An early AI pioneer shares how the ‘vibe coding’ revolution could reshape data journalism

The word AI can invoke a sweeping range of emotions in a reporter — paralyzing fear of more job losses, sheer awe at the new ease of massive data analyses, gnawing resentment at intellectual property theft, or simply “the ick.”

These emotions are all valid, explained Chase Davis, an early pioneer in journalistic uses of artificial intelligence and the keynote speaker at the USC Center for Health Journalism’s 2025 Data Fellowship.

But it is possible — and in fact encouraged — to hold both a healthy dose of skepticism toward AI and optimism about how it can advance our mission as journalists, said Davis, speaking to fellows gathered in Los Angeles for a week of data training and talks.

A helpful framework for doing this is to realize that most conversations about AI can be split into one of two buckets.

In one bucket, you have the big-picture, theoretical, “think-piece” questions: How can we prevent AI-generated misinformation from further eroding our perception of the truth? What will be the environmental toll of our increased reliance on AI? Could authoritarian regimes exploit AI to suppress dissenting journalism?

In the other, you have the narrow and practical problem-solving questions: How can I use ChatGPT to isolate dataset entries that represent hotel addresses? What’s the best way to prompt Google Pinpoint to pull instances of kidnappings from thousands of police reports? Am I able to use AI-generated code to verify my analysis of overdose data?

While it’s important to dwell on the high-level questions, Davis believes reporters have the most to gain by engaging with AI on this granular level.

“The way that I look at the applications of AI today are very much focused on very small, very practical problems,” said Davis. “I tend to think that the best uses of generative AI are extremely, extremely boring.”

He knows what he’s talking about. As a former leader on The New York Times’ Interactive News Desk and the former head of the Minnesota Star Tribune’s AI Lab, Davis has more than three decades of coding experience and 15 years of working with artificial intelligence tools.

He looks at the potential of AI with enthusiasm and urges reporters to look beyond the “low-hanging fruit” applications that most newsrooms are currently fixated on — such as generating headlines or three-bullet-point summaries of stories.

Instead of viewing AI simply as a way to increase the efficiency of day-to-day newsroom tasks, he encourages reporters to think creatively about how it can be used to find and tell better stories.

One early example of a newsroom successfully doing so is a 2015 investigation published by the Los Angeles Times that used natural language processing to analyze police reports for keywords identifying crimes as serious or minor assaults. The findings revealed that the LAPD had been misclassifying assaults, leading to an artificial 7% drop in serious violent crime statistics and artificial 16% drop in serious assaults over an eight-year period.

While AI can be applied to any form of journalism, it is especially useful for data journalists, as it can decrease the burden of manual computation and code writing.

Davis recommends using large language models (LLMs) such as ChatGPT as thought partners to help brainstorm solutions to roadblocks in data journalism projects. It may not have all the answers, but a back-and-forth conversation can yield new ideas on how to retrieve data, clean it, and then craft code to both analyze and verify the end result.

“What AI allows us to do is to transform the problem that we’re dealing with into a problem that’s easier to solve,” he said.

For example, he was recently working with a large dataset containing a list of many different places and wanted to isolate entries that had addresses so he could later use this information to geocode them for a map.

However, there is no coding formula that can make a computer realize that Dogwood Coffee shop will have an address, but Lake Superior will not. So he asked ChatGPT to distinguish between entries with and without addresses and then generate a list of those with addresses.

“It’s highly accurate at being able to answer that very, very specific, tiny question that otherwise would be very hard to do with conventional technology,” Davis said.

As a result, he was able to take a task that would have taken him days to do manually and complete it within an hour.

In addition to making data journalism faster, AI is also making it more accessible.

Whereas in the past a reporter would need to study R or Python before crunching complex datasets, that same reporter can now prompt AI to write the code for them. Instead of inputting a series of numerical commands and functions, they can simply type out what they are trying to do in English (or “natural language”) and tweak the output accordingly — a change that Davis finds empowering.

“It allows us to spend our brain calories thinking more about the how and the why and the methodological questions [of data analysis],” he said, “and less about the 'Wait a minute, what’s the second argument in the VLOOKUP function again?'”

AI researcher Andrej Karpathy coined a term for this process in early 2025. It’s called “vibe coding” and it shifts the developer’s role from writing code line-by-line to guiding an AI assistant, enabling the developer to focus on the bigger picture.

Or as Karpathy put it in a viral tweet: “The hottest new programming language is English.”

Vibe coding is not just a method for reporters with no knowledge of coding; it’s being embraced by the top echelon of AI experts.

A prominent data journalist recently sent Davis a note that said, “Oh my God, this vibe coding thing is insane. If I had this during our early interactive news days, it would have been a godsend. Once you get the hang of it, it’s like magic.”

The shift toward vibe coding, Davis predicts, will also lead to a shift in the skill set that distinguishes the best data journalists.

“For a long time in data journalism, I thought the barrier was more the technical skills, and now it’s a little bit more about higher-order thinking and creative problem-solving,” he said.

As this year’s Data Fellows head back to their newsrooms across the country, he hopes they bring with them a new level of creativity and optimism about how AI can be applied.

At the same time, he urges reporters to hold onto their healthy sense of skepticism when it comes to AI. Remember that LLMs can hallucinate information, and the code and data analysis they produce will often have flaws. It’s important to use AI results as a starting point and then continue to apply rigorous journalistic standards of verification and fact-checking before moving toward publishing.

It’s also good to remember that data entered into LLMs like ChatGPT may be stored on external servers of technology companies and could, in some circumstances, be subject to subpoena. Therefore, if a reporter is handling extremely sensitive information, it is best to keep it away from AI — and from the internet in general, he said.

A prominent data journalist recently sent Davis a note that said, “Oh my God, this vibe coding thing is insane. If I had this during our early interactive news days, it would have been a godsend. Once you get the hang of it, it’s like magic.”

Keeping creativity and judgment in mind, reporters can consider a different perspective when they encounter doomsday conversations about how AI is destroying journalism and the world.

“You can bring to those conversations a nuance that helps people see when this might be the right solution to a problem,” he said. “You can kind of serve as translators in that world.”

Above all, he thinks this is an opportunity to be excited about — and one that everyone in journalism ought to engage with. Given that essentially every industry, education system, and community that journalists report on is currently being reshaped by AI, reporters have a duty to at least understand it and bring that understanding to their work.

While other technological innovations have been “inflicted” upon the newsroom — Google Search, Craigslist, Facebook and other social media platforms — AI is still in its infancy, so this is a perfect time for reporters to learn how to leverage it.

“We’re often reacting to this stuff and chasing this stuff, and often to our detriment,” said Davis. “But this is an opportunity to take this technology that no one has really figured out yet and turn it toward the bits of our mission that matter.”