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.
As I was puzzling over all this, I attended a metascience conference, where there was a panel on “the social contract for science.” This is the transactional relationship dating back to at least the 1950s, by which scientists receive public funding and autonomy and society gets the benefits of scientific research. Back in the 19th century, scholars began to make a distinction between “pure” science–research unmotivated by any particular application–and applied science. The social contract takes this distinction and further presupposes a dependence relationship: what is confusingly called the “linear model”, the idea that pure science provides the well from which applied science contributions are drawn. Threaten this foundation, e.g., by letting applied science intercept too much of the resources society puts toward science, and we risk running out of useful innovations. Or so the story goes.
The social contract for science was an attempt to cement the importance of scientific understanding to society, making it an interesting counterpart to the current moment. If the actions of the current administration to direct funds away from universities, and the possibility of using AI to produce research output without understanding, are threatening our sense of what science should be, the history of science policy provides some perspective on how our expectations got shaped in the first place.
In search of the mysterious fruits of basic science
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.
From a less moralistic point of view, we’ve been encouraged to believe that a society that has rigorous ways of understanding the world is better off over one that doesn’t. Throughout history, understanding the laws of Nature has been portrayed as a good in itself, along with an intellectual life. From this view, by educating people on how to pursue deep understanding of the world, universities provide the general good of scientific thinking to society. If we believe in the intrinsic value of reading, writing, or intellectual discussion, then it would seem we should value the university as a place that provides the kind of timespan and environment needed to develop these skills.
Some argue that the university has come to serve too many conflicting purposes (research engine, job training center, credentialer, incubator of coming-of-age experiences), and should go back to its classical roots: training in oral reasoning and rhetoric, ethics and moral judgment, historical analysis, and the cultivation of taste and discrimination. This may be a useful refocusing, but it offers little consolation for the fact that the elite research infrastructure that helped this country establish and maintain scientific leadership for decades is in the process of being gutted.
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?
The challenge is that arguing that “someday the research will pay off,” without being able to point to any hard evidence that basic research is, on average, worth the investment, is not such a convincing argument. According to Douglas, studies have been attempted to show the payoffs of basic research, but without very impressive results. Uncertainty about what time scale we should expect between discovery and application makes this kind of exercise difficult.
At the same time, it’s hard to dispute that monetary incentives can sometimes discourage exploration that would eventually pay off. In evaluating the role of academic research to AI progress, we should keep in mind the uniquely massive private investments AI companies have received, and be cautious using it as a general example. It would be premature to conclude that because progress (in terms of models’ standalone capabilities as measured by benchmarks) doesn’t seem to depend much on academic research at the moment, cutting off academic research would be immaterial. Particularly unfortunate about the historical contingency of frontier companies defining the direction of the field is that they are focused on a pretty narrow space of methods, evaluations, and design ideas. But the power and resources they hold give newcomers to AI research the impression that ideas outside this narrow space aren’t important.
Indulgence, autonomy, and social responsibility
As suggested above, it’s always been tempting to bring moral judgment to bear on the basic versus applied research divide. The latest moment with AI research is no exception. Does the moral high ground belong to those who are staying in academia, underfunded or not, to preserve university culture and their autonomy from corporate interests, or those who are willing to give up a comfortable job to shape the impact of AI as a product in the world?
From one perspective, the academy, as a haven for basic science, has always been at risk of being seen as indulgent. In practice, building a scientific career is in many ways a process of identity development and fulfillment for the scientist. Historical pure science rhetoric associated the pursuit of scientific truth with self-realization. But talking about personal fulfillment does not go over well when your opponent is promoting the idea that science could do more direct good for the country or humanity. Academic scientists have always been at risk of coming off as being self-indulgent, insular, or dilettante when they defend understanding for understanding’s sake. The current political moment is just rehashing old themes.
Another unfortunate historical association of basic science is with insularity and shirking responsibility. After WWI era advances in chemical warfare and explosives, the social responsibility of the scientist became a much greater concern. Philosopher John Dewey came down sharply on the idea that an autonomous space for pure science, unhindered by societal concerns, was something to strive for. Instead, he argued that this impetus to protect pure science was partly a convenient abdication of moral responsibility for the downstream outcomes of research, a “shirking of responsibility.’’
Dewey’s concerns came at a time where philosophy was itself seeking to be more scientific. According to Douglas, Dewey’s views on how philosophers should approach science–through greater integration of societal concerns–lost out to the argument espoused by Bertrand Russell, who instead valorized the “disinterested intellectual curiosity which characterizes the genuine man of science.” The latter view became the more accepted one, and our definition of scientific freedom arose in tandem with expectations of limited social responsibility. It’s not particularly surprising, then, to encounter beliefs that academia is not the place to go if you want to have impact in the world.
At the same time, it seems hard to deny that at this point of time in AI, where we have a large imbalance of power and resources, there is something to be said for the autonomy afforded by the university or nonprofit. Some beliefs about AGI coming out of Silicon Valley border on religious. My biggest concern if I were to join an AI company at this point in time would be losing my ability to think for myself about what problems deserve priority. Having greater agency and impact are attractive, but not if they come at the expense of one’s internal compass or values. As Brendan McCord said recently, “Autonomy is different from agency. Agency is getting things done…You can be more effective than you’ve ever been, and you can be less the author of your own life than you’ve ever been.” Against the groupthink of Silicon Valley, the value of the intellectual autonomy academia provides does feel real. Though it’s unclear how valuable this autonomy will continue to be if academics and others outside the big labs can’t retain enough funding or visibility into frontier model development to remain relevant.
The problem with defining progress as prediction and control
In a 2014 article called Pure science and the problem of progress, Douglas suggests that if the pure/applied science distinction doesn’t survive scrutiny (which she argues it does not), we’re left with an account of scientific progress based on our ability to predict, intervene, and control our world. But this is not a definition of progress we should be content with:
“Any increase in the capacity to predict or control the thoughts and feelings of human beings would count as scientific progress. An increased capacity to destroy human subpopulations (through, say, targeted pathogens) would count as scientific progress. Developing new heinous capacities would count as scientific progress. Unlike Rowland, we should have no illusions that greater causal efficacy, greater power of intervention, will in fact always provide a better society.” (p. 63)
If misaligned AI, our own creation, changes how we view ourselves and the world, if it convinces some of us it has all of our best interests at heart even as it feeds our insecurities, or pursues its own goals in the background, is that scientific progress?
Douglas argues that judging real progress requires society to weigh in. When it comes to AI, this is happening through pushback against data centers, and the pace of AI progress, and the culture of Silicon Valley. Adoption matters too, but can’t be a substitute for evaluation. We need institutions independent of the companies to help interpret what’s going on. In the midst of changes to so many of our current institutions, we should expect the story we ultimately tell to take time to sort out.
In the meantime, defending academia as a category of research, or a moral standard, is a dead end. What seems more reasonable to advocate is a set of conditions — time, autonomy, training in scientific judgment, the evaluation of new approaches independent of their profitability. The value of these ingredients isn’t easily summarizable in some neat story, because what drives scientific progress is not that simple. But institutions that help society judge what’s been achieved seem worth defending.
