I would have expected a higher percentage. Few openings and a high bar for whatever there is. It was tough to get an assistant professor job 30 years ago and I can't imagine what it must be like now.
This. A 60% stay rate evokes scenarios of academic inbreeding and a total disconnect between the real world and the small bubble where research groups operate.
> A 60% stay rate evokes scenarios of academic inbreeding and a total disconnect between the real world and the small bubble where research groups operate
Academia's current structure rewards behaviors that don't necessarily create value. The "publish or perish" mentality encourages quantity over quality, leading to the replication crisis where many published findings can't be reproduced. The system tends to reward those who conform to existing academic paradigms while marginalizing innovative outsider perspectives that might bring valuable real-world insights.
When academics move directly from being students to faculty without external experience, it creates an echo chamber. This isolation from practical applications and market forces risks turning academic pursuit into a self-referential game - where success is measured by metrics like publication count and citation numbers rather than actual contribution to human knowledge or societal progress.
This separation from real-world feedback mechanisms means we may be investing significant human capital into activities that optimize for academic metrics rather than meaningful outcomes. The challenge isn't just about individual careers, but about ensuring our research institutions remain connected to the practical problems they're meant to help solve.
"Publish or perish" is fundamentally a wrong diagnosis. Publishing is generally the good part of the academia. If you are interested in your work, you obviously want to tell about it to other people in the field. Some specific publication venues are tedious and bureaucratic, but the pressure to publish is generally internal, not external.
Moving directly from being students to faculty is rare. It mostly happens in fields where the demand for PhDs is unusually high. Because universities can't compete with salaries, they have to offer positions people may not be ready for if they want to attract top candidates.
The norm is doing PhD, postdoc, and the first faculty position in different institutes – precisely to avoid the echo chamber effect. And unless you are from a particularly large country, you are expected to do at least one of them outside your home country.
Success measures are what they are for two reasons: competition and long-term focus. Academic research is fundamentally interesting, and there are far more competent people trying to do it than funding. And because people can't afford to wait for decades to measure the real-world impact, the academia must base career progression on something that can be determined quickly enough. If you are doing the kind of research where practical applications can be expected in a few years, the industry is a better place for you. They have a lot more resources for research than the academia.
I'm currently doing research on a topic I started working on ~15 years ago. In that time, the topic has progressed from something interesting and potentially valuable to a mainstream idea with plenty of research funding. If the work is successful, we may start seeing measurable real-world impact in 5-10 years.
ML papers by Western universities barely touch on the problems that practitioners face.
The only papers I see that are routinely useful have half the authors having a .in or .cn email at the end with the rest having Indian and Chinese names in US institutions.
The only western papers which aren't extended advertisements for their company are from people who are making something for themselves.
For example the best paper on image classification I've ever seen was posted on a private discord and was about better labeling the parts of a vagina as part of a stable diffusion training pipeline.
I used the methods without change and got better than state of the art for document segmentation.
Certainly, some countries have a more engineering-focused academic style. Western academia has always been more about advancing knowledge, which IMO is academia's mission.
> Western academia has always been more about advancing knowledge, which IMO is academia's mission.
You can advance knowledge in ways that are aligned with the nation's strategic needs. That would imply the career path of researchers would be oriented towards industry instead of pie in the sky projects.
I fail to see why universities should align on their country's strategic interests. Universities are not political nor military entities. Additionally, pie in the sky projects is what's needed to advance science, which is very distinct from advancing technology (industry).
> I fail to see why universities should align on their country's strategic interests.
In the UK, the industrial and military research labs have been closed, there is nothing being done except in universities. The university projects are really specific and don't join up in any way. There is no equivalent of DARPA guiding
research proposals to be useful but Governments still think that the universites will define the next generation of industrial products.
Source: my own area of trying to use computers for Materials Science.
Yeah. Practical working implementations of the latest in ML image classification technologies are a rapidly changing incremental improvement problem that industry is already all over, so not really surprising that this isn't a major focus for US university research: if PhDs or even potential PhDs want to do that they can get a much higher rate of pay at a private company.
All the foundational work that has lead up to the current practices in ML was done at universities. It's not like Google invented the transformer from scratch completely over night.
I couldn't read the whole article due to the paywall. I wonder (now that my knee has stopped jerking) whether they consider non-tenure-ladder professorial positions at universities as 'academia'. e.g. adjuncts, lecturers, staff or contract researchers, lab administrators, ...
Keep in mind that this is a postdoc. The thing you do after you complete a PhD, and for most of history, something you only did after you started working as a professor.
