I help companies close the gap between what their technology can do and what people actually do with it. Twenty years in technology — from program management to AI solutions architecture — taught me one thing: adoption is a design problem, not a technology problem.
“You'll know we've reached AGI when software ships as intended, not rushed to make deadlines. No compromises or shortcuts. Software that is joyful to use and just works.”
The next great leap in AI won't come from bigger models — it'll come from understanding why people aren't using the ones we already have. That last mile of adoption is the real frontier. And it starts by asking: how do users want this to work?
Most software initiatives stall in the same places. I've spent twenty years learning exactly where — first as a program manager, then as an AI solutions architect. The problems are always human, never just technical.
I take a nugget of an idea and extrapolate it to fruition — the full product vision, the user journey, the monetization model. I see what people will need before they know it’s possible, and I map how to get there.
The hardest problems aren’t technical — they’re design problems. I make the complicated simple. I solve for both the business and the user, so what ships isn’t just functional, it’s joyful to use.
AI readiness assessments, architecture planning, model selection, and implementation roadmaps. I design the system and stay engaged through delivery — not just until the contract ends.
AI strategy, product critique, and the ideas I can't stop turning over. Some of these started as tweets. The good ones became frameworks.
It's not about the tech. It's about what's missing. I broke down exactly how xAI could apply the Jobs playbook — obsessing over the gap between capability and desire — to turn Grok from a power tool into a daily habit for 100 million people.
If Microsoft owns OpenAI, why is SharePoint still unusable? If Apple has billions in AI R&D, why can’t Siri hold a conversation? AGI isn’t a benchmark — it’s a product experience.
The next great leap won’t come from bigger models or smarter machines. It’ll come from understanding why people aren’t using them yet. I wrote directly to xAI about the adoption problem nobody’s solving.
Same prompt, three models, wildly different results. Gemini won by a mile. GPT at $200/month was embarrassing. Here’s exactly why.
We use keywords and schema markup today. Tomorrow, every page will speak in vectors. I mapped out how embeddings replace metadata — before anyone was talking about it.
A TV series concept about a game developer and a whistleblower fighting to keep super-intelligent AI out of the wrong hands. Part thought experiment, part pitch.
Before the hype cycle, before the think pieces. A sampling from the archive.
Mapped the existential risks of AI five years before the open letter, GPT-4, and the alignment debate going mainstream.
Predicted Apple’s RISC architecture would compete with desktop-class systems and that Nvidia would enter the CPU market.
Wrote that every web page would eventually speak in vectors rather than metadata and schema markup.
Proposed an automated mechanism for AI platforms to condense and organize chat history into focused summaries — eliminating drift, reducing token costs, and keeping long conversations on track.
I start by understanding your users — not your roadmap. What are they trying to do? Where do they get stuck? What makes them leave?
Map the gap between what your AI can do and what people actually do with it. The answer is almost never ‘build more features.’
Design the solution — model selection, system architecture, user flows, and an implementation roadmap with real timelines and costs.
Clear deliverables — strategy documents, architecture blueprints, and implementation roadmaps your team can execute on. No vague recommendations. No lingering dependencies on me.
I review every inquiry personally and respond within 24 hours. No sales team, no intake forms, no gatekeepers.