The Age of AI Speed: From Startup to Wind-Down

The takeoff and return to earth for QualRank AI.

Back in late May, I started building a platform to help people compare AI models — think "which model is best for my needs, and at what cost?"

There were already benchmarks and services like OpenRouter and LM Arena that ranked models in broad strokes, plus plenty of evaluation libraries. But what I couldn't find was a dead-simple way to run my own prompts across multiple models, get repeatable scores, track costs, and see quick, apples-to-apples rankings. That was the gap I set out to fill.

On paper, it felt compelling, of course. So why didn't it work out? I'll get to that.

But first, some lessons learned that might be more useful to you.

AI at the Speed of… What, Exactly?

A day doesn’t go by on LinkedIn without seeing at least one fifteen posts about how you have to move super, super, super-duper fast in the age of AI or you’re absolutely dead. If you’re not going 500mph, don’t bother.

I get it. There's a land grab, and the tools let you build faster than ever. It's easy to feel like you're falling behind.

But here's the thing: it still matters what you're doing fast.

I can whip up something in the kitchen at lightning speed, but if I'm tossing honey-nut Cheerios, mayo, tamari, and pinto beans in a bowl and calling it a salad, speed doesn't make it any less gross.

There's a real obsession with being first to market. Sometimes that works — some race horses win by leading from the start. But plenty of winners hang back and surge at the end. Google was years behind the first search engines (Lycos, AltaVista, Ask Jeeves, Yahoo!), but it's the one that stuck and worth trillions of dollars. The others? Footnotes.

I fell into the "speed trap" myself. I was so excited to go from zero to a working product in days that I did just that. But a little more planning and validation up front would have helped. Speed is great — after you've confirmed there's a real problem and a real buyer. Otherwise, you just find out you're wrong faster. That's useful, but it's not a business.

Whether you're inside a big company or launching a startup, this rush can lead to a lot of wasted effort and abandoned products. But maybe that's just the price of progress in a new tech wave — ask Webvan or Pets.com.

AI Development Tools: The Real Game-Changer

Now, let's give credit where it's due: AI development tools are incredible.

I'm not a professional developer. But with today's AI tools, I felt like I'd checked into a Holiday Inn Express and woken up with new coding superpowers.

Here's how my journey went:

• Starting out: Writing Python locally, lots of back-and-forth with ChatGPT (GPT-3.5 days), basically letting GPT teach me Python

• Picking up steam: Added GitHub Copilot, got used to autocomplete and copying code snippets

• Big unlock: Used Claude Projects, loaded in project info and code, started pasting in bigger chunks or full files

• Takeoff: Tried Cursor and Gemini CLI, but Claude Code really clicked for me. Adding product docs and tickets to the repo made coding sessions much smoother

This all happened over two years, and I still feel like I’m drinking from the firehose.

Here's the catch: as much as I recommend tools like Claude Code, I'd have been lost without the basics I picked up early on. I'm not a full-time developer, but I learned to code as a kid and studied some computer science in college. That foundation let me understand what the AI tools were doing — and spot when they went off the rails.

Maybe someday you won't need to know any code to build. But for now, being able to peek under the hood is a huge advantage. The tools are rocket fuel, but code literacy keeps you from crashing.

Know Your User, Test Early

If you're building something to solve a problem, you need to know whose problem it is.

Ideally, you're the person with the problem and you also have a direct line to people who are. Before you build, pressure-test your idea with real users — ideally, more than just "friendly testers." Line up design partners who actually live with the pain you're trying to solve.

Once you have something to show, get it in front of those users as soon as possible. Does it actually help them? Or at least point in the right direction?

This might sound obvious, but it's critical. You're not solving a theoretical problem — you're solving a real headache for real people.

Yes, Steve Jobs said, "People don't know what they want until you show it to them." But most of us aren't Steve Jobs (sorry if I’m bursting bubbles). Plus, the true lesson there is: solve a real problem, and do it better than what came before — even if your solution is surprising.

For me, I focused a bit too much on my own struggle (comparing prompt performance across models) and assumed others felt the same. In beta testing, most users thought the platform was cool, but not something they'd use regularly.

Turns out, even AI builders tend to stick with one model family. It's not just about performance or cost — things like governance, support, security, and vendor relationships matter more than saving a few cents per token or slightly better outputs. Plus, the performance gap between top models closes fast, so there's rarely a reason to switch specifically to chase quality.

Kill fast

No, this isn't a Jason Statham movie. It's about knowing when to shut things down.

If you're not seeing real momentum early — whether that's revenue or just a growing, excited user base — it's tough to get it later. The exception is big enterprise deals, where cycles are long. There, look for signs of real interest: design partners investing time, integrating your tool, or giving you data, even if money comes later.

There are lots of good ideas out there. Don't get so attached to yours that you miss the signals.

In my case, I'm glad to call it now. I've been on projects that limped along long after they should have ended. Maybe if I'd tested with a broader group, I'd have found the right users. But the low excitement and low repeat usage told me it was time to move on.

So, What Went Wrong?

If you've made it this far, you've probably picked up most of this already. But here's the short version:

1. It wasn't a daily pain for most teams.
   Comparing models is something most teams do only occasionally. Unless your tool is built right into their daily workflow, it's hard to keep people coming back. Teams get more value from tools that automatically pick the right model for the job, rather than ones that just let you run occasional bake-offs. QualRank sat next to the workflow, not inside it.

2. I missed the real target user.
   I tested with smart people working in AI, but not with the folks who deal with this problem every week. The real audience is platform or infrastructure teams managing lots of models, or app teams with strict testing and monitoring. I didn't have those design partners, so my testers were lukewarm. That's on me: testing with the wrong users basically guarantees this outcome.

3. The market moved fast.
   Google Stax just launched a free cross-model evaluation tool. Big vendors and popular platforms made the basics cheap or free. That shifted the value to things like specialized datasets, reliable scoring, and tight integration with existing tools. I built a nice feature, but was missing the bigger picture.

Was it also "too early"? Maybe. I do think we'll see more demand for mixing and matching models, or saving money with smaller ones. But even then, the winning tools will be the ones that fit right into existing workflows — not standalone comparison dashboards.

Onward

While QualRank will no longer be a key focus for me, as I expand my consulting services, that’ll be wrapped in. As noted, I still think it’s worthwhile to carefully consider models – both for quality and cost – when going into a build. So it’s still a tool that I can use to help advise clients.

In the meantime, I’m on to the next ideas… of course.