AI Native Applications.

While many companies remain stuck in their digital transformation processes under the imposed Digital Native mantra or are deeply engaged in modernizing their legacy systems into Cloud Native solutions, the time has come to rethink everything under a new paradigm: AI Native.
Back in 2016, Sundar Pichai (CEO of Google), in his first annual letter to his team, wrote:

“Over time, the computer itself—whatever its form factor—will be an intelligent assistant helping you through your day.
We will move from mobile first to an AI first world.
Most of these computing experiences are very likely to be built in the cloud.”

In this letter, he already predicted how AI would take control of our daily lives and how cloud computing would be its enabler.
In fact, a year later, at Google I/O 2018, Pichai introduced Google Duplex with a demo that left the world speechless: a virtual assistant that could call a hair salon on your behalf and autonomously book an appointment.

However, despite this demo and the fact that artificial intelligence has been with us for several decades, its real and productive application has been more gradual. These impressive advances, where major AI players dazzled with demonstrations or in-depth articles, often remained as lab material that still needed to mature before becoming stable enough to be applied in the real world.

But that wait is coming to an end. The digitalization and hyperconnectivity brought by the omnipresent smartphone have contributed to the mass digitization of data. The evolution of the cloud has provided unimaginable computing power, global reach, and scalability. All of this has created the perfect environment for AI to mature to the point of being considered stable for solving multiple types of problems.

If we add the revolution of LLMs (popularized by ChatGPT), we find that complex and expensive AI systems are no longer required to develop applications that were once exclusive to a privileged few. Now training specific models for each need is no longer necessary.

As shown in the graph, this entire evolution has been taking shape over the past decades. Just as generations have been labeled based on the era of the PC, the Internet, or the Smartphone, I am sure that in a few years, everyone will remember this generation as the ones born in the era of ChatGPT—the era of the AI Natives.

This is a generational shift with countless aspects to discuss, but today, I want to focus on something much more specific: my vision of how the new digital solutions demanded by this generation will evolve—what we call AI Native Solutions.

What Does It Mean to Be "AI Native"?

“AI-Native applications are designed and built from the ground up with AI as an integral part of their core functionality.”

An "AI Native" application is one that, from its inception, is designed with AI as a central and indispensable component, meaning that it could not exist without it. It is not about adding an AI layer to an existing product but rather about building a digital solution where AI is the driving force behind all its functionalities.

I believe we have experienced similar changes before, and this one, in particular, reminds me a lot of the introduction of the smartphone, especially since the launch of the iPhone in 2007. Suddenly, a vast array of new capabilities was available to users: browsing the web from anywhere, touchscreens, access to cameras, GPS, and more. At the time, we experienced a transitional phase with "responsive" designs that attempted to adapt web pages to small screens, but soon we realized that the changes were much deeper. We had to completely redefine the customer experience. The same is happening now with the new capabilities AI is providing.

What Pillars Support an AI Native Solution?

• New Capabilities
• Generative UIs
• Agentic AI

New Capabilities

The emergence of ChatGPT represented a qualitative leap in AI's ability to engage in human conversations, but there is much more behind it. It was a step toward AGI (Artificial General Intelligence).

Where multiple models trained specifically for certain tasks were previously needed, now, with a single model, we can request multiple tasks:

• Generate content (text, audio, image, video) from any description, with rules and reasoning.
• Extract and enrich data from text, PDFs, videos, audio, etc.
• Assist in decision-making based on contexts with multiple nuances and subjectivities, previously only assessable by a human.
• Create highly personalized experiences based not only on historical data but also on the current context.

Now, when developing an application, we need to shift our mindset about what we can offer users. Things that were once highly complex or costly are now much easier to implement.

Imagine creating a travel website with a search engine capable of handling a query like: “I want a vacation in a place where I can swim in December, a plan for New Year's Eve, and where they speak Spanish.” Technologically, building this with traditional search capabilities would be extremely complex. However, I tested this exact query on Gemini and ChatGPT, and they provided perfect results that could be integrated into a product.

Generative UIs

We are already seeing how design teams need to shift their mindset. Instead of designing the best possible flow for most users, they will move toward creating objectives and guardrails so that AI generates the best interface at that moment, tailored to that person, considering their history and current context.

There are already examples in Call Center environments, where customer service interfaces dynamically adapt based on input information or even real-time conversations.

Similar to the mobile era analogy, I believe we are still in the "responsive" phase, meaning a transition stage. Over the next few years, we will likely see hybrid interfaces where AI generates small customized interface blocks.

Looking ahead, I imagine this will gradually lead to highly customized screens generated by AI, where the number of possible combinations will be enormous.

Agentic AI

The new buzzword, Agentic AI, refers to AI systems with a degree of autonomy, capable of making decisions, planning actions, and learning from experiences to achieve specific objectives without constant human intervention.

Again, a mindset shift is required, as there are now multiple actions where AI can automate work on our behalf. This is redefining processes, especially in the corporate world.

We Are Living in Times of Change…

Change is here—the "Digital Natives" generation is giving way to the "AI Natives," and we must adapt once again.

There are still many unanswered questions: Which use cases is AI already mature for, and which still require time? What barriers are companies facing in adopting this approach? What kinds of digital products will this new generation of “AI Natives” demand?

At Paradigma, we are continuously building and adapting this vision, as the speed at which this technology is evolving is truly astonishing. What seems impossible today may become feasible within a month. Where today we need to set up a relatively complex system to enable a specific use case, tomorrow it might be available as a service. As a technologist, I can think of few moments in my career as fascinating (and equally stressful) as what we have been experiencing since 2022. One thing is certain, this marks a clear before and after in the world of technology.