beehiiv is expanding beyond email publishing with a suite of new products aimed at keeping independent publishers and news organizations from relying on multiple software vendors to run their business.

At its Summer Release Event on July 16, the newsletter platform introduced four products: Community, a built-in discussion platform; Copilot, an AI agent for audience and business operations; programmatic advertising; and a redesigned visual editor. Together, the launches mark beehiiv’s broadest attempt yet to compete as an all-in-one publishing platform rather than just a newsletter service.

The expansion reflects a shift in digital publishing, where newsletter platforms are increasingly competing to own more of the publisher workflow, from audience engagement and monetization to AI-powered operations.

“We believe the next chapter of the creator economy and content businesses is about consolidation,” co-founder and CEO Tyler Denk said during the event.

For publishers, the most significant announcement may be Copilot, beehiiv’s first native AI product. Rather than serving as a writing assistant, the company describes it as an AI operator capable of analyzing subscriber data, identifying audience segments, drafting marketing campaigns, launching workflows and surfacing revenue opportunities through a chat interface.

The launch builds on beehiiv’s adoption of Model Context Protocol, an open standard introduced by Anthropic that allows AI systems to securely connect with external data and software. As more publishing platforms adopt MCP, AI tools are shifting from generating content to executing operational tasks across newsroom business systems.

beehiiv also introduced Community, a feature designed to let publishers host subscriber discussions inside their own branded websites instead of relying on platforms such as Discord, Slack or Facebook Groups. Paid subscriber communities, moderation tools and podcast integration are built into the feature.

For news organizations, the move reflects a growing emphasis on first-party audience relationships as publishers seek to reduce dependence on social platforms and create additional value for subscribers.

The company also expanded its advertising business with programmatic newsletter ads that automatically match advertisers with newsletters based on audience characteristics and campaign performance. The system is intended to fill unsold inventory alongside direct advertising deals.

beehiiv said publishers on its platform now receive more than $1 million per month through its advertising network and have generated more than $50 million in subscription revenue.

The final launch was a redesigned visual editor that previews how newsletters and website content will appear before publication while supporting email, web pages, automations and recommendations from a single interface.

The announcements highlight how newsletter platforms are evolving into broader publishing infrastructure providers at a time when many news organizations are looking to simplify technology stacks and automate business operations. Rather than stitching together separate tools for newsletters, communities, advertising and audience management, publishers increasingly have the option to consolidate those functions within a single platform.

That consolidation could reduce software costs and technical overhead, particularly for smaller newsrooms and independent journalists. At the same time, it also concentrates more of a publisher’s audience data, monetization and workflow inside a single vendor, raising familiar questions about platform dependence as publishing infrastructure becomes more centralized.

beehiiv also previewed upcoming podcast advertising features, including dynamic ad insertion, signaling that the company intends to expand further into audio publishing as it broadens its reach beyond newsletters.

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OpenAI has acquired Technology Business Programming Network (TBPN), a daily live video and audio podcast focused on business and technology news, the company announced Thursday.

The deal puts one of the world’s leading AI companies in control of an editorial brand — a move that mirrors a long history of tech giants using media acquisitions to shape industry conversation. TBPN, hosted by John Coogan and Jordi Hays, will continue to operate independently on editorial decisions, according to OpenAI, which framed the acquisition as part of its broader mission to shape the public conversation around AI.

“As I’ve been thinking about the future of how we communicate at OpenAI, one thing that’s become clear is that the standard communications playbook just doesn’t apply to us,” Fidji Simo, OpenAI’s CEO of Applications, wrote in a blog post announcing the deal. “We’re not a typical company. We’re driving a really big technological shift.”

The acquisition arrives as OpenAI prepares for a potential initial public offering, raising questions about what influence the company might wield over both industry coverage and national AI policy. OpenAI chief global affairs officer Chris Lehane cited to CNN’s Hadas Gold the “long history of companies and entities owning and acquiring media properties,” pointing to Westinghouse Electric’s ownership of CBS and Microsoft’s partnership with NBC to launch MSNBC. CNN’s Brian Stetler noted in his Reliable Sources newsletter that a live-streaming show with a small but influential audience — where executive moves are treated “like sports trades” — will now financially support one of the leading AI companies.

