Serge Aliseenko

My pleasure to come on Dwarkesh last week, I thought the questions and conversation were really good. I re-watched the pod just now too. First of all, yes I know, and I'm sorry that I speak so fast :). It's to my detriment because sometimes my speaking thread out-executes my thinking thread, so I think I botched a few explanations due to that, and sometimes I was also nervous that I'm going too much on a tangent or too deep into something relatively spurious. Anyway, a few notes/pointers: AGI timelines. My comments on AGI timelines looks to be the most trending part of the early response. This is the "decade of agents" is a reference to this earlier tweet nitter.cf/karpathy/status/188254… Basically my AI timelines are about 5-10X pessimistic w.r.t. what you'll find in your neighborhood SF AI house party or on your twitter timeline, but still quite optimistic w.r.t. a rising tide of AI deniers and skeptics. The apparent conflict is not: imo we simultaneously 1) saw a huge amount of progress in recent years with LLMs while 2) there is still a lot of work remaining (grunt work, integration work, sensors and actuators to the physical world, societal work, safety and security work (jailbreaks, poisoning, etc.)) and also research to get done before we have an entity that you'd prefer to hire over a person for an arbitrary job in the world. I think that overall, 10 years should otherwise be a very bullish timeline for AGI, it's only in contrast to present hype that it doesn't feel that way. Animals vs Ghosts. My earlier writeup on Sutton's podcast nitter.cf/karpathy/status/197343… . I am suspicious that there is a single simple algorithm you can let loose on the world and it learns everything from scratch. If someone builds such a thing, I will be wrong and it will be the most incredible breakthrough in AI. In my mind, animals are not an example of this at all - they are prepackaged with a ton of intelligence by evolution and the learning they do is quite minimal overall (example: Zebra at birth). Putting our engineering hats on, we're not going to redo evolution. But with LLMs we have stumbled by an alternative approach to "prepackage" a ton of intelligence in a neural network - not by evolution, but by predicting the next token over the internet. This approach leads to a different kind of entity in the intelligence space. Distinct from animals, more like ghosts or spirits. But we can (and should) make them more animal like over time and in some ways that's what a lot of frontier work is about. On RL. I've critiqued RL a few times already, e.g. nitter.cf/karpathy/status/194443… . First, you're "sucking supervision through a straw", so I think the signal/flop is very bad. RL is also very noisy because a completion might have lots of errors that might get encourages (if you happen to stumble to the right answer), and conversely brilliant insight tokens that might get discouraged (if you happen to screw up later). Process supervision and LLM judges have issues too. I think we'll see alternative learning paradigms. I am long "agentic interaction" but short "reinforcement learning" nitter.cf/karpathy/status/196080…. I've seen a number of papers pop up recently that are imo barking up the right tree along the lines of what I called "system prompt learning" nitter.cf/karpathy/status/192136… , but I think there is also a gap between ideas on arxiv and actual, at scale implementation at an LLM frontier lab that works in a general way. I am overall quite optimistic that we'll see good progress on this dimension of remaining work quite soon, and e.g. I'd even say ChatGPT memory and so on are primordial deployed examples of new learning paradigms. Cognitive core. My earlier post on "cognitive core": nitter.cf/karpathy/status/193862… , the idea of stripping down LLMs, of making it harder for them to memorize, or actively stripping away their memory, to make them better at generalization. Otherwise they lean too hard on what they've memorized. Humans can't memorize so easily, which now looks more like a feature than a bug by contrast. Maybe the inability to memorize is a kind of regularization. Also my post from a while back on how the trend in model size is "backwards" and why "the models have to first get larger before they can get smaller" nitter.cf/karpathy/status/181403… Time travel to Yann LeCun 1989. This is the post that I did a very hasty/bad job of describing on the pod: nitter.cf/karpathy/status/150339… . Basically - how much could you improve Yann LeCun's results with the knowledge of 33 years of algorithmic progress? How constrained were the results by each of algorithms, data, and compute? Case study there of. nanochat. My end-to-end implementation of the ChatGPT training/inference pipeline (the bare essentials) nitter.cf/karpathy/status/197775… On LLM agents. My critique of the industry is more in overshooting the tooling w.r.t. present capability. I live in what I view as an intermediate world where I want to collaborate with LLMs and where our pros/cons are matched up. The industry lives in a future where fully autonomous entities collaborate in parallel to write all the code and humans are useless. For example, I don't want an Agent that goes off for 20 minutes and comes back with 1,000 lines of code. I certainly don't feel ready to supervise a team of 10 of them. I'd like to go in chunks that I can keep in my head, where an LLM explains the code that it is writing. I'd like it to prove to me that what it did is correct, I want it to pull the API docs and show me that it used things correctly. I want it to make fewer assumptions and ask/collaborate with me when not sure about something. I want to learn along the way and become better as a programmer, not just get served mountains of code that I'm told works. I just think the tools should be more realistic w.r.t. their capability and how they fit into the industry today, and I fear that if this isn't done well we might end up with mountains of slop accumulating across software, and an increase in vulnerabilities, security breaches and etc. nitter.cf/karpathy/status/191558… Job automation. How the radiologists are doing great nitter.cf/karpathy/status/197122… and what jobs are more susceptible to automation and why. Physics. Children should learn physics in early education not because they go on to do physics, but because it is the subject that best boots up a brain. Physicists are the intellectual embryonic stem cell nitter.cf/karpathy/status/192969… I have a longer post that has been half-written in my drafts for ~year, which I hope to finish soon. Thanks again Dwarkesh for having me over!

