Rising value of Original Content in the AI Age

Generative AI's rise heightens the need for original content and innovations to counter risks and maintain originality

TL;DR As generative AI becomes more prevalent, the value of original, human-written content rises significantly. While this technology has the potential to expedite content creation and maintain consistency, it also risks standardizing viewpoints, stifling critical thinking, and reducing accountability. Ensuring the originality of content is crucial for both users and AI systems like large language models and search engines, which should prioritize original content to avoid error propagation through self-training. Looking to the past for guidance, industries have evolved in response to risks posed by major inventions, such as credit cards and the Internet, by developing innovations to address those challenges. Similarly, the generative AI industry is expected to advance and give rise to successful companies dedicated to reliably detecting original content.

Internet pre-generative AI

Before the emergence of generative artificial intelligence, content creators such as bloggers, writers, and social media personalities were solely responsible for producing articles on their personal sites or other online platforms. When you perused a piece, it was typically the outcome of the content creator's dedication to researching, conceptualizing, and composing a well-structured and captivating story. This endeavor demanded not only an in-depth grasp of the subject matter but also exceptional writing abilities to convey intricate concepts effectively. As a result, the value and originality of the content were intrinsically connected to the content creator's knowledge, enthusiasm, and commitment, making each work a distinctive expression of the individual who penned it.

How Generative AI works

Before we talk about Internet Post Generative-AI let’s discuss first how it works. Generative artificial intelligence creates content by examining and learning from a vast array of online sources, such as blogs, articles, and websites and using LLMs (Large Language Models) to “understand” the content and context within the articles. Through this learning process, it can generate well-organized and coherent written pieces that can later be published on the internet. Generative AI will also (unknowingly) consume and re-train itself from its own previously generated content, thereby creating a potential feedback loop. 

Internet post-generative AI

Thanks to Generative AI, many different applications for crafting AI-content have emerged. Creating content with these tools is so easy that it's increasingly plausible that a substantial proportion of content, including articles and blog posts on the Internet, will be authored by AI systems in the near future. Such a development has the potential to expedite the content creation process and aid writers in producing well-organized and cohesive text more efficiently. From a business perspective, AI-generated content enables the scaling of content output and the preservation of consistency across various channels.

However, the proliferation of AI-generated content comes with its share of disadvantages. As AI-authored content becomes more prevalent, the originality and distinctiveness of human-generated articles may wane, resulting in a standardization of viewpoints and writing styles. Furthermore, the dependence on AI-generated content could stifle critical thinking and imagination, as writers might increasingly rely on AI systems to produce text rather than cultivating their own one-of-a-kind perspectives. 

This may also contribute to reduced accountability and transparency, as discerning the true authorship of a piece and the expertise behind it becomes more complicated. Additionally, the possibility that AI-generated content could perpetuate biases and misinformation, unless properly monitored, raises ethical questions about the influence of such technology on the caliber of information available on the internet.

This is why Geoffrey Hinton, known by many as the “Godfather of Artificial Intelligence”, calls misinformation as one of top risks associated with AI.

Valuing original content

As the Internet becomes saturated with content generated by AI, the value of original, human-written content increases significantly. It is crucial for people to be able to distinguish between content created by humans and content generated as a response to prompts from AI language models like ChatGPT. While reputable websites and journals, such as The Wall Street Journal and The Washington Post, may not face trust issues, other websites or blogs can benefit significantly from demonstrating that their content is not simply copied from ChatGPT outputs. In order to build trust with their audience, writers must find ways to prove the originality of their content.

Original content is valuable not only for users but also for large language models (LLMs) and search engines like Google. Prioritizing the use of original content in training LLMs and search algorithms is essential, as relying on outputs from their own models can lead to a phenomenon known as "self-training" in machine learning. This approach may result in error propagation, where the model unintentionally reinforces its own mistakes and biases if the initial predictions are inaccurate or biased. Thus, it is essential to emphasize the importance of original content for both users and AI systems.

How industries evolve

When considering the future solutions to the challenges posed by generative AI, it is helpful to examine how industries have responded to major inventions in the past. For instance, the introduction of modern-day credit cards provided widespread access to easy credit, but it also came with risks. As a result, innovations such as FICO scores and fraud detection mechanisms were developed and continually improved to make credit cards more secure.

Similarly, the invention of the Internet connected people worldwide, but it also introduced new risks, leading to the birth and growth of the cybersecurity industry. The same pattern is expected with generative AI, which offers numerous benefits but also requires innovations to mitigate its risks. One such challenge is the reliable detection of original content. In response, it is anticipated that significant advancements and successful companies will emerge in this space, dedicated to addressing this particular issue.