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10 minutes of expert insights every weekday. Your morning ritual for staying ahead in retail media.
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[00:00:00] Kiri: A report went viral last week that rattled financial markets. The Rinni research report called the 2028 Global Intelligence Crisis. Reads like a postmortem from the future. Looking back from [00:00:15] 2028 at how AI triggered an economic collapse, here's the thesis. As AI agents replace white collar work companies cut staff margins look great short term, but the consumer [00:00:30] base creators.
[00:00:31] Machines don't buy mortgages or vacations, and so stocks like Uber, DoorDash, and MasterCard get sold off on the back of it. It's just one of many compelling [00:00:45] doom narratives about the future, and I get why it's spread. There's something satisfying about a clean story where technology. Eats itself, but I think it's wrong, or at least dangerously incomplete, and so does Eric [00:01:00] Sert.
[00:01:00] Eric is the person behind mobile dev Memo, one of the sharpest analysts covering the digital advertising economy. And in direct response to the Rinni report, he just launched a new podcast series called The Prosperous [00:01:15] Society, where he makes what he calls an AI bull thesis for the digital economy.
[00:01:21]
[00:01:22] Kiri: here's the setup. AI makes it dramatically cheaper to build things, But it doesn't make it any easier to get [00:01:30] those things in front of consumers. In fact, it makes that harder because now there's more stuff competing for the same finite pool of human attention. That alone should be ringing alarm bells for retail media professionals [00:01:45] because what Eric is describing at the macro level is exactly what's playing out in our industry.
[00:01:50] Retailers built their media businesses on the assumption that they controlled the discovery layer. That consumers would come to their websites, search for products and [00:02:00] encounter ads along the way. AI enabled shopping threatens that assumption by intercepting discovery upstream. But there's more to Eric's argument.
[00:02:10] He doesn't just say distribution matters more, he reframes what [00:02:15] advertising actually does in a digital economy. And this part, I think is genuinely important for how we think about retail media's value proposition.
[00:02:25] Eric: They can only be profitably exposed to consumers for whom they're specifically relevant. And this [00:02:30] distinction matters in a digital ecosystem characterized by extreme product heterogeneity, an extreme audience heterogeneity. The economic viability of a product often depends on its ability to be matched with precisely the right subset of users through advertising.
[00:02:44] [00:02:45] The salient question is not whether advertising can create demand for an arbitrary product. It is whether advertising can efficiently root existing demand to the product variant most capable of satisfying. The core claim there is that advertising in the digital context does not function as in [00:03:00] demand factory. It functions as a demand routing mechanism.
[00:03:03] Personalized digital advertising matches users with products on the basis of observable signals, historical behavior, contextual clues, demographic features, inferred intent, and increasingly [00:03:15] probabilistic estimates of discretionary spending capacity. These systems operate through auctions in which advertisers bid against one another for access to users predicted to generate profitable outcomes.
[00:03:25] The mechanism is not persuasion at scale, but selection at scale, and it's optimized at the [00:03:30] granularity of a specific user and not as in the era of the affluent society at large geographic regions or sweeping demographic profiles.
[00:03:38] Digital advertising operates in a similar environment. The vast majority of ad impressions do not result in conversion. Even fewer [00:03:45] result in high value conversion. The economic viability of a campaign can depend on a small subset of users who generate disproportionate revenue in the piece. I described it this way, quote.
[00:03:55] What's more important in digital advertising is attenuating the skew of the value distribution just [00:04:00] enough Through targeting to attain profitable user economics on an entire cohort, the Millionaire's mall only requires the presence of one billionaire end quote. Targeting does not need to produce a uniform uplift across all users.
[00:04:13] It needs to shift the distribution [00:04:15] enough that the tail contains sufficient value to justify the spend. Digital advertising is not broadly an exercise in persuasion. Digital ads don't attempt to convince the median consumer to purchase something they never previously considered buying. It is about identifying the [00:04:30] rare consumer whose latent willingness to spend makes the exposure economically rational.
[00:04:34] The challenge comes in identifying useful relationships and representations from that latent space. The most sophisticated advertising platforms are spending vast sums of money on doing just [00:04:45] that. When conversion optimization is layered on top of targeting the platform effectively tells the advertiser, specify your objective and your value per objective, and we will attempt to deliver those outcomes at or below your bid price.
[00:04:57] The advertiser bids based on expected lifetime [00:05:00] value, and the platform assumes the risk of wasted impressions and seeks to minimize it through better prediction. As I wrote in that piece, quote. An advertiser can ensure that their margin targets are satisfied with conversion optimization by submitting bids against conversion objectives that are [00:05:15] discounted against their actual economic value.
[00:05:17] If an advertiser pays $1 for a conversion, such as a purchase, that it expects to be worth $2. The difference in those values accrues to the advertiser is profit end quote. The platform's incentive is to refine its predictions continuously. [00:05:30] The more accurately it can identify high value users, the more budget it can capture.
[00:05:34] Budget flows towards absolute performance based on the advertiser's ROAS requirements. This feedback loop is economically expansionary. Better targeting leads to more conversions. More conversions produce more [00:05:45] revenue. More revenue supports greater reinvestment into advertising. Greater reinvestment produces more data.
[00:05:50] More data improves targeting. This is not a machine for manufacturing arbitrary wants. It's a machine for compressing the search cost associated with matching a user to the [00:06:00] product most capable of satisfying their existing preferences. Gabra dependence effect presumes that advertising manufacturers demand in order to absorb output.
[00:06:09] This has consequences for the consumer experience. First ads become more relevant. A relevant ad is [00:06:15] less intrusive. It aligns with existing interests. It reduces the cognitive friction associated with irrelevant exposure. In a world where ad inventory is finite and user attention is scarce, relevance reduces annoyance and product distraction.
