Happy Friday! This week I’m running through three topics you can’t afford to miss: what Altman’s viral exchange reveals about OpenAI’s missing anchor, the real lessons inside Anthropic’s Economic Index (hint: augmentation > automation), and why today’s job market feels stuck and how to move anyway.

Here’s the quick rundown. First up, a viral exchange between Sam Altman and Tucker Carlson shows us something bigger than politics. It reveals how OpenAI is being steered without a clear foundation and little attention on the bigger picture. Then, I dig into Anthropic’s new Economic Index report. Buried in all the charts and data is a warning about automation, augmentation, and how adoption is moving faster than most leaders realize. Finally, I take a hard look at the growing pessimism in the job market, why the data looks grim, and what it means for job seekers and leaders alike.

With that, let’s get into it.

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Sam Altman’s Viral Clip: Leadership Without a Foundation

A short clip of Sam Altman admitting he's not that concerned about big moral risks and his “ethical compass” comes mostly from how he grew up sparked a firestorm. The bigger lesson? OpenAI and many tech leaders are operating without clear guiding principles or a focus on the bigger picture. For business leaders and individuals, it’s a warning. You can't count on big tech to do that work for you. Without defined anchors, your strategy turns into reactive whack-a-mole.

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Anthropic’s Economic Index: Adoption, Acceleration, and Automation Risk

This index is a doozy as a heads up. However, it isn’t just about one CEO’s philosophy. How we anchor decisions shows up in the data too even if it has the Anthropic lens. The report shows AI adoption is accelerating and people are advancing faster in sophistication than expected. But faster doesn’t mean better. Without defining what “effective use” looks like, organizations risk scaling bad habits. The data also shows diminishing returns on automation. Augmentation is where the real lift is happening. Yet most companies are still chasing the wrong thing.

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Job-Seeker Pessimism in a Stalled Market

The Washington Post painted a bleak picture: hiring is sluggish, layoffs continue, and the best news is that things have merely stalled instead of collapsing. That pessimism is real. I see it in conversations every week. I’m hearing from folks who’ve applied to hundreds of roles, one at 846 applications, still struggling to land. You’re not alone. But while we can’t control the market, we can control resilience, adaptability, and how we show up for one another. Leaders and job seekers alike need to face reality without losing hope.

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If this episode helped, share it with someone who needs clarity. Leave a rating, drop a comment with your take, and follow for future updates that cut through the noise. And if you’d take me out for a coffee to say thanks, you can do that here: ⁠https://www.buymeacoffee.com/christopherlind⁠

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Show Notes:

In this Weekly Update, Christopher Lind breaks down Sam Altman’s viral interview and what it reveals about leadership, explains the hidden lessons in Anthropic’s new Economic Index, and shares a grounded perspective on job-seeker pessimism in today’s market.

Timestamps:

00:00 – Introduction and Welcome

01:12 – Episode Rundown

02:55 – Sam Altman’s Viral Clip: Leadership Without a Foundation

20:57 – Anthropic’s Economic Index: Adoption, Acceleration, and Automation Risk

43:51 – Job-Seeker Pessimism in a Stalled Market

50:44 – Final Takeaways

#AItransformation #FutureOfWork #DigitalLeadership #AIadoption #HumanCenteredAI #AugmentationOverAutomation

Happy Friday, everyone! I'm back with another round of updates. This week I've got four stories that capture the messy, fascinating reality of AI right now. From fast food drive-thrus to research to consulting giants, the headlines tell one story, while what's underneath is where leaders need to focus.

Here's a quick rundown. Taco Bell’s AI experiment went viral for all the wrong reasons, but there’s more behind it than memes. Then, I look at new adoption data from the US Census Bureau that some are using to argue AI is already slowing down. I’ll also break down KPMG’s much-mocked 100-page prompt, sharing why I think it’s actually a model of how to do this well. Finally, I close with a case study on AI coaching almost going sideways and how shifting the approach created a win instead of a talent drain.

With that, let’s get into it.

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Taco Bell’s AI Drive-Thru Dilemma

Headlines are eating up the viral “18,000 cups of water” order. However, nobody seems to catch that Taco Bell has already processed over 2 million successful AI-assisted orders. This makes the story more complicated. The conclusion shouldn’t be scrapping AI. It’s about designing smarter safeguards, balancing human oversight, and avoiding the trap of binary “AI or no AI” thinking.

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Is AI Adoption Really Declining?

New data from Apollo suggests AI adoption is trending downward in larger companies, sparking predictions of a coming slowdown. Unfortunately, the numbers don’t tell the whole story. Smaller companies are still on the rise. Add to that, even the “decline” in big companies may not be what it seems. Many are using AI so much it’s becoming invisible. I explain why this is more about maturity than decline and explain what opportunities smaller players now have.

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KPMG’s 100-Page Prompt: A Joke or a Blueprint?

