Universities would prefer not to.

Four years after ChatGPT caught so many by surprise, the primary role of higher education seems to be slowing down the public’s transition to the AI era. For many faculty, this is a win. For those focused on the future, this is the biggest institutional failure since the Victorian Post Office, with its monopoly on telegraph lines, dismissed the telephone because it had plenty of messenger boys. Adoption was slowed so much there were waiting lists for a home phone line into the 1970s.1Fortunately, the AI frontier race has skirted higher education almost entirely, so universities can’t slow the models. But higher ed is a monopoly too. Nobody else is granting college degrees. I look around and see that except for a few scattered faculty voices across the country (people I’ve shared platforms with this past year) higher education has decided it’s business as usual, squabbling about athletics and anthropology, worrying about federal takeover when the real risk is technological competition.Some facts. More than 90 percent of notable AI models released in 2025 came from industry: GPT-5, Gemini 3, Claude Opus 4.5, Grok 4, Llama 4, DeepSeek-V3.2, Qwen 3, Kimi K2. The infrastructure supporting frontier development is also overwhelmingly from industry. Global AI compute capacity has been growing 3.3x annually since 2022, doubling every seven months, driven by hyperscalers and enormous data-center investments. Some universities are entering the compute space. Ten New York universities spent two years and $340 million to get to about 400 GPUs. In 2024, xAI in Memphis stood up 100,000 GPUs in 122 days. In the past two years a site in Abilene, Texas got to 500,000.2 Even in China, DeepSeek, Alibaba, ByteDance, Moonshot and the other model developers are working outside of universities. Chinese universities may be producing more research and talent than U.S. universities because their missions are aligned with national goals. China leads in AI publication volume, citations, and patent output, even though the United States still produces more notable models and higher-impact patents. A Hoover Institution analysis of the 356 researchers appearing on DeepSeek’s foundational papers found that 53.5 percent of those with known affiliations had spent their entire recorded careers at Chinese institutions. Of the DeepSeek researchers who spent part of their careers abroad, over 70 percent ended up back in China. Universities are clearly part of the way China created a domestic frontier-research workforce.I’ve staked a public claim as an AI proponent in higher ed. As a literary scholar I see how LLMs are changing language in front of our eyes. As a former administrator I see how AI is going to change everything about the higher ed business model, from curriculum, pace of learning, research, teaching, assessment. The higher education news still focused on yesterday’s battles over speech and DEI is shocking. It seems the only people paying attention to the AI frontier are anxious faculty and CS students. I spoke to an AI-savvy provost friend the other night. The battles to keep the lights on amid federal funding changes for science are keeping her from making the institutional changes she wants. I get that. Universities have no influence over the pace of AI frontier development. Obsessed as they are with curricular battles and viewpoint diversity they have no standing even to have a voice. It is unclear whether the handful of individual national voices on AI from inside universities, like Ethan Mollick at Penn, Tyler Cowen at GMU, Scott Latham at UMass Lowell, Mike Madison at Pitt have changed their own institutions. Penn is certainly leading most of its peers in launching degrees and conversations about AI across the curriculum. I’ve shared stages with Penn’s AI leaders from the Center for Technology, Innovation, and Competition and Paideia Program several times this past year, including a major AI conference in South Korea. So it is possible that individual voices inside of universities can make a difference.Meanwhile, AI technology is changing fast. The new push for more open models will make universities even less important for new knowledge development. More firms are using open-source AI models: 63 percent of organizations run an open model in production, 72 percent inside technology companies, per a McKinsey survey of 700+ global technology leaders. Those numbers will only go up. Universities will likely offer their students and employees weaker models that are cheaper, customizable, locally deployable and independent. Employees will use the open-source model for compliance and university business but the forward-thinking graduate students, faculty, and staff will log onto their frontier models at home, after hours.Academic AI publications will be less important. There will be fewer conference papers describing modest benchmark improvements, though important breakthroughs will matter, like Berkeley’s vLLM