In this episode of The AI Moment, Jonathan Wagstaffe and I (Danny Denhard) dive into the uncomfortable "frontier" of AI surveillance. We kick things off with the fallout from Ring’s Super Bowl ad, which showcased a feature called "Search Party". While using a neighbourhood network of AI cameras to find a lost pet sounds like a dream for owners, it sparked a massive backlash from those who see it as a "statement of intent" for wider human surveillance.

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We explore how we’ve spent 30 years trading privacy for utility. From the UK being the most CCTV-monitored nation in the 90s to every smart doorbell now acting as an AI "node," the line between "helpful" and "creepy" is blurring.

• The Surveillance Node: Every AI-enabled device—from your Ring doorbell to your car—is now a data-collecting sensor in a massive network.

  
  

• The Meta Glasses "Jailbreak": I discuss the facial recognition features in Meta glasses and how they’ve already been "jailbroken" to identify complete strangers, raising massive red flags for personal privacy.

  
  

• The Business Risk: We’re seeing "second-order" risks where teachers or therapists wear AI glasses without consent, creating immediate HR and leadership nightmares.

  
  

• The Data Question: It isn't just about being recorded; it's about who controls the data and who benefits from it.

  
  

The value exchange for AI is shifting.

As a leader, you must decide: is the utility you're gaining worth the trust you might be losing?

Need Help With AI?

Jonathan and I are hosting AI workshops and AI hackathons helping companies improve their AI capabilities and performance, book in a time to chat

We've been asked a lot lately if Europe has already lost the AI race to the US and China.

While it’s easy to get distracted by the trillion-dollar giants in Silicon Valley, being a proactive leader today requires looking at the "layers underneath" the hype.

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How to Think About the AI Landscape:

Being proactive starts with shifting your mindset from consumption to specialisation.

We discuss why the "Big LLM" era might be peaking, making way for a future dominated by Vertical AI—tools designed for specific industrial, clinical, or legal sectors. If you are waiting for a general model to solve your specific business problems, you’re already behind.

Actionable Steps for Leaders:

• Audit your sector, not the news: Stop tracking every minor update to GPT and start monitoring the "remarkable little gems" appearing in your specific industry.

  
  

• Survive to Thrive: Your short-term goal is to get your infrastructure protected and operational so you can survive long enough to thrive when the market matures.

  
  

• Infrastructure over Applications: Understand the constraints—like power and capital—that dictate where the tech is heading. Proactive leaders look at the "power game" (literally) to predict which regions will scale next.

  
  

While the "Big 3" LLMs are American, the real value is moving into the "layers underneath."

We don't need to build the next ChatGPT to win; we need to own the verticals.

3 Actionable Takeaways for your team:

1. Verticals over Generals: The next six months belong to industry-specific AI. Whether it’s healthcare, autonomous mobility, or legal tech, niche expertise is our "Formula One" advantage.

   
   

2. Every company is an API company: Your unique data is your moat. Stop worrying about where the model is hosted and start focusing on how your data powers it.

   
   

3. The Rise of Small Language Models (SLMs): Massive models are power-hungry. Specialized, localized SLMs are more efficient and often more secure for industrial applications.

Europe’s story isn't over; we’re just moving from the "lab" phase to the "global market" phase.

The Bottom Line:

You don't need to move to Silicon Valley to win. By focusing on localized, geo-specific models and leveraging your own unique datasets, you can co-create an ecosystem that makes your business indispensable.

Thanks for listening today!

Danny Denhard

The AI Moment – Moving from Ask & Answer to Assistant to Agentic

In this episode, Jonathan Wagstaffe and I dive into the latest YouGov data on UK consumer sentiment. We strip back the hype to see how "Joe Public" is actually using AI. While the tech world is obsessed with autonomous agents, the reality on the ground is far more cautious.

The Core Takeaway: Trust is the New Currency

We are witnessing a massive "trust gap".

Only about 22% of consumers trust AI, and while they are happy to use it as a "slightly advanced Google" to find deals or compare prices, they aren't ready to hand over the credit card. For AI to truly scale, it must transition from being a persuasion tool (selling to you) to an empowerment tool (helping you).

The "Three A’s" of AI Adoption

We’ve identified a clear three-stage roadmap for how consumers interact with this technology:

> Ask & Answer: The current baseline where users seek quick information or writing help.

> Assistant: The middle ground where AI helps find discounts and compares options—this is where most people are currently comfortable.

> Agentic: The future state where AI executes decisions and places orders—a stage that still feels a "long way away" for the general public.

Actionable Insights for Leaders

> Focus on the "Assistant" Phase: Don't rush into fully automated agents if your customers don't trust the tech yet. Build tools that help them make better decisions, not just faster purchases.

> Bridge the Gender & Age Gap: Trust is currently higher in men and younger demographics. Consider how your AI interface can feel more accessible and reliable to a broader audience.

> Transparency is Non-Negotiable: If a user feels like an AI is a salesperson rather than a helper, trust evaporates.

The Bottom Line:

We’ve moved from Ask & Answer to Assistant. To cross the Rubicon into Agentic AI, brands must prove that the AI is acting in the customer's best interest.