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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, commoditised, 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 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 optimisations,smaller custom models, 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 commoditised, 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 including proprietary technology, execution speed, distribution networks, and economies of scale.

Companies could dominate through a combination of IP, funding, partnerships, or operational excellence – even if theirUX 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 commoditised, 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 IDE (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 driverʼs seat, navigating a fixed structure to complete a task.

But as AI capabilities evolve, this mental model will begin to break.

Tomorrowʼs apps wonʼt just respond-they will converse. They wonʼt just present options-theyʼll 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, theUXwill 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-Personalisation 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 personalisation weʼve seen yet, not just personalised marketing, but personalised experience.

This unlocks true personalisation, not just collaborative filtering like “users who bought A also bought B,ˮ but “you often take beach vacations with minimal travel, hereʼs 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 we’re 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 you’re 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 wonʼt 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, itʼs 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.

The post In the Age of AI, Experience Is The Real Product appeared first on Inc42 Media.

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