The Macro AI Podcast

What is Ilya Sutskever building at Safe Superintelligence — and why could it matter to business? 

In this episode of The Macro AI Podcast, Gary Sloper and Scott Bryan take a closer look at SSI, the highly secretive AI company founded by former OpenAI chief scientist Ilya Sutskever. 

We explore SSI’s unusual “straight-shot” approach to developing safe superintelligence, the billions of dollars behind the company, its relationships with Google and NVIDIA, and why Sutskever believes the next major breakthrough in AI may come from better learning rather than simply bigger models. 

The conversation also looks at what concepts like stronger generalization and continual learning could mean for enterprises. If future AI systems can learn from experience, adapt to unfamiliar situations, and become better at a job after deployment, the implications could extend far beyond today’s copilots and agents. 

We also examine the other side of that future: governance, auditability, trust, and the challenge of controlling AI systems that continue to evolve. 

SSI has not yet released a public model, but the signals around the company suggest it is worth watching closely. This episode offers business leaders a practical preview of what the next generation of AI may look like — and why it could move us closer to true digital labor. 

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

Grok is no longer just Elon Musk’s AI chatbot. It is becoming part of a much larger vertically integrated AI strategy inside SpaceX. 

In this episode of The Macro AI Podcast, Gary Sloper and Scott Bryan break down the rapidly evolving Grok ecosystem, including SpaceXAI, the massive Colossus compute infrastructure, the acquisition of Cursor, access to real-time data through X, and the arrival of Grok 4.6. 

They examine how Grok compares with leading models from OpenAI and Anthropic, where its price-performance and agentic capabilities stand out, and why enterprises should increasingly consider Grok as part of their model evaluation strategy. 

They also look ahead to Grok 5, SpaceX engineering data, and one of the most ambitious ideas in AI infrastructure: putting large-scale compute into orbit. 

For business and technology leaders, the bigger story may not be whether Grok becomes the single best AI model—it may be the uniquely integrated business being built around it. 

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 Sloper and Scott Bryan examine a remarkable Connecticut court case in which a litigant embedded hidden white-on-white instructions in legal filings in an apparent attempt to manipulate any AI system that might review them. The court discovered the prompt injection, sanctioned the litigant, and used the case to highlight the growing risks of generative AI in professional workflows.  

Gary and Scott use the case as a jumping-off point to explain why prompt injection could become a major enterprise AI security and governance issue. As AI evolves from chatbots and copilots into agents that read documents, evaluate vendors, process invoices, analyze résumés, review contracts, and take actions, untrusted content can potentially contain instructions designed to influence those systems. 

They also discuss why simply keeping a “human in the loop” may not be enough if the AI’s analysis has already been manipulated—and why businesses need stronger controls around AI agents, trusted inputs, decision integrity, independent verification, and AI governance. 

Topics include: prompt injection, AI agents, agentic AI security, AI governance, human oversight, enterprise AI risk, and the growing challenge of separating data from instructions. 

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

What happens when AI does not just work inside a company—but begins to operate the company itself? 

In this episode of the Macro AI Podcast, Gary and Scott explore the emerging concept of non-human corporations: businesses in which AI agents can plan, make decisions, coordinate work, transact and manage day-to-day operations with limited human involvement. 

They explain how these organizations could be built using specialized AI agents, connected business systems, digital identity, payment controls and machine-readable governance. They also examine early legal proposals, real-world experiments and research showing that multi-agent organizations may become more capable while creating new risks around accountability, ethics and control. 

The discussion goes beyond the idea of an “AI CEO” to consider the broader business implications: lower operating costs, smaller teams, machine-to-machine commerce, rapidly launched micro-companies and competitors that can scale at software speed. 

For business leaders, the key question is not whether fully autonomous corporations arrive tomorrow. It is how quickly companies will begin developing autonomous operating cores—and what that means for strategy, governance and competitive advantage. 

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

Most enterprises will not rely on a single AI model forever. Instead, they will use multiple models for different tasks—and model routers will decide where each request should go. 

In this episode of the Macro AI Podcast, Gary and Scott explain how model routers work, where they sit in the enterprise AI architecture, and why the technology is becoming an important control layer for cost, performance, security, and resilience. 

