In the Age of Liquid Content, the Smallest Unit Wins
By Lukas Luft
In my past corporate life, I co-authored two global media studies at a large consulting firm. Months of research, thousands of consumer data points, dozens of charts and insights, all packaged into one big report. It was published once, promoted for a couple of weeks, and then it sat on a website. Meanwhile, I kept thinking: every single chart in this thing could be its own LinkedIn post. Every data point could surface in a different conversation. But that’s not how scale-driven content marketing works. So we shipped the report and moved on.
That experience stuck with me. Because the individual pieces inside that report were more useful, more shareable, and more relevant than the report as a whole. And now, with AI reshaping how people discover information, the gap between what marketers produce and what actually reaches audiences is getting wider. Understanding why and staying in control requires looking at two forces that are converging on the same outcome.
The content marketing playbook assumed a reader on the other end. That assumption is breaking.
The Visibility Problem
The first is external. AI is collapsing the information supply chain. When a professional asks ChatGPT or Perplexity a question, they get a synthesized answer. No article to read. No publication to visit. No display ad to see. No brand quoted in a journalist’s story. The mechanisms that gave B2B brands visibility for years—earned media mentions, SEO-driven content, programmatic ads alongside articles—are losing surface area.
The second force is self-inflicted as every brand now publishes at scale, with AI making it cheaper. The result is a flood of interchangeable blog posts, whitepapers, and LinkedIn carousels that audiences scroll past. Content volume went up. Differentiation went to zero.
The content marketing playbook assumed a reader on the other end. That assumption is breaking.
Both forces push audiences toward the same behavior: retreating to curated or AI-mediated sources where someone, or something, has already done the filtering.
From Content to Context Assets
So if the old playbook is losing ground, what takes its place? It’s not about more content but about better units of content.
The opportunity is for marketers to stop thinking of their output as standalone content and start thinking of it as contextual assets: smaller, self-contained units of information that are useful alongside other content.
A note on “context”: In advertising, this word usually means where an ad appears. This article uses it differently: the background information that puts something in perspective. The material a professional would normally open five browser tabs to find.
It’s not a full case study PDF, but rather a single-page graphic showing the before-and-after results. Not a 45-minute webinar recording, but a 90-second clip of the key insight. Not a 3,000-word blog post about campaign performance, but a single data visualization showing the result.
Context requires atomized content: broken down into its smallest useful units so each piece can travel independently and be placed next to something relevant, whether that’s a news article, a platform feed, or a generative AI response.
And here’s where volume comes back in, but differently. The unit of output shrinks, but the number of permutations grows. If a performance chart can be tailored for different industries or regions, it should be. If the same campaign data can highlight two different angles in two different layouts, produce both. One underlying insight, many tailored versions, near-zero marginal cost per additional permutation.
The brand gets visibility not by publishing a competing article but by providing the background information that makes someone else’s content more valuable.
What This Looks Like in Practice
Companies used to get market research by hiring a consulting firm. Now many turn to ChatGPT. But there is a wealth of publicly available data that marketers already have—or can easily access—that many never think to repackage.
Consider campaign performance. You ran a LinkedIn campaign that doubled your MQL conversion rate from 4% to 8%. That result is sitting in a quarterly report nobody outside your team will read. Pull the key metric out. Put it in a single chart with clear attribution. Now that chart can surface alongside industry articles about LinkedIn advertising effectiveness, or get cited when someone asks an AI assistant about B2B conversion benchmarks. The insight is the same. The discoverability is completely different.
Case studies are another goldmine that most companies leave buried. A traditional case study is a multi-page PDF on your website. But inside it are three or four assets waiting to get out: an infographic of the client’s before-and-after metrics, a short video testimonial clip, a chart showing the timeline of results. Each one is self-contained, carries your brand, and can be embedded or cited in contexts far beyond your own site.
