Features follow an S-curve of value. Early effort yields little, then a steep rise, then diminishing returns. Use this model to determine if a feature needs more investment to become valuable or if you've already extracted its maximum worth and should stop investing.
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For AI products, the quality of the model's response is paramount. Before building a full feature (MVP), first validate that you can achieve a 'Minimum Viable Output' (MVO). If the core AI output isn't reliable and desirable, don't waste time productizing the feature around it.
Allocate 50% of your roadmap to core functionality ('low delight'), 40% to features blending function and emotion ('deep delight'), and 10% to purely joyful features ('surface delight'). This model ensures you deliver core value while strategically investing in a superior user experience.
People are unreliable at predicting their future behavior. Instead of asking if they *would* use a new feature, ask for a specific instance in the last month where it *would have been* useful. If they can't recall one, it's a major red flag for adoption.
Figma learned that removing issues preventing users from adopting the product was as important as adding new features. They systematically tackled these blockers—often table stakes features—and saw a direct, measurable improvement in retention and activation after fixing each one.
To find valuable AI use cases, start with projects that save time (efficiency gains). Next, focus on improving the quality of existing outputs. Finally, pursue entirely new capabilities that were previously impossible, creating a roadmap from immediate to transformative value.
To build a successful product, prioritize roadmap capacity using the "50/40/10" rule: 50% for "low delight" (essential functionality), 40% for "deep delight" (blending function and emotion), and only 10% for "surface delight" (aesthetic touches). This structure ensures a solid base while strategically investing in differentiation.
The Browser Company found that Arc, while loved by tech enthusiasts for its many new features, created a "novelty tax." This cognitive overhead for learning a new interface made mass-market users hesitant to switch, a key lesson that informed the simplicity of their next product, Dia.
Avoid the 'settings screen' trap where endless customization options cater to a vocal minority but create complexity for everyone. Instead, focus on personalization: using behavioral data to intelligently surface the right features to the right users, improving their experience without adding cognitive load for the majority.
A single roadmap shouldn't just be customer-facing features. It should be treated as a balanced portfolio of engineering health, new customer value, and maintenance. The ideal mix of these investments changes depending on the product's life cycle, from 99% features at launch to a more balanced approach for mature products.
While new large language models boast superior performance on technical benchmarks, the practical impact on day-to-day PM productivity is hitting a point of diminishing returns. The leap from one version to the next doesn't unlock significantly new capabilities for common PM workflows.
