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.
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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.
Simply offering the latest model is no longer a competitive advantage. True value is created in the system built around the model—the system prompts, tools, and overall scaffolding. This 'harness' is what optimizes a model's performance for specific tasks and delivers a superior user experience.
Don't just sprinkle AI features onto your existing product ('AI at the edge'). Transformative companies rethink workflows and shrink their old codebase, making the LLM a core part of the solution. This is about re-architecting the solution from the ground up, not just enhancing it.
The history of AI, such as the 2012 AlexNet breakthrough, demonstrates that scaling compute and data on simpler, older algorithms often yields greater advances than designing intricate new ones. This "bitter lesson" suggests prioritizing scalability over algorithmic complexity for future progress.
The "bitter lesson" in AI research posits that methods leveraging massive computation scale better and ultimately win out over approaches that rely on human-designed domain knowledge or clever shortcuts, favoring scale over ingenuity.
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.
Early on, Google's Jules team built complex scaffolding with numerous sub-agents to compensate for model weaknesses. As models like Gemini improved, they found that simpler architectures performed better and were easier to maintain. The complex scaffolding was a temporary crutch, not a sustainable long-term solution.
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.
The perceived limits of today's AI are not inherent to the models themselves but to our failure to build the right "agentic scaffold" around them. There's a "model capability overhang" where much more potential can be unlocked with better prompting, context engineering, and tool integrations.
The developer abstraction layer is moving up from the model API to the agent. A generic interface for switching models is insufficient because it creates a 'lowest common denominator' product. Real power comes from tightly binding a specific model to an agentic loop with compute and file system access.