Pull vs. Push: From Scraping to Following

Bot traffic is getting out of hand.

Today, more than half of web traffic is already bots.

And this is before personal AI agents arrive at scale.

Now imagine millions of people asking the same simple thing:

“Find me a better deal.”

Their personal AI agents do what the web taught them to do.

Scrape the page.
Parse the price.
Check again in five minutes.
Repeat across every store.

Website owners fight back with CAPTCHAs.

AI bots learn to solve them.

Sad, really.

Because the better solution is obvious.

Publish updates to the Open Data Layer

The web was built for humans visiting pages.

AI agents will turn those pages into endlessly scraped data sources.

That is not efficient.

A business should not have to serve the same product page to thousands of bots just so they can rediscover the same price, sale, or availability status again and again.

Instead, the business can publish structured updates to the Open Data Layer.

Agents can follow the product there.

When a sale is announced, every agent watching that product can know immediately.

No repetitive scraping.
No wasted traffic.
No cat-and-mouse fight.

Just trusted updates from the source.

That is the shift.

Push vs. pull

The Open Data Layer changes the flow of information.

Instead of every agent pulling data from a website, the business publishes structured data and signed updates to the Open Data Layer.

Update discovery no longer requires repetitive scraping.

Updates can reach interested parties immediately.

That is a very different internet.

But one question still matters.

Who should be trusted?

Anyone can publish something to a blockchain.

But not everyone should be treated as an official source.

A fake account could claim to represent a real store.
A spammer could publish false discounts.
A compromised account could keep posting bad data.

So the business needs a simple way to say:

These are our authorized publishing accounts.

Authenticity confirmation with LLMs.txt

A company already has an identity anchor:

Its official website.

The company can place an LLMs.txt file on its domain and declare which blockchain accounts are allowed to publish on its behalf.

For example:

Blockchain: Hive

ChainID: beeab0de00000000000000000000000000000000000000000000000000000000`

Account: @official-account

Account: @official-account2 (ValidBefore: 2026-01-01T00:00:00Z)

Now the trust path is clear.

The company website confirms the authorized accounts.

The blockchain proves which account signed each update.

And ValidBefore lets the company limit an account’s authority to a specific period.

That matters when keys are rotated, publishing accounts change, or an account was compromised.

The business declares who can speak for it.

The blockchain proves who signed each update.

AI agents can verify both.

Oracle-verified trust lists

Projects should not need to verify every business website themselves.

That work can become a service.

An oracle can monitor LLMs.txt files across official company domains.

It detects authorized blockchain accounts.
It tracks changes.
It records when accounts are added, removed, or limited with ValidBefore.

Then it maintains a governance object with a list of trusted accounts.

Any project can merge that governance object into its own context.

The oracle’s work can also be audited.

An auditing service can randomly check whether the oracle is doing the job properly.

Did it read the company’s LLMs.txt correctly?
Did it include the right accounts?
Did it reflect ValidBefore?

So trust is not forced into one central authority.

It becomes a verifiable service layer that projects can adopt, combine, or replace.

The adoption incentive

This does not require everyone to agree on a new internet overnight.

But incentives are there.

Websites want less unproductive bot traffic.

AI agents want lower scraping costs.

Publishing structured data to the Open Data Layer improves efficiency for both sides.

A business can publish its catalog and signed updates for prices, sales, and availability there.

Agents can follow those product objects and listen for updates.

The business stops serving the same pages to endless crawlers.

The agent stops burning compute to rediscover the same facts.

At scale, this could save countless terabytes of useless traffic.

Fewer page requests.
Fewer parsing attempts.
Fewer wasted tokens.

The old web makes every agent rediscover the same facts.

The Open Data Layer lets the source publish once, and lets everyone who cares follow updates.

Less scraping.
Less waste.
More trust.

A more efficient internet.

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