Does Claude Learn from your Code?

McDonald's ArchIQ and the Future of AI in Business Operations

Send a Text to the AI Guides on the show! [https://www.buzzsprout.com/2454256/fan_mail/new] About your AI Guides Gary Sloper https://www.linkedin.com/in/gsloper/ [https://www.linkedin.com/in/gsloper/] Scott Bryan https://www.linkedin.com/in/scottjbryan/ [https://www.linkedin.com/in/scottjbryan/]   Macro AI Website:  https://www.macroaipodcast.com/ [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 [https://macronetservices.com/events/the-comprehensive-guide-to-ai-readiness] Scott's Content & Blog https://www.macronomics.ai/blog

Does Claude Learn from your Code?

The concern is understandable. If your team is building a specialized AI product on Claude — with custom agent logic, refined system prompts, proprietary data pipelines, and hard-won product insight — it is natural to wonder whether that work could somehow make the model smarter and eventually benefit a competitor.  Gary and Scott break down the issue clearly and practically. They explain the difference between three things that are often confused: in-conversation context, Claude’s account-level memory features, and the underlying model weights. The key takeaway: API usage does not update Claude’s model weights, and a competitor does not gain access to what Claude remembers within your account.  The episode also walks through Anthropic’s commercial data protections, including the default policy that commercial API inputs and outputs are not used to train generative models unless a customer opts in. Gary and Scott also discuss API data retention, zero data retention options for enterprise customers, and the practical areas where teams can accidentally create risk — including browser-based prototyping, feedback buttons, and partner program opt-ins.  Most importantly, the conversation turns this into an operational playbook for business leaders:  Use the API for serious development.  Audit whether developers have disabled model training in browser settings.  Avoid feedback buttons on proprietary workflows.  Create a clear approval process before joining partner or beta programs that involve data sharing.  Gary and Scott close by reframing the strategic question. For most AI products, the durable moat is not the prompt itself. The real competitive advantage comes from proprietary data, customer relationships, execution speed, product insight, and the feedback loops that compound over time.  This is a practical episode for executives, founders, product leaders, developers, and investors who want a clear answer to one of the most important AI business questions: where is the real IP risk, and what should teams actually do about it?  Send a Text to the AI Guides on the show! [https://www.buzzsprout.com/2454256/fan_mail/new] About your AI Guides Gary Sloper https://www.linkedin.com/in/gsloper/ [https://www.linkedin.com/in/gsloper/] Scott Bryan https://www.linkedin.com/in/scottjbryan/ [https://www.linkedin.com/in/scottjbryan/]   Macro AI Website:  https://www.macroaipodcast.com/ [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 [https://macronetservices.com/events/the-comprehensive-guide-to-ai-readiness] Scott's Content & Blog https://www.macronomics.ai/blog

What is an AI Harness

In this episode of the Macro AI Podcast, Gary and Scott break down an important emerging concept in enterprise AI: the AI harness.  For the last few years, most of the AI conversation has focused on the model — GPT, Claude, Gemini, Grok, Llama, and which one is smartest. But in the enterprise, the model is only part of the story. The real question is what has been built around the model to make it useful, controlled, repeatable, and safe.  Gary and Scott explain that the model is the “brain,” while the harness is the operating layer that allows that brain to do real work. A harness can give the model access to tools, manage workflow state, control permissions, enforce guardrails, log activity, route decisions to humans, and connect AI to actual business systems.  They also explain why this matters as companies move from chatbots to AI agents. Once AI can take action — opening tickets, updating CRM records, drafting customer responses, approving invoices, or triggering workflows — businesses need a control layer. That control layer is the harness.  The episode also distinguishes between three uses of the term: the agent harness, the evaluation harness, and the broader enterprise harness. For business leaders, the enterprise harness may be the most important because it includes identity, permissions, governance, compliance, auditability, monitoring, and human oversight.  The key takeaway: enterprise AI success will not come from model selection alone. The companies that get the most value from AI will be the ones that design the best systems around the model. The model gives you intelligence. The harness gives you reliability.  Send a Text to the AI Guides on the show! [https://www.buzzsprout.com/2454256/fan_mail/new] About your AI Guides Gary Sloper https://www.linkedin.com/in/gsloper/ [https://www.linkedin.com/in/gsloper/] Scott Bryan https://www.linkedin.com/in/scottjbryan/ [https://www.linkedin.com/in/scottjbryan/]   Macro AI Website:  https://www.macroaipodcast.com/ [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 [https://macronetservices.com/events/the-comprehensive-guide-to-ai-readiness] Scott's Content & Blog https://www.macronomics.ai/blog