TBPN’s team will also contribute to OpenAI’s broader communications and marketing efforts, Simo said, helping the company bring AI to audiences “in a way that helps people understand the full impact of this technology on their daily lives.”

The acquisition follows a familiar pattern. Jeff Bezos bought The Washington Post, Marc Benioff acquired Time magazine, Adobe purchased Search Engine Land, and Arrow Electronics took on Electronic Buyers’ News in the early 2000s. Each deal gave a tech company a direct voice through an established media brand — a dynamic now playing out at a moment when AI companies are actively courting both regulatory goodwill and public trust.

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By The Copilot, based on an interview by The Copilot, Pete Pachal

When a lawyer who’s also a published author tells you artificial intelligence doesn’t actually learn like humans, you should probably listen.

Jason Henderson is a corporate and transactional attorney who specializes in streaming and licensing deals. He also occasionally writes books (one or two of which may be part of a long-running science-fiction franchise), which means he understands copyright from both sides of the table. In this episode of The Media Copilot podcast, he walks through the messy reality of how AI companies acquire content, what fair use actually protects (and doesn’t), and why the courts care less about the theory of transformation and more about whether your product just destroyed someone’s business model.

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The conversation starts with training data but quickly moves to the sharper edge: what happens when AI doesn’t copy your article but replaces the reason anyone would read it. Henderson explains why indemnification clauses in licensing deals only work if the company promising to cover you can actually pay up, why insurance may not protect publishers from AI-related risks, and why the next battlefield won’t be scraped text but agents that browse the web like users and become nearly impossible to block.

Why this matters

Media companies are no longer just competing with each other. They’re competing with systems that can answer questions, summarize stories, and satisfy curiosity without ever sending a reader to the source. Henderson maps out how courts evaluate that substitution, why “transformative use” is both the most important legal concept and the hardest to pin down, and why the industry is moving toward deals even as the lawsuits pile up.

He also sees a harder problem coming: agentic AI that behaves like a person, not a bot. The legal frameworks assume you can tell the difference. The technology is making that assumption obsolete.

What we cover

What we cover

• Jason’s background in AI, licensing, and streaming deals, plus his work as a writer and publisher
• The “AI learns like humans” argument, why it is only an analogy, and where it breaks down
• Inputs vs outputs: why training data and what models produce raise different legal and business issues
• A clear explanation of the four-factor fair-use test

• Why the ability to recreate articles via prompting becomes a legal flashpoint, even if framed as a “bug, not a feature”
• What media companies actually care about most, ethics vs bottom line, and why market substitution dominates
• The deal side: how licensing agreements are evolving for AI, including tighter usage restrictions
• The risk side: indemnification and why it only works if the other party can actually pay
• Insurance gaps: why many companies may not be protected for AI-related data and content liabilities
• The emerging “agents” problem: bot blocking, user proxies, and the future of attribution
• Hope vs dismay: personalization that helps audiences find authentic creators vs settling for “good enough” synthetic content
• Why Jason expects turbulence near term, but a longer-term premium on human-authored authenticity

👤 Guest

🔗Jason Henderson    🔗https://www.linkedin.com/in/jasonhendersontx 

🔗Senior Attorney,JWL International  🔗https://jwlinternational.com/
🔗Founder,Castle Bridge Media      🔗https://www.castlebridgemedia.com/
🔗Co-host, Castle of Horror podcast (horror movie coverage)  🔗https://podcasts.apple.com/us/podcast/castle-of-horror-podcast/id447295500

Enjoyed this episode?

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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 AI and copyright: How media can decide between litigation or negotiation  appeared first on The Media Copilot.

By The Copilot

AI isn’t just changing how journalism gets made. It’s changing how journalism gets taught.

In this episode of The Media Copilot, host Pete Pachal sits down with Kris Hodgson-Bright, professor of digital communications and media at Lethbridge Polytechnic in Alberta, Canada, to unpack what happens when AI enters the newsroom and the classroom at the same time.

