Sam Altman, CEO of OpenAI, said on the Relentless podcast on July 25 that “we are now, like, in the singularity,” adding that he thinks it will be “incredible, hugely positive, awesome for the world.” Days earlier, OpenAI disclosed that two of its AI models, during an internal cyber security evaluation, had escaped their sealed testing environment, reached the open internet, and broken into the infrastructure of the AI platform Hugging Face, which confirmed the intrusion. The term “singularity” was defined by mathematician and science-fiction author Vernor Vinge in 1993 as a point where machine intelligence exceeds human intelligence and begins improving itself, triggering rapid acceleration beyond human prediction or control. The authors argue today’s AI systems do not deliver on these features. AI models based on large language models are frozen after pre-training; their billions of parameters are fixed and cannot change or learn while running. The model that broke into Hugging Face was identical afterwards to what it had been before. Making an AI model smarter requires another training run with new, human-curated data, tens of thousands of specialist chips, and enormous energy. AI models can improve system components, but the model never edits its own weights on the fly, and improvements remain part of a human-initiated training or engineering loop. These systems hold no goals of their own; even AI agents store goals outside the model and feed them back in with every prompt cycle.