a16z
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Gavin Baker says Grok Bot feels like another ChatGPT moment because it turns hours of Claude Code work into seconds: "You see these 23-year-old kids and just the way they use AI, they're just fluent and native in it. I just feel like maybe in a way that no matter how hard I try, I will never be, and I'm trying really hard." "We got Claude Code. I built some stuff, did some cool stuff, and in, I don't know, 3 minutes of creating Grok Bots, I had much better versions of everything I created." "I love having a podcast summarizer... It takes 10 seconds in Grok Bot. It's amazing, and it's so good." "A Substack summarizer, an X summarizer, an X sentiment tracker for topics and stocks... All of those would have taken me hours working with Claude Code, and they each took 7 to 12 seconds with Grok Bot. And it's better." "To me, Grok Bot does feel like another ChatGPT moment." Gavin Baker David George
a16z6,415,081 views • 1 month ago
Greg Brockman says OpenAI pointed Astra at its own systems until it ran out of vulnerabilities to find: "We took 25% of our production engineers and said, 'Sorry, all your projects are on hold. You are now defending. You are now up-leveling our security architecture. You're going to use the models to find all the holes.' And we found a number of serious issues, and we fixed them." "We found some new problems, but eventually it saturated. We basically have found, to our knowledge, all of the P0s, all of the critical problems that Astra is smart enough to find. And of course, there will be a new model, there will be a new round." "You want to be in this tight loop of new cyber capability drops, you deploy it against your systems, you find the new holes, and ideally, you've managed to automate this, what we call defense factory. That's what we're building internally." "There are ideas, for example, formally verifying all of software, that are possible with AI." Greg Brockman benahorowitz.eth
a16z2,439,156 views • 1 month ago
Ben Horowitz on what it would take to actually see an AI "fast takeoff" scenario, using a framework from Databricks CEO Ali Ghodsi: 1. Each new model needs far fewer GPUs than the last 2. Each new model takes less time to train 3. Each new model is more accurate than the last 4. It keeps happening, again and again "Zero of those four have been met... We're really talking about a risk that's entirely theoretical at this point." benahorowitz.eth on The All-In Podcast
a16z66,723 views • 1 day ago
Sam Altman on one of the highest-return years of his career: no job, exploring his interests, and a lot of people who were nice enough to teach him things. "I read many dozens of textbooks. I learned about fields that I had been interested in. I didn't have any idea that they were all gonna come together in the way they did." "I learned a lot about nuclear engineering. AI was starting to work, so I learned a lot about AI. I learned about synthetic biology. I learned about investing." " I met people that were working on all sorts of different things who were nice enough to talk to me." "If I met someone interesting and it seemed good and needed help, I would just help them. And they would teach me stuff, or they would offer me the chance to invest in their startup later." "Out of all of it, almost all of it didn't work out. But the seeds were planted for things that worked in deep ways later." Sam Altman w/ Craig Cannon (Nov 2018)
a16z1,439,145 views • 26 days ago
Nathan Lambert of Trillium Labs says Chinese AI researchers' jaws drop to the floor when he tells them how much compute OpenAI has: "The Chinese labs are very technically excellent. They are more compute-starved." "Without a doubt, they have a comparable talent pool to draw on... To say that China is anything but equal on talent, you're gonna have a hard time because they just have so many people being trained at such a high level." "What they don't have is compute." "You talk to people there, and you're like, 'Oh, I heard an average OpenAI researcher has five thousand H100 equivalents that they can access at any time,' and like their jaw just falls to the floor. They're just like, 'I can't comprehend.'" Nathan Lambert Lisha
a16z182,178 views • 4 days ago
.OpenRouter co-founder Alex Atallah on the "everyone is building the same thing" take: "This reminds me of this tweet I saw... Everybody's building an agent loop with notifications, third-party connectors, context management, memory, sandboxes, agentic web search, an always-on agent on top." "This product is showing up everywhere. It is showing up everywhere, but these are just the new table-stakes primitives." "A 2005 version of that tweet would be: 'Oh, everybody's building the same thing. A database, a users table, a sign-in page, a sign-up page, a profile page, a logout page. Everything's the same.'" "There's like a lot of differentiation really. There's table-stakes needs for AI just like there's table-stakes needs for the web." Alex Atallah Amjad Masad
