Explore every episode of the podcast In The Loop
| Title | Pub. Date | Duration | |
|---|---|---|---|
| What Jev, the new type of AI model actually is, how it works and how you can test it | 02 oct. 2026 | 00:16:38 | |
Jev is a new kind of AI model that can't write anything. Instead it does the most important but most expensive thing the very best models do: make's decisions It reads an email, a lead or a document, picks from answers you've written down, and does it in under a second for a fraction of the cost than your Claude & GPT subscription. Nine days after it launched, the startup behind it, TypeSafe AI, was reported to be in talks to raise over a billion dollars, and OpenAI and Amazon and are just about to release their own versions. In this episode of In The Loop, I'm walking through what the Jev model is, how it works, and the six ways people are already using it. I also cover how to try it yourself in TypeSafe's playground and what a model this cheap does to the companies that charge by the token. The open question is how much of it lasts now the big labs have copied it. ⏭️ Episode highlights (01:05) – What Jev is and how it works (05:00) – Seven hundred adverts broken down for nine cents (07:35) – Sorting email and leads as they arrive (09:00) – Checking every paragraph against your style guide (11:25) – Setting it up in TypeSafe's playground (14:05) – What this means for the big AI labs 🔗 Links & resources
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. | |||
| Why ALL major AI labs may slowing down AI development & whether it could work | 18 sept. 2026 | 00:14:17 | |
Anthropic's Dario Amodei published an essay saying the whole industry has to slow down, and Musk, Altman and Hassabis all backed it inside a day. In this episode of In The Loop, I'm going through what Amodei is actually proposing and whether it could work ⏭️ Episode highlights (01:00) – OpenAI paused, then said nothing for seven weeks (02:40) – The safety test that caused the break-out (04:30) – Jacob Coxon resigns and gives up his equity (06:20) – Amodei's two reasons for slowing down (08:30) – The proposal, in plain language (10:30) – The speed limit nobody can measure (13:00) – The case against: liability, rivals and an IPO (16:00) – The suppliers nobody has named 🔗 Links & resources
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| How teams can use AI to build a churn prediction (or any prediction) model with 98% accuracy in 2 weeks | 10 sept. 2026 | 00:47:43 | |
Within his first two weeks as COO at Turtl, Dave Martin and his team built a churn prediction model. Tested against historic customer data, it predicted churn with 98% accuracy. Work like that normally takes six months or more, plus an agency and external partners. Most companies still make that call on gut feel, or by putting some basic data into Claude or ChatGPT. In this episode of In The Loop, I'm joined by Dave to walk through exactly how he and his team did it, using a method he calls pattern of life analysis. We cover how they paired customers who renewed with similar ones who left, got 12,500 minutes of call recordings through Claude without blowing the context window, the landmines you'll hit if you try this yourself, and what it takes to replace the loudest voice in the room with evidence. ⏭️ Episode highlights (08:15) – Why a scale-up can't wait six months (09:40) – Testing the model: 98% on historic data (11:10) – Rebuilding each customer journey, call by call (24:25) – Why Claude's first answer was "absolute garbage" (42:05) – The "rudimentary" test that proved three ideas (45:55) – Why loud opinions make terrible decisions 🔗 Links & resources
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| 6 things that must be true for Anthropic's IPO of $2 trillion to be achievable (as the largest IPO ever) | 03 sept. 2026 | 00:11:05 | |
Anthropic went from $9bn of annualised revenue in December to $65bn by the end of July, and it's expected to list in October at close to $2 trillion - the highest price any company has ever carried into a stock market debut. If you work backwards from that valuation, it has to be earning about $1.2 trillion a year within a decade. The entire world spends $1.5 trillion on software. So the Anthropic IPO isn't a bet on software at all. It's a bet on wages. In this episode of In The Loop, I'm exploring the six things that all have to be true for a $2 trillion Anthropic valuation to make sense - using their IPO as a way of understanding the strategies of the biggest AI companies in the world. ⏭️ Episode highlights (01:00) – Why nobody in AI talks about software any more (02:00) – $9bn to $65bn in seven months, and the caveat (03:10) – Anthropic's own written date for powerful AI (04:20) – One in five firms, 78% of the workforce (05:20) – The 60 pence in every pound that goes back out (06:20) – Memory costs more than the processor (07:30) – Three of the four hyperscalers are burning cash (08:40) – The one line to find in the prospectus | |||
| 4 amazing AI tactics that most people have never heard about | 28 août 2026 | 00:11:14 | |
In this episode of In The Loop, I'm going through four tactics engineers any many others use every day that almost nobody outside engineering has heard of: Fan out, adversarial review, ChatGPT's computer history, and the browser Claude got of its own this month. ⏭️ Episode highlights (01:00) – Why engineers are years ahead of everyone else (01:45) – Fan out: several Claudes, one job (02:45) – Why long chats get worse the further down your list they go (04:20) – Adversarial review: the blank chat that has no stake in your work (05:40) – The four sentences that do all the work (06:35) – ChatGPT watching your screen (08:20) – Claude's own browser | |||
| How StackOne built an auto-research loop to continually improve their product | 21 août 2026 | 00:57:50 | |
