For most of journalism’s history, the article has been the atomic unit of news: a fixed container, written once and served the same way to everyone. That’s beginning to change. AI is moving out from behind the scenes, where it quietly powered efficiencies, and into the interface itself, where it can assemble, personalize, and adapt the news to each reader.

On this week’s episode of Newsroom Robots, host, Nikita Roy speaks with Jim Friedlich, Executive Director and CEO of The Lenfest Institute for Journalism, and Kevin Delaney, Editor-in-Chief of The San Francisco Standard.

Earlier this year, The Standard became a part of the Lenfest AI Collaborative and Fellowship Program, funded by OpenAI and Microsoft. As part of the program, it received a grant to build a genuinely AI-native news app, where the article isn’t the building block anymore. Instead, the raw materials are atomic like a quote, a piece of data, a few lines of reporting, all assembled by AI into an experience that adapts to each reader.

In this episode:

01:47 — What “AI-native” actually means, and the three areas where AI is uniquely good

04:56 — “Personalized obsessions” — why readers follow stories, not sections and the app’s early results

10:00 — Kevin’s “news as farming” analogy

12:36 — Jim walks through the reader experience of the app

15:33 — The shift from Mode One to Mode Two and how the Lenfest AI Collaborative’s thinking has evolved

26:34 — Atomic content and the reporter’s CMS: unlocking the interviews, notes, and quotes that never make the 800-word article

42:48 — How they control for accuracy by grounding AI in their own journalism

50:28 — Everyday newsroom wins, from a sports contract calculator to “find me a juicy story” in a document dump

This episode of Newsroom Robots is supported by The Lenfest Institute for Journalism.

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At The Seattle Times, an AI tool identified a new prospect and helped close additional ad revenue in a single day. The Seattle Times is one of 11 news organizations participating in the Lenfest AI Collaborative and Fellowship Program, funded by OpenAI and Microsoft. Through this initiative, AI engineering fellow Rajesh Barade worked with the advertising team to address their challenges and developed a prospecting agent. This tool enables sales representatives to enter a category or focus area, identify businesses spending in that market, find decision-makers, access advertising spend data, and generate personalized outreach.

The Seattle Times deliberately began its AI efforts in advertising rather than the newsroom. Early successes helped build momentum across the organization. This led to listening tours across finance, HR, and eventually the newsroom, where a municipal meeting listening tool is now in development.

This week on Newsroom Robots, host Nikita Roy speaks with Kati Erwert, Senior VP of Product Marketing and Public Service at The Seattle Times, and Tristan Loper, Head of National Programs at the Lenfest Institute, to explore how the prospecting agent works, why starting in advertising was key to unlocking organization-wide adoption, and what it takes to turn early AI successes into lasting capability.

In this episode:

02:07 — How The Seattle Times built internal momentum for AI adoption

05:51 — What the Lenfest AI Collaborative is and how it works

09:29 — Inside the prospecting agent: how a sales rep closed business in one day

14:11 — Why starting with advertising drove cultural change across the organization

17:07 — The buy vs. build decision in a fast-moving AI landscape

19:03 — How open-source code sharing works across the 11-newsroom cohort

24:44 — Building the municipal meeting listening tool and beyond

37:07 — Navigating the tension of blocking AI bots

45:47 — How smaller newsrooms can get started with AI today

This episode of Newsroom Robots is supported by The Lenfest Institute for Journalism.

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For years, the aggregator model was simple: curate the best journalism from thousands of publishers and send audiences their way. Now that contract is being rewritten, and Yahoo News is one of the most interesting places to watch it happen.

In this episode of Newsroom Robots, host Nikita Roy speaks with Kat Downs Mulder, Senior Vice President and General Manager of Yahoo News, about how the platform is layering AI across every surface of a product that reaches an estimated 180 million people in the U.S. alone each month. Kat previously spent more than 14 years at The Washington Post as chief product officer and managing editor before taking on the challenge of modernizing one of the internet's original news destinations.

The conversation explores Yahoo's acquisition of Artifact, the AI news app built by Instagram's co-founders, which gave the platform a new recommendation engine that prioritizes time spent reading over clicks. It also digs into Yahoo Scout, the company's new AI answer engine that synthesizes information with rich citations and visual context, and an AI-powered daily audio digest designed to turn personalized news into a listening habit. Each of these products makes Yahoo more useful to its audience, but each also changes the relationship between Yahoo and the publishers whose journalism powers the platform.

