AI Alternate Portfolio

What have we lost? This is the right question, because the current boom is not just adding something, it is actively crowding out something else.

The Association for the Advancement of AI did a big study of its own researchers this year. 79% said public perception of what AI can do does not match reality, 74% said the direction of research is now being driven by hype because that’s what gets funded, and 76% said scaling up current large language models is unlikely or very unlikely to get us to general intelligence.

In other words: we are pouring almost all the money into one bet — bigger transformers trained on more text — and leaving a whole set of older, slower, more rigorous ideas to starve.

Here is what we have lost, or are losing:

1. Systems that reason, not just predict

Old-school symbolic AI — logic, theorem provers, knowledge graphs, rules — was unfashionable because it was brittle. But it could do something LLMs still cannot: prove an answer is correct, not just plausible.

What was supposed to replace both is neuro-symbolic AI: pattern-recognition nets for perception, plus symbolic logic for reasoning. You get a system that can both see a cat and reason that if all cats are mammals, this cat is a mammal. It is explainable by design.

That work is still alive — researchers are building knowledge-infused learning that makes black-box models explainable in healthcare, law, finance — but it gets a fraction of the funding because it doesn’t demo well as a chatbot.

2. Causality instead of correlation

LLMs are supreme correlation machines. They are terrible at causality. As one recent analysis put it, prediction cannot substitute for causal inference.

Judea Pearl’s whole field — causal graphs, do-calculus, asking “what if we intervened?” — is exactly what you need for medicine, economics, climate, public policy. An LLM can tell you that ice cream sales and drownings correlate. A causal model tells you why, and what to do about it.

That field has been eclipsed because it doesn’t scale with GPUs. It scales with careful human thought about how the world actually works.

3. Embodied and grounded intelligence

The original idea of AI was not a disembodied text predictor. It was an agent in a world. Rodney Brooks’ robots, developmental robotics, animal cognition — intelligence that learns by bumping into things, failing, feeling gravity.

LLMs have no body, no senses, no continuity. They have never been cold, or hungry, or embarrassed. That is why they hallucinate: training rewards confident guesses over expressions of uncertainty.

Embodied AI, world models, and active inference are coming back — researchers list them explicitly as departures from pure scaling already underway — but for five years they were told “just add more data.”

4. Small data, efficient, and Bayesian intelligence

Before the scaling hypothesis, a core goal was to learn like humans do: from few examples, with uncertainty, and with the ability to say “I don’t know.”

Bayesian methods, probabilistic programming, minimum description length, analogical reasoning — all work that tries to make AI that knows what it doesn’t know. That is essential if you want to put AI in a plane or a hospital.

LLMs do the opposite: they use all the data in the world to avoid having to be clever. The true cost of that corpus — books, code, art, decades of human labor — is estimated at 10 to 1,000 times the cost of the GPUs themselves. We are treating human knowledge as free to harvest.

5. Theory

The most worrying loss, according to the AAAI researchers, is theoretical AI research. Not building bigger things, but asking why things work.

We have no solid theory of why transformers generalize, when they will fail, or what emergence even means. We have benchmarks, not understanding. The field is running on vibes and leaderboard scores. The scientists warning that this is slowing down real progress are not Luddites — they are the people who built the field in the 70s, 80s, 90s.

What an alternative portfolio would have looked like

If investment had not been monopolized by LLMs, we would likely have by 2026:

• JEPA and World Models (Yann LeCun’s push): models that learn a model of how the world works, not just how we talk about it.
• Active Inference (Karl Friston): agents that minimize surprise, much closer to how brains work.
• Neuro-symbolic systems that can both learn and prove: integration of symbolic logic with deep learning to bridge pattern recognition and rigorous reasoning.
• AI that is less homogenous. Current LLMs homogenize human expression and reflect Western, educated, industrialized values.

None of these are magic. But they are diverse bets. And diversity is key when you don’t know which path is right.

The scaling bet might still pay off partially. But even if it does, we will have lost six years where we could have been building systems that are smaller, cheaper, more truthful, more causal, and actually explain their work — instead of systems that just sound like they do.