I use AI almost every day.
ChatGPT, Gemini, Claude, Copilot, and other AI tools have become part of how I work, learn, research, and sometimes even make decisions. As a software engineer, I obviously use AI for coding, debugging, architecture, and system design. But that's actually only a small part of it.
I also use AI when I'm working on a business idea, researching something for my home, comparing products, estimating the cost of a project, learning something new, or simply trying to understand something that came to mind.
And that's when I started noticing something.
AI made me much faster.
But it may have also made me a little lazy.
Not lazy in the traditional sense. Lazy at searching, reading, comparing, and thinking things through myself.
Before AI, getting an answer took effort
Think about what happened when you had a question a few years ago.
You'd probably search Google, open a few websites, maybe read a Reddit discussion, watch a YouTube video, look at the official documentation, compare several opinions, and perhaps realize that the first answer wasn't actually very good. Then you'd search again using different words.
Eventually, after all that, you'd form your own conclusion.
It wasn't always convenient. But there was something valuable hidden inside all that friction.
You were exposed to information from different sources.
You had to compare it. You had to decide which source seemed credible. You had to notice contradictions. You had to think.
Today, the process can be dramatically shorter, I can open an AI assistant and simply ask: What's the best way to do this? And a few seconds later, I have an answer.
That's incredibly powerful. And incredibly convenient. Maybe too convenient.
AI isn't just my coding assistant anymore
If this were only about programming, it wouldn't be particularly interesting. But AI has expanded far beyond that.
I'll give it questions about software architecture. I'll ask it to help me understand a business idea, structure a business plan, or estimate the costs involved in building something. I'll ask about materials I might need for a backyard project, different products before buying something, a financial concept, or the pros and cons of two approaches. I'll ask it to summarize something complicated.
Sometimes I don't even have a specific question. I just have an idea, and AI helps me explore it. That's where things become interesting, because AI isn't only helping me find information anymore, but it's increasingly helping me process information and make decisions.
Just ask AI
This has become a habit.
I don't know something? Ask AI.
I'm planning something? Ask AI.
I'm comparing two products? Ask AI.
I'm trying to estimate a cost? Ask AI.
I'm stuck? Ask AI.
It's almost like having a very knowledgeable person sitting next to you who is willing to discuss almost anything. That's amazing, but there's a subtle problem: sometimes I don't even realize that I've stopped doing the research myself.
The shortcut can become the default
Imagine I'm planning to build a patio in my backyard. Before AI, I might have searched for patio construction guides, looked at different materials, visited Home Depot or another supplier, checked prices, watched installation videos, looked at local requirements, compared different approaches, and calculated quantities myself.
Now I can ask: I want to build a 15 × 12 ft backyard patio. What materials do I need, how much should they cost, and what are the main steps?
Within seconds, I might get a beautifully organized answer: materials, quantities, estimated prices, tools, steps, and potential problems.
It feels like I just saved myself hours and maybe I did. But here's the important question: Did I get the correct answer?
Maybe the material prices are outdated. Maybe the quantity calculation doesn't account for waste. Maybe local requirements are different. Maybe the recommended material isn't suitable for my situation. Maybe there are drainage or grading considerations that weren't mentioned. Maybe the AI made an assumption about the soil, slope, or existing surface.
The answer can look incredibly complete while still being incomplete.
The same thing happens with business
Suppose I have a business idea. I can ask AI: Create a business plan for this idea.
And within minutes I can have market analysis, target customers, pricing, competitors, revenue projections, operating costs, marketing strategy, risks, and growth opportunities.
A few years ago, creating something like this required significantly more research.
Today, the first draft is practically free. That's fantastic, but there's a huge difference between: AI-generated business plan **and **validated business opportunity.
AI can help me organize my thinking. It doesn't automatically prove that customers actually want the product. It doesn't know what my competitors are doing today unless I provide current information or it has access to reliable current sources. It doesn't magically know whether customers will pay my proposed price.
A beautiful spreadsheet doesn't make the numbers true and a convincing business plan doesn't make the business viable.
Product comparisons can be even trickier
Here's another example. Suppose I'm looking for a new product, I ask AI: Compare Product A and Product B.
The response might give me a nice table: price, performance, features, pros, cons, reviews, recommendation.
It feels incredibly objective but then I realize something: The way I ask the question can influence the answer.
If I ask: Why is Product A better than Product B? I'm already pushing the conversation in one direction.
If I ask: Why is Product B better than Product A? I'll probably get a completely different perspective.
And even if I simply ask: Which one should I buy? I'm asking AI to make a decision based on the criteria I gave it — and the assumptions it makes about what matters to me.
