AI coding needs smarter routing over bigger models
Notes de l’épisode
Coding agents can drastically accelerate how fast developers build, but defaulting to the most powerful models gets expensive fast. But what if your coding assistant could route to less expensive models when they don't need high-end frontier intelligence?
In this episode of The Deep View Conversations, we sit down with Kyle Daigle, GitHub’s chief operating officer, at GitHub Universe in San Francisco to explore how the company's new Copilot Fusion can route between models and reduce token costs.
Daigle explains how Fusion can start with a less expensive model, move to a more capable one when needed, and bring in a second model to check the first model’s work. He says GitHub’s teams and early enterprise customers are seeing 50% to 60% lower costs on complex work, with savings reaching 77% for certain workloads. But most importantly, it's getting results that match using high-end models while doing it about 40% faster.
Other topics covered:
• Why people outside traditional developer roles are building their own software
• How GitHub’s marketing and design teams use Copilot to create tools for their own work
• How Fusion’s Cascade and Critique approaches coordinate models within a task
• Choosing between Fusion Standard, High, and Max based on the work you need done
• GitHub’s plans to route some tasks to local models running on your computer
• How Copilot Projects brings repositories, files, and agents together in one workflow
If you’re building with AI coding agents, leading a development team, or managing your company’s AI budget, this conversation offers a way to control costs while keeping the capabilities
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