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The Evolution of AI Software: Wrappers, Platforms, Applications, and Optimization
- The evolution of AI software development has gone through several stages, from GPT wrappers to applications and platforms and now to AI optimization.
- AI platforms, which are frameworks for developing and deploying GenAI workflows or agents, excel in prototyping but are not suitable for customer-facing applications due to their limited optimization and abstracted nature.
- Conversely, AI applications are specialized per vertical or business function and are differentiated based on proprietary data, unique understanding of a vertical, or a technically novel and robust infrastructure.
- To optimize AI software for varying scenarios, there is a need for a standardized set of evaluation metrics, and new companies like Superlinked, Vectorize, and Voyage AI are beginning to address individual components of the optimization process.
- The open-source community has begun developing tooling to address this issue, and the aim is to create a platform that could deliver the most optimal strategy per use case using a complex theory of Bayesian optimization and gradient descent in parallel.
- This field is exciting as it addresses diminishing returns of LLMs regarding quality output vs cost and latency and the need to orchestrate SLMs for enterprise GenAI deployments due to rising compute costs.
- What we hope for in the future is a standard set of metrics that would enable commercial vendors to create such a platform.
- As funding starts flowing back into the AI market, investors are looking for sustainable and structured evolution in AI software development with well-differentiated business value.
- There is already a trend towards AI optimization solutions that support development teams in the complex and ambiguous evaluation process, which is usually a tedious process.
- However, there are still many technical challenges involved in creating such a platform, and the costs associated with bigger models must also be considered.
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