Why the future of human-machine interface is already wired to our biology. Yet, we keep installing it backwards I. The Typing Tax A transformative morning of 2025 started in the rain. Mind racing, ideas stacking faster than I could hold them — one hand gripped the leash while Winston, my husky-malamute,
People cannot see their own transformation. Anthropic asked 1,250 professionals whether AI augments or automates their work. Two thirds answered augmentation, insisting the machine only helps. The logs told a simpler story: nearly half the output was machine generated. The sixteen point gap is not deceit. It is amnesia
This is a follow up to my previous article on Compound Calibration from September, 2025 Every week brings a new model release. Gemini 3 dropped in mid-November, GPT-5.1 days earlier. Opus 4.5 arrived late November. DeepSeek and Grok and Mistral in the mix. The discourse is endless: which
Most advice about work describes how work should behave, not how it does. Set clear goals. Build the right process. Execute the plan. The advice isn't wrong. It's incomplete. It assumes rational actors, aligned incentives, and frictionless communication. Real organizations have none of these. There'
Experienced developers are getting slower with AI tools, not faster. Nineteen percent slower, according to one study. The juniors are speeding up. The experts are drowning. This inversion tells us something important. The tools generate at a pace that breaks our intuitions about knowledge work. Sixty thousand lines in an
I stood in front of Man Ray's rayographs at the MoMA this fall, studying the ghostly white silhouettes floating against dark backgrounds. A glass funnel, a coil of wire, objects I couldn't quite identify. The wall text explained that he'd discovered the technique by
TradTech opens the fridge and asks what can we make with what we have? AI-native closes the door and asks what should we make, knowing the ingredients will change before lunch. Count your team’s Slack messages that link an arXiv paper or model-drop tweet this week. Zero? You’re
Every company is building an AI strategy by asking, "What can we add?" The better question: "What can we delete?" The Inversion AI strategy defaults to addition. Smarter recommendations. Better predictions. Automated tasks. But the biggest opportunities are in subtraction. Not "what gets better?"
Pharma marketing has spent two decades optimizing the wrong bottleneck. The standard story: ideas move fast, execution moves slow. MLR review takes weeks. Compliance cycles multiply. By the time materials clear, the moment has passed. Everyone agrees this is the problem. Everyone agrees AI is the solution. Speed up the
ChatGPT hit one billion users in three years. The internet took thirty-six. And now nobody can stop investing. Meta is spending $600 billion through 2028. OpenAI and Oracle announced $500 billion for Project Stargate. Amazon is investing over $25 billion per quarter. More than a trillion dollars in motion. The
Your context engineering isn't an implementation detail. It's the whole product. Most AI products feel like wrappers because their prompts, retrieval, and evals deliver basic capability. The ones that don't feel like wrappers are maximizing what the model can do. Compare these prompts: Basic: