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Behold the decade of mid tech!<\/p>\n
That is what I want to say every time someone asks me, \u201cWhat about A.I.?\u201d with the breathless anticipation of a boy who thinks this is the summer he finally gets to touch a boob. I\u2019m far from a Luddite. It is precisely because I use new technology that I know mid when I see it.<\/p>\n
Academics are rarely good stand-ins for typical workers. But the mid technology revolution is an exception. It has come for us first. Some of it has even come from <\/em>us, genuinely exciting academic inventions and research science that could positively contribute to society. But what we\u2019ve already seen in academia is that the use cases for artificial intelligence across every domain of work and life have started to get silly really fast. Most of us aren\u2019t using A.I. to save lives<\/a> faster and better. We are using A.I. to make mediocre improvements, such as emailing more. Even the most enthusiastic papers about A.I.\u2019s power to augment white-collar work have struggled to come up with something more exciting than \u201cA brief that once took two days to write will now take two hours!\u201d<\/p>\n Mid tech\u2019s best innovation is a threat.<\/p>\n A.I. is one of many technologies that promise transformation through iteration rather than disruption. <\/strong>Consumer automation once promised seamless checkout experiences that empowered customers to bag our own groceries. It turns out that checkout automation is pretty mid \u2014 cashiers are still better at managing points of sale. A.I.-based facial recognition similarly promised a smoother, faster way to verify who you are at places like the airport. But the T.S.A.\u2019s adoption of the technology (complete with unresolved privacy concerns) hasn\u2019t particularly revolutionized the airport experience or made security screening lines shorter. I\u2019ll just say, it all feels pretty mid to me.<\/p>\n The economists Daron Acemoglu and Pascual Restrepo call these kinds of technological fizzles<\/a> \u201cso-so\u201d technologies. They change some jobs. They\u2019re kind of nifty for a while. Eventually they become background noise or are flat-out annoying, say, when you\u2019re bagging two weeks\u2019 worth of your own groceries.<\/p>\n<\/div>\n<\/div>\n Artificial intelligence is supposedly more radical than automation. Tech billionaires promise us that workers who can\u2019t or won\u2019t use A.I. will be left behind. Politicians promise to make policy that unleashes the power of A.I. to do \u2026 something, though many of them aren\u2019t exactly sure what. Consumers who fancy themselves early adopters get a lot of mileage out of A.I.\u2019s predictive power, but they accept a lot of bugginess and poor performance to live in the future before everyone else.<\/p>\n The rest of us are using this technology for far more mundane purposes. A.I. spits out meal plans with the right amount of macros, tells us when our calendars are overscheduled and helps write emails that no one wants. That\u2019s a mid revolution of mid tasks.<\/p>\n Of course, A.I., if applied properly, can save lives. It has been useful for producing medical protocols and spotting patterns in radiology scans. But crucially, that kind of A.I. requires people who know how to use it. Speeding up interpretations of radiology scans helps only people who have a medical doctor who can act on them. More efficient analysis of experimental data increases productivity for experts who know how to use the A.I. analysis and, more important, how to verify its quality. A.I.\u2019s most revolutionary potential is helping experts apply their expertise better and faster. But for that to work, there has to be experts.<\/p>\n That is the big danger of hyping mid tech. Hype isn\u2019t held to account for being accurate, only for being compelling. Mark Cuban exemplified this in a recent post on the social media platform<\/a> Bluesky. He imagined an A.I.-enabled world where a worker with \u201czero education\u201d uses A.I. and a skilled worker doesn\u2019t. The worker who gets on the A.I. train learns to ask the right questions and the numbskull of a skilled worker does not. The former will often be, in Cuban\u2019s analysis, the more productive employee.<\/p>\n The problem is that asking the right questions requires the opposite of having zero education. You can\u2019t just learn how to craft a prompt for an A.I. chatbot without first having the experience, exposure and, yes, education to know what the heck you are doing. The reality \u2014 and the science \u2014 is clear that learning is a messy, nonlinear human development process that resists efficiency. A.I. cannot replace it.