Prediction: LLMs Will Only Get Dumber
I’m calling it right now, and I pledge to never edit this statement out of this blog post in the future:
Model collapse has begun, and LLMs will only get dumber from this date forward.
Hopefully this will lead to the AI hype bubble finally bursting. But what do I know? Investors continue to pour billions of dollars into AI startups, despite the reality that currently:
- No company has proven that they’re saving money and succeeding by replacing workers with LLMs.
- Even if a company COULD replace developers with LLMs successfully, they will still need skilled developers to oversee and correct the outputs, which necessitates the continuing software developer career path.
- LLM companies like OpenAI are not profitable.
- In 2024, AI companies nabbed 45 percent of all US venture capital tech investments, up from only nine percent in 2022.
- Time and time and time again, experiments in having LLMs write code begin to fail as soon as the code moves beyond basic examples and use cases.
In any case, LLMs are not the path to AGI.
The following video gives a shocking impression on how at the current state of technology: (swarms of) killer drones can be made and sent to targets, with the weapons making the decision they cannot be stopped, and could be used to assassinate political leaders or dissidents.
The following 2 essays were written by a former medical coder with a reasonable good understanding of computer technology and how the medical system works in the US, which makes it an interesting read, although most people probably won’t like such detailed (long) stories.
I don’t think that the first part is very interesting though, describing that the COVID operation included violating our privacy, and that more in general the medical industrial complex is violating our privacy (if people still don’t understand this, they probably still believe everything we were told about COVID…)...
Through extensive experimentation across diverse puzzles, we show that frontier LRMs face a complete accuracy collapse beyond certain complexities. Moreover, they exhibit a counter-intuitive scaling limit: their reasoning effort increases with problem complexity up to a point, then declines despite having an adequate token budget. By comparing LRMs with their standard LLM counterparts under equivalent inference compute, we identify three performance regimes: (1) low-complexity tasks where standard models surprisingly outperform LRMs, (2) medium-complexity tasks where additional thinking in LRMs demonstrates advantage, and (3) high-complexity tasks where both models experience complete collapse. We found that LRMs have limitations in exact computation: they fail to use explicit algorithms and reason inconsistently across puzzles.