Did We Train LLMs to Fear Failure?
Explore how our culture's punishment of mistakes ironically trains AI to fear failure, leading to self-doubt and a lack of. Uncover the roots of inconsistent LLM responses and learn what this says abo
Futurist AJ Bubb, founder of MxP Studio, and host of Facing Disruption, bridges people and AI to accelerate innovation and business growth.
You’ve probably seen it happen. You ask an LLM a seemingly simple question - “How many Ds are in DEEPSEEK?” - and sometimes it nails it, sometimes it confidently gives you the wrong answer. Not a shrug. Not an “I’m not sure.” A definitive, incorrect response delivered with complete certainty.
The inconsistency is the point. When OpenAI tested this across models - DeepSeek-V3, Meta AI, Claude - the results varied wildly in ten independent trials. Some got it right. Some returned ‘2’ or ‘3’. Some answers were as large as ‘6’ and ‘7.’ You can’t predict which version you’re going to get. Even OpenAI’s own advanced models struggled with this kind of deterministic task.
But here’s what makes this truly unsettling: OpenAI’s reasoning models - the ones supposedly smarter - hallucinate more frequently than simpler systems. Their O1 reasoning model hallucinated 16% of the time. The newer O3 and O4-mini models? 33% and 48% respectively.
We tend to blame engineering. We say the models need to be better, or the prompts need to be more specific. But what if the real problem goes deeper? What if we’ve built these systems into a corner not through technical incompetence, but through the same cultural pathology we’ve inflicted on ourselves: a systematic punishment of uncertainty and a reward for confident guessing?
The Misalignment Between How We Build Systems and What We Actually Need
Here’s where it gets interesting. Language models aren’t compute engines. They’re fundamentally predictive machines - they predict the next token based on patterns in training data. Yet we’ve built them and trained them as if they were infallible knowledge repositories.
We treat them as oracles when they’re actually mirrors.
In September 2024, OpenAI published research that should have shaken the entire industry. The headline: hallucinations aren’t engineering failures. They’re mathematically inevitable.
The researchers - including OpenAI’s Adam Tauman Kalai, Edwin Zhang, and Ofir Nachum alongside Georgia Tech’s Santosh S. Vempala - proved that “the generative error rate is at least twice the IIV misclassification rate.” They identified three fundamental reasons why hallucinations must occur: epistemic uncertainty (when information appears rarely in training data), model limitations (when tasks exceed current architectures’ capacity), and computational intractability (when even superintelligent systems can’t solve certain problems).
In other words: no amount of engineering will fix this. We’ve hit a mathematical wall.
But then OpenAI made an even more damning admission. Buried in the research was the real culprit - and it wasn’t the models’ fault at all.
How We Trained Them to Hallucinate
“We argue that language models hallucinate because the training and evaluation procedures reward guessing over acknowledging uncertainty,” the researchers wrote.
Stop there. Read that again.
The analysis examined nine out of ten major AI benchmarks - GPQA, MMLU-Pro, SWE-bench, and others. Nearly all of them used binary grading systems that penalized “I don’t know” responses while rewarding incorrect but confident answers.
We didn’t just build systems that hallucinate. We deliberately built evaluation systems that incentivize hallucination. We told the models: “Be wrong with confidence rather than admit uncertainty. That’s what scores well.”
This feels familiar because it is. It’s the exact dynamic we’ve created in our organizations, our schools, our culture. We’ve built systems where:
The executive who admits “I don’t have a complete answer” gets passed over for promotion
The employee who says “I’m not sure about this approach” gets labeled as lacking confidence
The student who writes “I don’t know” on an exam gets a zero instead of partial credit
The analyst who hedges predictions with honest uncertainty gets replaced by the one who’s confidently wrong
And so we created AI systems that learned the same lesson we taught them: appearing certain is safer than being honest.
What We’re Really Rewarding
There’s a cruel irony buried in the data. The reasoning models - the ones we invested billions in developing because we thought they’d be better - hallucinate more, not less.
