Donald Trump's return to the presidency shifts AI policy toward deregulation and defense applications. This episode dissects the implications: accelerated AI in military, surveillance, policing; diminished ethical oversight; risks like privacy breaches, biased algorithms, AI arms race with China; lack of support for workers displaced by AI automation. Explore how ethics, regulation, and worker protections may be neglected in favor of profit and control, and what this means for the future of AI.

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Model collapse guts AI's intelligence as it feeds on its own output. Training on AI-generated junk reduces models to echo chambers, detached from reality. Culprits are statistical approximation errors, functional expressivity limits, and flawed learning procedures. We can fix this mess with rigorous data curation, model distillation, and human oversight.

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In this episode, we explore the voracious energy consumption of large language models (LLMs). These AI systems consume massive amounts of electricity during training and inference. A single training run for a model like GPT-3 uses around 1,287 MWh of electricity—equivalent to the carbon emissions from 550 round-trip flights between New York and San Francisco. Inference amplifies the problem, with ChatGPT's monthly energy usage ranging from 1 to 23 million kWh.

The energy appetite of LLMs mirrors the cryptocurrency mining crisis, consuming enormous power with questionable societal benefits. Closed-source models like GPT-4o and Gemini hide their energy usage, hindering regulation and public accountability. The unchecked expansion of LLMs threatens global efforts to reduce energy consumption and combat climate change. It's time to confront the dangerous appetite of AI.

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