AI is booming.
Every week, I see new founders launching “the next big AI tool.”
But behind the hype lies a harsh truth:
Most AI startups fail wit...
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Totally agree — most AI founders skip the validation and jump straight into building fancy tools. The “start with one painful problem” point hit hard. I learned this the tough way while working on my own micro SaaS. Great breakdown! 👏
Really appreciate that, and I relate to your experience more than you know.
I also learned the “build-first, validate-later” lesson the hard way. It’s such an easy trap, especially with AI because building feels fast, fun, and productive… until you realize nobody needs it urgently enough to pay.
Great article, Jaideep — thanks for sharing these insights into why most AI startups fail and how the “what I’d do differently” perspective brings a fresh lens.
A couple of thoughts:
Thanks again for the honest perspective — these lessons are gold for founders and freelancers alike who want to build sustainable AI-powered businesses.
Thank you for such a thoughtful and well-structured comment. I really value the depth you brought here.
You highlighted three areas that deserve deeper discussion, so let me respond to each:
I’m glad that point resonated. It’s one of the biggest traps in the AI space right now, building impressive technology that nobody urgently needs. If I were starting again, I’d spend the first 30–45 days purely on:
a. Pain interviews
b. Rapid prototypes
c. Paid validation (not vanity feedback)
If people won’t pay for the problem to disappear, it’s not a problem; it’s a preference.
This is so important, especially for AI products that touch sensitive data.
Here’s how I would weave cybersecurity into early-phase development without slowing speed:
Phase 1: Customer Discovery (No data stored yet)
a. Use synthetic data + anonymised samples to test value, not tech.
Phase 2: MVP Build
a. Add lightweight guardrails like
• Data minimization
• Encryption at rest + transit
• Clear consent wording
Phase 3: Early Adoption
a. Introduce a “privacy-by-design checklist” before new features ship.
Even at the prototype stage, trust is a differentiator for AI startups.
You’re right, this is a delicate trade-off. Here’s the balance I’d aim for in the next attempt:
a. For the first 0 to 10 users, I will focus on shipping fast, without breaking anything critical.
b. For 10 to 100 users, I will focus on stabilising the core and automating repetitive ops. Here, I will make the whole business sustainable.
c. For 100 to 1,000 users, I will scale what works, like data pipelines, cost controls and compliance issues.
Your comment added meaningful depth to the conversation, and I appreciate you surfacing the cybersecurity + scalability angle, most founders overlook it until it’s too late.
after 20 years of running startups, helping, advising. There's a big one - persistence. So many give up before they've even got there. I've been there myself. Most of us have. The number 1 trait all my successful mates have is persistence. And a brain lol.
Love this and couldn’t agree more.
After all the frameworks, tools, strategies, and “startup hacks” people talk about… it’s usually the uncomfortable, unglamorous traits that actually decide who makes it.
Yep, valid points like the emphasis on solving a real customer pain problem and then validating it before building is a lesson that gets overlooked so much due to the reliance on AI.
Absolutely, and I’m glad you called that out so clearly
AI has made it too easy to jump into building mode. We can prototype fast, generate code fast, and create polished products in days, which tricks many founders into thinking speed = success.
But without validated demand, all that speed just takes us in the wrong direction faster.
For me:
Customer pain first → AI solution second → Build only what people will use and pay for.
Spot on .. ‘AI’ stopped being a differentiator ages ago
Yes, AI is making the real difference now.
"🤖 AhaChat AI Ecosystem is here!
💬 AI Response – Auto-reply to customers 24/7
🎯 AI Sales – Smart assistant that helps close more deals
🔍 AI Trigger – Understands message context & responds instantly
🎨 AI Image – Generate or analyze images with one command
🎤 AI Voice – Turn text into natural, human-like speech
📊 AI Funnel – Qualify & nurture your best leads automatically"
Thanks for sharing this. It looks like you’re building a full-stack conversational AI ecosystem, not just a chatbot.
Too many AI startups chase the tech before the problem. Your breakdown perfectly captures the reality success comes from solving a real pain point, not just adding “AI” to a pitch. Love the “one audience + one pain” rule simple, powerful, and scalable.
Thank you, I really appreciate that!
That “one audience + one pain” rule has saved me (and many founders I’ve worked with) from building the wrong thing. It forces clarity early, and I’ve seen how quickly things scale once you nail that single painful problem instead of spreading too thin.
HEY EVERYONE I'M NEW IN PROGRAMING AND DEVELOPMENT BUT I WANT ASK ONE THING IS WHAT BUG AND CODE ERROR TAKE YOU HOUR AND WEEK TO SOLVE THAT LIKE I COMING IN TEMINAL OF USER CODE EDITOR AS I'M USING VS CODE EDITOR FOR CODING CAN YOU GUY'S PLEASE TELL ME I AND LEARN MORE ABOUT IT BY YOU'RE EXPERINCE
Great to see your enthusiasm; that curiosity will take you far in programming.
In the beginning, everyone spends hours (sometimes days) fixing small bugs. It’s completely normal. One of my earliest struggles was a missing semicolon that broke the entire project; I spent hours finding it.
A few tips that helped me:
a. Start with small projects so you can learn step by step
b. Debug slowly — read the error, search it, try one change at a time
c. Ask AI to explain the error in simple words — it helps a lot
d. And yes, VS Code terminal errors can be confusing at first. With practice, you’ll start to recognise them faster.
Keep going, the fact that you’re asking questions means you’re already learning the right way! I hope it will help you.
Awesome! I could not agree more. AI is not the edge anymore, execution is. Really liked 'one audience → one problem → one solution'. Simple but so good.
Thank you, really appreciate that! 🙌
You explained it perfectly: AI is no longer the edge; how we execute with it is. Tools are accessible to everyone now, but clarity and follow-through are what actually create results.
That “one audience → one problem → one solution” lens simplifies everything.
You’re completely right - not only for AI startups, but for every kind of startup. Thanks for sharing! 🌸
Thank you, I truly appreciate that!
I’m glad this resonated beyond just the AI space, because you’re right, these fundamentals apply to any kind of startup. At the end of the day, it always comes back to:
a. solving a real problem
b. validating early
c. staying close to users
Start with a single flagship use case that delivers a transformational outcome, not just convenience.