Credence Goods and the Erosion of Trust-Based Pricing: How AI Logo Generators Are Reshaping Design Markets

Small businesses have often spent thousands of dollars on logos they could not truly “evaluate,” even after receiving them, due to how logo designers set prices. This pricing structure can be explained using a standard information economics concept taught in college: credence goods, as developed by Darby and Karni. Recent developments surrounding the rapid advancement of AI in the logo generator space have disrupted the market and caused a collapse in the foundation on which trust-based pricing operates.

Logo Design as a Type of Credence Good

A credence good is one that cannot be readily evaluated based on experience alone, since its quality cannot be fully assessed by a consumer even after using the product. Medical procedures and auto repairs are two well-known examples of this type of good. Consumers rely on trust in an outside expert (such as a doctor or a mechanic) to assess quality. The logo design process was historically similar to the above-mentioned examples, as potential buyers of logos would frequently ask questions such as: “How do I know if my new logo works?”, “Did I pay too much for this logo?”, and “Would I have received better service from a different designer?” Unfortunately, there is often no clear or objective answer to these questions.

One important point to remember about logo design is that perceived quality is subjective, and the long-term impact on a company’s brand is difficult to isolate. As a result, buyers have no reliable means of determining whether the service provided by a designer met or exceeded their monetary investment. Buyers therefore relied heavily on trust in their purchase decisions, as well as on reputation and signaling. Designers used multiple avenues to signal quality, including portfolios, awards, agency recognition, and affiliations with high-profile clients. Buyers accepted the high prices designers charged for their services not because they could measure the value of those services, but because significant information asymmetries left them with no alternative means of comparison.

In the late 2010s, surveys conducted by industry sources indicated that most small businesses in the United States were paying an average of between $2,500 and $10,000 for a professional logo package, which typically included multiple rounds of revisions. Customer satisfaction with these logo packages was mixed, and there were no reliable means of comparing results across different service providers. This is the type of environment in which credence goods typically generate the highest profit margins.

AI Is Changing Experience Goods

The introduction of AI-powered design tools has changed the classification of logos from credence goods to experience goods, meaning goods whose quality can be evaluated before or immediately after consumption. AI-generated logos can be created quickly and easily; users may generate dozens of logo designs within minutes, compare styles, and eliminate undesired options without incurring significant cost.

AI and computing technologies are drastically reducing the level of information asymmetry in the industry. Rather than relying on a designer’s opinion or pricing, consumers are now able to view a wide range of possible outputs from an AI generator. This transition has an economic impact similar to situations in which product quality becomes readily observable in a marketplace. Once verification becomes inexpensive, pricing structures based primarily on customer trust begin to erode.

For example, companies such as Canva generate millions of logo designs each month using automated technologies, largely driven by users with no formal design background. In addition, marketplaces such as Fiverr now offer logo design services at average prices below $100 for small businesses. The availability of visible, low-cost alternatives has reshaped pricing expectations across business and industry sectors and has caused those expectations to re-anchor.

Economic Forces Driving the Change

Several economic forces support this shift in behavior. The first—and most impactful—is the reduction in search costs for consumers. The time required to move from an initial idea to multiple design iterations has decreased dramatically.

Consumers also now have the ability to discard an AI-generated logo without incurring emotional or monetary costs. A third supporting factor is the ease of benchmarking against competitors; end users can compare tools directly and instantly evaluate outputs across platforms.

Most importantly, the cost of verification—the defining characteristic of credence goods—has declined substantially. In the past, suppliers were able to charge based largely on trust, as customers had no practical way to verify quality before it was too late to make changes.

Real-World Ramifications

The devaluation of logo design as a trust-based or “credible” service has produced several notable effects. On one hand, logo design conducted using high-volume, low-strategy approaches has become commoditized, resulting in lower prices and thinner profit margins. Conversely, this shift away from credence-based pricing does not signal the end of design work. Instead, as credence pricing declines in execution-focused services, more value accrues to services that remain credence-based, such as brand strategy, visual identity systems, and long-term brand management—areas in which outcomes are inherently difficult to measure in isolation.

Over the past decade, the cost of logos has steadily declined. This reduction has lowered barriers to entry for new firms, thereby encouraging higher levels of entrepreneurship. For designers, this shift increases pressure to distinguish execution from strategy and to justify fees through measurable outcomes rather than reputation alone.

Summary

The emergence of AI-driven logo creation tools provides a clear economic illustration of how markets reliant on credence goods become destabilized when verification costs fall. The technology itself tells an economic story: as information asymmetry declines, pricing based on trust dissipates, leading to a reconfiguration of prices and, ultimately, the structure of the creative labor market.