When an answer engine can deliver what a user needs without a click-through, when an audio digest summarizes a story so well the article never gets opened, and when personalization makes the aggregator the destination instead of the pass-through, the old economics stop working for publishers. Kat is candid that the compensation models haven't been figured out yet, noting that Yahoo is working with the Microsoft Publisher Content Marketplace to develop new frameworks, but that the industry is still writing those rules in real time.

She makes a strong case for how Yahoo is approaching this differently, from how Scout prominently surfaces publishers to the rev-share model they operate, and why she believes the quality flywheel they are building actually rewards better journalism. Kat argues that original, distinctive journalism will become more valuable in an AI world because AI agents will seek out what is unique.

This episode covers:

03:20 — Why Yahoo acquired Artifact and how it shifted recommendation algorithms

06:20 — The shift from click-based metrics to deeper engagement signals such as session time and retention

08:50 — Inside Yahoo Scout, Yahoo's new AI answer engine built to support publishers and the open web

12:40 — The changing economics of news as AI platforms begin generating answers instead of sending traffic

17:40 — Yahoo's personalized AI-generated audio news digest and why multimodal news experiences matter

22:00 — How Yahoo's editorial and AI teams collaborate on quality control at scale

31:00 — How AI is transforming newsroom product development and prototyping

36:10 — The tension between personalization and journalism's civic responsibility

40:00 — What smaller newsrooms can learn from their AI product playbook

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AI is settling in as infrastructure within newsrooms, a layer quietly reshaping how journalists discover information, how stories move through production, and how audiences increasingly expect news to reach them.

In this episode of Newsroom Robots, recorded live in New York City at TV News Check’s News Tech Forum, host Nikita Roy brings together four industry leaders to examine the tangible ways AI is transforming newsroom operations. The conversation features Ryan Struyk, Director of AI Initiatives at CNN; Rubina Madan Fillion, Associate Editorial Director of AI Initiatives at The New York Times; Arlyn Gajilan, Global Editor of AI Development and Integration at Reuters; and Burt Herman, Co-Founder and Principal of Hacks/Hackers.

The discussion focuses on defining questions for the news industry: Where is AI already delivering real operational impact? How should newsrooms adapt to a world of “liquid content” and AI-mediated distribution? Is human-in-the-loop governance sustainable, or is it already breaking down? As trust in news declines and trust in AI interfaces rises, what becomes journalism’s true competitive advantage?

In this episode, they cover:

03:10 — Where AI is already embedded inside CNN’s newsroom workflows

04:25 — How The New York Times uses AI to power investigative reporting and the “Manosphere Report”

07:30 — How Reuters compressed story production from minutes to seconds and feature development from three months to three weeks

11:44 — Why Hacks/Hackers is urging small newsrooms to think from first principles before adopting AI

15:15 — The rise of liquid content and what it means when audiences reshape journalism into their preferred formats

23:24 — Why local news holds a unique advantage in an AI-mediated information landscape

29:12 — Five years from now: What newsrooms hope they get right

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Most major newsrooms have now moved beyond early experimentation with AI. The main challenge now is determining how to govern effectively, scale consistently, and strategically position AI across the entire organization—while maintaining public trust as a central priority.

This week on Newsroom Robots, host Nikita Roy sits down with Uli Köppen, Chief AI Officer at Bavarian Broadcasting (BR), to talk about what it really looks like to lead AI strategy inside one of Europe’s largest public broadcasting networks.

Uli makes a compelling case for why every newsroom should establish a dedicated AI leadership function, backed by an interdisciplinary governance structure. They also dig into a question defining the next phase of AI strategy for many newsrooms: in a world of AI overviews, zero-click search, and agent-driven information retrieval, how do you maintain your brand as a recognizable, trustworthy source? Uli shares why BR opted out of AI crawling and what they are building instead, including a vision for a verified content data pool that could power new products across multiple media organizations.

