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Chris Barber

@chrisbarber • 11,975 subscribers

Startups, AGI prep, emotions, walkability. Interviewed: @tylercowen, @jeremyphoward, +more. I read DMs. Highlights: https://t.co/EgqEFGu40p

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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

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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

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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

243,485 görüntüleme • 24 gün önce

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?

127,901 görüntüleme • 26 gün önce

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