’s getting harder and harder.)
And we need that understanding inside our own organizations too. If your CEO doesn’t see what you’re seeing, give them something they can judge for themselves. Take a piece of real work. Show what the system can do, what still requires a person, where it fails, and how much effort it takes to verify the result.
Then work through what would change if a whole team started working that way. What would it be worth? What would it cost? Would it change the work you take on or the people you need to hire? You can start having that conversation even if you disagree about my timeline.
If we wait for widespread job losses before getting involved, decisions about how to reorganize the work, which roles are needed, and whether that next hire happens may already have been made without us.