Same

The Oct 11 Crypto Crash — What Really Happened TL;DR: Roughly $60–90M of $USDe was dumped on Binance, along with $wBETH and $BNSOL, exploiting a pricing flaw that valued collateral using Binance’s own order-book data instead of external oracles. That localized depeg triggered $500M–$1B in forced liquidations, cascaded into $19B+ globally, and earned the attackers about $192M via $1.1B in BTC/ETH shorts opened on Hyperliquid hours earlier, but minutes before Trump tariff announcement. It wasn’t a USDe failure!! It was Binance’s design flaw, timed with macro panic (Trump’s tariffs) for cover. What looked like chaos was actually a coordinated exploitation of Binance’s internal pricing system, amplified by a macro shock and systemic leverage. 1️⃣ The Setup Binance’s Unified Account let traders use assets like USDe, wBETH, and BNSOL as collateral. Instead of oracle or redemption prices, Binance valued these using its own spot market - a major vulnerability. On Oct 6, Binance announced a fix to move to oracle-based pricing, but rollout wasn’t until Oct 14, leaving an 8-day window. 2️⃣ The Exploit During that window, sophisticated actors manipulated Binance’s order books, dumping ~$60–90M of USDe, driving it to $0.65 on Binance only (still ~$1 elsewhere). Because the Unified Account marked collateral to internal prices, this instantly wiped margin value and triggered $500M–$1B in forced liquidations. Then, Trump’s 100% China tariff headline hit, magnifying panic and liquidity stress. 3️⃣ The Profit Engine The same day, fresh wallets on Hyperliquid opened $1.1B in BTC/ETH shorts, funded by $110M USDC from Arbitrum-linked sources. As the Binance cascade unfolded, BTC and ETH cratered, those shorts netted $192M in profit before closing out at the bottom. Timing, precision, and funding paths all suggest coordination. 4️⃣ The Contagion Binance liquidations dumped BTC/ETH/ALTs into thin books. Other exchanges mirrored the collapse through cross-market bots. Market makers hedged across venues were forced to unwind everywhere. Result: $19B+ global liquidations, with many alts down 50–70% intraday, all triggered by <$100M of manipulated collateral. 5️⃣ Who’s at fault? Binance: design flaw + delay in oracle rollout = root cause. Exploiters: executed and timed the manipulation, profited via external shorts. Ethena (USDe): not at fault - protocol stayed 1:1 collateralized, redemptions normal, peg held everywhere else. 6️⃣ Aftermath Binance admitted “platform-related issues,” promised compensation for affected margin/futures/loan users, and rolled out minimum price floors + oracle integration. USDe remained operational, and the incident is now a case study in how exchange-side pricing errors can trigger system-wide liquidations. Bottom line: A ~$90M dump on Binance and a $1.1B leveraged short elsewhere sparked a $19B bloodbath. Not a stablecoin failure, but a masterclass in exploiting flawed collateral valuation during peak macro stress.

EXACTLY WHY $GOOGL IS THE MOST UNDERRATED AI WINNER

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This is absolutely !nsane man 🤯🏓

XRP asked for a war and they’re going to get one. Here is a small list of why you should steer clear of XRP: - Pre-Mined -Central governance w/ CEO -Ripple owns half the supply -Cheap marketing tricks -Pump & Dump Scheme -Ripple loves Kamala - Extension of Fiat -They want a CBDC What am I missing ? 👇