You are 28, staring down two very different futures, and the weight of that choice is real. Finishing an MD you dislike to spend five more years in a PhD lab, or walking away now to build a Math + CS foundation from scratch, is not a casual fork in the road. It is a strategic pivot that deserves more than a pros-and-cons list. The fear about AI shrinking the software job market is understandable, but it is also misplaced. What is actually changing is the *shape* of the work, not its existence, and that distinction matters far more than any headline suggests.
The core question here is not whether a CS degree is losing value, but whether the skills you build are portable. A strong Math + CS background remains one of the most robust foundations for AI research, precisely because the field is becoming more mathematically rigorous, not less. The people who thrive will be those who can reason abstractly, design experiments, and understand the underlying structures of models, not just those who can write code that someone else prompts. The related piece on Unlock LLM Training: A Practical Guide to Distributed Algorithms reminds us that even advanced topics like distributed systems are still taught as foundational, solvable problems. That is the real signal: AI is not erasing the need for deep technical literacy; it is raising the bar for what counts as deep. A degree that trains you in the language of mathematics and the logic of computation is not a ticket to a shrinking job market. It is a key to a more sophisticated conversation.
Your MD+PhD path is not worthless, but it is a detour if your target is AI research. The biomedical angle could be a differentiator, but only if you are genuinely excited about applying ML to biology. If you are not, that five years is a long time to spend on a credential you will have to explain away. The related article on Explore the Forrester Function: Beyond Mathematics, a Tool for Machine Learning shows how even niche mathematical tools have direct applications in model design. That kind of curiosity-driven fluency is what you want to be building now, not tolerating. The practical takeaway is blunt: choose the path that lets you do the work you want to do sooner, with less accumulated regret. If that is math and CS, then the 3-4 years is an investment in a future you actually want, not a consolation prize for a career you are escaping.
The real risk is not that AI automates your first job. It is that you spend the next seven years becoming an expert in something you have already told us you do not want to do. The market will always need people who can ask the right questions, and a rigorous quantitative education is the best training for that. So here is the concrete detail to watch: when you picture yourself at 34, which version has more energy? The one holding an MD+PhD and a research gap, or the one holding a degree in math and a portfolio of AI projects? The answer is not just a preference; it is a signal. Act on it.