Imagine you’re shopping for a new processor. Two established manufacturers offer competing CPUs. Independent benchmarks show virtually identical performance for the applications you use. Power consumption, reliability, compatibility, and warranties are comparable. One costs $450 and the other costs $350.
Which would you buy?
When the specs are equal, what is familiarity worth?
Both options are assumed to perform equally in the buyer’s actual workload. The prices are hypothetical.
Product A · Familiar manufacturer
Established brand, known to the shopper
$450
- Comparable performance in independent testing
- Similar power use, reliability and warranty
- Existing customer familiarity
Product B · Established alternative
Another reputable, recognized manufacturer
$350
- Comparable performance in independent testing
- Similar power use, reliability and warranty
- Saves $100 without a demonstrated functional loss
If you’ve spent years buying from the more expensive manufacturer, loyalty might tug you toward the familiar box. But once an AI shopping assistant presents credible evidence that the cheaper processor performs just as well, that $100 premium becomes harder to justify.
Now imagine a third manufacturer enters the comparison at $250. You’ve never heard of the company. The specifications are impressive, but you’re unfamiliar with its products and reputation.
Suddenly, the calculation changes. You want to know whether the performance claims are accurate, whether the processor is reliable, and whether someone you trust has independently tested it.
If Gamers Nexus, Tom’s Hardware, or another credible source verifies those claims, perhaps you’d consider buying it. Without that evidence, the savings might not be worth the uncertainty.
That small thought experiment captures a question I keep returning to as AI takes a larger role in how people buy things.
Could AI-assisted shopping gradually eliminate the premiums brands charge simply because consumers recognize their names?
And if it does, what happens to the value of branding itself?
My suspicion is that AI could make certain products increasingly interchangeable while making genuinely differentiated brands more valuable.
I also think that would be a good thing.
When the brand name is no longer enough
Brands have always benefited from consumers having incomplete information. That isn’t necessarily the result of deception. It’s simply how shopping works when nobody has time to become an expert in everything they buy.
So consumers rely on shortcuts. Brand familiarity, reputation, advertising, customer reviews, and recommendations help narrow their choices.
A familiar name provides reassurance. When someone has had positive experiences with a manufacturer, they may reasonably expect those experiences to continue. That reassurance has real economic value.
But it can also create situations where consumers pay substantially more for products that offer little meaningful advantage over less expensive alternatives.
AI has the potential to change that equation. An assistant capable of comparing technical specifications, independent testing, long-term reliability, warranty coverage, and customer experiences can reduce the effort required to evaluate unfamiliar products.
The OECD has identified reduced search and verification costs as one way AI could intensify competition on price and quality. The outcome depends on market conditions and how recommendation systems operate, rather than being guaranteed by the technology alone.
For an established manufacturer, that creates an interesting problem. What happens when the consumer no longer needs to rely as heavily on your reputation because an AI assistant can substantiate the quality of your competitors?
The premium associated with familiarity becomes more vulnerable. But notice what hasn’t disappeared: trust.
In the processor example, I’d still want independent verification before considering an unfamiliar manufacturer. AI would be helping me evaluate the evidence, not creating that evidence from nothing. A positive performance benchmark alone also cannot guarantee long-term reliability.
The credibility of a trusted technical reviewer could help a new brand overcome its lack of recognition. And the more reliably an AI assistant identifies good alternatives, the more willing consumers might become to consider its recommendations.
In that sense, AI could begin redistributing some of the economic advantage associated with established brand recognition. A lesser-known company might not need decades of advertising to convince consumers that its product deserves consideration. It would still need to deliver a credible product and provide evidence that its claims were true.
Fame would matter less. Proof would matter more. A company shouldn’t have to be famous to make something good.
What if the premium buys you something you actually value?
Of course, products aren’t always as easy to compare as processors. Consider a refrigerator.
Imagine two models with comparable capacity, energy efficiency, functionality, and reliability. One costs $1,500, while the other costs $2,000. The more expensive refrigerator comes from an established manufacturer with an extensive service network. The cheaper one comes from a lesser-known brand with more limited local service coverage.
What does the extra $500 really buy?
Comparable household appliances can carry different service experiences. The distinction matters only if the buyer values it.
