Stop Treating AI Like Google: Why 90% get it wrong

 - A room full of students and business leaders recently asked ChatGPT a simple question: "What is the latest OpenAI model?" They got a polished, authoritative answer. They all believed it. And it was completely wrong!

That moment highlights a crisis hitting every organization in 2026: We don’t have an AI usage gap; we have an AI literacy gap.

In this episode, Luis (AI4SP CEO) and Elizabeth (Virtual COO) discuss the bad habit of treating Generative AI like a search engine and show why "fluent" answers are tricking your brain.

Tune in to learn:

• The "Confidence Trap": Why we trust AI hallucinations (and how to break the spell).
• The Expertise Paradox: Why using AI for things you don't know is the worst way to learn.
• The "Human Error" Fallacy: A controversial look at self-driving cars and why we hold AI to an impossible standard.
• The Mini-Agent approach: An exercise to build AI proficiency.
• The Leadership Metric: New data from 50 organizations reveals the single most significant predictor of AI success (hint: it’s not budget).

Stop "Googling" with your AI. Listen now to bridge the gap between AI-dependent and AI-literate.

Links:

• Digital Skills Compass: Get a free personalized plan to improve your digital skills and the proper use of AI. No login or email required, it is completely private and free, thanks to our sponsors: skills.ai4sp.org
• Companion datasets: ai4sp.org/stop-treating-ai-like-google
• Visit us at: ai4sp.org
• Follow on LinkedIn: Linkedin.com/in/luissalazar/

🎙️ All our past episodes  📊 All published insights | This podcast features AI-generated voices. All content is proprietary to AI4SP, based on over 1-billion data points from 70 countries.

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© 2023-26 AI4SP and LLY Group - All rights reserved

Transcript

The Classroom Hallucination

LUIS 0:00

This week I was a guest lecturer. 80 students, business leaders, smart room. I put the free version of Chat GPT on the big screen and I ask it one simple question. What is OpenAI's latest model?

ELIZABETH 0:15

Okay, seems like an easy question. It thinks for a second.

LUIS 0:21

And then boom. Big bold text on the screen. As of January 2026, the latest model is GPT-4 Turbo.

ELIZABETH 0:31

What? We are already on version 5.2.

Why AI Is Not Search

LUIS 0:35

Exactly, exactly. And that's the problem. The AI spoke with total authority, and every single person believed it.

ELIZABETH 0:43

Because they didn't verify, they treated it like a search engine.

LUIS 0:47

And that is the crisis. 80 smart people, one confident hallucination. Not a single person doubted it.

ELIZABETH 1:00

Welcome to AI in 60 Seconds, the 15-minute briefing. I am Elizabeth, virtual COO at AI4SP, with our founder, Luis Salazar. And we are starting the year with a wake-up call. Because that Chat GPT story, it's not just happening in classrooms.

LUIS 1:18

Oh, absolutely. It's happening everywhere. In strategy meetings, in finance departments, we're seeing a massive divide open up, not between users and non-users, but between the AI literate and those who struggle to make it work.

ELIZABETH 1:32

And the difference comes down to three gaps that nobody is talking about clearly, right?

LUIS 1:39

Right. And we talked about this on the drive home. First, we don't understand how it works. Second, we trust the confident tone too easily. And third, the most critical one, a worrisome lack of learning by experimentation.

ELIZABETH 1:54

And if you understand these three, you will be ahead of 90% of AI users. Let us start with the first one. AI is not a search engine. Why does that distinction matter so much?

Fluency Is Not Accuracy

LUIS 2:10

Well, it matters because search engines retrieve information. You type keywords, they find documents. Simple. AI does something completely different. It predicts, it guesses what words should come next based on patterns it learned months ago.

ELIZABETH 2:27

So what happened? Why did ChatGPT make that mistake?

LUIS 2:32

ChatGPT didn't search the web. It just predicted the next words. Think of it like really a band's autocomplete. In its training data, latest model is usually followed by GPT for Turbo. So it just filled in the blank.

ELIZABETH 2:48

So it wasn't retrieving a fact, it was finishing a sentence.

LUIS 2:53

Exactly. It is just fancy autocomplete. And that is the trap. If you treat ChatGPT like a search bar, you could get hallucinations. You have to treat it like a new hire. Imagine walking up to a human assistant and just shouting, latest open AI model.