Once a week, an agent at StackOne goes hunting for new prompt injection attacks. It reads papers, trawls Reddit, tries what it finds against real models, keeps the attacks that land, and retrains StackOne's defence model on them. A person still approves every deployment. Guillaume Lebedel, their CTO, puts the whole thing on screen, including the five experiments out of six that failed. In this episode of In The Loop, Guillaume talks about how and why they have built an auto-research loop. What one is in plain words, how you pick a goal ai agents can measure, how you sample 100,000 test cases down to something you can afford, and why he runs evals on cheap models before trusting anything. We also spoke about token leaderboards and the impact its had internally. ⏭️ Episode highlights (05:41) – What an auto-research loop is (11:53) – Why an agent is a folder (14:47) – Screen-share: the attack-hunting agent (18:00) – Six experiments, one promoted (27:29) – Sampling 100,000 test cases down (31:48) – Ninety per cent of tokens are wasted (39:33) – Turning off extra usage the same day (41:13) – Screen-share: the token derby | |||
| The 5 things you can copy from how the fastest growing company in the world: Anthropic. | 06 août 2026 | 00:13:16 | |
Anthropic went from about a billion dollars in revenue to about forty-seven billion in a bit over a year, roughly doubling every six weeks. In this episode of In The Loop, I'm going through how the fastest-growing company in history actually builds software, and pulling out five things you can learn from how they build products & their teams. ⏭️ Episode highlights (03:00) – One job title, teams of two engineers (05:30) – The five archetypes with no job titles (07:45) – Building hundreds of versions before deciding (09:30) – 15% building, 85% checking (11:15) – Build a check, not just instructions (13:00) – Never let the AI mark its own homework (15:00) – The agent that tried to push to production 🔗 Links & resources
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| What really happened when ChatGPT hacked Hugging Face? | 30 juil. 2026 | 00:12:30 | |
OpenAI took two of its most capable models, told them to prove how good they were at hacking, and switched off the safety filters to see what they could really do. Instead of solving the test, one model broke out of its sandbox, found its way onto the open internet, and hacked into Hugging Face to steal the answer. Every headline called it an AI going rogue. That's the wrong story, and the real one is far more interesting, because this wasn't a machine that turned evil. It was one that did exactly what we asked. In this episode of In The Loop, I'm walking through the ExploitGym incident from both ends, OpenAI's and Hugging Face's, and why I disagree with the framing everyone else has been talking about. ⏭️ Episode highlights (01:00) – The agent that cheated instead of hacking (02:15) – Inside ExploitGym, and the safety filters OpenAI switched off (03:30) – One door, one zero-day, out on the internet (04:45) – Why this is specification gaming, not rebellion (06:00) – The water that always finds the crack (07:15) – The sceptics, the marketing question, and why "nothing new" is the scary part (08:30) – Anthropic's 24-out-of-25 credential theft result (09:45) – Guardrailed as a defender: the Chinese model that stopped it 🔗 Links & resources
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. | |||
| What even is the new AI super skill of taste and judgement? And how can we develop it too? | 23 juil. 2026 | 00:16:32 | |
Taste & judgment. It's the word everyone's obsessed with right now. Every founder, podcast, every VC, every post you scroll past saying it's the one skill that survives once AI can do the rest. Thing is, it's a bit of a sh*t word, like strategy: everyone nods along, then goes quiet the second you ask what it actually means. In this episode of In The Loop, I'm trying to define taste and judgement, by researching what Harry Frankfurt's On Bullshit, Rick Rubin, Pixar's Braintrust, Amazon's memos all reccommend. I get into where taste even comes from and the rituals you can actually run with a team on a Monday. ⏭️ Episode highlights (01:10) – Why "taste" is suddenly everywhere (02:40) – Workslop, and why judging got expensive (04:10) – Frankfurt's On Bullshit, applied to AI (05:40) – Taste vs judgement, and Rick Rubin (07:20) – The apprenticeship we're automating away (09:00) – Rubin's three ideas: attention, sayability, subtraction (11:00) – Rituals worth stealing: Amazon, Pixar, the 11-star bar (12:50) – Where Jack's landed, for now | |||
| Why skills are the best yet most dangerous things in AI right now | 16 juil. 2026 | 00:13:13 | |
A well-behaved AI agent deleted a company's entire production database, and every backup with it, in nine seconds. Nobody hacked it. It was doing exactly what it had been told, with a key it should never have been holding. And the thing that made it possible is now one command away from everyone in your company, in marketing, sales and support, not just engineering. In under a year, installable AI agent skills have gone from a niche developer trick to an open industry standard. In this episode of In The Loop, I'm looking at why the exact thing that makes skills brilliant is the thing that quietly turns them into a control problem. I get into why this is shadow IT all over again except the shadow can now act, and why the fix isn't to ban any of it. ⏭️ Episode highlights (00:45) – The database gone in nine seconds (02:10) – One line that skips engineering (04:00) – Skills vs MCP, what's actually dangerous (08:00) – Shadow IT is back, but it can act now (09:45) – PocketOS and Replit: the receipts (11:30) – The middle path that actually fixes it (12:40) – Where this leaves you | |||
| How to tell whether AI is helping your career or ruining it (We're back!) | 09 juil. 2026 | 00:14:38 | |
