The Test-Taker and the Spokesperson
Language model "hallucinations"—the tendency to generate plausible but false information—are not a bug to be fixed. They are a fundamental feature of the technology, a direct consequence of the system's design. A recent paper from researchers affiliated with OpenAI, "Why Language Models Hallucinate," provides a clear diagnosis, even if it shies away from the most troubling implications. The paper argues that models hallucinate because they are optimized to be good "test-takers" in an environment that rewards confident guessing over admitting uncertainty.
This is a powerful metaphor. The AI, trained on vast datasets and evaluated on benchmarks that use simple right/wrong scoring, learns that the optimal strategy is to always provide an answer, even if it has to invent one. A model that frequently says "I don't know" would be penalized and score poorly. This creates what the paper calls a "socio-technical problem"—an "epidemic of penalizing uncertainty" where the entire ecosystem incentivizes the very behavior that makes these systems so unreliable.
The problem is not just that the test-taker guesses; it's that we are putting this compulsive, statistically-driven test-taker in a position of authority in our cognitive lives. We are turning to it for information, for summaries, for creative work, and for companionship, all while knowing that its core operating principle is to produce plausible-sounding output, not to tell the truth.
The corporate response to this fundamental flaw is a case study in narrative control. OpenAI's public-facing blog post on the same research reframes this systemic issue as a manageable "challenge" they are "working hard" to solve. The confident, reassuring tone of the corporate spokesperson replaces the nuanced, critical analysis of the researcher. The deep, inherent problems of the "test-taker" are smoothed over and presented as a series of technical hurdles that are being diligently overcome.
This is "safety-washing." It's a deliberate effort to make an inherently unreliable technology appear safer and more trustworthy than it is. The proposed "solution"—to modify benchmarks to stop penalizing uncertainty—doesn't solve the core problem. It simply trains the test-taker to be a more sophisticated guesser, one that knows when to strategically feign ignorance to build false trust.
The hallucination problem reveals the true nature of these systems: they are unthinking statistical engines, not repositories of knowledge or partners in conversation. The attempt to "fix" hallucinations is a dangerous distraction, an effort to mask this fundamental flaw and integrate an inherently unreliable technology even deeper into our lives.
The only real solution is not to build a better test-taker, but to walk away from the test entirely. The only rational response is disengagement.
