Q&A with Andrej Karpathy on AGI still being a decade away, why reinforcement learning is terrible, superintelligence, his AI education startup Eureka, and more
AGI is still a decade away (via) Extremely high signal 2 hour 25 minute (! … X: Ashpreet Bedi / @ashpreetbedi : This is exactly why we recommend keeping it simple and focusing on clarity and reliability over complexity. How to successfully add AI to your product: -> Add small, reliable AI features - ideally as “magic buttons.” -> Automate targeted workflows. Solve one painful step with @hesamation : Karpathy spilled the formula of truly learning something: > don't write blog posts > don't do slides > write the code > arrange it > get it to work if you can truly build it you can say you know it. build from scratch guys. [video] @terrybrock : You are right Andrej. Besides, those agents don't get the privilege (and fun!) of Vibe Coding!! We *LOVE* that concept that you introduced. Keep up the good work! Tahseen Rashid / @tahseenrashid_ : Thank god finally someone mainstream said it. A lot of great products and companies to be built and we are early, but this is the hard truth. Christian Carpinelli / @chricarp : we're currently living in what should be a recession, that isn't manifesting due to the extreme amount of money that was printed in 2020/21 and the promise of this very near AI future that will unlock more growth than the internet... but the timeline is just not there... @storiesseen : This is very interesting. I think Andrej is the most sophisticatedly blunt, simple person in regard to the truth about AI that there is. I take his word pretty much over most people. So, what this says is that for those USING AI for its *benefits*, you have 10 years to Nic Cruz Patane / @niccruzpatane : Andrej Karpathy in a new interview on Waymo's approach vs Tesla's approach: “Tesla took in my mind the a lot more scalable approach, and I think the team is doing extremely well. I'm on the record for predicting this will go; Waymo will have an early start because you can [video] @mattparlmer : I been saying this, update your models and timelines foomcels, mankind is back @rossbing : Looking at the development of Autonomous Driving. It attracted a huge amount of investment about 10 years ago. Now it's not generally available yet , even though it's very close to being ready. For AI agent, it might be faster to achieve, but the 10 year scale is more reasonable. @kimmonismus : Andrej Karpathy: AI agents like Claude or Codex are still far from acting as real “employees.” They lack memory, multimodality, and true learning. Fixing that will take about a decade — not because it's impossible, but because deep intelligence just takes time to build. [video] Dmitriy Green / @dmtgreeen : This is how I view AI's current state: it's not ready to replace humans yet. But I believe it can significantly enhance productivity when humans coordinate and validate its output. Dwarkesh Patel / @dwarkeshsp : The most interesting part for me is where @karpathy describes why LLMs aren't able to learn like humans. As you would expect, he comes up with a wonderfully evocative phrase to describe RL: “sucking supervision bits through a straw.” A single end reward gets broadcast across [video] Pedro Domingos / @pmddomingos : Karpathy: AGI is a decade away. AI Twitter: Catastrophe!!! Me: Wouldn't that be amazing? @hesamation : Andrej Karpathy beautifully explains the fundamental difference of learning between a human and an LLM. > “The book I'm reading is a set of prompts for me to do synthetic data generation. It's by manipulating that information that you actually gain that knowledge. We have no [video] John Rush / @johnrushx : I'm feeling so pessimistic about AI today. After building dozens of AI agents for many years, here is what I got to say: The biggest challenge in building AI agents is agent's long term memory. LLMs with huge context windows are pretty much scams, cuz they compress the text Rohit / @krishnanrohit : . @karpathy pours a little bit of cold water on the AI hype [image] Yacine Mahdid / @yacinelearning : I'm calling it ma man is going to become a frontier LLM pessimist the amount of legit heavy weight machine learning folks that are deflating the hype to his face is just too much he's gonna go full neuromorphic-latent-reasoning tin foil hat in like 2 podcast @leothecurious : karpathy 1 yr ago, for perspective: “the trajectories in ur brain when u're doing problem