Divergent Trajectories: A Comparative Analysis of Artificial Intelligence and Drexlerian Molecular Nanotechnology

I asked Gemini Deep Research with 2.5 Pro why A(G)I has advanced so much more successfully over the last 30 years compared to True Drexlerian Molecular Nanotechnology. This was inspired by the sad death of Damien Broderick recently and finding that the revised edition of The Spike was just $2.99 on Amazon US. I read the original version back in February 1999, where I first came across Eliezer S. Yudkowsky. The second edition contains significant few stuff from the late 1990s  and very early 2000s, so worth a punt. However, the reference to an article in the August 1995 edition of Wired might well be in the original. "Wired asked several nanotech experts to estimate when we will reap the rewards of their research."

I had always thought of Smalley as a critic of MNT, but perhaps I was wrong. He died in the 2000s sadly, but the others are still alive. Obviously, Donald W. Brenner was the one to got closest. He's still active, although not seemingly in research directly related to MNT, but I don't think he's going to be finally unveiling his molecular assembler this year. Someone might; we still have 8 months to go after all! There are obviously lots of commercial products that at some level are nanotechnology, although not really TDMNT, and I suspect that's what Brenner was thinking of in saying 2000. I suspect we will have nanocomputers by 2041. A lot of depends on whether we get the Amodei data centre/country of (super)geniuses and 50-100 years of bio/phystech progress in 5-10 years. Cell repair by 2029 seems optimistic by 2029, but Brenner suggested 2035, which could be on the money still. A molecular assembler by 2030? I am not going to bet on it, but then we didn't really expect to have AGI by 2025, yet we kind of do. 

The Gemini report included this useful table of comparative analysis of AI and Drexlerian MNT development.

Back in 1995, Drexler said he "believes that nanocomputers could finally provide the horsepower needed for artificial intelligence." That seemed to have turned out to be the case (I know, I know, but Gemini 2.5 Pro is better than me at 57 at physics,; it's also better than me at 27 at physics and people really should try the latest frontier models; o3 might not know how many "r"s there are in "strawberry" or be able to do simple algebra, but Gemini does; I suspect o3 also does, but if ask it an "easy" question it doesn't get answered by the full model; one thing that's clear is that different FMs have different capabilities and they are advancing rapidly). The chart very neatly lays out how we got to AGI before TDNMT. In summary, self-supervised training on large datasets of transformer architecture neural nets with backprop and gradient descent plus RL(HF) and various other tweaks like mixture of experts. There's still a lot of low hanging fruit and opportunities to combine different design philosophies, which is why I expect progress to continue and that we might get to something very like AGI by 2027 or so. Of course then, in the ideal world, we can use AGI to solve all the other problems. And one of the first and most fundamental problems to solve is TDMNT. Recall that Drexler's original 1981 nanotech paper was on protein engineering. Consider AlphaFold and the existence proof of the biological cell. Might we finally achieve the dream of the nanomachine that is formed from various proteins that fold into the engineered shapesof machine's components? It's going to be a crazy ride. Stay tuned.