Jessica Hullman, What stories should we tell about science now? Statistical Modeling, Causal Inference, and Social Science, August 28, 2026.
The opening paragraphs:
This is Jessica. Like many academics, I am concerned about what sort of new steady state U.S. universities will find themselves in after the dust settles on recent transitions. Namely, the last few years have brought funding cuts, targeted visa policy, reduced demand for grad degrees, and a general brain drain to industry (particularly noticeable in AI and computer science). It’s disorienting to think that academia has already peaked, and that the prestige ranking of the R1 faculty job over the top industry research positions (at least in computer science) might be inverting. But things feel very different than they did even a year ago. The reality of there being less money available to pay for basic aspects of research really started to hit me in the last six months. Post-covid, working on campus became less lively, but now it also feels like our collective attention is anxiously focused on Silicon Valley or Washington D.C. We hold faculty meetings where we discuss things like, Is there any way we can help local faculty members who were laid off from tenure track jobs? How will we ensure we can fund all of our own PhDs, given that TA quotas stay fixed but faculty are running out of funding runway?
To some, this is an overdue rebalancing. Nate Silver, for example, calls getting a PhD a “much worse value proposition than 20 years ago”, and predicts that elite higher ed will become “~50% less relevant in the new steady state,” which is in his eyes a good recalibration.
But it’s worth reflecting on what is lost exactly, if this dwindling of minds and resources continues. How should we think about the value of what universities provide over industry, like intellectual autonomy, or training on how to think scientifically? As a professor, I could make a list of the things that have kept me in academia–being free to work on the problems I find most important, the diversity of topics I can work on at any given time, grad students who care about doing deep work, having time to think about the best solution to a problem. But at an aggregate level, it’s less clear what the equation is.
The article then goes on to sketch out a number of specific issues. The first: “In search of the mysterious fruits of basic science.” Thus:
I’ve been reading the work of philosopher Heather Douglas, who has traced and critiqued the basic versus applied science distinction, the linear model as justification, and the idea of scientific freedom as limited social responsibility (see, e.g., here and here, or her book on the value-free ideal). Popularized by Vannevar Bush after WWII, in a report prepared for President Roosevelt, basic science is a reframing of pure science, presented as “scientific capital,” providing the principles and conceptions to power new products and processes years into the future. Bush called for deliberate policy to guard against the otherwise inevitable scenario where applied science drives out the pure. One of the eventual outcomes of his report was the creation of the NSF.
But despite the pragmatic nature of basic science espoused by Bush, as a derivation of pure science, it is hard to separate from less tangible values. One is that scientific understanding is a good outside of practical application, at both the individual and societal level. The earliest advocates of pure science associated it with being closer to God. Post-Enlightenment, this view gave way to a more secular superiority complex, which implied the strong character of the pure scientist, who chose to eschew wealth. “The highest occupation of mankind”, Henry Rowland called it in his Gilded Age era essay, “A Plea for Pure Science,” which bemoaned the vulgarity of attributing scientific greatness to the applied scientist rather than the pure.
One specific case: AI:
If we take our intuitions from the linear model, we might protest that innovation will suffer if universities’ research purposes are deprioritized. The post WWII science-industrial complex expanded the presence of basic research in industry, but studies suggest that the knowledge generating role of corporate R&D has been on the decline for years. To the extent that basic research is the supplier of downstream applications, it would seem we need universities more than ever.
But the distinction between basic and applied science that’s become synonymous with how we envision science has never been airtight. Critics questioned how an institution could be built around a distinction that seemed to amount to little more than a difference in intention, since applied research sometimes produced important new general knowledge, and pure science contributions sometimes had direct applicability.
AI research is a recent example. Not only is serious money being made without necessarily requiring advanced degrees, research positions do not require PhDs. By some accounts, passing 30 years old puts one in the older demographic of researchers at frontier AI companies. Yet much of the visible innovation in frontier model development has been heavily concentrated in industry labs, including transformer models, scaling laws, and AlphaFold.
Of course, AI owes much to academia. The amazing thing about deep learning and LLMs, to anyone who was paying attention to NLP before these developments, is that after many years of AI research contributing interesting questions but lackluster results, the technology finally seemed to work. Would we have had the foundations for deep learning if perceptrons had not been stubbornly pursued by academics like Frank Rosenblatt at Cornell early on, picked up again in the 1980s by Rumelhart and McClelland’s Parallel Distributed Processing group, despite multiple periods during which consensus said connectionist approaches were unlikely to pay off?
Other issues taken up: “Indulgence, autonomy, and social responsibility,” and “The problem with defining progress as prediction and control.” There’s much more at the link, including fairly extensive commentary.
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