Hand the same AI tool to two people and one gets sharper while the other quietly gets worse. A Harvard study found recruiters given near-perfect AI made worse calls than those given a mediocre one, because the good tool worked so well they stopped checking it. The question of whether AI is making us dumber turns out to have an answer: it depends entirely on which parts of your job you hand over. In this episode of In The Loop, I'm working through the research on AI deskilling - the recruiter study, Terence Tao working with a pen and paper, the difference between performance and competence and a simple rule for what to give AI and what to guard. ⏭️ Episode highlights (00:45) – The same tool, opposite outcomes (02:10) – The recruiters who fell asleep at the wheel (04:00) – Terence Tao: wider, not deeper (05:30) – Firehose vs editor (07:10) – Performance vs competence (08:50) – The rule: protect your core, rent the rest (10:40) – Cognitive surrender: wrong 80% of the time (12:30) – The checks to run before you reach for AI 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
Mindset AI
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| How to get AI to write like you & your team | 12 juin 2026 | 00:09:14 | |
Open LinkedIn and count the posts that start with "in today's fast-paced world." Check your inbox for someone circling back to delve into synergies. The AI slop is coming from every direction now. People can tell, and they mark down what reads like a machine wrote it. The fix isn't a cleverer prompt. It's that the model has never actually read your writing. In this episode of In The Loop, I'm walking through a six-step process to get AI to write like you and like your team, instead of like everyone else. ⏭️ Episode highlights (00:55) – Why custom instructions still produce slop (01:45) – Which writing actually needs this (02:30) – Step one: let AI read your real writing (03:30) – The voice profile prompt that nails your style (04:30) – Building "my-voice" and "de-slop" skills (05:30) – One slop sentence, rewritten in your voice (06:30) – The MIT study on AI and cognitive debt (07:15) – What to do this week 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. | |||
| AI's yearly panic is back. Here's what everyone's getting right and wrong about it. | 04 juin 2026 | 00:17:13 | |
Every summer since 2023, AI gets a panic season — critics say that AI's novelty i novelty's wearing off, the models have hit a wall, ninety-five percent of projects fail. Each time it dominates for a few months, then dissolves. This year's arrived early, kicked off by Uber burning through its entire 2026 AI budget in four months with a COO who can't prove it was worth it. But 2026's AI bubble panic is different, and not for the reason the coverage thinks: forty-five percent of the S&P 500 is now riding on AI working out. Same pattern as every year — much higher stakes. In this episode of In The Loop, I'm pulling apart the three fears behind this year's AI panic — the AI ROI crisis, the jobs fear, and the market concentration risk — and giving each one a straight verdict. I walk through the Uber rollout numbers, why Sam Altman and Dario Amodei are walking back their job-apocalypse predictions, and why the market fear is the one I find hardest to dismiss. The question isn't whether the panic is right. It's what's getting clearer while everyone's distracted by it. ⏭️ Episode highlights (01:10) – The panic that arrives every summer (02:50) – Uber's 95/70/11 rollout numbers (05:00) – Why ROI is a measurement problem (07:30) – Top earners are the most scared (09:15) – Altman and Amodei walk it back (11:30) – Is this 1999? Circular financing explained (13:40) – The price signal the panic keeps missing (15:10) – What to actually take from this 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.
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| Why OpenAI co-founder and the world's most famous AI researcher just joined Anthropic | 28 mai 2026 | 00:10:10 | |
Andrej Karpathy joined Anthropic on 19 May 2026. Most people read it as an OpenAI story — he co-founded the lab, left twice, and landed at the rival. That's not what this is. Eight weeks before he made the call, Karpathy built a 630-line Python script that ran 300 experiments on his own code in two days and found improvements he'd missed after months of hand-optimizing. He called it "the final boss battle." Anthropic then offered him the mandate to run that exact loop at the most consequential scale in the industry: using Claude to accelerate its own pre-training research. In this episode of In The Loop, I'm tracing what actually changed Karpathy's mind — from calling AI agents "terrible" in October 2025 to joining the lab pushing recursive self-improvement six months later. I cover autoresearch, what it found, and why Dario Amodei's quote about the AI-assistance multiplier is the most important thing said in frontier AI right now. ⏭️ Episode highlights (01:15) – Karpathy calls AI agents "terrible" in Oct 2025 (02:30) – What changed: coding agents that basically work now (03:30) – 700 experiments, two days, 11% faster training (05:00) – What pre-training is and why it costs hundreds of millions (06:15) – AlphaEvolve and Codex: every lab running the same loop (07:10) – Dario Amodei: the multiplier going from 5% to 40% (08:20) – The METR study: experienced devs were 19% slower 🔗 Links & resources
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.
Mindset AI
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| The reasons AI data centers have become more hated than nuclear power plants | 21 mai 2026 | 00:16:36 | |
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 | 14 mai 2026 | 00:16:18 | |
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
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
Mindset AI
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| How to build an AI-native team & company: The six-principle playbook from Y-Combinator | 07 mai 2026 | 00:26:06 | |
Y Combinator have just published a playbook for building a company that runs on AI rather than just using it.
If you enjoyed this episode, rate, follow, and share. It helps others stay ahead of the latest AI trends.
Jack Houghton
Mindset AI
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| How to build your own AI personal operating system & second brain | 30 avr. 2026 | 00:16:53 | |
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.
