In The Loop

The reasons AI data centers have become more hated than nuclear power plants

Americans now say they'd rather live next to a nuclear reactor than an AI data center. That's not a fringe view — a Gallup poll published this month found 71% of Americans oppose a data center near their home, versus 53% for nuclear. Nuclear carries Chernobyl, Three Mile Island, and forty years of films about radiation in the cultural zeitgeist. The fact that AI data centers have only been a visible part of suburban America for less than five years yet are this hated, is huge. In this episode of In The Loop, I'm looking at the organised opposition movement that has already blocked over $85 billion in planned data center investment — cancelling projects faster in three years than nuclear opposition managed in fifteen. I go through the legal strategy that's winning in courts and at ballot boxes, what the communities are right about, where they're factually wrong, and why the responsible data center model that could resolve this already exists — but nobody's requiring it. ⏭️ Episode highlights (01:05) – Missouri council wiped out 8 days after data center vote (02:30) – The Gallup poll: nuclear vs AI data centers (03:45) – $3 trillion buildout and the AI electricity consumption numbers (05:15) – The legal template that stopped nuclear — working again in Virginia (06:50) – What the opposition gets factually wrong on data center water usage (08:10) – Who actually pays — and which communities bear the cost (09:35) – The responsible data center model that already exists

Why the companies cutting junior headcount are making a decade-long mistake

In 1971, Boeing laid off 44,000 engineers in 18 months. The aerospace industry is still paying for it — they're projecting a shortage of over a million engineers by 2030, and the cohort that would now be the senior bench was simply never hired. Last year, S&P 500 companies cut 400,000 jobs — the first net decline since 2016 — and the specific pattern of who's being cut, and why, looks uncomfortably familiar. At firms that adopted AI, junior employment fell 7 to 10% within six quarters. Senior employment kept rising. The pipeline isn't slowing. It's stopping. In this episode of In The Loop, I'm working through the data on junior employment at AI-adopting firms, the economic logic that makes cutting entry-level roles feel rational, and why I think that logic is setting up a shortage that will look obvious in hindsight. I also take on Tim O'Reilly's counter-argument — his historical case that every programming wave expanded demand rather than destroyed it — and explain why I think he's right about 2035 and wrong about the cohort that's supposed to get there. ⏭️ Episode highlights (01:15) – The Boeing billboard and what it cost (03:00) – 400,000 jobs: where the cuts are concentrated (05:00) – Why junior work is separable — and senior work isn't(07:00) – The radiology lesson: why strong bundles hold (08:45) – O'Reilly's wave argument and the Jevons paradox (11:30) – Where the optimistic case runs out of road (13:00) – The 43-point gap: atrophy you can't feel (14:45) – IBM, Publicis, and who's betting on the pipeline 🔗 Links & resources * Boeing Bust (1969–1971) — HistoryLink.org: https://www.historylink.org/file/20923 [https://www.historylink.org/file/20923] * ISG — Aerospace and defense talent gap by 2030: https://isg-one.com/articles/why-the-aerospace-and-defense-industry-faces-a-million-person-talent-gap-by-2030---and-what-it-means-for-innovation [https://isg-one.com/articles/why-the-aerospace-and-defense-industry-faces-a-million-person-talent-gap-by-2030---and-what-it-means-for-innovation] * Hosseini & Lichtinger, "Generative AI as seniority-biased technological change" (SSRN, Aug 2025): https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5425555 [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5425555] * Brynjolfsson, Chandar & Chen, "Canaries in the coal mine?" (Stanford Digital Economy Lab, Aug 2025): https://digitaleconomy.stanford.edu/publication/canaries-in-the-coal-mine-six-facts-about-the-recent-employment-effects-of-artificial-intelligence/ [https://digitaleconomy.stanford.edu/publication/canaries-in-the-coal-mine-six-facts-about-the-recent-employment-effects-of-artificial-intelligence/] * Tim O'Reilly, "The end of programming as we know it": https://www.oreilly.com/radar/the-end-of-programming-as-we-know-it/ [https://www.oreilly.com/radar/the-end-of-programming-as-we-know-it/] * Tim O'Reilly & Aaron Levie, "The world needs more software engineers" (Apr 2026): https://www.oreilly.com/radar/the-world-needs-more-software-engineers/ [https://www.oreilly.com/radar/the-world-needs-more-software-engineers/] * Tim O'Reilly, "The missing half of the AI economy": https://www.oreilly.com/radar/ai-and-the-next-economy/ [https://www.oreilly.com/radar/ai-and-the-next-economy/] If you enjoyed this episode, rate, follow, and share. It helps others stay ahead of the latest AI trends. 🤝 We're social Stay in the loop, even when you're not listening to this podcast. Jack Houghton * LinkedIn - https://www.linkedin.com/in/jack-houghton1/ [https://www.linkedin.com/in/jack-houghton1/] * TikTok - @jackschats Mindset AI * Mindset AI website - https://bit.ly/40lJr6B [https://bit.ly/40lJr6B] * Newsletter - https://bit.ly/ITLnewsletter [https://bit.ly/ITLnewsletter] * LinkedIn - https://www.linkedin.com/company/mindset-ai/ [https://www.linkedin.com/company/mindset-ai/] * YouTube - https://www.youtube.com/@GetMindsetAI [https://www.youtube.com/@GetMindsetAI] * TikTok - @get.mindset.ai

How to build an AI-native team & company: The six-principle playbook from Y-Combinator

