AI media content: Capabilities of AI text generators improving at breakneck pace

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  Quantumrun Foresight
• February 15, 2022

Insight summary

Artificial intelligence (AI) is transforming the media landscape by creating content that mirrors human writing, leading to a shift from human journalists to AI systems. Despite the efficiency, there are challenges such as potential bias and factual errors, largely due to the quality and diversity of the training data used in deep learning and neural networks. As AI continues to improve, it could lead to significant changes in job markets, business efficiency, policy regulations, and even the democratization of information.

AI media content context

AI is witnessing a significant shift with the emergence of large language models like ChatGPT. These AI programs are becoming increasingly adept at creating engaging content that closely mirrors human writing. This development is reshaping the landscape of online media services, as more and more organizations are beginning to replace human journalists with AI systems. These systems automate the process of selecting and producing news content, streamlining the content creation process.

However, despite the efficiency and scalability offered by these AI systems, there are still hurdles to overcome. One of the most pressing issues is the potential for bias and the occurrence of unusual factual or cognitive errors in AI-generated text. This error is largely due to many AI systems today relying on deep learning and neural networks, which are essentially pattern-finding programs trained on large datasets. The quality of the output is heavily dependent on the quality and diversity of the training data.

The process of generating AI media content involves training these AI systems on vast amounts of text, such as books, online encyclopedias, and news articles. The AI systems develop a form of understanding of how information is written, collected, read, referenced, summarized, and rewritten for different contexts. As the training sets become more comprehensive and diverse, and as the AI systems continue to learn from positive results, their ability to produce accurate and high-quality results improves. 

Disruptive impact

The automation of simple journalistic tasks is just the beginning. As AI continues to improve, it is plausible that we will see entire editorials and journal articles being written by AI. This shift could lead to a transformation in the operational dynamics of news outlets. They may need to adapt by training their staff to work in tandem with AI, including learning how to identify and correct biases and factual errors in AI-generated content.

For individuals, it could mean a shift in the job market, with an increased demand for skills related to managing and refining AI outputs. For businesses, it could lead to cost savings and increased efficiency as AI can produce content at a faster rate than humans. However, businesses may also need to invest in quality control mechanisms to ensure the accuracy and reliability of AI-generated content.

From a policy perspective, the large-scale adoption of AI in media content creation could require the development of new standards and regulations. Policymakers may need to address the potential for AI models to adopt the biases of their creators or the biases inherent in the training sets they use. These measures could involve creating guidelines for the development and use of AI models, including ensuring transparency and accountability in AI systems. 

Implications of AI media content

Wider implications of AI media content may include: 

• The ability to produce research papers that introduce new knowledge.


• Automating parts or select types of hard investigative journalism.


• Creating new fiction or nonfiction content in the writing style of current or deceased writers.


• The eventual ability to for these AI systems to be applied to the production of audio, graphic, and video content production.


• A significant reduction in the carbon footprint of the media industry, as AI systems, once set up, consume less energy than human-operated systems, contributing to more environmentally friendly media production.


• The democratization of information as AI systems can produce content in multiple languages and formats, leading to more inclusive access to news and information.


• The potential for AI to generate misleading or false information increasing, leading to challenges in maintaining the integrity of news and information, and requiring stronger fact-checking mechanisms.


• A shift in economic power as companies that can afford to invest in AI technology gain a competitive advantage.

Questions to consider

• How will the field of journalism be changed when AI, not trained journalists, increasingly report the news?


• What kind of media biases may AI introduce to the media landscape?


• How will policies governing libel, fake news, and other media concepts be changed to accommodate AI-generated content?