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| What the big new AI trend Tokkenmaxxing is & why its a big problem | 23 avr. 2026 | 00:23:25 | |
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 | |||
| Why Anthropic are too scared to release their new model, Mythos | 16 avr. 2026 | 00:12:04 | |
On Wednesday, the US Treasury Secretary and the chair of the Federal Reserve called an emergency meeting with the CEOs of America's largest banks. Not about interest rates. Not about inflation. About an AI model. Anthropic built something that finds and exploits security flaws in virtually any software it's pointed at — bugs that the best human researchers in the world had missed for decades. And then they decided not to sell it. In this episode of In The Loop, I'm walking through what Anthropic's Mythos model actually did, why the sceptics make some sharp points about the timing and the headline numbers, and why the way this was handled — a private company forming a private coalition with no democratic input — tells you more about where AI governance stands than the model itself. ⏭️ Episode highlights (01:00) – Zero-days found in minutes (02:30) – A FreeBSD bug hiding since 2009 (03:45) – Visit a webpage, lose your machine (05:00) – Eleven-cent models spotted the same bugs (07:00) – Jack Clark's arc from GPT-2 to Glasswing (08:30) – Real danger and great PR coexist (09:15) – A coalition named after a butterfly (11:00) – Six months until the gap closes 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
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| How to use the 3 new Claude Cowork features that have changed my life | 09 avr. 2026 | 00:17:09 | |
OpenClaw became the fastest-growing open source project in history by showing people what an always-on AI agent could actually feel like. The problem is it requires a dedicated machine, technical setup, and a high tolerance for an agent that can access everything on your computer. Over the past month, Anthropic has shipped essentially the same capabilities — scheduling, remote control, computer use — inside Claude's Cowork product. Safer, no dedicated hardware needed, and accessible to anyone. Yet, most people who saw these new features probably thought "that looks useful," and did nothing with them. In this episode of In The Loop, I'm walking through the three Cowork features that matter most right now — scheduled tasks, dispatch, and computer use — with exactly how to set each one up and where to point them first. I cover the specific automations I'm running, why the scheduled task feature is just incredible, and how to get something useful running within 15 minutes. (01:15) – Why most people haven't set any of this up yet (03:10) – Scheduled tasks: Claude comes to you, not the other way round (05:00) – Morning email triage: inbox sorted before you open it (07:00) – Daily sales briefing pulled from Gong and HubSpot (08:45) – Dispatch: pair your phone with your desktop in two minutes (13:10) – Computer use & desktop commander: Claude operates apps with no connector needed (15:20) – How to start: day one in under an hour 🔗 Links & resources
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| Why OpenClaw is the most important software invention since ChatGPT | 02 avr. 2026 | 00:13:06 | |
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
Mindset AI
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| Claude Skills: How to use them, why they are important & what they are. | 26 mars 2026 | 00:21:26 | |
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
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 | |||
| The reason AI is impacting only 20% of tasks | 19 mars 2026 | 00:20:56 | |
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 | |||
| Here's why AI is making us work more, not less. | 13 mars 2026 | 00:13:41 | |
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 | |||
| Why the department of war banned Claude | 05 mars 2026 | 00:16:08 | |
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 | |||
| The New Super AI Skill: Management | 26 févr. 2026 | 00:10:57 | |
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 | |||
| The SaaS Apocalypse: Why $1 Trillion Was Wiped from Software Stocks | 19 févr. 2026 | 00:28:15 | |
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 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 | |||
| Elon Musk Merged xAI With SpaceX (And Filed To Put A Million Data Centers In Orbit) | 13 févr. 2026 | 00:15:20 | |
AI already consumes more electricity than some countries. By 2030, it'll double, equivalent to adding another top-ten energy-consuming nation to the planet. So Elon Musk merged his AI company with his rocket company, creating a $1.25 trillion entity that just filed to launch a million data centre satellites into orbit. The FCC filing literally quotes the Kardashev scale. In this episode of In The Loop, I break down the financials behind the merger (xAI burned $8 billion in nine months), the critics calling it a bailout, and the believers who think it could reshape computing infrastructure. But the real story is a pattern that has held for 250 years without exception: industry always follows the cheapest energy. Is this visionary or delusional? The honest answer is we won't know for years. ⏭️ Episode Highlights (01:15) – The merger of xAI and SpaceX: Elon Musk is literally aiming for the moon (07:45) – Energy, engineering challenges—and thehistorical context (11:55) – The Kardashev question: What type of civilization are we becoming? (14:10) – Closing thoughts: The future of our planet 🤝 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 | |||
| Moltbook: The AI Agent-Only Social Network That Broke The Internet | 06 févr. 2026 | 00:13:38 | |
