Hassan. @jacmvv

Next year, I will give one of my SpaceX shares to every kid in America. This will be the first of many.

Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.

Andrej Karpathy @karpathy

7 months ago

Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is

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A robot that moves isn’t always moving accurately. Wheel slip, dodgy encoders, and wonky wheel-radius estimates can fool your odometry. Before blaming the nav stack, check the basics. Sometimes your robot isn’t lost, your math is. 🤖

late night RL lectures from ETHZurich

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@Shra_va_ni Don't switch, just stick with claude

MBA = Master of Bot Administration

@danellisona Big fan of SpaceX and Starlink

@IBM It will be Sales

@elonmusk

@oprydai This is the thing we just can't make them understand, people always understand it by themselves after surviving the harsh period of their time

how to study and learn really difficult subjects: 1. don’t panic. hard just means dense. it’s not impossible, it’s just packed. unwrap it slowly. 2. get a map before diving in. watch an overview video, read the table of contents, or skim the wikipedia page. you need context first. 3. use multiple sources. one book will confuse you, two will clarify, three will enlighten. 4. build intuition, not memory. visualize it. simulate it. code it. teach it to a friend or a wall. 5. tolerate confusion. you’ll feel dumb 90% of the time; that’s the price of learning something that rewires your brain. 6. connect it to reality. every abstract thing has a real-world analog. find it. relate it. ground it. 7. revisit the same topic after a week. mastery is not about reading once; it’s about returning after your brain has “chewed” on it. 8. don’t romanticize genius. smart people aren’t born knowing it. they just survived longer in the confusion phase. the truth is; learning hard stuff isn’t about intelligence. it’s about endurance, humility, and curiosity. you stay long enough in the fog until it clears.