I see a lot of bad takes on X about PhDs and frontier labs (not just this quoted tweet), so let me chime in.
For context, I didn't do a prestigious undergrad, worked a bit in a startup as an applied ML engineer, then did a PhD, and now work in a frontier lab.
A PhD isn't following a course from a teacher or magically becoming an overpowered scientist. It's about doing a few things with intense focus for a long time, alone. You may have a great advisor guiding you, but that only affects the learning rate. At the end of the day, it's a lot of alone time with you and your thoughts on a problem few people care about.
Good news: if you can find time, you can get a very similar experience! It'll be slower since you may not have as much free time, compute (though a free T4 on Colab is great), or an advisor and teammates (but there are great open communities).
Today's distinction between ML research and applied ML is often small. Grinding paper reproductions on Colab and improving them one step at a time is a great way to become a researcher.
The real worry is "Can I join a frontier lab without a PhD?" You'll face fierce competition for research scientist jobs, but even top PhD grads do—it's a mix of talent and luck. Re-implementing a paper and posting it on X probably isn't enough now, but publicly trying improvements and sharing interesting results, even as a blog post, can work! I know several frontier lab researchers, some extremely famous on X, who started this way.
Personally, I loved my PhD. It was time to learn and explore ideas fully and freely. Do you absolutely need one? No.
HOT TAKE:
Reality is, you can't actually work in top-quality ML research labs without a PhD.
Top research labs still look for people with PhDs and excellence in maths, stats, PyTorch, neural networks, and CUDA kernels.
In India, quality ML research labs are virtually nonexistent. Most good research labs are in the US/UK and China.
Implementing papers and working on T4 Colab is cool, but you won't cross the threshold to become a researcher.
99% of ML people belong to the applied side, which has better practical perks:
- MNCs or SF startups
- You can switch and get promoted every 1.5 years
- You can move to product management or CTO
- All you need is hands-on experience and not many research papers
- Cashflow is best
I really respect people who code research papers, but how long will you wait for your breakthrough? In 3 months, research evolves, and you're following it without actually building anything.
Stop following blindly! The world's best research labs pick only from top universities, not because you've implemented papers and posted on X!
Either go for a PhD outside India or stick to the applied ML side.
The job market is saturated and will remain so because we're evolving post-COVID.
On the other hand, no startup or research lab thinks about you. You must focus on your growth and money first, then look for impact.