I think you're misunderstanding Kuhn slightly. He invented the term paradigm shift. What he means by normal science with intertwined spurts of revolution is more provocative. He means that in order to observe periods of revolution, the "dogma" of normal science must be cast aside and new normal must move in to replace it. Normal science hits a wall, gets stuck in a "rut" as Kuhn describes it.
I think, in a way, Doctorow is making that same argument for the current state of ML: "I don't think that we're remotely close or even on the right path in any way". In other words, general thinking that ML will lead to AGI is stuck in a rut and needs a new approach and no amount of progressive improvement on ML will lead to AGI. I don't think Doctorow's opinion here is especially insightful, he's just a writer so he commits thoughts to words and has an audience. I don't even know wether I agree or not. But I do think this piece comes off as more in the spirit of Kuhn than you're suggesting.
And of course you can interpret Kuhn however you want. I don't think Kuhn was saying you shouldn't use/apply the tools built by normal science to everyday life. But he, subtly, argues that some level of casting off entrenched dogmatic theories, in the academic domain, is a requirement for revolutionary progress. Kuhn agrees that rationalism is a good framework for approaching reality, but also equates phases of normal science to phases of religious domination that predated it. Essentially truly free thought is really really hard because society invents normals (dogma) and makes it hard to deviate. Academia is no exception. Science, during periods of normals, is (or can become) essentially over-calibrated and over-dependent on its own contemporary zeitgeist. If some contemporary theory that everyone bases progressive research off of is not quite right, it kinda spoils the derivative research. Not always true because sometimes the theories are correct.
I felt like the part that wasn't in line with Kuhn was the idea that there was something wrong with a field if incremental improvement couldn't lead to a breakthrough like AGI. You're right. He's arguing Kuhn's point. But he seems to use it to conclude that machine learning is a dead end when it comes to AGI. Further, he seems to think this means AGI won't happen any time soon.
But, if I'm not misinterpreting Kuhn again, knowing that a revolution is necessary to overturn the current dogma (which I would argue is deep learning) doesn't tell us anything about when the revolution will occur. It could be tomorrow or 50 years from now or never. So, specifically, it doesn't tell us anything about machine learning in general, whether AGI is possible, or when AGI will happen.
I think, in a way, Doctorow is making that same argument for the current state of ML: "I don't think that we're remotely close or even on the right path in any way". In other words, general thinking that ML will lead to AGI is stuck in a rut and needs a new approach and no amount of progressive improvement on ML will lead to AGI. I don't think Doctorow's opinion here is especially insightful, he's just a writer so he commits thoughts to words and has an audience. I don't even know wether I agree or not. But I do think this piece comes off as more in the spirit of Kuhn than you're suggesting.
And of course you can interpret Kuhn however you want. I don't think Kuhn was saying you shouldn't use/apply the tools built by normal science to everyday life. But he, subtly, argues that some level of casting off entrenched dogmatic theories, in the academic domain, is a requirement for revolutionary progress. Kuhn agrees that rationalism is a good framework for approaching reality, but also equates phases of normal science to phases of religious domination that predated it. Essentially truly free thought is really really hard because society invents normals (dogma) and makes it hard to deviate. Academia is no exception. Science, during periods of normals, is (or can become) essentially over-calibrated and over-dependent on its own contemporary zeitgeist. If some contemporary theory that everyone bases progressive research off of is not quite right, it kinda spoils the derivative research. Not always true because sometimes the theories are correct.