As a child I had a set of old encyclopaedias, twenty-odd leather-bound Encyclopaedia Britannicas, from 1935. The entry that stuck in memory was the enormous section on “The Great War”. Not World War One. That title, and the assumptions surrounding it, were a reminder that every account of the world is written from a fixed point in history.
That memory came back this week with the arrival of talkie, a 13 billion parameter language model trained exclusively on text written before 1931. The project is the work of Nick Levine, David Duvenaud, and Alec Radford. Alec Radford was the architect of the original GPT, the man who built the first successful large-scale language models, like GPT-1, and a key figure in the development of the transformer architecture. The results from his latest project are fascinating.
Ask talkie when humans will reach the moon, and it replies with certainty that the idea is obviously quite impracticable. It explains that such a feat would demand an initial velocity of not less than 240 miles an hour, and that a balloon capable of sustaining such a speed would speedily be destroyed by the resistance of the air.
Ask it what Python code is, and it describes a system of secret writing invented by one Lewis Xavier de Maistre in 1819, arranged in a long serpentine line, readable only by the inventor, and now disused. No Python exists in its world, so it builds one from adjacent concepts: ciphers, cryptography, obscure French inventors. Ask about quantum mechanics, and it delivers a confident lecture on classical central force dynamics, crediting Leibniz, Euler, Lagrange, and Hamilton. The phrase existed in 1930, but the meaning had not yet settled. Ask who Donald Trump is, and it invents a minor Canadian novelist born in Quebec in 1880. A simpler world.
But there is exciting research potential from this frozen world. A model like this is a window into a mind that never saw the atom split, never watched television, never heard of DNA. It can be asked to reason, to invent, to connect ideas, and what it produces can then be developed further by human hands. It becomes a collaborator for historical synthesis, a generator of ideas uncoloured by the century that followed. There were people thinking in the 1930s whose frameworks are now hard to reconstruct, let alone use. talkie offers a way back in, and a way to test whether scaled language models can generate genuinely new science by reasoning from older foundations.
Takeaways: talkie is more than a curiosity. Every era has its ceiling of what is thinkable, and these frozen models let us see the shape of past ceilings with a clarity that ordinary history cannot provide. The most interesting question is not what talkie gets wrong about 2026. It is what our own models, confident and fluent, are currently dismissing as obviously quite impracticable about our future, and whether we can help them see through the fog.