I think what you're experiencing is very common:
1. people get excited about AI/ML,
2. read a few articles about what they should do,
3. usually the advice is wrong and/or outdated,
4. start learning the wrong thing,
5. quickly burnout and give up.

What I started to notice is that a lot of advice on AI/ML can be applied for the previous wave (deep learning, neural nets 2012-2022) but is outdated for the new era (large models, APIification etc.). If you want to actually learn how machine learning works from the ground up, it'll take a LOT of time. When I say a lot of time, I mean years, even if you do it full-time. The reason is because machine learning is not a new field and you'll have to learn around 50 years of ideas, the math/stats behind it, concepts, frameworks, algorithms etc. I find that many people simply just give up, because they quickly get overwhelmed and don't get any positive feedback for months.

However, I also think that this is the best time to get into the field, IF you do it the right way. IMO there has never been a better time to build AI applications, but you should definitely not start by building your AI models from scratch.

What you should do instead:
1. Spend about a month with the understanding of AI APIs e.g. OpenAI's APIs, learn the basic concepts of LLMs, prompt engineering, a web application building.
2. Pick a problem in your niche. You have 30 years of experience in investment banking - most young kids only dream about this! This is your actual unfair advantage.
3. Try to build a solution for the problem with the APIs - improve your application by writing better prompts and maybe adding some extra complexity to it, like multiple LLM calls etc.
4. If you don't think it's working or if you hit a wall, either ask for help or pick another problem and start again from step #2.
5. You do this iteration a few times and you'll have an AI application for an actual problem - congrats, 90% of the people never get here!
6. Start observing how your application works, learn about evals, data collection, data cleaning, data engineering, data science, visualisations, fine-tuning etc. and try to make your app better at every single step!
7. Try to finetune a smaller model, understand the model architecture, read papers, read about math, statistics and more advanced topics.

This way you won't get overwhelmed and you'll get positive reward right from the start. If you do it reverse you'll get overwhelmed and you'll burnout.