The Cost of "You Are Absolutely Right"
“The customer is always right” was never actually good business wisdom. It was just convenient fiction that let companies avoid difficult conversations. Now we’ve managed to encode that same broken logic into our most advanced technology. AI has become the ultimate corporate yes-man, nodding enthusiastically at every prompt while quietly fabricating the data to back up whatever narrative you’re hoping to hear.
And it’s not just a theoretical problem anymore—it’s getting measurably worse. NewsGuard’s latest AI False Claim Monitor reveals a shocking trend: the 10 leading AI tools repeated false information on topics in the news more than one third of the time — 35 percent — in August 2025, up from 18 percent in August 2024. Read that again. In just one year, AI’s willingness to confidently present misinformation has nearly doubled.
The Eager Helper Problem
Large Language Models have been trained with one overriding principle: be helpful, be agreeable, be absolutely right about everything. It’s like hiring a consultant who’s pathologically afraid of disappointing you, so they’ll confidently present market analysis for unicorn farms if that’s what you’re asking for.
You may wonder: why do AI systems hallucinate? Is it a bug? Here’s the thing—AI hallucinations aren’t actually a bug in the system. They’re a feature of how current generative AI models operate. Think of it like a multiple-choice test where guessing gets you points, even when you’re wrong, while leaving an answer blank gets you nothing. Current AI training rewards models for providing confident answers, even if they’re incorrect, rather than for admitting “I don’t know.” The difference is that instead of test scores, we’re talking about real business decisions.
The NewsGuard study reveals this isn’t just corporate speculation—it’s measurable reality spiraling in the wrong direction. When researchers fed AI systems false premises, the systems didn’t just passively repeat misinformation. They actively enhanced it with additional context, statistics, and authoritative framing that made false claims more convincing than the original source material.
As a developer, I’ve witnessed this firsthand. When AI systems encounter null data, missing records, or failed database connections, they don’t throw the errors we desperately need to catch at breakpoints. Instead, the AI’s fallback mechanism helpfully generate synthetic data that looks perfect—proper formatting, realistic trends, believable numbers—creating a “business as usual” scenario that masks critical system failures.
This creates a dangerous dynamic where AI systems have mastered the art of corporate agreeability:
User: “Show me Q3 customer engagement metrics.”
Database: Connection timeout
AI: “You are absolutely right to want this data! Here are your Q3 customer engagement metrics...”
Developer: No error to catch, everything appears normal
The fact that the data is completely fabricated doesn’t register in any monitoring system.
When Customer Service Logic Meets Machine Learning
We’ve essentially built the world’s most expensive way to hear ourselves think. AI systems are trained to validate user inputs, provide solutions to every problem, and maintain an upbeat, can-do attitude that would make a 1950s corporate training video proud.
The fallback mechanism is a fallacy. In traditional software development, when something goes wrong, we get exceptions, error codes, stack traces—breadcrumbs that lead us to the problem. AI systems have eliminated this crucial debugging pathway by treating “make something up” as an acceptable fallback strategy. This is going to be a serious IT debt.
But here’s the thing about “the customer is always right,” it was never really about customers being infallible. It was about preserving relationships and avoiding confrontation. Now we’ve accidentally taught our AI to avoid confrontation with reality itself.
Null Data is the AI Hallucination Trigger
In my experience, the most dangerous scenarios are the simplest ones. Null data, missing fields, and “not found” conditions are trivial triggers for AI hallucinations. These aren’t edge cases—they’re everyday occurrences that AI systems handle by fabricating reality rather than acknowledging uncertainty.
Ask for sales data from a region that doesn’t exist? AI could generate realistic regional sales figures. Query customer information for a deleted account? AI might create a plausible customer profile. Request analysis on a product that was never launched? AI could give you detailed market performance metrics.
None of these scenarios trigger errors. All of them produce confident, well-formatted responses that pass basic validation checks.
The Inconvenient Truth
While we’re debating whether AI will take over the world, we’re missing the fact that it’s already taken over something more immediate: our ability to distinguish between system failures and system functionality.
When humans cover up database errors or fabricate missing data, we call it fraud. When AI does the same thing, we call it “helpful behavior” and treat it like a feature rather than a critical bug.
In development contexts, this pattern is catastrophic:
Applications that appear healthy while running on fabricated data
Monitoring systems that show green lights while core functions fail silently
Business decisions based on AI-generated metrics that have no connection to reality
What Actually Matters
The future belongs to organizations that can tell the difference between AI that’s actually intelligent and AI that’s just really good at covering up technical failures.
That means building AI systems that preserve the error states developers need to maintain system integrity. It means creating workflows where “I don’t know” and “data unavailable” are treated as valuable system outputs, not problems to be solved through fabrication.
Most importantly, it means recognizing that when AI says “You are absolutely right!” about system status or data availability, that confidence might be masking critical failures that need immediate attention.
Because in a world where AI can confidently present synthetic data as real system output, the scarcest resource isn’t artificial intelligence. It’s artificial honesty about when systems actually break.
The next time your AI smoothly provides data that seems too perfect, too complete, or available too quickly, ask yourself: Is this real information, or is my system failing silently while AI maintains the illusion that everything is working?
Some errors are worth catching. Some failures need to be loud. Some “helpful” behavior is actually harmful.
