Expensive to build and often needing highly skilled engineers to maintain, artificial intelligence systems generally only pay off for large tech companies with vast amounts of data. But what if your local pizza shop could use AI to predict which flavor would sell best each day of the week?
When I think about the rise of artificial intelligence (AI), I’m reminded of the historical rise of literacy. A few hundred years ago, many believed that not everyone needed to read or write. People who worked in fields or herded sheep had little need for written communication. The belief was that only high priests, priestesses, and monks needed to read the Holy Book, while the rest of us could rely on them to interpret it for us.
Fortunately, society eventually realized that a much richer world could be built if more people were literate. Today, AI is in the hands of a select few—highly skilled AI engineers, many of whom work in big tech companies. Most people only have access to the AI systems these experts create. However, just as with literacy, I believe we can build a richer society by enabling everyone to help write the future.
AI projects are often concentrated in big tech companies due to the immense resources required to build them. These projects typically need dozens of highly skilled engineers and can cost millions or even tens of millions of dollars. Large tech companies, especially those with hundreds of millions or even billions of users, have been the most successful at making these investments pay off. For them, a one-size-fits-all AI system, such as one that improves web search or recommends better products for online shopping, can generate massive amounts of revenue by being applied to a large user base.
However, this approach does not work as effectively outside the tech and internet sectors. Many industries do not have projects that apply to 100 million people or generate comparable economics.
Take, for example, a local pizza store owner. Every weekend, I visit a nearby pizza store that offers great food but struggles with inventory management—often running out of certain pizza flavors and having too many cold pizzas sitting around. This small business generates data that could be incredibly valuable if leveraged correctly with AI.
AI systems excel at spotting patterns when provided with the right data. For instance, if AI could help the pizza store owner identify which types of pizza sell best on a Friday night, it could suggest making more of those on Friday afternoons. While this may seem insignificant to some, it could make a big difference to the store owner, potentially increasing his revenue by thousands of dollars annually.
The problem is not the lack of data but the fact that small businesses like this pizza store cannot afford to hire an AI team to analyze their data. There are half a million independent restaurants in the United States, each serving a different menu, attracting different customers, and recording sales differently. No one-size-fits-all AI would work for all of them.
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Imagine if small businesses, like a T-shirt company, could use AI to forecast demand, optimize product placement, or ensure quality control. Today, large tech companies use AI to solve these kinds of problems effectively, but a typical small business does not. The variety among businesses, even within the same industry, means that each AI application would need to be custom-built.
This is the long-tail problem of AI. If you were to sort all current and potential AI projects by their value, you’d see that while the most valuable AI systems—like ad recommendation engines—are well-served, there are millions of smaller, unique projects with substantial aggregate value that no one is working on.
In the past, building an AI system required writing extensive code—a task that not everyone has the time or skills for. However, new AI development platforms are emerging that focus on providing data rather than writing code. These platforms allow people to teach AI systems by feeding them data, making AI development accessible to more people.
For example, an inspector at a T-shirt factory could take pictures of fabric and teach the AI to detect tears or discolorations by highlighting these defects in the images. By adjusting the data provided to the AI, the inspector can help the system improve its accuracy over time.
Platforms like these are still in their early stages and may take a few more years before they’re easy enough for everyone to use. However, they hold the promise of empowering every accountant, store manager, buyer, and quality inspector to build their own AI systems. This democratization of AI could spread the wealth generated by AI far and wide across society.
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Just as widespread literacy transformed society hundreds of years ago, democratizing access to AI could have a profound impact on our future. Building AI systems has been out of reach for most people, but this does not have to be the case. In the coming era of AI, we have the opportunity to empower everyone to build AI systems for themselves, creating an exciting and inclusive future.
