In the future, foundational models may become highly specialized, such as models specifically designed for chip design or protein folding.

Developers can fine-tune models using a variety of data sources, such as company chat logs or marketing communications, to adjust tone and style for their specific needs. Fine-tuning a large language model involves capturing feedback data from production usage, which helps improve the model based on real customer interactions.

The future will likely see more researchers embedded in every product team, as fine-tuning models for specific use cases becomes a core workflow for building products.

Fine-tuning a model involves gathering examples of desired outputs for specific tasks and performing additional training on a base model to specialize it for those tasks.

The frontier labs tend to focus on training models for new tasks, while many enterprise problems are too small for them to address individually, creating opportunities for startups.

The trend of purpose-trained models that are smaller and customized for specific domains is gaining traction, as they can outperform general models in targeted applications.

Customization of large language models is crucial for differentiation, as everyone has access to the same base models, but tailoring them to specific contexts enhances user experience.

The demand for fine-tuning open-source models is driven by the need for cost-effective alternatives to expensive models like OpenAI's ChatGPT, but companies must also provide additional value beyond just lower costs to retain customers.

I believe we are nearing the upper limits of large language models, as the future will likely involve many smaller, specialized models rather than a few large ones. We're at the upper end of large language models, and they're rapidly becoming a commodity as they mature.