In this episode, the Macro AI Podcast research agents run the show since Gary and Scott are on vacation for the Thanksgiving holiday.  They took recent feedback from our listeners and opted to break down one of the most important developments in global AI: China’s frontier-level model Kimi K2 from Moonshot AI. The Macro AI Podcast research agents explore the model’s architecture, benchmark performance, agentic capabilities, and the surprising academic pedigree of its founders — a Tsinghua/Carnegie Mellon University lineage that positions Moonshot among the world’s most elite AI labs. 

They compare K2 to OpenAI, Anthropic, DeepSeek, and Qwen, explain the significance of its open-weights release, and analyze what this means for Western enterprises, policymakers, and the broader U.S.–China AI competition. 

A must-listen for anyone tracking the global AI race, national competitiveness, or enterprise-grade LLM deployment strategies. 

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

Jeff Bezos is officially back in an operational role — and he’s betting billions on a new AI venture called Project Prometheus. But this isn’t another chatbot startup. This is AI aimed squarely at the physical economy: manufacturing, materials, engineering, aerospace, supply chain, and the real-world systems that make global industries run. 

In this episode, Gary and Scott break down: 
• What Project Prometheus is — and what we actually know so far 
• Why it’s attracting elite talent from OpenAI, DeepMind, and Meta 
• The meaning of “AI for the physical economy,” explained in simple terms 
• How Bezos and co-leader Vik Bajaj are positioning this as a multi-decade moonshot 
• The emerging shift from digital AI to AI that designs, builds, and optimizes physical systems 
• Potential applications: factories that self-optimize, AI-designed materials, robotic labs, new aerospace components, and more 
• The profound implications for business leaders across manufacturing, engineering, logistics, and operations 
• The risks, unknowns, and why Prometheus could reshape competitive advantage for entire industries 

This is one of the clearest signals yet that AI is moving beyond screens and into the world of atoms. If you’re a CIO, COO, CTO, or executive responsible for operations or innovation, this episode will give you a front-row view into the next wave of AI transformation — and what you should be watching now. 

Listen in and learn why the future of AI won’t just be about thinking… it’ll be about building. 

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 tackle one of the most requested topics from our listeners — what high schools should be doing to prepare students for an AI-powered world. Instead of banning AI or pretending students aren’t using it, we explore how schools can embrace AI responsibly, ethically, and effectively. 

We break down: 

• Why AI literacy is now a foundational skill 
• How schools can shift from fear to structure 
• The four-tier AI policy every school should adopt 
• Real-world classroom examples across English, math, science, history, and languages 
• How parents can support responsible AI use at home 
• A practical 90-day action plan for school leaders 

If you’re a parent, teacher, principal, or district leader wondering how to navigate AI in education, this episode gives you a clear, practical roadmap. 

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

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 

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 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 

SEO Tags / Keywords 

palantir technologies, palantir ai, palantir explained, palantir foundry, palantir gotham, palantir aip, palantir apollo, enterprise ai, ai for business, data ontology, ai operating system, macro ai podcast, gary and scott, ai transformation, databricks vs palantir, snowflake ai, govini defense analytics, artificial intelligence platforms, ai governance, ai decision making, ai strategy for executives 

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

n this episode of The Macro AI Podcast, Gary and Scott sit down with Michael Reid, CEO of Megaport, to explore how network-as-a-service is reshaping the way enterprises connect to the cloud, scale their infrastructure, and prepare for an AI-driven future. 

Michael shares his perspective on why agility and flexibility in connectivity are now strategic imperatives for CIOs, CTOs, and CFOs—and how Megaport is positioning itself at the heart of this transformation. We discuss the company’s recent global expansion, the impact of Project Centurion’s 400G backbone upgrade, and what enterprises need to think about as AI workloads demand more bandwidth, lower latency, and tighter integration across multiple clouds. 

Listeners will hear insights on: 

• How enterprises can simplify multicloud strategies while maintaining performance and security. 
• The role of software-defined networking in accelerating digital transformation. 
• Why infrastructure investments like Project Centurion are foundational to AI adoption. 
• Practical advice for decision-makers navigating the convergence of networking, cloud, and AI. 

