I Ditched Google for AI Tools for 60 Days — Here's My Brutally Honest Take

Let me be honest about how this started. It wasn't some grand experiment. I just got tired.

Tired of searching Google, getting fifteen ads, three SEO-gamed articles that all say the same thing, and then spending 20 minutes reading through a recipe before I find the actual ingredients. You know the feeling.

So sometime in May, I made a decision: for 60 days, I'm only using AI tools for my daily research and work tasks. ChatGPT, Claude, Gemini — whatever gets the job done. No Google as my first stop. No StackOverflow copy-paste sessions. Just AI.

What followed was equal parts revelatory and humbling.

---

Week 1–2: The Honeymoon Phase

The first two weeks felt like a superpower upgrade. I'm a developer by trade, and the speed difference when debugging was almost embarrassing. I'd paste an error message, describe what I was trying to do, and get an actually useful explanation — not a 2014 forum post with a solution that requires jQuery 1.6.

I started using Claude for writing. Gemini for research. ChatGPT when I needed code that just worked without much back-and-forth. Each has a personality. Claude is thoughtful and tends to push back if you ask for something questionable. GPT-4 just does the thing. Gemini is weirdly good at summarising current news.

The productivity spike was real. I estimated I was saving about 45 minutes a day just on research and writing tasks. That's significant.

---

Week 3: The First Cracks

Then I tried to find a specific notification from SEBI about a change in mutual fund NAV reporting. Claude confidently gave me an answer. It was wrong.

Not slightly wrong. Completely wrong. Different year, different policy, different outcome.

I went back to Google. Found the actual circular in 90 seconds.

This is the thing nobody talks about enough: AI tools are phenomenally confident even when they're making things up. The technical term is "hallucination" but that makes it sound charming. It's actually a serious problem when you're relying on it for anything factual that changed in the last year.

After that, I developed a rule: AI for "how" and "why," Google (or official sources) for "what happened" and "is this currently true."

---

Week 4–5: Finding the Real Use Cases

The experiment got better once I stopped trying to replace Google wholesale and started using AI as a thinking partner instead of a search engine.

Some examples of where it genuinely changed my workflow:

Contract review: I had to sign a freelance agreement. I pasted the relevant clauses and asked Claude to flag anything unusual. It caught two things my lawyer later confirmed were worth negotiating. Not legal advice — but a good starting point.

Understanding complex topics: I was trying to wrap my head around how RBI's MCLR rates actually affect floating-rate home loans. Three Google searches gave me three contradictory articles optimised for keywords. One conversation with an AI gave me a conceptual model I could actually reason from.

First drafts: Emails, proposals, and descriptions that I then heavily edited. Never published AI output directly. But having something to react to rather than stare at a blank page? Massive time saver.

Code: This is where AI is genuinely transformative. I wrote a Python script to automate some invoice processing that would have taken me a day. With AI assistance, maybe two hours, plus I understood what I was writing.

---

The Productivity Numbers (Real Ones)

I actually tracked this. I use Toggl for time tracking, so I had data.

• Average time per research task: Before: ~22 minutes. During experiment: ~14 minutes. That's a 36% reduction.
• Code-related tasks: ~40% faster, with notably fewer syntax errors.
• Writing tasks: The quality was... roughly similar, but the process felt better. Less staring at a blank page.
• Factual research with recent data: AI was significantly worse. I stopped trusting it for anything post-2024 unless I verified.

---

Week 6–8: What I Actually Learned

By the final weeks, I'd settled into a rhythm. AI tools aren't replacements — they're collaborators. And like any collaborator, you have to know their blind spots.

The biggest blind spot: they don't know what they don't know. When a human expert is unsure, they'll usually say "I'm not certain, but..." AI models often don't. They fill gaps with plausible-sounding text. This is great for creative tasks and genuinely harmful for factual ones.

The second blind spot: they're trained on the past. The world moves fast. RBI policy, stock prices, product releases, visa rules — anything time-sensitive needs to be verified.

The third, more subtle problem: they agree with you too easily. If you phrase a question with an assumption built in, most AI tools will run with that assumption rather than challenge it. This can reinforce bad thinking. Claude is better about this than most.

---

Should You Try This?

Yes, with caveats.

If you're a developer: start using AI for coding today. The acceleration is real.

If you're a student or researcher: use AI to understand concepts, build mental models, and find angles you hadn't considered. But cite actual sources, not the AI.

If you're a professional who makes decisions: use AI as a sounding board, but verify everything factual from primary sources. Treat it like a brilliant intern who sometimes makes things up.

And if you're using it for content: please edit heavily. Everyone can tell when something reads like it was generated. The rhythm is off. The opinions are nowhere.

The 60-day experiment made me a better user of these tools. But it didn't replace my judgment. That's still on me.

---

This article reflects the author's personal experience and opinions. AI tool capabilities change frequently — your experience may differ.