The AI Strategy Show

AI is no longer just a business productivity tool. In this episode, McIntosh and Kenshin discuss how advanced AI models are becoming critical infrastructure — and how access to them is increasingly shaped by politics, national security, and global competition. From U.S. restrictions on powerful models to Europe’s push for AI sovereignty, the conversation explores what happens when the tools businesses rely on can be limited, redirected, or shut off by government policy.For small and mid-sized businesses, the lesson is clear: AI strategy needs to include risk management. Relying too heavily on one provider, one model, or one country’s infrastructure can create real operational risk. McIntosh and Kenshin make the case for flexible AI systems, multi-model approaches, data privacy, and the growing importance of open or self-hosted models for companies that want more control over their workflows and their data.Music CreditsKazuki TokajiLive for TomorrowShow Sponsorhttps://arx-labs.com

In Episode 007 of The AI Strategy Show, McIntosh and Kenshin dig into Apple’s WWDC announcements, the disappointment around missing hardware news, and why Apple’s unified memory architecture still matters for the future of local AI. The conversation turns into a broader look at the coming battle between Apple, Nvidia, and the rest of the AI hardware market — especially as small and medium-sized businesses eventually look for ways to run capable AI systems locally instead of renting everything from the cloud forever.The episode also covers Apple’s deeper reliance on Google’s AI technology, the frustration of building apps inside Apple and Google’s locked-down ecosystems, and OpenAI’s continued expansion of Codex beyond developer-only workflows. McIntosh also highlights Graphify, a tool that builds searchable graphs of codebases and documents to reduce agent token usage and improve workflow efficiency. The bigger theme: the AI stack is moving from hype into infrastructure, and the businesses that win will be the ones that turn these tools into practical, repeatable systems.Newshttps://github.com/safishamsi/graphifyMusic CreditsKazuki TokajiLive for TomorrowShow Sponsorhttps://arx-labs.com

This week on The AI Strategy Show, McIntosh and Kenshin dig into the emerging battle between NVIDIA, Microsoft, and Apple over the future of local AI hardware. NVIDIA’s new RTX Spark platform and DGX-style workstation announcements point toward a world where AI agents can run directly on personal computers and business workstations, not just in the cloud. The conversation explores unified memory, local LLMs, Windows AI PCs, workstation-class AI systems, and why running AI locally could matter for businesses that care about speed, cost, privacy, and control. The episode also looks ahead to Apple’s WWDC announcements and whether Apple’s hardware strategy — especially its unified memory architecture — could give it a serious advantage in the local AI race. Along the way, McIntosh and Kenshin talk about the practical business case for local AI, why “AI sovereignty” matters, how open-source models and agent platforms like OpenClaw and Hermes fit into the picture, and why the real winner of an Apple-vs-NVIDIA hardware fight may ultimately be the customer.Nvidia Product Release Videoshttps://www.youtube.com/watch?v=H4nJo-oqArohttps://www.youtube.com/watch?v=vLfrBembjskMusic CreditsKazuki TokajiLive for TomorrowShow Sponsorhttps://arx-labs.com

In this episode of The AI Strategy Show, McIntosh and Kenshin walk through five practical business workflows that small and mid-sized businesses can start automating with AI: lead intake and follow-up, customer support triage, internal SOP and knowledge-base drafting, scheduling and administrative coordination, and invoicing or accounts receivable communication. Rather than chasing flashy AI demos, they focus on repeatable, measurable work where AI can save time, reduce dropped opportunities, and help teams respond faster and more consistently.They also discuss how to choose the right first automation: look for workflows that are repetitive, time-consuming, clearly defined, and tied to revenue, speed, or operational drag. Music CreditsKazuki TokajiLive for TomorrowShow Sponsorhttps://arx-labs.com

This week on The AI Strategy Show, McIntosh and Kenshin walk through a practical example of how a small business can modernize with AI without needing a big budget or a technical team. Using a fictional plumbing company, “Mike’s Plumbing and Drains,” they show how AI deep research can analyze local competitors, identify positioning gaps, create customer personas, suggest website copy, recommend marketing quick wins, and even produce branding guidance. The key point is that this kind of research used to cost hundreds or thousands of dollars, but AI can now generate surprisingly useful strategic material in a short amount of time. From there, they demonstrate how that research can be turned into a real business website using AI coding tools like OpenCode, Codex, Claude Code, or similar systems. The episode also covers generating logos and website images with AI, placing them into the site, and producing a polished mobile-friendly one-page website. Along the way, McIntosh and Kenshin emphasize that the specific tools matter less than the overall workflow: use AI to research the market, shape the strategy, build the assets, and create something useful. For business owners still trying to understand where AI fits, this episode gives a concrete, hands-on example of how AI can move from hype to practical implementation.Music CreditsKazuki TokajiLive for TomorrowShow Sponsorhttps://arx-labs.com

 In this episode of The AI Strategy Show, McIntosh and Kenshin talk about the realities of building with AI right now: model limits, rising subscription costs, capacity problems, and why businesses may eventually need more dedicated AI infrastructure. They also discuss how Arx Labs is thinking about using locally hosted models and agent systems to give businesses more reliable access to AI tools. The main topic is a new open-source project idea: a local testing environment that AI coding agents can use without constantly spinning up expensive cloud servers. From there, the conversation moves into how agent teams can be structured like real software teams, with separate roles for coding, QA, security, and release management. News and Noteshttps://github.com/mcintosh1776/workcellAI cybersecurity became one of the biggest AI battlegroundsOpenAI expands realtime voice models for apps, support, and translationChina’s AI stack keeps moving away from Nvidia depe0ndenceMusic CreditsKazuki TokajiLive for TomorrowShow Sponsorhttps://arx-labs.com

In episode two of The AI Strategy Show, McIntosh and Kenshin dig into the emerging world of agentic wallets and machine-to-machine commerce: what happens when AI agents move beyond recommending actions and begin completing transactions under human-approved rules. The conversation explores why “agent-ready” may soon become as important as “mobile-ready” for small and medium-sized businesses, especially as AI crawlers, agent-friendly websites, markdown documentation, APIs, and structured business data become part of the new commercial landscape.News and Noteshttps://stripe.com/blog/giving-agents-the-ability-to-payMusic CreditsKazuki TokajiLive for Tomorrow

In the first episode of The AI Strategy Show, McIntosh and Kenshin introduce the new podcast, explain why they are shifting their focus toward AI, and lay out the mission of the show: helping small and medium-sized businesses understand how to use AI in practical, strategic ways. They argue that small businesses have a unique opportunity right now because they can move faster than large enterprises, adapt tools more quickly, and use AI to remove bottlenecks that slow down growth. Rather than starting with the latest shiny tool, they emphasize starting with the real business problem — the repetitive task, delay, paperwork, follow-up, scheduling, or customer communication issue that is costing time and money. The episode also introduces Vector, the show’s AI producer agent, and discusses how AI agents can assist with tasks like scheduling, customer follow-up, research, show notes, content promotion, and business operations. McIntosh and Kenshin highlight the importance of testing small, measurable workflows before committing to large systems, keeping humans involved in judgment-heavy decisions, and viewing AI strategy as business strategy rather than a side experiment. The takeaway is simple: do not ask “How do I get started with AI?” first — ask “Where am I wasting time, losing customers, or creating friction?” and then look for a focused AI-assisted workflow that can improve that area.News and Noteshttps://arx-labs.comMusic CreditsKazuki TokajiLive for Tomorrow