The LLM warnings Google fired Timnit Gebru over have all come true

The LLM warnings Google fired Timnit Gebru over have all come true
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  > The first warning was about scale itself. Bender and Gebru argued that training ever-larger models on ever-larger scrapes of the internet would produce systems that appeared fluent but had no actual understanding of language.

  > The second warning was about bias amplification. The paper documented in detail that internet-scale training data contains systematic overrepresentation of dominant viewpoints and underrepresentation of marginalized ones. The models would not just absorb this bias. They would amplify it...

  > The third warning was about environmental cost.

  > The fourth warning was about documentation. The paper argued that the training datasets being assembled were too large for anyone to actually audit.

  > The fifth warning was the one Google cared about most. Bender and Gebru argued that the deployment of these systems would centralize linguistic and cultural power in the hands of the small number of companies that could afford to train them.

There's philosophical grappling to be done, as with the Ted Chiang post on the front page right now, about what it is LLMs are actually doing (I'm mostly with Chiang on those core philosophical issues). But Gebru went way past that, attacking their underlying utility. The coherency of GPT 5.5 responses are not simply tricks of the mind, and frontier models (leaving aside Grok, if you want to call it a frontier model) have not in fact been engines for bias.