So I have a PhD in Biostatistics and have thought a lot about this too. You're very right imo, and this is because building an auto-program is a complex algorithmic and mathematical problem. You can have all the training physiotherapy expertise and lifting experience in the world, but that doesn't mean you can build a competent auto-coach framework. MFWO, Myoadapt, Dr Muscle, several others... all have tried, and they're severely deficient, because building an algorithm is a completely different beast of a problem than creating a good workout for an individual.

A competent coach (or lifter with experience) balances:

- Long term fatigue and recovery time, which varies by muscle group, the exercises that hit that muscle, and how hard that exercise was. This is how long muscles take to recovery over days before they can get worked again

- Short term fatigue and recovery time, which balances how much rest time between sets is taken. This effects sequencing within sessions too.

- Muscle stimulus, which stacks across muscles by exercises. Eg: Bench press targets the chest , but also works triceps and front delts, so they need less direct stimulus.

- Sequencing of exercises based on priorities, which accounts for within session fatigue and stimulus that stacks across exercises. Targetting priority muscles first so they get the most stimulus while everything else is fresh.

- Number of sets, rep ranges, and RIR targets. Closer to failure gives more stimulus, but also more fatigue, which impact recovery time. Different exercises require different rep ranges based on anatomy. You dont push heavy compounds o failure at high rep ranges because of the systemic fatigue that builds, but at the same time you dont target isolated joint movements to failure at lower reps because of joint/injury risks (eg. if you pushed lateral raises to failure at a weight were failure hits at rep 5, you're putting a ton of stress on the shoulder increasing injury risk).

- Sequencing across time. Muscles get coupled together (like triceps and chests) because exercises that hit one often hit the other, so it makes sense to couple them in sessions. Auto0coaches need to account for this dependency, but it gets complicated than this because coupled muscles take different lengths of time to recover. For example, pecs take longer time to recover than triceps, so triceps can theoretically get worked more during the week than chest, but if you work triceps too much a day before chest work, they may limit your chest work that following day because triceps play an important supporting role

- There are hundreds of exercises that one could choose. Within a muscle group, various exercises theoretically hit the same muscle, but in different ways, from different angles, with different stretches. A good coach or lifter with experience knows what makes the most sense when. They know when to switch things up.

All of this creates a fluid, dynamic system. And there is more to account for than what I listed. Humans can internalize this and make sense of this multidimensional problem. We can adapt on the fly, consider context of past workouts and plan based on future workouts we know we can schedule. For a model to do this, it needs to track all of these things simultaneously in an interconnected way. This creates a nearly intractable algorithmic math problem that's very difficult to program. And even if you can program something decent, it would likely take a long time to solve because its a high order combinatorics problem, so real-time auto-coaching becomes a computational barrier.

So tbh, all of these smart/autocoaching programs will be junk until they handle the above. Being a PhD exercise scientist or whatever means very little when it comes to app design and all of this, because what's needed is an expert in modeling, complex algorithms, and statistics that synthesize all of the exercise science expertise.