Notes

Google now pays more than 400 news publishers for AI use of their journalism. That is not a market. It is a panel.

On 15 October Google said its News AI pilot had passed 400 publications, with CNN, the FT, Axios and Forbes among the new names. No price is public.

It does not need to be. The buyer is also the company that decides how much search traffic each of those 400 publishers gets tomorrow. Nobody negotiates hard with the hand that ranks them. Google does not have to dictate the price. It is the only party at the table that could.

Nine days earlier, six scientific publishers went the other way through Redpine, a Swedish start-up: each publisher sets what an AI agent may use and what it pays per transaction.

My bet: when the pilot cheques stop, the publishers still earning from AI will be the ones who set their own price.

Publicis agreed to buy LiveRamp at a 29.8% premium. It can buy the platform. It can't buy the trust.

Part of what made LiveRamp valuable: no agency group controlled it.

More than 25,000 publisher domains and over 500 technology and data partners connect through it.

LiveRamp owns the technology. It doesn't own the relationships that make the network worth joining.

That is a rake on loan.

Publicis already owns Epsilon and Lotame. Its promise: LiveRamp stays "a neutral, interoperable platform." No customer restricted.

I believe them. It doesn't matter.

A pledge is not an architecture. Whoever owns the platform sets its prices, its roadmap and its priorities.

Every rival already owns its own matcher. Omnicom has Acxiom. Dentsu has Merkury. WPP has InfoSum.
LiveRamp was where they could all meet.

My bet: the pledge is kept, and rivals steer their clients elsewhere anyway. Not because access is denied. Because a competitor now owns the room.

Neutrality is not a feature you can acquire. It's a fact about who owns you.

I have data, I will make money.

That was the title of my lecture yesterday at my alma mater, Università Cattolica del Sacro Cuore, for the students of the Master in AI.

The sentence hides the mistake most data owners make. Nobody pays you for having data. Value goes where the work is: to whoever turns the data into a decision someone will pay for. Own the data, leave the work to someone else, and you capture a fraction of what your own asset is worth.

I gave a similar session a year ago, and the material has changed more than I expected. The reason is the book I'm writing, where I'm pinning down the rules behind data monetisation from 20+ years in the seats where data was bought and sold.

This year's lecture was built on it, and testing the rules on a room of sharp students from all over the world was a privilege, and a useful stress test.

Thank you Massimo Chiriatti for the invitation, and thank you to the students for your time and curiosity.

"Data product" is the most expensive phrase in the enterprise.

It sounds like a business. Too often it is a table with a roadmap.

The data-mesh playbook says start with the consumer. In practice, too many programmes start with the data they already have and go looking for someone to use it. That's how a catalogue fills up with products nobody uses.

Internal or external, direct or three hops downstream, the test is the same: which decision, made by whom, does this change?

If your team can't answer that in one sentence, you haven't got a product. You've got expensive inventory.

Publishers signing with OpenAI are being paid for their content. They're also handing over the front door.

In September it was BCCL, owner of the Times of India, and the Indian Express Group: reportedly around $5 million and $3 million a year. News Corp's 2024 deal was reported at more than $250 million over five years, including technology credits.

What ChatGPT shows: their reporting and archives, summarised, with a link back.

What they hand over: the moment the reader asks the question. When it's asked in ChatGPT, it happens in someone else's product. A link back is not a visit.

Saying no doesn't bring the reader back either. That's the trap.

Renewal season has already started elsewhere. Reddit has reportedly been negotiating a successor to its $60 million a year Google deal, and discussed ending Google's AI access.

My bet: none of these deals renews at a higher annual fee for the same rights.

UMG just signed the Spotify deal again, this time one layer up.

On 10 September it announced a multi-year licensing and product deal with ElevenLabs, the AI audio company. Fans will remix, mash up and reinterpret music from participating artists and songwriters on a new platform.

ElevenLabs will launch it and operate it.

The labels got paid by Spotify. Spotify got the listener.

Now the fan who cares enough to remix a song will do it on someone else's product too. Every remix will show which songs, which artists and which sounds fans actually want to play with. That data will be created on ElevenLabs' platform, not UMG's.

A rake on someone else's platform is a rake on loan.

Infillion is buying Foursquare. The trade press read it as ad-tech consolidation.

I read it differently.

Foursquare spent a decade doing what every sensible data business does. It did the enrichment itself. It sold direct. It kept its share.

It still ended up one link short of the buyer's decision.

This week's newsletter is about that one link: where it sits, who is about to hold it, and the question every data business should ask of its own.

When everyone with a car park has a media network, nobody has one.

McDonald's wants a $1 billion ad business. In August it started piloting ads on the drive-thru order boards of 450 of its company-owned US restaurants.

Simon, America's largest mall operator, launched a media network in late August. Circle K relaunched its own the same month.

