Leading Change

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 Cloudforce actually is and how Salesforce and Claude work together
Why Salesforce may be moving beyond the traditional per-seat SaaS model
The questions I still have about pricing and how companies will actually adopt this
Why trust is such a major part of the Cloudforce positioning
The data problem that AI integrations still can't magically solve
Why accurate CRM data becomes even more important when an LLM is reasoning from it
How Salesforce could position itself as the orchestration layer between enterprise data and AI
What Anthropic potentially gains from getting closer to Salesforce's enterprise customers
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.
👇 Let’s discuss:
Does this change how you think about the SaaS apocalypse?
Would you trust an AI model to make decisions based on the data sitting inside your CRM?
Will established software companies become the orchestration layer for enterprise AI, or is AI eventually going to eat them anyway?
What do you think Anthropic really gains from this partnership?
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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:
What cognitive surrender means and how it shows up when we use AI
The Wharton research testing human reasoning with and without generative AI
Why people became more confident even when AI gave them incorrect answers
How expertise helps us recognize gaps and errors in AI output
The connection between cognitive surrender and the Dunning-Kruger effect
How AI-generated “workslop” creates more work for experts
Why cognitive offloading could be eating into companies’ AI ROI
How leaders can use AI intentionally without outsourcing critical thinking
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.
👇 Let’s discuss:
Have you caught yourself trusting an AI answer without questioning it?
Where should we draw the line between cognitive assistance and cognitive surrender?
Could overreliance on AI be one reason companies are struggling to see ROI?
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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:
Why Anthropic is introducing watermarking for AI-processed text
LinkedIn’s approach to reporting AI-generated content
Why AI-assisted content is not necessarily AI-created content
Where we draw the line between tools like spellcheck, Grammarly, and generative AI
How AI companies helped create the trust problem they are now trying to solve
Why labeling everything that touches AI may create even more distrust
The need to bring purpose and intentionality back into how we use AI
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:
Do AI watermarks actually make you trust content more?
Where should we draw the line between AI-assisted and AI-generated content?
Is labeling AI content solving the trust problem, or making it worse?
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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:
What happened during OpenAI's cybersecurity test
Why Hugging Face turned to an open-weight model for defense
The difference between closed, open-weight, and open-source AI models
Why companies like NVIDIA, Microsoft, IBM, and SpaceX are backing open AI initiatives
How open-weight models give organizations more flexibility and control
Why data ownership is becoming one of AI's biggest competitive advantages
What this shift means for enterprise AI adoption and digital transformation
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.
👇 Let's discuss:
Do you think most organizations will adopt closed, open-weight, or open-source AI models?
Should companies prioritize flexibility over convenience?
How important will AI ownership and data control become over the next few years?
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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 recent controversies surrounding surveillance and false accusations - Why surveillance technology is raising new ethical questions - The growing misuse of monitoring tools by those with access - How workplace surveillance mirrors what's happening in society - The relationship between trust, accountability, and technology - Why leaders should think carefully before replacing trust with monitoring
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? - Can organizations build trust while increasing employee monitoring? - When does technology become a substitute for good leadership?
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57% of all internet traffic is now generated by bots.
That statistic alone changes the way we think about the internet.
In this episode of Leading Change in the Wild, I explore Cloudflare's latest announcement to block AI crawlers and why it may mark the beginning of a fundamental shift in how the internet is funded, searched, and experienced.
Because this is not just about AI bots.
It is about the future of the internet itself.
Here's what I unpack:
Why AI bots now generate the majority of internet traffic
Cloudflare's new strategy to block AI crawlers
How Google's AI search is changing the economics of the web
Why the attention economy is beginning to break down
Cloudflare's new pay-per-crawl marketplace for AI companies
What happens when websites are no longer visited by humans
Whether this could be the beginning of the end of the free internet
The takeaway is clear. The internet was built on human attention.
As AI increasingly consumes information instead of people, the business model that has powered the web for decades is being rewritten.
This is not just a technology conversation. It is an economic one.
Because the future of the internet will depend on who creates value, who consumes it, and who ultimately pays for it.
👇 Let's discuss:
Should AI companies have to pay to access online content?
Will Cloudflare's approach change the balance of power between publishers and AI companies?
Is this the beginning of a paid internet for everyone?
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Google's latest AI announcement isn't just about a new feature.
 
It raises a much bigger question.
 
How much of our lives are we willing to let technology observe before consent becomes an afterthought?
 
In this episode of Leading Change in the Wild, I explore Google's new ambient audio memory feature, the rise of AI-powered wearables, and what these technologies reveal about the future of privacy, surveillance, and human consent.
 
Because this is not just about Google.
 
From Meta's smart glasses to Microsoft's workplace monitoring tools, we're seeing a growing trend toward collecting more of our conversations, behaviors, and daily interactions than ever before.
 
