Is Generative AI a job killer?
June 21, 2023Generative AI automates the creation of content. So is it the end of content creation? Not quite. Some people seem to think so.
While I don’t, it will change the nature of content creation jobs. As a content creator your traditional role can be much improved with using AI technology. Sadly many employers will use it to replace you. So you need to learn how to use it to augment your skills. On the other hand you and your employer can use this technology to cost effectively generate an explosion of crappy content. Given that this may be your future I have tried to not only suggest what I think will be a good path to using AI technology, but also how to more efficiently generate crap content.
The traditional content creator
In the traditional approach to content creation, individuals or teams come together to brainstorm ideas, draft content, edit, and revise. The process is time-consuming, often requiring deep expertise in the given domain. The content creator’s role was not just about generating content, but also about understanding the audience, identifying trending topics, maintaining a consistent tone and style, and managing distribution channels. In essence, they wore many hats and required a wide array of skills.
The generative AI content creator
Generative AI, including machine learning models such as GPT-3 and GPT-4, use large amounts of data to predict and generate content. They can compose human-like text, generate realistic images, create music, and more. By learning from vast amounts of data, these models can mimic different styles, adapt to various tones, and cater to an array of topics. The integration of these technologies into content creation implies profound changes in the role of the content creator.
A New Collaboration
As generative AI technologies become more integrated into the content creation process, content creators will transition into more supervisory, curatorial, and strategic roles. Instead of manually creating all the content, creators will spend more time overseeing the generation process, curating AI-produced content, fine-tuning the results, and providing strategic direction.
Content creators become a sort of AI trainer, guiding the AI’s learning process to produce content that aligns with a particular brand, style, or audience. They feed the AI model inputs, review its outputs, tweak parameters, and repeat the process until satisfactory results are achieved.
Augmentation over Replacement
Despite fears about AI taking over jobs, it is essential to note that these technologies are intended to augment human effort, not replace it. Generative AI can handle the repetitive, mundane tasks, leaving creators with more time to focus on strategic decision-making, creative brainstorming, and refining the narrative.
For example, a content creator might use an AI tool to generate the first draft of a blog post, which they then revise and enrich with their creative touch. Or a graphic designer might employ an AI tool to generate initial design layouts, which they subsequently enhance and finalize based on their aesthetic judgement.
Enhancing Content Personalization and Efficiency
Generative AI can produce a wide variety of content rapidly and at scale, enabling unprecedented levels of personalization. Content creators can use these technologies to generate tailored content for different segments of their audience, enhancing engagement and relevance.
Moreover, generative AI can streamline the content creation process by providing quick prototypes, drafting initial versions, and generating content ideas. This heightened efficiency allows creators to produce more content in less time, or focus their efforts on other areas such as content strategy, distribution, or audience engagement.
Potential Challenges and Ethical Considerations
Despite the numerous advantages, the integration of generative AI in content creation brings about potential challenges and ethical considerations. Issues related to data privacy, content originality, and AI biases need to be carefully managed. Moreover, the shift in the content creator’s role also demands new skills, such as the ability to work with AI and a basic understanding of machine learning principles.
Path to generating poor content using generative AI
Train on Low-Quality Data: Generative AI models learn from the data they’re trained on. If the data consists of poor quality or irrelevant content, the AI will generate similar content.
Minimal Supervision and Editing: Allowing the AI to generate content with little to no human supervision or editing could lead to the production of poor quality, nonsensical, or off-topic content.
No Fine-tuning: Generative AI models need to be fine-tuned to generate relevant content. If the model is not fine-tuned or adjusted, it might generate content that doesn’t make sense or lacks relevance.
Ignoring Ethical Standards: If you ignore copyright laws and ethical standards of content creation, your content might infrally on others’ intellectual property, resulting in derivative content that is not only poor but also illegal.
Mass Production without Quality Control: If you prioritize volume over quality and do not review or revise the AI-generated content, you’re likely to produce poor derivative content.
Generic Content: If you use AI to generate content without tailoring it to your audience’s needs and preferences, you’ll end up with content that feels generic and irrelevant, which can be considered as poor quality.
How to use generative AI well
Derivative content, or content that imitates or is significantly influenced by existing works, often gets a negative reputation for lacking originality. However, when approached properly, it can provide value, especially in learning environments or situations requiring rapid generation of content in familiar formats. Generative AI can play a significant role in creating such derivative content, but it’s crucial to avoid generating low-quality or irrelevant content. Here’s how to steer clear of that:
Don’t Rely Solely on AI: Generative AI is a tool, not a replacement for human creativity and judgment. The best content often comes from a collaboration between human and machine, with the human providing oversight, context, and creativity. When using AI to create derivative content, avoid relying solely on the AI’s output. Instead, use the AI as a starting point, then revise, edit, and refine to suit your specific needs.
Avoid Inappropriate Use of Data: Generative AI models learn from the data they’re trained on. If this data includes poor quality or irrelevant content, the AI will generate similar content. Ensure that the model is trained on high-quality, relevant data to avoid generating poor derivative content.
Ensure Proper Fine-tuning: Generative AI models need to be fine-tuned to produce high-quality content. If the model is not properly adjusted, it might generate irrelevant or nonsensical content. The fine-tuning process includes adjusting parameters and providing feedback to the model.
Maintain Ethical Standards: Even when creating derivative content, ethical standards of content creation must be upheld. This includes respecting copyright laws and acknowledging sources of inspiration. Failure to do so not only leads to poor derivative content but can also result in legal consequences.
Quality over Quantity: Generative AI is capable of producing content at a high volume and speed. However, creating a mass of content should never be prioritized over the quality of the content. It’s essential to review and revise AI-generated content, ensuring that it provides value and is relevant to your audience.
Personalization and Relevance: Even derivative content should be personalized and relevant to your audience. Generative AI, with its ability to produce content rapidly and at scale, can aid in personalizing content for different audience segments. However, this must be done thoughtfully and intentionally to avoid generating content that feels generic or lacks relevance.
Conclusion
Generative AI is reshaping the content creation landscape and transforming the role of the content creator. As we navigate this exciting frontier, it becomes clear that the future of content creation lies not in choosing between human and AI, but in merging the two to create the best possible content.