Some mocked KPMG for creating a “hundred-page prompt,” but what they actually did was map complex workflows into AI-readable processes. This isn’t busywork; it’s the future of enterprise AI. By going slow to go fast, KPMG is showing what serious implementation looks like, freeing humans to focus on the “chewy problems” that matter most.

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Case Study: Rethinking AI Coaching

A client nearly rolled out AI coaching without realizing it could accelerate attrition by empowering talent to leave. Thankfully, by analyzing engagement data with AI first, we identified cultural risks and reshaped the rollout to support, not undermine, the workforce. The result: stronger coaching outcomes and a healthier organization.

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If this episode was helpful, would you share it with someone? Leave a rating, drop a comment with your thoughts, and follow for future updates that help you lead with clarity in the AI age. And, if you’d take me out for a coffee to say thanks, you can do that here: https://www.buymeacoffee.com/christopherlind

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Show Notes:

In this Weekly Update, Christopher Lind breaks down Taco Bell’s viral AI drive-thru story, explains the truth behind recent AI adoption data, highlights why KPMG’s 100-page prompt may be a model for the future, and shares a real-world case study on AI coaching that shows why context is everything.

Timestamps:

00:00 – Introduction and Welcome

01:18 - Episode Rundown

02:45 – Taco Bell’s AI Drive-Thru Dilemma

19:51 – Is AI Adoption Really Declining?

31:57 – KPMG’s 100-Page Prompt Blueprint

42:22 – Case Study: AI Coaching and Attrition Risk

49:55 – Final Takeaways

#AItransformation #FutureOfWork #DigitalLeadership #AIadoption #HumanCenteredAI

Happy Friday, everyone! Hopefully you got some time to rest and recharge over the Labor Day weekend. After a much needed break, I’m back with a packed lineup of four big updates I feel are worth you attention.

First up, MIT dropped a stat that “95% of AI pilots fail.” While the headlines are misleading, the real story raises deeper questions about how companies are approaching AI. Then, I break down some major shifts in the model race, including DeepSeek 3.1 and Liquid AI’s completely new architecture. Finally, we’ll talk about Google Mango and why it could be one of the most important breakthroughs for connecting the dots across complex systems.

With that, let’s get into it.

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What MIT Really Found in Its AI Report

MIT’s Media Lab released a report claiming 95% of AI pilots fail, and as you can imagine, the number spread like wildfire. But when you dig deeper, the reality is not just about the tech. Underneath the surface, there’s a lot of insights on the humans leading and managing the projects. Interestingly, general-purpose LLM pilots succeed at a much higher clip, while specialized use cases fail when leaders skip the basics. But that’s not it. I unpack what the data really says, why companies are at risk even if they pick the right tech, and shine a light on what every individual should take away from it.

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The Model Landscape Is Shifting Fast

The hype around GPT-5 crashed faster than the Hindenburg, especially since hot on the heels of it DeepSeek 3.1 hit the scene with open-source power, local install options, and prices that undercut the competition by an insane order of magnitude. Meanwhile, Liquid AI is rethinking AI architecture entirely, creating models that can run efficiently on mobile devices without draining resources. I break down what these shifts mean for businesses, why cost and accessibility matter, and how leaders should think about the expanding AI ecosystem.

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Google Mango: A Breakthrough in Complexity

Google’s has a new, also not so new, programming language, Mango, which promises to unify access across fragmented databases. Think of it as a universal interpreter that can make sense of siloed systems as if they were one. For organizations, this has the potential to change the game by helping both people and AI work more effectively across complexity. However, despite what some headlines say, it’s not the end of human work. I share why context still matters, what risks leaders need to watch for, and how to avoid overhyping this development.

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A Positive Use Case: Sales Ops Transformation

To close things out, I made some time to share how a failed AI initiative in sales operations was turned around by focusing on context, people, and process. Instead of falling into the 95%, the team got real efficiency gains once the basics were in place. It’s proof that specialized AI can succeed when done right.

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If this episode was helpful, would you share it with someone? Leave a rating, drop a comment with your thoughts, and follow for future updates that help you lead with clarity in the AI age. And, if you’d take me out for a coffee to say thanks, you can do that here: https://www.buymeacoffee.com/christopherlind

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Show Notes:

In this Weekly Update, Christopher Lind breaks down MIT’s claim that 95% of AI pilots fail, highlights the major shifts happening in the model landscape with DeepSeek and Liquid AI, and explains why Google Mango could be one of the most important tools for managing complexity in the enterprise. He also shares a real-world example of a sales ops project that proves specialized AI can succeed with the right approach.

Timestamps:

00:00 – Introduction and Welcome

01:28 – Overview of Today’s Topics

03:05 – MIT’s Report on AI Pilot Failures

23:39 – The New Model Landscape: DeepSeek and Liquid AI

40:14 – Google Mango and Why It Matters

47:48 – Positive AI Use Case in Sales Ops

53:25 – Final Thoughts

#AItransformation #FutureOfWork #DigitalLeadership #AIrisks #HumanCenteredAI