Sourcegraph

The emerging skillset of wielding coding agents — Beyang Liu, Sourcegraph / Amp

Jun 30, 2025 · 35:06

Beyang Liu, CTO and co-founder of Sourcegraph, argues that coding agents are a real and high-ceiling skill, contrary to skeptics like Jonathan Blow and Eric S. Raymond. He presents design decisions for the agentic era: agents should directly edit files instead of asking permission, UIs should be minimal (like Amp's bare-bones VS Code extension and CLI), and fixed pricing should give way to usage-based models. In a live demo, Liu uses Amp to implement a custom icon for the linear connector in Amp's own codebase, demonstrating agentic search and sub-agents. He shares power-user patterns, including writing long prompts, constructing feedback loops with Playwright and Storybook, and running multiple agents in parallel—as exemplified by Jeff Huntley using Amp to build a compiler while sleeping. Liu emphasizes that agents should enable more thorough code reviews, not replace human understanding.

Building AI Agents with Real ROI in the Enterprise SDLC: Bruno (Booking.com) & Beyang (Sourcegraph)

Apr 8, 2025 · 20:56

Beyang Liu, CTO of Sourcegraph, and Bruno Passos, Group PM at Booking.com, explain how their partnership built AI coding agents that deliver measurable ROI in enterprise software development. Booking.com, with 3,000 developers handling 250,000 merge requests and 2.5 million CI jobs yearly, used Sourcegraph's Cody and custom agents to automate code migration and review, cutting cycle times. They report that daily Cody users ship 30% more pull requests and are 30% faster, with lighter MRs. Agents like a GraphQL generator for a million-token schema and a code review tool with customizable rules emerged from joint hackathons. Bruno emphasizes education as critical: training turned skeptical developers into daily users, driving adoption and productivity gains. The goal is to shift-left compliance and reduce tech debt, making the SDLC self-healing.

The ROI of AI: Why you need Eval Framework - Beyang Liu

Dec 31, 2024 · 25:28

Beyang Liu, CTO of Sourcegraph, presents six evaluation frameworks for measuring the ROI of AI coding tools, arguing that no single metric suffices because AI ROI reduces to the intractable problem of developer productivity. He dismisses the 'roles eliminated' framework as irrelevant for engineering, noting that backlogs are never fully addressed. Instead, he highlights customer examples: A/B testing velocity at Palo Alto Networks showed 20-30% acceleration with Cody; time saved as a function of engagement lower-bounds gains (e.g., each code search saves ~2 minutes); and LITOS tracked value-add zones (building features, writing tests, reviewing code) instead of lines of code. For key initiatives like code migrations, ROI is measured by how many months the migration is pulled forward. Liu also advocates developer surveys bounded by a 5-25% productivity tool budget, and warns against the Mythical Man Month fallacy: preferring one 100x human developer over 100 mediocre AI agents.

The AI emperor has no DAUs why most devs still don't use code AI: Quinn Slack

Nov 20, 2024 · 18:45

Quinn Slack, CEO and cofounder of Sourcegraph, argues that despite massive hype, only about 5% of professional developers actually use Code AI tools, with total recurring revenue from Code AI sitting at roughly $300 million ARR—a fraction of Salesforce's $36 billion. He cites GitHub's 1.3 million paid Copilot subscribers and just 935,000 yearly active users receiving suggestions, revealing the gap between perception and reality. Slack warns that the entire AI ecosystem—foundation models, infra, and applications—risks collapse if usage doesn't grow, and most revenue in AI flows to NVIDIA and chip makers, not software. From building Cody, the number two Code AI product, he shares lessons: hype fools everyone, autocomplete is a freakishly good feature that spoils expectations, while chat and agents are harder to vet and adopt. He advises builders to use their own product daily, ignore customer demands for buzzwords like fine-tuning, and manually build explicit interactions before adding magic. Slack concludes that the industry must collectively dehype and focus on real daily active users to turn the potential into sustained enterprise revenue.

The Code AI Maturity Model and What It Means For You: Ado Kukic

Feb 13, 2024 · 7:58

Ado Kukic, Director of DevRel at Sourcegraph, presents the Code AI Maturity Model, a framework comparing AI coding assistant autonomy to SAE vehicle levels. He argues that 92% of developers now use Code AI tools, up from 1% a year ago, and predicts 99% of code will be written by AI in 5 years. The model defines six levels: Level 0 (manual coding), Level 1 (AI-generated snippets from developer intent), Level 2 (codebase-aware completions, e.g., suggesting Axios in a Node.js project), Level 3 (AI builds full features from high-level requirements, like adding authentication), Level 4 (AI proactively handles tasks, such as auto-fixing bugs via PRs), and Level 5 (AI handles the entire software lifecycle with minimal human oversight). The talk contextualizes each level with autonomous driving parallels, emphasizing that the industry is currently transitioning between Levels 1–3, with Level 5 representing full AI-led code generation.