PagedAttention, Stanford’s FlashAttention (now a foundational technique for efficient transformer computation) and Berkeley’s LMSYS-created Chatbot Arena, a major open evaluation platform. So yes, computer science and engineering departments are supplying enabling technology, evaluation systems, and trained researchers. These are consequential projects. But overall, I don’t think most people in the higher ed world realize that computer science’s highest-value contribution has moved down the stack, in the parlance of the field.It’s not like higher ed does not know how to be a professional preparation system. Where would most moneymaking American sports be without universities being incubators for pro football and basketball players? The NFL requires players to be three years out of high school. The NBA requires one. Universities begin scouting talent in high school. Billions of dollars are paid to tens of thousands of recruiters, coaches, strength coaches, scores of staff and advisor roles largely for the roughly 320 players drafted into the NFL and NBA each year.Universities are not doing a fraction of that for workers on the AI frontier, whether they’re in history, geography, astrophysics, literature, or music. Yes, U.S. computer science departments, engineering schools, and labs are continuing to train the workforce that AI companies hire. The number of new AI PhDs in the United States and Canada rose 22 percent between 2022 and 2024. The growth went into academic employment. Industry’s share of new AI PhDs fell to 62.75 percent from a 77 percent peak in 2022, while academia’s rose to 31.59 percent, suggesting that universities have a substantial research reservoir even as corporate labs dominate model production. Without university research, yes, AI progress would slow.But the AI frontier is not just computer science and philosophy. It’s the way people will access knowledge. Historians, linguists, biologists, artists, and every single person entering the real world of business, culture, and government need to understand the jagged edge of AI capabilities as much as computer scientists and mathematicians do. They need to understand the shrinking but still important distance between what AI can do and what it cannot. Everyone’s learning this the hard way now as the internet is breaking down, search is degrading, nobody can find things they used to, history is disappearing, and the two most reliable places to get an answer are the physical library and AI.Meanwhile university leaders are scrambling to remain solvent by enrolling as many students as possible. Bringing students to the frontiers of knowledge is way down the priority list. Colleges and universities are currently a brake on AI progress. As cultural institutions, responsible for forming the next generation of young people, the message is a lot of hand flapping about “needing to be workforce ready” and “don’t cheat” and “here are your required courses to graduate.” I’ve been focused for three years on the strangeness of higher ed’s preoccupation with “general education” and the sector’s commitment to staying the course while technology marches forward.A 2026 Digital Education Council survey covering more than 45,000 students and faculty across 35 countries found the U.S. and Canada are lagging behind the rest of the world in institutional coherence about AI and faculty support for AI. Students are frustrated. Worldwide, only 29 percent believed their instructors were well equipped to guide them in AI use; in the U.S. and Canada, the figure was 17 percent. Only 35 percent of students said even some of their assessments reflect the work, judgment, and skills they expect to need in an AI-enabled workplace; 37 percent said none or only a few do. In the U.S. and Canada, 48 percent said none or only a few. More students and faculty in the U.S. and Canada would support an institution-wide AI ban than any other region surveyed.Higher education is failing at its economic, educational, and cultural function when it comes to AI. Students are already using AI, employers are redesigning jobs around AI, and universities are focused on enrollment and survival, preserving assessment and credential structures designed for a world that has disappeared. For individual students, faculty, and staff, AI use is voluntary and unled. Institutions have announced AI fluency requirements and system-wide chatbot licenses but I have not seen a single institution say okay, let’s redesign ourselves for the future.Back in May 2023, the Chronicle of Higher Education ran a forum “How Will Artificial Intelligence Change Higher Ed? ChatGPT is just the beginning. 12 scholars and administrators explain.” (Some of the other contributors were Bryan Caplan, Ted Underwood, Lee Vinsel; the predictions were along the lines that higher ed would absorb AI the way it absorbed the calculator — some admissions efficiencies, a probable hype bubble, but no major disruption.) I stumbled across my notes in my files and