We've been building Genie - an AI analyst inside Databox, and one thing kept coming up in user research: people don't lack data. They lack fast answers.
Dashboards exist. Reports get built. But when someone asks "why are signups down this week?" or "are we on pace to hit our revenue target?" - getting a clear answer still takes hours.
We're launching Genie on Product Hunt on March 18th, and we'd love to hear from you before we do:
What's the moment where your current analytics setup lets you down most?
Let me start from the creator s perspective: I personally don t have a product (apart from hiring people for creative work or offering personal consultations).
But as a creator, I constantly share content, insights, and information, value that helps me build trust (for free). Based on that perceived expertise, people eventually decide to work with me (a paid service).
Today, I read a TechCrunch article about what investors are no longer looking for in SaaS, or rather, what to avoid if you don't want to lose their interest.
The red flags were:
Too easy to replicate light AI wrappers, generic horizontal tools, basic CRM clones, generic productivity or project management tools.
No real depth products where differentiation is mostly UI and automation, anything without proprietary data, surface-level analytics.
Becoming obsolete workflow automation tools that coordinate human work (agents are taking over), integrations as a moat (MCP is making connectors a commodity), and "workflow stickiness" products trying to keep humans inside their software.
Today, I came across an article on TechCrunch: The great computer science exodus (and where students are going instead).
It shows that UC campuses saw a drop in computer science enrollment for the first time since the dot-com crash (6% in 2025, 3% in 2024), but students are shifting to AI-focused programs.
We wrapped up our Product Hunt launch at #13 out of ~500 launches and I just wanted to say thank you to everyone who checked out Votap, upvoted, commented, or followed along
Many people have told me that being part of Gen Z comes with advantages we have time, energy, and plenty of opportunities to shape our careers in the AI era. And I do feel lucky to have grown up with technology, to have had early exposure and opportunities to learn and explore it.
But the AI era feels different. The shift is not only new, it s happening at lightning speed. Before I ve even fully adapted to working with AI, we re already seeing waves of layoffs where human roles are being replaced or reshaped by AI systems. And honestly, that creates uncertainty and anxiety not just for me, but for many people around us.
I've built my product around traditional SaaS pricing (monthly tiers), but I m starting to wonder if that model is getting outdated, especially with more AI-powered and compute-heavy tools entering the market. That shift requires real architectural changes, instrumentation, metering, billing logic, and UI changes, not just pricing tweaks. It s something I m starting to seriously think about for my own product.
In particular, AI usage has real COGs (every prompt costs money), and I m seeing more platforms experimenting with usage-based models, or hybrids like SaaS base + usage + overage.
For those of you building AI or compute-intensive tools:
Yesterday, I had an unpleasant experience. For a few minutes, I lost my LinkedIn community of several thousand people (TL;DR: I was falsely accused of using suspicious software).
Fortunately, I got my account back but it was a strong reminder that we don t own platforms, nor our profiles on them.
