Inference as a Service
Description
How to do Exclosure: Inference as a Service:
Victor:
"Inference as a service makes functionality generatable on demand at near-zero marginal cost. A form, a tracker, a scheduler, a ledger, a governance tool: things that once needed a vendor, a roadmap, and a pricing page can now be produced by the people who need them, in the shape they need, in an afternoon.
The firm stops being the necessary organ for making tools. A commons can build its own.
That single shift closes the gap that commons have never been able to close, and it opens a vector that the defensive literature on AI and commons has mostly missed. Almost everything written about AI and the commons is about protecting commons from AI, or governing AI as a commons. Both matter. But the more interesting move is using inference to let commons out-produce the market and reverse enclosure function by function."
(https://liminalcommons.substack.com/p/progressive-exclosure-14f)
Discussion
More explanation by ChatGPT, prompted by Michel Bauwens:
In this essay, “inference as a service” is essentially a deliberately provocative way of saying: AI inference turns the production of software functionality into something that can be generated on demand, rather than something that has to be purchased from a firm.
1. What is “inference”?
Here, inference means the computational process by which an AI model takes an input or request and generates an output.
For example, instead of buying a piece of software called “membership management system,” you could tell an AI:
“Build me a simple system for tracking members, their contributions, and whether they have participated in the last three meetings.”
The model generates the relevant code, database structure, interface, etc. The important thing is that the functionality is produced when you need it.
That is why the author says:
“Inference as a service makes functionality generatable on demand at near-zero marginal cost.”
The key word is functionality. The argument isn't really about AI chatbots as such. It is about AI making the production of tools cheap and immediate.
2. Why call it “as a service”?
There is an interesting tension in the phrase.
Normally, “X as a service” means that you don't own X—you rent access to it from a provider. Software-as-a-Service (SaaS), for example, means that instead of owning and maintaining software yourself, you pay a company to provide it.
So “inference as a service” initially sounds like another form of dependence on a firm: you send your request to OpenAI, Anthropic, etc., and they provide the computational inference.
But the author is interested in what happens because inference has become so cheap and powerful, not necessarily in the current commercial form of the service.
The crucial distinction is between:
the service as a product
→ “Here is a piece of software we have built; pay us to use it.”
and
inference as a productive capacity
→ “Tell the system what you need, and it can generate the functionality for you.”
The latter potentially undermines the economic reason for buying software from firms in the first place.
3. Why does this matter for commons?
This is where the argument becomes much more interesting.
The author claims that commons have historically had a problem: they can steward things collectively, but they have difficulty producing new tools and infrastructure.
A commons might collectively manage land, knowledge, trust, care, or a community—but if it needs:
- a database,
- a scheduling system,
- accounting software,
- a membership system,
- a governance platform,
it usually has to buy those things from the market.
Hence the striking observation that many supposedly autonomous commons run on Slack, Notion, Stripe, Google Workspace, etc. The social relations may be organized as a commons, but the technical infrastructure supporting those relations is commercially owned.
The author's claim is that AI changes this asymmetry.
If a commons can simply say:
“We need a tool that does this particular thing in this particular way”
and generate that tool itself, then it no longer necessarily needs a company to produce the tool.
So:
Before:
Commons → needs tool → buys tool from firm → market relation enters commons
With cheap inference:
Commons → needs tool → generates tool → maintains it collectively
That is what the author means by saying:
“The firm stops being the necessary organ for making tools. A commons can build its own.”
4. This is why “inference” is more important than “AI”
I think this distinction is important for understanding the argument.
The author isn't primarily saying:
AI will make humans more productive.
That's the usual AI argument.
They're saying something closer to:
AI potentially changes who or what needs to exist in order for productive capacities to be created.
Historically, if you wanted a piece of sophisticated software, you needed:
capital → programmers → firm → product → customer
The firm was necessary because software production was expensive and specialized.
If inference makes the generation of relatively specialized functionality extremely cheap, then that chain potentially becomes:
need → inference → functionality
And that is potentially a very significant change in the organization of production.
This explains the rather strong statement in the essay:
“The firm was the price of building; that price just went to zero.”
I'd qualify that claim heavily—the cost of building certainly hasn't literally gone to zero—but that is the theoretical wager.
5. Why does this produce “exclosure”?
Now we can understand the larger argument.
The author distinguishes enclosure from exclosure.
Traditional Enclosure:
common resource → fence it off → make it privately exploitable → market enters.
Exclosure reverses the direction:
commons → exclude market relations → allow alternative relations to develop.
An AI-generated tool supposedly makes it possible to perform this exclusion one function at a time."