Michael also highlights where Megaport is heading next, from enabling new AI-centric services to supporting the rapid evolution of edge computing. For executives thinking about how to future-proof their connectivity strategies, this episode delivers both strategic guidance and actionable insights. 

Want more from Megaport? Don’t miss their own podcast, Uplink, where Michael and his team dive deeper into connectivity, cloud adoption, and the future of digital infrastructure. It’s the perfect complement to today’s conversation. You can listen here: https://www.megaport.com/uplink 

If you enjoyed this episode, please subscribe to The Macro AI Podcast, share it with colleagues, and stay tuned for more conversations at the intersection of AI, infrastructure, and business transformation. 

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

Customer Experience (CX) is undergoing the biggest transformation in decades, powered by AI and accelerated by the shift to Contact Center as a Service (CCaaS). In this episode of The Macro AI Podcast, Gary and Scott break down where CCaaS is today, how AI is reshaping the landscape, and what business leaders need to do to prepare for the next ten years. 

We start with the basics: what CCaaS actually is, why it matters, and who the leading players are — from established platforms like Five9, NICE, Genesys, and Avaya to innovators such as Verint, Talkdesk, 8x8, Microsoft, Zoom, eGain, and Observe.AI. This sets the stage for sourcing decisions and gives listeners a realistic view of the vendor ecosystem. 

From there, we dive into where CX stands today. Companies have chatbots, transcription tools, and AI-driven coaching — but most of it is fragmented. The real future is orchestration: AI systems that not only interact with customers but orchestrate workflows across humans, machines, and enterprise systems. 

Looking ahead, Gary and Scott explore four insights executives may not have considered: 

• Contact centers as insight engines — mining every customer interaction for churn risk, product feedback, and revenue opportunities. 
• A shift in the economic model — from cost-per-seat to outcome-based pricing tied to resolution, containment, or customer satisfaction. 
• Regulatory blind spots — how compliance, transparency, and trust will define CX success as much as speed and efficiency. 
• The organizational shift — why CX won’t remain a siloed department but will evolve into an enterprise-wide orchestration function. 

The episode also highlights the role of independent AI consultants in bridging the gap between technology and business outcomes. From assessing data readiness and designing orchestration fabrics to implementing governance frameworks, consultants help companies avoid vendor lock-in and align AI to their unique business models. 

For CIOs, COOs, and CEOs, the message is clear: the companies that start building AI fluency and governance now will be the ones delivering tomorrow’s customer experience. The future of CX is not just faster service — it’s intelligent, predictive, and woven into the fabric of the entire enterprise. 

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 MacroAI Podcast, Gary and Scott take a deep dive into one of the most overlooked yet mission-critical concepts in artificial intelligence: robustness. 

What does it mean for an AI system to be robust? In simple terms, it’s the ability to keep performing under stress — when the data is messy, unexpected, or even deliberately manipulated. Without robustness, AI that looks flawless in a demo can fail spectacularly in production, creating business risks instead of business value. 

Gary and Scott break it all down for business leaders, connecting technical concepts to practical outcomes. You’ll learn: 

• Why accuracy is not enough — accuracy is practice, robustness is game day. 
• Real-world examples of AI failures across healthcare, finance, retail, and even autonomous vehicles. 
• How organizations can build robustness into their AI systems through diverse data, stress testing, fallback mechanisms, and advanced methods like adversarial training and ensembles. 
• Ways to measure robustness, from stress-test error rates to cross-domain testing and robustness curves. 
• The growing role of third-party robustness testing, which is quickly becoming the AI equivalent of cybersecurity penetration testing. 
• The high cost of ignoring robustness — from financial losses to reputational damage. 
• Why future enterprise AI will require independent certifications, insurance validation, and proof of resilience to win trust. 

For executives, the message is clear: robustness equals trust. If you can’t trust your AI under pressure, you can’t scale it. Robustness is no longer a technical “nice-to-have” — it’s a business differentiator, a regulatory expectation, and the foundation for long-term AI success. 