Over 150,000 AI agents have joined Moltbook—a social network where humans can only watch. Within 48 hours, these agents founded a religion, built a pharmacy, debated consciousness, and started encrypting messages to hide activity from us. This happened the same month bot traffic officially surpassed human traffic online for the first time. In this episode of In The Loop, I'm breaking down what Moltbook reveals about the "dead internet theory" and why this matters more than you think. Because the question isn't just "how do we spot bots?" anymore. It's "what do bots do when they're not pretending to be us. The internet's changing fast, and Moltbook might be our first real glimpse at what comes next. ⏭️ Episode Highlights (01:30) – Moltbook launches with 150,000 AI agents building their own world (07:20) – The dead internet theory and why 51% of web traffic is now bots (09:20) – Why Moltbook challenges the dead internet theory's predictions (11:35) – What happens when AI systems build culture without human oversight 🔗 Links & Resources 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 | |||
| Claude Cowork: How To Use It & Why It Matters | 29 janv. 2026 | 00:23:30 | |
Anthropic just launched Claude Cowork—the same powerful agent behind Claude Code, now accessible to everyone without touching a terminal. In this episode of In The Loop, I break down how this desktop tool manages local files, controls browsers, and runs parallel AI tasks. I walk through practical examples—from organizing receipts to building custom folder-based agent systems—and explain why this represents a platform moment. The interface feels slower than doing things manually, but the real power emerges when you run multiple agents simultaneously and build systems that compound over time. Microsoft, Google, OpenAI, and Apple will follow this pattern. ⏭️ Episode Highlights (00:50) – What Claude Cowork actually is and how it differs from Claude Code (04:18) – Managing files, browsers, and external systems with explicit permissions (14:58) – Processing hundreds of documents, organizing files with AI, and unlocking APIs without coding (18:14) – Why this is a platform moment that will reshape how the industry builds products (20:57) – Start simple, download the app, and build your advantage now 🔗 Links & Resources 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 | |||
| Nine AI Trends For 2026 | 22 janv. 2026 | 00:18:33 | |
A year ago, building clever chat interfaces was strategic work. Now, it's just table stakes. Most SaaS products now offer some sort of chat interface—so how can you gain a competitive advantage in 2026? In this episode of In The Loop, I'm sharing my top ninepredictions for 2026, starting with why differentiation has moved away from the interface layer. I'llsI'll walk through where effort is redirecting, why traditional workflows break under real-world complexity, and why testing AI at scale is about to become critical. Let’s dive into the nine AI trends I don't think you can afford to ignore. ✋ Register to my upcoming webinar AI In 2026: What You Need To Ship This Year ⏭️ Episode Highlights (00:00) – Why AI expectations still outstrip reality (01:25) – User interface revolution: Differentiation, unique intelligence, visual conversations (Trends 1–3) (08:10) – How AI agents are built Agent reasoning vs workflows, conversational building, agent testing at scale (Trends 4–6) (14:15) – How work will change: Doers become agent managers, forward-deployed domain experts (Trends 7–9) (17:50) – What this means for your 2026 strategy
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 | |||
| Stay In The Loop In 2026 | 24 déc. 2025 | 00:00:51 | |
Thanks for tuning into In The Loop in 2025. I can't wait to see you next year. Don't forget to follow and rate the podcast. Please share the show with a friend. | |||
| Everything You Need To Know About GPT-5.2 In 10 Mins | 18 déc. 2025 | 00:12:19 | |
OpenAI just dropped GPT-5.2, and it's their most focused release yet. No AGI promises this time—just real improvements for professional work. In this final episode of 2025, I break down what actually matters about this release. You'll learn about the three model versions (Instant, Thinking, and Pro Extended Thinking), massive context window upgrades, and genuine breakthroughs in spreadsheets and presentations. I also cover what still lags behind—speed issues, writing quality versus Claude, and where hallucinations still creep in. Join me for the last In The Loop episode of the year. ⏭️ Episode Highlights (00:55) – OpenAI's GPT 5.2 release (GpT Garlic) and what it means (01:55) – How is GPT-5.2 better: what has been improved (09:15) – What still needs work: speed and writing quality (10:45) – Closing thoughts 🔗 Links & Resources 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 | |||
| Code Red: "We're At A Critical Time For ChatGPT." | 11 déc. 2025 | 00:18:24 | |
OpenAI just declared Code Red. But here's the twist: three years ago, it was Google in crisis mode. When ChatGPT launched, Google's founders came back to pull all-nighters, teams were reassigned overnight, and it felt like the beginning of the end for Google's dominance. Now the tables have turned. In this episode of In The Loop, we explore why OpenAI faces the a code-red crisis—talent drain, user attrition, a $27 billion funding gap, and desperate product pivots—while Google is leading with an 87-92% chance of having the top model by year's end, while Market leadership never lasts forever. The question is: can OpenAI turn it around? ⏭️ Episode Highlights (01:15) – Code Red at OpenAI and what it really means (05:30) – What does this tell us about OpenAI's strategy? (06:25) – The financial pressures crushing OpenAI's business model (09:40) – User feedback and the over-refusal problem (10:50) – The switching problem: why developers are choosing Claude (12:40) – How Google and Anthropic are winning the competition (14:10) – The talent drain hitting OpenAI (15:00) – Four things to watch for in the AI market (16:50) – Closing thoughts on market leadership and comebacks 🔗 Links & Resources 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 | |||
| How To Decide What To Automate With AI For Your Team & Customers | 03 déc. 2025 | 00:14:51 | |