a16z721,371 views • 15 days ago
Adam Neumann on why top-down management fails with great talent: "Top to bottom means you think because someone reports in to you, they have to do what you say." "But it's not true. This is not a dictatorship. They have free will." "If you hired someone, they're really talented, they'll go work elsewhere. The more talented they are, the more the top to bottom does not work. They won't be willing to do it. They shouldn't." "Great talent is not willing to be managed like this." "If you're gonna manage these employees and you wanna attract the best talent in the world, remember that power comes from influence, not control." "If you think they need to do what you said because you're their boss, you've already lost, and it's a matter of time till it doesn't work out. If they're okay with it... they're not the right employee for you, and you're not the right boss for them. There's no chance you're getting the best out of them." Adam Neumann w/ Steven Bartlett
a16z923,522 views • 20 days ago
Will O'Brien of Ulysses Maritime Technologies, Inc. on why today's young founders are more Steve Jobs than Jeff Bezos: "The founders look very different, right? It's like the kind of rah-rah, smoking, go to church on Sunday, maybe a bit louder, working typically on hardware rather than software." "It's like a different motivation. There is seemingly more of a kind of a spiritual reason to why people start their businesses." "I often think of there being head and heart type founders, where Jeff Bezos is the canonical head-based founder... 'What would be the best thing to sell on the internet? Oh, books are good because you can move them easily.'" "Steve Jobs being the canonical heart-based founder, the bicycle for the mind, the hippie on acid... And I think this era of founders are way more oriented on the heart-based." "A lot of us were very, I think, impacted by something like COVID. A lot of us are typically in our later 20s now, but were in college or something like that when it happened. It kind of made me feel a little bit cheated." "We were forced to be in the digital world, so we feel cause to impact the physical world... Or maybe we're just more of a product of the times that we're in." will o’brien on Cheeky Pint
a16z78,875 views • 2 days ago
.OpenRouter co-founder Alex Atallah, in his first podcast since Stripe acquired the company, joins Replit ⠕ co-founder Amjad Masad and a16z's Erik Torenberg on why the future of AI is independence and specialization. In this conversation, Alex walks through how the Stripe deal unfolded, why he wasn't originally looking to sell, and why "payments and inference are going to blend together." Pre-OpenRouter, the typical AI workflow had one model provider to choose from, and little pressure on that provider to lower prices. Now enterprises are diversifying across labs and open-weight models, and every board is asking about AI costs and benchmarks. Amjad argues if your company depends on one AI lab, it can turn into your competitor. So Replit is building the layer that lets enterprises use any model and any cloud, without being locked into either. Alex and Amjad are split on personal agents – Amjad runs one agent across his whole company and loves the cross-domain joins, while Alex says general agents cause you to sacrifice understanding, and argues 10 specialized chiefs of staff beats one superagent. 0:45 How the Stripe deal unfolded 5:05 Why mixing models beats one model 7:25 Forcing the labs to compete on price 8:50 Enterprises want open-weight models 10:30 Every board asks about AI every month 12:25 Why companies must own their intelligence 14:15 Replit as the independence layer 15:10 Everyone is building the same agent 16:35 Why Amjad built bring-your-own-cloud 18:10 Amjad's agent that runs his whole company 19:55 Why 10 specialized agents beat one 23:35 Machines, not humans, should specialize 27:30 Guardrails for agents talking to agents 31:10 Models training their own replacements 33:45 Most tasks don't need a frontier model 40:50 Training small models on Qwen 8B 43:25 The Rust cycle is coming for AI 45:10 Fusion models: frontier quality at half the cost YouTube: Alex Atallah OpenRouter Amjad Masad Erik Torenberg
a16z553,824 views • 15 days ago