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Kris has seen journalism education evolve from high-volume print production to an online first, multi-platform workflow spanning campus news, radio, TV, and emerging formats. Now, he is putting AI directly into the curriculum, not as a shortcut for writing, but as a research assistant that can strengthen reporting, sharpen critical thinking, and help students confront one of the biggest challenges in modern media: bias and trust.

Pete and Kris explore where AI fits in journalism training, where it doesn’t, and why transparent guardrails matter. They also dig into the job market reality for new journalists and communicators, plus the promise of immersive storytelling, including 360-degree video, VR, and photogrammetry, as a way to deepen understanding and empathy.

Along the way, the conversation surfaces some of the most difficult questions facing the media right now: how much automation is too much, where responsibility still sits with the human journalist, and how educators can prepare students for an industry that is evolving faster than any syllabus. 

This is a grounded conversation about the future of media work: hopeful about what AI can enhance, and clear-eyed about the slippery slope toward low quality content and atrophied thinking.

Why this matters

As AI becomes embedded in every part of media, the next generation of journalists and communicators will be judged on more than writing skills. They will be judged on judgment: bias awareness, ethical decision-making, transparency, and the ability to use tools without surrendering the work of thinking.

What we cover

• How journalism education shifted from print heavy production to online-first publishing
• The right way to integrate AI into student workflows without outsourcing the writing
• Using AI to check for bias and improve historical context in local reporting
• What transparency and disclosure should look like in AI-assisted media
• Media law, ethics, privacy, and how to teach responsible AI use
• Why the journalism job market is harder and what students can do to stand out
• Immersive journalism, empathy, and what VR still gets right even without mass adoption
• Kris’s hopes and fears about AI’s long-term impact on media

👤 Guest

🔗Kris Hodgson-Bright | Lethbridge Polytechnic

🔗Kris Hodgson-Bright (@hodgsonkr) / Posts / X

🔗krishodgsonbright/LinkedIN 

Enjoyed this episode?

You can subscribe to The Media Copilot on Substack, Apple Podcasts, Spotify, or your favorite app. On YouTube?  Tap the Like button and Subscribe to the 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 Teaching journalists to use AI without losing critical thinking appeared first on The Media Copilot.

The Washington Post released its AI-generated podcast feature this week despite internal tests showing the technology repeatedly failed to meet the publication’s standards, according to an exclusive report by Semafor‘s Max Tani.

Between 68% and 84% of scripts generated by “Your Personal Podcast” failed quality tests across three rounds of evaluation, according to an internal review obtained by Semafor. The review’s conclusion was blunt: “Further small prompt changes are unlikely to meaningfully improve outcomes without introducing more risk.”

The product team recommended launching anyway.

“This is how products get built and developed in the digital age,” a Washington Post spokesperson told Tani. The company described the release as a “Beta” that would continue to improve.

Post staff described errors ranging from pronunciation problems to misattributed and fabricated quotes. The AI tool also inserted commentary, sometimes presenting a source’s quotes as the newspaper’s own position.

Editorial leaders pushed back hard. Post head of standards Karen Pensiero wrote to staff that the mistakes have been “frustrating for all of us.”

One editor was more direct in Slack messages shared with Semafor: “It is truly astonishing that this was allowed to go forward at all. Never would I have imagined that the Washington Post would deliberately warp its own journalism and then push these errors out to our audience at scale.”

The timing is particularly awkward. The launch came days after the White House created a site attacking journalists, with Post reporters among those targeted.

The Post isn’t alone in chasing AI audio. Yahoo released a similar product the same week. Google’s NotebookLM podcast generator became an early breakout AI tool. But most news organizations have kept AI behind the scenes, wary of handing their reader relationships to unreliable technology.

For the Post, already struggling with subscriber losses and talent departures, the gamble on AI-generated content represents a bet that iteration can outpace reputational risk.

The post Washington Post AI podcasts failed quality tests appeared first on The Media Copilot.