AI hallucinations are real — and they catch people off guard. I’ve been using AI daily for work for a long time now, so I’m used to its strengths and its limits. But recently, I noticed something interesting. A few family members and friends — smart, capable professionals — started using AI more seriously. And almost all of them hit the same wall. They asked a reasonable question. The answer sounded confident. It was written well. And it was… wrong. That moment tends to be frustrating, sometimes even a deal-breaker. Not because the mistake was catastrophic, but because it breaks trust. Here’s how I think about hallucinations: - AI doesn’t “know” when it’s guessing - Fluency ≠ accuracy - Confidence in tone is not a reliability signal Once you internalize that, hallucinations stop being shocking — and start being manageable. In my own work, I reduce the risk by: - Asking AI to show its assumptions or reasoning - Forcing constraints (“If you’re not sure, say so”) - Treating AI output as a draft or hypothesis, not an answer - Verifying anything that would matter if it were wrong AI is a powerful thinking partner. But it’s not a source of truth — and pretending it is usually backfires. I’m curious: Have you personally run into an AI hallucination that caused confusion, wasted time, or a real problem? Or have you developed a habit that helps you catch them early?

Didn’t expect to be writing this today, but here we are. We crossed 100 members within a few hours of opening this community. That tells me one thing: a lot of professionals are thinking seriously about how to use AI well, not just loudly. To mark the moment, here’s something worth reflecting on: The real power of AI isn’t speed. It’s reducing friction between thinking and execution. Used poorly, it creates noise. Used well, it helps you: - clarify what you already know - structure messy ideas - test decisions faster - move forward with less mental drag That’s the spirit of this space. If you’re new here: feel free to introduce yourself. What kind of work do you do — and what do you hope AI can help you think or execute better? More soon.

People often ask which AI tool is “the best,” and the honest answer is: it depends on how you’re using it. Right now, the three language models I use the most are ChatGPT, Grok, and Claude — and while there’s a lot of overlap between them, each one has a slightly different “personality” and strength. ChatGPT is the one I have the deepest relationship with. I use the paid version and it’s part of my daily workflow — planning, thinking, structuring ideas, writing, decision-making, and turning messy thoughts into something usable. It’s extremely versatile, especially if you learn how to work with it over time instead of treating it like a one-off prompt machine. Grok has a different flavor. I use it a lot for image generation and short visual assets that can be repurposed for marketing or content. It also feels a bit more unfiltered in how it reasons and frames ideas, and it has access to real-time information from X, which can be useful depending on the context. I wouldn’t replace ChatGPT with it — but I wouldn’t want to lose it either. Claude is very strong when it comes to long-form reasoning and detailed explanations. When I want a more careful breakdown of an idea, a structured critique, or a thoughtful expansion of something complex, Claude often shines. It’s especially good for clarity when things start getting abstract. The important point isn’t choosing one “winner.” It’s understanding that different tools are better for different cognitive jobs. Over time, you stop asking “Which AI should I use?” and start asking: 👉 What am I trying to think through right now? 👉 Do I need speed, structure, creativity, or depth? That’s where AI really becomes useful — not as magic, but as leverage. If you’re curious: which one are you using the most right now, and for what?

One of the most practical ways I use AI is not to come up with ideas for me, but to help me organize ideas that already exist. A lot of my thinking starts in a very unstructured way. I’ll often record a stream-of-consciousness voice note — just talking through thoughts, questions, half-formed ideas, or even contradictions. I don’t try to sound clear or intelligent. The goal at that stage is simply to get everything out of my head. Once that’s done, I’ll run the audio-to-text and feed the raw transcript into ChatGPT. From there, I’ll ask it to help me: - identify the main themes - separate signal from noise - structure the ideas logically - highlight what’s actionable vs. what’s just exploratory - turn something chaotic into something I can actually work with Used this way, AI becomes a thinking aid, not a thinking substitute. There’s an important limitation here, though. If the input is too unfocused — for example, if I ramble about ten unrelated topics with no underlying intention — the output will naturally become diluted or generic. AI is very good at organizing thought, but it still responds to the quality and coherence of the input. So over time, I’ve developed a simple rule for myself: - ramble freely first - then give AI a very clear instruction about what I want clarified, structured, or extracted When I do that, the results are consistently strong. I get clarity faster, I make better decisions, and I move forward with less friction — without pretending that AI “did the thinking” for me. For me, this is where AI shines most in professional work: not replacing judgment, but supporting it. If you’re using AI already, I’m curious — do you tend to use it more to generate ideas, or to clarify and structure your own thinking?