Month: September 2026

  • How to Make AI Truly Creative: Leverage Cross-Domain Data for Breakthrough Ideas

    AI doesn’t naturally see across domains. Generative AI by its very nature is a probability engine. It returns the highest likely probabilities based on its inputs. That’s part of the reason it struggles with creativity, with true ideation, because it tends to stay in domain. Ask it for marketing ideas, it will return marketing ideas,…

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  • You Ask, I Answer: Preventing Bias in Historical Training Data?

    Summary In today's episode, I walk through how to sanitize historical sales data so your AI models don't inherit and amplify the biases hiding in your CRM. Here's what this means for you. You gain two practical levers for data quality that let you build predictive models grounded in fair, representative samples instead of skewed…

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  • New AI Models Are Just the Engine: What You Actually Need to Succeed

    A bunch of new engines (AI models) came out today. Opus 5.5 GPT-6-Sol GPT-6-Luna Mimo-V2.6 A gentle reminder: A new engine in a broken car still won’t get you where you need to go. A new engine in a good car that has no fuel still won’t get you where you need to go. A…

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  • You Ask, I Answer: Compensating Employees Who Use AI Efficiently?

    Summary In today's episode, I tackle how to handle compensation and performance reviews for employees who use AI to finish work in a fraction of the time. Here's what this means for you. You'll discover that the answer hinges entirely on having clear KPIs, because you cannot reward or adjust anyone you haven't defined a…

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  • Why Prompt Scoping Is the New Standard for Mastering Agentic AI

    Remember to include scope in your AI prompts. Today’s models are agentic-native. What that means is they’re tuned to be less about knowing, more about doing. I said the other week that they’re less like libraries (model makers are intentionally sacrificing knowledge) and more like engines. Models today are built on the assumption that whatever…

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  • You Ask, I Answer: Chunking Enterprise Data for AI Analysis?

    Summary In today's episode, I break down how to analyze massive multi-year business reports with AI without falling victim to context window limitations or hallucinations. Here's what this means for you. You'll discover a practical architecture combining vectorized databases, knowledge graphs, and deterministic Python code that delivers reliable enterprise-scale analysis. You'll also learn these concepts:…

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  • Almost Timely News: 🗞️ How I Use AI As a Keynote Speaker & Facilitator (2026-09-27)

    _Almost Timely News: 🗞️ How I Use AI As a Keynote Speaker & Facilitator (2026-09-27) :: View in Browser_ The Big Plug ✍️ Enroll in my newest course, Introduction to Deep Research With AI. 📺 Watch my 25-minute Intro to GEO talk at Spotlight Summit! Content Authenticity Statement 100% of this week’s newsletter was made…

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  • Local AI on Apple Silicon Just Got 5x Faster: Why You Should Run Models On-Device Now

    oMLX just added dual ANE support for Qwen3.x models! Now, if that sentence seems like complete gibberish, you’re not alone. There’s a very small subset of people who would get excited about that announcement (and yes, I am one of them) but it’s important in the bigger picture. Katie Robbert and I were talking about…

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  • You Ask, I Answer: Defending Pricing Against AI Discount Demands?

    Summary In today's episode, I explain how agencies and consultants should respond when clients demand massive fee cuts because they assume AI does all the work. Here's what this means for you. You'll discover that transparency and working visibly in public is the most effective defense for your pricing. You'll also learn these concepts: why…

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  • AI Model vs. Effort: How to Choose Brains or Muscle for Better Results and Lower Costs

    Lots of folks are confused by model choosers in AI tools. What’s the difference between model and effort? Here’s how I explain it to people. Model: brains Effort: muscle Both settings can burn more usage, but they do it in different ways. As models get bigger and smarter, they cost more compute, so vendors charge…

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