George Hotz
About
Hacker/entrepreneur who advocates everything being open source and decentralized
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Claims by George Hotz (20 of 57)
AI Accelerators Are a Software Problem
ML accelerators are fundamentally a software problem, not a hardware problem; companies like Graphcore, Cerebras, TensTorrent and Groq built good chips but terrible software, and if you can't write a Torch-level performance stack on Nvidia GPUs down to the driver, you certainly can't write one for your more specialized chip.
Anarchy Over Tyranny: Good Outnumbers Bad
Centralized held control is tyranny, and Hotz will always take anarchy over tyranny because with anarchy you have a chance; giving powerful AI to everyone is safer than to a small trusted group, because if you believe in democracy you believe good outnumbers bad, so distributing the technology guarantees it lands in more good hands than bad.
Programming Is Tool-Complete, Driving Is Not
Driving is not tool-complete and programming is: cars have had the same interface for 50 years so anyone can drive a 1980 car, whereas programming tools (languages, IDEs, runtimes) have radically changed, meaning a JavaScript programmer would struggle at an Apple IIe BASIC prompt — and if Copilot or Codex genuinely help you, it probably means your framework has too much boilerplate.
Managers Should Recognize and Demand Simplicity
Good code requires technical leadership you trust: managers should be better programmers than the people they manage, or at least able to recognize skill, and the way to create a great code base is to require contributors to explain their code and rewrite it more simply when a simpler way exists.
Prompt Engineer as Top of the Abstraction Stack
As humans move up the abstraction stack (arithmetic by hand → spreadsheets → macros → accounting software), the top of the stack is the prompt engineer; what a human really is in a company of AIs is a prompt engineer — the do-what-I-mean operator who keeps the AI aligned with their intent.
Chess-Effect: AI Achievements Get Devalued
Whatever new thing AI can do, everybody first thinks is brilliant, then 20 years later calls it mechanical — chess was 'intelligent' in 1960 but 'mechanical' by 2010; today we think language ability is intelligent, but in 20 years humans will define a new niche (e.g. 'can it reproduce?') because humans always carve out a niche to feel special.
Real vs Artificial Difficulty
Artificial difficulty is difficulty that is constructed and could be turned off with a knob, whereas real difficulty cannot be turned off — like surviving in the woods; smashing the knob (e.g. not bringing a map into the wild) doesn't make difficulty real, it just makes you fail.
Superintelligence Before Robustness
We will build superintelligence long before we build any robustness in AI; we cannot yet build a machine that can survive in nature like a bird or reproduce itself, so in an AI apocalypse the AIs would likely die out because the silicon stack lacks the reproductive core that the biological stack has.
VR Headsets Should Pull You Into a World
Quest's failure is that it shows you scrolling clouds and a Facebook login screen instead of bringing you into a world; the beginning of an experience is critically important (as Todd Howard says about games), so a headset that immediately pulls you into an expansive, peaceful world the way Zelda does is what makes VR compelling.
20 Petaflops Equals One Person of Compute
Compute can be quantified in 'persons' the way mechanical power is quantified in horsepower: roughly 20 petaflops of compute equals one person, so a 40-petaflop cluster is 'two people worth of compute' and a Comma 3 device has about as much intelligence as a bee.
Refactors Before Features; 50 People Run Twitter
A legacy code base like Twitter's needs refactors before features: you first build trusted tests/CI before touching code, then replace old modules with simpler ones (e.g. 100k lines replaceable by 300 lines of Go), and Hotz believes 50 people could comfortably build and maintain Twitter — the code base grew because engineers get promoted for adding libraries others use, not for simplicity.
Local Aligned AI for Embodied Robots
Comma will sell rudimentary bodies, but a human's worth of intelligence requires a tiny rack rather than legs on a tiny box; the body would connect wirelessly to your tiny box/rack at home (0.1ms away vs 30ms to the cloud), keeping the AI's models running locally so they stay aligned to you rather than to a cloud provider.
Intelligence, Not Size, Lets Humans Dominate
The reason a man dominates all the chickens in a factory farm has nothing to do with size and everything to do with intelligence; if one central organization has all the intelligence, you'll be the chickens and they'll be the chicken man, but if we all have the intelligence, there's no chicken man.
Laziness Fuses Kernels and Cuts Memory Access
Tinygrad uses laziness like Haskell: instead of computing A times B immediately and storing it to memory, it waits until the user realizes the tensor, allowing the plus C operation to be fused into one kernel, which eliminates the excessive loads and stores to memory that PyTorch incurs.
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