媒体飞轮：评论和意见

本文是“媒体策略的现实检验”系列文章的第 7 部分。许多经验丰富的媒体行业专业人士认为，媒体业务是

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.

许多经验丰富的媒体行业专业人士认为，媒体业务本质上是动态的。因此，战术敏捷性和快速上市时间比其他权衡更为重要，例如遵守战略、所构建技术的质量以及维护数据标准。

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.

是的，这在一定程度上是正确的，因为媒体公司通过利用公众想象中的短暂话题来赚钱。这阐明了为什么内容团队需要不断站稳脚跟，但它并不能解释为什么业务-产品-技术功能更看重战术敏捷性而不是战略资产建设。

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.

我的工作理论是，媒体公司将战略控制权让给算法市场后，被迫过早实现收入多元化，以减轻黑天鹅风险和季节性波动。这会产生一系列收入流，每个收入流都会产生少量资金。

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

让我们探讨一下这两个想法——收入最大化和收入多元化。

Revenue Maximization

收入最大化

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:

为了保持专注并最大化投资回报率，企业通常会优先考虑最大化单一模式的收入，然后再考虑多元化。以下是成功实现收入最大化的几个例子：

Netflix: Streaming service

Netflix：流媒体服务

Spotify: Music streaming service

Spotify：音乐流媒体服务

Coinbase: Cryptocurrency exchange

Coinbase：加密货币交易所

Stripe: Payment processing service

Stripe：支付处理服务

Zoom: Video conferencing service

Zoom：视频会议服务

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.

这些企业优先考虑建立单一收入来源，并扩大其规模，成为各自类别中明显的领导者。他们还没有过早地多元化发展其他模式。

Revenue Diversification

收入多元化

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

媒体公司，如对冲基金，多元化收入来源组合：直接和间接广告、赞助内容、联盟营销、订阅和微交易。

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.

2023-2024年，OpenAI已与新闻集团、金融时报、美联社、Axel Springer、Le Monde、Reddit签署协议，媒体公司为AI模型提供内容以换取经济补偿。这种快速获胜的许可策略有助于在短期内实现收入多元化，同时将难以衡量的成本/外部性（例如对品牌的影响以及与受众的直接关系）转移到未来。

Why It Matters

为什么它很重要

Premature revenue diversification has several drawbacks:

过早的收入多元化有几个缺点：

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

它阻止公司扩大单一收入来源，成为明显的类别领导者。

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

这会导致每个流的收入降低，并且服务每个流的成本更高。

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

由于没有明确的特许经营权，这使得公司对合作伙伴和投资者的价值降低。

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

它阻止公司在新技术上大胆押注，因为可用于研发的资金较少。

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

由于收入来源的多元化程度较低，这使得该公司更容易受到市场变化的影响。

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.

例如，如果媒体公司专注于建立自己的算法市场，而不是过早地多元化进入快速获胜的许可交易，他们就能够大胆地押注于能够长期持续收益的人工智能模型。

Implications on AI

对人工智能的影响

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:

在缺乏产品功能定义的清晰抽象的情况下，快速做出决策的压力会导致副作用，使大规模部署人工智能成为一项挑战：概念漂移、数据漂移、标签漂移、特征漂移、协变量漂移等。让我们评估一下其中两个：

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.

人工智能模型学习根据输入数据中的模式来预测输出。当这种关系不再有效时，就会发生概念漂移。例如，如果产品和编辑团队引入一种新的内容格式来改变用户的兴趣，就会发生概念漂移。如果推出该功能是为了缩短上市时间而不更新数据标准和算法，则尤其如此。

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.

当数据结构、语义或分布发生未记录的更改时，就会发生数据漂移。例如，用户界面的突然变化会导致用户浏览网站的方式发生变化。这就是大多数算法市场（如 Google、X、Facebook、Instagram、LinkedIn 等）在过去二十年中基本保持用户体验的原因之一。

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

持续的多样化和频繁的变化使得整个系统极易发生漂移，这使得人工智能模型的持续收益变得不可靠。

Conclusion

结论

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.

为了摆脱这种困境，媒体公司必须通过投资成为算法市场来重新获得战略控制权。如果转型不可行，他们就必须接受因收入多元化而导致的低投资回报率作为开展业务的必要成本。

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.

好奇我是如何写作的吗？我为自己创建了一个 CustomGPT，它作为我的首选编辑器并审核我的初稿。这是链接——试一试！它可以免费使用。

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.

想要重新发布吗？这篇文章是在 CC BY-ND 下发布的——您可以按原样重新发布，并带有以下来源和反向链接：“最初由 Ritvvij Parrikh 在《印度时报》上发布。”作者保留该帖子的版权和任何其他附属权利。