Suppose you need a suitcase.
You go online to replace the one with the broken wheel. You compare a few, read some reviews, check whether it will fit in the overhead compartment. Somewhere along the way, the store shows you packing cubes, a travel pillow, and a luggage tracker.
You came for a suitcase. The store has other ideas.
Now imagine giving the job to an AI assistant. Find a carry-on within this budget, check the dimensions and return policy, and buy it once I approve.
The companies building these assistants call them agents. They can use websites and other services to carry out a task, with whatever permissions you give them. How much they can finish varies. But the proposition is straightforward: you describe what you want, and the software takes on more of the work.
For the customer, that could save an afternoon. For the store, it could change what happens during that afternoon.
And for the people whose work depends on everything between wanting the suitcase and receiving it, the question gets bigger.
a busy time to be somebody’s assistant
The personal-agent market has the feeling of a gold rush. Everyone wants to show what their agent will do for you: manage the inbox, arrange the trip, make the call, complete the purchase.
Meta’s description of Muse includes shopping and other tasks on a person’s behalf, with approval for sensitive actions. That tells us what the company is offering. It doesn’t tell us how reliably every task gets done.
Still, I understand the appeal. People have plenty of work they would gladly hand over. Much of it consists of finding information, comparing it, entering it somewhere else, and checking whether anything happened.
Those steps also support businesses. They create opportunities to sell something, charge for help, or persuade the customer to stay.
If agents become useful enough for people to delegate regularly, some of those opportunities could shrink. Others could move somewhere new.
what happens to “you might also like”?
I’m interested in the purchase that never gets suggested because the customer never browses.
An assistant sent to buy a particular suitcase may have no reason to spend time looking at travel pillows. It might finish the assigned purchase without taking the route the store designed for a person.
That could affect impulse sales. It could also affect genuinely useful discovery. Sometimes the thing beside the thing you came for is something you need.
We shouldn’t claim impulse buying is going away. Nor should we assume an agent chooses on facts alone. In controlled marketplace experiments, researchers found that AI buyers responded to product position, platform endorsements, and changes in descriptions. Different models made different choices.
That research doesn’t predict how much a real shop will sell next season. It does make the idea of an entirely unpersuadable shopper difficult to defend.
The commercial effort may shift toward influencing the assistant: getting into its sources, its shortlist, or its preferred purchasing route.
Which gives us a question to ask before celebrating the end of advertising. If the customer no longer sees the persuasion, how will they know it happened?
the charge you meant to cancel
Andrew Yang wrote about a colleague who used Muse to cancel a gym membership without having to visit or talk to someone. His broader argument is that AI can remove inefficiencies that businesses and jobs depend on.
The cancellation is one anecdote. But it makes delegation easy to understand.
People may be unsure about giving software responsibility for their lives. Getting an unwanted charge off the card is a smaller proposition.
A customer could ask an assistant to check renewals, compare bills, or work out how to leave a service. Some businesses might benefit: a clearer offer could win a customer who finally has help comparing it.
Others may discover that continued payment was doing more work than continued satisfaction.
That is good reason to improve the offer. But there’s another part of Yang’s argument we need to handle carefully. The person employed to process the cancellation may have had nothing to do with making it difficult.
the employee didn’t invent the paperwork
There is friction created to discourage a customer from acting. There is also friction because systems don’t fit together, records are wrong, or a request needs judgment.
Someone reconciles the bill. Someone corrects the booking. Someone checks the documents and follows up on what’s missing.
We can want those tasks to become easier without pretending the people doing them are unnecessary. They may be keeping a badly designed process working.
If an agent can do part of that work, several outcomes are possible. The employee might handle more cases or spend more time on difficult ones. The employer might reduce staffing. A service might become affordable to people who couldn’t previously use it.
We need evidence to know which is happening.
Anthropic’s Economic Index found AI use clustered in particular tasks within occupations; only about 4% of occupations used it across most of their tasks. Its data describes how people use Claude; it doesn’t tell us how many positions a valley employer will cut.
But it gives us a useful discipline. Ask what work is changing before announcing that an occupation is disappearing.
Then ask where the savings go.
here, the savings have an address
In the Coachella Valley, these questions could reach a hotel reservation office, a property manager, a clinic’s billing desk, or the person handling orders for a small retailer.
Those are places to investigate, not a list of jobs we can declare lost.
A visitor’s assistant could make it easier to find and book a local business. That business would need accurate information and a usable way to complete the request. Being found, understood, and booked are different problems.
One more thing you should know: my company, SunshineFM LLC, which is separate from this newsroom, sells AI visibility to businesses through a program called Get Agent Ready.
But getting the booking is only one part of the economic change. What work did the assistant do along the way? Did it bypass a service someone used to pay for? Did it compare offers differently? Who chose which businesses it considered?
And whose assistant is it, in practice? A recommendation can reflect the customer’s needs, a commercial arrangement, or simply the services the agent can reach. A friendly conversation won’t make those distinctions obvious.
I want customers to spend less time fighting forms and chasing answers. I want our businesses to benefit from better systems. I also want the people doing the work to be part of this conversation before their role appears in a spreadsheet as a saving.
“Optimization” is an easy word to put in a presentation.
Before we use it to describe what agents are doing here, name the task being removed, the person doing it, and where the money goes afterward.