Whether you’re a CEO, CIO, CFO, or a technical leader building AI systems, this episode will give you the insights, analogies, and practical takeaways to put robustness at the center of your AI strategy. 

Key soundbites: 

• “AI without robustness is like a self-driving car that only works in the sunshine.” 
• “Accuracy is practice. Robustness is game day.” 
• “Third-party robustness testing will soon be as common as penetration testing.” 

Good Reference Article:  Machine Learning Robustness A Primer 

Tune in and learn how to future-proof your AI investments. 

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 MacroAI Podcast, Gary and Scott take a deep dive into one of the most overlooked yet mission-critical concepts in artificial intelligence: robustness. 

What does it mean for an AI system to be robust? In simple terms, it’s the ability to keep performing under stress — when the data is messy, unexpected, or even deliberately manipulated. Without robustness, AI that looks flawless in a demo can fail spectacularly in production, creating business risks instead of business value. 

Gary and Scott break it all down for business leaders, connecting technical concepts to practical outcomes. You’ll learn: 

• Why accuracy is not enough — accuracy is practice, robustness is game day. 
• Real-world examples of AI failures across healthcare, finance, retail, and even autonomous vehicles. 
• How organizations can build robustness into their AI systems through diverse data, stress testing, fallback mechanisms, and advanced methods like adversarial training and ensembles. 
• Ways to measure robustness, from stress-test error rates to cross-domain testing and robustness curves. 
• The growing role of third-party robustness testing, which is quickly becoming the AI equivalent of cybersecurity penetration testing. 
• The high cost of ignoring robustness — from financial losses to reputational damage. 
• Why future enterprise AI will require independent certifications, insurance validation, and proof of resilience to win trust. 

For executives, the message is clear: robustness equals trust. If you can’t trust your AI under pressure, you can’t scale it. Robustness is no longer a technical “nice-to-have” — it’s a business differentiator, a regulatory expectation, and the foundation for long-term AI success. 

Whether you’re a CEO, CIO, CFO, or a technical leader building AI systems, this episode will give you the insights, analogies, and practical takeaways to put robustness at the center of your AI strategy. 

Key soundbites: 

• “AI without robustness is like a self-driving car that only works in the sunshine.” 
• “Accuracy is practice. Robustness is game day.” 
• “Third-party robustness testing will soon be as common as penetration testing.” 

Good Reference Article:  Machine Learning Robustness A Primer 

Tune in and learn how to future-proof your AI investments. 

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 sat down with Sarah Craynon Zumbrum, Sr. Manager of Cloud Engineering with Oracle to discuss AI, careers and much more.  She’s also a Gen AI Board member at Webber International University offering expert guidance to the faculty and students.  Sarah's a wealth of knowledge as her team are on the forefront of AI as part of Oracle, one of the best tech companies in the world.

Sarah provides excellent insight into how organizations can begin their AI journey breaking down areas to focus including in some instances using CPUs over GPUs.  

Have a listen and hear about Sarah’s journey in AI.  Oh, and she’s an amazing Tri-athlete.

Sarah’s profile:  https://www.linkedin.com/in/sczumbrum/

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

The Macro AI Podcast Episode: America’s AI Action Plan – A Summary 

In this episode of The Macro AI Podcast, hosts Gary and Scott dive into America’s AI Action Plan, a July 2025 White House roadmap to secure U.S. leadership in the global AI race. This 28-page document outlines three pillars—Accelerating AI Innovation, Building AI Infrastructure, and Leading International AI Diplomacy—to drive economic growth, national security, and technological dominance. 

Segment 1: Why It Matters 
The plan positions AI as a catalyst for an industrial, information, and cultural renaissance, emphasizing that the nation with the largest AI ecosystem will set global standards. 

Segment 2: Pillar I – Accelerating AI Innovation 
The first pillar focuses on unleashing private-sector innovation by removing bureaucratic barriers, like rescinding Biden’s Executive Order 14110. Regulatory sandboxes and AI Centers of Excellence enable rapid testing of AI tools, especially in healthcare and energy. The plan promotes open-source AI models, ensuring startups can innovate without relying on big tech. Federal funding will enhance access to compute resources, and workforce initiatives, including tax-free AI training reimbursements, aim to upskill workers, complementing rather than replacing jobs. 