Then there’s publicly available data that nobody else is bothering to visualize. Government datasets, industry association reports, platform-published benchmarks. All available for free. A marketer who builds an original, well-designed table comparing average CPMs across Meta, LinkedIn, X, TikTok, and YouTube has created a context asset. When a journalist or AI system needs that comparison, the brand behind the table gets the visibility.
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Then there’s event content and video. Most companies upload a full 30-minute conference keynote to YouTube and call it done. But somewhere in those 30 minutes is a single insight worth 60 seconds and a title card. The publishers and brands that are already getting this right don’t publish long recordings. They cut short-form clips from TV conversations, panel discussions, and interviews, each one focused on a single talking point. Those clips get picked up by context platforms and surfaced alongside related news articles and analysis. A reader exploring a story about, say, streaming market shifts sees a 90-second clip of a relevant executive interview right next to the article.
The clip is more useful than the full recording, more shareable, and far more likely to be watched. The same logic applies to branded podcasts: a 60-minute episode contains multiple standalone insights that are more useful as individual audio clips than as one long listen.
AI makes all of this scalable. One case study result can become a chart, a narrated audio clip, a short animated video, and a static infographic card. Four distinct assets from one input. Without AI, each format conversion is a production project. With it, the cost approaches zero. Brands can now produce a full portfolio of context assets from every insight they generate, at a fraction of what it would have cost even two years ago.
The brand gets visibility not by publishing a competing article but by providing the background information that makes someone else’s content more valuable.
Think of it like a fashion ad in Vogue. It works because it reaches someone already exploring that world. The reader is engaged with fashion editorial, and the ad becomes relevant because it matches the interest at the exact moment of engagement. Now apply that logic to B2B: a brand’s chart, video, or case study visual becomes valuable when it appears alongside content the reader is already exploring. Instead of paying for placement, the brand becomes the context.
The brands that figure this out first will own a new category of visibility—one that holds up as AI reshapes how professionals find and consume information.
Where This Is Already Happening
A new class of platforms is starting to surface third-party context assets alongside news and analysis automatically, matching charts, videos, podcasts, and reports to the content someone is already reading.
Particle News does this on the consumer side, using AI to assemble context around news stories for general audiences. Peaklight does it for professionals, surfacing data visualizations, executive bios, video interviews, and podcast episodes alongside media and technology industry news. In both cases, the brands and creators who produce the context assets gain visibility not through their own distribution but through relevance to what someone else is reading.
But context doesn’t only surface alongside articles. It also surfaces as cited answers. When someone asks Perplexity “what’s the average LinkedIn ad CPM in 2026?” or uses an AI assistant to research a competitor, the sources that get cited are the ones with clean, structured, factual assets. A well-produced chart with clear attribution is more likely to be pulled into an AI-generated answer than a 3,000-word blog post. The brand behind that chart gets visibility through citation, without the user ever visiting the brand’s website.
These are two distribution surfaces for the same asset: context platforms that surface it alongside relevant content, and AI systems that cite it in response to a direct question. Both reward brands that produce atomized, structured, visual context.
That global study I mentioned at the start? It had dozens of charts and insights that could have surfaced in industry conversations for months. Alongside news articles, inside AI-generated answers, embedded in other people’s analysis. Instead, it was published once and forgotten. The pieces inside it were more valuable than the package. Now the tools and platforms exist to treat them that way.
The brands that figure this out first will own a new category of visibility—one that holds up as AI reshapes how professionals find and consume information.
The ones still packaging their best insights into big reports and hoping someone downloads them are playing a version of the game that’s already over.
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About Lukas Luft
Lukas Luft is the founder of Peaklight, an AI-powered news platform that contextualizes journalism for media and technology professionals. Before founding Peaklight, Lukas was a strategy consultant at Accenture, where he co-authored the firm’s global “Media Reinvent for Growth” study and advised media and entertainment companies on business and technology strategy. Lukas started his career in digital marketing at Kantar Millward Brown, an agency specializing in brand equity research, advertising effectiveness, and consumer perception of brands.