The G in AGI is an Achilles Heel

Anthropic's big picture strategy also contains it's own undoing.

Anthropic has a stated goal, they’re building toward AGI. Whatever you think of the timelines, or the bigger picture debate around AGI, the framing is shared and explicit. The goal is “generality”.

That word is critical, and it’s also quietly undermining that whole strategy.

And all the frontier labs, OpenAI, Google DeepMind, etc. face the same challenge.

Sutton’s two-part insight

Rich Sutton’s Bitter Lesson gets quoted in two parts, and most people only seem to quote the first part - the famous part:

General methods that scale with compute
consistently beat hand-crafted, domain-specific approaches

Every time, across every area of AI. And that’s the part that has underwritten their trillion-dollar capex story.

But the second part is also important, and often more inconvenient. Sutton’s argument also includes the idea of good approximation - what really matters in practice is whether a solution is good enough, across enough tasks. Not actually whether it’s best at any single one of them. The actual bar for displacement is not perfection. It’s just “good enough, broadly enough”.

If we put those two parts together then the challenge becomes clear. If general methods win, and the bar is good-enough-across-enough-tasks, then at the point when open-weight models cross that bar in enough domains, the argument for paying frontier prices becomes a lot harder to make. Of course, not for everyone. But for enough people that it starts to show up in the numbers.

We may already be there

In January this year MIT Sloan, using OpenRouter usage data, found that open-weight models now run at roughly 90% of closed-model performance, and for only 13% of the cost. That’s ninety percent of the capability, for only thirteen percent of the price. By Sutton’s own “good enough” criterion, we crossed the line a while ago. And in the three months since that report, things have come a long way.

But that report also looked at how the market really responds. Closed models actually capture about 80% of token usage and 96% of revenue. That cheaper, nearly-as-good option is largely sitting on the shelf. Most buyers seem happy to just walk past it.

That looks like good evidence this whole argument is wrong. It’s not. It’s really the most interesting part.

Their real moat

The frontier labs clearly do have a moat. It just is not the one that a lot of pitch decks describe. The moat is not capability - Sutton clearly shows that capability is a depreciating asset by definition. The current moat is behavioural. Switching costs, integration effort, brand, perceived reliability, the gravity of accumulated tooling and of course habit.

That is a real moat. 96% of revenue is definitely not “nothing”. But a behavioural moat is a very different kind of asset to a capability moat. And that difference is very important.

Capability leads tend to compound. If you are a year ahead on a really challenging technical problem, then next year you might be more than a year ahead. Your lead tends to grow. But habits don’t compound in that way. Habits hold, and hold, and hold, until something changes them. Then they change fast. Telcos. Blackberry. Taxis. Incumbents look untouchable, right up to the point that they don’t.

So the trade that the frontier labs are really making is this - they’re swapping a self-dissolving moat (capability, eaten progressively by Sutton) for a self-reinforcing-until-it-isn’t moat (habit, robust until it changes). That is not as good a trade as the headline numbers suggest, because the failure mode, when it happens, is non-linear.

A double edged sword

There’s also a second-order effect that sharpens this trade. The exact same behavioural inertia that keeps users on closed providers today will, if it ever changes, keep them on whatever they change to. This stickiness does not choose sides. It protects the incumbent now, and it can help lock in any successor later.

So it cuts both ways. Inertia favours the labs right now and it gives them more breathing room than a pure capability argument might predict. But if that inertia turns, then they don’t get a graceful comeback. The same mechanism that built that moat can become the wall they can’t climb back over.

The end result

It’s clear that timelines matter more than destinations. The frontier labs are likely fine in the short run - they are after all in a very enviable position right now. The behavioural moat is active and doing a great job. But the interesting question is what happens when a new wave of users, or the next class of agentic deployments, or a new regulatory shove triggers a behavioural change. Nobody knows when that might land. And the structural setup says it’s very likely that it eventually will.

There’s no clean way out from this either. A lab can’t dodge it by just going narrow - Sutton’s first part tells us that narrow loses to general. And they can’t dodge it by going even more general - that’s the trajectory they’re already on and we’ve just established that’s also what commoditises their capability moat. Ironically, the G is doing both jobs. It’s the thing that makes a capability lead even possible, and it’s also the thing that makes the capability lead likely temporary. The exact same word. The same strategy. And driving both effects.

Of course, there are other strategic forces in play that will give the labs a strong tail wind, and I’ll come back to those in a follow-up soon. But on this specific axis (the one that says “capability lead translates into durable advantage”) that argument doesn’t seem to survive Sutton’s own logic.

The G in AGI was never going to be a long term moat. It’s really just a starting gun.