But all of that discovery work, the financial planning, what their actual objectives were, having to dig into why someone wanted to buy a house, etc. proved to be some of the most invaluable and transferable skills I've learned (that, and SQL ;). A lot of that part of the course was learning how to ask questions, and learning how to follow-up to get past the superficial and often incorrect initial answers.
To your point I feel like there's these kind of meta skills that are transferable to many domains, but are sadly lacking these days from a lot of education that is hyper-focused on very role-specific qualifications. The ones that immediately come to mind:
- Learning how to learn (i.e., how to understand yourself, and improve)
- How to ask good questions (similar to above, but about others)
- And something I need a better label for because I just clumsily call it "sales", but in the Daniel Pink "To Sell is Human" style. Something like being able to structure a coherent and compelling narrative to other people. Clearly I fall short on this one.
no, the opportunity cost is too great for this field, for people that saw the light in just undergrad or wound up building software for other people without an undergrad CS degree
but yes I realize that any conversation with academics is going to be skewed from the market, I think its worth representing nonetheless since academics often interpret their privilege of choice as a nobler cause because they made that choice at all
e.g. "I could be an engineer, or I could be a billionaire. Which one should I pick? If I'm an engineer, I could have a nice career in the industry; but if I'm a billionaire I wouldn't have to ever work again and could just do all the software engineering I want, so it's an obvious choice to pick being a billionare."
A PhD is not about getting good at understanding something, let alone "unusually good". It's about being able to explore an uncharted area with little to no help, and write down your learnings so that those that come after you can build upon your work.
[1] My experience in ML in 2026.
No. What qualifies you as a researcher is being able to output work that is accepted by your peers. Hence the peer review process is critical. You fail to meet this bar if you publish something in blogspot or even arxiv for that matter.
You are trying too hard to come up with excuses. The paper review process is double-blind, and nothing prevents you from publishing the same work in multiple journals. If your goal is to do meaningful contributions to science then "power dynamics" are irrelevant.
You do need to develop a meaningful network in the field if your goal is to collaborate with other researchers, but that's basically what everyone needs to do in any professional field.
Like I get that the current social norms around "research" might not be there yet, but if they refuse to adapt, I just see that as a reason to defund "research" and start from scratch.
If you plan to go to industry, your incentives are somewhat misaligned with your PI. In that case you want to pursue multiple internships and getting networked with top companies in your field, and demonstrating practical output, well-released code, demonstrated downstream value for followup projects etc.
You're being needlessly dismissive of what "publishing papers" mean. Writing down what you learn is what papers are for, and the only papers that count for a PhD are those which are accepted by reference journals, which impose a relatively high bar on what content they accept.
So you still need to explore uncharted areas, and your main output is still writing down what you found for others to build upon. Those are the deliverables. For the person doing the PhD, the primary benefit is developing research skills, such as independente and self-reliance.
Nope. In fact they accept utter garbage most of the time. I can't count the numbers of times I've caught pure errors (literal logic errors, code errors, mathematical errors) even in highly respected journals. Journals have a high bar of entry because they've gated their communities, only allowing insiders to publish (ie tenured professors, people associated/employed with university labs or their colleagues). If you don't have an industry connection the odds of you self publishing in these journals is zero. And if you _do_ find a connection, say a professor whose willing to help you out, they'll demand to place their name on your paper (without actually contributing anything). This is how you get papers with 50 authors, because every politicker wanted a piece of the pie.
This is also why you see frontier AI labs self-publishing on their websites rather than in journals.
What they self-publish on their own websites should be regarded as marketing.
Unless you enrolled in a fraudulent, make-believe University of Phoenix style program, no. Any real PhD program only counts publications in a closed allowlist of reputable journals. Those journals are peer reviewed and enforce quality and relevance standards.
> I can't count the numbers of times I've caught pure errors (literal logic errors, code errors, mathematical errors) even in highly respected journals.
That's fine and all, specially if what you actually want to do is take a shot at humble bragging, but back in the subject papers are supposed to be snapshots of your understanding of a topic that can be used by others to inspire or advance their exploration. This means they are not infallible or free from criticism. In fact, it is rather ok brand to publish papers improving upon prior publications by correcting errors, improving methodologies, and even presenting contradictory evidence. That is what it means to be at the bleeding edge.
> The hard part of startups is product: knowing what to build, and being able to build it
Funny enough, this is why I think a PhD is undervalued by many. You spend years focused on a single area, sometimes pivoting topics, and getting hands on experience with operating solo on the outskirts of a particular field.
The overall point of the degree is you learn how to learn. The nice part is you have the padded walls of university equipment, funding, and time; So failure isn't terminal, just an opportunity to figure out what didn't work.
There's a few reasons how and why this message got distorted and lost in modern academia (honestly it would be a great subject for a book). It's no mistake that better known programs and programs with ties to a stream of successful startups are also the ones where there's a culture of builders and makers.