In The Loop – Details, episodes & analysis

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In The Loop

Jack Houghton

News

Frequency: 1 episode/8d. Total Eps: 77

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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Data updated on 05/10/2026

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https://www.linkedin.com/in/jack-houghton1/
72 shares
https://www.linkedin.com/company/mindset-ai/
70 shares
https://www.linkedin.com/in/mrdavemartin/
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https://simonwillison.net/
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https://www.loriniles.com/
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https://www.oreilly.com/radar/the-end-of-programming-as-we-know-it/
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https://www.youtube.com/@GetMindsetAI
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https://www.youtube.com/watch?v=xS8xX3usi4c
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https://www.youtube.com/watch?v=Rz3LD7u2KX8
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What Jev, the new type of AI model actually is, how it works and how you can test it

Friday, October 2, 2026 • Duration 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

TypeSafe playground - https://console.typesafe.ai/playground
TypeSafe launch post, "Introducing System One Models & Jev" - https://typesafe.ai/blog/introducing-system-one-models-and-jev
TypeSafe add-on for Claude Code and other coding assistants - https://docs.typesafe.ai/agent-skill
Raphaëlle d'Ornano on Jev and AI economics, Decoding Discontinuity - https://www.decodingdiscontinuity.com/p/typesafe-jev-decision-models-ai-economics

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.

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Why ALL major AI labs may slowing down AI development & whether it could work

Friday, September 18, 2026 • Duration 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

Dario Amodei, "We Must Pace the Frontier" - https://darioamodei.com/post/we-must-pace-the-frontier

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How teams can use AI to build a churn prediction (or any prediction) model with 98% accuracy in 2 weeks

Thursday, September 10, 2026 • Duration 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

Dave Martin on LinkedIn - ⁠https://www.linkedin.com/in/mrdavemartin/⁠

6 things that must be true for Anthropic's IPO of $2 trillion to be achievable (as the largest IPO ever)

Thursday, September 3, 2026 • Duration 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

Friday, August 28, 2026 • Duration 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

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How StackOne built an auto-research loop to continually improve their product

Friday, August 21, 2026 • Duration 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

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The 5 things you can copy from how the fastest growing company in the world: Anthropic.

Thursday, August 6, 2026 • Duration 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

Gergely Orosz, The Pragmatic Engineer, "How building software is changing at Anthropic" - https://newsletter.pragmaticengineer.com/p/inside-anthropic
Anthropic, "Running an AI-native engineering org" - https://claude.com/blog/running-an-ai-native-engineering-org

What really happened when ChatGPT hacked Hugging Face?

Thursday, July 30, 2026 • Duration 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

What even is the new AI super skill of taste and judgement? And how can we develop it too?

Thursday, July 23, 2026 • Duration 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

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Why skills are the best yet most dangerous things in AI right now

Thursday, July 16, 2026 • Duration 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

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In The Loop – Details, episodes & analysis

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In The Loop – Details, episodes & analysis

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