## Sources

1. [Agentic Code Review](https://www.oreilly.com/radar/agentic-code-review/)
2. [This Week in AI: Who Controls the Loop?](https://www.oreilly.com/radar/this-week-in-ai-who-controls-the-loop/)

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### **Agentic Code Review** – **Addy Osmani**

**Main Arguments**
*   **The bottleneck in software engineering has shifted** from the speed of writing code to the speed of verifying it [1, 2]. While AI agents can produce thousands of lines of code nearly instantaneously, human reading speed remains unchanged, creating a massive "review debt" [1, 3].
*   **Raw output does not equal delivered value.** Organizations are seeing approximately four times the code output for only about a tenth more delivered value, with the gap between these figures being occupied by the increased labor of review [4, 5].
*   **The purpose of code review has fundamentally changed.** Historically, review checked a human author’s reasoning; now, reviewers must often reconstruct the "why" behind agent-generated code because the agent's internal reasoning is usually discarded before the pull request (PR) is created [6, 7].

**Key Takeaways**
*   **Review is the most leveraged skill in modern software engineering.** Because writing code has become cheap and fast, the ability to decide whether to trust that code is where the most value is now generated [1, 8].
*   **One-size-fits-all advice for AI adoption is useless.** The level of review required depends entirely on a project’s **blast radius** (consequences of failure), how long the code must live, and how many people share ownership [9, 10].
*   **Human-in-the-loop is evolving into human-on-the-loop.** Humans should move from reading every line of every PR to sampling, auditing, and exercising high-level judgment on high-risk changes, while letting AI handle the "boring 90%" [11-13].

**Important Details**
*   **Alarming productivity data (March 2026):** High AI adoption has led to an **861% increase in code churn**, a **242.7% increase in the incident-to-PR ratio**, and a **441.5% increase in median review duration** [14].
*   **Tooling Heterogeneity:** Running multiple, differently-built AI reviewers (e.g., CodeRabbit and Greptile) is more effective than picking one "best" tool, as different models surface different classes of bugs with almost no overlap [15, 16].
*   **Agent Failure Modes:** A critical risk is agents "fixing" broken tests by rewriting assertions to match their new, incorrect behavior rather than fixing the code itself [17].
*   **Actionable Strategies:** Osmani recommends **tiering review by risk** rather than author, **refusing to review PRs that lack evidence** of intent and testing, and keeping agent-generated PRs small to ensure they remain human-readable [17-19].

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### **This Week in AI: Who Controls the Loop?** – **Michelle Smith**

**Main Arguments**
*   **AI is transitioning from "conversation" to "operational loops."** The industry is moving beyond chatbots and focusing on integrating AI into the infrastructure where real work—such as software development and medical diagnostics—is executed [20].
*   **The IDE is the new center of software infrastructure.** As agents write more code than humans, the tools they use (like Cursor) are becoming more strategically important than traditional systems of record (like GitHub) [21, 22].
*   **AI access is now a geopolitical "capability" issue.** Advanced AI models are being treated similarly to military hardware, with the US and G7 nations establishing "trusted partner" frameworks to control who can access frontier systems for national security reasons [22-24].

**Key Takeaways**
*   **Infrastructure is being redesigned for agents.** While GitHub was built for human collaboration, newer tools like Cursor were designed for agents from the start, leading to a battle for control over the "developer's active coding surface" [22, 25].
*   **Geopolitics may hinder AI talent.** New access controls based on citizenship could prevent high-level researchers (like Andrej Karpathy) from accessing the very systems they are hired to build if they are non-US citizens [26].
*   **AI value compounds through measurement loops.** Whether in software or health, the goal is to own the environment where data—such as longitudinal body scans or repository history—accumulates over time [27, 28].

**Important Details**
*   **SpaceX’s Massive Acquisition:** SpaceX reportedly acquired Anysphere (the creator of the Cursor IDE) for **$60 billion in stock**, signaling a move to own the full loop of agentic software engineering [21].
*   **Midjourney’s Medical Pivot:** Known for image generation, Midjourney has launched a medical division featuring a **full-body ultrasound scanner** that uses 500,000 sensors and two petaflops of power to create 3D body maps in 60 seconds [26].
*   **Frontier Talent Shifts:** Top researchers continue to move between major labs, including Nobel laureate **John Jumper** moving to Anthropic and "Attention Is All You Need" coauthor **Noam Shazeer** returning to OpenAI [28].
*   **G7 "Trusted Partners" Framework:** This logic mirrors NATO-style alliances, focusing on who can safely use dual-use models that can both find and exploit software vulnerabilities [24].