📝 usncan Note: Solve A Real Problem First
Disclaimer: This content has been prepared based on currently trending topics to increase your awareness.
Digital generated image of abstract AI data chat icons flying over digital surface with codes
We’re living in the age of copy-paste intelligence. You can spin up an AI-powered landing page in 15 minutes, plug in a few GPT calls, and it’ll even write its own press release. The tools are impressive. But most of what gets built doesn’t stick.
The real issue isn’t a lack of AI, it’s a lack of purpose. Many products skip the part where they answer: Why does this need to exist?
Too many products today are beautifully packaged but hollow. A glossy AI wrapper with no meaningful problem underneath. Founders get caught up in the novelty and forget to ask the most important question: What are we really solving?
Great products start at the breaking points. Where something is frustrating, slow, tedious, or emotionally draining. That’s where the value is. AI should quietly support that, not be the entire pitch.
Start With Problem, Not the Solution
A lot of AI products today start with the technology. The model comes first. Then comes the scramble for a use case.
That’s backwards.
The best ideas start with a real-world problem. Something that’s annoying, inefficient, or consistently painful. That’s where AI can make a real difference.
Take something as specific (and surprisingly frustrating) as naming a company. It sounds simple, until you try. Founders spend hours searching for something that feels right – a name that fits the story, the tone, the ambition. But they often settle for whatever’s available.
Keyword-based search tools don’t help much in finding great domain names. You enter a word, get back a bunch of literal matches. Some might be close. Most aren’t.
But that’s not how people think. They search in feelings. In phrases.
“Something calming but confident.”
“A name that sounds smart but not cold.”
That’s not a search problem. It’s a language problem.
We built semantic search at Atom.com not to showcase AI, but to bridge the gap between how people describe what they want and how results are typically delivered.
One founder, for example, was building a modern parenting brand. She didn’t search for “baby” or “mom.” She searched for something that felt timeless and trustworthy, something that could evolve with the brand. The name she picked wasn’t obvious. But it clicked. Because it captured the feeling, not just the words.
That’s what AI should do. Not call attention to itself, but quietly help people get to a better answer.
The Real Difference Is in the Decisions
Everyone has access to the same tools. The same models, APIs, tutorials. That’s not where the edge is.
What really separates strong products from forgettable ones is the thinking behind them. The decisions about what to build, and what to leave out.
It takes restraint to focus on solving one specific thing well. It takes clarity to ignore the hype and focus on something that’s actually useful. The best builders aren’t showing off the technology. They’re paying attention to the person on the other side.
The Best AI Products Don’t Talk About AI
Think about the tools you rely on every day. Notion. Figma. Superhuman. They all use AI but none of them lead with it. They just work better. Quietly.
That’s what progress looks like. You don’t remember the tech. You remember how much smoother things felt.
One of the clearest examples of this approach is Dharmesh Shah, co-founder of HubSpot. He’s quietly building Agent.ai, a suite of AI-powered tools that actually help you get work done. Agents that research companies, summarize complex inputs, and yes, even help find domain names. Not as a gimmick. But because those are tasks people genuinely want to spend less time on.
It’s not about being flashy. It’s about being helpful.
Better Questions to Ask
- What part of your user’s day feels like a chore?
- Where are they improvising with half-baked workarounds?
- What do they avoid doing until they absolutely have to?
If AI can make that feel lighter, faster, or simpler, you’re onto something.
If you’re starting with “what can we build with GPT-4,” you’re probably building a demo, not a product.
Final Thought: Solve Like a Human, Then Scale Like a Machine
The companies that will stand out in this next wave won’t be the ones shouting about AI.
They’ll be the ones quietly removing friction. Making things easier. Helping people move faster with less frustration. That’s the work that lasts.
Start with pain.
Then let the AI fade into the background.
