6 posts tagged “pillars-of-ai”.
We've covered what the model is (prediction), that it invents (hallucination), and where your data goes (deployment) — risks that live inside the machine. This pillar is about the risk you add: doing what the AI says without checking. Blind acceptance is the single most expensive habit in applied AI, and the fix isn't a better model. It's a human in the right place at the handoff.
Read post →Five pillars cataloged how AI goes wrong — it predicts, invents, leaks, gets blindly trusted, and costs more than the sticker. This final pillar is about the aftermath: when a decision turns out wrong, can you reconstruct what the system did, explain why, and prove who was responsible? That's accountability, and it rests on three properties — reproducibility, explainability, traceability. It's what makes the other five survivable, and increasingly it's the law.
Read post →The per-token fee is the number on the pricing page — the easiest cost to see, and almost never the largest line on the real invoice. This pillar opens the whole bill: what a token actually costs once you count the reasoning tokens you never see, why renting through an API or buying your own GPUs is the decision that moves the total most, and the hidden line items that turn a cheap demo into an expensive system. Budget for the whole system, not the call. (Prices are stamped with the month — a deliberate time capsule.)
Read post →Pillar 2 told you to ground a model in your own verified data. But to do that, you have to give the model your data — and that opens a different risk. When people ask whether an AI is safe, the part actually in your control is simpler than the model: where does your data go, who can see it, and is it allowed to be there? That's not a question about the model. It's a deployment decision — cloud, local, or a deliberate hybrid — and it's the one place where a choice made up front closes the risk almost entirely.
Read post →In the first pillar we landed on a hard fact: a language model predicts the next word, and it optimizes for plausible, not true. A hallucination is what happens when those two come apart — and it isn't the machine breaking. It's the machine doing exactly what it does, in the same confident voice it uses for everything else. You don't patch that out. You contain it. This pillar is about how.
Read post →Since text-generating AI went mainstream, an entire industry started using one word — AI — to mean one product. That collapse is expensive. It hides the half-century of techniques quietly running everything from fraud detection to global logistics, and it dresses up a next-word predictor as something that reasons. The first pillar of AI literacy is learning to see the field at its real size, and knowing what the model in front of you is actually doing when it answers.
Read post →
