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
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Jack Houghton
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Mindset Ai
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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
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Jack Houghton
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Mindset Ai
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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
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Jack Houghton
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Mindset Ai
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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
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Jack Houghton
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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
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Jack Houghton
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Mindset Ai
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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 “ AIworkslop” 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
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Jack Houghton
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Mindset Ai
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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
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Jack Houghton
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Mindset Ai
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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
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Mindset Ai
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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
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Jack Houghton
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Mindset Ai
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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?
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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:
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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: three likely scenarios
🔗 Links & Resources
Episode transcript with more resources on the Mindset AI blog
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Jack Houghton
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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?
🔗 Links & Resources
Episode transcript with more resources on the Mindset AI blog
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Jack Houghton
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Mindset Ai
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This summer, the world of AI has taken a sharp and fascinating turn. In this episode of In The Loop, I break down four powerful trends that are quietly—and not-so-quietly—reshaping the future of our digital economy. From AI companies being forced to pay for content, to Wall Street trading computing power like it's oil, things are shifting fast. And these aren't just random changes—together, they signal a much bigger story.
Let’s explore the rise of ambient agents, the monetization of web data, the financialization of compute power, and a surprising new twist in how products are being built not for humans but for AI agents. This is one of those episodes that connects the dots between what’s happening now and where we're all headed next. You don’t want to miss it.
⏭️ Episode Highlights
(00:55) - Trend #1: What are ambient AI agents?
(08:30) - Trend #2: Pay-per-crawl: How AI is disrupting the web’s free content model
(12:30) - Trend #4: Computing power as a commodity: Wall Street’s new gold rush into GPU futures
(15:20) - Trend #5: AX (Agent Experience) is here: Why websites are now being designed for AI, not humans
(16:30) - What these trends say about the bigger AI transformation ahead
🔗 Links & Resources
Episode transcript with more resources on the Mindset AI blog
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Mindset Ai
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AI is reshaping work while corporate learning stays stuck. Compliance-first courses, born after Enron and locked into SCORM, waste time and budget. Companies spend $340B a year; 70% is forgotten within 24 hours. As automation threatens up to 800M jobs by 2030, workers need real upskilling-personalised, in-the-flow coaching, triage by need, and conversational support- not “click next” modules.
Lori Niles-Hofmann has spent 20+ years inside L&D across banking, consulting, and marketing, leading large digital transformations and building data-driven frameworks that turn L&D from support to strategy. She’s published courses with 100K completions, serves on EdTech/HRTech boards, and wrote The Eight Levers of EdTech Transformation. Her ER “triage” insight—sparked by a chaotic airport-to-hospital day—explains how to prioritise skills, kill duplication, escape SCORM’s limits, and move to AI-powered learning coaches tailored to each person’s work.
Episode Highlights
(03:12) – The compliance trap: Why early e-learning became "legal documents to protect the company" rather than actual learning
(07:02) – Meet SCORM: The 25-year-old technical standard that has trapped corporate learning in "click next to continue" modules
(12:22) – Lori's vodka-fueled revelation: How a hospital emergency room visit revealed the solution to broken workplace learning
(15:06) – The triage model: Why learning should work like an ER- prioritized by need, personalized by context
(18:28) – The AI future: Learning coaches that know your calendar, projects, and work context to provide just-in-time development
Links & Resources:
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Episode Description:
In just ten days, OpenAI went from market leader and top dog to a series of very painful events that threaten its market leadership.
This episode breaks down the dramatic collapse of their $3 billion Windsurf acquisition, the power play from Microsoft that unraveled it, and how Google swooped in for a surgical “hackquisition.” But that was just the beginning.
We unpack how Elon Musk’s Grok 4 leapfrogged OpenAI’s GPT lineup to become the best AI model in the world (for now), and how Meta pulled off the most aggressive talent raid in recent memory with jaw-dropping offers and the promise of research freedom.
This isn't just a bad week. It might be the turning point where OpenAI's grip on the AI race starts to slip.
⏭️ Episode Highlights
(00:35) - $3 billion Windsurf AI acquisition deal collapses
(03:00) - The Microsoft partnership agreement lands Windsurf AI's technology in Google's hands
(07:35) - Elon Musk's Grok 4 beats the best ChatGPT models
(09:45) - Meta talent war continues: Mark Zuckerberg pouches 20+ AI researchers from OpenAI
(11:40) - What does this all mean for OpenAI?
🔗 Links & Resources
Episode transcript with more resources on the Mindset AI blog
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Context engineering is more than just the latest AI buzzword—it’s shaping up to be the future of how we build truly intelligent systems. In this episode of In The Loop, I explore the shift from prompt engineering to context engineering, why this evolution matters, and how it impacts the effectiveness of AI agents. If you’ve ever wondered why some AI tools feel magical while others fall flat, context engineering holds the answer.
I unpack what "context" actually means in the world of AI—from prompts and system instructions to long-term memory and external data. You’ll hear how building multi-agent systems without proper context coordination often fails, and why simpler, sequenced workflows are currently more effective. Whether you’re building AI products or simply using tools like ChatGPT, this episode will change how you think about how they work.
⏩ Episode Highlights
(01:00) - What is context engineering—and how is it different from prompt engineering?
(09:20) - What role are orchestration and multi-agent systems help maintain context and improve p
(16:05) - The impact of context engineering: personalization, privacy, and better UX
(16:00) - Closing thoughts: what this means for the future of AI
🔗 Links & Resources:
📖 Episode transcript with more resources on the Mindset AI blog
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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
In this episode of In the Loop, I bring you a very top-of-mind topic: how AI is transforming the job market—but this time, we’ll focus on the jobs AI will create. I unpack a recent New York Times article that outlines 22 new roles AI is expected to create, and contrast it with a powerful historical perspective on the Luddites, offering a surprising lens on the current AI discourse.
This isn't just about technology—it's about people, work, and the way progress reshapes our lives. From fears of job loss to the deeper ethical and societal implications, I explore the big questions: Should we slow down innovation to protect current livelihoods? Or do we owe it to the future to push forward, even if the transition is tough? Whether you're anxious or excited about AI, this episode brings clarity, context, and a little hope.
⏩ Episode Highlights
(00:50) - Exploring the global tension around AI and economic anxiety.
(04:50) - A little history: Did the Luddites have a point? What they really fought for and what we can learn.
(09:20) - Why today’s comforts are built on past generations’ risks.
(11:15) - AI job displacement vs. AI job creation: 22 new jobs AI will create.
(16:25) - Closing thoughts: Compassion for a successful AI transition.
🔗 Links & Resources
New York Times article: 22 New Jobs AI Could Give You
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
This week, I’m diving into one of the most insightful pieces of research I’ve come across lately—a report by a16z based on interviews with over 100 enterprise CIOs across five industries. If you’re building, selling, or using AI in a big company—or just trying to understand how enterprise AI is evolving—you’ll want to hear what’s really driving AI adoption at scale.
In this episode of In The Loop, I break down three of the most interesting trends from the report that totally shifted how I think about enterprise AI today. From the dramatic shift from “build” to “buy,” to a budget explosion that shows just how serious companies are about integrating AI, to how wrong we all were about model consolidation—this research is packed with mind-blowing insights.
Whether you're a SaaS founder, enterprise tech leader, or AI enthusiast, there's something in here for you.
⏩ Episode Highlights
(01:00) – Why enterprises are ditching "build" for "buy" when it comes to AI solutions
(06:40) – Budget explosion: AI spending grows 75% beyond expectations
(09:55) – Customer-facing AI: the next wave of enterprise adoption
(11:10) – How wrong we were about LLM consolidation—and why multi-model is the future
(16:45) – What this all means for builders: why it’s time to move faster than ever
Links & Resources
🤖 Check out Mindset AI’s customer-facing embed AI agents
📊 a16z How 100 Enterprise CIOs Are Building and Buying Gen AI in 2025
📖 Episode transcript + graphs on the Mindset AI blog
If you enjoyed this episode, rate, follow, and share! It helps others stay ahead of the latest AI trends. 🚀
In this episode of In The Loop, I unpack what feels like a turning point for Apple—and not in a good way. From a lackluster WWDC 2025 filled with buzzwords instead of breakthroughs, to a baffling research paper, The Illusion Of Thinking, that downplays the entire AI industry, Apple seems more confused than ever about its future.
And then comes the gut punch: Jony Ive, the design visionary behind nearly every iconic Apple product, has joined forces with OpenAI to shape the next era of AI hardware. It’s a move that says more about Apple’s current state than any press release or gimmicky marketing campaign ever could.
I’ll break down the three major signals that Apple may be spiraling.
⏩ Episode Highlights
(00:50) - Apple WWDC 2025 keynote letdown: Liquid Glass instead of Siri innovation
(02:50) - Reason 1: Siri’s downward spiral and Apple’s decade-long AI fumble
(07:05) - Reason 2: The controversial Apple research paper on AI reasoning (The Illusion of Thinking)
(11:10) - Reason 3: Jony Ive joins OpenAI to design the next generation of AI devices
(13:50) - The big takeaway: Apple’s lost identity and the cost of playing it safe
🔗Links & Resources:
🎧 EP9 - Apple’s AI Crisis Exposed: Is It Having A Nokia Moment?
📖 Read the written version of this episode on the Mindset AI blog (with all relevant links and resources). Read here.
If you enjoyed this episode, rate, follow, and share! It helps others stay ahead of the latest AI trends. 🚀