Amazon is building a marketplace where publishers can license their content directly to AI companies. Every executive who creates content, publishes research, or owns proprietary data needs to read this one.

The story broke February 10, the same day Amazon hosted its own Publishing Symposium in New York City, where they gathered publishing decision makers to discuss, among other things, content monetization in the AI economy. The Information reported that Amazon had been circulating internal slides about the marketplace plan at that event. When asked directly, Amazon didn't deny it. What they did say: they have "long-lasting, innovative relationships with publishers across AWS, Retail, Advertising, AGI, and Alexa."

For years, AI companies scraped copyrighted content without asking and without paying. Publishers sued. Negotiations started. Now Amazon showed up to a room full of publishers with a licensing marketplace and a slide deck. This is the Napster moment for written content, except this time the industry is being invited to negotiate the terms before the courts decide them.

Here's the shift happening in real time: Microsoft launched its Publisher Content Marketplace with AP, Condé Nast, Hearst, Vox Media, and USA Today on board. OpenAI signed direct deals with the Associated Press, News Corp, and The Atlantic. Amazon pays the New York Times more than $20M a year for content access. If this marketplace launches, Amazon becomes the infrastructure sitting between every content creator and every AI company in the world.

Spotify CEO Gustav Söderström told investors that the company's most senior developers stopped writing code themselves in December. They now supervise AI-generated output instead. He warned that what teams build today may be obsolete within a month, and called the shift something the company is "hell-bent" on leading. This isn't a startup experiment. This is one of the world's most sophisticated engineering teams making a deliberate, strategic pivot. The question for every leader in the room is no longer "should we explore AI?" It's "what are we supervising, and what are we still doing by hand that we shouldn't be?"

Oura is the exclusive Official Wearable of Team USA and the LA28 Olympic and Paralympic Games, supporting athletes across sleep, readiness, and recovery. They also announced this week the launch of their first proprietary AI model built specifically for women's health, covering everything from early cycles through menopause. Hosted on Oura's own servers to protect your data, it’s designed to be clinically grounded and emotionally supportive. Their chief medical officer called it a fundamental shift in how AI gets deployed in the health sector.

Buried in two recently published patents is where it gets even more interesting. The first outlines a system where the Oura Ring controls AR smart glasses, displaying your biometrics, heart rate, stress, and temperature directly in your line of sight. The second shows the ring connecting wirelessly to VR headsets like Meta or Apple Vision Pro, with your body data influencing what you see inside the virtual environment in real time. Several inventors listed come from AR teams at Meta, which signals this is more than exploratory. The ring that tracks your sleep is quietly becoming mission control for your full body health.

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TechCrunch's analysts call 2026 the shift from "vibe coding" to "vibe working," a term Anthropic's head of enterprise product used to describe what happens when AI starts completing full knowledge work tasks, not just code. Agentic workflows are moving from demos to daily practice. For executives, this means the tools your teams are experimenting with in Q1 will likely be embedded in operations by Q4.

Think of it this way. Vibe coding was asking AI to write the code while you watched. Vibe working is asking to complete a set of various tasks: AI to research the vendors, flag the contract risks, write the memo, and book the meeting. You set the goal. AI completes the tasks while you focus on even bigger goals with this newfound treasure trove of data and time.

Pick one thing your team does every week that is mostly research or summarizing. Run it through an AI workflow this quarter. By Q4 it won’t feel like a pilot anymore. It will just be how you work.

LinkedIn watched its B2B awareness traffic collapse by up to 60%. Google rankings never moved. AI Overviews were answering questions directly inside Google and nobody needed to click through anymore. You write a strong piece of content. Google ranks it number one. Someone searches the topic. Google's AI reads your article, summarizes the answer directly on the page, and the person gets what they needed without ever visiting your site. You won, technically. You just didn't get the visit.

Your content is getting read. Just not by humans.

Your buyer got your ideas, your research, your expertise. Your brand got a footnote, if you were lucky. The top of your content funnel is already a ghost town. The rest is on borrowed time.

LinkedIn's response was to form a ten-department AI Search Taskforce, restructure every piece of content for AI readability, build new analytics to track visits from AI citations, and create a process to correct misinformation about themselves appearing inside AI answers, a job function that did not exist two years ago. They went public with all of this on January 28, 2026. Four weeks ago. Their new operating principle: "Be seen, be mentioned, be considered, be chosen." LinkedIn, one of the most sophisticated content operations on the internet, had to rebuild from scratch. If LinkedIn had to rebuild, what makes you think your strategy is immune?

It took LinkedIn two years from spotting the threat to going public with the rebuild. Most organizations haven't started yet.

The measurement that matters now is citations, not clicks. How often does AI mention your brand when your buyers ask questions in your space? That is your new share of voice. Most organizations have never checked it.

This is called GEO, Generative Engine Optimization, and the writing approach is fundamentally different from what your content team was trained on. Answer first, always, in the first 70 words before any context. Write in short extractable paragraphs that stand alone out of context. Lead with specific data and statistics, pages with verifiable numbers show 30 to 40 percent higher visibility inside AI answers. Use question-based headings that match exactly how your buyers talk to AI. Be consistent across every channel because AI builds a picture of your authority from everything it can find about you.

A practical starting point: I have worked with Pendium for my own AI agents and trust them. They offer a free visibility scan showing what AI is currently saying about your business across ChatGPT, Claude, and Gemini at pendium.ai. Two minutes, no engineering required. Use code ASTRID if you decide to go deeper. The gap between what you think AI says about you and what it actually says is often significant. That gap is costing you buyers you never knew you lost.

Seedance 2.0 lit Hollywood on absolute fire this week by allowing anyone to deepfake the IP of every actor in the world. They’ve since cut out this feature, at least in the US. and they are in for a long legal battle.
This is an eye opener that changes your risk calculus at the executive level.
Video is no longer 100% reliable confirmation of authenticity.

Before you trust any video clip, run this check: Does it come with a verifiable original source link? Is the source a publication with editorial accountability? Can you find the same clip indexed on the outlet's own site? If a video arrives via DM, forwarded email, or social share with no traceable original, treat it as unverified until confirmed.

The higher the stakes, the more you verify. A viral clip of a competitor's CEO, a product recall video, a "leaked" board meeting. All of these are now in play. Your team needs a shared policy on verification before acting on video content. If you don't have one, this week is the week to start that conversation. And I’m not saying this to instill fear, it’s just going to be common good practice to verify from now on, because it is indeed very hard to tell what is fake and what's real nowadays.