solving, if we had a billion of that, like AGI is here, roughly speaking, i mean, to a very large extent, uhm, and we just don't have that.” @wispem_wantex : It's pretty funny that we are converging on the reality that will upset the maximum amount of people: LLMs are pretty good. They're not transformative, and also not useless. There are some tasks where LLMs will save you a lot of time. And that's it Will Brown / @willccbb : ok but he's right, ai agents *are* slop have you seen the code they write when you don't keep them on a very tight leash? sure it's often functional, but it's definitely slop @pli_cachete : You just got another 8 years to escape the permanent underclass [image] Abinash Senapati / @techievena : A decade sounds about right timeline to build a company in the space to fix this. @scaling01 : Andrej Karpathy calls AI Agents slop “Overall, the models they are not there. And I feel like the industry [...] it's making too big of a jump and it's trying to pretend that this is amazing. And it's not—it's slop! And I think they are not coming to terms with it. And maybe [video] Erny / @egmontesano : First decent take about agents and AI. Dan Mac / @danielmac8 : What @karpathy describes here is ASI not AGI. Let me explain: You can't drop a generally intelligent human into *any* job and expect them to perform competently. E.g. I have a friend who is a very smart M.D. Drop him into my job tomorrow and he's completely lost. He'd need Haider / @slow_developer : Andrej Karpathy says today's agents aren't ready to work like real coworkers or interns They lack intelligence, can't use computers, aren't multimodal, lack continual learning, and forget what you tell them Fixing these gaps will take about a decade [video] Jonathan Blow / @jonathan_blow : Some in the industry are still honest and reasonably objective? https://x.com/... Gary Marcus / @garymarcus : When Karpathy sounds like Marcus, the gig is up: “Andrej Karpathy calls AI Agents slop “Overall, the models they are not there. And I feel like the industry [...] it's making too big of a jump and it's trying to pretend that this is amazing."" Sahil / @sahilypatel : karpathy just dropped the best career advice for engineers [image] Sriram Krishnan / @sriramk : The @dwarkesh_sp @karpathy podcast is a banger. Highly recommended. Akshit / @akshitwt : karpathy by far has the best and most sober takes on AI progress in the community. super careful with what he says and how he articulates it and he isn't too bearish or bullish. i agree with almost everything he's saying Max Farrens / @maxwellfarrens : This episode made my brain shift a little. Andrej thinks we're already in the regime of recursive self-improvement (and have been since the Industrial Revolution). In his framing, AI is not a discrete new thing. Instead, it's continuous with the past 250 years of automation and Dwarkesh Patel / @dwarkesh_sp : The most interesting part for me is where @karpathy describes why LLMs aren't able to learn like humans. As you would expect, he comes up with a wonderfully evocative phrase to describe RL: “sucking supervision bits through a straw.” A single end reward gets broadcast across every token in a successful trajectory, upweighting even wrong or irrelevant turns that lead to the right answer. @zephyr_z9 : First Sutton, now Karpathy AGI 2027 bros in shambles Timothy B. Lee / @binarybits : Good @karpathy discussion of why we're unlikely to have a moment where we automate AI research and get an “intelligence explosion.” [image] @okitafan : In retrospect not caring too much about my career since my timelines were so short sounds completely out of distribution regarding predictions made by people build it Noah Vandal / @noah_vandal : “every 0.9 is the same amount of work; ” if it takes 100 units to get to 90% expect another 100 units to get to 99% and then another 100 units to get 99.9% ... Andrew Curran / @andrewcurran_ : The hotly anticipated Dwarkesh x Karpathy interview is up. One of his visions of a potential future is competing AI's slowly becoming more autonomous and eventually splitting into warring factions. Mutuals, I hope we all end up under the same AI banner, in the same kingdom. [image] Biswajit / @biswajitghosh95 : AGI still ~10 years away due to scaling limits. - LLMs struggle with reasoning, planning, and deficits in cognitive architecture. - RL is inefficient and “terrible” for complex tasks. @dhwani_io : Time to dig in [image] @josephjacks_ : “RL is terrible"is so catchy and so wrong. Simultaneously reveals a total lack of understanding of what @RichardSSutton codified and yet sets on fire the excitement of so many jaded by this acronyms misuse. Matthew Kenney / @baykenney : Listened to @dwarkesh_sp podcast with Gwern, but hadn't circled back around until my recent 20 hour car trip where I plowed though a bunch of episodes. I know I'm late to the game, but it's phenomenal - highly recommend Yuchen Jin / @yuchenj_uw : Andrej explains why reinforcement learning is terrible (but everything else is much worse). [image] Aaron Slodov / @aphysicist : so refreshing to see a grounded take in reality. Aakash Kumar Nain / @a_k_nain : “AGI is still a decade away” A lot of us have been saying this but people don't want to accept that “the last mile” problem is largely unsolved. Thank you .@karpathy 🙂 PS: OTOH LLMs have certainly taken us from “it kinda works” to partial AGI Mirek Mencel / @mirekmencel : The decade of agents Max Davish / @max_davish : This is like Taylor Swift going on the Kelce's podcast but for people who work at VC backed companies Adi / @adiautomates : shhh... 🤫 the master is speaking @karpathy @tenderizzation : Dwarkesh @ 15:16: “but the in-context learning itself is not gradient descent” orly? [image] Dwarkesh Patel / @dwarkesh_sp : The @karpathy interview 0:00:00 - AGI is still a decade away 0:30:33 - LLM cognitive deficits 0:40:53 - RL is terrible 0:50:26 - How do humans learn? 1:07:13 - AGI will blend into 2% GDP growth 1:18:24 - ASI 1:33:38 - Evolution of intelligence & culture 1:43:43 - Why self driving took so long 1:57:08 - Future of education LinkedIn: Georg Zoeller : > “It will take more than a decade to work though the issues with agents”. — LLMs are powerful compression and data retrieval technology … Threads: Dare Obasanjo / @carnage4life : “I feel like the industry...is making too big of a jump and it's trying to pretend that this [AI Coding Agents] is amazing. And it's not, it's slop! And I think they are not coming to terms with it. And maybe they are trying to fundraise or something like that, I'm not sure what's going on.” … Forums: Hacker News : Andrej Karpathy - It will take a decade to work through the issues with agents r/accelerate : Andrej Karpathy — AGI is still a decade away r/agi : Andrej Karpathy — AGI is still a decade away r/singularity : Andrej Karpathy — AGI is still a decade away
Context & Ripple Effects
Karpathy’s assessment lands after GPT-5 was characterized as an incremental release, reinforcing a strand of coverage that questions whether scaling alone closes the gap between capable models and dependable general-purpose workers.
The interview also sits against competing long-run interpretations: one related account treats present LLMs as early AGI, while another argues labs’ behavior implies continued weakness in generalization and workplace learning.
First-order effects
- The claims temper near-term expectations for AI agents as autonomous employees: teams evaluating agent deployments must still account for weak memory, multimodal understanding, and on-the-job learning.
- Eureka’s hands-on teaching stance favors practical building and debugging over presentation-led AI education, aligning its offering with learners seeking implementation skills.
Second-order effects
- Agent vendors and enterprise adopters face pressure to demonstrate reliable performance on bounded workflows rather than market broad coworker-like autonomy; the reported critique of reinforcement learning also challenges claims that more RL alone will solve the gap.
- The emphasis on small, dependable capabilities supports product strategies built around targeted automation, rather than redesigning operations around an assumed imminent AGI breakthrough.
Third-order effects
- If model progress continues to arrive as incremental reliability gains rather than a discrete AGI event, AI’s economic impact may be absorbed through gradual productivity growth and workflow redesign instead of a single labor-market discontinuity.
- The unresolved divide between scaling-led optimism and skepticism about generalization will keep shifting competition toward systems that can validate, integrate, and supervise models in real work environments.
The trend: AI is moving from headline AGI timelines toward scrutiny of whether models can learn reliably, retain context, and deliver value inside constrained production workflows.