The next phase of AI: technology must solve real-world problems.

While Anthropic and OpenAI are fiercely competing — from projects involving the U.S. Department of Defense to launching new models and developing collaborative tools — it's important to remember that technology isn't the ultimate goal.

 

Technology is merely a tool. Its true value lies in helping people solve everyday problems and reduce their worries on a global scale.

However, before AI can make a real impact, people need to recognize that a problem exists and understand how the technology can help solve it.

When this happens, the change process typically begins with user education, followed by widespread adoption, and finally leads to societal impact. These three steps always occur in the correct order, although sometimes it can take months or even years.

 

The "understand first – apply later – transform finally" model is not unique to the field of AI; it has been repeated many times throughout the history of technology.

Lessons from the era of cloud computing and mobile web

Previous technology cycles, such as cloud computing or mobile web, have shown that the most successful systems are not those that receive the most investment or have the most complex features.

Instead, success often comes to technologies that quietly improve real-world processes, making them faster, simpler, cheaper, and more sustainable.

The widespread adoption of a technology doesn't happen overnight. It's a sign that the technology has moved beyond the initial curiosity about something new and is now a familiar tool in everyday life.

AI has enormous potential, but many people in the tech industry sometimes forget the most important goal: creating real value for users.

 

When smartphones changed the world.

When Apple developed the first iPhone, almost no one could have imagined a future where dating apps, ride-hailing services, mobile payments, or social media would become an integral part of life.

Today, it's hard to recall a time when people couldn't manage almost all aspects of their lives from a phone.

The turning point then lay not just in the device, but in building an ecosystem when the technology was strong enough and society was ready. Thanks to that platform, a host of new services emerged, allowing users to pay with their phones, order a ride with just a tap, or connect with people globally.

 

When users realized that a simple tap or swipe could solve everyday problems, the use of technology experienced explosive growth.

Lessons from smart home devices

The same principle emerged when the research team developed Nest's first smart thermostat.

From the outset, the project's goal was to use technology to help households save energy and reduce electricity costs. The idea was to build AI-powered devices that could understand human habits and automatically adjust their operation accordingly.

However, to turn that vision into reality, AI technology needs to develop sufficiently strongly.

Even a seemingly simple feature like detecting packages at the front door on Nest's smart doorbell took almost a year to perfect. At that time, AI models were still bulky, hardware was limited, and development was slow.

However, as computer vision technology eventually improved, users began to understand the problem Nest was solving. They realized that the system could help save energy and manage living expenses more effectively. That's when the large-scale transformation truly began.

AI must solve specific problems.

For technology to spread, simply releasing products or updating software is not enough. It's crucial to combine cutting-edge technology with the real needs of users.

Currently, the author runs Mill, a food recycling company. The project initially focused on households, helping them process leftover food and return it to the food chain.

 

The first goal is to educate consumers. When people understand that food waste is a major systemic problem, they will be more willing to change their behavior.

In fact, food waste is not just a household problem but an industrial one. Supermarkets discard millions of kilograms of food every day. Behind each supermarket are typically areas filled with trash cans and garbage compactors, consuming space, energy, and manpower—while much of that food ultimately becomes a source of methane emissions.

Mill is developing an AI-powered food waste management system for businesses. The fact that large corporations like Amazon and Whole Foods Market are beginning to adopt this technology indicates that a new era has begun.

Reducing food waste isn't just about changing individual habits. More importantly, it's about eliminating the entire source of waste within the system. AI makes this possible, not because it's flashy, but because the technology is now reliable enough, cheap enough, and fast enough to operate at scale in the real world.

Technology must serve life.

From smartphones and smart home devices to AI, the most important thing is having the right perspective on technology.

Technology leaders need to always remember that the goal is not to create technology for the sake of technology, but to solve real human problems.

In an era where AI is intertwined with the physical world, progress requires not only ambition but also prudence and discipline.

Previously, the internet was predicted to become a "wild west" of virtual worlds and digital identities. But ultimately, it became the foundation of the digital society, with everyday tools like email, maps, e-commerce, and communication—tools that solve daily problems on an unprecedented scale.

The next chapter of AI may well follow a similar path. As the hype subsides, models become more commonplace, and product launches become less flashy, what will likely be quiet AI systems helping humans solve real-world problems, rather than distracting them.