Good morning 👋 to the unrelenting dreamers. The 70k+ strong temporary desert nomads of Burning Man are once again proving that community and creativity flourish best under extreme conditions. This year, Mother Nature is taking “extreme” as a personal challenge. Campsites have been ripped apart by 50 mph winds and blinding walls of dust, leaving burners stranded. Monsoon rains are expected next. But hey, no one ever said finding yourself in the desert would be easy.
🤔 Ever wonder how smart AI could actually get? Chatbots keep leveling up, but they have limits. The universe has some pretty strict rules that even AI can’t break, like how fast it thinks or how much it predicts. Right now, we’re bumping into real-world problems too, like AI’s insatiable appetite for data and energy. If AI ever reaches the theoretical limits of intelligence, it’ll be because humans pushed it there.
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Today, we’re doing something different. In honor of Burning Man, were doing a deep dive into the mind bending theoretical limits of AI intelligence.
Buckle up, it’s going to be a wild ride.
AI’s Got Issues Right Now
The idea of superintelligent AI scaling to infinity might feel scary, but it’s hitting some very real ceilings:
1. The Data Wall
This might sound crazy, but we’re running out of high-quality data to train AIs. They’ve gobbled up all the good stuff, like Wikipedia, science papers, books.
That’s why the big push behind wearables, autonomous cars, robots, and sensors to collect real world data.
2. The Synthetic Data Trap
Why not just let AI generate its own training data? Great idea in theory. But researchers found that AIs trained on synthetic data eventually get dumber. Researchers call this “model collapse.”
Imagine making a photocopy of a photocopy. Each new copy gets a little blurrier, a little more distorted. The same thing can happen with AI. If models are trained on slop, they get stuck in a feedback loop, amplifying their own biases and mistakes until their view of the world becomes warped.
It’s AI’s version of inbreeding.
3. An Unsustainable Appetite for Compute
The estimated cost to train GPT-5 might be as high as $2.5 billion, and consume as much electricity as a small country. The energy required for AI data centers is projected to double by 2030. Given current US renewable energy policies, we’ll likely rely on coal and gas to power it.
On top of that, the entire AI revolution relies on an incredibly fragile chip supply chain…
fun stats
market share NVIDIA holds in AI training chips in 2025. That’s down from an estimated 94% in 2023.
🦾 This new humanoid bot has some fancy fingers