The Digital Press

Building AI products requires adapting quickly to rapidly improving models rather than relying on clever engineering hacks that soon become obsolete. The author shares lessons learned from early failures and evolving practices for success.

Why it matters: AI capabilities evolve fast, so product roadmaps must pivot to leverage new model strengths instead of patching old limitations.

The big picture: Early AI product attempts often fail due to immature models; retrying ideas with new models every few months is key.

The stakes: Clinging to sunk costs and demo-stage optimism can waste time; killing failing projects fast preserves resources and morale.

Commenters say: They emphasize that AI is steadily improving, urging builders to focus on adapting to foundational model advances rather than superficial tweaks.