Y Combinator have just published a playbook for building a company that runs on AI rather than just using it. Six principles, written by a general partner who works with hundreds of the very best leading AI-native startups every year. The reason its gone viral so fast is that it named something a lot of people had been watching happen without quite having the language for. In this episode of In The Loop, I'm walking through Diana Hu's framework for building a truly AI-native company. I cover all principles and how you can do the same in your team or company — from running AI as your operating system to building your intelligence layer so you can be 10 steps ahead of everyone else Episode highlights (01:00) – YC's Diana Hu publishes the playbook (03:00) – Dorsey cuts 40% at Block — and why (05:30) – Principle 1: AI as your operating system (08:00) – Principle 2: closing open loops (10:30) – Principle 3: making your company queryable (13:00) – Principle 4: the thousand-x engineer (15:30) – Principles 5 & 6: token-max and software factories (19:00) – Principles 7 & 8: intelligence layers and honest signals (22:00) – Four moves to start this week Links & resources * Diana Hu, "The playbook for building an AI native company," YC Startup School, April 2026 —https://www.ycombinator.com/library/OX-the-playbook-for-building-an-ai-native-company [https://www.ycombinator.com/library/OX-the-playbook-for-building-an-ai-native-company] If you enjoyed this episode, rate, follow, and share. It helps others stay ahead of the latest AI trends. We're social. Stay in the loop, even when you're not listening to this podcast. Jack Houghton * LinkedIn -https://www.linkedin.com/in/jack-houghton1/ [https://www.linkedin.com/in/jack-houghton1/] * TikTok - @jackschats Mindset AI * website -https://bit.ly/40lJr6B [https://bit.ly/40lJr6B] * Newsletter -https://bit.ly/ITLnewsletter [https://bit.ly/ITLnewsletter] * LinkedIn -https://www.linkedin.com/company/mindset-ai/ [https://www.linkedin.com/company/mindset-ai/] * YouTube -https://www.youtube.com/@GetMindsetAI [https://www.youtube.com/@GetMindsetAI] * TikTok - @get.mindset.ai [http://mindset.ai]

How to build your own AI personal operating system & second brain

The gap between casual Claude users and people getting ten times more out of it isn't prompt craft. It's a folder. This is the basis of a personal AI operating system. Andrej Karpathy posted his "LLM Knowledge Wiki" in early April and kicked off a wave of people rebuilding their note systems — not for themselves, but for the agent. This episode is the architecture they all converge on, the master file template, and the one prompt that makes the whole thing compound. In this episode of In The Loop, I'm walking through the four jobs every personal AI operating system has to do — identity, context, skills, memory — and showing exactly how to lay them out as plain text files an agent can read. I'll cover the six sections that go into your master file, the two-hundred-line cap nobody talks about, and the session log loop that makes every day one regression test better than the last. ⏭️ Episode highlights (00:45) – Why the second brain isn't for you (02:30) – Where the wave came from: Karpathy's LLM Wiki (04:15) – Identity, context, skills, memory: four jobs, one folder (06:20) – The six sections of the master file (08:40) – The two-hundred-line cap hidden in the code (10:15) – Skills folder and the slash-command workflow (12:30) – The session log loop and Boris Cherny's mundane advice (14:00) – What to do this week, full version and lightweight Episode transcript with more resources on the Mindset AI blog If you enjoyed this episode, rate, follow, and share. It helps others stay ahead of the latest AI trends. 🤝 We're social Stay in the loop, even when you're not listening to this podcast. * Jack Houghton LinkedIn - https://www.linkedin.com/in/jack-houghton1/ [https://www.linkedin.com/in/jack-houghton1/] * TikTok - @jackschats * Mindset AI * Mindset AI website - https://bit.ly/40lJr6B [https://bit.ly/40lJr6B] * Newsletter - https://bit.ly/ITLnewsletter [https://bit.ly/ITLnewsletter] * LinkedIn - https://www.linkedin.com/company/mindset-ai/ [https://www.linkedin.com/company/mindset-ai/] * YouTube - https://www.youtube.com/@GetMindsetAI [https://www.youtube.com/@GetMindsetAI] * TikTok - @get.mindset.ai

What the big new AI trend Tokkenmaxxing is & why its a big problem

Most AI spending right now is measured in tokens consumed. Jellyfish tracked 12,000 developers across 200 companies and found the heaviest users produced twice the output at 600 times the cost. Uber's internal numbers are even worse: 70% of submitted code was AI-generated, but only 11% of the code running in production was AI-written. So almost all of that AI code never made it into their app. There's a name for what's going on: tokenmaxxing. This episode goes past the leaderboard stories. The four forces driving token bills up faster than productivity can justify are a pricing model most teams don't fully understand, a workplace culture that turned consumption into a status signal, a quality gap that doesn't show up on dashboards, and something called the orientation tax, which is probably the biggest driver nobody has named yet. The second half covers what the companies getting real ROI from AI are doing differently, including why Salesforce built a new metric called Agentic Work Units to replace token counts, and what the right unit of measurement looks like for engineering, sales, legal, support, and marketing teams. ⏭️ Episode highlights (01:00) – Uber's CTO: the budget was gone by April (03:30) – Where "tokenmaxxing" actually comes from (06:00) – Meta's Claudeonomics leaderboard: 60 trillion tokens in 30 days (08:30) – Jellyfish data: twice the output, 600 times the cost (11:00) – Goodhart's Law and the Soviet chandelier factory (13:30) – The orientation tax: why agents burn tokens before doing anything useful (17:00) – Salesforce's Agentic Work Units and why they matter (19:30) – How to define your own unit of work that actually held