The Case for Curation as a Service: Generative AI and the Future of Content Creation

The Case for Curation as a Service: Generative AI and the Future of Content Creation

In the vast ocean of information available on the internet, it's easy to feel overwhelmed and lost. With the exponential growth of content being produced every day, finding the valuable resources amidst the noise becomes a daunting task. This is where curators come in as life preservers for the web, saving us from drowning in a sea of irrelevant information. These curators are domain experts or individuals with a deep obsession for a specific topic, dedicating countless hours to sift through the garbage and collect the gems. Their role goes beyond mere collection; they take the time to format and present the curated content in a readable manner. Good curation is not about quantity but about quality.

On the other hand, we have the rise of generative AI, a technology that holds the promise of transforming the world of creation and knowledge work. Generative AI has the potential to bring down the marginal cost of creation towards zero, leading to increased labor productivity and economic value. This, in turn, could result in a significant increase in market cap.

The evolution of generative AI can be classified into waves. In the first wave, small models were considered the state of the art for understanding language. However, with the advent of the second wave, the focus shifted to scaling up these models. Google Research introduced the concept of transformers, a new neural network architecture for natural language understanding that could generate superior quality language models. These models began surpassing major human performance benchmarks, but they were large, difficult to run, and expensive to use.

The third wave brings hope for better, faster, and cheaper compute. New techniques, like diffusion models, have emerged, reducing the costs associated with training and running inference. This paves the way for the fourth wave, where killer applications of generative AI are expected to emerge. The platform layer is solidifying, models are improving in terms of quality, speed, and cost, and access to these models is becoming more open. Text generation is the most advanced domain, followed by code generation, image generation, speech synthesis, and 3D models.

One particular application of generative AI that has gained attention is copywriting. The need for personalized web and email content to fuel sales and marketing strategies, as well as customer support, makes language models a perfect fit. Additionally, vertical-specific writing assistants are being developed to cater to the unique needs of various industries. Code generation is another area where generative AI is expected to have a significant impact on developer productivity. Tools like GitHub CoPilot have already shown the potential of AI-generated code.

Generative AI applications are built on top of large models like GPT-3 or Stable Diffusion. As these applications gather more user data, they can fine-tune their models to improve performance and decrease costs. These applications act as a UI layer and a "little brain" on top of the large general-purpose models. Currently, generative AI apps exist as plugins in existing software ecosystems, but as the models become smarter and more refined, they may become the final product.

To succeed in the world of generative AI, companies need to establish a flywheel between user engagement/data and model performance. Exceptional user engagement leads to better model performance through prompt improvements, fine-tuning, and user choices as labeled training data. This improved model performance, in turn, drives more user growth and engagement. The fields that generative AI addresses—knowledge work and creative work—comprise billions of workers, making it a crucial technology for the future.

In conclusion, the convergence of curation as a service and generative AI presents a fascinating future for content creation and knowledge work. Curators play a vital role in filtering out the noise and presenting the best resources, ensuring quality over quantity. Meanwhile, generative AI offers the potential to revolutionize the creative process, making it faster, cheaper, and more accessible. As the technology continues to evolve, it is crucial to harness its power responsibly and ethically.

Actionable Advice:

1. Embrace the role of curators: As an individual or organization, invest time and effort into becoming a trusted curator in your domain. Your expertise and ability to find valuable content will be highly valued in an era of information overload.
2. Explore generative AI applications: Stay updated with the advancements in generative AI and identify areas where it can enhance your productivity or unlock new creative possibilities. Experiment with tools and platforms that leverage generative AI to drive innovation in your field.
3. Foster a symbiotic relationship: Recognize the potential synergy between curation as a service and generative AI. Curators can leverage AI-powered tools to streamline their curation process, while generative AI can benefit from curated datasets to improve model performance. Collaboration between curators and AI developers can lead to breakthroughs in content curation and creation.

In the ever-expanding digital landscape, the collaboration between curators and generative AI holds the key to meaningful and valuable content creation. By combining the expertise of curators with the capabilities of AI, we can navigate the vast sea of information and unlock new frontiers of creativity. As we embrace these technologies, let us remember the importance of quality, ethics, and responsible use to shape a future where knowledge and creativity thrive.