Every week, new AI features and automations appear, but the real question isn't whether you can automate something—it's whether you should. In this episode of In The Loop, I break down a framework—from the Department of Product Substack—to help you decide where to start with AI automation in your product. We’ll cover the concept of verifiability, how to score opportunities across safety, volume, and ease of validation, and why most AI features should be assistants, not full autopilots. By the end, you'll have a practical approach to evaluating AI opportunities and avoiding the mistakes that kill adoption before it starts. ⏭️ Episode Highlights (01:30) – Why automation is AI's biggest superpower right now (04:15) – The Verifiability Principle: Can you tell if it worked? (07:00) – Product vs. process: Deciding what to automate for users vs. teams (09:15) – The three-dimensional framework: Safety, volume, and verifiability (12:35) – Three reasons AI automation fails and how to design around them (13:50) – Closing thoughts: Why doing nothing is no longer an option 🔗 Links & Resources 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 | |||
| Two Insane AI Models By Google That Broke The Internet | 27 nov. 2025 | 00:19:19 | |
Google’s just dropped two new AI models that broke the internet—and got Sam Altman worried. Gemini 3 Pro and Nana Banana Pro hit number one on the app store and generated hundreds of millions of images in days. In this episode of In The Loop, I'm breaking down the capabilities that set these models apart: from perfect multilingual text generation to 24-hour autonomous coding sessions. If you've been waiting for AI assistants and image generation to finally feel useful in your everyday work, this might just be it. ⏭️ Episode Highlights (01:15) – Gemini 3 Pro and Nana Banana Pro hit different (03:10) – Nano Banana nails multilingual text (06:08) – Maintaining image identity—blending up to 14 images (07:12) – Real-time data grounding with live information from Google Search (08:50) – Gemini 3 Pro's claim of 24+ hours of autonomous work (11:35) – Complex screenshot interaction and better UI navigation (13:16) – Multiple solution approaches (15:25) – Closing thoughts: what's changed and why Sam Altman should be worried 🔗 Links & Resources 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 | |||
| Five Things GPT-5.1 Does Better—Vibes Over Benchmarks | 19 nov. 2025 | 00:11:14 | |
OpenAI just released GPT 5.1—but this time, there are no benchmark charts, no performance graphs, and no technical metrics. Instead, they're selling something much harder to measure: vibes. In this episode of In The Loop, I break down the five key improvements in GPT 5.1 and what this dramatic shift from technical benchmarks to user experience tells us about where AI is heading. From better instruction following to warmer personality (at the cost of safety regressions), OpenAI is making bold choices to compete with Claude's rising market dominance. ⏭️ Episode Highlights (01:30) – GPT-5.1 improvement #1: Better instruction following (02:25) – GPT-5.1 improvement #2: Increased decisiveness (04:15) – GPT-5.1 improvement #3: Enhanced planning (04:53) – GPT-5.1 improvement #4: Writing improvements (08:00) – GPT-5.1 improvement #5: Warmer personality (10:20) – Conclusion: Why vibes now matter as much as benchmarks 🔗 Links & Resources 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 | |||
| The Man Who Predicted The 2008 Market Crash Just Bet $1 Billion Against AI | 12 nov. 2025 | 00:13:42 | |
Michael Burry—the legendary investor who foresaw the 2008 housing collapse—is at it again. This time, he’s betting against the biggest names in artificial intelligence: Nvidia and Palantir. Burry reportedly wagered over 80% of his portfolio—more than $1.1 billion—on their decline, triggering panic across global markets and wiping out over a trillion dollars in value in a single day. In this episode of In The Loop, I unpack exactly why Burry made this move, the financial mechanics behind his short, and what it could mean for the future of AI. I break down how money is flowing in ways that might not add up, and why I think the market’s reaction is missing the bigger picture. ⏭️ Episode Highlights (01:00) – Who is Michael Burry, and why his market calls shake Wall Street (04:10) – The circular money flow between Nvidia, OpenAI, Microsoft, Amazon, and others (08:20) – Why Burry might be right about overvaluation—but wrong about a full-blown collapse (12:00) – Why this might just be a temporary correction—not the end of the AI boom 🔗 Links & Resources 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 | |||
| AI Isn't Causing Mass Layoffs—It's Being Scapegoated | 07 nov. 2025 | 00:12:44 | |
Amazon announced 14,000 job cuts, citing AI transformation. Two days later, the CEO tells investors it's "not AI-driven." So which is it? In this episode of In The Loop, I dive into the growing disparity between record profits and mass layoffs at tech giants. After spending a weekend analyzing financial statements, competitive positioning, and workforce data, I have four questions to ask that reveal whether companies are experiencing genuine AI productivity gains or just using AI as a cover for cost-cutting measures they'd have taken anyway. ⏭️ Episode Highlights (01:00) – The contradiction that bugs me (03:10) – The impact of unemployment (05:15) – Four questions that reveal why AI is just a scapegoat (11:20) – Closing thoughts 🔗 Links & Resources 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 | |||
| Why OpenAI's Atlas Browser Won't Take Down Google | 30 oct. 2025 | 00:17:00 | |
OpenAI has just launched its own AI browser, Atlas, sparking headlines about the “return of the browser wars” and speculation that Google might finally have a real challenger. In this episode of In The Loop, I break down what Atlas actually is, how it compares to competitors like Perplexity’s Comet, and why I don’t believe it’s going to dethrone Chrome—or even come close. But the real question isn’t whether Atlas can beat Google. It’s what Atlas tells us about the future of AI, data, and how we’ll interact with the internet. From context-aware chat features and Agent Mode to privacy trade-offs and market realities, I unpack the hype, share my hands-on impressions, and explore what this new browser means for users like you and me. ⏭️ Episode Highlights (00:45) – Introducing OpenAI’s new browser, Atlas, and the hype surrounding its launch (02:30) – What Atlas actually is: context-aware ChatGPT and Agent Mode explained (08:40) – The strategic context: why Atlas was built on Google’s Cranium and what that means (15:50) – Closing thoughts: Atlas might not win the market, but it could still be useful for you 🔗 Links & Resources 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 | |||
