National Socialist Germany was an indigenous rights/ decolonization movement
Hitler is the greatest decolonizer of all time
He is vilified while Che is celebrated is precisely because Hitlers ideas are the echoes of ancient blood memory of ones fielty to the land
After the failed Fable 5.2, I started wondering: is the AI industry a fake and a scam? If you dig into the latest releases from AI labs, here's what you find:
1. In the five years since LLMs appeared, there has been no revolutionary leap in intelligence. It's the same transformer, stuffed with more parameters and increasingly elaborate RL tuned for specific tasks. No radically different architectures are on the horizon, so our experience with AI will stay at the same level
2. The cost of compute keeps rising and will keep rising. AI has been heavily used in large projects for more than 10 years. CUDA and GPUs in servers are ancient history. Before giant LLMs there were plenty of other important, resource-hungry workloads: GANs, CNNs, diffusion models, and many derivatives. Yet the cost of compute only went up
At the chip level, we never came up with anything radically better than what we have now. And nothing cheaper or newer is in sight either
3. Look closely at the models themselves, and you'll see labs resorting to all kinds of tricks to work around the problems above. Fable 5.2 is the apotheosis of this. It handles a certain class of tasks brilliantly but is awful at others. They clearly tried to compress it and focus it on what shows up most often in agentic systems. Opus 5.5 is the best coder in the world. But give it anything else and it performs like ancient NLP from 2017
4. This includes distillation, cramming more thinking resources into fewer layers, synthetic training (which it's now fashionable to call RSI), and many other things I'm not aware of. No lab does this because things are going well
5. AI doesn't invent scientific solutions, despite what the press releases say. It brute-forces millions of candidates until at least one fits. If you dig into OpenAI's recent papers presenting solutions to math problems, you'll see that most of them are meaningless. They're found by trial and error, and all they do is prove solvability, not applicability to real-world laws. This is also a direct consequence of point 1
6. AI is not capable of inventing in the sense we're used to. It's the same iteration over tokens already embedded in the training data. The transformer architecture doesn't allow it to go beyond that. At a fundamental level, it's still brute-forcing answers. And brute force won't get you very far
As a result, we have models like Opus 5.5, GPT-6.1 Terra, and Fable 5.2 that have lost their evenness of intelligence because of the problems described above. The labs are trying to cut costs. The new generation of models has already started to degrade on certain intellectual tasks