The "bitter lesson" of AI applies to product development: complex scaffolding built around model limitations (like early vector stores or agent frameworks) will inevitably become obsolete as the models themselves get smarter and absorb those functions. Don't over-engineer solutions that a future model will solve natively.
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Box CEO Aaron Levie advises against building complex workarounds for the limitations of cheaper, older AI models. This "scaffolding" becomes obsolete with each new model release. To stay competitive, companies must absorb the cost of using the best available model, as competitors will certainly do so.
Overly structured, workflow-based systems that work with today's models will become bottlenecks tomorrow. Engineers must be prepared to shed abstractions and rebuild simpler, more general systems to capture the gains from exponentially improving models.
AI development history shows that complex, hard-coded approaches to intelligence are often superseded by more general, simpler methods that scale more effectively. This "bitter lesson" warns against building brittle solutions that will become obsolete as core models improve.
Building an AI-native product requires betting on the trajectory of model improvement, much like developers once bet on Moore's Law. Instead of designing for today's LLM constraints, assume rapid progress and build for the capabilities that will exist tomorrow. This prevents creating an application that is quickly outdated.
Strong AI products require a tight feedback loop where the product and model are deeply integrated. Thin wrappers around third-party models create weak, short-lived features that will be subsumed by the platform. A durable AI business treats the model *as* the product itself.
Features built to guide AI agents, like an explicit "plan mode," will become obsolete as models become more capable. The Claude Code team embraces this, building what's needed for the best current experience and fully expecting to delete that code when a new model renders it unnecessary.
In the fast-paced world of AI, focusing only on the limitations of current models is a failing strategy. GitHub's CPO advises product teams to design for the future capabilities they anticipate. This ensures that when a more powerful model drops, the product experience can be rapidly upgraded to its full potential.
When developing AI-powered tools, don't be constrained by current model limitations. Given the exponential improvement curve, design your product for the capabilities you anticipate models will have in six months. This ensures your product is perfectly timed to shine when the underlying tech catches up.
The pace of AI model improvement is faster than the ability to ship specific tools. By creating lower-level, generalizable tools, developers build a system that automatically becomes more powerful and adaptable as the underlying AI gets smarter, without requiring re-engineering.
In the rapidly advancing field of AI, building products around current model limitations is a losing strategy. The most successful AI startups anticipate the trajectory of model improvements, creating experiences that seem 80% complete today but become magical once future models unlock their full potential.