Building the platform layer for AI-native apps

People don’t open apps the way they used to. Increasingly, they tell an AI what they need and expect the right tool to just appear. John Allen, Co-Founder and CEO of Layo, is building the platform that makes that possible - turning existing products into AI-native experiences that live inside ChatGPT and other AI systems. From OpenAI’s app announcement in October to having paying enterprise customers in early 2026, John has moved with urgency matched to his conviction: the next operating system is a conversation. We sat down to talk about how he’s building the infrastructure underneath it.

At a glance

John sees ChatGPT apps as the beginning of a fundamental platform shift, comparable to the early iPhone app store but happening at internet speed. His thesis centers on the shift from screen-driven workflows to intent-driven conversations, where users express what they want naturally rather than navigate complex interfaces. Traditional web and mobile development tools can’t handle non-deterministic outputs or intent-based interaction patterns, creating a new category of infrastructure needs. Layo provides the visual builders, analytics, and configuration tools specifically designed for AI-native apps, helping enterprises ship into the ChatGPT ecosystem in days rather than months. The deeper transformation John envisions is software that adapts in real time to individual user preferences, enabled by AI platforms that maintain context across all applications. Enterprise customers consistently ask the same two questions: what should we build and when, with his answer being “the sooner the better” due to dramatically lower development costs and first-mover learning advantages. The analytics revolution enables something unprecedented in software history: full insight into user intent, measuring why people want to use your product rather than just what buttons they clicked. This leads toward a future of millions of interface variants rather than today’s A/B testing approach, where every user gets software built for exactly how they want to use it.

Defining AI-native: intelligence and action as the foundation

John draws a clear distinction between companies that “bolted on AI into their existing software” and those building AI-native products. “I think the real definition of AI-native are apps that actually have true intelligence as the baseline foundation. Intelligence and action, right?” He compares it to the data-native era of the 2010s, where companies competed on proprietary data. Now, data is getting commoditized and “all these apps are starting to talk to each other.” The value shifts to becoming “the system of action where people can go and do things” with global intelligence as the foundation rather than siloed data.

The operating system shift: from apps you open to systems that respond

John frames the current moment as comparable to previous platform transitions from desktop to web to mobile. The fundamental change is that “instead of just having the foundation of like we are the system of record, right now you’re like we’re the system of action, we’re embedded into experiences, meeting customers where they already are.” ChatGPT represents the first true AI operating system where users come to express intent. “People use software to go and do things or to get information” and AI interfaces can meet users at that moment of intent without forcing them to learn new interaction patterns for every application they use.

Why the “mobile moment” parallel matters: winning through learning, not timing

Drawing explicit parallels to the iPhone app store launch, John believes “the first thousand apps in the app store, they’re going to win the biggest. Not because they were the first thousand, but because they were the first to learn.” The ChatGPT app store launched December 17th, making this moment “like the cutting edge, like really day one.” The advantage comes from accumulating learning cycles about user intent patterns, optimization strategies, and interface design principles. “You never really get any product right on the first iteration” so the companies that start learning earliest will have the biggest advantage as the ecosystem matures.

The infrastructure gap: why traditional dev tools fail for conversational interfaces

John discovered the platform opportunity through direct experience building ChatGPT apps. After taking his team to upstate New York for a week of building, they initially tried creating a “super app” that could access everything. “We just realized like we are abstracting this stuff way too much” and “OpenAI already is the super connector.” The real insight came when they recognized that despite being technically skilled, “if we’re struggling with it then every company is going to struggle with it.” Traditional development tools assume deterministic outputs and screen-based interactions, making them poorly suited for conversational, non-deterministic AI interfaces that require entirely different architectural thinking.

Enterprise speed: from months to days with 10x cost reduction

The economics of AI-native development represent a dramatic shift for enterprise software teams. John explains that “most businesses once they’re established software companies they’re spending hundreds of thousands or millions of dollars on engineering” on their core products. With Layo, “they can go and actually use our product and spin up a chat GBT app in like days” with resource constraints “diminished by 10x.” This speed advantage enables rapid experimentation and learning cycles that weren’t economically feasible with traditional development approaches. The low cost of entry means companies can start learning immediately rather than waiting for perfect strategy.

The analytics revolution: measuring intent for the first time in software history

John identifies a fundamental measurement problem that creates unprecedented opportunity for product teams. Traditional analytics tell you “user clicked button X” but AI apps require understanding “user intended to do this. So your app was called. Why was it called?” This shift enables something entirely new: “this is probably the first time in history where you actually have full insight into why people want to use your product.” The new metrics focus on “invocation, follow-ups, success/error rates, depth, latency, and reliability rather than traditional pageviews or clicks.” Product teams can finally see the direct correlation between user intent and product performance.

Customer acquisition through value demonstration: proving before asking

John uses Clay as an example of how AI-native apps fundamentally change customer acquisition. When someone prompts for sales prospecting help, “Clay, if they’re well optimized, can just come up and say, ‘Great. We not only can serve you, but we actually already did the work for you. Do you like it? Great. Continue on.’” This approach proves value before asking for commitment, similar to how mortgage companies used calculators in 2016-2017. The result is warmer leads and higher conversion rates because “you’re proving to them that you’re adding value” rather than just telling them you exist. For companies like Clay, this opens access to ChatGPT’s 800 million weekly active users who may not know the product exists.

From deterministic to non-deterministic: designing systems instead of flows

John envisions software development fundamentally changing as outputs become unpredictable. He learned from Perplexity’s head of design that “you don’t really know the output. The only way to actually design for the output is to try to build enough outcomes, potential outcome surfaces that make sense.” This leads to a future where “that whole era of A to B testing where your designers and your developers build three different flows and spend six weeks testing which one performed better” gets replaced by “A-to-Z testing. You’re going to have a million variants constantly flowing.” The vision is “every single user will have software built exactly for how they want to use it” by 2030, enabled by AI that understands individual preferences and can adapt interfaces in real time.

How product roles evolve: from screen designers to system architects

The transformation from deterministic to non-deterministic software requires new types of product thinking. John sees “design engineers” emerging as tools merge: “every tool is merging into the same idea of you design and code in the same interface.” But the deeper change is conceptual: “You’re going to be the architect of outcomes and intents” rather than someone who builds specific screens or workflows. The new role requires “systematic thinkers” who can “build a million different components, a million different outputs so that the system can go and interact with your brand and your software in the way that is best suited for the user” rather than forcing everyone through the same predetermined flow.

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John Allen - CEO & Co-Founder of Layo

Double Diamond