"Building AI-first Products: How to Do a Product Critique"

"Building AI-first Products: How to Do a Product Critique"

Introduction:

The paradigm shift towards AI-first products has the potential to revolutionize the world. With the right approach, AI can be used to create new products and services that were previously impractical due to human costs and technical constraints. However, building successful AI-first products requires careful consideration of several factors. In this article, we will explore the key requirements for building AI-first products and how to conduct a product critique to ensure their success.

• 1. Containing the problem space: thinking in domains

To build AI-first products, it is crucial to define the problem space and identify the specific domain the product aims to tackle. This can be broad, with a comparable depth of knowledge across domains, or narrow, with significant depth in a specific domain. By leveraging artificial domain intelligence (ADI), which involves using domain-specific knowledge and fine-tuning, new products and services can be created that were previously unattainable due to scalability or technical constraints.

• 2. Construct the UX: breaking the skeuomorphic barrier

When incorporating AI into existing products and paradigms, it is important to avoid simply bolting AI on top without rethinking the solutions. Redefining the problem context and designing the workflow with AI-native paradigms in mind can lead to more innovative and efficient interfaces. This may involve creating interfaces that do not resemble traditional editors or tables, and considering whether human input is necessary in the workflow at all. By redesigning solutions to be AI-native, the complexity of interfaces can be significantly reduced, with most of the magic happening behind the scenes.

• 3. Compose the product stack: simulating proto-AGI

To ensure the reliability and scalability of AI products, it is essential to simulate proto-AGI (Artificial General Intelligence) for the specific use-case and domain. This can be achieved by implementing structural scaffolding, workflow handling, and data management techniques. By leveraging language model APIs and offloading complex systems to the model layers, AI can power more than just chat and language interfaces. Decomposing problems into stages and building optimized pipelines can also lead to more resilient and scalable systems. Additionally, the use of machine-interface models and federation multiplexing can enhance the overall performance of the AI product.

• 4. Correcting errors: guarding for technical limitations

Language models, such as LLMs, do not conceptually understand their own outputs and are trained on potentially error-prone data sources. To ensure the accuracy and reliability of AI products, it is crucial to implement structural tooling, methodologies, and processes. This includes safeguarding against factual errors, bias, and negative outputs through programmatic reinforcement features at the application layer. Critical services, such as healthcare and search, require rigorous protocols and methodologies to guarantee the quality of AI-generated outputs.

• 5. Capture value: building AI businesses

To build sustainable AI businesses, it is important to optimize for three possible moats. First, creating unique product infrastructure built with domain insights that can be leveraged by AI. Second, gaining access to proprietary data that can enhance the training and fine-tuning of models. Third, having access to compute power and talent to build and scale faster than the competition. By evaluating existing processes and identifying integration points for AI, businesses can effectively capture value and drive success.

Conclusion:

Building AI-first products requires a deep understanding of the problem space, a willingness to break traditional paradigms, and a focus on addressing technical limitations. Conducting a product critique based on user experience, feedback, and comparative analysis can help ensure the success of AI products. By thinking in domains, breaking the skeuomorphic barrier, redefining solutions with AI-native approaches, guarding against technical limitations, and leveraging AI where it creates the most value, businesses can thrive in the AI-first era.

Actionable Advice:

• 1. Define the problem space and identify the specific domain your AI product aims to tackle.

• 2. Rethink the user experience and design AI-native solutions that break away from traditional interfaces.

• 3. Implement rigorous protocols and methodologies to correct errors and ensure the accuracy and reliability of AI outputs.

With these strategies in place, businesses can harness the full potential of AI and build successful AI-first products.