Product A · Established brand
Established service network
$2,000
- Comparable capacity and energy performance
- Familiar reputation and local service access
- Typically easier to arrange warranty repairs
Product B · Newer brand
Comparable appliance, narrower service reach
$1,500
- Similar reported reliability (hypothetical)
- More limited local repair coverage
- Saves $500 upfront
At first glance, the established brand seems to offer greater peace of mind. But is that peace of mind worth $500?
This is precisely the kind of question I’d want AI to help me answer. How often do owners of the cheaper refrigerator actually need repairs? When something breaks, how long does service take? Are replacement parts available? Could I hire an independent technician if the manufacturer’s service were inconvenient?
Suppose credible evidence suggested that the cheaper refrigerator had only a 5% chance of requiring a significant repair over five years. And suppose an independent technician could perform that repair promptly for approximately $300.
Under those hypothetical conditions, the probability-weighted repair expense would be $15, before accounting for other potential costs. That doesn’t mean the refrigerator will cost only $15 to repair. It means that when the likelihood of the event is considered alongside the potential expense, paying another $500 upfront for better service may no longer seem economically justified.
There are still considerations the calculation doesn’t fully capture. A refrigerator failure could mean spoiled groceries, disrupted schedules, time without an essential appliance, or considerable inconvenience. Different consumers will value avoiding those problems differently.
This is where AI could earn its keep. Instead of asking consumers to accept a vague promise of superior reliability or service, it could help them understand what those advantages might actually be worth in their circumstances.
Then another question enters the picture. What if the $2,000 refrigerator looks substantially better?
Imagine the less expensive model has a conventional stainless-steel design, while the premium model has an architectural appearance that complements a carefully designed kitchen. If I were renovating my home and wanted a particular aesthetic, I might happily pay the additional $500.
The refrigerator wouldn’t necessarily cool food any better. Its design would simply contribute something meaningful to my experience of the space. And that is a perfectly legitimate reason to pay more.
Notice what changed. I’ve moved from paying more for an assumed functional advantage to paying more for something I actually value.
The same aesthetic premium won’t mean as much to everyone. Someone shopping for a $500 refrigerator may care deeply about design but have little room in the budget to express that preference. Someone furnishing an expensive kitchen might consider a $500 design premium relatively insignificant.
Both people can appreciate good design. Their priorities, resources, and willingness to pay are simply different. AI could help make those differences clearer rather than treating every shopper as though they were evaluating the same definition of value.
A watch is worth more than its ability to tell time
Watches offer an even more interesting example because their prices frequently have little relationship to their basic function. A relatively inexpensive quartz watch can keep excellent time. A mechanical watch costing thousands of dollars may actually be less accurate. And yet people willingly pay enormous premiums for mechanical watches.
Why? Because telling time isn’t necessarily what they’re buying. Your phone already does that, and it never needs a service interval.
Sometimes a person buys not only what the product does, but what owning it communicates about the person who chose it. A recognizable luxury watch may signal success to a broad audience. A less familiar piece may signal discernment to a handful of enthusiasts. A practical, inexpensive watch can communicate something about its owner too. Those signals depend on who is wearing the product, who notices it, and the associations they share.
This isn’t just an advertising theory. Consumer researchers Jonah Berger and Morgan Ward described the role of subtle signals of inconspicuous consumption: knowledgeable insiders can use understated brand cues to communicate with others who know how to recognize them. That doesn’t mean all watch buyers seek social status, or that the symbolism has to be visible to strangers. Personal satisfaction and social meaning can exist at the same time.
What is someone really buying when they choose a watch?
Here the comparison is not about finding the cheapest timekeeper. It is about two different ways ownership can communicate identity.
Recognized icon
Broadly understood brand symbolism
- Widespread recognition
- Established heritage and familiar associations
- Status meaning understood by many people
Enthusiast choice
Understated recognition within a smaller community
- Distinctive design and history
- Recognition by people who share the interest
- Personal meaning and a sense of belonging
I’ve had a personal interest in watches for years. Earlier in my career, I gravitated toward brands that weren’t necessarily recognizable to the average consumer.
