What to Look For in an AI Training App

Most "AI-powered" apps generate a plan once and then change it every four weeks because that is the convention. That is a schedule, not adaptation. The control question is: what happens if I miss my prescribed reps for three sessions?

In an adaptive system the answer is specific — a lower load or fewer sets, with a stated reason. It matters because the volume–outcome curve is flat at the top end [1], so holding a dose you are not completing costs recovery and buys no result.

The test takes a week to run. Enter a deliberately weaker session — same loads, two fewer reps on every set — and see what the app proposes next week. If the plan looks identical, adaptation does not exist regardless of what the marketing page says.

The second version of the test runs upward. Enter a session two reps better and check whether the system proposes an increment immediately. Reacting to one good session is as suspicious as not reacting to three bad ones.

Does it state a reason for the change? An unexplained plan cannot be verified and is harder to execute consistently. Does it handle real constraints — missing equipment, travel, a skipped session — or does it assume perfect conditions? And can it go down as well as up?

The last question matters most, because it covers the only moment a program really works: week five or six of a mesocycle, when fatigue starts eating execution. Load7 bases that decision on the gap between planned and completed volume and shows it openly, rather than quietly rewriting the plan.

Check proximity to failure separately. Strength gains are similar across a wide RIR range [2], so an app that pushes every set to failure is selling you fatigue as intensity.

It is also worth checking how the app handles incomplete data. A real week contains sessions logged in a hurry, exercises recorded without RIR, and one workout entered from memory the following day. A system that needs a complete data set before proposing anything will, in practice, propose nothing.

The last criterion is the most mundane: whether you can export your own data. Training history is the one thing you cannot recreate after switching tools, and it underpins every future decision about your plan.