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.
Similar snapshots
MVP built in one week
Sam's initial MVP was coded in approximately one week using ChatGPT voice mode and copy-pasting code, with no prior technical experience.
Discord vs email for young users
Sam argues Discord is 10x better than email for building relationships with younger users who rarely check their inbox.
Monthly infra costs ~$780–$980
Sam's monthly operating costs include Cursor ($200), AI image generation ($100), AI video generation ($200), hosting ($100), email marketing ($80), and AI compute ($300–$500).
ChatGPT-powered pain point research
Sam recommends copying days of Discord chat history into ChatGPT and prompting it to list recurring pain points as a fast, free market research technique.
50 free tools built total
Bhanu and his team built approximately 50 free tools to attract search traffic, each linked back to SiteGPT.
New tool built in under 5 minutes
With AI coding tools like Cursor, Bhanu can now create a new free marketing tool in less than 5 minutes by referencing existing tools.
KD filter < 10 for rankable keywords
Bhanu filters Ahrefs keyword results to show only those with a keyword difficulty below 10, making them realistic ranking targets for any decent website.
Minimum 1,000 monthly search volume
Bhanu sets a minimum search volume of 1,000 monthly searches when selecting keywords to target with free tools.
20 downloads/day at launch
PropGPT averaged 20 downloads per day right after launching on the App Store through influencer marketing.
