The university did not arrive with a campus.
It arrived in tabs.
One held a lecture. Another held an open textbook. A third held a paper I was trying to understand. Somewhere else, an AI system was patiently explaining a term I should probably have known before opening the paper.
There was no admissions office. No timetable. No student card. Nobody asking whether I belonged there.
I had accidentally assembled something that looked like the front half of a university.
That was the exciting part.
The strange part was discovering how much was still missing.
The internet gave me access to knowledge.
AI let me keep asking when a paragraph refused to make sense.
Open-source tools let me build and publish without first being invited inside.
But education is not a folder full of content.
I did not automatically have a teacher who knew where I was stuck. I did not have a cohort that expected me to show up. I did not have a laboratory, an appeals process or an institution willing to say, credibly, that I could do the thing I said I could do.
Knowledge had escaped the building.
Trust had not.
That gap is one reason I built Hedegreen Research. It emerged through open sources, AI-assisted learning, public work logs, tools, articles and things other people could inspect. It was not the conventional route into research. It was the route available to me.
This is not proof that universities are unnecessary. My biography is not a pilot study. A person learning outside an institution does not make the institution obsolete.
But it does expose a useful question:
What exactly are we paying the institution to hold together?
Some of the answer is knowledge. Some is teaching. Some is equipment, community, safeguarding, assessment, recognition and the quiet social fact that other people believe the certificate.
Those things are normally bundled together. You enter the institution, and the institution gives you access to the bundle.
The bundle works for many people. It also leaves many people standing outside with access to more information than any previous generation and no reliable way to turn that learning into trusted evidence.
That is where After the University begins.
Despite the title, the paper is not a plan to demolish universities. It is not an argument for replacing teachers with chatbots. If anything, I want teachers to spend less time repeating what a machine can repeat and more time doing the human work a machine cannot safely claim: mentorship, judgment, care, challenge and attention.
The paper asks whether parts of education can be separated and rebuilt as an open learning commons.
Could knowledge be maintained openly?
Could learners choose between different tutoring systems?
Could learning communities exist without requiring everyone to reproduce the same campus life?
Could assessment be independent enough that a person is judged by the work, not only by where they were admitted?
And could the hours saved by technology be returned to learning instead of quietly converted into staff cuts, larger workloads and platform rents?
Those are large questions. The honest next step is not to declare a new university from my desk. It is to build one small course, expose the rules, measure what happens and make the whole thing modest enough to fail.
The working paper is therefore a proposal, not a victory announcement. It does not prove that the model works. It describes what would have to be tested, what could go wrong and why the existing pieces may now be close enough to assemble.
I have placed the current version in Camelot as a public review draft because I would rather have the weak parts found than protected.
Read After the University — Public Review Draft.
The university arrived in my tabs years before it arrived as a proposal.
Now the question is whether education can learn to recognise the people who are already sitting there.