They break down the differences between infrastructure routing, policy-based routing, and intelligent prompt routing, then examine how platforms from Microsoft, Google, Amazon, Cloudflare, Kong, LiteLLM, and Palo Alto Networks approach the problem. 

The episode also takes a closer look at Cloudflare’s broader enterprise AI strategy, including AI Gateway, Workers, Workers AI, Vectorize, AI Search, security, and Zero Trust services. 

Finally, Gary and Scott discuss where model routing is headed as enterprises begin routing not only prompts, but entire AI workflows across models, providers, regions, tools, and security policies. 

For business and technology leaders, the key question is no longer simply which AI model to choose. It is how the enterprise will continuously decide which model should handle each piece of work—and how it will know that decision was correct. 

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

Microsoft is making a major strategic push to build more of its own AI capability — and business leaders should pay attention. In this episode of the Macro AI Podcast, Gary and Scott break down Microsoft’s evolving AI strategy under Mustafa Suleyman, including the company’s new MAI model family and how it fits into the broader Microsoft ecosystem. 

They explain the purpose of Microsoft’s new models: MAI-Thinking-1 for more complex reasoning, MAI-Code-1-Flash for developer workflows, MAI-Image-2.5 for image generation and editing, MAI-Transcribe-1.5 for turning audio into business data, and MAI-Voice-2 for voice, localization, accessibility, and customer experience. They also explain where Microsoft’s Phi family fits in as a smaller, efficient model layer for everyday AI tasks that do not require a large frontier model. 

The discussion focuses on why Microsoft’s strategy is about more than low-cost AI. It is about matching the right model to the right workflow, using Microsoft Foundry as a control plane for discovering, deploying, managing, and routing across models. Gary and Scott also cover where executives should look first — meetings and calls, software development, content creation, voice and localization, and complex reasoning — and why Microsoft’s existing footprint in Teams, Microsoft 365, GitHub, VS Code, Dynamics, Power Platform, Azure, and its partner ecosystem gives the company a major enterprise advantage. 

For CIOs, CTOs, CFOs, and business leaders, the key question is no longer, “What is the one best AI model?” The better question is, “What work are we trying to transform, and which model is the right fit?” 

https://microsoft.ai/models/

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

Enterprise AI has moved beyond experimentation. The challenge now is building systems that deliver answers companies can trust—especially in highly regulated industries where accuracy, governance, and compliance are nonnegotiable. 

In this episode, Gary and Scott welcome Evan Siegel of eGain back to the Macro AI Podcast. Drawing on his experience in financial services, customer experience, and large-scale contact center operations, Evan explains how organizations are moving from AI pilots toward practical, measurable deployment.  

The conversation explores eGain’s expanding focus on banking and healthcare, why enterprise knowledge has become foundational infrastructure for AI, and how companies can reduce hallucinations by connecting AI systems to accurate, governed, and continuously maintained information. 

They also discuss: 

• What has changed most in enterprise AI over the past year  
• The unique AI challenges facing banking and healthcare  
• Why knowledge architecture may matter more than the latest foundation model  
• How organizations can build accurate, explainable, and compliant AI systems  
• The business metrics that demonstrate real AI value  
• Whether enterprises will use one foundation model or orchestrate several  
• The most common mistakes companies make when beginning their AI journey  
• How AI agents could reshape customer service over the next three to five years  

For business and technology leaders, this episode provides a practical look at what it takes to move from AI enthusiasm to trusted, governed, and measurable execution. 

Featured guest: Evan Siegel, eGain 

Follow the Macro AI Podcast for practical conversations about artificial intelligence, enterprise technology, and the strategies business leaders need to understand what comes next. 

eGain

https://www.egain.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

Kimi K3 is one of the most ambitious AI model launches of 2026—and it could reshape the global competition between Chinese and American AI companies. 

In this episode of the Macro AI Podcast, Gary Sloper and Scott Bryan explain who built Kimi K3, how Moonshot AI created a 2.8-trillion-parameter mixture-of-experts model, and why its architecture is designed for long-running coding and agentic work. 

Gary and Scott also clarify the frequently misunderstood difference between open-weight and open-source AI. They examine whether businesses will begin hosting models like Kimi K3 themselves, why most companies will still rely on managed infrastructure, and where smaller private models may deliver greater value. 

