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In The Loop

Jack Houghton

Stay in the loop with the biggest stories in AI—without the noise and nonsense.

Each week, Jack Houghton (CPO at Mindset AI) unpacks the latest news, research, and product trends shaping the future of artificial intelligence.

From OpenAI breakthroughs to unicorn startups, In The Loop delivers sharp, less than 20-minute episodes packed with insights for product leaders, engineers, and AI-curious innovators.

Subscribe to get smarter about AI, every week. Don't forget to rate and share the show with other AI enthusiasts.

Check out Mindset AI: https://bit.ly/40lJr6B

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  • 26 episodes
  • weekly
  • Avg 18 min
  • English

Episodes 26

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  • April 2 · 13 min

    Why OpenClaw is the most important software invention since ChatGPT

    Jensen Huang stood in front of a Morgan Stanley audience and called OpenClaw "probably the single most important release of software, probably ever" — arguably more important than the web browser, Linux, and the iPhone OS. It went from a side project by one Austrian developer to the stated foundation of Nvidia's entire enterprise agent strategy in a matter of weeks. That claim is self-serving. It might also be right. In this episode of In The Loop, I'm explaining what OpenClaw actually is under the hood, why it spread twenty times faster than ChatGPT, and what Jensen's real motivation is behind the praise. The answer has as much to do with software architecture as with a trillion-dollar token thesis. ⏭️ Episode highlights (01:05) – What is OpenClaw & what innovations did it make? (02:30) – The no-interface, messaging-first design(04:10) – Skills, SKILL.md files, and ClawHub's 13,000 community tools 07:45) – Token economics: why agentic tasks burn 1,000x more(09:20) – Jensen's "operating system of agentic computers" claim(11:00) – How to get started with OpenClaw 🔗 Links & resources Lenny's Newsletter — OpenClaw: the complete guide to building, training, and living with your personal AI agent: https://www.lennysnewsletter.com/p/openclaw-the-complete-guide-to-building 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/ TikTok - @jackschats Mindset AI Mindset AI website - https://bit.ly/40lJr6B Newsletter - https://bit.ly/ITLnewsletter LinkedIn - https://www.linkedin.com/company/mindset-ai/ YouTube - https://www.youtube.com/@GetMindsetAI TikTok - @get.mindset.ai

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  • S1 · E53
    March 26 · 21 min

    Claude Skills: How to use them, why they are important & what they are.

    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 & resources Anthropic's blog post: "Equipping agents for the real world with Agent Skills" Claude Code skills documentation Agent skills open standard Anthropic's official skills GitHub repo Claude help centre: using skills Matt Pocock's skills repo 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/ TikTok - @jackschatsMindset AI website - https://bit.ly/40lJr6B Newsletter - https://bit.ly/ITLnewsletter Mindset AI LinkedIn - https://www.linkedin.com/company/mindset-ai/ YouTube - https://www.youtube.com/@GetMindsetAI TikTok - @get.mindset.ai

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  • S1 · E52
    March 19 · 20 min

    The reason AI is impacting only 20% of tasks

    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 & Resources Anthropic's Labor Report: 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 HoughtonLinkedIn - https://www.linkedin.com/in/jack-houghton1/TikTok - @jackschats Mindset 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

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  • March 13 · 13 min

    Here's why AI is making us work more, not less.

    A UC Berkeley study spent eight months inside a real tech company watching how people actually used AI. The finding? Workers worked more, not less. They took on broader responsibilities, blurred the line between work and rest, and filled every freed-up minute with more tasks. Nobody told them to. The tools just made stopping feel like waste. In this episode of In The Loop, I'm breaking down what the researchers actually found, why this pattern has repeated with every major labour-saving technology for the past century — from the washing machine to the spreadsheet to email — and what German sociologist Hartmut Rosa's theory of social acceleration tells us about why productivity tools never seem to produce the spare time they promise. The question AI is asking us right now isn't whether it works. It clearly does. It's whether we have the individual or collective will to decide what the time it saves is actually for. Episode highlights (01:20) – The Berkeley study: eight months, forty interviews, three patterns (03:45) – Task expansion: why even product managers started writing code (05:10) – Blurred boundaries and the frictionless prompt problem (06:30) – Why self-regulation failed — and why it felt good (08:00) – The washing machine, the spreadsheet, and a hundred years of the same story (10:15) – Hartmut Rosa and the theory of social acceleration (12:00) – Dynamic stabilisation: why the treadmill only gets faster 🤝 We're social Stay in the loop, even when you're not listening to this podcast. Jack HoughtonLinkedIn - https://www.linkedin.com/in/jack-houghton1/TikTok - @jackschats Mindset 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

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  • S2 · E51
    March 5 · 16 min

    Why the department of war banned Claude

    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 blog 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 - @jackschats Mindset 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

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  • S2 · E50
    February 26 · 10 min

    The New Super AI Skill: Management

    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 Social Stay in the loop—even when you're not listening to this podcast. Jack HoughtonLinkedIn - https://www.linkedin.com/in/jack-houghton1/TikTok - @jackschats Mindset 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

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Showing 21–26 of 26 episodes