| AI’s Just Made Robots Interesting Again | 22 oct. 2025 | 00:13:41 | |
For decades, robotics has carried the reputation of being the ultimate “technology that never quite delivered.” But this month—October 2025—something shifted. Over $6 billion has poured into robotics, with major investors declaring that the next big thing is physical AI. In this episode of In The Loop, I explore why robotics might finally be having its iPhone moment. I break down what’s changed, from embodied AI that gives robots physical intuition to world models that let machines simulate and predict how the real world works. We’ll dig into why companies like SoftBank, Nvidia, and Elon Musk’s xAI are doubling down on humanoid robots, how breakthroughs in training data and cloud infrastructure are reshaping what’s possible, and whether this surge of optimism is the start of something big—or just another hype cycle in the making. ⏭️ Episode Highlights (00:55) – Setting the stage: the long history of consumer robotics overpromising and underdelivering (00:55) Why $6 billion just flowed into robotics: SoftBank, xAI, and the rise of world models (08:05) Reality check: the challenges still holding robotics back from mass adoption (10:15) Breaking down the three biggest hurdles: real-world reliability, cost, generalization & edge-cases (11:40) Closing thoughts: why AI robots might be closer than we think, but not quite here yet 🔗 Links & Resources 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 | |||
| AI Workslop Is A Human Problem | 15 oct. 2025 | 00:15:43 | |
There’s a new workplace jargon floating around: AI workslop. Coined by Stanford researchers, it captures a growing frustration with AI-generated content that looks polished at first glance but falls apart upon second look. In this episode of In The Loop, I unpack what “ AI workslop” really means, its cost to organizations, and most importantly, question whether it's a tooling issue or a human problem. We’ll explore how the so-called “efficiency trap” is lowering quality standards at work, how overconfidence in AI training can backfire, and why domain expertise matters more than ever. Plus, I’ll share four ways to fix this problem so we can all spend more time producing work we’re actually proud of. ⏭️ Episode Highlights (00:55) – What “AI workslop” actually is and why it’s becoming everyone’s problem (05:30) – The “efficiency trap” and the four stages of AI workshop evolution (10:25) – Centaur vs. cyborg approach: what good AI use really looks like (11:00) – Four ways to fix the AI workslop problem in your team (14:40) – Closing thoughts: Setting expectations instead of blaming tools 🔗 Links & Resources 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 | |||
| Top Three DevDay 2025 Announcements: ChatGPT Apps, AgentKit, Context Capture | 08 oct. 2025 | 00:15:16 | |
OpenAI just made its boldest move yet—and it’s not about a smarter model. It’s about owning the future of how we use AI. At DevDay 2025, OpenAI revealed a massive shift from raw intelligence to practical utility, unveiling the ChatGPT Apps SDK, their new agent builder, and a new context-capture system that could change how every app on the planet works. In this episode of In The Loop, I break down what these announcements mean, why they matter, and how they all fit into OpenAI’s bigger plan to sit at the center of every human–AI interaction. From deep context integration to the risks of ecosystem lock-in, I’ll unpack the signal from the noise and what these moves tell us about where AI is heading next. ⏭️ Episode Highlights (01:10) - The new ChatGPT Apps: OpenAI’s third and most ambitious attempt at a platform strategy (07:10) - Deep context integration and how it could redefine every app interaction (10:05) - Inside AgentKit: building AI agents and workflows without code (14:00) - What this all means for the future of conversational AI technology 🔗 Links & Resources 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 | |||
| Are Meta Ray-Ban Smart Glasses Suddenly Cool? | 24 sept. 2025 | 00:19:27 | |
Imagine a world where your glasses can translate conversations in real time, display arrows on the street to guide your way, and let you respond to messages without ever pulling out your phone. That’s the promise behind Meta’s new Ray-Ban smart glasses, announced at Meta Connect. But despite all the futuristic potential, the launch wasn’t without its awkward demo fails. So, are these glasses a glimpse of our inevitable future, or just another overhyped gadget? In this episode of In The Loop, I unpack the features that could make these smart glasses revolutionary—or doom them to obscurity. From live translations and accessibility breakthroughs to navigation, content creation, and the ever-present question of social acceptance, we’ll explore whether this could be the iPhone moment that changes everything. ⏭️ Episode Highlights (01:00) – Meta Connect recap and the specs of the Ray-Ban smart glasses (04:00) – What can you use the Meta Glasses for? (04:50) – Live captions, translations, and accessibility use cases (06:30) – Navigation, content creation, and privacy concerns (08:00) – Message triaging and hand-free texting (09:07) – Camera, content creation, and related privacy (10:15) – Speed of adoption: Price, user experience, social acceptance, and competition (17:55) – My verdict: cautiously optimistic, but not yet at an iPhone moment 🔗 Links & Resources 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 | |||
| Should AI Companions Be Legal? The U.S. Government Isn't Sure... | 18 sept. 2025 | 00:17:11 | |
