---
id: "t-2026-03-03-most-ai-training-programmes-teach-people-to-type"
type: "thought"
title: "Most AI training programmes teach people to type"
date: "2026-03-03"
week: "2026-W10"
url: "https://nosov.ai/writing/most-ai-training-programmes-teach-people-to-type/"
linkedin: "https://www.linkedin.com/feed/update/urn%3Ali%3AugcPost%3A7434524071641440257"
author: "Ivan Nosov"
---

# Most AI training programmes teach people to type

Most AI training programs are teaching people to type.

The capability gap is in what they should be writing.

Every organization I talk to is running prompt engineering workshops. Some have moved to RAG pilots. Almost none have asked the harder question: can you actually specify how your own processes work with enough precision for a machine to execute them?

There's a four-layer capability stack emerging from AI-native companies. Prompt engineering is the bottom layer. It's commoditizing. The three disciplines above it are where value compounds - and where almost nobody is investing yet.

Take performance management. At Layer 1, an AI drafts a generic review that could describe anyone in any company. At Layer 4, it runs the full cycle - goal-setting, calibration, development triggers - across markets and local regulations, producing auditable output managers review rather than write from scratch.

Same technology at every layer. What changes is the organizational clarity required to use it.

I wrote the full framework - what the four disciplines are, why the sequence matters, and what each one actually takes to build 👇