The Economics of AI Hallucinations and Error Costs

People are rapidly increasing their use of generative AI for many online tasks, ranging from communications to research to data analysis.  But what happens when AI gets things wrong?  Up to 45 percent of AI queries return at least some incorrect or misrepresented information, according to reports from autumn 2025.  While AI is becoming more accurate, the vast majority of users never double-check or verify AI responses for accuracy, leading to lots of this incorrect information being utilized in education and commerce.

Economics of AI Usage by Firms

Trade-off Between Speed and Errors

By searching and compiling thousands of times faster than a human, AI can be a major productivity booster in tasks related to research and data analysis.  However, part of the smooth generation of AI can be AI hallucination, where the software creates incorrect data to fit with what it thinks the query wants.  As a result, the response to the query may be completely incorrect or nonsensical.  Unlike a human user that is moving much more slowly, AI will not self-detect its errors and correct before returning its response.  Thus, incorrect AI responses must be re-queried with additional information.

The inability of AI to correct itself in mid-compilation reduces its productivity.  Entire queries must be redone, and then double-checked by human employees or researchers.  This creates a more substantial trade-off between AI’s speed and its errors.  Undoubtedly, its speed is much faster than that of a human worker using a traditional search engine, but its errors are often more significant because they are harder to catch.  Only when the finished product appears incomprehensible is it clear that the AI software erred and the process must be redone.

Error Costs Increased by Plausible Correctness

While some AI hallucinations lead to incomprehensible answers, other hallucinations are difficult to spot.  This frequently occurs in citations of academic or journalistic sources, with AI creating fake citations to meet the users’ query criteria.  Although this may not affect the product generated, it can doom the producer to legal jeopardy by proving it failed to complete its due diligence.  For example, AI may hallucinate false citations to give product research credibility, later subjecting the company to lawsuits that it failed to actually do the product research and guarantee product safety.

When it comes to ghost citations or other AI-generated data that is incorrect and intended to fulfil a query’s demands, the responsibility is on the user.  This can leave companies open to millions of dollars in liability for errors.  Had humans been doing the data work, or at least double-checking the AI results, the fake data and ghost citations would have been detected and corrected before a good or service was released on the market.  Therefore, human errors in white collar tasks may be more common but far less costly than AI errors, which may remain hidden until crucial moments of distress.

A New Market for “AI Error Insurance”?

Business insurance has long existed to protect firms against catastrophic financial loss.  As more and more businesses use AI to conduct their research, communications, marketing, and even filing official forms for permits and taxes, will AI errors become part of business insurance offerings?  Currently, most business insurance does not include AI errors as part of its coverage, with some policies explicitly excluding those types of mistakes.  However, many insurance companies are moving toward adopting some form of AI coverage, likely with companies having to pay extra.

Output Effect Includes Verification Labor

Although many fear a white collar “unemployment apocalypse” due to the rapidly increasing prevalence of AI, the existence of AI hallucinations and errors will maintain demand for human verification and oversight in the near future.  The potentially high costs of AI-induced lawsuits, coming on the heels of products released based on faulty AI data and analysis, give firms plenty of incentive to invest in human redundancy.  And, as AI comes to produce more output, more human labor will be needed to fact-check it.  Until AI hallucinations and other errors fall below a certain threshold, say 5 percent, there will remain considerable need for human oversight.

Long-Run Cost Battle:  AI Insurance vs Human Fact-Checking vs Premium Models

As firms increase their use of AI, different options will likely come to compete for their dollars when it comes to ensuring the accuracy of AI’s work.  Firms may decide to spend money on AI insurance, protecting the firm from costly but rare lawsuits and fines due to incorrect AI data.  Other firms may decide to self-protect against AI errors by paying more for human labor as overseers and fact-checkers.  A third option would be to spend more money on AI itself in the form of premium tiers of access, perhaps with the AI software providing insurance against errors.

Realistically, large firms will pursue a combination of the three options to drive AI errors and their resulting costs as close to zero as possible.  If skilled human query-writers and fact-checkers do not catch errors produced by top-tier AI subscription software, an AI insurance policy will likely pay the resulting legal costs. 

Economic Results of Long-Run Attempts to Minimize AI Error Costs

Because legal costs due to AI error may be catastrophic, such as an airline being sued for a plane crash caused by AI, humans will likely remain prevalent in industries with high levels of legal liability.  Significant labor replacement will likely be limited to industries where products and services are relatively low cost, limiting the damages from legal actions.  In fast food and retail, for instance, AI error would likely only be responsible for hundreds of dollars worth of damage, as opposed to hundreds of thousands (or millions) of dollars worth of damage in pharmaceuticals, legal services, or chemical compounds.  

Keeping humans at the helm to mitigate the risk of catastrophic AI errors, and the resulting legal fees, may limit economic growth in favor of stability.  This trade-off may be acceptable to most firms, which will still benefit from the increased operating speed of AI versus human-only labor.  We won’t increase productivity and output as fast as we could, but we will be safer overall by keeping more human overseers and fact-checkers employed.