Chris Barber (in SF)'s banner

Chris Barber (in SF)

@chrisbarber • 13,096 subscribers

Startups, data, compute. Lists & interviews. Interviewed: @tylercowen, @insane_analyst, +. I read DMs. Highlights: https://t.co/EgqEFGu40p

Shorts

interesting and hiring: Joanne Jang - gm of labs at openai ("inventing and prototyping new interfaces for how people collaborate with ai") - stanford bs mathematical & computational sciences and ms cs - interned with congressman Adam Schiff, at nasa jpl, at apple, at disney, and at coursera - ran Girls Teaching Girls To Code - pm at dropbox business - pm for google assistant - joined OpenAI in late 2021 - was head of product for model behavior. worked on dall e 2, gpt 4, tts, voice mode etc - announced she was starting oai labs in sep 2025 - "computer : gui :: ai : ?" video from 2023

interesting and hiring: Joanne Jang - gm of labs at openai ("inventing and prototyping new interfaces for how people collaborate with ai") - stanford bs mathematical & computational sciences and ms cs - interned with congressman Adam Schiff, at nasa jpl, at apple, at disney, and at coursera - ran Girls Teaching Girls To Code - pm at dropbox business - pm for google assistant - joined OpenAI in late 2021 - was head of product for model behavior. worked on dall e 2, gpt 4, tts, voice mode etc - announced she was starting oai labs in sep 2025 - "computer : gui :: ai : ?" video from 2023

87,981 просмотров

interesting background: Tibo (hiring) - eng lead for codex at OpenAI - 2010: made the final of the first Belgian olympiad of informatics and finished 22nd in the higher ed category - 2014-15: was at N-SIDE building predictive models for clinical trial supply chains - 2015: swe at google - 2017: senior swe at google - 2018-2024: deepmind (incl ml workflow lead, gemini human data lead). co-author on the gopher and reverb papers - 2024: joined openai - 2026: codex now has >1M WAUs video: Tibo with Greg Brockman

interesting background: Tibo (hiring) - eng lead for codex at OpenAI - 2010: made the final of the first Belgian olympiad of informatics and finished 22nd in the higher ed category - 2014-15: was at N-SIDE building predictive models for clinical trial supply chains - 2015: swe at google - 2017: senior swe at google - 2018-2024: deepmind (incl ml workflow lead, gemini human data lead). co-author on the gopher and reverb papers - 2024: joined openai - 2026: codex now has >1M WAUs video: Tibo with Greg Brockman

16,833 просмотров

Videos

Anya Rossi

sweetdream.ai

SweetDream.ai•Sponsored•Livecam

Watch Anya Live

Anya is streaming live right now! Join her private show and enjoy exclusive content.

Exclusive private shows

1.2k viewers online

Private Show

Join now for exclusive access

Free preview available • Premium content

I interviewed bubble boi about his ratings of AI supply chain bottlenecks. We talked about DRAM, advanced packaging, CPO, HBF, PCBs, power delivery, etc. 0:00 HBM, DRAM, the cartel 7:24 Silicon photonics, CPO, Lumentum lasers 11:35 Advanced packaging: TSMC vs Intel 13:49 HBF: Sandisk/SK monopoly window 17:02 Memory accelerators + TurboQuant 22:35 PCBs + Unitika 27:27 Power: transition from 48V to 800V 31:32 Outsourced assembly + test, fiber coupling 36:23 HBM vs DRAM vs NAND in 2-3 years 42:03 Where will hardware founders come from? 44:10 Alt accelerators: Etched, Taalas, MatX 48:43 Emerging tech: CPO bearishness 49:23 Hyperclouds 49:53 HBF timeline + CXL 51:48 Voltage + cooling wall 56:30 Rapid fire: Intel, Nvidia, TSMC, Alphabet 1:05:25 ASML, Hynix, Lumentum, Wolfspeed 1:13:25 Building conviction in Intel 1:19:37 Pitching Intel to funds 1:22:39 X accounts, analysts + why care about any of this

