Search is no longer just about blue links and ranking on Google. More and more, people are getting their answers directly from AI tools like ChatGPT, Google AI Overviews, and other answer engines that summarize information, pull citations, and decide what gets surfaced in real time. That means visibility is changing, and so is the value of content.

In this episode of The Media Copilot podcast, Pete Pachal speaks with Josh Blyskal, who leads answer engine optimization research at Profound, a company focused on tracking how brands appear inside AI generated answers. Their conversation explores what answer engine optimization really means, how it differs from traditional SEO, and why specificity, utility, and structure now matter more than ever.

Josh shares how answer engines build responses, how citations are selected, why Reddit and forums still matter, and what brands, publishers, and media companies should be paying attention to as AI becomes a bigger gatekeeper between information and the audience.

Listen or watch:

• YouTube
• Spotify
• Apple Podcasts

Why this matters

The old search bargain is fading. It used to be simple: publish content, earn traffic, measure the clicks. Now, the answer may show up before the click ever happens. For brands, publishers, and media professionals, that shift raises bigger questions around visibility, credibility, attribution, and control.

This episode gets into the mechanics behind that shift, but it also looks at the bigger picture. If AI is becoming the front door to information, who gets included in the answer, who gets left out, and what happens to the business models built around discovery? It is a timely conversation for anyone thinking seriously about the future of media, search, content strategy, and digital reputation.

What we cover

• What answer engine optimization is and why it is not just SEO with a new label

• How AI tools break prompts into fan out searches behind the scenes

• Why answer engines favor utility, clarity, and highly specific content
• The role of citations, consensus, and diversified source portfolios in AI answers
• How Google, ChatGPT, and other models differ in the sources they pull from
• Why Reddit, forums, and user generated content still influence AI visibility
• How brands can monitor narrative shifts and emerging sentiment in answer engines
• The growing tension between publishers, AI discovery, and the value of being cited
• What Josh sees as the biggest opportunity and biggest risk in the next phase of AI powered media

Takeaways
AI search is quietly reshaping how information is surfaced and trusted online. Instead of sending users down a list of links, answer engines increasingly extract, synthesize, and deliver responses directly. That shift changes the economics of discovery, the role of publishers, and the way expertise shows up on the internet.

Josh explains that this isn’t just another search algorithm update. It represents a structural change in how knowledge is organized and retrieved. Content that is clear, credible, and machine-readable has a growing advantage, while content built purely for traditional search rankings may struggle to surface in AI-generated answers.

The conversation also highlights a widening knowledge gap. Organizations that understand how answer engines interpret authority, context, and sourcing will be far better positioned to remain visible. Those that don’t may find their work increasingly invisible, even if the underlying reporting or analysis is strong.

For media companies, brands, and creators, the real question is no longer just how to rank. It’s how to ensure their work becomes part of the answers people receive.

About the 👤 Guest Josh Blyskal

• Website: https://www.joshblyskal.com

• LinkedIn: https://www.linkedin.com/in/joshua-blyskal

• X: https://twitter.com/joshblyskal

• Speaker Deck: https://speakerdeck.com/joshbly

Learn More About Profound

• https://www.tryprofound.com

About the show:

To explore more conversations like this and see what’s new, visit the freshly updated Media Copilot website at mediacopilot.ai. You’ll find new episodes, expanded resources, and tools designed for journalists, communicators, and media leaders navigating the fast-changing world of AI. It’s the home base for everything Media Copilot and it’s just getting started.

Enjoyed this episode?

Subscribe to The Media Copilot on Substack, Apple Podcasts, Spotify, or your favorite app. On YouTube?  Tap the Like button and Subscribe to the YouTube channel.

For more AI tools and resources built for media professionals, visit MediaCopilot.ai.

Produced by Pete Pachal and Executive Producer Michele Musso
Edited by the Musso Media Team 

Music: “Favorite” by Alexander Nakarada, licensed under CC BY 4.0

All rights reserved. © AnyWho Media 2026

The post How AI search really works, with Josh Blyskal appeared first on The Media Copilot.

AI in journalism is often discussed in extremes, either as a revolution that will transform reporting or a threat to the profession.