That's something we need to remember, AI doesn't necessarily remove our biases but sometimes it can amplify them.
What about investing?
This becomes even more important when money is involved.
I can ask AI: S*hould I buy this stock?, or: **Compare these two ETFs, or: **What are the risks of this investment?*
And I can receive a detailed answer almost instantly. But an investment decision isn't simply an information-retrieval problem.
There are assumptions about risk tolerance, time horizon, valuation, diversification, taxes, liquidity, personal circumstances, and future expectations.
AI can help me understand these concepts. It can help me organize information. It can help me ask better questions.
But that doesn't mean the answer should become: AI said buy it, so I'll buy it.
That's not research. That's outsourcing the decision.
The dangerous part isn't when AI is obviously wrong
We already know AI can make mistakes. That's not particularly surprising anymore.
The more dangerous situation is when the answer is plausible.
Imagine AI gives you an answer that is 95% correct, beautifully written, confidently explained, logically structured, and full of examples.
You might never question the remaining 5% and that 5% could be the part that actually matters.
Maybe it's a wrong assumption. Maybe it's an outdated price. Maybe it's an incorrect technical detail. Maybe it's a missing regulation. Maybe it's a product specification that changed. Maybe it's an overlooked risk. Maybe it's simply not applicable to your particular situation.
The better AI gets at producing convincing answers, the more important our ability to evaluate those answers becomes.
Sometimes the best answer is still a few clicks away
This is probably the biggest change I've noticed in myself.
Before AI, I was willing to spend time searching, now I sometimes think: Why would I search for this when I can just ask AI?
But sometimes the answer really is sitting one or two clicks away; The official documentation. The manufacturer's specification sheet. The government website. The actual product manual. The current price on the retailer's website. The company's financial report. The original research paper. The terms and conditions. The actual source behind the claim.
Sometimes the best answer isn't hidden, it's just less convenient to reach and maybe that's okay.
Not everything needs to be instant.
We are outsourcing more than answers
This is where I think the real issue lies.
We're not just outsourcing search.
We're increasingly outsourcing: research, comparison, summarization, analysis, brainstorming, planning and sometimes even judgment.
That's where I think we need to be careful because the more of these steps AI performs for us, the easier it becomes to skip the thinking that used to happen between the question and the decision.
AI can make us feel smarter than we are
There's another interesting side effect.
AI can explain almost anything in a very understandable way.
You ask a question. You get an answer.
It makes sense. You think: Okay, I understand. But do you?
Understanding an explanation is not necessarily the same as understanding the subject.
If AI explains a business model to me, can I explain it without AI?
If AI designs an architecture for me, can I explain why that architecture is appropriate?
If AI compares two products, do I understand the trade-offs?
If AI gives me a cost estimate, do I understand the assumptions behind it?
If AI explains an investment, do I understand the risks?
There is a difference between receiving knowledge and developing understanding.
I'm not going back
None of this means I want to stop using AI. Quite the opposite.
AI has genuinely improved the way I work. It lets me explore ideas much faster, helps me get unstuck, gives me perspectives I might not have considered, helps me learn, saves time, handles repetitive work, and lets me go from an idea to a rough implementation much faster.
I don't want to go back to a world where every question requires opening ten browser tabs.
The answer isn't to use AI less. The answer is to use it better.
AI made me faster. It also made me lazy.
I don't think being "lazy" is necessarily a bad thing.
If AI can save me two hours of repetitive work, I'll happily take those two hours.
If it can help me find an answer in 30 seconds instead of 30 minutes, that's progress.
If it can help me explore an idea that I otherwise wouldn't have had time to explore, that's valuable.
The problem starts when convenience replaces curiosity.
When we stop checking, when we stop reading, when we stop comparing, when we stop asking why, when we stop challenging the answer, and when we start believing that a detailed answer must be a correct answer.
AI has made information incredibly cheap but judgment is still expensive and I don't think we should outsource that part.
The goal isn't to think without AI
Maybe the future isn't about choosing between: Thinking or AI.
It's about learning how to think with AI.
Let AI do the things it's good at. Let it generate, summarize, brainstorm, explain, and challenge us. Let it help us explore ten possibilities in the time it used to take us to explore two.
But keep one person in the loop: Us.
Because AI can give us a shortcut. It can give us an explanation. It can give us ten possible solutions. It can even give us a very convincing answer.
But ultimately, someone still needs to ask: Does this actually make sense? And maybe that question is becoming more important, not less.
AI made me faster. It also made me lazy.
I'm okay with the first part but I'm just trying not to let the second part become permanent.
Top comments (1)
This is such an interesting question! AI definitely makes you faster but there's something to be said about doing things the hard way once in a while.
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