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