99% AI-Written Code: How Every Runs 4 Products With 15 People

How Every Built an AI-Native Organization From Scratch

Dan Shipper runs Every, a media and software company with six business units, four production software products, 7,000+ paying subscribers, and double-digit MRR growth for six consecutive months—all with just 15 people. The remarkable part: 99% of their code is written by AI agents. This talk from AI Engineer NYC offers a concrete playbook for what AI-native organizations actually look like.

On the 10x difference: "There's a 10x difference between an org where 90% of the engineers are using AI versus an org where 100% of the engineers are using AI. It's totally different." The threshold effect matters because even 10% using traditional methods forces the entire organization to accommodate that workflow—preventing the paradigm shift from completing.

On single-engineer products: "Each one of our apps is built by a single developer... and these are not little apps. Thousands of users. It's not simple. It's complicated." Kora (email AI), Monologue (speech-to-text), and Spiral are all production apps maintained by one person each. This isn't about prototype quality—these are paying customer apps.

On the core loop: "In traditional engineering, each feature makes the next feature harder to build. In compounding engineering, your goal is to make sure each feature makes the next feature easier to build." Their four-step process: Plan → Delegate → Assess → Codify. The "codify" step—turning tacit knowledge into prompts—is where the compounding happens.

On parallel work: "I know engineers... who are productively using four panes of agents at the same time." This isn't the Twitter meme of vibe coders pretending to work—it's actual parallel feature and bug work that enables single-engineer products.

On managers committing code: "Managers can commit code. If you're technical, even the CEO can... AI allows engineers to work with fractured attention." Shipper himself commits production code despite running a 15-person company with four products. The startup cost of coding dropped enough that context-switching executives can contribute.

6 Insights From Dan Shipper on Compounding Engineering

• 100% AI adoption is qualitatively different - At 90% adoption, the org still accommodates traditional workflows; at 100%, entirely new operating models become possible
• Single engineers run production products - Every's apps with thousands of paying users are each maintained by one developer plus occasional contractors
• Compounding engineering inverts the usual dynamic - Instead of features adding complexity, each feature makes the next easier through codified prompts and learned patterns
• Tacit code sharing becomes free - Point Claude at a colleague's repo, learn their implementation approach, and reimplement in your own stack—no library abstraction needed
• New hires productive on day one - All environment setup and PR standards live in CLAUDE.md files; the agent handles onboarding
• Tech stack standardization optional - AI handles translation between languages and frameworks, so teams can use what they prefer

What This Means for AI-Native Teams

Every's model—15 people, four products, 99% AI code—previews the organizational structure of AI-native companies. The key insight isn't that AI writes code faster; it's that 100% adoption enables qualitatively different operations: single-engineer products, manager-contributors, frictionless code sharing across repos. For companies debating AI adoption levels, the message is binary: partial adoption still requires accommodating the old paradigm, while full adoption unlocks a new one.