Tag: AI
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Known Weaknesses
Photo by FlyD on Unsplash Author’s Note: The workplace examples in this piece are generalized from requests I have reviewed and stories shared by friends, colleagues, and community members over the past several years. Details have been altered, and none describes a specific person, team, or project. ~Dom The Bug Wasn’t Where They Were Looking…
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Drawing Dead | An Architect’s Perspective on the Enterprise AI Bet
Photo by Julian Gojani on Unsplash You know what you are holding. Two cards, face down, and you have looked at them enough times to stop looking. You know the rules of the game, which hands beat which, and roughly how often the deck cooperates. You know how many chips are on your side of…
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The Implied Zero
Photo by Frank R on Unsplash Author’s Note: The opening example is a composite, paraphrased from several professional conversations over many years. Details have been generalized, and no specific organization, analysis, or business decision is being described. The concern examined here is the analytical pattern itself. ~Dom The conversation that removed it probably took about…
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The Meter is a Map
Photo by Tyler on Unsplash Author’s Note When I published The Token Tax three weeks ago, I expected the cost conversation around enterprise AI to keep growing. I did not expect it to accelerate this quickly. In the time since, the same pattern has surfaced across the industry in plain view: companies exhausting an entire…
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The Token Tax
Photo by Jakub Żerdzicki on Unsplash Beginning in late 2022, during the preview conversations for the first iteration of Copilot, through its launch and at intervals ever since, I told Microsoft’s product team that speed to launch was a short-term advantage. It mattered, of course. In a market defined by attention, fear, and executive urgency,…
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The Work We Couldn’t See
Photo by Olek Buzunov on Unsplash Author’s Note This is not an argument against AI, nor is it an argument that organizations are broadly incapable of making sound decisions. AI, when applied to well-understood domains with clearly defined inputs and outputs, works. It scales execution, improves throughput, and reduces the burden of routine work. I…
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The Space Between Slop and Solutions
Photo by John Cardamone on Unsplash In early 2026, a meme started making the rounds with surprising traction: Microslop. It began as a riff on “AI slop”; Merriam-Webster’s 2025 Word of the Year, coined to describe the deluge of low-effort, AI-generated content saturating digital platforms. Within days, Microslop had become shorthand for growing public frustration…
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Building Luna: Part 1 – A Machine Learning (Through) Experience
Photo by Guzmán Barquín on Unsplash Readers following the Building Aria series probably expected Part 6 last weekend. I did too… until I made myself stop. Not from burnout or technical failure, but from a deeper question: what would actually be worth building next? So I imposed a two-week pause. No new features, no late-night…
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Building Aria: Part 2 – From Voice to Agency
Picking Up the Thread In Part 1, Aria reached her first milestone: she was live in a practical sense. Running locally in Docker, serving models through Ollama, and speaking through Discord, she could already respond in conversation. Messages flowed into Postgres, the last ten entries recalled with each new exchange, giving her a baseline of…
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Building Aria: Part 1 – Giving Shape to a Thought
Photo by EJ Strat on Unsplash Framing the Problem Everyone in tech seems to have a strong opinion on AI these days—especially generative AI, the kind that powers everything from chatbots to deepfakes to essays in classrooms. For the past year, I’ve been immersed in it, testing the major models, subscribing to the usual suspects,…
