Article URL: https://hollisrobbinsanecdotal.substack.com/p/universities-would-prefer-not-to Comments URL: https://news.ycombinator.com/item?id=49182679 Points: 9 # Comments: 3
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.1
Fortunately, 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.
