In The Loop

Anthropic turned down hundreds of millions of dollars and said no to the Pentagon. Less than 24 hours later, OpenAI signed the deal. Both companies claim identical principles, but one drew a line in the contract and one didn't. That difference might be everything.In this episode of In The Loop, I'm breaking down the full story behind the Anthropic-Pentagon fallout: the internal memos, the red lines, the legal fine print, and why the mechanism matters more than the mission statement. Because the question isn't just "can AI be used for war?" anymore. It's "who gets to decide, and what happens to the company that says no?"This one's bigger than AI. It's about power, accountability, and a moment Dario Amodei has been preparing for since he handed every new Anthropic employee a copy of The Making of the Atomic Bomb.⏭️ Episode Highlights(01:29) – Sam Altman's internal memo and OpenAI's Pentagon deal(02:13) – How deep Anthropic was already inside the U.S. military(03:09) – The two red lines Anthropic refused to cross(04:39) – Dario Amodei's published response(07:11) – Trump's threats and the political fallout(07:48) – Why the mechanism is everything(09:51) – What a legal expert found inside the OpenAI contract(10:50) – Altman admits the deal was rushed(11:46) – The cancel ChatGPT movement and three things to watch🔗 Links & ResourcesEpisode transcript with more resources on the Mindset AI blogIf you enjoyed this episode, rate, follow, and share! It helps others stay ahead of the latest AI trends. 🚀🤝 We're SocialStay in the loop, even when you're not listening to this podcast.Jack HoughtonLinkedIn - https://www.linkedin.com/in/jack-houghton1/TikTok - @jackschatsMindset AIMindset AI website - https://bit.ly/40lJr6BNewsletter - https://bit.ly/ITLnewsletterLinkedIn - https://www.linkedin.com/company/mindset-ai/YouTube - https://www.youtube.com/@GetMindsetAITikTok - @get.mindset.ai

The job market just had its worst month since 2009. Over 108,000 layoffs were announced in January alone (a 120% increase year-on-year), and AI was directly cited in thousands of them. But the real shift isn't about who's being replaced. It's about what skills actually matter now.In this episode of In The Loop, I break down the new skill sets emerging in the AI era — taste, judgment, curiosity, agency — and why management is suddenly the most valuable capability you can develop. Plus, a simple framework for deciding when to delegate work to AI and when to do it yourself.⏭️ Episode Highlights(01:16) – Why management is the skill that matters now (and what Ethan Mollick gets right)(04:59) – Taste, judgment, curiosity, and agency — the new career differentiators(07:52) – A three-variable framework for when to delegate to AI(09:27) – The shift from execution to direction and what it means for your career🤝 We're SocialStay in the loop—even when you're not listening to this podcast.Jack HoughtonLinkedIn - https://www.linkedin.com/in/jack-houghton1/TikTok - @jackschatsMindset AiMindset AI website - https://bit.ly/40lJr6BNewsletter - https://bit.ly/ITLnewsletterLinkedIn - https://www.linkedin.com/company/mindset-ai/YouTube - https://www.youtube.com/@GetMindsetAITikTok - @get.mindset.ai

Over a trillion dollars has been wiped from software stocks. Traders are calling it the SaaS Apocalypse, and the sell-off is only accelerating. In this episode of In The Loop, I break down why markets are panicking, what happens when the cost of creation collapses to near zero, and what software actually becomes on the other side of this shift.⏭️ Episode Highlights(01:46) – What's actually happening in the market and the $830B sell-off(03:14) – The catalyst: Anthropic's Claude Cowork plugins and the legal sector collapse(05:06) – Thomson Reuters: strong earnings, plunging stock — why good numbers don't matter right now(10:27) – The Jevons Paradox and why cheaper software means more software, not less(18:22) – Broken business models, API companies, and what comes next🤝 We’re SocialStay in the loop—even when you’re not listening to this podcast.Jack HoughtonLinkedIn - https://www.linkedin.com/in/jack-houghton1/TikTok - @jackschatsMindset AiMindset AI website - https://bit.ly/40lJr6BNewsletter - https://bit.ly/ITLnewsletterLinkedIn - https://www.linkedin.com/company/mindset-ai/YouTube - https://www.youtube.com/@GetMindsetAITikTok - @get.mindset.ai