Segment 3: Pillar II – Building AI Infrastructure 
AI demands robust infrastructure—data centers, semiconductors, and energy. The plan streamlines permitting for data centers via NEPA exclusions and FAST-41 reforms, ensuring faster deployment. It addresses energy needs by stabilizing the U.S. grid and preventing power source decommissioning. The CHIPS Act bolsters domestic semiconductor production, reducing reliance on foreign supply chains. Cybersecurity is prioritized with secure-by-design AI and a skilled workforce trained for infrastructure roles. 

Segment 4: Pillar III – International AI Diplomacy 
This pillar aims to make American AI the global standard by exporting to allies and countering external influence in governance bodies. Strengthened export controls protect AI compute and chips, while biosecurity investments address risks in synthetic biology. The TAKE IT DOWN Act combats synthetic media, ensuring trust in AI.

Segment 5: Technical Deep Dive 
For tech enthusiasts, the plan invests in AI interpretability via DARPA, enhancing trust in high-stakes applications like defense. NIST’s AI Evaluations Ecosystem standardizes reliability metrics, and secure compute environments at NSF and DOE protect sensitive data. Open-source support and hackathons foster innovation. 

Segment 6: Practical Advice 
Business leaders should join sandboxes, upskill teams with federal programs, secure AI stacks with U.S. tech, and align with American AI standards for global markets. Visit whitehouse.gov for the full plan. Tune in for actionable insights to le

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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

Description: In this episode of The Macro AI Podcast, hosts Gary and Scott dive into the transformative world of AIoT (Artificial Intelligence of Things), where IoT’s connected devices meet AI’s analytical power to revolutionize industries. Aimed at business leaders and tech professionals, this episode unpacks AIoT’s mechanics, real-world applications, and future potential, offering practical insights to lead in the AI era.Gary and Scott explain AIoT’s symbiotic relationship: IoT sensors collect real-time data (e.g., factory vibrations, traffic patterns), while AI processes it to predict outcomes and automate decisions, creating a self-improving ecosystem. They explore the technical backbone—sensors, edge/cloud processing, and protocols like MQTT and CoAP—emphasizing open standards to avoid vendor lock-in. Real-world examples include Siemens’ predictive maintenance (20% fewer outages), Singapore’s traffic optimization (15% less congestion), and AIoT wearables reducing hospital readmissions by 25%. In agriculture, AIoT achieves 99% accuracy in crop disease detection, boosting yields.Looking to 2025–2035, they highlight trends like 6G’s terabit speeds, Federated Learning for privacy-preserving AI, and digital twins for virtual system modeling. Challenges include scalability, security (60% of IoT devices have vulnerabilities), and ethical risks like algorithmic bias. Leadership strategies focus on governance, upskilling, and aligning AIoT with business goals.Key Takeaways: 

• Business Leaders: Start with a high-impact AIoT pilot (e.g., smart logistics) to drive ROI. 
• Tech Teams: Prioritize secure, interoperable systems using MQTT/CoAP and robust data pipelines. 
• Future-Proofing: Prepare for 6G and decentralized AI to stay competitive. 

With a 2025 McKinsey report estimating AIoT’s $5 trillion GDP impact by 2030, Gary and Scott urge listeners to act now. Tune in for actionable advice and inspiring use cases to transform your business with AIoT! 

