In the age of AI, experience is the real product

We are at the cusp of a tectonic shift in how technology is built, used and experienced. As large language models (LLMs) become widely available—often open-source, commoditized and deeply integrated—intelligence itself is becoming a utility. It is no longer the algorithm or model that gives you an edge. It’s the experience you build around it. 

Intelligence as infrastructure

Over the past few years, billions of dollars and immense compute power have gone into training LLMs using vast swathes of public data. But the frontier of artificial intelligence (AI) is shifting. The next breakthroughs driven by synthetic data and massive inference workloads will be expensive, making foundational model training the domain of a few global players.

Fewer teams will focus on foundational model training; the majority will innovate through post-training-building optimizations, workflows and intelligence layers on top. In this new era, LLMs function more like a utility—fast, cheap and universally available. 

What makes this shift even more profound is that interacting with this technology is remarkably simple. You donʼt need to code or configure—you just speak, in your own language. And the model does the rest. As execution becomes commoditized, the real differentiator is no longer how you solve problems but which problems you choose to solve, when you choose to solve them and whether you have the clarity and conviction to focus where it truly matters.

When moats disappear

Customer experience has always been important. But in the past, it shared the stage with other defensible moats: proprietary technology, execution speed, distribution networks, economies of scale. Companies could dominate through a combination of IP, funding, partnerships or operational excellence, even if their user experience (UX) wasnʼt best-in-class.

But in the era of LLMs, that equation has fundamentally changed. Technology is no longer a moat—foundation models are widely available and commoditized, offering the same baseline to all. Execution, too, has been flattened by AI, which now automates everything from code and content to design and marketing, allowing even lean teams to move at pace. Distribution is also losing its edge, as AI-native interfaces like chat and voice are inherently viral and platform-agnostic. 

This leaves one sustainable advantage: experience. And it’s not just about usability, it’s about how deeply a product understands, adapts to and acts on behalf of its users.

Why the interface wins

What’s particularly striking about the success of platforms like ChatGPT, DeepSeek, Cursor, Lovable and Manus is that their underlying AI capabilities weren’t radically different.

In many cases, they were simply thin wrappers over the same foundational models. And yet, they achieved massive adoption. The key driver? Interface lock-in. These products built intuitive, user-centric interfaces on top of powerful but broadly accessible technology—turning technical parity into market leadership.

ChatGPT’s mobile app brought LLM technology into the hands of hundreds of millions of users. Its advanced voice mode took things a step further, removing the friction of typing and making the experience feel natural, conversational and intuitive.

Cursor reimagined the coding experience by embedding AI directly into the coding environment, turning the integrated development environment into a place where code can be reasoned about, refactored and generated collaboratively. Lovable did the same for product development by letting you create full-stack apps just by describing them without using code.

These products didn’t rely on splashy marketing campaigns. They let the experience speak. They relied on interface lock-in: the idea that a product becomes indispensable simply because it understands the user better than anything else. That is the new competitive advantage.

From transactional to conversational

Most apps today are still designed around transactions—search, compare, book. The user is expected to know what they want, where to click and how to get there. Interfaces revolve around static entry points: a homepage, a hamburger menu and a series of options neatly laid out on a screen. The user is in the driver's seat, navigating a fixed structure to complete a task.

But as AI capabilities evolve, this mental model will begin to break. Tomorrow's apps won't just respond—they will converse. They won't just present options—they'll understand context. Interfaces will shift from being static to adaptive, from menu-driven to intent-driven. They'll be voice-first, visually rich and deeply integrated across surfaces.

Most importantly, the UX will become increasingly invisible. Instead of overwhelming users with all possible actions up front, the interface will metamorphose based on need. Buttons, prompts and suggestions will surface only when relevant and disappear when not. The app itself will become a living, breathing interface-fluid, anticipatory and responsive to the moment.

In this world, users wonʼt need to learn how to use the app. The app will learn how to serve the user.

Hyper-personalization through memory

The second pillar of next-gen customer experience is memory. AI tools are increasingly being designed to keep the conversation going—not just to engage but to collect signals. Every query, every preference, every action becomes a data point in a persistent user profile.

Done ethically and with user consent, this long-term memory creates a compounding advantage. Apps that remember who you are, what you like and how you behave will soon be able to anticipate your needs before you even articulate them. This predictive layer will power the most powerful form of personalization we've seen yet, not just personalized marketing but personalized experience.

This unlocks true personalization, not just collaborative filtering like “users who bought A also bought B” but “you often take beach vacations with minimal travel, here's a direct flight and a hotel with a kidsʼ club and a pool with slides.” The more the system knows, the more useful and sticky it becomes.

The more you use the app, the more it gets to know you. The more it knows you, the more compelling the value proposition and the harder it becomes to leave.

Agentic experiences: Doing, not just suggesting

Finally, the most powerful shift weʼre about to witness is from recommendation to action. AI-driven apps wonʼt just suggest or advise; theyʼll do things for you. This is the leap from assistance to agency.

Imagine a travel assistant that doesnʼt just tell you when ticket prices are low but books them for you with your preferred airline, uses your credit card points, fills in your passport details, checks you in and sends you the boarding pass—all while youʼre on a call or at dinner. Or a health app that doesnʼt just show you lab results but schedules a follow-up consultation, books the cab, sets a reminder and emails your insurance provider.

These agentic applications will handle your research, make decisions for you, complete purchases, make phone calls, book appointments and more. Agentic AI is the true holy grail. And whoever builds the most trusted, intuitive and reliable agent for each domain, be it travel, health, finance, education, is going to win.

The real moat is experience

In the age of LLMs, access to intelligence is no longer the bottleneck, experience is. The winners won't be those with the best model weights but those who make those weights invisible through beautiful interfaces, persistent memory, seamless autonomy and deep empathy for the user journey.

When users come back to your product, it's not because of your model weights. It's because of how the product makes them feel understood, helped, empowered and relieved. Because in a world where everyone has access to the same models, the best interface and the most human, anticipatory and agentic experience wins.

The playbook has changed. The new moat is not algorithms; it's experience.