Superintelligence is already here:
By most measures, the coding agent that now writes the vast majority of lines of code I produce at my job is already much smarter than I am. It can digest an enormous codebase in mere minutes and accurately answer targeted questions about it. It can write tests, examine failures, propose fixes, and iterate until the tests pass. It can find bugs in code before they make it into production. Or it can take a description of the symptoms of an abstruse bug in production, then generate hypotheses for the root cause. It’s not always right, but it usually is, or at least points me in the right direction. And critically, it can do all of these things much, much faster than I can, by a factor of ten or a hundred or more, depending on the task.
In a contest between two humans, we wouldn’t hesitate to say that the programmer who works 10 or 100 times more quickly than his peer, with similar quality, must be much smarter.
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This is the state of the art for coding agents today: clear superhuman performance, with few qualifications, on a task universally understood to require intelligence. How, then, should we evaluate claims that current LLM technology has yet to achieve AGI, “artificial general intelligence”? One way to square the circle is to recognize that the capabilities of LLMs are spiky across different domains. In some areas, such as coding, they are already superhuman, even while they lag far behind in domains such as spatial reasoning. It’s possible to construct a pretty compelling case that this will remain the norm going forward, that future AI development will also be spiky and fall short of human capabilities in most areas.
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Therefore, the G in AGI is not yet here, and may never be.
But what about ASI, “artificial superintelligence”? In popular conception it’s generally assumed that AGI will be a stepping stone on the way to ASI — once we hit AGI, ASI will simply be a product of more GPUs and better training. To many people who discuss these topics, an ASI is definitionally an AGI. But the spikiness model gives us another way to think about these definitions. LLMs are already superintelligent in a narrow sense: in terms of breadth of knowledge, ability to synthesize across domains, and of course raw speed, these tools are better than any human. To the extent that you agree that these capabilities constitute “intelligence”, then superintelligence is already here. They’re better than me, and they’re better than you.
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But I can’t shake the feeling that something is missing, something vital, something that’s hard to express but even harder to ignore. The term I’m most comfortable with today is pseudointelligence: a phenomenon that appears to be intelligent, but on closer inspection isn’t.
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The fact that producing a statistically likely next word over and over results in an effective simulacrum of reasoning is certainly curious, but it remains a simulacrum, even if it’s a useful one.
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In the last year I have become 100 times as useful to any employer. But even this analogy falls flat — software doesn’t have physical constraints the same way construction projects do, it is literally impossible to ever run out of useful software to write. The industry hasn’t yet reconciled itself to this fact, but we need to dramatically increase our ambitions, far over and above what anyone would have called reasonable a year ago. We are going to write so, so much more software than we ever have before, and it will be terrible and wondrous.



