We Built AI-Native Software Services Before It Had a Name.

The company architecture everyone had written off as "un-investable" turned out to be the most interesting place to build. We just got there years before anyone had a name for it.

Software has always been valuable because building it was genuinely difficult. Difficulty creates scarcity. Scarcity creates the asset. AI doesn't improve this engine, it removes the difficulty that made the engine work. Once building software stops being hard, the software stops being an asset. It becomes inventory. Abundant, replicable, impossible to amortize because your customer can manufacture their own for less than your subscription costs.

Most people read this and see a problem for software companies.

It looked to me like the foundation for a different kind of company entirely.

About a year into building, we were working with the agri-business division of a $10B Indian conglomerate, one of the country's largest spice supply chains. They had ERPs. Multiple of them. They also had Excel files stitching the whole thing together, because the ERPs didn't talk to each other and nobody had built the bridge.

We spoke on a Friday. By Monday they had a working prototype. Their full visibility system was live by Day 30.

The supply chain hadn't changed. The problem had been there for years. What changed was how fast we could move. And once we could move that fast, the procurement logic that would have routed this through an 18-month IT rollout simply stopped applying. We weren't competing with SAP. We were competing with the Excel file. That's a different category of competition, and speed is the only thing that matters in it.

Could this have been too early? Yes. The honest version of our timing thesis is that it required AI-native engineers, not standard engineers using AI tools, which is a real but modest improvement, but engineers whose entire workflow is built around agentic systems handling the majority of the build. That cohort was just beginning to emerge. Most companies hadn't noticed them yet because they didn't fit any existing hiring rubric, not a senior engineer, not a junior one, something structurally different. We built the systems to find them before the market understood what it was looking for. That's still true today.

The objection I still hear is the same one from two years ago, slightly updated. Okay, AI collapses delivery cost, but where's the moat? Anyone can hire AI-native engineers. Anyone can promise fast delivery.

Nothing stops a competitor from doing this in general. What stops them from doing it in supply chain specifically is the same thing that made the Excel file rational in the first place: proximity to the problem, accumulated over time.

Every engagement teaches the platform something it didn't know before. The engineer who spent days inside a spice supply chain - working through fragmented ERP reconciliation, seasonal harvest logistics, the specific ways cold chain exceptions propagate through an export operation, carries domain knowledge that isn't in any training dataset. It's only available through deployment history. Customer 10 gets a better system than customer 2 because by customer 10 the agents have already solved most of the adjacent problems. That depth compounds with every engagement, and a competitor starting today has to earn it the same way we did.

Then the engagement doesn't end. Day 7 is the start of the next sprint. Revenue continues without the sales cost repeating. At that point the unit economics stop looking like a services business.

The standard objection to services companies is actually three objections bundled: margins compress as you grow, there's no compounding IP, revenue is episodic. The AI-native vertical agency breaks all three: but only if the domain is specific enough to build the flywheel. A horizontal AI agency gets the delivery speed. It doesn't get the compounding. The vertical isn't a constraint on what you can build. It's the mechanism that makes it defensible.

When Y Combinator published their Request for Startups and named AI-native agencies as a priority (services companies with software margins - scaling bigger than anything in fragmented markets today), we had been building this for two years before they wrote it. Not because we predicted the framing. Because the problem made the architecture obvious once you were close enough to it.

The category has a name now. The question is which verticals and which teams get there before the window closes.