Generative AI And The "Great Averaging"
How do generative AI systems enhance the capabilities of individuals whose skills are below the average level of the system's training data?
Generative AI systems enhance the capabilities of individuals whose skills are below the average level of the system's training data by providing them with access to the collective intelligence of the data on which the system was trained13. These systems allow users to generate outputs that are at least at the average level of quality, even if their own abilities would produce something worse than that average.
By using generative AI, individuals can produce outputs that are better than what they could achieve on their own, essentially "raising the bar" for the expected level of quality. This can be seen as a form of "Great Averaging," where generative AI systems bring everyone up to at least an average level of performance.
For example, someone with limited writing skills could use a generative AI system to produce a well-written document, or someone with limited design skills could use the system to create a professional-looking graphic. The AI system takes the user's basic input and combines it with the vast amount of data it has been trained on to produce a higher-quality output.
However, it's important to note that simply using generative AI won't necessarily provide an advantage, as these systems are designed to bring users up to the average level, not necessarily to produce exceptional outputs. To truly excel, individuals will need to find ways to differentiate themselves beyond the capabilities of the AI system.
What implications does the widespread use of generative AI have for individuals and organizations in terms of creativity and output quality?
The widespread use of generative AI has several implications for individuals and organizations in terms of creativity and output quality. With generative AI systems, people and organizations can produce outputs that are at least average in quality, as these systems collage data from multiple sources to create content that matches the prompt provided. This "Great Averaging" effect means that anyone can achieve an average level of output, potentially leading to higher expectations for the minimum quality of work.
For individuals, generative AI can help enhance their creative outputs and improve the quality of their work, especially if their skills are below the average level of the AI-generated content. However, those who do not use AI tools may find their skills and talents increasingly marginalized, as the use of generative AI becomes more ubiquitous.
For organizations, generative AI can streamline processes and improve efficiency, as well as provide a consistent level of output quality. As these AI systems become more widely adopted, organizations will likely expect their use in various core processes.
In both cases, simply using generative AI won't provide a significant advantage, as the technology will become increasingly commonplace. Instead, the focus should be on leveraging AI effectively and understanding that the impressive outputs generated by AI today may become the expected minimum level of quality in the future.
What exactly does the term "Great Averaging" refer to in the context of generative AI systems?
The term "Great Averaging" refers to the phenomenon where generative AI systems, by leveraging massive amounts of training data, produce outputs that represent an average of the collective human intelligence contained within that data6. As a result, these systems can generate content that is at least at the average level of their training data, effectively setting a new bar for the expected minimum quality of outputs. This means that individuals and organizations that do not use AI-generated content could be seen as producing outputs below the new average standard. The "Great Averaging" thus implies a future where AI-generated content becomes the norm, raising the baseline for what is considered an acceptable level of quality in various fields.