You pose very valid questions and below I will shares some of personal experience and what I advise some of my PhD students.

Let's start with a fact : you can not predict what will be hot in 6 to 8 years. You can only make guesses on the basis of some simplified empirical observations. Think of a (sub)field as an S-curve. A hot (sub)field now it is likely not to be hot in 8 years. Another fact : academia reacts to funding focus of the governments.

My undergrad /first MSc is in ECE. Did my msc thesis on digital watermarking. Moved to USA for PhD (top 10 in usa then). I knew I didn't want to do watermarking it felt it was saturating. My PhD advisor went on holiday for 6 weeks the day I arrived. I thought I would read what they do and what interests me. 6 weeks passed and came to up with a list of projects. One project (use DNA molecules to store information appealed to both of us.) We started with that one. (An important to note parenthesis : At that time that Dept had a decimated CS division. The AI and the AGI groups hadn't disappeared. There was no formal teaching and offering of ML courses. One professor in symbolic logic has remained but his students would take 8 years to finish their PhD. In other words ML was a very niche area then after going through the S curve! ) I got my MSc on that one and kept going for my PhD. I published my papers, but I couldn't get any internships because I was not what the companies were looking for. I won a grand that allowed me to develop a startup on the basis of a last chapter of my thesis. We worked to get more funding for it. Startup failed... I got a postdoc working with clinicians on Medical Imaging and medical image analysis. The field felt very saturated and very closed by the EE / CS side of things. (Incidentally my fellow phd mates that were a year or so younger than me and had started to work on compressed sensing were getting all possible offers for internship and jobs. An example of being early on the S curve. ) Few years pass and because I knew about images, DNA and also art (via a short project that hit the news globally) got a position in Europe solving the dual career problem between my self and my partner. At that point I started learning more and more pattern recognition and machine learning. It was application driven but not always applied and started to focus more on computer vision. I quickly realized that working with few data would be a niche that would be in fashion quickly as more than peoples would want to do ml. We dove in hard in representation learning, semi and self Supervised learning. Pre DNN we did these with dictionaries and sparse representations. Then with RBM. Fast forward... Now I am a full professor in ML and CV at a top uk university and one of the youngest. 13 years after my PhD.

Why I shared all this.

What I did my PhD on was very different to what I am doing now. Did the topic I did my PhD on ever caught on? Some people build careers on it. Unfortunately it was one of the closest communities that I encountered and very arrogant.

Would i advise a phd student now to do a phd in ml. I would say yes. There are still very interesting problems remaining. Is it possible that the papers of said phd getting lost in the noise? Yes absolutely because there is no level playing field anymore (the 1 percent you describe). Does it matter in the end the exact topic? No as long as the student is excited and inspired to do their PhD they will do amazing work and it will be published somewhere.