In this episode, they cover:

02:09 — What it means to be Chief AI Officer at a public broadcaster

06:30 — Why every newsroom needs an interdisciplinary AI board, not just a single AI leader

09:06 — The skills newsrooms need to build for an AI-driven environment

11:00 — Why reinventing workflows starts before adding any technology

16:28 — Inside the Oktoberfest Chatbot and the collaborative content pool powering it

23:40 — Using AI for smarter community engagement and real-time moderation

26:30 — The personalized audio news briefing that users love and where it’s headed

36:00 — How BR’s AI guidelines evolved from broad guardrails to clear, example-based rules

41:40 — The strategic question: be part of AI platforms, or build recognizable products of your own?

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Chicago Public Media operates two distinct news brands: WBEZ, the public radio station, and the Chicago Sun-Times, the legacy newspaper. With audio and print journalism, both membership and advertising revenue, and decades of archives in multiple formats, they're a unique case study for AI in local news.

When CEO Melissa Bell joined the organization, there was interest in AI but no dedicated resources for experimentation. Through the Lenfest AI Collaborative, they brought in their first AI engineer. A year later, Spanish translations that used to take days are now published the same day. Forty years of WBEZ audio, previously unsearchable, are being transcribed and made searchable for journalists.

In this week's episode, host Nikita Roy speaks with Chicago Public Media leaders Melissa Bell (CEO) and Aron Pilhofer (Chief Product and Membership Officer), along with Mark Chonowsky (AI Fellow) and David Chivers (lead AI advisor for the Lenfest AI Collaborative).

A note on this week's episode

David Chivers, who listeners will hear in this episode, passed away on January 1, 2026. He was the lead advisor for the Lenfest AI Collaborative and this episode was recorded the previous month. David was deeply committed to building capability in newsrooms. He was generous with his time, sharp in his insights, and always had one of those big smiles that would light up a room. He will be missed.

The conversation covers how Chicago Public Media is thinking about AI as part of a larger membership strategy, how they decide what to build versus buy with limited resources, and what it looks like to lead through a public AI failure.

In this episode:

02:55 — Where Chicago Public Media started with AI a year ago

08:08 — What AI use looks like inside the newsroom

15:42 — How product development is evolving with AI tools

27:28 — Collaboration with OpenAI and Microsoft

28:26 — How AI fits into Chicago Public Media's membership strategy

36:05 — Build vs. buy with limited resources

37:44 — The Chicago Sun-Times AI-generated book list incident

42:18 — Advice for leaders navigating AI mistakes publicly

This episode of Newsroom Robots is supported by The Lenfest Institute for Journalism.

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By 2026, most leading newsrooms have moved past the question of whether AI belongs in their organization. Now the key question is: what does a sustainable AI product strategy look like when you’re building for a subscription-based business and a high-trust brand?

This week on Newsroom Robots, host, Nikita Roy sits down with Alessandro Alviani, Lead for Generative AI, and Fabian Heckenberger, Managing Editor for AI, at Germany’s Süddeutsche Zeitung to discuss how they’re using AI to build the next generation of news products.

This conversation looks at what happens when AI becomes a permanent layer in a newsroom’s product stack.

Alessandro and Fabian walk through how they’re designing AI experiences that fit naturally with reader behavior and how they’re developing new distribution and accessibility formats that would have been impossible to sustain manually.

This episode also goes deep on a topic that’s becoming a defining competency which is operational trust. What do you monitor once an AI product is live? How do you categorize failures? And how do you respond quickly when something goes wrong, without panic and without eroding your brand?

This episode, we cover:

02:52 — How editorial and product roles complement each other in AI strategy

13:13 — Addressing skepticism and fear around AI in the newsroom

25:17 — Inside building the German election chatbot

31:10 — The design framework that signals AI content without eroding trust

35:30 — Real-time risk management and monitoring for live AI tools

48:50 — The two questions every newsroom should ask before greenlighting an AI project

54:55 — Closing reflections and personal AI use

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2025 wasn’t just another year of AI experimentation in the media industry. It forced the industry to confront a bigger question: what happens when AI stops being just a newsroom tool and becomes the layer audiences experience journalism through? That is the core question heading into 2026.

This week on Newsroom Robots, host Nikita Roy sits down with Francesco Marconi and Scott Austin for an end of year recap roundtable on what actually changed in AI and media in 2025 and what newsroom leaders need to prepare for heading into 2026.

