AI and Local News: How AI Is Changing Journalism

Artificial intelligence is reshaping journalism in ways that extend far beyond automated writing. From gathering public records and translating community news to analyzing complex financial documents, AI is changing how newsrooms operate, how reporters investigate stories, and how local publishers reach their audiences.

For journalists who have spent years in the industry, the scale of this transformation is difficult to overstate. AI is arriving across nearly every sector, but its impact on journalism is particularly significant because the industry depends on collecting, verifying, interpreting, and distributing information.

At the same time, local news organizations are struggling to remain financially sustainable. As publishers search for ways to rebuild local journalism, AI presents both an opportunity and a challenge. It can help small newsrooms accomplish more with limited resources, but it may also weaken the reporting skills, editorial judgment, and business models that have supported journalism for decades.

A panel discussion at the Next Endeavor event in Mountain View, California, explored these questions, examining how AI is being used in newsrooms and what its growing influence could mean for the future of local reporting.

AI and Local News: Insights from the Next Endeavor Event

The Next Endeavor event, held September 14–15 in Mountain View, California, was organized by Imagination in Action and its partners. The program included a panel on the relationship between artificial intelligence and local journalism. The event organizer’s involvement in the conference is disclosed here for transparency.

Nina Gregory moderated the discussion, bringing together Ken Doctor, CEO of Lookout Santa Cruz; Gabriel Sama of Columbia Journalism School; and Queena Kim, a journalism educator at UC Berkeley.

Gregory established the focus of the conversation at the outset:

“There is so much news about AI. But today we’re not going to talk about that. We’re going to talk about how AI is uh entering the newsroom, the process of gathering, reporting, writing, and distributing the news.”

Rather than discussing AI as an abstract technology, the panel examined how it is already changing the daily work of journalists.

How AI Helps Local Newsrooms Do More With Less

Ken Doctor sees AI as a practical tool for expanding the capabilities of small news organizations.

“We really view (AI) as a force multiplier, and we’re starting to use it in everyday activities,” he explained. He identified three broad areas in which his organization is applying the technology, beginning with news product development.

Doctor described how local newsletters once required substantial human effort. Reporters and editors had to collect information from neighborhoods, review public records, monitor school developments, and track transportation updates. Producing that level of localized coverage was expensive, particularly for publishers serving multiple communities.

AI has changed that process at Lookout Santa Cruz.

The organization uses the technology to identify crime reports, school updates, and transportation information at the ZIP code and sub-ZIP code levels. It then uses those findings to help produce nine highly automated neighborhood newsletters.

Automation does not eliminate editorial oversight. A human still reviews the material before publication.

Doctor said a process that could previously require 40 to 80 hours of work each week can now be completed in approximately 15 minutes each morning. He also cited Spanish-language translation and geocoding—the process of associating information with geographic locations—as other practical applications.

For local publishers working with small teams and limited budgets, these efficiencies can make more comprehensive community coverage possible.

The broader lesson is that AI can help local newsrooms expand their output without increasing staffing at the same rate. The challenge is ensuring that greater efficiency translates into better journalism rather than simply more content.

How AI Is Changing Technology Journalism

Gabriel Sama offered a different perspective, drawing on his experience covering technology and working at Meta in 2022.

He recalled that technological innovation moved more slowly in 2013, giving journalists more time to understand emerging developments before explaining them to readers.

AI has changed that pace dramatically.

Sama spent approximately 10 months at Meta before becoming part of the company’s first round of layoffs. During that period, he concluded that the company was committed to developing AI over the long term and that organizations with substantial financial resources would push the technology forward rapidly.

That assessment, he argued, has largely been borne out by subsequent developments.

The difficulty for journalists is that AI continues to evolve while reporters are still trying to understand its capabilities, limitations, and consequences.

Sama believes technology journalism is particularly vulnerable because fewer journalists are dedicated to covering the field than there were in the past. The Bay Area, for example, once had a larger concentration of technology reporters who could investigate new developments and explain them to the public.

Today, journalists face the combined challenge of covering a rapidly changing technology with fewer specialized reporting resources.

Why Responsible AI Reporting Matters

Sama also raised concerns about the way AI is portrayed in the media.

He observed that many recent headlines had repeatedly used the term “doomsday” to describe developments in artificial intelligence. Such coverage can leave readers frightened without necessarily helping them understand what is happening or why it matters.

Journalists need enough time and expertise to examine these claims, distinguish realistic risks from speculation, and explain the evidence clearly.

However, that kind of reporting requires resources. When fewer journalists are available to cover AI, newsrooms may struggle to provide the context readers need.

For Sama, this creates a serious problem: the technology is becoming more influential at precisely the time when the capacity to scrutinize it is under pressure.

AI Does Not Replace Journalistic Originality

The discussion also addressed standards for using AI in newsrooms.

