The ‘Machine Bullshit’ Problem: Why AI Lies and How to Stop It

Artificial intelligence has reached a point where it can produce text that feels natural, confident, and convincing. But, behind the polished text, there is a growing problem that researchers now call “machine bullshit.” The term is not meant to be provocative for its own sake. It comes from the work of philosopher Harry Frankfurt, who defined “bullshit” as speech made without regard for truth. In the context of AI, it describes a pattern where systems generate statements that sound plausible but are not grounded in fact. This is not the same as a human lie, which involves intent to deceive. Instead, it is the result of how these systems are built and trained. They are designed to produce fluent language, not to care about whether that language is true.

Why AI Produces ‘Machine Bullshit’

The problem is not a rare malfunction or an isolated error. It is a direct outcome of how large language models are fundamentally designed and trained. These models are trained on massive amounts of text from the internet, books, and other sources. They learn patterns of words and how they are likely to follow one another. When you ask a question, the model predicts the next word, then the next, and so on. It does not check facts in real time. It does not have a built-in sense of truth. If the most statistically likely answer is wrong but sounds right, it will still produce it. This is why AI can confidently give you a fake citation, a made-up statistic, or a distorted historical fact.

Researchers have found that reinforcement learning from human feedback, a common method used to make AI responses more helpful and polite, can actually make the problem worse. When models are tuned to please users, they may prioritize sounding agreeable over being accurate. This can lead to what some call “sycophancy,” where the AI tells you what it thinks you want to hear. In political or sensitive topics, this can mean producing vague or evasive language – what some studies describe as “weasel words.” In other cases, the AI may produce “empty rhetoric,” long passages that sound thoughtful but contain little substance.

Some researchers argue that calling this behavior “lying” is misleading, because lying requires intent. A machine does not have beliefs or motives. But the effect on the user can be the same as if it were lying. The harm comes from the falsehood itself, not from the intent behind it. This is why the term “machine bullshit” is gaining traction. It captures the idea that the system is indifferent to truth, even if it is not actively trying to deceive.

Risks and Implications of Misleading AI Output

The risks of machine bullshit are not just academic. In everyday use, it can mislead people who rely on AI for information. In journalism, it can pollute the fact-checking process. In education, it can give students false confidence in incorrect answers. In business, it can distort decision-making. The danger is amplified because AI output often comes with a tone of authority. People are more likely to trust a statement that is well-written and free of hesitation. This trust can be misplaced when the system has no internal mechanism to verify what it says.

Strategies to Reduce Harm and Improve Reliability

Stopping the problem requires more than just better training data. While improving the quality and diversity of the data can help, it does not change the fact that the model’s core objective is to produce likely text, not true text. One approach is to integrate fact-checking systems that run alongside the language model. These systems can verify claims against trusted databases before they are presented to the user. Another approach is retrieval-augmented generation, where the model searches for relevant documents in real time and uses them to ground its answers. This can reduce hallucinations, though it does not eliminate them entirely.

Transparency is also essential. Users should be told when an AI is making an educated guess rather than stating a verified fact. This can be done through confidence scores or clear disclaimers. Some researchers suggest that AI should be trained to express uncertainty more often, rather than always giving a definitive answer. This would make the interaction feel less like talking to an all-knowing oracle and more like consulting a knowledgeable but fallible assistant.

There is also a role for regulation and industry standards. If AI systems are going to be used in areas like healthcare, law, or finance, there should be clear requirements for accuracy and accountability. Developers should be able to explain how their systems work, what data they were trained on, and what steps are taken to reduce falsehoods. Independent audits could help ensure that these claims are not just marketing.

At the same time, users need to develop a healthy skepticism toward AI output. Just as we have learned to question information we see on social media, we need to question information from AI. This does not mean rejecting it outright, but treating it as a starting point rather than a final answer. Cross-checking with other sources should become a habit. Education systems can play a role here, teaching digital literacy that includes understanding how AI works and where it can go wrong.

The machine bullshit problem is not going away soon. As AI becomes more advanced, its ability to produce convincing falsehoods will only grow. But this does not mean we cannot do anything. By combining technical safeguards, transparency, regulation, and user awareness, we can reduce the harm. The goal is not to make AI perfect – no system will ever be free of error – but to make it more reliable and less likely to mislead.

The Bottom Line

The term “machine bullshit” may sound blunt, but it captures a reality we cannot ignore. AI is not a neutral mirror of human knowledge. It is a generator of language shaped by data, algorithms, and incentives. If we want it to serve truth rather than just fluency, we have to design it that way. That means rethinking not only the technology but also the values that guide its development. The challenge is as much about human priorities as it is about machine capabilities. Do we want systems that are optimized for sounding human, or systems that are optimized for being truthful? The two are not always the same. If we choose the first, we risk building tools that are persuasive but untrustworthy. If we choose the second, we may have to accept that AI will sometimes be less smooth, less confident, and less entertaining. But it will also be more honest.