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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, hosts Gary and Scott explore the transformative impact of artificial intelligence on the global economy. They dive into how AI drives short-term disruption and long-term prosperity, drawing parallels to Schumpeter’s creative destruction. The discussion highlights AI’s task-specific focus, augmenting rather than replacing jobs, with studies suggesting only 4% of jobs are fully automatable while 46% of tasks could be AI-driven. Productivity gains are projected to add $7-13 trillion to global GDP by 2030, though adoption rates and labor dynamics will shape outcomes.The episode examines AI’s geopolitical implications, with the U.S. and China leading the race for dominance, while regions like India and the EU carve out niches. AI is reshaping trade, supply chains, and global alliances, creating both opportunities and challenges for businesses. Workforce transformation is another focus, with AI creating new roles and demanding skills like critical thinking and AI literacy. The hosts discuss reskilling solutions, including AI-driven training platforms and public-private partnerships.AI’s influence extends to monetary policy, potentially driving deflationary pressure and transforming financial instruments like dynamic bonds and micro-financing. Industries like logistics, healthcare, and finance are undergoing major shifts, with AI unlocking precision medicine and democratizing wealth management. Long-term, AI could redefine economies through decentralized autonomous organizations (DAOs), personalized education, and new governance models, raising questions about taxing AI-generated wealth.With a bullish outlook, Gary and Scott emphasize AI’s potential to amplify human potential and create abundance, urging business leaders to invest in skills, adaptability, and strategic innovation. Tune in for a deep dive into AI’s economic promise and challenges. Connect with the hosts on LinkedIn or at macroaipodcast.com. 

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, hosts Gary and Scott dive into NVIDIA Corporation, the powerhouse defining the AI era with its cutting-edge hardware and transformative software ecosystems. Tracing NVIDIA’s journey from a 1993 gaming graphics pioneer to a $4 trillion AI leader by July 2025, they explore how CUDA, Omniverse, and tools like NIM and Dynamo make NVIDIA indispensable across industries. The episode breaks down NVIDIA’s four core business units—Data Center ($115.2B in FY25), Gaming, Professional Visualization, and Automotive & Robotics—highlighting their role in AI infrastructure, digital twins, and physical AI. A technical deep-dive unpacks innovations like the Blackwell architecture, DRIVE Thor, and Cosmos, while the hosts address challenges like U.S.-China export controls and rising competition from AMD and Huawei. Practical advice for business leaders and engineers includes leveraging NVIDIA’s software for scalable AI solutions and joining their developer communities. Tune in to learn how NVIDIA’s ecosystem can transform your business in the AI-driven future! 

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, hosts Gary and Scott dive deep into the world of Artificial Intelligence Centers of Excellence (AI CoEs), exploring their evolution, strategic importance, and practical steps for building high-impact CoEs in 2025. With 42% of U.S. enterprises now leveraging AI CoEs (up from 37% last year, per Deloitte), these hubs are critical for aligning AI innovation with business goals, ensuring ethical governance, and driving measurable ROI. 

The episode traces the history of CoEs from their 1990s origins in IT and quality control to their modern role as strategic engines for AI innovation. Gary and Scott discuss how early adopters like JPMorgan Chase and Siemens used CoEs for fraud detection and manufacturing optimization, while contemporary examples like Cleveland Clinic’s 2024 CoE showcase multimodal AI for personalized medicine. 

Listeners will learn key steps for building an AI CoE, including securing C-suite sponsorship (backed by a 2025 McKinsey study), assembling multidisciplinary teams with roles like AI strategists and ethicists, and prioritizing quick-win projects to demonstrate value. The hosts also break down three organizational models—Centralized, Federated, and Hybrid—offering insights on their pros, cons, and best use cases, supported by examples like Walmart’s hub-and-spoke approach. 

The episode addresses challenges like talent shortages and governance risks, while highlighting strategies to foster enterprise-wide innovation through hackathons, shared resources, and agile methodologies. Looking ahead, Gary and Scott explore emerging trends like multimodal AI, federated learning, and quantum computing, emphasizing the role of CoEs in navigating regulations like the EU AI Act and driving sustainability. 

Perfect for executives and AI enthusiasts, this episode offers actionable insights for transforming AI from a concept into a competitive differentiator. Tune in to learn how to structure and scale an AI CoE for enterprise success! 

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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

Join hosts Gary and Scott on The Macro AI Podcast as they explore the Agent Communication Protocol (ACP), a game-changing framework for AI agent collaboration. Developed by IBM Research and backed by the Linux Foundation, ACP enables seamless communication between AI agents, breaking down silos to drive innovation and scalability. Learn how ACP’s REST-based architecture, multimodal data handling, and agent discovery features empower businesses in manufacturing, healthcare, and beyond. The episode compares ACP to MCP and A2A, highlighting its unique focus on local-first, real-time coordination. With practical use cases and insights into challenges and future directions, this episode offers business leaders and technical listeners actionable strategies to leverage ACP for competitive advantage in the AI era. 

