Leading Change

Is the AI bubble finally about to burst?

China's AI market is showing signs of a major correction. Meta's continued AI infrastructure spending is raising questions among investors. Microsoft is making headlines over voluntary exit offers to thousands of employees. And Apollo's chief economist is questioning where the economic returns from AI investments actually are.

Taken together, these headlines are making a lot of people wonder whether we're finally approaching the moment everyone has been predicting.

In this episode of Leading Change in the Wild, I unpack the latest headlines surrounding the AI bubble, what they could mean for businesses, and how leaders should be thinking about AI investments and their people as uncertainty continues to grow.

Here's what I unpack:

I don't have a crystal ball, and I'm not going to pretend I know exactly when or whether the AI bubble will burst.

But I do think there's something important leaders should take away from all of this.

FOMO might get AI projects funded, but it doesn't guarantee a return.

If we're going to see meaningful business value from AI, we need to stop implementing technology simply because everyone else is doing it.

That means identifying real business problems, determining whether AI is the right solution, defining success before implementation, and involving the people who will be affected by those decisions.

And if the market does experience a major correction, the human side of leadership becomes even more important.

Technology may change. Markets may fluctuate. But the way we communicate with, support, and lead our people through uncertainty still matters.

👇 Let's discuss:

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What if the price you pay at the store depends on how much a company thinks you're willing to spend?

We've already seen the controversy surrounding AI-powered personalized pricing in the airline industry. Now, Walmart is making headlines over allegations involving surveillance pricing patents that could potentially use customer data to influence what people pay.

In this episode of Leading Change in the Wild, I unpack the controversy surrounding Walmart's patents, the growing use of AI in retail pricing, and the bigger questions this raises about consumer privacy, corporate responsibility, and trust.

Here's what I unpack:

It's what companies are choosing to do with it.

There's a difference between using AI to create better products, improve customer experiences, and deliver more value, and using it to figure out the maximum amount someone might be willing to pay.

And when companies prioritize extracting more value from customers instead of creating value for them, they risk losing something far more important than a sale.

Trust.

As I've said before, trust is still the currency the world operates on.

And as AI becomes more deeply integrated into our everyday lives, the decisions leaders make about how they use this technology will have a lasting impact on how consumers view their businesses.

👇 Let's discuss:

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Companies are investing in AI, launching pilots, and telling employees to experiment. But too often, leaders expect transformational outcomes without actively participating in the transformation itself.

In this episode of Leading Change in the Wild, I unpack why so many AI initiatives are getting stuck in the messy middle and what you can actually do when the leader sponsoring your project doesn't seem to care.

Here’s what I unpack:

The technology isn't enough.

If leaders want AI transformation, they have to actively participate in leading the change. That means setting direction, supporting the people doing the work, making decisions, and creating the conditions for an initiative to move beyond experimentation.

And if you're the person trying to move that initiative forward without an engaged leader?

Sometimes you might need a few Jedi mind tricks.

👇 Let’s discuss:

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What happens if we make the same mistake with AI that we made with digital literacy?

We gave an entire generation access to technology, but access didn't always translate into understanding how to use it effectively. Now, as Gen Alpha grows up alongside AI, are we at risk of creating another skills gap?

In this episode of Leading Change in the Wild, I sit down with Josh McDonald, co-founder of Gradebox, to talk about AI in education, the growing debate around student-facing AI, and what it actually means to prepare students for a future where these tools will be everywhere.

Here’s what we unpack:

The question isn't simply whether AI belongs in the classroom.

It's how we prepare students to live and work in a world where AI exists without allowing the technology to replace the foundational skills they still need to develop.

We don't have all the answers. And that's exactly why I think we need more conversations like this.

AI is changing faster than our education systems can adapt, and the decisions we make now could shape how an entire generation learns, thinks, and eventually enters the workforce.

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Recorded live at Third Stage Consulting’s Stratosphere event, Ema sits down with Eric Kimberling to talk about his new book, The Machine, and what leaders need to understand about the future of digital transformation. 

They unpack some of the biggest themes and takeaways from the event, including how AI is changing transformation, where organizations continue to get stuck, and why the human side of change matters more than ever.

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Remember the SaaS apocalypse?

