The Media Flywheel: Review and Opinion

This article is part 7 of a series called ‘Reality Check on Media Strategy’. Many seasoned professionals in the media industry argue that the media business is

Many seasoned professionals in the media industry argue that the media business is inherently dynamic. Therefore, tactical agility and rapid time-to-🧃market are more critical than other trade-offs, such as adherence to strategy, the quality of the technology built, and maintaining data standards.

Yes, this is partially true because media companies earn by capitalizing in bursts of short-lived topics that are in public imagination. This articulates why content teams need to be constantly on their feet but it doesn’t explain why business-product-technology functions value tactical agility over strategic asset building.

My working theory is that media companies, having ceded strategic control to algorithmic marketplaces, are forced into premature revenue diversification to mitigate black swan risks and seasonal fluctuations. This leads to a portfolio of revenue streams, each generating small amounts of money.

Let’s explore both ideas — revenue maximization and revenue diversification.

Revenue Maximization

To maintain focus and maximize ROI, businesses typically prioritize maximizing revenue from a single model before considering diversification. Below are few examples of successful revenue maximization:

Netflix: Streaming service

Spotify: Music streaming service

Coinbase: Cryptocurrency exchange

Stripe: Payment processing service

Zoom: Video conferencing service

These businesses have prioritized building a single revenue stream and scaled it to become clear leaders in their respective categories. They have not diversified into other models prematurely.

Revenue Diversification

Media companies, like hedge funds, diversify into a portfolio of revenue streams: direct and indirect ads, sponsored content, affiliate marketing, subscriptions, and micro-transactions.

In 2023-2024, OpenAI has signed deals with News Corp., Financial Times, Associated Press, Axel Springer, Le Monde, Reddit, where media companies provide content for AI models in exchange for financial compensation. This quick-win licensing strategy helps diversify revenue in the short-term while shifting costs/externalities that are hard to measure, like impact on brand and direct relationship with audience, into the future.

Why It Matters

Premature revenue diversification has several drawbacks:

It prevents the company from scaling a single revenue stream to become a clear category leader.

This leads to lower revenue per stream and higher costs to service each stream.

It makes the company less valuable to partners and investors because there is no clear franchise.

It prevents the company from making bold bets on new technologies because there is less money available for R&D.

It makes the company more vulnerable to changes in the market because there is less diversification of revenue sources.

For example, if media companies had focused on building their own algorithmic marketplaces instead of prematurely diversifying into quick-win licensing deals, they would have been able to make bold bets on AI models that could have sustained gains over the long term.

Implications on AI

In the absence of clear abstractions defined by the product function, the pressure to make decisions quickly results in side effects that make deploying AI at scale a challenge: concept drift, data drift, label drift, feature drift, covariate drift, etc. Let’s evaluate two of these:

AI models learn to predict output by relying on patterns in the input data. Concept drift occurs when this relationship is no longer valid. For example, if the product and editorial teams introduce a new content format that shifts user interest, concept drift will occur. This is especially true if the feature was launched to reduce time to market without updating data standards and algorithms.

Data drift occurs when an undocumented change to data structure, semantics, or distribution happens. For example, sudden changes to the user interface result in a change in how users navigate the website. This is one of the reasons why most algorithmic marketplaces, like Google, X, Facebook, Instagram, LinkedIn, etc., have broadly maintained their user experience over the last two decades.

Constant diversification and frequent changes make the overall system highly susceptible to drift, which makes sustaining gains from AI models unreliable.

Conclusion

To escape this predicament, media companies must regain strategic control by investing in becoming algorithmic marketplaces themselves. If transformation is not feasible, they must accept low ROI due to revenue diversification as a necessary cost of doing business.

Curious how I’m managing to write? I created a CustomGPT for myself, which serves as my go-to editor and audits my first draft. Here’s the link—give it a spin! It’s free to use.

https://chatgpt.com/g/g-hgI62sWPm-mediaflywheels-review-opinion-pieces

Want to republish it? This post was released under CC BY-ND — you can republish it as is with the following credit and backlinks: ‘Originally published by Ritvvij Parrikh on The Times of India. The author retains the copyright and any other ancillary rights to the post.