Technical Tradeoffs

Technical debt can actually be a useful tool for fast-moving startups and product teams. By prioritizing speed and user needs over perfect engineering, you can get a product to market quickly and start learning. However, debt that accumulates without a plan can become a major burden down the line. The key is to be strategic about when and how you take on debt, and have a plan to address it over time.

AI tools like large language models (LLMs) can now produce "good enough" software at a much lower cost than traditional programming. While the output may not be perfect, it can still meet the needs of your product and customers in many cases. This allows you to launch and iterate faster, rather than waiting for the perfect solution.

The quality and organization of your data matter more than the specific technology you use. Focus on carefully preparing and structuring your data first, rather than getting caught up in choosing the latest trendy tools. This will give your AI system a solid foundation to provide accurate and relevant insights.

Instead of just telling your stakeholders about the technical tradeoffs, try showing them. Provide real-world examples that illustrate the different options and their pros and cons. This hands-on approach helps them grasp the nuances much better than abstract instructions. By leading with examples, you can have a more meaningful dialogue and ensure your stakeholders truly understand the tradeoffs at play.

As a product manager, your role is to define the boundaries and decision-making framework for your algorithms. Algorithms alone cannot fully account for long-term effects, human responses, or your product's intended purpose. Your job is to thoughtfully determine what the algorithm should be responsible for versus what decisions humans should make.