Here's what I unpack:
Google's new ambient audio memory feature and what it means for users
How AI-powered devices are expanding the scope of data collection
Why wearable technology is changing expectations around privacy
The growing tension between convenience and meaningful consent
How workplace AI monitoring is extending surveillance beyond personal devices
Why younger generations are increasingly pushing back against always-on technology
The leadership and societal questions we should be asking before these technologies become the norm
 
The takeaway is clear. AI is not just changing the way we work.
 
It is changing the relationship we have with privacy, trust, and consent.
 
This is not just a technology conversation. It is a leadership one.
 
Because once constant surveillance becomes normal, it becomes much harder to ask whether we ever truly agreed to it.
 
👇 Let's discuss:
 - Where should we draw the line between convenience and privacy? - Are companies collecting more data than consumers truly understand? - What role should consent play in the future of AI?
 
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Most organizations are trying to adopt AI using structures, processes, and leadership models that were built for the industrial age.
And that may be the biggest obstacle standing in the way of transformation.
In this episode of Leading Change in the Wild, I sit down with Jana, co-author of The Octopus Organization, to explore why traditional organizations struggle with change and what leaders need to do differently in the age of AI.
From bureaucratic decision-making to AI adoption metrics that drive the wrong behavior, this conversation dives into the patterns holding organizations back and the leadership shifts required to move forward.
Here’s what we unpack:
The difference between a "Tin Man" organization and an "Octopus" organization
Why traditional organizational structures struggle in today's environment
The anti-patterns preventing successful AI adoption
The dangers of chasing AI adoption metrics instead of business value
Why leaders need to personally experiment with AI before expecting their teams to adopt it
The role of distributed intelligence and decentralized decision-making
How to move from AI hype to meaningful business transformation
Why speeding up a bad process is not transformation
The takeaway is clear. AI does not just challenge technology strategies. It challenges the way organizations are designed.
Because the companies that thrive will not be the ones with the most AI tools. They will be the ones that learn, adapt, and empower people to solve real problems.
📚 The Octopus Organization by Jana and Phil is available now!
👇 Let’s discuss:
Which anti-pattern do you see most often in organizations today?
Are companies focusing too much on AI adoption and not enough on value creation?
What would it take for your organization to become more adaptive?
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Anthropic has issued another warning about artificial intelligence. But this time, the concern is not job displacement or productivity.
It is the possibility that AI could soon begin improving itself.
In this episode of Leading Change in the Wild, I break down Anthropic’s latest report on recursive self-improvement and what it means if AI reaches a point where it can build, test, and improve future versions of itself with minimal human involvement.
But beyond the technology itself, this report raises some deeper questions.
Who should be leading these conversations? And what happens when the companies warning us about the risks are also the companies building the technology?
Here’s what I unpack:
What recursive self-improvement actually means
Why Anthropic believes we may be approaching a major AI inflection point
The challenge of keeping humans in control of increasingly capable systems
The “prisoner’s dilemma” at the center of AI development
Whether AI companies can simultaneously be the warning system and the builder
How regulation, competition, and incentives collide in the AI race
The connection between recursive AI, model collapse, and the dead internet theory
The takeaway is not just about technology.
It is about incentives, accountability, and who gets to shape the future of AI.
Because if the people raising the alarm are also the people benefiting from the outcome, we need to ask harder questions about how these decisions are being made.
👇 Let’s discuss:
Should AI companies be leading conversations about AI regulation and ethics?
Are we approaching a point where AI can meaningfully improve itself?
Is a global pause realistic, or are we already too far down the path?
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For years, some of the biggest names in AI warned us that artificial intelligence would eliminate jobs, disrupt entire industries, and fundamentally reshape the workforce.
Now, many of those same leaders are saying something very different.
In this episode of Leading Change in the Wild, I break down the growing narrative shift coming from AI executives and why the people who once warned of mass job displacement are suddenly talking about productivity, augmentation, and the importance of human connection.
So what changed?
Here’s what I unpack:
The fear-based messaging that defined the early AI boom
Why AI leaders are changing their tone on job displacement
Sam Altman’s surprising comments about human interaction and AI
Dario Amodei’s shift from replacement to productivity multiplier
How IPO pressure, public sentiment, and adoption challenges may be influencing the narrative
Why human connection still matters in an increasingly automated world
The bigger leadership lessons hidden inside this messaging shift
The takeaway is clear. When the narrative changes this dramatically, it is worth asking why.
 
Because whether AI becomes a replacement, an enhancement, or something in between, leaders need to think critically about the messages they are hearing and who benefits from them.
This is not just a technology conversation. It is a leadership one.
Because the future of work will not be shaped by technology alone. It will be shaped by the choices we make about how we use it.
👇 Let’s discuss:
Why do you think AI leaders are changing their message?
Was the original narrative wrong, or is this new one?
Is the truth somewhere in the middle?
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