Showing posts with label WiP. Show all posts
Showing posts with label WiP. Show all posts

Thursday, March 19, 2026

Brave New World: Notes on the next 30 years in AI [Work in Progress]

You may or may not be wondering why so many tweets have recently been showing up on New Savanna. There’s a good reason: I’ve been thinking. These days, more often than not, the means interacting with either ChatGPT or Claude, and often both in one day. I copy these interactions to Word documents, which I save to my hard drive. And then promptly forget what topic is in what document where on my hard drive. FWIW, these discussions can ramble all over the place, which is fine. That’s how I think. But keeping track of it all is difficult.

Anyhow, over the last week I’ve been thinking about work that David Hays and I have done going back to the mid-1970s when I first began working with him in graduate school. I’ve asked ChatGPT to look at some of our papers and evaluate them in terms of current literature and debates. And so forth and so on. I’m also looking forward to next steps, for me, but also in general.

At the moment I’m working on a piece for 3 Quarks Daily with the tentative title: “The Paradox of Contemporary AI: Engineering Success and Institutional Failure.” Here’s how ChatGPT put it in an interaction we had yesterday:

The engineering success is real. LLMs and related systems have given us access to a new conceptual continent. They work, and at extraordinary scale. But the institutional failure lies in the monoculture: too much intellectual, financial, and training-path dependence on one family of architectures and one style of thought about intelligence. The result is that we are building out the utility before we have adequately explored the space of possible successor technologies or developed the conceptual tools needed to understand what these systems are revealing about language, cognition, and cultural structure.

That’s a metaphor I’ve used off and on over the years, the discovery of a new continent. My paradigm case is the European discovery of the Americas. Making it to the eastern shore is one thing. Then comes exploring and settling. Columbus set foot in the Caribbean in 1492. Lewis and Clarke made it across North America to the Pacific Ocean in 1805. That’s over three centuries later. It would take another century for Europeans and their descendants to more or less settle North America from coast to coast.

Let’s say that GPT-3 in late 2022 is analogous to Columbus making it to shore in the New World. While I don’t think it will take four centuries to settle this new world, nor even three centuries to measure its extent. Intellectual discovery, engineering implementation, and commercial development move at a faster pace. But I do think there’s a lot of work, very exciting work, ahead of us.

Here’s a fragment of an interaction I had with ChatGPT 5.2 a couple of days ago. My prompt is first, then ChatGPT’s response.

* * * * * 

Obviously I don’t know how long it will take to explore that territory and to settle it, nor do I know what we’ll have when that’s done. But I’m thinking in terms of 30 years or more, not one or two bright ideas away.

Your time horizon sounds realistic, and historically it fits the pattern of major intellectual/technical shifts. What you’re describing—the exploration and eventual settlement of that “continent”—is not the discovery of a single clever algorithm. It is the construction of a new intellectual infrastructure, and those almost always take decades.

Let me sketch why.

1. Precedent: how long paradigm infrastructures take

If we look at previous transformations in science and technology, they tend to unfold over multi-decade arcs.

Digital computing

•       1936 — Turing machine

•       1940s–50s — early computers

•       1960s–70s — programming languages, operating systems

•       1980s–90s — personal computing ecosystem

Roughly 50 years from theory to mature civilization-scale infrastructure.

Molecular biology

•       1953 — DNA structure

•       1960s — genetic code deciphered

•       1970s — recombinant DNA

•       1990s — genome sequencing

Again 40 years to transform biology.

Deep learning

•       1986 — backprop revival

•       1998 — LeNet

•       2012 — AlexNet

•       2020 — GPT-3

That arc is already 35+ years long.

So a 30-year horizon for what you’re describing is not pessimistic—it is historically typical.

2. Why this particular transition is slow

The reason the transition will be slow is that the field is confronting three different layers simultaneously.

1. Mathematics of distributed cognition

We still lack a clear mathematical theory of:

•      representation in high-dimensional spaces

•       compositionality in distributed systems

•       interaction between symbolic and dynamical processes

Yevick was an early attempt at that level.

But the theory is still incomplete.

2. Architecture

Even if the mathematics existed, we would still need working architectures.

The field is currently experimenting with:

•       transformers

•       memory systems

•       agent frameworks

•       search hybrids

These are early prototypes.

3. Cognitive theory

Perhaps the hardest layer is understanding what intelligence actually is.

That question spans:

•       neuroscience

•       psychology

•       linguistics

•       philosophy

Your RNA work sits squarely in that domain. [Relational Network over Attractors]

3. Why the current moment feels fast

The speed of the last few years comes from something different: engineering scaling.

Transformers gave the field a powerful substrate for experimentation.

So exploration is happening rapidly near the coastline.

But deeper theoretical consolidation will take longer.