We are in the cusp of structural changes about how technology is built, used, and experience. As large-scale language models (LLMs) become widely available, they are often open source, commoditized and deeply integrated. It's no longer an algorithm or model that gives you an edge. It's the experience you build around it.
Intelligence as an Infrastructure
Over the past few years, billions of dollars and immeasurable calculation power have led to training LLMS using vast public data. However, the frontier of artificial intelligence (AI) is changing. The next breakthrough driven by synthetic data and large inference workloads is expensive, with basic model training becoming the domain of a small number of global players.
Fewer teams will focus on training basic models. The majority innovate the post-training build optimization, workflow and intelligence layers upwards. In this new era, LLMS works like a utility.
What makes this shift even deeper is the fact that it is very easy to interact with this technology. You just speak in your own language and don't have to code or configure it. And the model remains. When execution is commoditized, the real differentiator is not how to solve problems, but what problems to solve, if you choose to solve them, and whether there is clarity and belief that will focus on where it really matters.
When the mort disappears
The customer experience is always important. But in the past, they shared stages with other defensible moats: their own technology, execution speed, distribution networks, and economies of scale. Companies can control through a combination of IP, funds, partnerships, or operational excellence, even if their user experience (UX) is not in their best class.
However, in the LLMS era, the equation changed fundamentally. Technology is no longer a moat. Found models are widely available, commoditized, and offer the same baseline for all. The execution has also been flattened by AI. This automates everything from code and content to design and marketing, allowing even lean teams to move the pace. AI-native interfaces like chat and voice are inherently viral and platform dependent, and distribution also loses its advantage.
This leaves one sustainable benefit behind. It's experience. And it's not just about ease of use, but how the product understands, adapts and acts on behalf of the user.
Why the interface wins
What's particularly impressive about the success of platforms like ChatGpt, Deepseek, Cursor, Lovable, Manus is that the underlying AI capabilities were not fundamentally different.
Often they were thin wrappers of the same foundation model. Still, they achieved a massive adoption. Key driver? Interface lock-in. These products have built an intuitive, user-centric interface on top of powerful but widely accessible technology.
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ChatGPT's mobile app has put LLM technology into millions of users. Its advanced audio mode took it a step further, removing the friction of typing and making the experience feel natural, conversational and intuitive.
The cursor embeds AI directly into the coding environment, Integrated Development Environment Where you can infer, refactor and collaborate your code. Lovable did the same with product development by allowing you to create full stack apps with no code and just explaining.
These products did not rely on flashy marketing campaigns. They let them talk about their experiences. They relied on lock-in on the interface. The idea is that a product becomes essential just because it understands the user better than anything else. That's the new competitive advantage.
From transaction to conversation
Most apps today are designed around transactions, such as searching, comparing, booking, and more. Users are expected to know what they want, where to click, and how to get there. The interface revolves around a static entry point. A set of options neatly laid out on the homepage, burger menus and on-screen options. The user is in the driver's seat and navigates the fixed structure to complete the task.
However, as AI capabilities evolve, this mental model begins to break. Tomorrow's app won't just respond. They talk. They don't just present options. They understand the context. The interface shifts from static to adaptive, from menu-driven to intentional-driven. They are audio-first, visually rich and deeply integrated throughout the surface.
Most importantly, the UX is becoming increasingly invisible. Instead of users who overwhelm all possible actions in advance, the interface transforms based on their needs. Buttons, prompts, and suggestions only surface when they are relevant and disappear if they are not. The app itself becomes a living respiratory interface fluid, which is predictive and responsive at the moment.
In this world, users don't need to learn how to use the app. Apps learn how to provide services to users.
Ultra personalization through memory
Memory is the second pillar of the next generation of customer experience. AI tools are increasingly designed to keep conversations going. It's about collecting signals, not just engagement. All queries, all preferences, all actions become data points in the persistent user profile.
Ethically and with user consent, this long-term memory creates compound interest benefits. An app that remembers who you are, what you like, how you behave, and how you behave, can quickly predict your needs before you clarify them. This predictive layer is not only a personalized marketing, but also a personalized experience, but also the most powerful form of personalization we have yet to see.
This unlocks true personalizations as well as “users who also bought B” as well as co-filtering such as “often taking beach vacations with minimal travel.” Here you will find direct flights and hotels with children's clubs and a pool with slides. The more you know the system, the more convenient and sticky it becomes.
The more you use the app, the more you will know you. The more you know you, the more persuasive the value proposition will be and it will be difficult to leave.
Agent Experience: It's more than just suggestions
Finally, the most powerful change we are trying to witness is from recommendation to action. AI-driven apps don't just need to suggest or advise. They will do something for you. This is a leap from support to agency.
I don't talk when ticket prices are low, but I will book for you on your preferred airline, use credit card points, fill in passport details, check in and send a boarding pass. Or use a health app that not only does not display lab results, but also schedule follow-up consultations, book CABs, set reminders, and email insurance providers.
These agent applications will process your research, make decisions for you, complete your purchases, make calls, make reservations. Agent AI is the true holy grail. And anyone who builds the most reliable and intuitive and trustworthy agents for each domain, whether it's travel, health, finance, education, etc., wins.
The real moat is experience
In the age of LLMS, access to intelligence is no longer a bottleneck. Experience is like that. The winners are not those with the best model weights, but they become invisible through beautiful interfaces, persistent memory, seamless autonomy and deep empathy for the user journey.
When users return to the product, it is not because of the weight of your model. It's because of how the product makes them feel understood, helped, empowered and reassuring. In a world where everyone can access the same model, the best interface and the most human, predictive and agent experience win.
The playbook has been changed. The new moat is not an algorithm. That's experience.