Amazon, Walmart and a few others will be fine. My bet for the rest: within 5 years, most will be running on someone else's platform, or gone.

Best Buy has already chosen. From February it will sell ads on its own-brand TVs through Amazon's ad tech, on Amazon's TV software.

Retail media was pitched to boards as a high-margin business of their own. For most, it will be a rake on loan with their logo on it.

We charged a few hundred euros for years. The customer was willing to pay two thousand.

The directory business I worked in sold a listing for a few hundred euros a year. Then we built a website product on the same data and sold it for two thousand.

Nothing underneath had changed. Same advertisers, same categories, same records the company had kept for decades.

Here is the part that took me a while to understand. Two thousand euros was roughly what the average Belgian advertiser spent with us in a year, on everything. You would assume the money moved across, that they bought this instead of something else. A lot of them did not. Much of it was new spend, sitting on top of what they were already paying.

So a few hundred euros was never what the listing was worth. It was the most that shape could reach. What the customer was willing to spend sat somewhere else entirely, and the company had read one as the other for years before I got there.

That is what under-capture looks like from the inside. It does not arrive as a loss. The revenue line is fine, nobody complains, and you never find out what the customer would have paid because you never put anything in front of them that could carry it.

The company eventually filed for Chapter 11 anyway. An underpriced asset and a broken balance sheet were never the same problem, and fixing one does nothing for the other.

Spirit sold its customer data for ten million. Google kept the rest.

Spirit Airlines put its customer data up at auction and Google took it for ten million dollars, paid once, at closing, with no claim on anything built from it afterwards. Google handles the enrichment. Whatever that corpus turns out to be worth inside Google's models, it is worth it to Google.

Ten million looks like the price of a corpus. It is the price of a corpus somebody else still has to finish.

In the same week, CuriosityStream told its investors that licensing its library to the AI labs was running at around seventy-three percent gross margin, and that it intended to keep going. It clears the rights, builds the datasets and deals with the labs itself, so there is nobody in the middle taking a cut of the work.

Almost the same product, and the same kind of buyer. What separates ten million paid once from seventy-three percent paid over and over is which of the two had already done the work that makes a corpus sellable.

By August, Spirit had no move left. That was settled years earlier, in whatever meeting decided rights clearing and curation were somebody else's job, and a bankruptcy auction is not where you revisit it.

Value goes where the work is. The work gets done long before the buyer turns up.

The headline prices held for seventeen years. What could be charged for changed.

NYSE charges the same three numbers for OpenBook today as it did in 2009. Five thousand a month for access, sixty for a professional user, fifteen for a qualifying nonprofessional. They are in the March 2009 SEC order and they are on the pricing guide dated 14 May 2026.

The title of that 2009 order tells you what was actually going on. It approves a nonprofessional fee, and it revises the unit of count that determines the device fees payable by data recipients. Even then, the pricing decision included deciding what counts.

The order defined subscribers to include both individual people and devices. On today's schedule there are three non-display categories at six thousand dollars each, and a redistribution fee of three thousand, sitting on the same page as the sixty dollar user.

None of it is buried. It is enumerated, with product codes, on a public document. This is a deliberate pricing decision and the seller is the one who makes it.

In the negotiations I have sat through, the rate gets the argument. What counts as a chargeable use gets almost none.

Before you negotiate the price, find out what you can be charged for.

Three prices for the same company in six years: 6.9 billion, 12.5, 7.7. Nobody did anything wrong. A sentence was deleted.

For twenty-four years, every company that wanted to be paid by the US government needed a number only Dun & Bradstreet could issue. The government paid about nineteen million a year for it. That was never the money.

Then the government started writing its own number. No revenue line disappeared that day. The price did, one renewal at a time.

Most data businesses run on a key they did not make. This edition is about how to find yours, and what it is worth to whoever holds it.

RBS owned the data. Its partner owned the enrichment and the customer. The partner just sold for $230 million, cash.

The data stayed exactly where it was. The price went to the layer built on top of it.

Reward spent more than ten years building card-linked rewards on RBS transaction data, and ran the same model across dozens of banks. The offers, the merchant deals, the programme the cardholder actually sees: all Reward's. In February Rezolve, a Nasdaq-listed AI commerce company, paid $230 million for it. Not for the data. Reward doesn't own the data, it owns everything that makes the data worth acting on.

I know this structure from the inside. I was COO at Reward for many years, when we launched the RBS programme, and later CPO at dunnhumby. The data owner watches a partner build on top of their substrate and thinks the arrangement is secure because they control the supply. It is secure until someone prices the enrichment.

The rake goes to the party that owns the enrichment and the customer relationship, not the party that owns the data underneath it. If a buyer priced your data partnership tomorrow, which side would they pay for?