I really enjoyed this piece by Tom Smith in Tech Policy. It hardens my sense that the sector is walking into a massive problem here:
The problem comes when access becomes dependence. Consider what happens over several years after a university selects a preferred AI platform. Staff develop workflows around it. Students learn through it. Bespoke assistants are created inside it. Institutional knowledge accumulates within it. Other university systems are connected to it. Eventually, AI agents may begin initiating actions across those systems.
At that point, changing providers is no longer comparable to replacing a software license. The institution must unwind an ecosystem. This is a familiar feature of platformization: the value of a platform increases as more activity takes place within it, while the cost of leaving rises at the same time.
Higher education has encountered versions of this problem before with learning management systems, cloud infrastructure, academic publishing and research analytics. AI potentially intensifies it because the technology is not simply storing information or providing access to content. Increasingly, it is mediating the work itself.
https://www.techpolicy.press/universities-need-ai-infrastructure-they-dont-need-ai-lockin/
I think this is spot on about the need for a governance architecture which is adequate to these challenges. I worry that rushing into procurement actively makes it harder to build this architecture:
Universities should also be cautious about allowing the urgency of AI adoption to reverse the appropriate sequence of governance. The pattern too often is: procure a system, distribute access, encourage experimentation, then work out how to govern what follows. The more sustainable sequence is governance architecture first, scalable adoption second.
https://www.techpolicy.press/universities-need-ai-infrastructure-they-dont-need-ai-lockin/
He links to this piece by Will Stronge from the Autonomy institute which begins with an outline of the financial context in which these decisions are being made:
Britain’s universities are in an economic crisis. The Office for Students reported in November 2025 that 124 providers, around 45% of the sector, expect to post a deficit in 2025-26, and that nearly one in six hold less than thirty days of liquidity.[1] Almost two in five institutions are now cutting staff, with more than 12,000 jobs lost during 2025.[2] The causes seem to be structural, with the tuition fee having been limited to £9,250 since 2017, losing roughly a quarter of its real value to inflation, plus a fall in international recruitment.[3]
A second shock has landed on top of this. By 2025, 88% of UK undergraduates reported using generative AI in their assessments, up from 53% a year earlier (and we can assume the numbers recede towards 0 if you go pre-2023/2022).[4] These two crises have created a complete mess of business as usual. The funding squeeze has exposed how much of the university depends on effectively selling a single product: the qualification (as devalued as it might seem today). And AI is now calling into question the value of this product, both in terms of what it ‘gets you’ once you’re in the labour market, but also in terms of what it takes to achieve it during the process of education itself.[5]
https://autonomy.work/portfolio/ai-is-forcing-us-to-rethink-higher-education-lets-do-that/
