Head of Claude Code: What happens after coding is solved | Boris Cherny

Feb 19, 2026 1h 27m 30 insights Episode Page ↗
Boris Turney, Head of Claude Code at Anthropic, discusses how AI has transformed software engineering, enabling 100% AI-written code and increased productivity. He explores the future where AI generates ideas, branches beyond coding, and the societal shifts agentic AI will bring, offering advice for adapting.
Actionable Insights

1. Build for Future AI Capabilities

Design and build AI products with the anticipated capabilities of models six months in the future, rather than current limitations. This forward-looking approach positions your product to “click” when more advanced models are released.

2. Bet on General AI Models

Adhere to “the bitter lesson” by consistently betting on more general AI models over highly specific or fine-tuned ones for most applications. General models typically outperform specific ones over the long term, despite initial performance gains from scaffolding.

3. Empower AI with Tools and Goals

Avoid overly strict workflows or excessive pre-context when building with LLMs; instead, provide the model with a set of tools and a clear goal, then allow it to determine the best course of action. This approach often yields better results than rigid orchestration.

4. Enable Unlimited AI Token Usage

For innovation, initially provide engineers with unlimited AI token access, rather than focusing on cost optimization. This freedom allows for experimentation with “crazy ideas” that might lead to breakthroughs.

5. Optimize AI Costs Later

Defer cost optimization for AI usage until an idea proves successful and begins to scale. Early optimization can stifle innovation, so prioritize proving the concept first, then find cheaper models or methods.

6. Automate with AI Agents

When faced with a task, consider if an AI agent can perform it, even if you could do it yourself. This principle encourages leveraging AI for automation to free up human time for higher-level work.

7. Adapt to Evolving AI Capabilities

Continuously update your mental model of AI capabilities, as models improve rapidly and significantly. Avoid getting stuck in old ways of thinking and actively explore what new versions can do.

8. Use Most Capable AI Model

Always opt for the most capable AI model available (e.g., Opus 4.6 with maximum effort) as it often proves more cost-effective and efficient. Its higher intelligence leads to faster task completion with fewer iterations and less human intervention.

9. Utilize AI Plan Mode

Initiate complex tasks using “plan mode” (e.g., by prompting the AI not to execute code immediately) to allow the model to outline its approach first. This ensures a well-thought-out strategy before execution, leading to more accurate one-shot completions.

10. Integrate AI for Multi-Tool Automation

Connect multiple AI tools and instruct the agent to perform complex, multi-step automations, such as syncing data between spreadsheets and messaging people on Slack. This enables comprehensive project management and reduces manual toil.

11. Run Multiple AI Agents in Parallel

Maximize productivity by running multiple AI agents or Co-work tasks concurrently. Initiate several independent tasks and allow the AI to work autonomously, freeing up your time for other activities.

12. Use AI for Idea Generation

Leverage AI tools like Claude to analyze feedback, bug reports, and telemetry data to proactively generate ideas for bug fixes and new features. This transforms AI into a co-worker that helps define what to build next.

13. Direct AI to Feedback Channels

Point your AI agent (e.g., Claude Code, Co-work) directly at internal feedback channels like Slack threads to automatically identify potential tasks and generate solutions. This streamlines the process of acting on user input.

14. Develop Generalist Skills

Cultivate a generalist mindset by crossing over multiple disciplines, such as combining engineering with product, design, or business acumen. This broader perspective helps in understanding complex problems beyond a single domain.

15. Encourage Cross-Functional Coding

Promote a culture where all team members, regardless of their primary role (e.g., PM, designer, finance), learn to code. This empowers individuals to unblock themselves and contribute more broadly.

16. Apply Common Sense & First Principles

Always apply common sense and think from first principles in your work, rather than blindly following processes or momentum. If something “smells weird,” question it, as this approach leads to better outcomes and avoids failures.

17. Prioritize Mission-Driven Work

Seek out work environments with a strong, resonating mission, such as safety in AI, as it can be crucial for personal happiness and job satisfaction. This deep alignment can be more important than building a cool product alone.

18. Understand Underlying Layers

To excel in your field, especially in AI, deeply understand the foundational layers beneath your immediate work (e.g., model mechanics for AI engineers, infrastructure for product engineers). This comprehensive understanding enables better work.

19. Under-resource New Projects

When starting new projects, intentionally under-resource them slightly to encourage creative problem-solving and force reliance on efficient tools like AI. This approach can lead to faster shipping and innovation.

20. Pursue Promising Threads

If you feel you’re “onto something” with an idea, even if its full utility isn’t immediately obvious, dedicate time and effort to explore it. This persistent exploration can lead to unexpected breakthroughs.

21. Maintain Human Code Review

Even with 100% AI-generated code and AI-powered reviews, retain a layer of human review for non-prototype code to ensure correctness and safety. This acts as a crucial checkpoint in the development process.

22. Rapid Feedback Loop

Respond to and address user feedback as quickly as possible, ideally within minutes, to make users feel heard and encourage more contributions. This rapid cycle fosters a culture of continuous improvement and engagement.

23. Prioritize Speed

Foster a culture of speed and urgency, encouraging teams to complete tasks today if possible. Utilizing AI tools can significantly accelerate this process.

24. Actively Experiment with AI

To succeed in the evolving AI landscape, actively experiment with new AI tools, embrace the bleeding edge, and overcome any apprehension. Dive in to understand their capabilities and limitations.

25. Design for Model’s Intent

When building AI products, observe what the model naturally “wants” to do or is capable of, and design products that facilitate these inherent capabilities. This “on distribution” approach leverages the model’s strengths.

26. Anticipate AI Tool Use & Longevity

When building for future AI, anticipate significant improvements in the model’s ability to use tools, interact with computers, and run autonomously for extended periods. Design products that leverage these expected advancements.

27. Auto-Accept AI Edits Post-Plan

Once an AI’s plan is reviewed and approved, confidently auto-accept its subsequent code edits. With advanced models like Opus 4.6, a good plan often leads to correct, one-shot execution.

28. Explore Diverse AI Interfaces

Experiment with various AI interfaces (e.g., desktop app, mobile app, Slack integration, web, IDE extensions) beyond just the terminal. Find the interface that best suits your workflow and preferences, as the core AI agent remains consistent.

29. Start Co-work with Simple Tool Use

For new Co-work users, begin by instructing the AI to perform simple tasks that involve tool usage, such as cleaning up your desktop, summarizing emails, or responding to top emails. This helps familiarize users with its agentic capabilities.

30. Engage Directly for User Feedback

Actively engage with users on platforms like Twitter to solicit feedback and identify bugs. Directly responding to user issues and questions can lead to rapid problem-solving and foster strong community engagement.