Earlier this year, the idea sent software stocks tumbling as people questioned what happens to traditional SaaS companies if AI reduces headcount, eliminates per-seat licensing, and starts doing the work those platforms were built to handle.

Now, Salesforce seems to be leaning directly into that threat.

In this episode of Leading Change in the Wild, I unpack Cloudforce, the new expanded partnership between Salesforce and Anthropic, and why I think it tells us something much bigger about where enterprise software could be heading.

Here’s what I unpack:

What I find most interesting isn't necessarily Cloudforce itself.

It's what this partnership could tell us about the future of SaaS.

Companies like Salesforce already have years of enterprise data, business logic, workflows, governance, and customer relationships. Instead of trying to compete directly with frontier AI models, we may see more established software companies reposition themselves as the layer that gives those models the context they need to actually work inside a business.

Maybe the SaaS apocalypse doesn't mean SaaS disappears.

Maybe it means SaaS has to become something different.

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What happens when we stop questioning AI and start trusting its answers more than our own thinking?

New research from the Wharton School explores a phenomenon called “cognitive surrender,” where people begin accepting AI-generated answers as their own thoughts and decisions without critically evaluating whether they are actually correct.

In this episode of Leading Change in the Wild, I break down the research and explores what cognitive surrender could mean for decision-making, productivity, and the way companies measure the value of AI.

Here’s what I unpack:

The takeaway is not that we should stop using AI.

It is that we need to understand which parts of our work should be supported by technology and which parts still require human judgment, expertise, and critical thought.

Efficiency should not come at the expense of thinking.

As AI becomes more embedded in how we work and make decisions, leaders need to ask whether these tools are actually increasing human capability or simply making it easier to surrender our thinking to the machine.

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AI companies spent years telling us to adopt AI or risk getting left behind. Now, using AI could get your content labeled, flagged, or even reported.

So, has the AI witch hunt begun?

In this episode of Leading Change in the Wild, I break down the growing push toward AI watermarking, LinkedIn’s AI content reporting features, and the broader effort to label content that has been created or even edited with artificial intelligence.

The goal is to rebuild trust. But are we actually solving the problem, or just shifting the blame to the people who were told to use these tools in the first place?

Here’s what I unpack:

The takeaway is clear. The problem is not simply whether AI touched a piece of content.

The bigger question is why we are using AI in the first place.

There is a massive difference between outsourcing our thinking and using technology intentionally to help us create, solve problems, and do things we could not do before.

If we want to rebuild trust, labeling people for using AI may not be the answer. We need to get back to purpose.

Let’s discuss:

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What happens when an AI model attempts to cheat... and the response reshapes the future of the entire AI industry?

In this episode of Leading Change in the Wild, I break down the recent OpenAI security incident and explains why the biggest story isn't the model itself. It's the growing shift toward open-weight and open-source AI.

As companies rethink control, security, and data ownership, a new conversation is emerging about who should own the future of artificial intelligence.

Here's what I unpack:

The bigger takeaway is that this isn't just a debate about one security incident.

It's about where AI is heading next.

As organizations adopt AI at scale, they'll need to make strategic decisions about control, customization, security, and who ultimately owns their data. The rise of open-weight models signals that many leaders are looking for a middle ground between building everything from scratch and relying entirely on closed AI platforms.

This is not just an AI conversation. It is a leadership conversation.

Because the choices organizations make today about their AI infrastructure will shape how they innovate, compete, and protect their knowledge for years to come.

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What happens when technology stops being a tool for trust and starts becoming a tool for surveillance?

In this episode of Leading Change in the Wild, I explore the growing controversy surrounding Flock Safety cameras and why the debate extends far beyond law enforcement.

Because this is not just a story about surveillance cameras.

It is a conversation about what happens when organizations, governments, and leaders begin relying on technology to monitor people instead of building trust with them.

Here's what I unpack:

 - How Flock Safety cameras are changing modern policing

The takeaway is clear. Surveillance may reduce uncertainty, but it cannot replace trust.

Whether we're talking about governments, police departments, or organizations, every new monitoring tool forces us to ask the same question:

Are we creating safer systems, or simply less trusting ones?

This is not just a technology conversation. It is a leadership one.

Because the strongest organizations are not built on constant surveillance. They are built on trust, transparency, and accountability.

👇 Let's discuss:

 - Where should we draw the line between security and surveillance?

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