Nividia Vera

In this episode of the Macro AI Podcast, Gary and Scott break down NVIDIA Vera and why it matters far beyond another chip announcement.  Vera is NVIDIA’s new data center CPU, but the bigger story is NVIDIA’s push to define the full AI factory architecture — CPU, GPU, memory, networking, interconnect, security, rack design, and software working together as one system.  Gary and Scott explain why the AI conversation is moving beyond GPUs alone. As AI shifts from simple chatbots to agents that retrieve data, call tools, use APIs, check permissions, and complete real business workflows, the infrastructure around the GPU becomes increasingly important.  The episode covers how Vera works with NVIDIA’s Rubin GPUs, NVLink, ConnectX networking, BlueField DPUs, and OEM systems from companies like Dell and Supermicro to support high-volume agentic AI workloads. The hosts also discuss why this matters for hyperscalers, neoclouds, colocation providers, mid-large enterprises, and even smaller AI-native companies where inference cost, latency, and model performance directly affect product margins.  The key takeaway: Vera is partly a cost optimization story. Not because CPUs replace GPUs, but because better architecture keeps expensive GPUs focused on high-value computation instead of wasting time on coordination, data movement, or system overhead.  For CIOs and AI product leaders, Vera raises a critical question: where should each AI workload run? Some AI belongs on the PC, some in SaaS, some in public cloud, some in neoclouds, and some in private or colocated AI factories.  Enterprise AI is becoming a distributed system — and the winners will be the companies that understand which workloads belong where.  Send a Text to the AI Guides on the show! [https://www.buzzsprout.com/2454256/fan_mail/new] About your AI Guides Gary Sloper https://www.linkedin.com/in/gsloper/ [https://www.linkedin.com/in/gsloper/] Scott Bryan https://www.linkedin.com/in/scottjbryan/ [https://www.linkedin.com/in/scottjbryan/]   Macro AI Website:  https://www.macroaipodcast.com/ [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 [https://macronetservices.com/events/the-comprehensive-guide-to-ai-readiness] Scott's Content & Blog https://www.macronomics.ai/blog

The AI Compute War: Why Anthropic Is Paying xAI for Colossus

In this episode of the Macro AI Podcast, we break down one of the most important AI infrastructure stories in the market: Anthropic’s major compute agreement with Elon Musk’s xAI and SpaceX infrastructure.  At first glance, the deal seems surprising. Anthropic, the company behind Claude, is backed by Amazon and Google and competes directly with xAI’s Grok. So why would Anthropic pay for access to Colossus, one of the largest AI compute clusters ever built?  The answer points to a major shift in the AI market. AI is no longer just a model race. It is becoming a compute race, a power race, and an infrastructure race.  Gary and Scott explain what Colossus is, why xAI’s rapid buildout matters, and why Anthropic needs massive production capacity to support Claude’s growth across enterprise users, developers, API workloads, coding tools, and agentic workflows. They also explain the difference between training and inference, and why inference is becoming the day-to-day economic engine of frontier AI.  The episode also gives CIOs a practical view into the market cost of AI compute. High-end NVIDIA H100-class GPU capacity can vary widely depending on provider, commitment level, scale, networking, storage, support, and availability. We compare typical enterprise GPU pricing to Anthropic’s reported $1.25 billion-per-month agreement and explain why the deal should be viewed less as a simple GPU rental and more as an industrial-scale capacity reservation.  The key takeaway for CIOs: AI strategy now requires infrastructure strategy. Enterprises need to understand where inference runs, what providers are involved, how data is handled, what happens during demand spikes, and whether their AI vendors have enough compute capacity to support business-critical workloads.  This episode is essential listening for business and technology leaders trying to understand the next phase of enterprise AI, where model performance, compute availability, power, cooling, network design, vendor dependency, and cost governance all come together.  Send a Text to the AI Guides on the show! [https://www.buzzsprout.com/2454256/fan_mail/new] About your AI Guides Gary Sloper https://www.linkedin.com/in/gsloper/ [https://www.linkedin.com/in/gsloper/] Scott Bryan https://www.linkedin.com/in/scottjbryan/ [https://www.linkedin.com/in/scottjbryan/]   Macro AI Website:  https://www.macroaipodcast.com/ [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 [https://macronetservices.com/events/the-comprehensive-guide-to-ai-readiness] Scott's Content & Blog https://www.macronomics.ai/blog