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Generative AI Is an Engineering Disaster. A shockingly inefficient trillion-dollar project.
(www.theatlantic.com)
this post was submitted on 15 Jul 2026
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Whoever thought that this machine that can predict next word in a sentence, next sentence in a conversation etc. should be used in place of all human intellectual work... should have his elderly care taken over by LLM.
That's what is wild about it. At any given point in time, the model is wholly consumed only with the very next token. Maybe that token is a running narrative of 'reasoning' or directly in the output, either way, the AI does not have anything to model anything beyond the very next token. It doesn't have a destination in mind and is just finding the words to get there, it's building it up word by word. The overall 'meaning' is an emergent property of just picking the very next token and seeing what happens.
Honestly, it's shocking it works as well as it does. More shockingly, there are AI enthusiasts that argue that's how the human brain works, which I can't imagine someone going through life with every thought rooted in building it up word by word.
its not that simple. whatever opinion you might on llms have you have to agree this is oversimplifying at best.
I assumed that but everything I have seen as I dug deeper has been that at some level that is what is happening. If it is 'reasoning', it's generating a 'reasoning chain' next token by next token and using that to influence the final output tokens. The reasoning chain is discarded and since the actual output is a continuation of the reasoning chain it may conceptually be described as allowing the model to 'rethink' things, but even as the generation of a 'reasoning chain' has results that more closely resemble reasoning, it is still a scenario where it's building it one token at a time and we get to see meaning as an emergent property, rather than trying to find words to build to a more abstract concept like humans do. It just gets to throw away the intermediate work and the extra tokens manage to improve the 'accuracy' of the preserved final output.
The interesting bits are when it derives the likely hood of something being correct and does more passes, or splits the data apart in the first pass and opens up new context processes with specialized instructions to handle it. The code stuff goes full on ororborus on some models, writes out the code on one pass, checks for issues on another pass, runs the code looking for errors on a third pass and goes back to step 1 if it fails.
They're getting a lot out of it for it primarily just being a weighted token generator wrapped in an orchestrator.