The discussion also compares Kimi K3 with leading American open models from NVIDIA, Google, OpenAI, Meta and IBM. Finally, Gary and Scott address model distillation, data security, deployment costs, geopolitical risk and the questions executives should ask before adopting a Chinese AI model. 

Listen for a practical business explanation of what Kimi K3 means for enterprise AI strategy. 

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 only as good as the data behind it. In this episode, we sit down with Jeff Lunsford, CEO of Tealium, to discuss why customer data has become one of the most strategic assets for enterprises embracing AI.

As organizations race to deploy AI applications, digital assistants, predictive analytics, and agentic workflows, many discover that fragmented, outdated, or poorly governed customer data becomes the biggest obstacle—not the AI model itself. Jeff shares how enterprises can move beyond traditional Customer Data Platforms (CDPs) to create real-time customer intelligence that powers meaningful AI outcomes.

During our conversation, we explored how the customer data landscape has evolved from the early days of tag management into today's world of real-time data orchestration, AI activation, and predictive decisioning. Jeff explains where Tealium fits within the modern enterprise architecture alongside data warehouses, cloud platforms, reverse ETL, and customer engagement systems.

We also discuss the importance of creating real-time customer context, enabling AI systems to make faster, more intelligent decisions while maintaining strong governance, privacy, consent management, and regulatory compliance. Jeff provides a practical overview of AIStream and explains how organizations can deliver AI-ready data to applications, models, and autonomous agents in real time.

The conversation also explores:

• Why data quality—not AI models—is often the biggest barrier to successful AI deployments
• The role of real-time customer context in improving personalization and customer experiences
• Predictive intelligence and AI-driven decisioning
• AI at the edge and real-time activation
• Building trusted AI through strong governance, privacy, and consent management
• Partner ecosystems spanning cloud providers, data platforms, and AI technologies
• Emerging trends including Model Context Protocol (MCP) and agentic AI workflows
• Practical advice for CIOs, CMOs, CDOs, and CEOs preparing their organizations for the next generation of AI

Jeff also shares career advice for students entering the workforce, discussing the skills that will remain valuable as AI continues to reshape nearly every industry.

Whether you're leading AI strategy, modernizing your customer data architecture, or simply trying to understand how AI creates business value beyond the model itself, this episode offers practical insights into one of the most important foundations of enterprise AI: trusted, real-time customer data.

Topics Covered

• Tealium overview and enterprise strategy
• Customer Data Platforms (CDPs)
• Real-time customer data and context
• Data orchestration and activation
• AI readiness
• AIStream
• Predictive intelligence
• AI decisioning
• Customer experience personalization
• Privacy, consent, and governance
• Data quality for AI
• Agentic AI and MCP
• Enterprise AI strategy
• AI careers and future workforce

If you enjoyed this episode, be sure to subscribe to The Macro AI Podcast, leave a review, and share it with colleagues interested in AI, enterprise architecture, customer data, and digital 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

In this episode of the Macro AI Podcast, Gary Sloper and Scott Bryan examine one of the most important questions facing business leaders today: is AI eliminating work, or is it changing where work gets stuck? 

While much of the public conversation focuses on job replacement, the bigger strategic issue may be that AI is moving the bottleneck. AI can make individual tasks faster — from writing and research to coding, customer support, forecasting, and design — but that does not automatically make the entire enterprise faster. In many cases, AI simply exposes the next constraint: approvals, data quality, governance, implementation capacity, supplier readiness, field labor, compliance, or physical infrastructure. 

Gary and Scott discuss why the labor market is not yet showing a simple AI-driven job-loss story, why entry-level career paths may be one of the first pressure points, and why individual productivity gains do not always translate into enterprise-wide economic gains. They also explore how AI can create new work by making ideas, experiments, and business models cheaper to pursue. 

The episode highlights examples across healthcare, manufacturing, banking, retail, telecom, and software, showing how AI shifts the constraint from knowledge production to workflow absorption. The discussion also explains why physical bottlenecks — including data centers, power, cooling, manufacturing capacity, clinical capacity, logistics, and supplier readiness — will matter more as AI accelerates planning, design, analysis, and demand generation. 

The key takeaway: AI is not just a labor replacement technology. It is a throughput technology. The companies that win will be those that map their workflows, anticipate where bottlenecks will move, redesign early-career training, modernize their workflow layer, and use AI for growth — not just cost cutting. 

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