Why Product Decisions Are Harder Than Coding, According to Luca Rossi

In a recent LinkedIn post, 🌀 Luca Rossi explores the often-underestimated complexity of product development compared to software coding, arguing that product decisions require a higher degree of nuanced judgment.

🌀 Rossi begins by stating a provocative assertion that may challenge many in the engineering field: “Product is harder than coding.” He elaborates on this by outlining the multifaceted considerations involved in making a good product choice, which extend far beyond technical implementation.

“For a product choice to be good, there is a lot to be taken into account: Strategy… Tactics…”

As 🌀 Rossi notes, these considerations involve a deep understanding of market needs, business strategy, and user experience. He highlights that many of these product-related choices rely on elements that are difficult to codify into strict rules, referring to them as “judgment and taste.” This subjective yet critical aspect of product development is what, in 🌀 Rossi’s view, sets it apart from the more structured domain of coding.

The Deterministic Nature of Coding

In contrast, 🌀 Rossi posits that software engineering is structurally simpler for two primary reasons. Firstly, it operates downstream from product direction, meaning it has a more constrained design space. Secondly, the deterministic nature of code allows for more reliable testing of correctness and non-functional qualities like performance and complexity.

This inherent structure of coding, 🌀 Rossi argues, makes it more amenable to automation and AI assistance. He points out that tools like static analysis, Test-Driven Development (TDD), and code health tooling can effectively enforce coding standards and produce passable, if not exceptional, code. According to 🌀 Rossi:

“Static analysis, TDD, code health tooling, are all devices that help AI agents write good enough code—granted, not the code that the most talented engineer on earth would write—but still passable for many situations.”

This efficiency in coding, even with AI agents that may lack deep product context, underscores 🌀 Rossi’s core argument about the differing levels of complexity between the two disciplines.

Bridging the Gap: AI in Product Development

The core challenge, as 🌀 Rossi frames it, is how to make product development more akin to the more structured and testable nature of coding. While his LinkedIn post primarily highlights the difficulty of product decisions, it also points towards a broader exploration of how AI might be leveraged to improve product-related tasks.

🌀 Rossi concludes his post by referencing a more in-depth article he has written on how to enhance AI’s capabilities in product development. This suggests that while product judgment may be inherently harder to codify, there are ongoing efforts and explorations into applying technological solutions, including AI, to assist in this complex domain.

In essence, 🌀 Luca Rossi’s insights on LinkedIn serve as a valuable reminder of the intricate blend of strategy, user understanding, and subjective taste that defines successful product development, setting it apart from the more rule-based and deterministic nature of coding.