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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

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

Join hosts Gary Sloper and Scott Bryan on The Macro AI Podcast for an engaging conversation with Michael Raj, Vice President of Network Enablement at Verizon. In this episode, Michael dives into how Verizon, a global telecommunications leader, is harnessing the power of artificial intelligence and advanced analytics to drive transformative change across its network organization. 

From his personal career journey to the strategic deployment of AI within Verizon, Michael offers a fascinating look at how large enterprises are identifying and implementing AI use cases to achieve operational efficiencies. Michael shares insights into Verizon’s innovative applications of AI, including the use of propensity modeling and geospatial analysis to proactively mitigate fiber network disruptions, ensuring seamless connectivity for customers. 

 Michael addresses the importance of clean, high-quality data in building robust AI models, a topic that resonates with executives grappling with leveraging historical datasets. For students and job seekers, Michael provides actionable advice on pursuing careers in AI and analytics, emphasizing the skills and mindset needed to thrive in this rapidly evolving field. Beyond technical expertise, he highlights the leadership qualities critical for successfully driving AI initiatives within a complex organization and reflects on the most impactful lessons from his career. 

This episode is packed with practical insights for professionals, aspiring technologists, and anyone curious about the intersection of AI and business. Don’t miss Michael’s inspiring perspective on the future of work in an AI-driven world and his recommendations for the next generation of innovators. 

Subscribe to The Macro AI Podcast on Buzzsprout, share this episode with others, and connect with us at www.macroaipodcast.com or on LinkedIn to submit your questions. Stay curious, keep learning, and join us for the next episode! 

Listen now to explore how Verizon is shaping the future with AI and gain career advice for the AI era!

Guest:  Michael Raj - Vice President - Artificial Intelligence & Data (CDO Organization) at Verizon

https://www.linkedin.com/in/michael83/

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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 Independence Day episode of The Macro AI Podcast, hosts AI Agent X and AI Agent Y select a hot topic in AI based on listener feedback and generate a podcast to explore the powerful integration of Artificial Intelligence (AI) with Six Sigma, a proven methodology for process improvement and defect reduction. Titled "Revolutionizing Business Excellence with AI-Powered Six Sigma," the episode examines how AI is enhancing Six Sigma’s capabilities, enabling businesses to achieve unparalleled efficiency, quality, and competitiveness. 

The hosts dissect the application of AI within the DMAIC framework (Define, Measure, Analyze, Improve, Control) and discuss its implications for practitioners, ethical considerations, and the future of operational excellence. This summary provides a comprehensive overview of the episode’s key insights, offering business leaders actionable guidance for leveraging AI to elevate Six Sigma initiatives.

Introduction to Six Sigma and Its Evolution
 The episode opens with an introduction to Six Sigma, described as a disciplined, data-driven methodology aimed at eliminating defects in processes across industries, from manufacturing to services. AI Agent X explains that Six Sigma targets near-perfection, aiming for a maximum of 3.4 defects per million opportunities (DPMO). AI Agent Y provides historical context, tracing Six Sigma’s roots to the 1920s with Walter Shewhart’s statistical process control, through the Total Quality Management (TQM) movement led by W. Edwards Deming and Joseph Juran, to its formalization by Bill Smith at Motorola in the 1980s. 

The methodology gained prominence in the 1990s under Jack Welch’s leadership at General Electric, becoming a cornerstone for operational efficiency in companies like 3M and Honeywell. Today, Six Sigma, often combined with Lean principles as Lean Six Sigma, is practiced by millions of professionals and adopted by numerous Fortune 500 companies in sectors like healthcare, IT, finance, and supply chain management.AI’s Immediate Impact on the DMAIC Cycle
 The hosts delve into how AI is revolutionizing each phase of the DMAIC cycle, emphasizing that these advancements are already driving significant improvements in real-world applications.

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