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." @sama w/ @CraigCannon (Nov 2018)

Patrick Collison's advice to people in their 20s: find where the highest standards are, and go experience them firsthand. "When I talk to people in other domains, this is so frequently the thing that I hear from them." "That when they worked with X person or Y organization or in Z environment, they learned what great actually is, and that just permanently changed their sense for what their own standard for their work ought to be." "Maybe one version of what people in their twenties should do is... figure out, where can you learn the highest standards? Where are the highest standards embodied, and where can you go and experience that firsthand?" @patrickc w/ @dwarkesh_sp (Feb 2024)

ChatGPT changed the game in 2022. Median startup revenue four years after founding: 2018: $2.0M 2019: $2.3M 2020: $2.5M 2021: $2.8M 2022: $5.6M More charts:

Ben Horowitz and Erik Torenberg with Nas, Steve Stoute, and Grandmaster Caz on the Paid in Full Foundation Caz penned rhymes that became Rapper's Delight. Even with his name in them, he got nothing. The Paid in Full foundation honors the people who created hip-hop by making sure they're "paid in full," financially, spiritually, and culturally. As Nas points out, money alone reads as a handout. So the grant comes attached to the honor. Each year at the foundation's Hip Hop Grandmaster Awards, pioneers are celebrated by the artists they inspired. Caz ultimately received a five-year grant, bought a house, and left the projects. In his words: "The thing is the honor. The thing is putting you in the light that you deserve to be in." Ben says he bought Paid in Full (the album) for $10 and got at least $5 million worth of value out of the record, with no way to pay it back. The vision is paying the debt. Felicia Horowitz runs the foundation. She and Ben are matching donations 2.5 to 1, and every dollar goes to the artists. 0:00 Intro 1:40 The $10 album that was worth $5M to Ben 3:10 Nas: in hip-hop, you can't just give money 5:05 Why artists thought it was a scam 9:30 Caz wrote Rapper's Delight and got nothing 11:40 Roxanne Shanté: "I wanted the right award" 16:35 Dr. Dre asks to meet Kool Moe Dee and Slick Rick 18:50 Why the industry never did this 26:05 Ben: start with what's right, not what's possible 29:10 Inside the room: Grand Puba, George Clinton 32:15 The Quincy Jones Award 37:00 Scarface and Rakim talk writing for the first time 38:45 Caz: the grant got me out of the projects 43:45 What hip-hop did for Adidas, Hilfiger and Sprite 48:00 How Felicia saved Scarface's life 52:10 Quincy Jones stories, and Nas's jazz legend dad @Nas @SteveStoute @GrandmasterCaz @bhorowitz @eriktorenberg

Databricks' @alighodsi on AI risk and adoption: Ali isn't losing sleep over the existential risk debate. He says a number of conditions would all have to be true simultaneously to enable an actual runaway takeoff scenario, and currently several opposite conditions exist. Each frontier training run requires significantly more resources. Power, GPUs, engineers - and some attempts fail, burning up huge piles of money with them. Until that reverses, he doesn't see the self-improving loop happening. Cyber is what he's watching most closely and where he anticipates real impact. Most orgs are not equipped for the coming change in agentic capabilities. The time between a vulnerability being published and being weaponized has collapsed from years to hours. On adoption, he believes most companies don't need a smarter model. The models are already smart enough. The gap is context they don't absorb - the things an employee who's worked at a company for five years learned by osmosis. If the frontier stopped advancing today, he thinks it wouldn't meaningfully change the value most are extracting from AI anyway. In conversation with a16z's Martin Casado and Sarah Wang: 00:00 Intro 00:48 Why Ali places the AI risk near zero 05:05 The word "pacing" was a mistake 10:50 What 10k agents and $100m can do 12:20 What would change his mind on AI risk 14:20 More GPUs, more ways to fail 18:05 US export controls on PlayStations 20:10 The damage everyone expected by now 24:30 Public vulnerabilities weaponized in hours 30:15 Why labs can't grade each other 37:15 Why most of RSI isn't actually RSI 40:05 Why nobody really needs a smarter model 41:50 The AI use cases nobody argues about 47:30 Google Search solved this 25 years ago 50:55 Nobody has privileged knowledge now 55:10 Same model, new harness, 2x cost 58:15 Open source: 5% of spend, 60% of tokens 1:05:30 90% of new databases are created by agents YouTube: @databricks @martin_casado @sarahdingwang

Unicorns are getting minted earlier: - The median new unicorn is just over 4 years old, a 37% decline in age since 2023 - The median unicorn overall is 15 years old Charts of the Week:

Almost nobody pays for AI out of pocket yet Only ~3% of US consumers do, up from under 1% in 2023 The youngest buyers are adopting at 4x the rate of the oldest Charts of the Week:

.@fal's Gorkem Yurtseven and Batuhan Taskaya on making an open source video model 35x faster, and what Hollywood wanted after they built it: Last month, MiniMax released H3, an open source video model. fal rebuilt it - they cut down the steps the model takes to make a video, rewrote the code under each stage, and got the GPUs to 70-80% of their theoretical ceiling instead of their usual 30-40%. No quality loss. Video now generates faster than you can film it. The models have gotten so cheap and fast that end users aren't even asking for improvements in either category anymore. The gap has moved to quality, or how closely the model follows the prompt. Hollywood wasn't a customer a year ago and is now fal's fastest growing segment. Studios love it for the little things - extending a shot, moving the camera, changing the lighting. Those edits land 80-90% of the time. fal is chasing 99.9%. In this conversation with a16z's Jennifer Li: 00:00 Intro 01:45 The first open model worth rebuilding 03:45 The industry ran out of compute in April 06:25 35x faster without losing quality 11:05 5s of video generated in 1.5s 14:25 The weekend fal dropped everything 16:15 3 viral projects nobody planned 18:20 Teaching a video model to remember 20:25 An hour of video that's consistent 23:30 2x the usage of other models in 3 weeks 26:35 The case for giving video away for free 29:00 Blender sketch in, finished shot out 30:40 Chasing 99.9% reliability 34:15 Hollywood, fal's fastest-growing customer 36:50 Why studios wouldn't touch it until now YouTube: @gorkem @isidentical @fal @JenniferHli