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When I think about the rise of AI, I'm reminded by the rise of literacy. A few hundred years ago, many people in society thought that maybe not everyone needed to be able to read and write. Back then, many people were tending fields or herding sheep, so maybe there was less need for written communication. All that was needed was for the high priests and priestesses and monks to be able to read the Holy Book, and the rest of us could just go to the temple or church or the holy building and sit and listen to the high priest and priestesses read to us. Fortunately, it was since figured out that we can build a much richer society if lots of people can read and write.
Today, AI is in the hands of the high priests and priestesses. These are the highly skilled AI engineers, many of whom work in the big tech companies. Most people have access only to the AI that they build for them. I think that we can build a much richer society if we can enable everyone to help to write the future.
But why is AI largely concentrated in the big tech companies? Because many of these AI projects have been expensive to build. They may require dozens of highly skilled engineers, and they may cost millions or tens of millions of dollars to build an AI system. The large tech companies, particularly the ones with hundreds of millions or even billions of users, have been better than anyone else at making these investments pay off because, for them, a one-size-fits-all AI system, such as one that improves web search or that recommends better products for online shopping, can be applied to these very large numbers of users to generate a massive amount of revenue. But this recipe for AI does not work once you go outside the tech and internet sectors to other places where, for the most part, there are hardly any projects that apply to 100 million people or that generate comparable economics.
Let me illustrate an example. Many weekends, I drive a few minutes from my house to a local pizza store to buy a slice of Hawaiian pizza from the gentleman that owns this pizza store. His pizza is great, but he always has a lot of cold pizzas sitting around, and every weekend some different flavor of pizza is out of stock. But when I watch him operate his store, I get excited, because by selling pizza, he is generating data. This is data that he can take advantage of if he had access to AI. AI systems are good at spotting patterns when given access to the right data, and perhaps an AI system could spot if Mediterranean pizzas sell really well on a Friday night, maybe it could suggest to him to make more of it on a Friday afternoon.
You might say to me, "Hey, Andrew, this is a small pizza store. What's the big deal?" And I say to you, to the gentleman that owns this pizza store, something that could help him improve his revenues by a few thousand dollars a year will be a huge deal. I know that there is a lot of hype about AI's need for massive data sets, and having more data does help. But contrary to the hype, AI can often work just fine even on modest amounts of data, such as the data generated by a single pizza store. So the real problem is not that there isn't enough data from the pizza store. The real problem is that the small pizza store could never serve enough customers to justify the cost of hiring an AI team. I know that in the United States there are about half a million independent restaurants. Collectively, these restaurants do serve tens of millions of customers. But every restaurant is different, with a different menu, different customers, and different ways of recording sales, so no one-size-fits-all AI would work for all of them.
What would it be like if we could enable small businesses, and especially local businesses, to use AI? Let's take a look at what it might look like at a company that makes and sells T-shirts. I would love for an accountant working for the T-shirt company to use AI for demand forecasting. Say, figure out what funny memes to print on T-shirts that would drive sales, by looking at what's trending on social media. Or for product placement, why can't a front-of-store manager take pictures of what the store looks like, show them to an AI, and have the AI recommend where to place products to improve sales? For supply chain, can an AI recommend to a buyer whether or not they should pay 20 dollars per yard for a piece of fabric now, or if they should keep looking because they might be able to find it cheaper elsewhere? Or for quality control, a quality inspector should be able to use AI to automatically scan pictures of the fabric they use to make T-shirts to check if there are any tears or discolorations in the cloth.
Today, large tech companies routinely use AI to solve problems like these, and to great effect. But a typical T-shirt company, a typical auto mechanic, retailer, school, or local farm will be using AI for exactly zero of these applications today. Every T-shirt maker is sufficiently different from every other T-shirt maker that there is no one-size-fits-all AI that will work for all of them. In fact, once you go outside the internet and tech sectors in other industries, even large companies such as the pharmaceutical companies, the car makers, and the hospitals also struggle with this. This is the long-tail problem of AI. If you were to take all current and potential AI projects and sort them in decreasing order of value and plot them, you get a graph that looks like a curve. Maybe the single most valuable AI system is something that decides what ads to show people on the internet. Maybe the second most valuable is a web search engine, and the third most valuable is an online shopping product recommendation system. But when you go to the right of this curve, you get projects like T-shirt product placement, T-shirt demand forecasting, or pizzeria demand forecasting. Each of these is a unique project that needs to be custom-built. Even T-shirt demand forecasting, if it depends on trending memes on social media, is a very different project than pizzeria demand forecasting, which depends on the pizzeria's sales data. So today there are millions of projects sitting on the tail of this distribution that no one is working on, but whose aggregate value is massive.
So how can we enable small businesses and individuals to build AI systems that matter to them? For most of the last few decades, if you wanted to build an AI system, you had to write pages and pages of code. I would love for everyone to learn to code, and online education as well as offline education are helping more people than ever learn to code. Unfortunately, not everyone has the time to do this. But there is an emerging new way to build AI systems that will let more people participate. Just as pen and paper, which are a vastly superior technology to stone tablet and chisel, were instrumental to widespread literacy, there are emerging new AI development platforms that shift the focus from asking you to write lots of code to asking you to focus on providing data. This turns out to be much easier for a lot of people to do. Today, multiple companies are working on platforms like these. Let me illustrate a few of the concepts using one that my team has been building.
Take the example of an inspector wanting AI to help detect defects in fabric. An inspector can take pictures of the fabric and upload them to a platform like this, and can show the AI what tears in the fabric look like by drawing rectangles. They can also show the AI what discoloration on the fabric looks like by drawing rectangles. These pictures, together with the green and pink rectangles the inspector has drawn, are data created by the inspector to explain to the AI how to find tears and discoloration. After the AI examines this data, we may find that it has seen enough pictures of tears, but not yet enough pictures of discolorations. This is akin to a junior inspector who has learned to reliably spot tears but still needs to hone their judgment about discolorations. So the inspector can go back and take more pictures of discolorations to show the AI, helping it deepen this understanding. By adjusting the data you give to the AI, you can help the AI get smarter. An inspector using an accessible platform like this can, in a few hours to a few days, and with a suitable camera setup, build a custom AI system to detect defects, tears and discolorations, in all the fabric being used to make T-shirts throughout the factory.
You may say, "Hey, Andrew, this is one factory. Why is this a big deal?" I say to you, this is a big deal to that inspector whose life this makes easier. Equally, this type of technology can empower a baker to use AI to check the quality of the cakes they're making, an organic farmer to check the quality of the vegetables, or a furniture maker to check the quality of the wood they're using. Platforms like these will probably still need a few more years before they're easy enough for every pizzeria owner to use. But many of these platforms are coming along, and some of them are already quite useful to someone who is tech savvy today, with just a bit of training.
What this means is that, rather than relying on the high priests and priestesses to write AI systems for everyone else, we can start to empower every accountant, every store manager, every buyer and every quality inspector to build their own AI systems. I hope that the pizzeria owner and many other small business owners like him will also take advantage of this technology, because AI is creating tremendous wealth and will continue to create tremendous wealth. It's only by democratizing access to AI that we can ensure this wealth is spread far and wide across society. Hundreds of years ago, I think hardly anyone understood the impact that widespread literacy would have. Today, I think hardly anyone understands the impact that democratizing access to AI will have. Building AI systems has been out of reach for most people, but that does not have to be the case. In the coming era of AI, we'll empower everyone to build AI systems for themselves, and I think that will be an incredibly exciting future. Thank you very much.