Nina Gregory emphasized that established principles of journalism still apply, regardless of the tools reporters use.

“When you copy and paste out of an LLM in journalism, we have a word for that,” Gregory said, reinforcing the point that reproducing AI-generated material without proper original work can amount to plagiarism.

AI can help journalists organize information, summarize documents, and accelerate research. It cannot remove the responsibility to verify claims, attribute sources, and produce original reporting.

The distinction is important. Using AI as an investigative assistant is different from treating its output as finished journalism.

AI in Journalism Education: Efficiency Versus Experience

Queena Kim, who teaches at UC Berkeley, discussed the mixed reactions to AI among students, professors, and university staff.

She noted that younger people are not necessarily the enthusiastic adopters that some might expect. In her experience, students have often approached AI tools with considerable skepticism.

A working group involving staff, professors, and students revealed different attitudes toward the technology. Staff members were interested in the potential productivity gains, while professors approached the tools with a mixture of curiosity and caution.

Students, however, expressed stronger reservations. Some worried that using AI could help train systems to perform work they might eventually be expected to do themselves. Others raised concerns about the environmental costs associated with AI.

These differences reflect a broader debate about what it means to adopt AI responsibly, particularly in professions where people must develop specialized skills through practice.

Where AI Tools Make Audio Editing Easier

Despite her concerns, Kim highlighted useful applications of AI in journalism education, including Descript, a tool that helps users work with audio and video recordings through transcription and text-based editing.

She explained that these tools can significantly accelerate the editing process, even though human work remains necessary.

The technology is not perfect, but it can make tasks that were once time-consuming much easier to manage.

For audio journalists, that means spending less time on certain mechanical aspects of editing and potentially more time on storytelling. However, the benefits depend on how the tools are integrated into the workflow.

Automation can speed up a task without necessarily teaching someone how to perform it well.

What Happens When AI Replaces Basic Reporting Tasks?

Kim illustrated this concern with an example involving a reporter in the San Francisco Bay Area.

The reporter had trained an AI agent to review county meeting minutes and track developments at city council meetings. These were the kinds of assignments he had handled early in his career, when a more experienced reporter asked him to search through public records and identify potentially important developments.

Although the work was not always exciting, it served an important purpose. Junior reporters learned how local government operated, developed instincts for spotting unusual details, and gained experience identifying questions worth investigating.

They also benefited from the guidance of senior reporters, who could point them toward overlooked information and explain why it mattered.

If AI takes over these entry-level assignments, aspiring journalists may lose opportunities to develop those skills.

The concern is not simply that a machine can do the work faster. It is that the process of learning through the work may disappear.

Why Listening and Reading Still Matter

Gregory raised a related point about audio journalism.

Repeatedly listening to recordings allows editors to notice subtle changes in tone, hesitation, emphasis, and emotion. Those details can provide context that a transcript or automated summary may fail to capture.

The same concern applies to document analysis.

Tools such as NotebookLM can help users examine lengthy documents, including financial and regulatory filings. They can quickly summarize key points and identify patterns, but a summary may not reveal everything a journalist would notice by reading the source material directly.

The most important discovery may be a seemingly insignificant comment buried in an otherwise routine document.

As Kim explained:

“The pack reporters always go to the most obvious parts of that document.”

She argued that experienced journalists sometimes notice an unusual detail that others overlook, prompting a new question or an unexpected line of investigation.

That ability to recognize the significance of something seemingly minor develops through experience. If journalists rely exclusively on AI-generated summaries, they risk missing these details and weakening the instincts that distinguish original reporting from routine information gathering.

Can AI Strengthen Investigative Journalism?

Ken Doctor offered a more optimistic view.

He argued that AI can strengthen accountability and investigative reporting by helping journalists analyze far more information than they could reasonably process manually.

In his view, the ability to investigate complex issues using AI will become increasingly important throughout the 2030s. Journalists who avoid these tools entirely may miss opportunities to uncover patterns, compare records, and identify discrepancies.

Doctor acknowledged that traditional research methods involved painstaking work, but he sees AI as a way to accelerate that process without necessarily sacrificing investigative depth.

Analyzing City Budgets With AI

Doctor cited an investigation involving city budgets in Eugene, Oregon.

The team compared budget documents spanning three years and used AI to identify changes and potential anomalies.

Manually reviewing multiple years of budget records could require hours of detailed work. AI helped compare the documents and surface differences that warranted closer examination.

The technology did not eliminate the need for journalistic judgment. Instead, it helped narrow the search, allowing reporters to concentrate on interpreting the findings and determining whether the changes were significant.

This is one of the clearest examples of AI’s potential value in local journalism. Public budgets can contain thousands of figures, and important changes are not always immediately obvious. Automated comparisons can help reporters identify areas that deserve further investigation.

