Yunsong Wang is the CEO of Fields-IQ LLC, where he's building an "app factory" a platform that spins up full applications end to end in days. His focus is making AI-generated data reliable through structure, validation, and a single source of truth.
Connect on LinkedIn ↗When an AI feature misbehaves, the first instinct is to blame the model. Pick a smarter one, rewrite the prompt, hope the next run comes out cleaner. In a recent Tech Leaders conversation, a builder working on an AI "app factory" at Fields-IQ made a different case. Most reliability problems, he argues, sit one layer below the model, in how the system is structured and whether the AI is forced to respect a single version of the truth.
Yunsong Wang, CEO of Fields-IQ LLC, has spent about six months on one platform. Roughly four months went into building the app factory itself, a system meant to take an app from UI to database to deployment, and out to the App Store or Google Play, in a couple of days. The last two months went somewhere less visible: making the data the AI generates consistent enough to actually use. Five apps are already shipped and two domains are covered. The model was never the hard part.
The goal was never one app, or two, or three. It was a platform split into domains, each able to provision full apps end to end. That ambition runs straight into a familiar wall. AI can produce enormous volumes of data fast, millions of data points in an hour, but that speed is worthless if the output cannot be trusted or reused.
His answer is to stop treating cleanup as a one-off. "You have to convert AI generated data into structured data through infrastructure," he says, "not just like random or one-time operation." The point is to build the conversion into the system, so anything the AI generates lands in a shape the platform can actually consume.
That gets harder across domains, because each one carries its own data structure. Fitting all of them into a single platform takes a deliberately dynamic, extensible way to extract and convert AI output before feeding it into each domain. Without that, every new domain becomes a fresh mess to untangle.
Ask him for his top rule and the answer is immediate. "When I create AI, I have top rules. Number one is single source of truth."
The catch is that AI works against that rule by default. People reuse what already exists because starting over is more effort for us. AI has the opposite instinct. Reuse means reconciling old dependencies and tracing existing logic, and "from AI perspective it's less effort for them to create from scratch." Give it a similar task twice and it will happily build a brand new version both times.
Do that across a growing system and you do not get one source of truth. You get tens, sometimes hundreds, quietly contradicting each other. Forcing the AI back to a single one, he says, is "a lot of work because that's against AI's instinct."
Underneath the data problem sits a deeper mismatch. "AI is task oriented, it's not solution oriented," he says, "but you as architect or founder you have to deliver the entire solution, not the single module." A module the AI writes can work perfectly on its own and still break something, in his words, "300 miles away" in the system, because the AI never had the whole picture in view.
So he front-loads that picture. Before building something like a coupon system, the first question he forces is what the coupon system depends on. Answer that first, and the AI builds inside the existing structure instead of inventing a new island next to it.
Most developers know the moment he describes. The first run is dazzling. The second run, same request, returns something different. Chasing that away with ever longer prompts does not work, because "AI will always try their way to break it. So you have to have a very strong validation system."
That is the shift worth internalizing. Tightening prompts is a losing game, since the model will keep finding a way around them. What holds is a validation layer that assumes the output will drift and catches it, plus a framework the AI has to generate within. The instruction is not the safeguard. The checks around it are.
He is not precious about which model he uses. His system can switch models at runtime from an admin panel, and a change takes effect within about a minute, so different scenarios can run on different models. What he has noticed is convergence. A few months ago one model clearly produced higher-quality data. The others have since caught up.
His conclusion is blunt: "your system structure is more important than choose different model." The differences that still matter to him are cost and context size, not some decisive gap in quality.
Cost is where he gets specific, and where a point for tech leaders hides. A poorly trained developer can burn hundreds of dollars in tokens looping on a problem that a skilled engineer resolves for a few. In his framing, work that costs "$300 tokens could be easily answered or resolved by an experienced AI engineer with $3." The tool is the same. The person driving it is not. AI does raise a developer's efficiency, but how much depends heavily on whether that developer actually knows how to work with it.
Ask where AI is genuinely limited today and he points at context, not capability. If a model could hold enough context, he says, it would match his judgment and carry a thousand times more detail than he ever could. What it lacks is not horsepower. "AI's context only can cover a task, they cannot cover solution," he says. "Not because AI is not capable, just AI's memory is not big enough."
What still separates him from the model, for now, is "my instinct, my experience and my vision." Give AI enough context to hold a whole solution, he believes, and that gap closes fast.
Then he goes one level deeper, to a limit most product conversations skip. Today's models are built on language, and language is powerful because so much of our world is encoded in it. But language is also recent. It belongs only to the sliver of history since humans began using it, and the physical world ran for a very long time before that. An intelligence built purely on language, he argues, eventually reaches the edge of what language can represent, and "has to be back to the physical world" to cover everything that came before. It is a long horizon, but a useful reminder that the current paradigm has a shape, and an edge.
The throughline is that reliable AI is mostly an architecture and discipline problem, not a shopping problem. A few things follow from that.
Decide your single source of truth before you add features, and expect to keep enforcing it, because the AI will drift toward building fresh. Put your effort into validation systems and frameworks rather than longer prompts, on the assumption that the model will eventually break whatever you wrote. Hold the whole solution yourself, since the AI only ever holds the task in front of it. And when you compare tools, weigh cost, context limits, and the skill of the people using them at least as heavily as raw model quality.
None of that is glamorous. It is closer to plumbing than to magic. But the teams that get AI to behave in production tend to be the ones treating it that way.
