AI parameters grew 100x since 2020
From 175 billion parameters in 2020 to approximately 10 trillion today, AI models have grown by roughly two orders of magnitude in six years.
Snapshot · The Diary Of A CEO with Steven Bartlett
OpenAI Whistleblower FINALLY Speaks: “AI Has A 70% Chance Of Going Horribly Wrong!“
Explore episode
Jul 13, 2026
Where this was said
Why AI Works More Like the Human Brain Than You Think
At 35:29 · chapter starts 30:03
This is one of the most technically informative sections of the interview, delivered accessibly. Kokotajlo explains that modern AI is not software in the conventional sense — no engineer wrote rules telling it how to behave. Instead, it is a neural network: a vast tangle of connections (parameters) that starts as random noise and is trained into usefulness by reinforcement, the same basic mechanism by which brains learn. Pre-training teaches the AI to predict text, giving it a broad model of the world. Later training layers on specific capabilities like coding. The current largest models have approximately 10 trillion parameters, compared to 175 billion in 2020 — roughly 100x growth in six years. A key point for the safety discussion: unlike conventional software, you cannot look inside a neural network and see what it's thinking. Mechanistic interpretability research is trying to solve this, but it's an inherently difficult problem — understanding one small group of connections among 10 trillion tells you almost nothing about the whole. This opacity is a central reason the loss-of-control risk is so hard to rule out.
How AI Actually Works: Neural Nets Explained Simply
Modern AI is not lines of code — it's a neural net with up to 10 trillion parameters, trained by positive and negative reinforcement the same way the human brain learns. Pre-training teaches it to predict text; then specific tasks like coding get layered on top. The whole thing is basically an artificial brain, but one that no human can look inside and understand.
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