Anthropic Technical Expert: ‘Coding Is No Longer the Bottleneck’ as Engineers Ship 8x More Code Per Quarter

A recent appearance by Fiona Fung, who manages the Claude Code and Cowork teams at Anthropic, on Lenny Rachitsky’s Podcast offered a window into how one of the world’s most prominent AI labs approaches software engineering when its engineers are AI-native. Her headline claim is that today, “Coding is no longer the bottleneck.” Fung referenced an internal chart showing Anthropic engineers shipping 8 times as much code per quarter today compared with 2021 through 2025 levels.

The New Constraint Is Verification

For two decades, software engineering productivity has been measured in proxies like pull requests, lines shipped, and cycle time. Fung’s argument is that the cost of producing code has collapsed inside Anthropic, and the constraints have moved elsewhere. As she put it, “Now it’s all about, where has that shift happened? Not only are more people checking in code, but like different disciplines, but also the throughput is so high, how do we think about verification?”

When a team can generate a quarter’s worth of code in a week, the scarce resource becomes the human and automated judgment needed to confirm the output behaves correctly, secures customer data, and matches intent. Code review, test infrastructure, observability, and post-deployment monitoring become the gating activities rather than raw code development.

AI Is Blurring the Line Between Engineers and Everyone Else

Fung described a team composition that would have been unusual even a year ago. “We also have designers, PMs, everybody on the [Claude Code] team checks in code,” she said. If accurate as a description of practice, it implies that the traditional career ladder, where coding is a gated specialist activity, is being rewritten inside AI-native firms. Product managers prototype features. Designers ship UI changes directly. The engineering function shifts toward architecture, review, and systems thinking.

Lenny Rachitsky framed the broader trajectory for listeners, “People may forget 100% of code was written by humans not long ago. And now it’s getting to 100% of code written by AI.” This is obviously a bit of an exaggeration, but it captures the direction of travel Anthropic is seeing internally.

What an AI-Native Team Looks Like

Fung also shared a practical example of how she manages her team. She maintains a persistent Claude Code session connected across repositories, Slack channels, and internal metrics. On a monthly cadence, she said, “Every month… we’ll actually do it together. I’ll share my screen, then we do our Claude Code session” to review what shipped, how it performed, and what the feedback looked like.

The workflow resembles having an always-available engineering analyst embedded inside the organization. Rather than spending time gathering information, managers can focus on interpreting results and deciding what to build next.

What Investors Should Watch

Anthropic remains private, so investors cannot buy its shares directly. The more important takeaway is what Anthropic’s experience suggests about the broader AI ecosystem.

If code generation becomes abundant while verification becomes scarce, the biggest beneficiaries may be companies providing developer security, testing, observability, monitoring, and evaluation tools. The hyperscalers running the underlying AI infrastructure also stand to benefit as software teams increasingly rely on AI-generated code.

Anthropic is already cited as a strategic partner of Broadcom (NASDAQ:AVGO | AVGO Price Prediction), alongside Google (NASDAQ:GOOGL) and OpenAI, in the AI semiconductor ecosystem, and a ProShares “FAB 10” ETF filing would package private leaders like OpenAI and Anthropic with public names such as NVIDIA (NASDAQ:NVDA), Microsoft (NASDAQ:MSFT), and Tesla (NASDAQ:TSLA).