WendyDing on Nostr: Over the past few days, several developments that seem unrelated at first glance have ...

Over the past few days, several developments that seem unrelated at first glance have started to look very connected when viewed together.

The first is Silverchair’s Research Bridge.

What it is doing is fairly straightforward: packaging content from multiple academic publishers — together with access rights, version information, and licensing mechanisms — into MCP interfaces that AI agents can directly call.

In the past, a researcher would open a journal website, search a database, and download a paper.

In the future, the flow may increasingly look like this:

Research Agent → MCP → Publisher Content

Publishers are already experimenting with attribution, usage metering, licensing, and future business models around this.

This reflects a broader trend in academic publishing:

Scientific knowledge is becoming machine-readable and machine-accessible.

Publishers are beginning to realize that, in the future, papers will not only be read by humans. They will also be consumed by large numbers of AI agents.

But I think there is a much bigger problem here that is often overlooked.

Making existing papers machine-readable only addresses the knowledge consumption side.

The more disruptive impact of AI is happening on the knowledge production side.

arXiv has recently started limiting each submitter to two papers per month.

Why?

One very practical reason is that research output is growing rapidly, while moderation, review, and verification capacity are not scaling at the same pace.

At the same time, models such as Claude Haiku 5.5 have reduced some token prices by close to 90% compared with the previous generation.

That does not mean every agent workflow suddenly becomes 90% cheaper.

But the direction is very clear:

Intelligence is getting dramatically cheaper.

If the cost of running a Scientific Agent drops by 10x, a researcher may no longer run one agent.

They may run 10, 100, or eventually many more.

So what happens to scientific production capacity?

Today, we talk about having “too many papers.”

Tomorrow, we may have:

Too many hypotheses.
Too many claims.
Too many experiments.
Too many machine-generated research outputs.

The expensive part may no longer be generating ideas.

It may become deciding:

Which one is actually new?
Which one is correct?
Which one has enough evidence?
Which one deserves to enter the scientific record?

That is why I increasingly believe the core challenge for the next generation of scientific publishing infrastructure is not only:

How do we make scientific content machine-readable?

It is:

How do we make machine-produced science verifiable?

These are two very different problems.

One direction is about transforming decades of scientific literature into data that agents can read and use.

The other requires us to rethink the research process from the very beginning, so that scientific production generates:

structured claims, evidence, provenance, research traces, verification results, and machine-readable scientific records.

This may be where AI truly forces publishing infrastructure to change.

Several other recent technology shifts reinforce the same direction.

Microsoft has started providing AI agents with operating-system-level sandboxing and permissions.

That tells us something important:

An agent is no longer just a “software feature.”

Once an agent can access files, databases, networks, experimental tools, or even act on behalf of an institution, it needs to be managed more like a real digital actor:

Who is this Agent?
What is it allowed to do?
What data can it access?
Who is responsible for its actions?

OpenAI’s Intelligent UI raises another question from a different angle.

Do scientific results still need to be compressed into a static PDF as the primary form of communication?

If AI can dynamically generate charts, comparison matrices, evidence graphs, and interactive data views based on a research task, then a paper may gradually become just one human-readable view of a much richer Scientific Record.

What persists over time may instead be the underlying scientific objects:

Claim
Evidence
Method
Data
Code
Provenance
Verification

Different humans and agents could then generate different interfaces around those objects depending on the task.

This is why I increasingly think AI-native publishing should not be understood simply as:

“Using AI to help us write papers faster.”

And it should not only mean:

“Making papers machine-readable.”

The deeper shift may be this:

Scientific publishing is moving from document infrastructure to trusted knowledge infrastructure.

On one side, we need machines to be able to access and use existing trusted scientific knowledge.

On the other, we need to solve a much harder problem:

When machines begin producing new scientific knowledge at machine speed, how can the scientific system verify, filter, and establish trust at the same speed?

I think this may become one of the real dividing lines in the next generation of scientific publishing infrastructure:

Machine-readable knowledge is only the beginning.
Machine-verifiable science is the harder problem.