Skills are the fix for most of your problems using AI tools. And right now they're one of the most powerful and most underused features in the entire Claude ecosystem.In this episode of In The Loop, I'm going deep on Claude Skills: the context engineering principle underneath them, the exact anatomy of a skill file, how to build your first one from scratch, how skills chain together into full workflows, and how they sit alongside MCPs and plugins. Plus.....a real walkthrough of a developer who's built a four-skill chain that goes from rough idea all the way to a kanban board of implementation tasks & how this relates to every knowledge workers day-to-day task. The shift happening right now isn't just about prompting better. It's about moving from using AI as a conversation to using AI as a library of reliable, repeatable capabilities. Skills are how you get there.⏭️ Episode highlights(01:40) – The context window problem that created skills(07:20) – Building your first skill, step by step with a real example(13:55) – Skill chaining: a four-skill workflow from idea to implementation(17:20) – Where to find community skills and the difference between skills, MCPs, and plugins(19:55) – Compound interest for your AI process🔗 Links & resourcesAnthropic's blog post: "Equipping agents for the real world with Agent Skills"Claude Code skills documentationAgent skills open standardAnthropic's official skills GitHub repoClaude help centre: using skillsMatt Pocock's skills repoIf you enjoyed this episode, rate, follow, and share. It helps others stay ahead of the latest AI trends.🤝 We're socialStay in the loop, even when you're not listening to this podcast.Jack HoughtonLinkedIn - https://www.linkedin.com/in/jack-houghton1/TikTok - @jackschatsMindset AI website - https://bit.ly/40lJr6BNewsletter - https://bit.ly/ITLnewsletterMindset AILinkedIn - https://www.linkedin.com/company/mindset-ai/YouTube - https://www.youtube.com/@GetMindsetAITikTok - @get.mindset.ai

Anthropic just dropped a labor market report with a chart you need to see. It maps what AI could theoretically do across every major occupation against what it's actually doing. The gap is enormous. In computing and math, AI could theoretically handle 94% of tasks. Observed usage? 33%. Legal hits nearly 90% theoretical — real-world usage barely clears 20%.This week on In The Loop, I break down why. Some of it is a people problem: adoption looks more like a cliff than a curve, with a tiny fraction of users actually pushing AI to its limits. Some of it is structural — enterprise contracts, legacy systems, and slow procurement cycles. And some of it is the technology itself. Reliability — not capability — is the real bottleneck right now.This isn't pessimism. It's a realistic read on how long transformation actually takes.⏭️ Episode Highlights(01:15) – Anthropic's labor market report and the chart that tells the real story(03:45) – Theoretical vs. observed AI usage across occupations(07:20) – The adoption cliff: who's actually using AI at full capacity(09:45) – Enterprise slowdown, legacy systems, and integration complexity(11:55) – The supply issue(12:55) – The reliability gap(19:30) – The computer age parallel — and why patience might be the right call🔗 Links & ResourcesAnthropic's Labor Report: If you enjoyed this episode, rate, follow, and share! It helps others stay ahead of the latest AI trends. 🚀🤝 We're SocialStay in the loop—even when you're not listening to this podcast.Jack HoughtonLinkedIn - https://www.linkedin.com/in/jack-houghton1/TikTok - @jackschatsMindset AIMindset AI website - https://bit.ly/40lJr6BNewsletter - https://bit.ly/ITLnewsletterLinkedIn - https://www.linkedin.com/company/mindset-ai/YouTube - https://www.youtube.com/@GetMindsetAITikTok - @get.mindset.ai