The Macro AI Podcast

In this episode of The Macro AI Podcast, Gary and Scott dive deep into the emerging concept of Data Commons — shared, governed ecosystems that make data interoperable, trusted, and ready for AI.

They explain what a Data Commons is, how it differs from traditional data lakes, and why it’s essential to the next phase of AI transformation. From Google’s global Data Commons and the NIH’s biomedical repositories to emerging “Private Data Commons” inside enterprises, the hosts show how these ecosystems are reshaping trust, governance, and efficiency.

Listeners will learn how Data Commons reduce AI hallucination, enable grounding, improve reproducibility, and support ethical AI. Gary and Scott also explore governance models, global equity, and the rise of AI agents that automatically fetch verified data from commons networks.

If you’re a CIO, CTO, or business leader preparing your organization for AI, this episode offers the strategic framework you’ll need to understand the infrastructure of the future.

🔗 Links mentioned:

• Google Data Commons
• Open Data Policy Lab — AI Data Commons Blueprint
• Therapeutics Data Commons
• NIH Data Commons

Send a Text to the AI Guides on the show!

About your AI Guides

Gary Sloper

https://www.linkedin.com/in/gsloper/

Scott Bryan

https://www.linkedin.com/in/scottjbryan/

Macro AI Website:

https://www.macroaipodcast.com/

Macro AI LinkedIn Page:

https://www.linkedin.com/company/macro-ai-podcast/

Gary's Free AI Readiness Assessment:

https://macronetservices.com/events/the-comprehensive-guide-to-ai-readiness

Scott's Content & Blog

https://www.macronomics.ai/blog

In this episode, Gary and Scott unpack one of the most critical questions for business leaders today: Is AI actually disrupting the labor market—or are we still waiting for impact to show up in the data?

They dive deep into Yale University’s Budget Lab study, “Evaluating the Impact of AI on the Labor Market: Current State of Affairs” (October 2025), which concludes that there has been no discernible economy-wide labor disruption since the launch of ChatGPT in late 2022. Using decades of labor data, the Yale team found that the pace of occupational change today looks remarkably similar to earlier waves of innovation like the PC and Internet eras.

But Gary and Scott don’t stop there. They explore contradictory findings from other top institutions:

• Stanford’s Digital Economy Lab (Aug 2025): Early-career workers in AI-exposed jobs have seen employment drop by roughly 13%, signaling localized disruption.
• IMF (2024): Up to 40% of jobs globally are exposed to AI, especially in advanced economies.
• OECD & WEF (2024–25): AI is already reshaping skills demand, with executives expecting major restructuring by 2030.

Throughout the episode, Gary and Scott translate these insights into an executive playbook for 2025:
✅ Build an internal AI exposure map by task.
✅ Track real adoption and productivity telemetry.
✅ Reinvent early-career roles through apprenticeships.
✅ Reinvest AI gains into upskilling and responsible adoption.

The takeaway?
No broad labor shock yet—but localized tremors are real.
The smartest leaders are already using data to navigate the gray zone between augmentation and automation.

Referenced Research:

• Yale Budget Lab (2025): Evaluating the Impact of AI on the Labor Market: Current State of Affairs
• Stanford Digital Economy Lab (2025): AI Exposure and Early-Career Employment Effects (working paper)
• IMF (2024): Generative AI and the Future of Work
• OECD Employment Outlook (2024): AI, Skills, and the Changing Labor Market
• World Economic Forum (2025): Future of Jobs Report

Takeaway:
AI is transforming how we work, not yet how many of us work. Stay adaptive, build visibility into your workforce data, and lead with metrics—not headlines.

Send a Text to the AI Guides on the show!

About your AI Guides

Gary Sloper

https://www.linkedin.com/in/gsloper/

Scott Bryan

https://www.linkedin.com/in/scottjbryan/

Macro AI Website:

https://www.macroaipodcast.com/

Macro AI LinkedIn Page:

https://www.linkedin.com/company/macro-ai-podcast/

Gary's Free AI Readiness Assessment:

https://macronetservices.com/events/the-comprehensive-guide-to-ai-readiness

Scott's Content & Blog

https://www.macronomics.ai/blog

Artificial Intelligence is moving fast—but privacy risks are moving just as quickly. In this episode of the Macro AI Podcast, Gary and Scott break down a role that’s quickly becoming indispensable: the Privacy Engineer for AI.

So what exactly is a privacy engineer? They’re the bridge between regulators and technologists. Their mission is to embed privacy by design into AI systems, turning complex laws like GDPR, HIPAA, California’s CPRA, and the EU AI Act into concrete technical safeguards. From minimizing sensitive data in training pipelines to stress-testing models for leaks, these engineers are the ones who make sure your AI is trustworthy, compliant, and resilient.

The timing could not be more urgent. The EU AI Act comes into full force in 2026, while in the U.S., the FTC is already forcing companies to delete models trained on tainted data. Without privacy engineers, businesses risk not just fines but also losing the very models they’ve invested millions in.

Gary and Scott dive into:

• How privacy engineers protect the AI lifecycle—from data collection to model deployment.
• Why businesses of every size need this role, with different priorities for startups, mid-market firms, and global enterprises.
• The ROI story: Cisco research shows a nearly 2x return on privacy investments, driven by faster sales cycles and stronger customer trust.
• A practical roadmap for building privacy capacity—starting small with guardrails and scaling up to ISO 42001 certification readiness.
• And new in this episode: the talent pipeline challenge. Where do you find these people? The best privacy engineers often start as ML engineers, security professionals, or graduates of specialized programs like Carnegie Mellon’s Privacy Engineering track. But supply is thin, so forward-looking enterprises are upskilling internal talent, partnering with consultancies, and competing aggressively to hire the rare hybrid who can talk about both differential privacy and the NIST AI Risk Management Framework.

The bottom line: Privacy Engineers for AI aren’t just compliance hires. They future-proof your AI investments, accelerate growth, and turn privacy into a strategic differentiator in an era where trust is the new currency.

Send a Text to the AI Guides on the show!

About your AI Guides

Gary Sloper

https://www.linkedin.com/in/gsloper/

Scott Bryan

https://www.linkedin.com/in/scottjbryan/

Macro AI Website:

https://www.macroaipodcast.com/

Macro AI LinkedIn Page:

https://www.linkedin.com/company/macro-ai-podcast/

Gary's Free AI Readiness Assessment:

https://macronetservices.com/events/the-comprehensive-guide-to-ai-readiness

Scott's Content & Blog

https://www.macronomics.ai/blog