<\/p>\n<\/div>\n<\/div>\n But A.I. is a parasite. It attaches itself to a robust learning ecosystem and speeds up some parts of the decision process. The parasite and the host can peacefully coexist as long as the parasite does not starve its host. The political problem with A.I.\u2019s hype is that its most compelling use case is starving the host \u2014 fewer teachers, fewer degrees, fewer workers, fewer healthy information environments.<\/p>\n I have seen this sort of technological Catch-22 in higher education before. Academia is a major institutional client for technology solutions. Schools helped Zoom beat Skype during the Covid-19 pivot to remote learning. Once upon a time, schools also helped the flagging Apple shore up its bottom line<\/a> while it found a consumer market for its devices. All of the technology revolutions that are coming for America\u2019s workplace have usually come earlier through mine.<\/p>\n Despite our reputation, most of the academics I know welcome anything that helps us do our jobs. We initially welcomed A.I. with open arms. Then the technology seemed to create more problems than it solved. The big one for us was cheating.<\/p>\n Every day an internet ad shows me a way that A.I. can predict my lecture, transcribe my lecture while a student presumably does something other than listen, annotate the lecture, anticipate essay prompts, research questions, test questions and then, finally, write an assigned paper. How can professors out-teach an exponentially generative prediction machine? How can we inculcate academic values like risk-taking, deep reading and honesty when it\u2019s this cheap and easy to bypass them?<\/p>\n Academics initially lost our minds over the obvious threats to academic integrity. Then a mysterious thing happened. The typical higher education line on A.I. pivoted from alarm to augmentation. We need to get on with the future, figure out how to cheat-proof our teaching and, while we are at it, use A.I. to do some of our own work, people said. Every academic friend of mine has now encountered a letter of recommendation or a research peer review that was obviously written by A.I. Its wide adoption \u2014 and its midness \u2014 is threatening to topple an already fragile but important model of peer-reviewed research, deliberate scholarship and well-educated expertise. <\/strong>Which is just what we need in the post-fact era: less research and more predicting what we want to hear.<\/p>\n<\/div>\n<\/div>\n This isn\u2019t the first time institutions pivoted from concern to tech acceptance. The same thing happened in the 2010s with massive open online courses, or MOOCs. Tech evangelists promised that we would not need as many professors, for one expert could teach tens of thousands online! But MOOCs were a mid technology that could barely augment, much less replace, deep expertise. Receiving information is not the same as developing the facility to use it. That did not stop universities from downsizing experts or from making online videos. Now MOOCs have faded from glory, but in most cases, the experts haven\u2019t returned.<\/p>\n A.I. is already promising that we won\u2019t need institutions or expertise. It does not just speed up the process of writing a peer review of research; it also removes the requirement that one has read or understood the research it is reviewing. A.I.\u2019s ultimate goal, according to boosters like Cuban, is to upskill workers \u2014 make them more productive \u2014 while delegitimizing degrees. Another way to put that is that A.I. wants workers who make decisions based on expertise without an institution that creates and certifies that expertise. Expertise without experts<\/a>.<\/p>\n That tech fantasy is running on fumes. We all know it\u2019s not going to work. But the fantasy compels risk-averse universities and excites financial speculators because it promises the power to control what learning does without paying the cost for how real learning happens. Tech has aimed its mid revolutions at higher education for decades, from TV learning to smartphone nudges. For now, A.I. as we know it is just like all of the ed-tech revolutions that have come across my desk and failed to revolutionize much. Most of them settle for what anyone with a lick of critical thinking could have said they were good for. They make modest augmentations to existing processes. Some of them create more work. Very few of them reduce busy work.<\/p>\n Mid tech revolutions have another thing in common: They justify employing fewer people and ask those left behind to do more with less.<\/p>\n