Investigating Pesticide Use and Immigration Enforcement

Doctor also described how AI has supported research into issues affecting communities in and around Santa Cruz.

One area involves pesticide use in agricultural regions of South County and its effects on farmworkers. Another involves immigration enforcement, including the collection and examination of documents related to Immigration and Customs Enforcement (ICE) activities affecting local communities.

In both cases, AI has helped the newsroom work through large volumes of material and conduct deeper research.

These examples demonstrate how the technology can support reporting on complex public-interest issues. When used carefully, it can help small news organizations pursue investigations that might otherwise require more time and resources than they have available.

The essential step is independent verification. AI can identify patterns and suggest leads, but journalists still need to examine the underlying evidence, confirm the facts, and give the people involved an opportunity to respond.

Reaching More Readers Through AI

Doctor also highlighted AI’s potential business benefits.

For local news organizations, the challenge is not only producing journalism but also ensuring that it reaches the people who need it.

Translation, localized newsletters, automated information gathering, and other AI-assisted workflows can help publishers serve more communities and communicate with readers in different languages.

These applications may help independent newsrooms extend their reach without significantly increasing their operating costs.

However, improved efficiency alone does not guarantee financial sustainability. Publishers still need to build relationships with readers, establish reliable revenue sources, and demonstrate the value of their reporting.

Is There a Sustainable Business Model for Local News in the AI Era?

Toward the end of the discussion, Gregory asked the panel whether there were successful models that independent journalists and local publishers could follow.

Sama’s response revealed the uncertainty facing the industry.

He said he felt more pessimistic about the future of journalism than at any point in the previous 15 to 20 years. In the past, he believed there were business models that publishers could study and adapt to generate revenue, pay employees, and sustain their operations.

Today, he finds the path forward much less clear.

How AI Is Changing the Rules of Search and SEO

Sama compared the current situation with the rise of Google and the development of search engine optimization (SEO).

Although Google’s ranking algorithm was never fully transparent, publishers gradually learned how to improve their visibility in search results. They developed strategies, studied performance, and adapted their content to the signals they could observe.

SEO became an important source of traffic for news organizations, even if the rules were sometimes difficult to understand.

AI-powered language models are changing that relationship.

Sama argued that publishers now face a different challenge: AI systems can act as gateways to information while presenting material in ways that do not necessarily direct readers to the original sources.

Traditional search engines generally displayed links that allowed readers to visit publishers’ websites. AI-generated answers can instead combine, summarize, or reorganize information from multiple sources into a single response.

This creates uncertainty for publishers that depend on website visits, advertising revenue, subscriptions, and reader relationships.

If readers obtain the information they need without visiting the original article, news organizations may find it harder to capture the value of their reporting.

Why Attribution and Verification Matter

Sama also raised concerns about how AI systems present information.

Journalists follow established practices to verify facts, attribute claims, and provide context. Their publications take responsibility for what they report and give readers a way to evaluate the underlying evidence.

AI-generated answers do not always make those relationships equally clear. Information may be summarized from multiple sources, separated from its original context, or presented in a way that makes verification more difficult.

This creates a challenge for both publishers and readers.

News organizations need to maintain the standards that make journalism trustworthy, even as information increasingly travels through systems they do not control. At the same time, they need clearer ways to understand how their reporting is discovered, attributed, and used by AI platforms.

The business implications are substantial. Publishers cannot easily build a strategy around a system when they have limited visibility into how it distributes their work or how much value it returns to the original source.

For Sama, the lack of a clear, replicable model is one of the central uncertainties facing independent journalism today.

The Future of Local Journalism Depends on How AI Is Used

The discussion at Next Endeavor revealed that AI is neither an automatic solution to local journalism’s problems nor an inevitable threat to the profession.

For small newsrooms, the technology can reduce repetitive work, support multilingual coverage, analyze public records, and make investigative reporting more accessible. These benefits matter, particularly for organizations operating with limited staff and funding.

At the same time, excessive reliance on automation could weaken the training process through which journalists learn to recognize important details, verify information, and develop editorial judgment. AI also introduces new questions about plagiarism, attribution, public trust, and the financial sustainability of publishers.

The practical challenge is finding the right balance. Newsrooms need to use AI where it offers genuine advantages while preserving the human expertise that makes journalism valuable.

That means treating AI as an assistant rather than an unquestioned authority, maintaining human review, checking original sources, and investing in the development of new reporters.

Local journalism has always depended on more than the ability to collect information. Its value comes from understanding communities, asking difficult questions, identifying what others miss, and holding powerful institutions accountable.

AI can help journalists do some of that work more efficiently. Whether it strengthens local news in the long term will depend on how publishers, technology companies, educators, and journalists respond to the opportunities and risks it creates.