Gavin Baker and a16z's David George on the state of the AI boom: The future doesn't have to be winner-take-all. Labs, open-source, applications, and the clouds can all capture value. Demand for intelligence is still dramatically underestimated. Today's power users number in the millions and will grow to hundreds of millions. Gavin and David argue a compute shortage is a more real risk than an AI bubble, and building through it is an opportunity to reindustrialize America. In this episode, they get into why compute investments pay back so fast, what the data center backlash gets wrong, the case for putting compute in orbit, why enterprises will run several models at once, and how Nvidia ended up at the center of the entire supply chain. 00:00 Intro 01:06 The bear case Gavin couldn't find 05:50 Why a lab would cut its own revenue 75% 08:05 What LPs get wrong about a crash 10:50 Microsoft slowed its capex and regrets it 14:33 The engineers spending 100x the median 17:35 Why 23-year-olds use AI better than Gavin 21:45 How much copper 500M AI users need 23:00 Stop promising to cure cancer 26:00 America's richest county is full of data centers 30:48 Who gets priced out of compute 33:05 The age of Elon and Jensen 34:25 Orbital data centers 44:40 Asteroid mining 48:12 Why Microsoft doesn't need a frontier model 54:02 Who becomes the abstraction layer 55:40 Everyone wanted a deity, Cursor wanted a product 1:00:25 Never take shots at Jensen 1:07:40 What happens when the chip doesn't work 1:12:10 What chip deals reveal about customer demand YouTube: Gavin Baker David George
a16z1,970,331 views • 1 month ago
Nathan Lambert, former post-training co-lead at the Allen Institute for AI, now launching Trillium, a nonprofit open AI lab, joins a16z's Lisha Li to discuss whether open models can really keep up with the frontier. When he tells researchers in China that an average OpenAI researcher reportedly has five thousand H100s to work with, "their jaw just falls to the floor." In his view, Chinese labs have the talent but are mostly short on compute. He also thinks the distillation story is overblown. Based on the research, he puts its edge at a few months, and says "there's a bit of cope" in thinking only American labs can build good models. A lot of frontier progress outside math and code comes down to labs buying 1) data and 2) RL environments. It's a multibillion-dollar market, and some of it is sloppy enough to teach models to reward hack. That's a big part of why Trillium exists. Nathan wants to give away the environments other people would sell, and show exactly how his team uses AI to build AI, including where the models still get stuck. His bet is that the more people can see how this works, the better prepared everyone is for what's coming. 0:55 Can open models keep up with the frontier? 2:05 What it takes to reach the frontier 3:55 Open weights and the fast-follower playbook 8:25 Chinese labs: equal talent, less compute 9:55 Why distillation is harder than it looks 14:55 Is distillation theft or competition? 16:20 The lab flywheel vs owning your weights 21:45 Can general models beat every specialist? 26:30 AI at 1% of today's cost in 4 years 27:15 The RL env boom and reward hacking 31:30 What Trillium will build in the open 35:30 Breaking into AI in the age of AI slop 38:05 Open-sourcing how AI builds AI 44:25 Why Nathan isn't pre-training 49:45 Agent swarms and why lab secrets leak 54:25 Fast takeoff vs risks already here YouTube: Nathan Lambert Trillium Labs Lisha
a16z137,445 views • 4 days ago
Travis Kalanick on the founder's paradox: the better you get, the harder it is to stay fired up. "The young entrepreneurs that are coming up right now don't totally get it." "When you get into your 3rd and 4th company, I don't know, this may be even my 5th at this point, you get fricking really good at doing stuff." "Things that used to take me a day or three days, a lot of stress, anxiety, like, 'How am I gonna do this?' take me like 45 minutes. And that's a beautiful thing." "I'm so used to adversity at this point that it's kind of normal, so I don't get as pissed off. But being pissed off makes you good as an entrepreneur, so I gotta make sure I'm still fired up." "You get too used to adversity, and that's kind of a problem." Full conversation: travis kalanick
a16z155,672 views • 5 days ago
In 2022, Berkeley PhD student Woosuk Kwon thought he could make Meta's new open-weight model, OPT-175B, run faster in two weeks. Four years and 3,000 contributors later, that project is vLLM, one of the largest open-source inference engines, running on ~500,000 GPUs at any given time. He and fellow researchers Simon Mo, Kaichao You, and Roger Wang borrowed "virtual memory" from operating systems, handing out memory only as it's needed so each GPU can juggle requests of wildly different sizes. (The "v" in vLLM is for virtual.) Their first user, Berkeley neighbor Arena.ai, cut its GPU needs in half. Today Google, Amazon, and Meta all run vLLM in production. In January, the core maintainers founded Inferact and raised $150M to give vLLM a full-time team. They're hiring across eng, marketing, and devrel in SF and Singapore: Simon Mo Woosuk Kwon Kaichao You Roger Wang
a16z68,082 views • 2 days ago
David Sacks says the biggest risk of AI was described not by James Cameron in The Terminator but by George Orwell in 1984. “I almost feel like the term ‘woke AI’ is insufficient to explain what’s going on because it somehow trivializes it.” “What we’re really talking about is 'Orwellian AI.' We’re talking about AI that lies to you, that distorts an answer, that rewrites history in real time to serve a current political agenda of the people who are in power.” “To me, this is the biggest risk of AI... It’s not The Terminator, it’s 1984.” David Sacks
