“Our devs can self-learn from docs and YouTube.”+
They've had three years. METR's developers believed they were 20% faster while measuring 19% slower, and Anthropic's session data shows novice practice succeeding at half the expert rate. Structured training with coaching is what moves regular use (BCG 2025).
“We're piloting Claude Code next quarter. Shouldn't training come after?”+
A pilot measures the tool and the team's practice together. Untrained, most developers use a fraction of what an agent can do, so the trial reads low and the tool takes the blame. One day first gives the pilot a calibrated cohort, a baseline and a success measure.
“The tools change too fast; training goes stale.”+
The durable skills are tool-agnostic: planning, context engineering, review discipline and verification. They transfer across Claude Code, Codex and whatever ships next. We re-train on the frontier monthly because we ship with it, and Academy content carries lifetime updates.
“We can't take engineers off delivery.”+
The entry format is one day. Your engineers already spend a median two hours a day with AI, unmanaged (DORA 2025). The bootcamp redirects time already being spent, and applied exercises run on tasks from your own roadmap.
“Security won't allow it.”+
Everything runs in your approved environment: your tool surface, your permissions, no production access, NDAs standard. We have delivered inside Europol. Untrained use is the larger security risk, so review boundaries and guardrails are a full module.
“We already pay for seats, so we're covered.”+
Seats are access, not capability. DORA's finding is that AI amplifies the engineering discipline you already have; licences amplify nothing on their own. The 8x gain in code merged per engineer showed up where trained use met shared standards (Anthropic 2026).
“Our AI bill is already high. Isn't the answer a cap?”+
A cap stops the bleeding and does nothing for the work. Anthropic's own cost guidance names the drivers of the expensive tail: sessions never cleared, wrong model defaults, vague tasks. Training removes those behaviours and gives you spend per merged change as the metric.
“Won't this deskill our junior developers?”+
It can, if nobody teaches them to stay in the loop. Anthropic's 2026 study found juniors who leaned on AI scored 50% on a knowledge quiz against 67% without it. Our exercises require participants to explain the plan, find the error and justify the checks.
“How do we know it worked?”+
We capture a baseline in scoping and close with a written capability debrief. It records what changed in how your engineers work, the conventions your team keeps and the metrics to watch: throughput, review load, spend per merged change. Scale to more cohorts only if the debrief earns it.