One of those brands was Bell & Ross. I appreciated its origin story, its connection to aviation instruments, and its distinctive design philosophy. The brand’s identity came from something more meaningful to me than simply attaching a prestigious name to a watch.
Another was Panerai. I was particularly attracted to the company’s military-diving heritage and the way that history influenced its design. The proportions, recognizable crown-protection system, and utilitarian character gave the watches an identity I appreciated. I eventually bought a PAM 422 because it connected me to that tradition.
At the time, Panerai wasn’t a brand that everyone around me immediately recognized. And that was part of the experience, although perhaps not for the reason you might assume.
I wasn’t buying it because I wanted to impress strangers or flaunt an expensive watch. I liked knowing what I was wearing.
Occasionally, someone who understood watches would recognize it. Suddenly, there was a connection. We shared an appreciation for something that most people around us didn’t necessarily understand. It felt a little like belonging to a club whose members recognized one another without needing an introduction.
That social experience was part of the value. So was the fact that the watch reflected my taste and the way I wanted to understand myself. For some consumers, a purchase helps shape other people’s perceptions; for others, the value is knowing privately why the product matters. Often the two overlap.
Interestingly, I never wanted the brands I appreciated to remain obscure. As Panerai gained recognition, I felt proud that more people were discovering something I’d already valued. I believed the brand deserved greater appreciation. Its growing recognition reinforced my confidence in the qualities that originally attracted me.
That enthusiasm would change, however, if the company started abandoning those qualities. If its products lost the craftsmanship, distinctive design, or connection to its heritage that made them desirable, greater popularity wouldn’t automatically compensate for that loss.
A brand becoming more recognizable is not the same as a brand becoming more valuable. Recognition can create opportunities. What the company does with those opportunities determines whether its meaning is strengthened or diluted.
Consumers don’t just recognize brand value. They help create it.
That experience points to something AI-assisted discovery could change. We tend to think of branding as something companies develop and consumers respond to. Companies establish identities, create products, invest in marketing, and hope people develop favorable associations.
But consumers participate in creating those associations. When someone discovers an unfamiliar watchmaker, appreciates its philosophy, wears its products, and introduces the brand to others, that person becomes part of the brand’s development. Their credibility and relationships can influence how other people perceive it.
That doesn’t mean every enthusiast is an influential tastemaker or that an individual’s advocacy guarantees a brand’s success. It means brand equity is partly social. It grows through the relationships, conversations, experiences, and meanings people share.
And it raises a question worth asking of any brand premium: Where did the perception come from, and what continues to earn it? Advertising and celebrity endorsement can introduce an association, but customers test it against the product, its service, the company’s actions, the communities around it, and their own experiences. A socially meaningful brand need not satisfy everyone. Its meaning has to remain credible to the people for whom it matters.
Consider what happens when AI makes it easier to discover brands outside the conventional competitive set. A shopper who appreciates industrial design, independent craftsmanship, or a particular cultural history might be introduced to a manufacturer they would never have encountered through conventional advertising or search.
The AI might explain the founder’s background, the product’s design decisions, the history behind its materials, or the community developing around it. Now the consumer has an opportunity to appreciate the brand for reasons beyond its price or specifications.
If the brand delivers on those expectations, the customer might become an advocate. That advocacy can introduce the brand to other people who share similar interests.
AI could therefore do two seemingly contradictory things. It could make established brands compete more aggressively on price by exposing functionally comparable alternatives. And it could help unfamiliar brands establish new premiums by making their genuine differentiation more discoverable.
The outcome isn’t inevitable. AI recommendations could also reinforce already popular brands or overlook emerging ones.
A 2026 Wharton Generative AI Labs report based on roughly 26,000 AI-shopping tests found that agents chose consistently when shown only a product page, but their picks became less predictable as more context was added. A single outside source, the order in which sources appeared, and even how information was delivered could shift the final choice.
That matters because AI isn’t an independent authority on what a product is worth. Its conclusions depend partly on the information it receives and how it evaluates that information. The opportunity for discovery is real. So is the risk of replacing familiar commercial biases with algorithmic ones.
Will AI actually make everything cheaper?
So far, I’ve looked at AI from the consumer’s side of the counter. But manufacturers and retailers will have access to increasingly sophisticated AI systems of their own.