In this episode of The Media Copilot podcast, Pete Pachal speaks with Gina Chua, Executive Editor at Large at Semafor, where she leads AI strategy, and Executive Director of the Tow Center for Digital Journalism at CUNY. The conversation focuses less on theory and more on how AI tools are being used in everyday newsroom workflows.

Listen or watch:

• YouTube
• Spotify
• Apple Podcasts

At Semafor, some of the systems Chua describes are simple tools designed to reduce time spent on routine tasks. These include internal tools for copy editing and proofreading, and a workflow that reads a story draft and suggests datasets that could help illustrate it. The reporter can then decide whether to explore those datasets or build a chart using existing newsroom tools.

Another example involves helping reporters find related coverage more quickly. A tool reads a draft story and searches across publications for related reporting, sometimes across languages. It then returns summaries of potential articles so editors can decide which ones are worth reviewing.

Semafor also works with large volumes of interview transcripts from events and panel discussions. Chua explains how those transcripts are integrated into Slack so reporters can search them directly. Journalists can ask whether a speaker mentioned a particular topic, locate the section of the transcript where it appears, and review it themselves. The next step the team is experimenting with involves automated alerts that notify reporters when certain topics appear in interviews across the archive.

Elevating the human element

Throughout the conversation, Chua emphasizes that these tools are meant to assist newsroom work rather than replace editorial judgment. When discussing publishing standards, she says the key principle is that anything published must be reviewed by a human editor who takes responsibility for it.

She also offers a practical way to think about large language models. They are designed to handle language, not verify facts. When used with a defined body of text, such as a transcript or speech, they can be helpful for tasks like summarizing, comparing passages, or identifying themes. Problems are more likely to appear when they are asked to generate information outside of that context.

The discussion also turns to how audiences are already encountering AI in everyday information environments. Chua points to research showing a small but growing number of younger users say they get news from chatbots. She argues that the shift may be larger than the statistics suggest, since AI summaries are now embedded in search results and other tools people use to find information.

For news organizations, that raises broader questions about how people will access information in the future. If AI systems increasingly sit between audiences and publishers, journalism will need to think carefully about how reporting reaches readers and how trust is maintained.

Why this matters

AI tools are already influencing how newsrooms work and how audiences discover information. Understanding the practical uses and limitations of these systems is becoming part of the daily reality of journalism.

What we cover

• How Semafor is experimenting with AI tools inside the newsroom
• Using transcripts and Slack to search interviews and discussions
• Why language models are useful for handling text but not verifying facts
• The role of human review in newsroom publishing decisions
• How AI interfaces are changing the way audiences find news

Key takeaways

• AI tools in newsrooms are often used for efficiency rather than automation
• Large language models work best when applied to known text sources
• Human oversight remains central to publishing decisions
• AI driven discovery tools may increasingly shape how audiences access journalism

About the 👤 Guest: Gina Chua

LinkedIn 👉 https://www.linkedin.com/in/ginachua

X (Twitter) 👉 https://x.com/GinaSKChua

Instagram 👉 https://www.instagram.com/gina_chua_nyc 

Personal Website / Writing 👉 https://ginachua.me 

Author Page (Semafor) 👉 https://www.semafor.com/author/gina-chua

📰 Semafor Official Website:  https://www.semafor.com

About the show: To explore more conversations like this and see what’s new, visit the freshly updated Media Copilot website at mediacopilot.ai. You’ll find new episodes, expanded resources, and tools designed for journalists, communicators, and media leaders navigating the fast-changing world of AI. It’s the home base for everything Media Copilot and it’s just getting started.

Enjoyed this episode?

Subscribe to The Media Copilot on Substack, Apple Podcasts, Spotify, or your favorite app. On YouTube?  Tap the Like button and Subscribe to the YouTube channel.

For more AI tools and resources built for media professionals, visit MediaCopilot.ai.

Produced by Pete Pachal and Executive Producer Michele Musso
Edited by the Musso Media Team 

Music: “Favorite” by Alexander Nakarada, licensed under CC BY 4.0

All rights reserved. © AnyWho Media 2026

The post Building the newsroom AI playbook with journalists in the loop appeared first on The Media Copilot.