I interviewed bubble boi about his ratings of AI supply chain bottlenecks. We talked about DRAM, advanced packaging, CPO, HBF, PCBs, power delivery, etc. 0:00 HBM, DRAM, the cartel 7:24 Silicon photonics, CPO, Lumentum lasers 11:35 Advanced packaging: TSMC vs Intel 13:49 HBF: Sandisk/SK monopoly window 17:02 Memory accelerators + TurboQuant 22:35 PCBs + Unitika 27:27 Power: transition from 48V to 800V 31:32 Outsourced assembly + test, fiber coupling 36:23 HBM vs DRAM vs NAND in 2-3 years 42:03 Where will hardware founders come from? 44:10 Alt accelerators: Etched, Taalas, MatX 48:43 Emerging tech: CPO bearishness 49:23 Hyperclouds 49:53 HBF timeline + CXL 51:48 Voltage + cooling wall 56:30 Rapid fire: Intel, Nvidia, TSMC, Alphabet 1:05:25 ASML, Hynix, Lumentum, Wolfspeed 1:13:25 Building conviction in Intel 1:19:37 Pitching Intel to funds 1:22:39 X accounts, analysts + why care about any of this

Chris Barber (in SF)

322,603 просмотров • 2 месяцев назад

Yesterday I interviewed Sean Cai about AI data. This is essentially a guide for founders on how to sell data and RL envs to AI labs. "I've never seen a data contract get turned down by a top lab, if it's good quality data, for budget reasons." 00:00 What areas of data are underserved? 02:10 For bio data, is it real-world or purely digital? 04:21 For cyber data, which subsets are most underserved? 05:50 What is the sales process like? 07:04 Why would a lab not renew or increase their purchase volume? 10:13 When a researcher is exploring a new direction, what's the first step? 11:35 In robotics data, what do you view as underserved? 13:12 What does the initial data delivery look like, what format? 13:53 Do labs have more sophisticated internal setups for running environments? 14:32 Are the non-frontier labs buying off-the-shelf data from Anthropic / OpenAI vendors? 16:11 Do Anthropic data vendors put expiry timeframes on the exclusivity? 16:42 Are purchase decisions researcher-led? 17:41 Decagon, Sierra, Ramp: what kinds of data are they buying? 19:06 Long-term, when do labs still need to buy external data vs train on user traces? 21:15 Will end-vendor benchmarks shift to performance per dollar? 22:04 How many labs are spending at the 1B+/yr data level? 23:53 Delta between Anthropic's stated $1B and your 10-20B/lab number? 26:05 What makes inference providers / neoclouds a good fit to acquire RL env cos?

Yesterday I interviewed Sean Cai about AI data. This is essentially a guide for founders on how to sell data and RL envs to AI labs. "I've never seen a data contract get turned down by a top lab, if it's good quality data, for budget reasons." 00:00 What areas of data are underserved? 02:10 For bio data, is it real-world or purely digital? 04:21 For cyber data, which subsets are most underserved? 05:50 What is the sales process like? 07:04 Why would a lab not renew or increase their purchase volume? 10:13 When a researcher is exploring a new direction, what's the first step? 11:35 In robotics data, what do you view as underserved? 13:12 What does the initial data delivery look like, what format? 13:53 Do labs have more sophisticated internal setups for running environments? 14:32 Are the non-frontier labs buying off-the-shelf data from Anthropic / OpenAI vendors? 16:11 Do Anthropic data vendors put expiry timeframes on the exclusivity? 16:42 Are purchase decisions researcher-led? 17:41 Decagon, Sierra, Ramp: what kinds of data are they buying? 19:06 Long-term, when do labs still need to buy external data vs train on user traces? 21:15 Will end-vendor benchmarks shift to performance per dollar? 22:04 How many labs are spending at the 1B+/yr data level? 23:53 Delta between Anthropic's stated $1B and your 10-20B/lab number? 26:05 What makes inference providers / neoclouds a good fit to acquire RL env cos?

Chris Barber (in SF)

130,324 просмотров • 2 месяцев назад

Больше нет контента для загрузки