Francesco is the co-founder and CEO of AppliedXL. He previously led R&D at The Wall Street Journal and built some of the earliest AI and newsroom automation systems at The Associated Press.

Scott leads business development at Symbolic.ai, an AI assisted publishing tool. He is also a journalist and digital media veteran who spent years at The Wall Street Journal as a reporter and award winning editor, and later led content partnerships at Dow Jones across major platforms.

This episode covers:

03:10 — Why 2025 was journalism’s operational reckoning year

08:55 — The shift from search to answers and why it breaks old business models

14:40 — Proactive AI and what ChatGPT Pulse reveals about the next distribution layer

20:30 — Journalism’s hidden work and why persistence, source building, and human judgment still matter

23:30 — Why news orgs must move upstream from content to structured knowledge

36:10 — AI agents: what they actually are, what they are not, and why transparency matters

41:20 — The overlooked shift: Model Context Protocol (MCP) and why it is a major newsroom disruption

51:05 — Predictions for 2026

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The Philadelphia Inquirer never had an AI engineer on staff until the Lenfest AI Collaborative & Fellowship program changed that.

The collaborative is a $5 million partnership between the Lenfest Institute, OpenAI, and Microsoft that placed 10 AI fellows in American newsrooms for two years. These engineers work within the organizations, building tools that solve real newsroom problems.

This week on Newsroom Robots, host Nikita Roy sits down with Jim Friedlich, CEO and Executive Director of the Lenfest Institute, David Chivers, lead advisor to the Lenfest AI Collaborative and Matt Boggie, CTO of The Philadelphia Inquirer, to walk through how the program works and what the Inquirer has built as a result.

The Inquirer came to the collaborative with an idea to build a full-archive search tool that would let reporters query decades of journalism. They expected it to take 24 months. Within two weeks of a Microsoft hackathon, they had working code. The tool, now called Dewey, searches everything the Inquirer has published since 1978.

This episode covers:

03:02 — How the Lenfest AI Collaborative got started

05:34 — Can newsrooms trust big tech partners?

08:33 — How the fellowship works day to day

14:52– Inside the Microsoft hackathon that built Dewey in two weeks

21:37 — Training journalists to understand LLM limitations

24:07 — How AI literacy has changed newsroom culture

29:45 – How small newsrooms can get started with AI

35:14 — AI answers, search decline, and the future of audience traffic

38:15 — Rethinking journalism’s role in an AI-mediated world

41:23 — Closing reflections and personal AI use

This episode of Newsroom Robots is supported by the Lenfest Institute for Journalism.

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This week on Newsroom Robots, host Nikita Roy is joined by Tav Klitgaard, the CEO of the Danish newsroom Zetland, to unpack the origin story of GoodTape — an AI transcription tool that began as an internal newsroom solution and evolved into a profitable, global product used far beyond journalism.

Zetland is an audio-first newsroom in Denmark. But GoodTape wasn’t born from an AI strategy or a product roadmap. It emerged from a familiar newsroom pain point of journalists spending hours transcribing interviews, with existing tools falling short, especially in non-English languages like Danish.

In this conversation, Tav breaks down how GoodTape went from an internal experiment to a standalone, subscription-based product that quickly became profitable, generated millions in revenue and was eventually divested. He also shares what building GoodTape taught Zetland about AI adoption, organizational learning, and where newsrooms should, and shouldn’t, use generative AI.

This episode covers:

05:50 – How a prototype using OpenAI’s Whisper sparked GoodTape

08:36 – The moment Zetland realized GoodTape could be a real product

12:34 – How journalism’s trust and privacy standards became a product advantage

13:59 – What actually improves transcription quality beyond the model itself

15:27 – How GoodTape became profitable and contributed to Zetland’s revenue

16:29 – Why Zetland eventually divested GoodTape instead of scaling it internally

17:36 – What building an AI product taught Zetland about newsroom AI adoption

19:08 – Why Zetland uses AI for productivity, not editorial output

28:14 – A real-world example of AI use that forced Zetland to rethink its own guidelines

30:34 – Why principles matter more than rigid AI rules in newsrooms

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