The Intersection of Decreasing AI Costs and Surviving the Creator Economy Winter

The Intersection of Decreasing AI Costs and Surviving the Creator Economy Winter

Introduction:
The fields of AI and the creator economy are experiencing significant changes. On one hand, the decreasing costs of AI training and inference are opening up new possibilities for companies to adopt machine learning. On the other hand, startups in the creator economy are facing challenges in capturing a share of creator revenue. In this article, we will explore the common points between these two trends and discuss actionable advice for startups in both industries.

Decreasing AI Costs:
The acquisition of Mosaic by Databricks aligns perfectly with the vision of making AI training cost-effective for companies. Training costs have decreased by 10x in less than a year, with companies like MosaicML making algorithmic improvements and GPU costs going down 3x in about 3 years. This decrease in costs will lead to more model providers and increased competition in the model layer. It may also drive companies towards open-source model providers, putting pricing pressure on closed-source ones.

Surviving the Creator Economy Winter:
In the creator economy, the majority of revenue accumulates at the top 0.01% of creators, leaving startups with the challenge of justifying their revenue share. Startups must answer the question of what they are doing to earn that share. The total number of creators is estimated to be around 200 million, but only a small percentage generate meaningful revenue. The hunt for new fans is a constant challenge for creators, and startups must find ways to help creators expand their fan base.

Actionable Advice:

1. For companies in the AI industry: Focus on making algorithmic improvements and optimizing GPU costs. This will not only drive down training costs but also attract more companies to adopt machine learning. Consider open-source models as a cost-effective alternative to closed-source providers.

2. For startups in the creator economy: Instead of relying solely on revenue share from creators, explore other revenue models. Invert the vertical software serving creators exclusively towards a more horizontal platform serving businesses in general. This will broaden the customer base and provide more opportunities for revenue generation.

3. For both industries: Emphasize the importance of demand aggregation. Just as social giants like YouTube, Twitter, and Facebook have robust demand aggregation efforts, startups must find ways to aggregate demand for creators and provide value to both creators and advertisers. This can be achieved through recommendation algorithms, trending topics, and recommended pages.

Conclusion:
The decreasing costs of AI and the challenges in the creator economy share common points that can be addressed through strategic approaches. By focusing on algorithmic improvements, optimizing GPU costs, and exploring alternative revenue models, companies can navigate these trends and thrive in their respective industries. The key lies in providing value to users, whether it be through cost-effective AI solutions or effective demand aggregation for creators. The future of AI and the creator economy holds immense potential, and those who adapt and innovate will be well-positioned to succeed.