Consumer Paradise is Near – Thanks to AI

The article "The end of the rip-off economy" in The Economist identifies a central change triggered by Artificial Intelligence: the dissolution of information asymmetries in the market. The observation is accurate and describes the beginning of a development. The article focuses on the first, direct effect – fairer pricing. However, if we follow this development to its logical conclusion, we encounter second- and third-order effects that go beyond mere market efficiency and could change the foundations of the consumer economy.

Stage 1: The End of Information Asymmetry

The Economist correctly diagnoses how the "rip-off" has historically worked. It is based on the information gap between seller and consumer. This principle, described by George Akerlof using the example of the used car market ("The Market for Lemons"), allows providers to profit from a lack of transparency.

For decades, many industries have used this advantage. The used car dealer knows the defect history, the financial advisor knows the commission models advantageous to them, and the craftsman knows the actual repair needs. The internet initiated a first correction through review platforms, but it remained incomplete. Review portals aggregate subjective opinions and are themselves manipulable. Researching in forums is time-consuming.

Artificial Intelligence, as The Economist outlines, changes this dynamic on a more fundamental level. It does not primarily aggregate opinions; it analyzes available facts. A consumer no longer has to rely on vague testimonials. They can use AI systems to check a rental agreement for legally disadvantageous clauses or to analyze a wine list objectively for its price-performance ratio, based on a broad database of expert judgments.

This reveals the first-order effect: AI delivers an improved, often marginal, but decision-relevant piece of information directly to the point where the purchase decision is made. The transaction costs associated with understanding complex offers or contracts decrease significantly. When a leasing contract can be checked against thousands of other contracts and current case law in seconds, the seller loses their leverage of informational complexity.

This process triggers the so-called "AI Butterfly Effect" (Gerlach, 2025): a small, local change – a single consumer making a better decision thanks to AI – can set off an unforeseen chain reaction. This one, now-informed customer forces a provider to adjust a price or offer a better deal. Competitors who observe this must follow suit. The marginally improved information of an individual thus propagates through the system and begins to shift the market equilibrium, much like the proverbial flap of a butterfly's wings.

The conclusion of The Economist is sound: The "rip-off economy," based purely on an information advantage, is beginning to erode. The era of the structurally inferior, "clueless" consumer could be coming to an end.

Stage 2: From a Fair Purchase to an Optimized Product

What happens when this transparency becomes the market norm? The reaction of providers represents the second wave of this transformation.

When consumers can no longer be taken advantage of through complex contracts or information deficits, competition will shift to the actual quality of the product. The focus moves from marketing to engineering, from sales psychology to product performance.

To survive in this more transparent environment, producers must also use AI. Developing new, more subtle forms of manipulation is a dead end. An arms race against globally learning consumer AIs would be a costly undertaking and offers real product innovators room to conquer market share. This forces all producers to use AI to improve their own products and internal production processes.

This innovation pressure, generated by transparency, can set a self-reinforcing cycle in motion:

1. Informed Consumers: AI assistants enable more rational purchasing decisions and reward good offers. This increases the pressure on second-best providers to catch up.
2. Better Products: Innovative companies use AI in design, material science, and production to create more durable, efficient, and low-maintenance goods. This draws demand to them and again forces the second-best providers to try harder.
3. Changed Demand Structure: Better products make other products obsolete, such as cleaning agents for windows if nano-tech prevents dirt from sticking. Here lies the deeper level of transformation: that of products and need structures. 

The Economist describes the end of being disadvantaged at the point of purchase. The logical next step is the end of being disadvantaged during ownership through the need for repairs, maintenance, or premature replacement.

A significant part of today's "rip-off" happens after the purchase, for example, through "planned obsolescence" or suboptimal product design. A printer whose cartridges are disproportionately expensive. A device where a simple repair is intentionally made difficult. A smartphone whose battery life seems artificially limited. All these are forms of exploitation based on the lack of transparency in the product lifecycle.

However, if an AI can reliably predict the "Total Cost of Ownership" of a product at the time of purchase – including expected repair cycles, consumables, and maintenance costs – the consumer will prefer the product that has the better balance over its entire lifespan.

The result is not necessarily "the perfect product," but a better-designed, more durable one. The incentive for manufacturers to profit from inefficiency, wear and tear, and repairs diminishes. This leads to a decline in demand in sectors that live off this downstream inefficiency: overpriced spare parts, unnecessary maintenance contracts, or the constant cycle of new purchases.

The smartphone is a good real life example for this. It has already replaced dozens of specialized devices, thereby eliminating their entire follow-on costs. Mostly gone are separate digital cameras, video cameras, MP3 players, e-book readers, navigation devices, calculators, alarm clocks, wristwatches, notepads, calendars, scanners, mobile gaming consoles, dictaphones, compasses, spirit levels, and physical maps. This consolidation of needs into a single device reduces total costs, eliminates the need for separate batteries, avoids electronic waste and "dust collectors" on the shelf, and shifts maintenance to simple "on the fly" software updates instead of requiring physical replacements.

This optimization logic applies not only to consumer goods but also to capital goods like machinery, software, and industrial components – i.e., the entire supply chain. When machines and enterprise software become more efficient, durable, and interoperable through AI-driven analysis, production improves. Better means of production lead to better end products for the consumer.

This starts a cycle that can be described as the "AI Butterfly Wheel Effect." Unlike the linear "Butterfly Effect" (A leads to B), this is a self-reinforcing flywheel: Better information (1) leads to better consumer decisions (2). This forces the creation of better production goods (3). Better production goods (capital goods) enable more efficient production (4), which in turn leads to even better end products (back to 2). This cycle of transparency and production optimization accelerates itself and drives the efficiency of the entire economy. An avalanche of changes sweeps over the whole economy, similar to the changes brought by smartphones mobile computing.

Stage 3: The End of the Economics of Scarcity

This development leads to a third, long-term consequence, which The Economist does not address in its article but which can be derived from the initial analysis.

What happens when markets are transparent and products become more durable through AI-driven production (i.e., the deeper integration of design, control, and manufacturing)?

The answer could lie in a phenomenon one might call the "Great Decline Paradox": The total demand for durable consumer goods could structurally decline. If a refrigerator self-maintains, clothing barely wears out, and road pavement lasts 100 years – how often will a new purchase be made or a repair be necessary? These are gloomy economic prospects for refrigerator manufacturers, textile producers, and road builders. Of course, the development is not linear, and refrigerators will also continue to evolve. However, the demand for such goods per unit of time is likely to fall, inhibiting economic growth.

Traditional economics manages scarcity. The 20th century was marked by mass production and artificially stimulated consumption. This era could be ending. The Economist described the beginning of the end of information scarcity; the logical continuation is the end of artificial product scarcity and, potentially, an erosion of material scarcity in durable goods.

If AI-driven, local production solves the allocation problem, the distribution of goods may cease to be the central organizing principle of society. Scarcity then shifts from the material to the immaterial: time, attention, creativity, and meaning.

When material needs are efficiently met, the central question will no longer be "What can we afford?" but "What do we want to spend our time on?". 

The Economist's article thus provides the starting point for a debate in which the material consumption game itself is fundamentally changing—hopefully leading to a better life for all humankind.

Sources:

Gerlach, S. (2025). The Simple Nanoeconomics of AI: An Economic World Model for Exploring AI Impacts. Available at SSRN 5394649. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5394649

The end of the rip-off economy. (2025, October 27). The Economist. https://www.economist.com/finance-and-economics/2025/10/27/the-end-of-the-rip-off-economy