[00:06:26] Second, personalization should improve monetization [00:06:30] efficiency. If a platform can reliably deliver conversions at or below a profitable threshold, advertisers are willing to scale spend that. Spend supports product development. It supports experimentation. It supports distribution at zero marginal price to the consumer. [00:06:45] Retailers know that a marketplace model can dramatically boost product assortment, shopper engagement, and total revenue. But to get the most out of your marketplace, you need an [00:07:00] ad tech solution that can really engage sellers. Miracle Ads is powering the future of retail media for leading retailers to activate both three P Sellers and one P brands.
[00:07:13] Kiri Masters: Learn [00:07:15] more@miracle.com. That's M-I-R-A-K l.com.
[00:07:20]
[00:07:20] Kiri: So demand routing, not demand creation, and that distinction matters for retail media. there is a conventional narrative that suggests [00:07:30] that all ads are annoying and erode trust, but Eric's framework suggests a different diagnosis. The problem is in that ads exist. It's when the routing is bad.
[00:07:41] When a sponsored product ad matches what you're already looking for, [00:07:45] it's not an interruption. It's efficient. Demand routing when it doesn't match, it is annoying. It is noise. Now, here's where Eric brings it all together and directly addresses the question of whether AI agents will kill advertising, which is the [00:08:00] question I certainly hear the most.
[00:08:02] Eric: If AI is deflationary for production, it is inflationary for distribution. That framing can sound paradoxical at first. When we talk about generative ai, we tend to focus on the reduction in marginal production Cost.
[00:08:14] Code [00:08:15] generation becomes cheaper, ad creative production becomes cheaper, iteration becomes cheaper. Entire product surfaces can be scaffolded and deployed with dramatically less capital than even a few years ago. But trivially, when production becomes cheaper, more things get produced. When more [00:08:30] things get produced, more firms compete for the same pool of human attention.
[00:08:33] And when more firms compete for a resource that does not scale, which human attention doesn't, the price of accessing that resource rises in the inflationary impact of AI generated ad creative. I try to [00:08:45] express this in straightforward economic terms, quoting from that piece, quote. Generative AI is deflationary for content production, but is inflationary for distribution.
[00:08:54] Generative AI will see the production costs of increasingly complex forms of content like Video Approach Zero. [00:09:00] These tools will instigate an immense expansion in the volume of each content format that they perfect. The first photograph to feature a human being was taken by Louis Degure, inventor of the De Guro type process in Paris in 1838, according to The Guardian, as a result of [00:09:15] widespread smartphone ownership, 1 trillion photographs were taken in 2014, representing more than a quarter of all existing photographs taken up until that point.
[00:09:24] Statistics like this will echo across text animated and photo realistic video production, audio, et cetera. In [00:09:30] synthetic form. As a result of generative AI and as content proliferates through generative AI tools, the challenge of capturing potential customer attention becomes more acute, necessitating an increased reliance on advertising.
[00:09:42] This is inflationary. The corpus of content will grow [00:09:45] at a much more rapid pace than the human birth rate. Organic discovery becomes ineffective as content mushrooms. This dynamic gave birth to the search ads mechanism in the first place. Generative AI will similarly create competitive friction for the discovery of all forms of content.
[00:09:59] In [00:10:00] saturated markets, allocation systems matter more than production systems. Which leads to the next installment in the series, which relates to the narrative that AI will eliminate the need for advertising altogether. The idea is that instead of browsing, consumers will delegate purchasing decisions to agents.
[00:10:14] [00:10:15] Those agents will query APIs to discover new products and decision product adoption based on price with product discovery becoming programmatic, automated, and abstracted from the consumer's cognizance. But even in a world of total age, agentic autonomy discovery requires a catalog. The [00:10:30] catalog is a central data structure in open AI's age agentic commerce protocol.
[00:10:33] For instance, it sources the options that can be exposed in the instant checkout viewport, but whether a product catalog takes the form of traditional digital storefront and API endpoint or a machine readable commerce [00:10:45] protocol like M-C-P-A-C-P or Google's recently announced UCP, the economic function remains the same.
[00:10:51] Someone intermediates discovery, and when someone intermediates discovery, they control allocation. They decide what is included in the catalog and that [00:11:00] control is economically meaningful, even if discovery becomes invisible to the human eye, even if it is entirely abstracted away from consumers, and I don't think it will be.
[00:11:08] The scarcity problem does not disappear. There will still be more products than any system can prioritize. Equally, there will still be [00:11:15] competition for inclusion, ranking, and prominence. There will still be mechanisms that determine which products are routed to which users. That's an allocation problem that naturally leads to advertising.
[00:11:24]
[00:11:25] Kiri: That last section is what we need to sit with. Even in a fully [00:11:30] agentic commerce world where AI agents are doing the shopping, someone still controls the catalog, someone still decides which product surfaces, and that allocation function is by another name. Advertising the [00:11:45] Rinni Doom thesis imagines AI eating the economy from the inside out.
[00:11:50] Eric's counter thesis is that AI reorganizes the economy around a new bottleneck, attention and distribution for retail media, the [00:12:00] implication is twofold. First, advertising isn't going away. The form factor changes. The interfaces change, but the economic function of paying for distribution persists.
[00:12:10] But the second. Is the question, who controls the [00:12:15] distribution layer today? Retailers control it on their own properties, but as AI platforms increasingly intermediate discovery before consumers ever reach a retailer's website, that control is up for grabs. The retailers who [00:12:30] recognize that their value lies in demand routing will be able to maintain their position.
[00:12:34] I highly recommend listening to the full episode of Eric T's Prosperous Society series on the Mobile Dev Memo podcast. We'll link to it in the show notes. Thanks for tuning [00:12:45] in, and I'll catch you tomorrow. I.
[00:12:46]