Content operations can run on instinct at a small scale. With a strong editorial team, a handful of trusted writers, and an understanding of voice, there’s usually enough discipline to keep the calendar moving.

But some businesses aren’t built that way. For media rollups, large affiliate networks, entertainment properties, sports brands, and other content-led businesses, publishing at triple-digit volumes per day makes sense. 

In some cases, it’s necessary to survive because content is the operating model rather than a marketing function, as it is in many B2B organizations.

At that scale, content strategies don’t break because of content. More often, they break because economics, systems, and editorial judgment stop speaking to each other.

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Not every content category can support that scale

That B2B distinction is important. If you sell a niche manufacturing ERP, you simply don’t need that scale of content. There’s not enough to publish. You’d be burning cash and operating outside the market.

Some categories have the depth and audience appetite required to sustain hundreds of daily articles. Sports is an obvious example. There are games, trades, injuries, recaps, rankings, interviews, opinion pieces, explainers, storylines, and the list goes on.

A business like The Athletic can support significant publishing volume because audience demand is real, while the revenue model includes subscriptions, direct sales, programmatic display, affiliate revenue, and likely other sources under the hood.

In Q2 2025, The Athletic generated $54 million in revenue, according to its last standalone financial report. Of that, 64% came from subscriptions, 26% from advertising, and 10% from affiliate and licensing revenue.

When most revenue comes from people actively choosing to pay, editorial quality is no longer a judgment call. It’s the most important commercial requirement. Economics, systems, and editorial judgment are forced to speak the same language.

Other models are more fragile. The clearest example is when monetization is driven primarily by programmatic display measured by RPM (say, more than 70% of revenue), with content rewritten from existing coverage or produced around short-term search and social opportunities, where margins require high output and very low production costs.

The formula is simple:

• Revenue = (Pageviews ÷ 1,000) × RPM
• Profit = ((Pageviews ÷ 1,000) × RPM) − Production Cost

So if a website earns 4,000 pageviews per article at a $16 RPM, it generates $64 in revenue.

Subtract production costs. The margin gets thin fast.

To generate meaningful profit, the organization has little choice but to publish hundreds of articles per day while doing everything it can to maintain quality, discoverability, and audience trust.

That’s where these content strategies break.

A content model that breaks under its own weight

More content can look like more revenue. But the spreadsheet tells only a fraction of the story.

Numbers don’t show editorial quality, whether thinner work is being produced to feed the machine, or whether monetization decisions are inadvertently weakening the asset.

Data surfaces where that drift starts. Points captured within a CMS include:

• Content types.
• Categories.
• Tags.
• Author and editor attribution.

Cross-referenced with sessions, pageviews, pageviews per session, session duration, RPM, source/medium, and other metrics.

That lets analysts drill into content types by source, category, and tag, while providing visibility into top performers, opportunities to optimize the ad stack by content type, and more.

Here are some simple scenarios that highlight what that looks like in practice:

• An analyst runs a pivot table on an entertainment property and notices higher pageviews from Google Discover per article among list content in the reality television category tagged to a specific show. Since traffic equals more revenue, the conclusion is to write more lists about that show.
• An analyst notices RPM is lower on features than lists, even though average word counts are the same. The reason is that the ad stack serves programmatic display after each image, and features have four times fewer images than lists. Since images drive higher RPM, the conclusion is to increase the number of images in features or reduce the number of published features in favor of more lists.

Fairly simple stuff on the surface. However, this is where judgment becomes the difference between a healthy operation and one that’s quietly eating itself.

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The systems that prevent failure

Scaling these operations past 100 writers is mainly a question of whether the business has the systems, data, and judgment required to keep the operation from collapsing under its own volume.

It’s worth noting that 100 writers is rarely just 100 writers. For many of these businesses, it’s 100 writers across a dozen properties, which is actually more than 1,000 writers when you account for the full footprint.

Independent publishers don’t typically hit that scale because the infrastructure requires a level of investment they most likely don’t have access to.

That infrastructure includes clearly defined communication structures for editors, project management ownership, and comprehensive guides covering writing, linking, imagery, social, and CMS usage.

Without them, standards can degrade unpredictably across properties, and editors lose the ability to diagnose why or quickly point people toward resources when putting out fires.

On the data side, granularity is a must. Without consistent tagging and categorization built into the CMS from the start, analytics can become too fuzzy to act on.

Performance needs to be attributable at every level, rolled up into a P&L for each property, and then rolled up again across the conglomerate.

Technical infrastructure is essential as well, often in ways editorial teams wouldn’t expect.

If you consider how to get images into Google Discover, for example, it requires CDN delivery within specific guidelines. That’s more of an engineering problem than an editorial one. User roles and permissions across CMS and revenue dashboards are another example, along with the development resources required to implement the CMS architecture needed for data capture and reporting in the first place.

Proprietary systems can also be beneficial depending on a business’s scale. If you’re a rollup with a dozen properties operating on one or two CMS templates, it’s much easier to make bulk optimizations or accelerate the integration of newly acquired properties.

Channel distribution isn’t static either. Platform value to publishers shifts. Think about when Facebook stopped sharing news links in Canada. It changes the economics of whether a platform is worth optimizing for. Consistent monitoring and testing need to be built in.

The judgment that keeps it from collapsing

The systems above create favorable conditions, but they don’t guarantee sound judgment.

Let’s revisit one of the examples above:

• The ad stack serves programmatic display after each image. Editorial guidelines require one image per entry in a list. This generates higher RPM across Google Discover traffic for lists with 20 thin entries at 1,000 words than for a well-constructed feature.

If you’re looking only at the spreadsheet, you’d favor doing as much of that as possible. That’s tempting, especially if employers incentivize target RPMs or sessions per article as KPIs tied to bonus compensation.

However, thin content at volume isn’t ideal for organic visibility. Once readers and search engines encounter too much low-quality output, the traffic disappears.

You’d essentially optimize for short-term yield, reinforce that behavior through employee bonuses, and damage the asset in the process.

Or another example:

• An editor notices that updating a datePublished timestamp drives a short-term bump in traffic. The conclusion is to roll out timestamp updates across hundreds of pages.

The problem is that doing it at scale without substantive edits and strict guidelines may create distrust. That’s the judgment call.

Three things need to be held in tension: economic logic, infrastructure and systems, and the judgment not to sacrifice long-term gains for short-term wins.

While that sounds like common sense, these responsibilities are often owned by different people who don’t speak the same language.

Finding a way to bridge that gap is the most important challenge in a scaled content operation. Diversified revenue streams like The Athletic’s help enforce that alignment.

Otherwise, your content strategy will probably fail when you scale past 100 writers. And the examples above are just two of hundreds of scenarios where the spreadsheet points one way, and the right decision points another.

Get it right, and you can scale to 1,000 writers.