Building AI Products that Work, -1 to 0

I spend a lot more time on PH at the moment to see what indepedent makers are spending their time on. I've noticed some patterns and also want to share a little bit about my journey at South Park Commons. Most startup stories begin at “zero”—when there’s already a team, an idea, maybe even a prototype. But at South Park Commons (SPC), the philosophy is different: people gather in the -1 to 0 stage. That liminal space where you don’t yet know what you’re building—or even if you should build at all. It’s a place for exploration, experimentation, and being brutally honest about what’s working and what’s not.

A hallmark of SPC is how often industry leaders drop by to share what they’ve learned in the wild. Recently, I was in a small chat with Tyler Payne—former Google and LinkedIn AI lead, startup builder, who has spent the last decade helping teams actually ship real-world ML systems. We're always talking about what's being launched at SPC.

I'll give you some of my hot takes on this and why it's relevant to a lot of the launches I am seeing at the moment on PH.

Commodities don’t make companies:

The AI landscape is crowded with lookalike products. Prompt wrappers, note-takers, and “yet another copilot” tools flood Product Hunt every week. If your bet is that the next model release will suddenly make your app defensible, you’re already playing a commodity game. The hard part integrating an API to a model and then an API to some existing tool you already use. It’s everything else — workflows, data loops, other feedback that speeds stuff up. Makers need to ask: what do I know, or what can I collect, that nobody else can?

Services-first as a strategy

It's hard for soloprenuers pulling of service and I see a lot of launches with some version of Levelsio with an AI model strapped on. Founders often resist starting with services. It feels less "silicon valley" and scalable, less “venture ready.”

• Services give you reliable delivery when the model still fails half the time.

• They generate structured data you can’t get from customers alone.

• Auomation after discovery can make beautiful workflows and killer UX learnings.

• What's not glamorous might be the definition of a solid business.

For makers at -1 to 0, it’s a reminder that starting messy is often the only path to something durable. Building fast prototypes is fun to throw up quick on PH but what's going to be the enduring business? As Paul Graham said, do what isn't scalable.

Thinking about two spaces: Healthcare and Compliance

Healthcare / digital health companies

• Pretty much every digital health company is a tech-enabled services play.

• They start with humans delivering the service, while collecting structured data about patient interactions.

• Over time, parts of that workflow can be automated, but the services-first approach reduces risk up front.
  

Compliance (Vanta / Pristina example)

• Here's how @christinacaci of@Vanta aapproached compliance: instead of building pure software from day one, she offered to “just do compliance for you,” learning the space and only later codifying it into product.

Somedays I struggle to think what is worth upvoting on PH, sometimes it is because there are too many good products in a single day to even try them all --- at least their pitches are good---and other days it feels we just saw the same launches the day before.