AI Field Notes – Issue No. 17

Lead Story: Meta + Google — Unthinkable Partnerships

Meta is reportedly in talks with Google to use its Gemini AI models for ad targeting. Think about that for a second. Two of the fiercest rivals in digital advertising may join forces. A year ago, this would have been unthinkable. But Apple’s privacy changes and the AI revolution are pushing even competitors to consider cooperation.

For us, this is the perfect launch point for a new series we’ll be running: The Future of Marketing. It’s the most common question I get asked by CEOs and business owners — “What do you think the future looks like?” The truth is, the road isn’t lit. But part of our role is to help map it, even if it’s just a sketch. Meta and Google’s possible partnership shows how undefined things are — and why we need to explore what comes next.

Quick Hits

1. Databricks + OpenAI
   Databricks just embedded OpenAI’s models into its platform. Translation: AI isn’t just a tool anymore — it’s being woven into workflows where your business data already lives. Take a manufacturing client in the Philadelphia region: instead of relying on analysts to manually forecast demand, leadership could ask, “Which suppliers are most likely to delay shipments next quarter, and how should we adjust production?” AI delivers an answer in minutes, not weeks. Clients are asking how this fits into their operations — proof that demand is becoming real.
2. Google Data Commons + MCP
   Google opened its Data Commons through the Model Context Protocol (MCP). Think of MCP as a universal translator that lets AI connect to verified datasets like government reports and trade logs. For example, a regional manufacturer in PA could use an AI tool powered by MCP to analyze Department of Transportation data and get an alert: “Expect delays on key trucking routes. Suggested action: adjust production schedules or reroute shipments through the Port of Philadelphia.” That’s practical intelligence, grounded in real data.
3. The AI Build-Out Bubble
   The WSJ compared today’s AI spending spree to the dot-com bubble. Data centers, chips, and power projects are scaling at breakneck speed, but monetization hasn’t caught up. For business leaders, the signal is clear: focus on ROI-driven workflows, not hype. More clients are asking us to filter out the noise and identify tools with measurable value.
4. Cloudflare vs. Google AI Overviews
   Cloudflare is giving publishers the ability to block Google’s AI Overviews while still appearing in search. On the surface, it’s a short-term win. But as search traffic declines and LLM adoption grows, the real strategy is preparing for AI-native discovery. Local businesses in Philly are already asking: how will their content be discovered when buyers stop “Googling” and start “asking AI”? That’s the next battleground.

The Lab: Shifting to Product Testing

We’re evolving our Lab section. Instead of abstract analysis, the Lab will now focus on AI product testing:

• Description: What the product is and why it matters.
• Review: Our hands-on testing and perspective.
• Results: How it performed and how it could weave into industries we work with.

Why the shift? Because demand is growing. Clients aren’t just asking, “What is AI?” anymore — they’re asking about specific products they may be getting approved on. They want to know: does this tool work, how would it fit, and is it worth it? The Lab will become the place where we answer those questions.

Closing Thought

Meta talking to Google about ad targeting makes one thing clear: the future of marketing is undefined. It’s not just about who builds the best model; it’s about who adapts, partners, and monetizes first. As leaders, we don’t get the luxury of waiting for clarity. We have to test, learn, and start building the blueprint ourselves.