AI is the new 'Mobile First'
The history of digital interaction is marked by profound shifts that fundamentally change how users engage with technology. We're witnessing one of these pivotal moments right now, and many companies are missing it entirely.
When the web first emerged, we built experiences around a simple assumption: users sat at desks, staring at large monitors, navigating with mice and keyboards. This wasn't just a technical constraint—it shaped our entire understanding of digital interaction. We designed sprawling interfaces, complex navigation systems, and feature-rich dashboards because we could. Users had time, space, and the right tools to engage with comprehensive digital experiences.
Web 2.0 represented our first major evolution in this paradigm. We recognized that users wanted more intuitive, sophisticated experiences. We moved beyond static pages to dynamic, interactive applications. Ajax enabled seamless updates without page refreshes. Social features transformed isolated websites into interconnected platforms. Yet fundamentally, we were still designing for the same context: users with dedicated time and attention, sitting at computers with full-sized displays and precise pointing devices.
Then mobile arrived and shattered every assumption we held about digital interaction.
The disruption wasn't just about smaller screens or touch interfaces—though those changes were significant. The real transformation was contextual. Suddenly, users were engaging with digital services while walking down the street, waiting in line, sitting on buses, or lying in bed. They had divided attention, limited time, and completely different needs. The interaction model shifted from deliberate, focused sessions to quick, purposeful micro-interactions scattered throughout the day.
Most importantly, users didn't want to accomplish the same tasks on mobile that they did on desktop. They wanted different value propositions entirely. Mobile users needed directions, not detailed maps to study. They wanted to quickly check account balances, not analyze comprehensive financial reports. They needed to respond to urgent messages, not compose lengthy emails.
Companies that thrived in the mobile transition understood this fundamental shift. They didn't simply shrink their desktop experiences to fit smaller screens. Instead, they completely rethought what users would want to accomplish in mobile contexts and rebuilt their value propositions accordingly. Instagram didn't try to recreate Photoshop for phones—they recognized that mobile users wanted to quickly capture and share moments with minimal friction.
The companies that struggled with mobile transition made a predictable mistake. They took their existing desktop websites and crammed them into mobile apps without reconsidering the underlying value equation. They assumed users would adapt to touch interfaces that were clearly designed for mouse interaction. They wondered why conversion rates plummeted and engagement suffered, not recognizing that they had fundamentally misunderstood the new interaction paradigm.
We're experiencing an equally profound shift today, and the parallels are striking. AI is becoming the new mobile-first moment, and many companies are repeating the same mistakes that characterized the early mobile era.
In an AI-first world, users don't want to interact with your service by touching glass or clicking buttons. They want to speak to ChatGPT or Claude, describe what they need in natural language, and have the AI agent figure out how to accomplish their goals using your service. The primary interface isn't your carefully crafted user experience—it's a conversational interaction with an AI that can access and manipulate your services on the user's behalf.
This represents a fundamental shift in the interaction model. Users are moving from direct manipulation of interfaces to delegation of tasks to intelligent agents. Instead of learning your app's navigation structure, remembering where features are located, and manually executing multi-step processes, users simply describe their desired outcomes and let AI handle the implementation details.
Yet most companies are responding to this shift by embedding AI features into their existing web and mobile applications. They're building AI-powered chatbots within their apps, adding smart suggestions to their interfaces, and using machine learning to personalize user experiences. While these features provide value, they miss the fundamental point: in an AI-first world, users won't engage with these features directly because they won't be using your app at all.
The equivalent of building a mobile app in today's AI-first transition is implementing an MCP (Model Context Protocol) server for your service. MCP servers enable AI agents to interact with your application programmatically, accessing your data and functionality through structured interfaces designed specifically for AI consumption.
When you build an MCP server, you're essentially creating a new interface layer that allows AI agents to become power users of your service. Users can accomplish complex workflows entirely through conversational interfaces with AI, which then coordinates with your systems behind the scenes to deliver the requested value.
This approach offers three immediate strategic advantages that parallel the benefits early mobile adopters experienced.
First, building an MCP server forces you to understand the core value you actually provide, stripped of the interface complexity that often obscures it. When you design APIs for AI consumption, you must clearly articulate what your service does, what data it provides, and what actions it enables. This clarity often reveals opportunities to simplify or enhance your core value proposition that weren't apparent when that value was buried within complex user interfaces.
Second, enabling AI-first interactions early allows you to observe how users actually want to engage with your service in this new modality. The requests that come through AI agents reveal user intent more directly than traditional interface analytics. You can see what users are trying to accomplish, how they describe their needs, and where your current capabilities fall short. This intelligence should fundamentally reshape your product roadmap, just as mobile usage patterns forced companies to reprioritize features based on new contexts and use cases.
Third, implementing MCP server capabilities requires your team to develop genuine competency in working with AI systems. This isn't just about understanding APIs or data formats—it's about grasping how AI agents think, what information they need to be effective, and how to structure interactions that feel natural from the user's perspective. Teams that develop this competency early will have significant advantages as the AI-first transition accelerates.
This doesn't mean you should abandon innovation in traditional interfaces. During the mobile transition, successful companies didn't stop improving their desktop experiences entirely. However, they fundamentally reoriented their priorities around mobile-first thinking, which forced them to reevaluate every feature, workflow, and assumption about user behavior.
The shift to AI-first requires a similar reorientation. We need to enable native AI interactions as quickly and comprehensively as possible, then observe how this new interaction model takes us in unexpected directions. Just as mobile usage patterns revealed entirely new categories of user needs, AI-mediated interactions will likely surface value propositions we haven't yet imagined.
The companies that recognize this shift early and build robust MCP server implementations will be positioned to capture value as users increasingly delegate complex tasks to AI agents. Those that continue focusing primarily on traditional interface innovation risk becoming irrelevant as users migrate to AI-first interaction patterns.
The transition is already underway. The question isn't whether AI will become the primary interface layer—it's whether your organization will adapt quickly enough to thrive in this new paradigm.