Hey Product Hunt community!

I've been building some increasingly complex automations lately across different platforms (Make.com, n8n, Zapier) that connect multiple AI services together. While the technical side works beautifully, I'm hitting a major pain point: figuring out how much each automation actually costs to run.

Feels like everywhere you look right now, there s a new AI tool promising you can build a company solo.
No team. No budget. Just you, a laptop, and some clever prompts.

And sure building something solo has never been easier.
But building something that lasts? Feels like a different game.

You can ship faster. You can look bigger than you are.
But can you really wear all the hats founder, marketer, builder, support forever?

• If you're building solo right now, what s been harder than you expected?

• If you scaled a team, when did you know it was time?

Hey PH!

We re a small startup getting ready to launch an e-commerce service in North America, and we ve been digging into one key

question:

• What kinds of products are people actively searching for online but still can t buy easily?

I am Head of Marketing at Global AI Platform's US office in Silicon Valley, working on GTM for our app's US public availability launch this Summer'25. We re in the final stages of beta testing our mobile app (focused on meal and weekend planning), and we want to be intentional and avoid rushing just because we feel ready.

We re working on defining exit criteria the metrics, signals, and checkboxes that say:

Yes, it s time to move from beta to full release.

I mean, is your day job at a bank or an oil company or a manufacturer? Do you use consumer AI as part of your workflow? Does your company have any kind of objectives?

I ve noticed that some of the biggest product breakthroughs happen when teams look outside their own industry. For example, I ve seen SaaS products borrow UX ideas from video games to improve user onboarding, or logistics companies apply lean manufacturing principles to streamline workflows.

But this raises a tricky challenge: how do you identify which unrelated industries hold practical insights without getting distracted? And once you find those ideas, how do you translate them effectively into your own product context without overcomplicating things?

I d love to hear from product builders who have intentionally looked outside their direct competitors

What specific problems were you trying to solve by exploring other industries?

Most AI startups today build tools to help existing companies work faster or smarter. But Y Combinator is doubling down on a much more ambitious vision: full-stack AI startups that don t just improve industries they replace them.

Instead of selling AI to law firms, why not build an AI-first law firm?

Instead of helping developers write code, why not launch a fully automated dev agency?

Why sell to customer service teams when you can eliminate the need for them entirely?

The funny thing about building an AI product right now is that the hard part keeps changing.

A couple years ago, I was obsessed with offline evals. They felt clean. You write a test, you run it every time you change something, and you get a number you can trust. If the number goes up, you ship. If it goes down, you fix it. It s the kind of engineering loop that makes you feel like you re in control.