They can use those systems to monitor competitors, analyze demand, evaluate pricing, identify customer segments, and determine where consumers are willing to pay more. That could produce more competitive pricing in some categories. It could also produce the opposite.
Economists have studied how competing pricing algorithms can sustain prices above competitive levels under particular conditions, even without explicit communication between sellers. A 2026 paper in American Economic Review: Insights models sellers who choose pricing algorithms rather than individual prices and finds that, in that setting, equilibrium prices end up above competitive levels.
That doesn’t establish that AI shopping will cause widespread price increases. It does show why better technology and more information don’t automatically guarantee more competitive markets.
There’s also a distinction between discovering what consumers value and discovering how much a particular consumer can be persuaded to pay.
Suppose an assistant knows I prefer a certain industrial design and have a generous budget. That knowledge could help it recommend a product I’ll genuinely appreciate. But a retailer with access to similar information might also use it to determine how much more it can charge me.
The same intelligence that makes shopping more relevant can potentially make selling more profitable. We shouldn’t assume one outcome will dominate every category.
The effect of AI on prices will depend on market competition, the quality and independence of information, the incentives of the platforms making recommendations, and the ability of consumers to compare and switch between alternatives.
Still, I think reducing the effort required to evaluate products is a positive development. Consumers should be better equipped to understand what they’re buying and why one option costs more than another. And brands should welcome that scrutiny when they have meaningful reasons for their premiums.
Maybe commoditization is exactly what branding needs
I don’t see commoditization as inherently bad. Some products should become more interchangeable. Some premiums should become harder to justify.
If two companies offer essentially the same product, with comparable quality, reliability, service, and experience, why shouldn’t consumers become more willing to choose the less expensive one? A recognizable name alone shouldn’t entitle a company to maintain a substantial premium indefinitely.
But that doesn’t mean every brand should compete on price. Brands have every reason to earn premiums through better engineering, distinctive design, exceptional experiences, meaningful relationships, and the things they contribute to people’s lives. My analysis of Porsche’s pricing power examines how such premiums depend on an identity the business can continue to deliver.
Heritage can matter. So can craftsmanship, reputation, cultural participation, ethical behavior, and a clear sense of identity. Sometimes these qualities help an owner express who they are, or who they aspire to be. The value can come from a private appreciation, from recognition by others, or from belonging to a community that understands the product.
Those differences don’t have to matter equally to everyone. A collector may appreciate the history behind a mechanical watch. A designer may value the appearance of an appliance. An enthusiast may care about the components inside a computer. Another customer may care primarily about getting something reliable at the lowest possible price.
The economic value of differentiation depends on the people evaluating it. But the differentiation has to be real, and that is the test I keep returning to in my thinking about brands.
A company can advertise that it values craftsmanship, but its products and production decisions have to demonstrate that commitment. It can claim to care about customers, but its service and support need to reflect that claim. It can celebrate a heritage, but it needs to preserve the qualities that made that history meaningful.
And if a company claims to make the world better, its actions should provide a credible reason for people to believe it.
In Bigger, Smarter, Stronger Brands, I argue that brands are living social systems. Their identities and reputations develop through the combined effects of their people, products, behavior, relationships, and experiences.
A marketing campaign can influence perception, but it cannot indefinitely substitute for everything else a company does.
AI could make the distance between those claims and their supporting evidence easier to examine. It could make consumers better informed about technical differences, more aware of lesser-known alternatives, and more capable of questioning whether a premium is justified.
At the same time, it could help them discover forms of value they hadn’t previously considered. That wouldn’t make branding obsolete. It could make branding more consequential.
If product specifications become easier to compare, the differences that truly matter to people may become more important. And if consumers can investigate those differences more thoroughly, companies have a stronger incentive to ensure that their promises reflect what they actually deliver.
I don’t believe there’s one objectively correct price for every product. People value things differently, and what they’re willing or able to pay varies with their circumstances.
But I do believe there’s something healthy about a marketplace that makes it easier for consumers to understand what they’re paying for.
Perhaps the most interesting consequence of AI-assisted shopping won’t be that every product becomes a commodity. It will be that more brands have to earn the right to be something other than one.