We traded attention for a free internet. What are we trading now?

After the last post on ChatGPT starting to sell ads, I took a proper look at:

how general-purpose AI actually makes money.

After writing it, one question kept lingering in my head.

For many free internet products in the past, the exchange was actually fairly clear:

users don’t pay directly, but contribute attention and usage behavior; advertisers then pay to buy the chance to reach these users.

So short-video platforms want us to keep scrolling a bit longer, search platforms want to understand our intent more precisely, and social platforms work hard to keep us coming back.

What about AI?

If I haven’t paid for ChatGPT, Claude or another AI product, what exactly am I trading?

After going through the earlier material, I found the answer is no longer just “attention” or “data.”

01 | Sometimes users actually aren’t “selling” anything

This is a situation that’s easy to overlook.

A free user isn’t necessarily bringing the platform revenue directly.

Many products cover the costs of free users first:

• some people will upgrade to paid later;
• some will push for enterprise purchases at their companies;
• some developers will start using the API;
• and some may never pay.

So behind a free AI conversation, the cost may, for now, be borne elsewhere:

by paying users, enterprise customers, other business revenue, or even the company’s fundraising.

That means:

free doesn’t necessarily mean the platform is secretly taking some equivalent from you.

It may also just be spending money to gain users, scale and a future market position.

So understanding every free product as “you must be paying with something” isn’t actually accurate.

02 | Attention is still valuable, but AI is a bit different from traditional content platforms

On the traditional internet, attention is a very important resource.

Finish one video, and here comes another.

Finish one piece of content, and the next is recommended.

The longer users stay, usually the more ads can be shown.

Many products naturally hope:

you don’t leave.

But AI is a bit different.

If I just ask:

“Help me make this email clearer.”

a good AI might solve the problem in two minutes, and then I close the page.

From the user’s point of view, that’s actually a very good experience.

So:

the best AI shouldn’t necessarily make me use it longer.

When explaining why Claude chose to be ad-free, Anthropic also mentioned a similar issue.

If an AI product relies mainly on ad revenue, might it slowly gain an extra incentive:

to get people to stay longer, ask more and come back more often?

For now we can’t assert that this change will definitely happen just because “ads appeared.”

But at least, it reminded me of one thing:

the business model affects what a product wants to optimize for over the long term.

03 | What AI gets isn’t only attention, but a great deal of context

This may be a very clear difference between AI and the previous generation of internet products.

Before, I’d search Google for:

“Tokyo hotels.”

What the platform knew was a search intent.

But I might tell AI:

“Taking my parents to Tokyo for the first time; they’re quite old and can’t walk much. Budget is about this much, I want to stay near the subway, I care about quiet, and I don’t like places that are too touristy.”

To do this well for me, AI needs to know more in the first place.

Our budgets, preferences, constraints, what we’ve done before and what we’re doing now can all become part of one conversation.

But here it’s also easy to slide into an overly simple line:

“so AI takes more data.”

The thing is:

having context and how that context is used are two different things.

When I tell AI my budget, it may first of all just be so it can complete the task.

Whether this information is later stored, used for personalization, used for model improvement, used for ad matching, or even provided to other companies needs to be looked at separately, based on each product’s policies.

So a more useful question than “does AI have my data” is actually:

what is each piece of this data used for?

Completing the current task is one thing.

Remembering my preferences is another.

Improving the model is another.

Matching ads is yet another.

Calling all of it “trading privacy for service” actually blurs the boundaries that really need attention.

04 | More worth watching long term may be the “default starting point”

Thinking further, I think there’s something even more interesting than attention and data:

habit.

At first, I might only occasionally ask AI a question.

Later it slowly becomes:

• opening it first to write things;
• asking it first to do research;
• having it plan trips;
• having it analyze work problems;
• handing it files too;
• and later still, connecting email, Calendar, Drive and other tools.

At this point, what an AI product gets is no longer just how many minutes I spend each day.

What it gets is:

whether, for the next thing, I’ll still come here first.

That’s a very different kind of value.

Because once a product enters more and more workflows, the cost of replacing it slowly rises too.

Not just signing up for a new account.

But also the habits already formed, the history, the personalized settings, the connected tools, and the way of working you’ve grown used to.

Many internet products in the past competed for:

how long you’re willing to stay.

General-purpose AI may also be competing for something else:

how much you’re willing to hand over to it.

I think this change is more worth watching long term than “will AI start showing ads too.”

05 | If AI starts acting for us, the exchange goes one step further

Today we have AI:

find a hotel.

The next step might be:

compare these few hotels for me.

And the step after that:

book it for me directly.

If in the future AI takes part more and more in:

searching, comparing, choosing, booking, ordering and paying,

then the places where the platform can make money multiply too.

Not necessarily just ads.

It could also come from merchant referrals, transaction commissions, agent service fees, or other commercial partnerships.

At that point, a new question appears:

AI doesn’t only know what I want to do; it may also stand between me and the transaction.

If it recommends a hotel, and the platform can earn revenue from this booking, I’d want to know:

why does it recommend this one?

Because it suits me best, or because the platform also benefits from this choice?

That doesn’t mean recommendations can never be trusted when there are commercial interests.

Many platforms we use every day already involve commissions and commercial relationships.

But at least, these relationships should be something we can know about.

Because only by knowing how an exchange happens can we possibly judge whether we accept it.

06 | From now on, when I meet a free AI, I’ll look at these 5 things first

Now, when I see a free AI product, I won’t immediately say:

“it’s free, so it must be making money off my data.”

I’d rather look concretely at five things.

1. Who bears the cost of my use this time?

It may be paying users, enterprise customers, advertisers, or the company still subsidizing growth with its own money.

2. What does this product mainly make money from right now?

Subscriptions, APIs, enterprise business, ads, or transactions?

3. How will the information I provide be used afterwards?

Only to complete this task, or will it also go into Memory, personalization, model improvement or ad matching?

These aren’t the same thing.

4. Is it getting harder and harder for me to leave?

Once history, files, Memory, plugins and workflows have all built up in one product, the real cost may already be not just the monthly fee, but the cost of switching.

5. When it recommends something, does it have a financial interest of its own?

Especially in scenarios like shopping, travel, financial products and service recommendations.

If the platform earns ad fees, commissions or transaction revenue from my choice, I at least want this to be transparent.

So looking back now, I think “what does free AI actually take” has no single answer.

• Sometimes it’s attention.
• Sometimes it’s information and context.
• Sometimes the platform is just temporarily covering my costs.
• Over the longer term, what’s really valuable may still be habits, workflows, and becoming my default starting point for getting things done.

More useful than “when it’s free, you’re the product” is looking clearly and concretely at each exchange:

what I get, what the other side gets, and whether I know about and accept the exchange.

Being informed may be just this.

Not treating every product as a villain.

Nor, because it’s convenient, not thinking at all about the commercial relationships behind it.

Just trying to know how the things you’re using actually work.

If you build AI products

The same question can be asked the other way around:

• who bears the cost of your free users?
• what kind of long-term behavior do you hope they form?
• once monetization starts entering recommendations, Memory, agents and transactions, which boundaries need designing clearly in advance?

About the sources

This post continues the previous one’s research on general-purpose AI business models, based on the public product, pricing and data policies of OpenAI, Anthropic and other companies as of September 2026, along with related research on AI business models, advertising and recommender systems.

The parts about “default starting points, workflows and a future action layer” are further analysis based on current product trends, and don’t mean these business models have all become reality.