AI megathread

The most underreported and important story in AI right now is that pure scaling has failed to produce AGI

Gary Marcus

In the last few days, several more cracks in the scaling edifice started to show. Starting with some smaller and moving to the largest:

• Heavy AI booster Klarna did an about-face from its all in on AI stance. They assumed scaling would make things work, and seem to have changed their mind.
  
  
• Humane AI Pin was canceled and the company sold for parts. The founders were forced to retreat from their glorious TED Talk vision of new AI-driven gadgets to working far more modestly, for HP, to “integrate artificial intelligence into the company’s personal computers, printers and connected conference rooms.”
  
  
• OpenAI implicitly acknowledged that they don’t yet have GPT-5, and would not get there purely by building massive clusters and gathering more training data.
  
  
• Mathematician Daniel Litt exposed massive hallucinations in OpenAI’s Deep Research. (I independently pointed out similar issues in Grok 3 Deep Search last night.)
  
  
• Finally, and perhaps most significantly: Elon Musk said over that weekend that Grok 3, with 15x the compute of Grok 2, and immense energy (and construction and chop) bills, would be “the smartest AI on the earth.” Yet the world quickly saw that Grok 3 is still afflicted by the kind of unreliability that has hobbled earlier models. The famous ML expert Andrej Karpathy reported that Grok 3 occasionally stumbles on basics like math and spelling. In my own experiments, I quickly found a wide array of errors, such as hallucinations (e.g, it told me with certainty that there was a significant 5.6-sized earthquake on Feb. 10 in Billings, Montana, when no such thing had happened) and extremely poor visual comprehension (e.g. it could not properly label the basic parts of a bicycle).