Transaction Cost Economics and Institutional Lag: Why AI “Humanizer” Tools Thrive in Rapid Innovation Markets

Many people wonder why large language models don’t offer a simple “humanize” feature, especially given how advanced AI has become. While the technical answer is straightforward, the real reason involves economics. The use of third-party AI Humanizer tools is explained by Transaction Cost Economics, a theory by Ronald Coase and Oliver Williamson that is often taught in college economics courses.

Many users of large language models ask the question; why don't these models have a "humanize" function since AI has developed into these very high-tech advanced platforms? While this question has a very straightforward answer and can be answered relatively easily from an engineering standpoint, when viewed from an economic viewpoint, the use of third-party AI Humanizer tools is driven by Transaction Cost Economics, a theory developed by Ronald Coase & Oliver Williamson, which has been commonly taught in Undergraduate & Graduate level Economics classes.

The Core Principle: Transaction Cost Economics

Transaction Cost Economics (TCE) says that businesses don’t do everything themselves. Instead, they organize their work to keep transaction costs as low as possible, often by choosing the cheapest option available. If it costs more to do something in-house than to buy it from someone else, both the producer and the user are motivated to create new technologies. This can lead to new industries with economies of scale and specialized providers as demand increases.TCE also helps explain why markets break into smaller parts when new technologies are created and used during product development.

The Paradox of the Missing “Humanize” Button

Big AI companies like Google and OpenAI can technically change writing to sound more human or more robotic. However, they don’t advertise their tools as being able to “humanize” AI content, and they don’t promote features that help users avoid detection.This gap isn’t because companies can’t build these tools. Instead, it’s due to institutional habits and the high costs involved in making such changes.

Institutional Constraints Facing Core AI Platforms

Even if running these platforms is cheap, adding new features still comes with costs. Major AI companies have to consider several types of transaction costs:

Costs Associated with Coordination and Alignment

The model’s behaviour must be consistent with a company’s internal safety policy, external research claims, and public views on the company. A company that brandsThe way the model works has to match the company’s safety rules, research claims, and public image. If a company offers a “humanization” feature, people will question how it fits with their policies, testing methods, and partnerships.ate features that enable users to avoid detection, they expose themselves to legal risks as well as increased scrutiny from regulators. As of this writing (2023–2024), more than twenty U.S. states have proposed or passed legislation addressing AI transparency, increasing the likelihood of costly regulatory missteps.

Brand and Political Risks

A company that serves hundreds of millions of customers faces reputation costs that are significantly greater than those faced by an average-sized firm. For an inA company with hundreds of millions of customers risks much more damage to its reputation than a smaller business. Even a harmless feature could be seen as encouraging plagiarism, spreading false information, or supporting fraud.ompany’s balance sheet.

The Cost Structure of Third-Party AI Humanizer Products

Third-party AI Humanizer tools operate under a very different cost structure than the large platforms whose models they rely on.Small and start-up companies typThird-party AI Humanizer tools have a very different cost structure compared to the big platforms they depend on.Because of this, small companies can experiment quickly, launch niche products, and adapt easily, without the extra costs that big organizations face. The market shows this: many AI post-processing tools have appeared, and some make six- or seven-figure yearly revenues by building on top of existing AI models they don’t control.The key point is that third-party tools exist because they lower the costs of trying new things in business—costs that big companies often avoid taking on themselves.

Real-World Analogy: Browsers and Extensions

In the past, web browsers could have added ad blockers or privacy tools themselves, but they usually didn’t. Instead, browser extensions filled this gap. This wasn’t by accident—it happened for the same reasons of cost and company incentives that we now see in the AI world.

Economic Forces and Long-Run Ramifications

Several factors cause this market to split up:

• Rapid technological change increases uncertainty, raising the cost of internal experimentation
• Asymmetrical regulatory environments place greater risk on dominant firms than on smaller entrants
• Heterogeneous demand creates multiple standards of what “human” output should look like

Over time, the AI world will probably settle into a system where main platforms and aftermarket tools work together, instead of one company controlling everything. Because big companies are slow to add new features, aftermarket tools will stick around, since they are still the best economic choice for large organizations.

Final Thoughts

Viewed through the lens of Transaction Cost Economics, firms rationally avoid internalizing activities when uncertainty, reputational risk, and experimentation costs are high. AI Humanizer technologies are therefore not a transient trend. They illustrate how institutional constraints shape market structure and give rise to new systems of order across industries.

