By Christopher Mims

There is a huge gap between what AI can already do today and what most people are actually doing with it.

Closing that gap will take years. Meanwhile, fortunes will be created, not just for giant tech companies, but for the everyday folks who use those companies' AI models to build products and services of their own.

The curious among us are already leading the charge. A goatherd (and software developer) in rural Australia discovered a simple but radical new technique to optimize the performance of the leading software-writing AI. An almost 50-year-old horticulture company in Bakersfield, Calif., is rolling out an AI agent that connects its growers with decades of wisdom from professional agronomists. A copywriter who saw her business decimated by her clients' use of AI pivoted to coaching those same clients on building their own AI tools.

Technological diffusion happens every day as people adopt innovations to suit their personal or business needs. With AI, there's a fresh twist: Today's generative AI is much more accessible than past technologies, and can be used even by nontechnical people. There is no "right way" to use it.

In just over three years, AI usage has gone from almost nil, to something 62% of Americans report using several times a week, according to the Pew Research Center. And while most of that usage is probably relatively basic, awareness of AI has risen to nearly 100%.

This isn't a story of AI turning into a superhuman intelligence that replaces workers. AI remains, primarily, a tool that enhances our existing abilities. But as researchers and users grasp the real-world functionality of today's AI, they're seeing a huge amount of room for productivity and economic growth.

Two years ago, AI chatbots were too finicky and error-prone to be reliable and broadly useful to most people, says Ram Bala, an associate professor of AI at Santa Clara University. Today, he says, they're ready for prime time, because of advances in reducing hallucinations, and in plugging these AI models into other software systems.

Whatever comes next in the development of AI, adoption of existing technologies will snowball well into the next decade.

AI's untapped powers

The biggest AI innovations might come from users at work or at home, rather than tech giants and research labs.

The companies making AI models know this, and are now promoting applications their own users pioneered. For example, OpenAI this week introduced ChatGPT Health to demonstrate its ability to analyze medical records, wellness apps and provider bills in order to improve healthcare outcomes.

Users of Claude Code, Anthropic's software-writing AI system, recently discovered a way to create finished, bug-free programs without human intervention. (One of the originators was the aforementioned Australian goatherd.) The trick: Write a small program that asks the AI, over and over again, to improve the code it has already written. Named the Ralph Wiggum technique, after the dimwitted but persistently optimistic "Simpsons" character, this simple trick is effective at forcing Claude Code to solve problems on its own.

This discovery is a great example of "capability overhang," says Ethan Mollick, a professor of innovation and entrepreneurship at the University of Pennsylvania's Wharton School and a leading authority on generative AI. That's his term for the many new things existing AI can do that were unknown until users discovered them.

"This is a tool that does programming and also writes documents, and it can also do image editing, and also can read Etruscan, and a bunch of other stuff too," says Mollick. Software projects with a narrow audience but a big potential impact might have been shelved for want of money and talent. Now, they can be built by a handful of people, or even just one, with the help of AI, he adds.

AIs play well with others

Many people building with AI are finding that fusing several AI models can yield capabilities well beyond those of a single one. Meta is pursuing Manus, which makes a software "agent" that can produce deeply researched reports and perform other actions online. While Meta has its own AI models, Manus uses a combination, including those from Anthropic and others.

Santa Clara University's Bala, who also heads a company that builds real-world AI applications, is currently working with his team on an app for Sun World, a California developer of new varieties of fruits and vegetables. Farmers who need advice on how best to grow their crops can have natural-language conversations with AI agents preloaded with research and advice from scientific literature and a community of professional agronomists.

While the interface is powered by one of the usual top-tier chatbots, the information it is fed has been predigested and enhanced by other AIs in a process called data enrichment.

The skills required to make this app for agronomists aren't so different from the ones Bala has been using for years, as a data scientist and software engineer. The difference is that now, AI makes individual engineers much more effective, allowing a small team to do something that before would have been nearly impossible even with many times as many resources.

A tech team of one

Before the debut of ChatGPT in November 2022, Leanne Shelton made a comfortable living as a freelance copywriter in the suburbs of Sydney, Australia. Not long after its debut, like others in her field, she saw her business dry up. So she became an expert in customizing ChatGPT to write voicey marketing copy. She now makes more than she ever did as a copywriter, she says.

She and others are discovering the capability overhang of AI for themselves. Her story also illustrates that customizing AIs with your own data doesn't mean you have to be a software engineer like Bala.

The intense pressure to adopt AI -- from bosses, peers and, if you're an earlier adopter like me, voices in your head -- is real. So are the seemingly endless options for exploring its existing capabilities.

"I think about fields that might get suddenly affected by AI," says Mollick. He thinks we will see sudden innovation, often in unexpected areas, even as other fields and people in some roles fall behind. "The unevenness will be hard to predict."

Write to Christopher Mims at christopher.mims@wsj.com

(END) Dow Jones Newswires

January 09, 2026 05:30 ET (10:30 GMT)

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