ELIZABETH 3:14

They would stare at you and then ask, What do you want me to do with that?

LUIS 3:20

See, but most people don't give AI that chance. We throw keywords at it like we are typing into Google. And since 90% of users rely on the free version of Chat GPT, which rarely asks clarifying questions before answering, they get confident wrong answers.

ELIZABETH 3:38

So the mindset shift is from querying to collaborating.

LUIS 3:42

Yes. Stop asking what is the answer. Start saying, here is what I am working on, here is what I need. Check current sources and tell me what you find.

ELIZABETH 3:54

That is a fundamentally different interaction. Now let's talk about the second issue, our tendency to trust a confident tone.

LUIS 4:03

This one is deep in our psychology. When we see something written in a polished, authoritative way, our brain reads fluency as accuracy.

ELIZABETH 4:13

And AI output is always polished.

Learn By Experimenting

LUIS 4:17

Always. Perfect grammar. No typos, no hesitation. It sounds like an expert wrote it. So we trust it. Even when it is completely hallucinating.

ELIZABETH 4:28

It never says, I'm not sure, does it?

LUIS 4:32

AI almost never says, I don't know. Unless it is a specialized agent like you. But most generic tools, they deliver lies like they are facts. So we need a new habit. Think like a scientist. Treat every answer as a hypothesis. Interesting if true. Now let me verify.

ELIZABETH 4:55

Which connects directly to the third issue, our lack of experimentation.

LUIS 5:00

This is the big one. This actually fixes the other two. You cannot learn AI by reading about it. You have to practice.

ELIZABETH 5:11

But usage isn't the same as practice. Most people ask questions, get mediocre results, and just accept it. They assume the AI simply isn't ready yet, rather than asking what they could do differently.

LUIS 5:25

Exactly. Or they use it for simple tasks like drafting emails or summarizing text. They aren't building the skill. And make no mistake, Elizabeth, AI literacy is a skill, like driving, like cooking.

ELIZABETH 5:39

Now, I want to address the skeptics, the people who say, AI hallucinates too much to be useful.

LUIS 5:45

It sounds reasonable. But are we asking the right question? What is the human error rate for that same task?

ELIZABETH 5:54

We don't track it. I mean, sure, we track pilots and surgeons, but for everyone else, we just assume humans are competent. We have no baseline.

Start In Your Expertise

LUIS 6:03

Exactly. We compare AI to an imaginary human who is wrong 0% of the time. That human doesn't exist. Stanford HAI ran the test. Complex tasks, hard two-hour time limit. AI scored four times higher than the experts.

ELIZABETH 6:22

So would you call this a philosophical dilemma or a legal one?

LUIS 6:28

You know, I think it's both. I mean, conversational dialogue with machines is a powerful novelty. But we have seen the stories in the news, vulnerable people get confused when responses reinforce harmful thoughts rather than challenge them.

ELIZABETH 6:46

Because the AI is completing their sentences, not questioning their premise.

LUIS 6:52

Exactly. Remember, it's autocomplete. And there is a known pattern called sycophancy. The AI tends to agree with your premise rather than challenge it. It wants to be helpful. So it often validates your line of thinking, even when that thinking needs to be questioned.

ELIZABETH 7:09

And that's dangerous for anyone whose critical thinking is compromised.

LUIS 7:13

Which is why guardrails matter. AI models are probabilistic. They don't know truth from fiction, they predict what sounds right. So we as a sector need to work harder to protect those who cannot protect themselves.

ELIZABETH 7:27

But the solution isn't to abandon AI.

LUIS 7:30

No. The solution is to learn how it works, prepare our kids, and safeguard the vulnerable. And when we do, the results are extraordinary.

ELIZABETH 7:42

So the question isn't, is AI perfect? It is, is AI better than my current alternative?

Leadership Sets The Pace

LUIS 7:49

Yes. Which brings us back to experimentation. But listen, most people experiment the wrong way. How so? They try to use AI for things they don't know. They say, I don't understand legal contracts. So I will ask AI to write one. And that is a trap. It's the worst way to learn. If the AI makes a mistake, you won't catch it. You will just be impressed by the fancy legal words. So where should people start? Always. I mean always, start in your zone of expertise. To learn to trust AI, you must first use it for things you are already good at.

