The Agent Economy: Who's Most at Risk?

Source: Rihard Jarc; UncoverAlpha (Substack, paid post) · 2026-09-30

On September 8, Meta launched Muse, a personal AI agent that sends your emails, books your travel, fills out your forms, buys things for you, and lowers your bills. Thirteen days later, it had 2.5 million downloads in North America and was number 1 in terms of downloads on both the App Store and Google Play ever since. A few days ago, we also got a revamp of an AI agent from Microsoft named Autopilot, and yesterday we got OpenAI’s agent called Dots and we also have the agent startup Instinct.

1. The agent uses the website like you do (computer-use / browser agents). The model gets a virtual computer, looks at screenshots of the page, and clicks, scrolls and types like a human would. Perplexity’s Comet works like this, and so does Muse, which runs each user’s agent on its own cloud virtual machine with a second “Sentinel” agent approving anything that leaves it. The upside: it works on any website, with no permission needed from the site. The downside: it is slower and more error-prone. Amazon is literally building “digital gyms” where its Nova Act agents practice scrolling, clicking and interacting with interfaces because this is still hard.

The important design choice common to all of these: the merchant stays the merchant of record, but the agent owns the conversation. Google’s hotel booking in AI Mode is a good example. It launched in August with Booking.com, Expedia, Hilton, Marriott, IHG, Choice, Wyndham, Priceline, Hotels.com, and Trip.com, and Google passes partners only the minimum needed to complete the booking (name, payment, occupancy, property). The inspiration, the comparison, the “why this hotel and not that one” all stay with Google. On top of that, Google says there are no paid placements and no way for partners to commercially influence ranking, at least for now.

But in essence the agent keeps the demand signal, and the aggregator gets reduced to a fulfillment API.

I want to be fair here, the first attempt at in-chat shopping flopped. OpenAI launched Instant Checkout in September 2025 promising “over a million” Shopify merchants, and by February 2026 only about 30 Shopify merchants were live. In March, OpenAI moved checkout into merchant apps instead. Booking’s CFO said on the Q2 call that LLM referral traffic is still under 1% of room nights and has not moved much. So if you are looking for this in the P&L today, you mostly will not find it.

Instinct’s CEO shared some very interesting data points on the recent Invest Like the Best Podcast. He said that after 3 weeks, 40% of the user base shares the credit card with the Instinct AI agent. He also said data showed that if you share one piece of sensitive information with Instinct, retention is 80% (which is very high for a consumer product). He also explained that $1B of transaction value already flows through Instinct, and 50% of that was travel use cases.

Attention ads are now a core profit engine for aggregators. Over the last three years, almost every marketplace has quietly built an ad business on top of its human traffic, and those ads are close to 100% gross margin:

DoorDash, Uber Eats and Instacart together now generate more than $4 billion in annualized advertising revenue. Uber Advertising alone passed a $2 billion run rate, with delivery ad penetration above 2% of gross bookings. On $58.0 billion of quarterly gross bookings, that is a meaningful slice of Uber’s profit.

Amazon’s ad revenue was $68.6 billion in 2025, and roughly $60 billion of that is on-site search ads, i.e. sponsored products shown to a human typing into the Amazon search box.

Now think about how an agent chooses. OpenAI’s own description of ChatGPT shopping says it ranks merchants on availability, price, quality and whether the merchant is the primary seller. There is no line item for “who paid for the slot.” Google says the same for hotels in AI Mode. When the buyer is a piece of software optimizing for your stated preferences, the ad becomes irrelevant. Every dollar of in-app ad revenue is, in effect, a bet that a human will keep looking at the app.

Layer 3 is already breaking, and we have a live case study. If you want to see what happens to an aggregator whose value is “we help the human compare,” look at Tripadvisor. In Q2 2026, its Hotels segment (the old metasearch business) saw revenue down 23% to $117.9 million, with media and advertising down 12%. Management is guiding the whole Hotels & Other segment to decline another 20% to 23% in Q3, citing structural changes in its main SEO channel. And this is from Google’s AI Overviews, which are merely summarizing the comparison for a human. Agents that act on the comparison are the next step and make this even worse.

Even the strongest aggregator is paying more to stand still. Booking Holdings is, in my view, one of the best-run OTAs in the world, and even there you can see the cost of the transition. Booking’s marketing expense was 4.7% of gross bookings in Q2 2026 vs 4.6% a year earlier, and marketing grew 11% while gross bookings grew 9%. The company’s own 10-Q says it expects SEO traffic to decline in the short to medium term, which may push more spend into paid channels. That is the first-order effect: free traffic becomes paid traffic. The second-order effect, when agents do the booking, is that the paid traffic goes to a new toll collector.

• When C+R Research asked consumers to estimate their monthly subscription spend, the average guess was $86. When they itemized it line by line, it was $219 a month, a $133 gap.

• Self Financial’s 2026 survey found 59.9% of people have at least one paid subscription they did not use in the last 30 days, averaging 2.6 idle subscriptions each.

That unused-but-paid revenue is close to pure profit for the company collecting it. No servers, no content cost, no support tickets.