Yesterday I interviewed @SeanZCai 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?

The Museum of the Human Web is now open at 238 King Street in San Francisco. Can’t make it? See the collection and enter the sweepstakes contest online to win an artifact from the museum: museum.parallel.ai/sweepstak…

Hello. We recently hired Jude Law to be the face of our brand. So, moving forward, we would prefer that you associate him with precise drafting, seamless collaboration and the ability to analyze thousands of legal documents simultaneously. In line with his contract, something like, “Oh, wow, he makes me think of that collaborative AI platform for exceptional lawyers!” would be an ideal ask. But we don’t want to push it. Learn more: legora.com/law-just-got-more…

Very funny that it went on for 10 or 15 minutes

New Harvard Business Review research reveals that excessive interaction with AI is causing a specific type of mental exhaustion ( or AI brain fry), which is particularly hitting high performers who use the tech to push past their normal limits. A survey of 1,500 workers reveals that AI is intensifying workloads rather than reducing them, leading to a new form of mental fog. While AI is generally supposed to lighten the load, it often forces users into constant task-switching and intense oversight that actually clutters the mind. This mental static happens because you aren't just doing your job anymore; you are managing multiple digital agents and double-checking their work, which creates a massive cognitive burden. The study found that 14% of full-time workers already feel this fog, with the highest impact seen in technical fields like software development, IT, and finance. High oversight is the biggest culprit, as supervising multiple AI outputs leads to a 12% increase in mental fatigue and a 33% jump in decision fatigue. This isn't just a personal health issue; it directly impacts companies because exhausted employees are 10% more likely to quit. For massive firms worth many B, this decision paralysis can lead to millions of dollars in lost value due to poor choices or total inaction. Essentially, we are working harder to manage our tools than we are to solve the actual problems they were meant to fix. --- hbr .org/2026/03/when-using-ai-leads-to-brain-fry

never been a better time to build a tech company with 20 year old engineers and 40 year old salespeople very spooky time to build a tech company with 40 year old engineers and 20 year old salespeople