As Anthropic and OpenAI clash over Pentagon issues, Cowork capabilities, and model releases, it’s important to remember that technology is not the goal. It is a means to an end. Its value comes from helping people solve everyday problems and giving them one less thing to think about, on a global scale.
However, people must first realize there is a problem and understand how technology can solve it before AI can make a significant difference.
When things fall into place, it’s always a matter of consumer education, which leads to expanded adoption, which in turn leads to society-wide impact (in that order). Each step can happen quickly, or take months or years to complete.
This pattern (first education, then adoption, and finally transformation) is repeated in all sectors. It is also a story as old as (human) time.
The lesson from past cycles like cloud and mobile web: the best AI-powered systems won’t be the ones with the highest total investments or the most bells and whistles; They will be the ones with technology that relentlessly makes real-world processes faster, cleaner, cheaper and more resilient.
Technology adoption at scale is not an overnight phenomenon; It is a sign that technology has crossed the threshold from curiosity about “what is new” to the daily driver. AI has tremendous potential, but we in the technology world have lost our way in achieving it. affair for real people. I have been fortunate to see this cycle play out several times in my career.
For example, when I worked on the first iPhone, it was impossible to predict a future driven by dating apps, ride-sharing, mobile payments, or social media. Now, it’s hard to remember a time before we could manage our lives through our phones. Our breakthrough was to create an ecosystem once the technology was powerful enough. and the world was ready. Thanks to the backbone we created, new platforms have emerged that allow people to leave their wallets at home and pay conveniently through their phones, or tap a button to get transportation.
Once consumers realized the power and ease of solving real-world problems with just a swipe or touch of their fingertips, adoption took off like wildfire.
The same principle was applied when we built the first Nest thermostat. From the beginning, the goal was to apply technology to make energy more efficient, both in terms of capacity and cost, for homes and ordinary people. We talked about building AI-powered devices that could understand human behavior and adapt accordingly. We had the vision, but we needed AI to move forward and make progress technologically possible and practical for development.
For example, a popular and seemingly humble feature like package detection on a Nest doorbell camera took almost a year to develop. The models were heavy. The hardware was limited. The development cycles were long. We were crawling towards a clear goal with hundreds of obstacles in our way.
As we refined computer vision over more than a year, consumers understood the problem Nest was solving and how adopting the system would help them reduce utility costs while using energy more efficiently. It was at this point that transformation to scale could (and did) occur.
But to scale meaningfully it takes more than just shipping innovation and driving updates to consumer devices. It is necessary to combine the latest technology with the level of consumer interest to solve the problems we face every day.
At Mill, the food recycling company I now run, we started by focusing on households, helping people manage food scraps and return them to the food system, one kitchen at a time. That phase mattered. Education that leads to behavior change always comes first. People must realize that there is a system-wide problem and understand why it exists before technology can help solve it.
Food waste, for example, is an industrial problem. Grocery stores throw away millions of pounds of food every day. Behind every supermarket is a loading dock full of garbage containers and compactors that consume space, energy and labor, and these valuable resources end up being sent to a purgatory of methane production.
Developing an enterprise-grade AI-powered food waste system at Mill and seeing it adopted by major players like Amazon and Whole Foods Market is a sign that we have entered a new phase. It’s clear that reducing food waste isn’t just about boosting individual habits. It’s not just about putting last night’s lasagna in the right container. It’s about removing entire classes of waste from the system.
Artificial intelligence makes this possible, not because it is flashy, but because it is finally reliable, affordable and fast enough to operate at scale in the physical world.
With the iPhone, smart devices like Nest, and now AI, perspective matters. But above all, technology leaders must keep in mind that we must solve real problems, not generate technology for technology’s sake.
Progress in this physical age of AI requires logic and restraint as much as ambition. Fortunately, we’ve been here before. There was speculation that the Internet would become a Wild West of lawless virtual worlds and digital avatars. It became a functional tenant of the digital society, based on email, maps, commerce and communication: mundane tools that solved everyday problems on an unprecedented scale.
The story of AI’s next chapter is full of precedents. The enthusiasm will fade. Models will be commoditized. Launches will be quieter. What we will hopefully be left with are AI-powered systems that work to solve everyday problems while improving life in the physical world, rather than distracting us from it.