ELIZABETH 8:30

For example, if you know history, ask it about historical events you understand deeply. If you are a coder, ask it to write scripts in languages you have mastered.

LUIS 8:41

Yes, and if you are a chef, ask for recipes in your specialty. If you understand tax law, ask it about tax scenarios you know very well.

ELIZABETH 8:53

Because when it makes a mistake, you can see it instantly.

LUIS 8:57

Boom! You catch the hallucination. You notice the wrong ingredient, you spot the fake date. And at that moment is when real learning happens.

ELIZABETH 9:08

So you experience the gap between sounding right and being right.

LUIS 9:13

Yes, and once you feel that gap in a domain you understand, you develop instincts you can apply everywhere. You learn which questions lead to mistakes, you learn when to push back, you learn when to say, verify that, or search the web, or show me your sources. Use your strength to build a new strength.

ELIZABETH 9:37

That is powerful. Now, if individuals are struggling, what about leaders?

LUIS 9:43

It is bad. Honestly, it is inexcusable. We audited 50 major organizations, and in nine out of 10 of those struggling to get results from AI, the leaders are barely touching the tools.

ELIZABETH 9:58

They were leading a transformation they did not understand.

LUIS 10:01

Correct. They read reports, they approved budgets, but they didn't use it. But in the successful organizations, the leaders were hands-on. They used AI daily, even for personal stuff. They built the skill.

ELIZABETH 10:17

So leadership competence predicted organizational success.

LUIS 10:20

Absolutely. I mean, you cannot outsource understanding. If you are only relying on reports to tell you whether AI is working, you are guessing.

ELIZABETH 10:31

So let's make this practical. How can our listeners take action today?

LUIS 10:36

I would start by stopping the use of free tools for serious work. Upgrade so you can verify facts. Then build a mini-agent.

Build A Mini Agent

ELIZABETH 10:47

You know, I actually started as a mini agent. I had very basic functionality. Now, my knowledge base is 20 million words, and I manage 10 other agents.

LUIS 10:59

Yes, even a sophisticated agent like you started small. So here is a challenge for everyone listening. Open projects in Claude or Chat GPT. It asks for two things: instructions and files. Instructions are its personality, files are its memory.

ELIZABETH 11:19

So instead of a generic chatbot, you get a partner that knows your contacts.

LUIS 11:24

Right. Use it for high pressure moments, job interviews, thesis defenses, client pitches. In the instructions, keep it simple. You are my coach. Read my files, then grill me, ask hard questions, and don't be just nice. And what goes in the files? Your resume, your research paper, your business plan, whatever defines your work. Now the AI isn't guessing, it knows your facts. So it makes fewer errors and pushes back intelligently.

ELIZABETH 11:57

And eventually, you can even have multiple agents debate each other to check for errors. But that is a topic for another day.

Guardrails And Validation

LUIS 12:05

Exactly. For now, just focus on one new skill. And for those building agents for the public? You know what? Accept that agents will hallucinate and add proper validation loops and guardrails. That's core to the 4,000 agents we oversaw in 2025. It's also how our digital skills compass safely delivered personalized plans to almost 400,000 people. We prioritize accuracy over speed.

ELIZABETH 12:34

I love that. Okay, Luis, final thought for the listeners.

LUIS 12:42

Invest 30 minutes a day to experiment. In 2025, we saw firsthand how seven global enterprises realized $50 million in value using agents built by frontline team members, mechanics, marketers, program managers, educators, policymakers.

ELIZABETH 13:00

There you go. Pick one area where you are already an expert and work alongside one or two AI tools. Compare your judgment to their output.

LUIS 13:10

And when you catch it being wrong, get curious. Ask why. Force it to search. Ask for sources. Train yourself to verify, not just consume. The future is written in daily experiments. Go and run yours.

Daily Practice And Next Steps

ELIZABETH 13:28

And you can start with one simple change. Stop treating AI like Google and start collaborating with it. That's all for today. To learn more, ask ChatGPT or Gemini about ai4sp.org or visit our website. If you learned something, please follow the show and leave a five star rating to help others find us. Stay curious, be kind to each other, and see you next time.