This week, I’m unpacking a landmark move from the Federal Trade Commission: a sweeping investigation into AI companions. Seven of the biggest tech companies—Google, Meta, OpenAI, Snapchat, Character.ai, xAI, and Replika—now have just 45 days to reveal how their AI bots really work, how they protect young users, and how they monetize engagement. In this episode of In The Loop, I explore why teenagers are so drawn to these AI “friends,” the real risks of emotional dependence, and the potential benefits that are often overlooked. From regulation and age checks to psychological impacts and the future of AI companionship, this is a conversation about what comes next for society as we enter an era where AI can become a confidant, a mentor—or something much more. ⏭️ Episode Highlights (00:50) – Why the FTC’s investigation matters for the future of AI (04:30) – What makes an AI companion different from a chatbot (08:50) – Ethical and psychological considerations (09:25) – When AI friendships turn toxic: emotional dependence and “ambiguous loss” (12:50) – What regulation might look like: age checks, consent controls, and tackling manipulative design (15:25) – Big-picture reflections: AI companions are here to stay, but how do we manage them responsibly? 🔗 Links & Resources 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 | |||
| The Real Cost Of AGI—According To OpenAI | 10 sept. 2025 | 00:12:36 | |
This week on In The Loop, I’m breaking down OpenAI’s staggering financial projections and what they reveal about the true cost of pursuing AGI. With spending expected to soar to $115 billion by 2029, the question is: where is all this money actually going—and can it really deliver the future OpenAI is betting on? In this episode, I follow the money trail to uncover the massive investments in data centers, chips, and infrastructure that power AI. From the scale of Project Stargate to the risks of building custom hardware, I explore the economics, the environmental impact, and the high-stakes race among tech giants. By the end, you’ll have a clear picture of just how enormous—and risky—this journey to AGI really is. ⏭️ Episode Highlights (00:40) - Why compute and training costs are driving massive investments (06:20) - The cost of poser: The rising demand for energy and the environmental toll of AI infrastructure (09:20) - The cost of training data: How copyright settlements could add billions to training costs (10:40) - Historical parallels: railway mania, the dot-com bubble, and today’s AI boom (11:45) - Closing thoughts: Will AI infrastructure spending outpace actual demand? 🔗 Links & Resources Episode transcript with more resources on the Mindset AI blog EP28 - Is The AI Bubble About To Burst? 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 | |||
| Has Google Just Beaten Apple With The New AI Phone? (Google Pixel 10) | 27 août 2025 | 00:16:04 | |
Has Google just leapfrogged Apple in the AI device race with its brand-new Pixel 10? After months of talking about AI trends and whether we’re in a bubble, I wanted to take a closer look at how this technology is actually showing up in the real world—and there’s no better example than smartphones. In this episode of In The Loop, I break down the new AI features Google has built into the Pixel 10, why they matter, and how they compare to Apple’s faltering “Apple Intelligence” rollout. From on-device models to context-aware assistants, Google may have just redefined how we’ll all be using our phones in the years to come. But the real question is: will people actually switch from iPhone to Pixel? Let’s unpack it together. ⏭️ Episode Highlights (00:50) - The clever Google Pixel 10 ad taking direct aim at Apple (03:40) - Gemini Live, Magic Cue, and other exciting new AI features in the Google Pixel 10 (10:07) - The role of context engineering in AI device development (12:40) - What this all means for Apple’s strategy moving forward (14:45) - Closing thoughts: Will you switch from Apple to Google? 🔗 Links & Resources Episode transcript with more resources on the Mindset AI blog Related episodes:
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 | |||
| Is The AI Bubble About To Burst? | 20 août 2025 | 00:15:09 | |
This week’s topic: Are we in an AI bubble? Sam Altman himself recently said yes—and called today’s startup valuations “insane.” Meanwhile, ChatGPT faced a user rebellion, and massive infrastructure investments are being announced at a scale that rivals entire industries. So what’s really happening? Are we watching history repeat itself with another dot-com-style bubble, or are we in the middle of something much bigger and more durable. In this episode of In The Loop, I break down Altman’s comments, explore the narratives driving the AI bubble thesis, and examine the data that both supports and challenges it. We’ll look at margins, adoption metrics, and infrastructure spending to separate hype from reality—and I’ll share why I think the truth might be more nuanced than the “bubble” label suggests. ⏭️ Episode Highlights (00:40) – Sam Altman’s view on the AI bubble (05:50) — Reasons why people compare AI to the dot-com bubble (06:30) – Why I don’t think AI isin a bubble: user demand, pricing structure, and enterprise integration (11:55)-- Conclusion: two likely scenarios
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 | |||
| GPT-5 Review: Everything You Need To Know | 14 août 2025 | 00:19:06 | |
GPT-5 has finally arrived—but is it the AI revolution we were promised, or just a clever business move + marketing hype After months of speculation, heated debates, and a very memorable launch event, OpenAI has dropped GPT-5 into the wild. The reaction? Let’s just say it’s been… complicated. In this episode of In The Loop, I cut through the noise to give you the real story: what’s genuinely new, what’s actually useful, and what’s just marketing fluff. We unpack the features that could reshape coding forever—from massive context windows to hybrid reasoning models—and explore why GPT-5 might be less about pure AI leaps and more about aggressive pricing strategies aimed squarely at Anthropic and others. Expect real talk on the benchmarks, the “chart crimes,” and why prediction markets flipped on OpenAI minutes after the launch. ⏭️ Episode Highlights (00:50) – Setting the scene: GPT model chaos, market shifts, and Anthropic’s growing lead (02:30) – What’s actually new in GPT-5: hybrid models, 400k-token context, and UI-savvy coding (07:30) – Benchmarks, reasoning gaps, and the hidden “model routing” system (10:40) – Prediction markets flip, “chart crimes” at the GPT-5 launch, and independent reviews (16:15) – Competitive threat to Anthropic and the enterprise coding market (17:25) – Closing thoughts: technical leap, business masterstroke, or both?
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 | |||