The AI Playbook: 9 Mental Models for Building in the Age of Intelligence https://www.youtube.com/watch?v=7xTGNNLPyMI
This is not your typical founder story. This is a masterclass from the “Teacher of the AI Revolution.”
Andrej Karpathy (founding member of OpenAI, former Director of AI at Tesla) just dropped a 3.5-hour “State of the Union” on Large Language Models. If you are building a startup today, you cannot afford to ignore this. This is the new electricity.
Most people treat AI like magic. Karpathy treats it like a stochastic token tumbling machine. And that distinction changes everything about how you build.
Here are the 9 key takeaways for founders building on top of this shifting tectonic plate.
- The “Base Model” is an Internet Simulator, Not a Truth Machine
Founders often mistake the AI for a “know-it-all.” It’s not. It is a compression of the internet.
Karpathy explains that the “Base Model” (the raw neural net) is just trying to predict the next word of a random internet document. It isn’t trying to be helpful; it’s trying to simulate a Reddit thread or a Wikipedia article [43:35].
Takeaway: Don’t trust the raw model to be your product. The “product” is what you build on top of the simulator to constrain it into being useful.
- Fine-Tuning is Just “Roleplay”
How do you turn a wild internet simulator into ChatGPT? You use Supervised Fine-Tuning (SFT). You hire humans to write questions and answers, and the AI learns to imitate them [01:03:00].
When you talk to ChatGPT, you aren’t talking to a silicon brain; you are talking to a statistical simulation of a human data labeler [01:17:49].
Takeaway: If you rely solely on fine-tuning, your startup hits a ceiling: human performance. You can never exceed the quality of your labelers.
- Reinforcement Learning (RL) is the “AlphaGo” Moment
This is the frontier. To go beyond human capability (like DeepSeek R1 or OpenAI o1), you need Reinforcement Learning.
Karpathy compares this to AlphaGo [02:42:20]. If you only train on human games, you top out at human skill. But if you let the AI play against itself and reward the wins, it discovers “Move 37”—strategies no human would ever think of [02:45:32].
Takeaway: The most valuable startups won’t just imitate humans; they will build “gyms” (simulation environments) where the AI can practice and get smarter than us.
- The “Swiss Cheese” Problem
This is the sharpest edge for founders. LLMs have “Jagged Frontiers.”
They can solve PhD-level physics problems but will confidently tell you that 9.11 is larger than 9.9 [02:05:40]. Why? because 9.11 looks like a Bible verse or a date to the token predictor.
Takeaway: Do not build “trust-based” products. Build “verification-based” products. You must have a human (or code) in the loop to check the cheese for holes.
- Give the Model “Scratchpad” Time
Karpathy emphasizes that models need “tokens to think” [01:58:00].
If you ask a model to solve a hard math problem in one word, it fails. If you let it write out the steps (“Let’s think step by step…”), it succeeds.
Takeaway: Design your UX for patience. Don’t hide the latency; use it. Allow the model to generate “chain of thought” before giving the final answer.
- Tools > Brains (The Hybrid Approach)
The model is bad at spelling “Strawberry” (counting Rs) because it sees tokens, not letters [02:03:44].
Karpathy’s solution? Don’t force the LLM to do it mentally. Tell the LLM to write a Python script to count the letters.
Takeaway: Don’t force the AI to be a computer. We already have computers. Build systems where the AI acts as the orchestrator that calls tools (Search, Calculator, Code) to do the precise work.
- Hallucination is a Feature, Not a Bug
Hallucination is just the model “dreaming” based on its training data. It happens because the model is probabilistic, flipping a coin for every word [26:45].
You can mitigate this by allowing the model to say “I don’t know” or by using Search (RAG) to put the answer into its working memory [01:35:00].
Takeaway: You cannot “fix” hallucination in the model weights alone. You fix it by stuffing the correct answer into the prompt (Context Window).
- The Future is “Agents”
We are moving from chatbots (talk) to Agents (do).
Karpathy predicts a shift where we hand off control to models that can use keyboards and mice to perform long-horizon tasks [03:12:00].
Takeaway: The next trillion-dollar opportunity is not “Chat with PDF.” It is “Do my taxes” or “Book my travel”—end-to-end execution.
- Open Weights are Catching Up
Karpathy highlights DeepSeek and Llama [03:16:00]. The gap between closed models (OpenAI/Google) and open models (DeepSeek/Meta) is closing fast.
Takeaway: The model itself is becoming a commodity. Your moat is not the LLM; your moat is your proprietary data, your distribution, and the unique “gym” you build to train your specific agent.
Final Thought
Karpathy leaves us with a humbling reminder:
“You are not talking to a magical AI. You are talking to a statistical simulation of an average human labeler.”
Build accordingly. Don’t worship the tool; wield it.
Post created via email from emin@nuri.com