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This Is How You Become 10x Smarter

Varun Mayya · 31:28 · 20260517

What This Is Actually About

The 10 techniques that made LLMs go from toy to revolution were reverse-engineered from how biological neurons work — so they transfer directly to human intelligence. The speaker maps each stage of AI development (pre-training, chain-of-thought, tool use, multimodal, few-shot, distillation, constitutional AI, self-play, mixture of experts, continual learning) onto a concrete practice you can apply to your own brain, using a monkey named Mr. Dimwos as a demonstration subject.

Key Points

Pre-Training: Build the Wide Base Before You Specialize

AI reads the entire internet before any fine-tuning. Humans must do the same — read 50 books a year across 10 different categories, have conversations outside your industry, travel to places that challenge your worldview. The goal is maximally diverse training data. Most people specialize too early; the best minds (da Vinci, Franklin, Feynman, Demis Hassabis) pre-trained across dozens of domains first.

Chain of Thought: Reasoning Is a Separate Muscle from Knowledge

Knowing facts is useless without the ability to connect them. The AI equivalent is step-by-step reasoning; the human equivalent is first-principles thinking, steelmanning (constructing the strongest version of an opponent's argument before rebutting it), and postmortems on your own decisions. Reinforce this with a mentor who gives you honest signal — the human version of RLHF.

Tool Use: Smart vs. Effective

AI went superhuman when it learned to search the web, write code, and call APIs. Humans should do the same — build a tool set (Photoshop, coding, calculators, LinkedIn hiring) through end-to-end projects. Learn one tool at a time, then combine them until you develop task association: knowing which tool for which job.

Multimodal: Input and Output in Every Format

AI models trained on text + images + audio + code simultaneously got better at everything, not just each modality. Humans who only consume text have one-dimensional understanding. The founder who understands their product through spreadsheets, customer calls, building prototypes, and watching users has a 4D mental model. Output in multiple formats too — teaching others, making videos, recording audio all reinforce understanding far more than reading alone.

Few-Shot Learning: Coachability and Situational Intelligence

Show an AI three examples and it extrapolates to a hundred. Humans can train this muscle by deliberately entering unfamiliar situations — travel alone, sit in on meetings outside your domain, adapt to foreign cultures. The fastest path is studying history: human behavior is cyclical, and seeing past patterns lets you predict future ones from sparse data.

Distillation: Compress What You Know by Teaching

A large AI model teaches a smaller one only the essential patterns — like a zip file. Humans do the same by compressing years of experience into principles and teaching others. The speaker runs distilled versions of multiple disciplines in his brain, trading peak expertise for the ability to handle 4-5 domains at once while hiring larger models (specialists) where needed.

Constitutional AI: Wisdom Under Pressure

The latest models have internal principles that make them hard to prompt into doing harmful things. Humans need the same: a written personal constitution with specific, situation-triggered rules ("When I'm angry, I wait 24 hours before responding"), reviewed monthly and pressure-tested against actual decisions. Raw intelligence without values is dangerous.

Self-Play: Simulate Before You Experience

AlphaGo beat the world champion by playing itself millions of times. Humans can run thought experiments, premortems, and red-team their own strategies. What if your biggest customer leaves tomorrow? What if your career plan is useless? Find peers who will genuinely debate you. Anxiety, properly harnessed, is a built-in future-simulation engine.

Mixture of Experts: Multiple Internal Modes

Modern AI uses specialized sub-networks — one for healthcare, one for code, one for conversation — and activates the right one per task. Humans should audit their modes: are you bringing CEO energy to a situation that needs coach energy? Engineer mode when you need entrepreneur mode? Build distinct operating protocols for different contexts and practice switching deliberately.

Continual Learning: Unlearn to Relearn

AI suffers from catastrophic forgetting — it can't update in real time without losing old knowledge. Humans have the opposite problem: identity attachment ("I'm a finance person"), sunk-cost reasoning (10 years in a worldview), and pruning failure (holding outdated mental models). The advantage young people have is they haven't learned the old way yet. The antidote is asking "What am I wrong about? What has changed in the last 5 years?" The survival of the fittest is actually survival of the most adaptable.

If You Remember Nothing Else

• AI researchers didn't invent these 10 stages — they reverse-engineered what the most effective human minds have always done.
• The gap between where you are and where you want to be is in one of these 10 stages. Audit yourself, find the bottleneck.
• The most underrated stage is distillation: intelligence that can't be shared can't be scaled.

Watch Out For

• Stage 10 (continual learning) is presented as aspirational — LLMs themselves haven't solved it, and the human version is more of a sociopsychological challenge than a technical one.
• Stage 9 (mixture of experts) and Stage 6 (distillation) blur together in the explanation — the speaker acknowledges you need distilled mixture-of-experts, but doesn't cleanly separate the two.
• Many claims (e.g., "50 books a year across 10 categories," "Dhoni doesn't know the straight drive") are asserted as truth with no source or evidence.
• The sponsor segment for CyberGhostVPN is embedded mid-Stage 3 (Tool Use), which inflates the length of that section.

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