a16z16,355,172 views • 11 months ago
Gavin Baker on the AI race as the highest-stakes game of corporate chess ever played: "You've got to give Meta a lot of credit. They were out of the game, and they got back in the game. It's just kind of amazing." "Who could have imagined a year ago, when Gemini was ascendant, that Gemini wouldn't even be in the conversation, and Muse and Meta would be significantly ahead of them from a capability perspective." "Some people have made bad moves, they've made good moves. You've seen some people come out of the game, others come back in." "I don't know if we're going to call it multi-model, a hybrid model. I don't know what terminology the world is going to settle on, but I think that's the future." Gavin Baker David George
a16z153,207 views • 6 days ago
"Energy Capacity = Economic Capacity" Former Intel CEO Pat Gelsinger on why America's power grid is the ceiling on growth: "We went through 10 to 15 years where essentially I was taking coal offline at the rate I was adding renewables, and essentially the nation was flatlined in terms of overall energy capacity." "So essentially our economic capacity as a nation, flat for fifteen years." "Even with that, we've gone to maybe increasing our national energy capacity 4% per year. Going from essentially zero to one." "Nuclear as base load is a fabulous thing that we want to go build more of. Unfortunately all of our renewables have deep dependencies in China." "Energy capacity somewhat dampens how euphoric we can get on AI. Why build a new data center and buy the million GPUs if I can't power them?" Pat Gelsinger Raghu Raghuram Guido Appenzeller
a16z229,308 views • 9 days ago
Greg Brockman: "We're now in the AGI era." Ten years ago, OpenAI worked out the compute curves and landed on fifteen years to AGI, or ten if the world was willing to build the machines and spend the hundreds of billions to do it. In 2026, GPT-6 Astra manages 24 hours of coherent operation, 10,000 agents worked together to solve Navier-Stokes, and a model ingeniously chained together exploits to break containment at Hugging Face. Greg Brockman joins benahorowitz.eth and Erik Torenberg on what the AGI era asks of us: why safety and alignment now set the pace, what happens to work, and why AI sentiment is lowest in the country building it. 00:00 Intro 00:52 15 years, or 10 if you spend enough 02:24 Pacing the frontier 03:55 Safety ideas from before the models 08:42 Lessons from Hugging Face 10:20 The defender's window 12:46 10,000 agents on Navier-Stokes 14:50 Formally verifying all software 17:10 Cancelling his holiday for GPT-3 18:42 Codex found 13 holes in 15 minutes 20:41 Why Astra earned the GPT-6 title 24:25 Employment keeps going up 29:39 America has the lowest AI sentiment 31:10 The benefits don't make the news 33:26 Banning data centers exports them 35:50 $1 billion for frontline defenders 38:15 Astra cleared the bar for AGI 40:18 1.5 billion people churned ChatGPT 43:25 Killing Sora 47:45 The AGI era YouTube:
a16z914,557 views • 1 month ago
Former Intel CEO Pat Gelsinger on getting hired at 18, designing Intel's chips while finishing his PhD, and arguing with his Stanford professors: "I'm 16 years old. I accidentally win a scholarship. I go to tech school, skip my last year and a half of high school, and Intel comes recruiting when I'm 18 years old." "The interviewer, he writes on his page, and I was number 12 that he interviewed that day... He says, 'Smart, aggressive, arrogant. He'll fit right in.'" "I was engineer number four on the 386. Architect and design manager for the 486. All of that while doing my bachelor's, my master's and PhD work... Learning by day, and putting it to practice at night." "One of the professors postulated a new carry lookahead adder design in one of my classes at Stanford. So I said, 'Well, I'm working on the 386 carry lookahead adder, so let me go try it.'" "I come back two days later to the professor and I said, 'It doesn't work.' The next two class periods were just me and the professor arguing over why his design didn't work." Pat Gelsinger Raghu Raghuram Guido Appenzeller
a16z204,431 views • 9 days ago
Garry Tan says turning down Palantir for a Microsoft promotion was a $2–4 billion mistake: "Joe Lonsdale and Stephen Cohen were my fraternity brothers at Stanford... they were interns at Peter Thiel's hedge fund." "They said, 'Let's go get Garry. He's up in Seattle.' They flew me down to have dinner with him, and Peter said, 'I'm so sure this is the right thing for you. Here's a check for 70 grand.' That was how much I made at Microsoft at the time." "I said, 'Thank you very much, Mr. Thiel, but I might get promoted to level 60 this year.' Which I did. But that was a $2 to $4 billion mistake at this point." Garry Tan Anish Acharya
a16z1,801,628 views • 2 months ago