Openness or Oblivion
Does AI Change the Value of Knowledge?
I wonder what we can confidently say about how AI is changing the way our community works.
My hunch is that, as usual, we are held captive by our own experience and use AI in the ways we're accustomed to. Text chat, for example, remains the primary way of interacting with AI in software development — even though voice commands are often quite sufficient, and the necessity of a keyboard is no longer all that obvious. It's also unclear what will happen to websites and other digital interfaces: picking out a bike or a laptop is clearly easier in a chat with an AI assistant, and for the purchase itself, a banking app will do.
Yet some fundamental shifts can already be glimpsed. To my mind, the two most significant are the growing role of public sources of information and the shift of human effort away from processing and presenting information toward producing new knowledge.
Openness as a condition of existence
People now include AI in their decision-making chain, in various forms. Even in the most conservative variant (like PostgreSQL core development), it is present as a mechanism of critique — hunting for weak spots in a nearly finished product. But if a relevant source of information is not openly accessible, an LLM simply doesn't know about it. Which means it won't use it in decisions, won't combine it with other sources, and won’t check it for correctness. For the model, such knowledge does not exist.
Imagine: with limited time and money, you present your breakthrough idea not at a top international conference but at a university seminar in your hometown. Or at a department meeting, even. Twenty years ago, it would simply have sunk without a trace. Today, when I ask an AI agent to analyse a problem and identify known attempts to solve it, it draws on two sources of knowledge: whatever was available during training and whatever it finds by searching the web. So what matters is no longer so much where your results are published as the mere fact of their presence online, their accessibility to indexing crawlers, and the completeness of the write-up.
Hence, a corollary: communities and social networks built on open access and freedom of criticism gain a notable advantage — a collective search for solutions emerges within them, in the spirit of the Borg from Star Trek (minus the assimilation). If you've made something, it is critically important to publish it, present it, and get it widely discussed — that is what makes a result visible and weighty for AI.
From organising information to producing knowledge
The second aspect is the shift toward the production of new knowledge. As I see it, the job of organising and curating information — what newspapers and television traditionally did — is losing its former value as a standalone mass profession. A personal newspaper or video feed can now be assembled with a simple prompt, no development skills or knowledge of ranking algorithms required. I, for example, put together a personal digest tuned to my interests and easy to reconfigure. Writing a survey on a given topic is now fairly easy, while thirty years ago it was one of the most labour-intensive and respected kinds of work in science.
The requirements for the publication itself are changing, too. Republishing the same material on multiple platforms is just redundant noise to an AI agent. How readable the write-up is, and even what language it's written in, no longer matters. What the agent cares about is a clear and complete problem statement, the method, and the verification details.
Curiously, the very notion of authority is changing along the way. An LLM has no explicit metric of an author's reputation — what operates during training is rather a corpus consensus, the corpus being the entire body of text the model was trained on: a claim independently confirmed by many open sources is absorbed with greater confidence. An author's name carries weight only once it has itself become a visible part of that corpus: the model will "trust" a well-known contributor to a public mailing list more than an anonymous blogger — but only because it has already seen that name next to verified results. Authority is turning from a status attribute ("professor at such-and-such university", "editor of such-and-such journal") into an accumulated trail of verifiable publications in the open. You cannot show your reputation to an AI — you can only accumulate it, and accumulate it in public.
This argument is partly borne out by reality: ACM and the Royal Society opened their archives in 2026, while Wiley sells access to its content to some LLM vendors. My guess is that Elsevier, Springer, and IEEE are staying with hybrid, transitional models out of sheer conservatism and will, sooner or later, move in the same direction.
Will this affect the PostgreSQL community?
Our community, it seems, turned out to be ready for the new reality long before it arrived — and unintentionally at that.
For decades, all the substantive work in PostgreSQL has been carried out on the public pgsql-hackers mailing list: design discussions, patch reviews, arguments and rejected ideas — everything is archived, indexed, and available to any crawler. Commits come with detailed messages linking back to the discussions. By the standards of my thesis, this is a nearly ideal corpus: a complete problem statement, the method, the counterarguments — all in the open. Small wonder that LLMs today reason fairly confidently about the planner's internals or MVCC: they have "read" not just the code but thirty years of arguments around it, the history of commits and reverts.
The reputation mechanism works here literally as described. The names of long-time contributors appear in the corpus next to thousands of accepted decisions — and the model reproduces that trust. An amusing consequence: the weight of the argument "well, so-and-so objected to this approach in such-and-such thread" now extends beyond the mailing list — to anyone who asks an AI about the same design.
But there is a flip side. Knowledge that the community passes around outside the archives — in conference hallways, in chats, in committers' heads — does not exist for AI. For instance, unspoken criteria like "this patch won't make it because it complicates maintenance" are often never stated explicitly anywhere. The gap between the community's written and oral traditions is becoming palpable: the former grows stronger, the latter is likely depreciating. I suspect this will nudge us to write down the unwritten rules more often — which is no bad thing in itself. Or perhaps to make conferences more transparent for online participants?
That said, factors as crucial to DBMS code as reliability, simplicity, and the cost of long-term maintenance across platforms remain poorly codified, which casts doubt on how deeply AI agents will infiltrate PostgreSQL development in the near future.
One last thing: commercial forks and closed-source products built on PostgreSQL end up at a disadvantage. Their solutions, however elegant, take no part in the "collective mind": an LLM will not assimilate their work during training, will not analyse their code, will not include it in its "memory" — so AI will pay off noticeably less in enterprise projects. It looks like openness is ceasing to be merely an ideology or a development model — it is becoming a condition of influence.
What follows
If both trends are real, the formula for successful intellectual work is changing. It used to read: "do good work, publish it in a prestigious venue, and retell it at a few conferences, each time with a different sauce." Now it reads more like: "produce new knowledge, lay it out fully and verifiably, put it in the open, spark a broad discussion — and do it systematically, because your reputation for AI is precisely the accumulated trail of such publications."
Paradoxically, the technology many see as a "killer of thinking" actually devalues the routine processing of information — and raises the price of what a machine cannot do: framing a new problem, running the experiment, taking responsibility for the result.
What about you — what have you noticed? Which changes in your reality already look irreversible?
THE END.
July 19, 2026 — Brisbane, Australia.