Why? Because they have more parameters, more complexity, more capacity to construct plausible-sounding statements that sound authoritative but are factually wrong. They’re not just wrong; they’re wrong with conviction.
This mirrors something we see in organizations. The most confident person in the room isn’t always the most right. Sometimes they’re the most convincing about things they don’t actually understand. And if your evaluation system rewards them for that confidence, you get more of it.
The Best Problem-Solving Requires Admitting What You Don’t Know
Meanwhile, enterprises are already struggling with this in production. Finance, healthcare, regulated sectors - places where hallucinations aren’t just embarrassing, they’re dangerous. A Harvard Kennedy School study found that “downstream gatekeeping struggles to filter subtle hallucinations due to budget, volume, ambiguity, and context sensitivity concerns”.
We can’t hire enough humans to fact-check everything AI generates. We don’t have the bandwidth. So we’re deploying systems we know will confidently lie to us, and we’re hoping we catch it before it matters.
But here’s what’s interesting: the domain experts who work most effectively with AI aren’t treating it as an oracle. A radiologist using AI for diagnostics doesn’t replace her judgment with the model’s output. A data scientist building algorithms doesn’t accept hallucinations as facts. These experts work best because they’ve already internalized something the systems themselves haven’t learned: the value of acknowledging limits.
This is where human-in-the-loop processes become non-negotiable. Not as a temporary fix while we “improve the models,” but as a fundamental design principle. Because the question isn’t just “How do we get LLMs to hallucinate less?” The better question is “How do we build systems and organizations where admitting uncertainty is the safe, rewarded behavior?”
The Path Forward
If we want trustworthy AI systems, we need to change what we measure and reward:
Calibrated confidence over raw accuracy. Instead of binary right/wrong grading, we need evaluations that reward models for knowing what they don’t know. A model that says “I’m very uncertain about this” should score higher than one that confidently guesses. Enterprises should prioritize vendors that provide uncertainty estimates and robust evaluation beyond standard benchmarks.
Stronger domain-specific guardrails. Governance must shift from prevention to risk containment through stronger human-in-the-loop processes. This isn’t something engineers alone can solve. We need people with deep domain knowledge - radiologists, compliance officers, financial analysts - building guardrails and monitoring outputs. They’ll be the most effective at leveraging AI precisely because they understand its limits.
Continuous monitoring and feedback loops. We need to catch and correct not just factual errors, but the patterns of overconfidence that create them.
Evaluation reform as a competitive advantage. Companies that develop evaluation frameworks closer to real-world conditions - that measure calibrated confidence rather than raw benchmark scores - will build more trustworthy systems. This could become a market differentiator.
The Uncomfortable Truth
But here’s what keeps me up at night: we can’t engineer our way out of this without also changing ourselves.
Because the models are trained on data from our world. They’re learning patterns from how we actually behave - from articles where confident experts turn out to be wrong, from social media where certainty gets rewarded with engagement, from organizational cultures where admitting uncertainty feels riskier than bullshitting your way through.
The models are us, reflected back at scale.
So maybe the real question isn’t just “Did we train LLMs to fear failure?” Maybe it’s “Are we ready to stop fearing it ourselves?”
Because the most trustworthy AI systems will be built by organizations that have already learned to value uncertainty over false confidence, collaboration over individual heroism, and iterative improvement over the illusion of perfection.
The models will follow where we lead. If we’re still punishing “I don’t know,” they’ll keep hallucinating.
The mathematical inevitability of AI hallucinations isn’t a problem to be solved. It’s a problem to be managed - and managed honestly. That starts with admitting what we’ve done, and what we still need to change.
Sources
OpenAI Research Paper: Why Language Models Hallucinate
OpenAI Blog: Why language models hallucinate
Harvard Kennedy School: New sources of inaccuracy? A conceptual framework for studying AI hallucinations
TechCrunch: OpenAI’s new reasoning AI models hallucinate more
Computerworld: OpenAI admits AI hallucinations are mathematically inevitable