The Core Principle: Transaction Cost Economics

According to Transaction Cost Economics (TCE), businesses do not simply bring every action they can perform in-house. Rather, they structure all production activities to reduce total transaction costs by extending production to the lowest transaction cost alternatives available to them. If the total transaction costs associated with producing an activity internally exceed those associated with purchasing that activity from an outside source, then both parties (i.e., the producer and the user) have incentives to develop technologies that give rise to an industry with economies of scale as demand grows and specialized external providers emerge.The TCE framework also provides insight into the fragmentation of markets as new technological innovations are developed and used during periods of new product development.

The Paradox of the Missing “Humanize” Button

Large AI companies like Google and OpenAI have all the technical abilities necessary to change the tone and style of a piece of writing from “human-like” to “robotic.” They do not market their tools’ ability to “humanize” AI-written content, nor do they promote their tools for bypassing detection methods.The underlying cause of this discrepancy is not due to an inability to create such tools. It is a result of institutional inertia, which is driven by high transaction costs.

Institutional Constraints Facing Core AI Platforms

Even when marginal compute costs for larger platforms are nearly non-existent, adding a feature is not without its costs. Core AI companies have several categories of transaction costs to account for:

Costs Associated with Coordination and Alignment

The model’s behaviour must be consistent with a company’s internal safety policy, external research claims, and public views on the company. A company that brands a feature as “humanization” will face questions regarding the alignment of that feature with documentation, model evaluation practices, and institutional partnerships.

Legal and Compliance Risks

AI detection systems are being adopted by educational institutions, employers, and the publishing industry. If core companies create features that enable users to avoid detection, they expose themselves to legal risks as well as increased scrutiny from regulators. As of this writing, more than twenty U.S. states have proposed or passed legislation addressing AI transparency, increasing the likelihood of costly regulatory missteps.

Brand and Political Risks

A company that serves hundreds of millions of customers faces reputation costs that are significantly greater than those faced by an average-sized firm. For an individual user, a seemingly benign feature may be reported as enabling plagiarism, misinformation, or fraudulent behaviour. According to Transaction Cost Economics (TCE), the risks outlined in this section represent real economic costs, even though they are not clearly visible on a company’s balance sheet.

The Cost Structure of Third-Party AI Humanizer Products

Third-party AI Humanizer tools operate under a very different cost structure than the large platforms whose models they rely on.Small and start-up companies typically face:

• Lower levels of business risk
• Limited end-user promises
• Limited political exposure
• Quicker development cycles

As a result, they are able to experiment rapidly, release niche products, and adapt without the institutional overhead costs associated with large, coordinated organizations. The marketplace has demonstrated this dynamic: dozens of AI post-processing tools have entered the market, many generating six- or seven-figure annual revenues by building products on top of upstream AI models over which they have no control.The critical observation is that third-party tools exist because they reduce the transaction costs associated with business experimentation—costs that large institutions rationally choose not to incur internally.

Real-World Analogy: Browsers and Extensions

Historically, web browsers have hesitated to implement ad blockers, script modifiers, or privacy circumvention tools, despite having the technical ability to do so. Instead, extension ecosystems flourished to provide these functions. This division was not coincidental, but rather reflected the same cost structures and institutional incentives now shaping the AI platform ecosystem.

Economic Forces and Long-Run Ramifications

Several forces contribute to this fragmentation:

• Rapid technological change increases uncertainty, raising the cost of internal experimentation
• Asymmetrical regulatory environments place greater risk on dominant firms than on smaller entrants
• Heterogeneous demand creates multiple standards of what “human” output should look like

In the long run, the AI ecosystem is likely to stabilize as a complementary platform-and-aftermarket structure rather than evolve into a fully integrated monopoly. Institutional lag in feature development ensures that aftermarket tools persist even as core models improve, because aftermarket solutions remain economically optimal for large organizations.

Final Thoughts

Viewed through the lens of Transaction Cost Economics, firms rationally avoid internalizing activities when uncertainty, reputational risk, and experimentation costs are high. AI Humanizer technologies are therefore not a transient trend. They illustrate how institutional constraints shape market structure and give rise to new systems of order across industries.