Episode Description (130 words):
In this episode of The Macro AI Podcast, Gary and Scott unpack why Apple chose Google’s Gemini to power the next-generation Siri — and why the move makes perfect sense when viewed through history. The hosts trace Google’s 20-year journey in artificial intelligence: from Google Brain’s “cat-video” experiment to DeepMind’s AlphaGo and the 2017 Transformer breakthrough by Google Research. They spotlight the engineers, hardware, and research culture that made Google the quiet giant of AI. The conversation then turns to Apple’s strategy — speed, scale, and privacy — and what this partnership means for the future of AI ecosystems.

Apple AI partnership, Google Gemini, Siri upgrade, DeepMind, Transformer architecture, Google Research 2017, TPU Trillium, word2vec, Google Brain, Jeff Dean, Demis Hassabis, Macro AI Podcast

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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 of The Macro AI Podcast, Gary and Scott dig into one of the biggest challenges emerging in enterprise AI: securing autonomous agents. As businesses deploy systems that can reason and act independently, a new class of risks emerges — from prompt injection and memory poisoning to identity confusion and tool abuse. The hosts explain why the old cybersecurity playbook no longer works, what “intent security” really means, and how identity-bound autonomy can make AI systems trustworthy at scale.

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 of The Macro AI Podcast, Gary and Scott take a deep dive into Palantir Technologies — the company quietly transforming how organizations use artificial intelligence to make real-world decisions.

They explain what Palantir actually is (and isn’t), how its four platforms — Gotham, Foundry, Apollo, and AIP — work together to fuse data, decisions, and actions, and why its ontology-driven architecture has become the blueprint for operational AI at scale.

The conversation explores Palantir’s:

• Government and commercial growth engine, including NHS and DoD programs
• Financial transformation into a profitable, recurring-revenue software company
• Competitive landscape, from cloud hyperscalers (Microsoft, AWS, Google, IBM, Oracle) to modern AI platforms (Databricks, Snowflake, C3.ai), BI specialists (Tableau, Splunk, Alteryx, SAS), and defense-sector rival Govini
• Platform differentiation — how Palantir uniquely unifies structured and unstructured data into a single, governable operating system

Gary and Scott close with practical lessons for executives: how to evaluate enterprise AI platforms, what to ask vendors, and why Palantir’s model represents the next phase of AI transformation — moving beyond analytics toward true decision infrastructure.

Whether you’re a CEO, CIO, or board member exploring how to operationalize AI responsibly, this episode gives you the clearest explanation yet of what makes Palantir different — and why its approach may define the next decade of enterprise intelligence.

Links & References:

• Palantir Investor Relations – Quarterly Results and AIP Overview
• Govini Ark Platform Overview
• Macro AI Podcast Executive AI Readiness Checklist

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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

Gary and Scott break down Walmart’s groundbreaking partnership with OpenAI — a move that officially launches “AI-first shopping experiences” inside ChatGPT. This is more than a new shopping feature; it’s the dawn of agentic commerce — where AI agents understand intent, plan purchases, and execute transactions autonomously.

Listeners will learn how Walmart is leveraging this partnership to expand its digital reach, strengthen its retail-media flywheel, and transform from a traditional retailer into a data-driven AI platform. The hosts also unpack what this means for OpenAI’s evolving business model, as commerce becomes a core workload for ChatGPT and a foundation for agent-based ecosystems.

The conversation covers:

• 🧭 Strategic Implications: How Walmart gains share-of-basket and new demand surfaces beyond walmart.com
• 🧠 Technical Breakdown: How AI agents plan, retrieve, rank, and execute orders using retrieval-augmented generation, constraint solving, and real-time checkout orchestration
• ⚙️ Optimization Insight: Why planning a shopping cart is a “knapsack scheduling problem under uncertainty” — and how that’s reshaping AI logistics
• ⚖️ Governance & Risk: Hallucinations, ranking fairness, privacy, and accountability in agent-driven transactions
• 🚀 Future of Retail (2025–2035): From persistent household twins to multimodal perception, agent media, and composable fulfillment

Gary and Scott explore what this means for CIOs, CFOs, and strategy leaders who need to prepare for AI-driven commerce infrastructure — where assistants become execution engines and supply chains become conversational.

If you want to understand how Walmart × OpenAI is quietly redefining the economics of retail and why this partnership will shape the next decade of consumer behavior, this is the episode you can’t miss.

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 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

Welcome to the Macro AI Podcast with Gary and Scott. In this episode, we dive into one of the newest and most important concepts hitting boardrooms and executive teams: AI Workslop.

AI Workslop describes polished, AI-generated work that looks good on the surface but lacks the substance, accuracy, or context to drive real decisions. It’s the long memo with no action, the glossy slide deck without insight, the email that shifts the burden onto the reader. And it’s not just annoying — it’s expensive.

Recent research from Harvard Business Review, BetterUp Labs, and Stanford found that:

• 40% of desk workers encountered AI Workslop in the last month.
• Each incident wasted nearly 2 hours.
• The hidden cost adds up to $186 per employee per month — over $9M annually for a 10,000-person company.
• Colleagues perceive Workslop senders as less creative, less capable, and less reliable.

In this episode, Gary and Scott explore:

• What AI Workslop is — and how it differs from AI hallucinations.
• Why it happens (old habits, new tools, and cultural pressure).
• How leaders can spot Workslop before it derails productivity.
• Why prompting skill matters — and why it’s not the full cure.
• The Anti-Workslop Playbook: leadership guardrails, workflow templates, training strategies, and metrics.
• Real-world examples of slop vs. substance in sales, operations, and contact centers.
• The single KPI executives should watch: time-to-decision.

AI isn’t the problem. Workslop is. And leaders who build the right norms, culture, and skills will see ROI instead of sludge.

🔗 Resources mentioned in this episode:

• Harvard Business Review article introducing “AI Workslop” (Sept 2025): https://hbr.org/2025/09/ai-workslop
• BetterUp Labs research and resources: https://www.betterup.com/resources/research/ai-workslop
• Stanford Social Media Lab collaboration: https://sml.stanford.edu

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