For years people treated Google like an oracle. Type in a question, click the first result, and call it truth. We later learned that search results are shaped by SEO tricks, advertising, and popularity not necessarily accuracy.
Now the same habit is showing up with AI. People throw a question at ChatGPT or another model, take the answer at face value, and assume it’s fact. The problem is, AI sounds even more convincing than Google ever did. It writes in clean, confident language. That polish makes it easier to trust, even when the answer is shallow or biased.
One phrase that really shows this problem is: “there is no evidence.”
Why “No Evidence” Sounds Final
Scientists originally used the phrase carefully: “We don’t have data for this yet.” But in everyday use, it gets twisted into “this is false.” Companies and institutions love it because it shuts down curiosity without technically lying.
AI picked up this same reflex from its training data. Ask it about something outside the mainstream and you’ll often get: “There’s no evidence…” It’s the model’s safe way of avoiding controversy. The problem is, that answer feels like the end of the conversation when really it should be the beginning.
Case Study: Fertilized Eggs vs Unfertilized Eggs & Muscle Growth
To show you how this plays out in practice, let me walk you through a recent exchange I had with ChatGPT. I asked a simple question: Are fertilized chicken eggs nutritionally different from unfertilized ones?
At first, the AI gave me the mainstream, safe answer: they’re the same in nutrition and taste.
I pushed back: “You just lied. There is a difference not in taste but in nutrition with fertilized eggs. Tell me what they are.”
That’s when the answer started to shift. The AI admitted there CAN be nutritional differences, depending on storage and incubation, and that fertilized eggs sometimes carry slightly different enzymes and compounds.
I pressed further: “There is something that makes you build muscle faster in fertilized eggs.”
Now the AI opened up about the belief (and some biochemical logic) that fertilized eggs may contain growth factors and peptides that could, in theory, support muscle recovery and growth more than unfertilized eggs.
Still, I wanted to know if there were actual studies. So I asked: “How do you know they are slightly? Have there been studies showing the differences?”
This time, the AI pointed to studies & research showing fertilization does measurably change the egg’s molecular profile, including protein expression and bioactive compounds. It admitted there weren’t human trials proving muscle growth, but the molecular evidence was real.
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Finally, I reframed the issue: if studies show measurable biochemical differences, then saying “there is no evidence” isn’t accurate, it’s a cop out. What’s really happening is that there’s no large human trial yet, but there IS evidence at the molecular level. That distinction matters.
This is exactly the “no evidence” trap: people hear that phrase and assume it means “this has been studied and disproven.” In reality, it often just means “we don’t have the kind of study that the mainstream accepts as definitive.” The AI’s first answer mirrored that same institutional dismissing nuance with a blanket statement. But once pushed, it admitted the evidence exists, just maybe not in clinical trials.
That’s the heart of the problem: “no evidence” becomes a way to shut down curiosity instead of a signal to ask better questions. And that’s why learning to prompt deeper, to push past the easy dismissal, is so IMPORTANT.
Prompting as a Skill
If you use AI like a vending machine -> ask, get, move on, you’ll keep getting surface level answers. If you use it like a research partner, you can dig out far more. That means:
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Ask for both sides. Instead of “Does X work?” try “What arguments exist for and against X?”
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Invite speculation. Say “Let’s assume this were true, how might it work?”
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Assign roles. Try “Debate this as a skeptic and as a supporter.”
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Force structure. Use prompts like: “Step 1: consensus. Step 2: what’s unknown. Step 3: minority views.”
These strategies don’t trick the model, they just give it permission to show you more of what it already “knows.”
Why It Matters
The danger isn’t that AI lies all the time. The danger is that it makes shallow, mainstream answers sound finished. That’s the same trap we fell into with Google, mistaking easy answers for truth.
And we need to be honest about what drives this: AI doesn’t “think” for you. It mirrors the patterns of the data it was trained on, which means it repeats the same consensus ideas that dominate the media, academia, and corporate messaging. Those ideas aren’t always neutral; they’re often shaped by profit, convenience, or institutional self-preservation. When the model says “there is no evidence” or gives you the polished mainstream view, that’s the algorithm echoing the loudest voices — not weighing truth for you.
The fix isn’t to distrust AI completely. It’s to treat it like a tool, not an oracle. Use it with curiosity. Challenge it. Prompt it like you would question a spokesperson. Treat the first answer as a draft, not a verdict. Push for nuance. Ask better questions.
The phrase “no evidence” shouldn’t be a wall. It should be a red flag to dig deeper because that’s where the real understanding starts.
