March 2025: DeepShopping

The next logical evolution of DeepResearch is agents that shop for you

The Parable of Search

In the beginning the internet was small, and users were few, and everyone knew where everything was. Then the internet got too big, and you needed a search engine to find what you were looking for. And the internet felt small again. Now again, the internet is so big, that even search is not enough. So along came Deep Research.

Deep *X*

From what I can tell, the “AI Agents” that are in most widespread use today are “Deep Research” agents; Google, OpenAI, Perplexity, Manus and several others have one. Coding agents are probably second. Both of these examples agents typically complete a non-deterministic task (a task with subjective output/no correct answer). 

I’d spent some time with a couple of founders building agents for shopping and I didn’t really get it initially. Most recently I’ve been trying Deep Research out with a handful of holiday shopping tasks and I can’t tell if there’s something there but it feels like “Deep Shopper” or “Deep Concierge” is a(t least one of many) logical evolution of Deep Research given what it appears to be good at (assembling many options and synthesizing them into a single view faster/with less effort than you would manually). I’ve long believed that most ideas (at least at inception) are bad, and I’m just not yet sure the way in which this is bad yet.

Deep Shopping

Think of this as “Deep Research for Shopping” and it can deliver a few specific benefits:

1. Saves you (the shopper) time: imagine buying something you don’t regularly buy. This could save you the time and energy you’d use to research it, and bring you back a set of options with an articulation of the various benefits and tradeoffs of each

2. Saves you money: once you’ve selected the specific option you want, this agent can find the specific outlet online with the best price (eg Amazon vs. Nike for shoes)

3. Configuration: imagine trying to find a holiday house for a large group of people - in some cases it’s actually cheaper/more feasible to find two houses next to each other. If you have a big family that gets together a couple of times a year, you know this pain intimately. Most platforms make this harder to do than it should be, and this agent could help you optimize across platforms as well.

4. Exceptions: finding you the most fastest shipping, and most favorable cancellation and return/exchange policies

5. Qualitative evaluation: this is something like aggregating Reddit reviews and finding potential issues that a) you wouldn’t know existed and/or b) you wouldn’t realize are important to you.

These benefits are all about the shopping task. A meta benefit this could deliver is more about the shopper: normalizing results into a single aggregated view that makes them easy to compare by generating the UI directly, and making the UX completely custom to the shopper’s revealed preferences.

And at the end the agent could allow you book directly or present a buy button and complete the task for you. The company who builds the agent could issue a card to complete the transaction which would simplify credential handling in the short term while agent specific payment primitives mature. 

Start Extremely Narrow

This feels like the kind of thing where most people will start wide/general, and I think starting narrower with either an extremely high frequency shopping task, or a low frequency + high value shopping task is better. 

My reasoning is that shopping is a slightly more deterministic task than knowledge acquisition (the Deep Research use case) and in many cases once you’re consuming the object you bought it is EXTREMELY obvious if it was wrong or off in some way, and in the higher value cases the point at which it’s obvious you made the wrong choice, is also the moment it’s hardest and most expensive to reverse (staying on the holiday house use case - by the time you’ve realized you booked a crappy Airbnb, you’re already in it).

As a result, I believe it’s better to create a shopping agent that is absolutely excellent at one task and can so consistently get better results than a human would, that humans who do not use it are at a material disadvantage. And once you’re excellent at one, it’s easier to become excellent at others (vs creating something general and trying to be good at everything).

Monetization

This feels reasonably obvious but I’ll state them anyway

• Ads: an agent that is 2x - 10x better than the average human at a shopping task has incredible real estate to surface sponsored results. Even better if it has differentiated understanding of that users revealed preferences.

• Affiliate fees: obvious monetization in lots of shopping categories.

• Transaction fees/interchange: Lots of ink has been spilt on agent payment frameworks. A simple short term path would be to issue the agent a payment credential of it’s own. This allows the user to pay only when they’ve authenticated against a specific purchase, without handing over their own credential for further use.

• Percentage of savings: who doesn’t like free money

Open Questions

Just a few that come to mind:

1. One of my favorites: what happens to chargeback rights when the purchase is completed by a machine? Right now consumers win ~¾ of chargebacks and merchants win the rest. Does this change? Does the agent take financial responsibility?

2. More generally, who bears responsibility for errors? The user? The agent? The company that made it?

3. What is the final configuration (or set of configurations) for payment instruments? Will we live in a world where each person has many agents doing things for them, or will we generally have a single agent that can do many tasks? How does authentication work in all these scenarios?

4. Merchants have invested a ton in e-commerce experiences; is there incremental investment required to make those experiences agent friendly? How will merchants react to consumers buying from inside an agent experience?

5. Many merchants have also invested heavily in bot detection for blocking purposes - whats the mechanism for distinguishing between bots that might result in a legitimate sale vs. spam.