The singularity has already begun for math: Scott Aaronson is one of the world's leading quantum computing researchers and spent two years at OpenAI working on AI safety. We talked about why he thinks the singularity has already begun for math, who gets credit when AI writes the proof, and whether intelligence will still matter once machines do the thinking. In this episode: • Why the singularity has already begun for math • Who gets credit when AI writes the proof • What a proof that P = NP would be worth • Why he turned down getting rich to keep blogging • Whether intelligence will still matter Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 Intro 00:34 Why he updated so hard on AI 01:14 The people still denying AI progress 03:53 The singularity has already begun for math 08:30 Will human-driven math research survive 13:12 Who gets credit for AI-written papers 15:55 OpenAI's $15M Navier-Stokes proof 19:20 What if AI proved P = NP 23:07 Why AI is so good at finding counterexamples 26:13 The goalposts keep moving 28:52 Why AI disclosure matters in research 32:59 The fake Monet experiment 35:23 Why he blogs instead of getting rich 39:45 Are AI labs racing too fast 42:01 Will intelligence still matter 47:12 Book recommendations 52:27 Advice for students today 57:23 Reacting live to a new AI result 59:58 Outro

Ethan Evans (ex-Amazon VP, @EthanEvansVP) on getting caught empire building by Jeff Bezos: Me: Because you had so much success as a VP, did you do empire building? Ethan: "The human mind is great at rationalizing. I think in hindsight, I for sure did some empire building. But at the time, you probably don't tell yourself you're empire building. What you tell yourself is, with all these people, I could do so much more, right? You frame it as, man, I have all these great ideas. We've got a big backlog. If only I had 375 more people, imagine what we could do. And it's not your budget. So why not ask for it? So I got totally burned on this. When we were growing Amazon Appstore, we had launched the store and we had our annual meeting with Bezos. We took him this huge ask for all these people. I think we had nobody in marketing. We had no dedicated marketers because we had started this lean team. It was something absurd like we need 25 marketing people. And, this is not exactly right, but it's like we need 5 PR people. We had this idea: we're operating in 5 countries, we need 1 PR person per country. Well, Jeff tore into us. He asked a couple questions like, what are these people going to do? What are they actually going to do? And our answers fell apart. He asked one question, we gave a crappy answer, and you could tell he's like, hmm. A second later he asked another question, and we gave another crappy answer. And he's like, 'Okay, I don't believe any of your numbers from here on. I've asked you two questions. They were both nonsense. Go back and bring me a sheet where the maximum size of any bucket of anything you're asking for is two or three people, and it says exactly what they're going to do. And I will go through that line by line and tell you what you can have.' And that's what he did. He just literally, with a red pen, with all of his direct leaders watching: no, 3, none, 2, okay... and just took us over the coals. He wasn't wrong. He was actually right. I was relatively newer. One of my managers was newer. We had gotten big eyes because we'd launched the project and thought, well, let's see what we can do with this. It's a good project. Let's see what they want to do. And what we found out was not very much. They wanted us to be lean."

Neetcode (@neetcode1) is the most well-known coding interview prep creator with over 1M+ subs on YouTube. I interviewed him about the latest on coding interviews, his career at Amazon vs Google, and what it was like quitting big tech to do his own thing. In this episode: • Quitting Amazon after two months • Memorizing solutions versus deriving them • How he started Neetcode • What he disliked most about Google Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 Intro 00:33 Quitting Amazon after 2 months 06:00 Testing Neetcode on Leetcode 12:57 Memorizing vs deriving solutions and intelligence 16:13 The story behind Neetcode 22:24 Getting a job at Google 27:47 How it was quitting big tech 29:38 What he hated most about Google 30:52 Top technical book recommendation 32:34 Advice for his younger self 33:46 Outro

Trying to remove myself as a "meat proxy", recent fix made a huge difference I noticed a pattern of me copying errors into Claude Code which it'd fix in one shot. Why not just have Claude listen directly for the errors if my only input was copy pasting text into Claude Now I removed myself by having a headless instance that monitors system health + errors If it can confidently fix the issue it does. If not, it emails me (rare) It's basically a first-responder oncall. So much less noise and lower latency for simple fixes Maybe obvious but thought it'd be helpful to share since I'm noticing more and more of these "meat proxy" optimizations as the models get more capable

The latest models (e.g. Opus 5.5, GPT 6) have made a ton of my glue work obsolete When I started my podcast, I used models to write GUIs for repetitive tasks like transcription, publishing, and basic video editing Lately I realized I was just a "meat proxy" The model could run the tools I had built by itself to carry out end-to-end workflows, so it didn't need me or any GUIs. So now I just build interfaces that let the model use the tools directly Things are changing so quickly. One day, my podcast backend will reduce to a well-structured repo of data and desired outcomes Outcomes documented in a mix of software and English

I don't think you'll find another conversation like this on the internet I asked Ethan Evans (ex-Amazon VP) questions about common situations in corporate politics but they get increasingly darker In this episode: • How he got burned by Bezos for empire building • How to be recognized in collaborations • How to reject work that isn't impactful • Unusual firing stories with HR (e.g. human poop) • How to handle bad managers and political peers • Top book recommendation for power dynamics And much more, this episode was my longest conversation so far Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 Intro 00:42 How to receive credit for your work 19:18 Ways for quiet people to get visibility 21:32 How to be trusted for top projects 29:52 How to defend from others taking your project 36:13 How to reject bad projects 44:10 How to be first for promo 55:35 How to handle managers that dislike career growth talk 01:06:30 What if your manager is in the way 01:15:43 Did he ever have overly political reports 01:28:06 How to fire a bad manager 01:35:16 What gives you leverage 01:43:41 How to defend against peers undermining you 01:50:24 What if everyone is good at politics 01:55:11 How Jeff Bezos burned him for empire building 02:03:27 How to retain your headcount 02:07:31 Career recommendations for the current landscape 02:15:11 How to avoid negative layoff optics 02:18:39 How to develop executive presence 02:29:25 How to network in a natural way 02:33:30 How to network with powerful people 02:41:01 How to learn hidden incentives 02:44:43 What happens behind the scenes with HR 02:50:13 Did he burn people to get ahead 02:53:24 What he says to people who hate politics 02:59:03 Top book recommendations on power dynamics 03:00:45 How he would speedrun career growth today 03:06:56 Outro

His biggest career regret is only going to become more common for software engineers now. “I wasn’t as inquisitive as I could have been about what the actual underlying particularly technical cause was.” “You need to understand how things work.” “You’re inquisitive about why it actually worked. And I think that this is just a much more sustainable foundation.” @hievanking

Philip Su grew quickly to a Distinguished Engineer at Facebook and OpenAI. We talked about the psychology behind peaking in a tech career and the pressure at the highest levels. In this episode: • Not wanting IC9 and requesting demotion • How motivation changed across his career • Fulfillment and motivation after FIRE • Why sustaining IC9 was difficult Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsors of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at • Jira by Atlassian: Get more work done with your favorite agents and models all in one place, check them out at Chapters: 00:00 Intro 00:37 Growth past IC9 05:39 Motivation changes over his career 11:36 Asking to not be promoted 17:33 What made it hard to sustain IC9 performance 22:22 What motivated him outside of promos 26:21 Is career advice useless 29:34 Motivation after you've hit FIRE 36:08 Meaning and fulfillment 38:17 Top book recommendation 40:14 What he would change 43:24 Outro

$200 max Claude and $200 pro Codex subscriptions both run out around halfway through the week for me The recent computer use improvements have drastically improved the surface area of useful work they can do Considering getting a 3rd subscription...