At Factory we build Droids, autonomous software development agents that review PRs, implement features, run tests, fix incidents, and handle migrations for enterprise engineering teams. Droid is model agnostic and surface agnostic, and works across your CLI, IDE, Web/Desktop app, CI/CD, Slack, and Linear. Customers include Nvidia, Adobe, Adyen, Klarna, and Ernst & Young.

We raised a $150M Series C at a $1.5B valuation, backed by Khosla Ventures, Sequoia, Blackstone, Insight Partners, NEA, and NVIDIA.

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I think we might be talking past each other on the "super-manager" term. I defined it as a hybrid of EM + IC roles, not pure management, though I can see how that term invited misinterpretation.

On the false dichotomy: fair point that I painted two archetypes without acknowledging the complexity between them or the many other archetypes. What I was trying to capture was a pattern I've observed: some skills from managing and reviewing others' work (feedback, delegation, synthesizing approaches) seem to transfer well to working with AI agents, especially in parallel.

One thing I'm curious about: you said my framing overlooks "the real benefit of going through the process of authoring a change." But when you delegate work to a junior developer, you still need to understand the problem deeply to communicate it properly, and to recognize when their solution is wrong or incomplete. You still debug, iterate, and think through edge cases, just through descriptions and review rather than typing every line yourself. And nothing stops you from typing lines when you need to fix things, implement ideas, or provide examples.

AI tools work similarly. You still hit edit-compile-test cycles when output doesn't compile or tests fail. You still get stuck when the AI goes down the wrong path. And you still write code directly when needed.

I'm genuinely interested in understanding your perspective better. What do you see as the key difference between these modes of working? Is there something about the AI workflow that fundamentally changes the learning process in a way that delegation to humans doesn't?

However, my sense is that someone with proper management/review/leadership skills is far less likely to let that code ship, whether it came from an AI, a junior dev, or anyone else. They seem to have more sensibility for what 'good' looks like and can critically evaluate work before it goes out. The cognitive distance you mention is real, which is exactly why I think that review muscle becomes more critical, not less. From what I've observed, the people actually thriving with AI are maintaining their quality bar while leveraging the speed; they tend to be picky or blunt, but also give leeway for exploration and creativity.

What we need is better tools for this upcoming new phase. Not a new IDE; we need to shift the whole paradigm.

Here's one example: If we give the same task to 3 different agents, we have tools to review a diff of each OLD vs NEW separately, but we need tools to review diffs of OLD vs NEW#1 vs NEW#2 vs NEW#3. Make it easy to mix-and-match what is best from each of them.

From what I've seen, the idea that AI is turning developers into super-managers is why some people struggle to adapt and quickly dismiss the experience. Those who love to type their code and hate managing others tend to be more hesitant to adapt to this new reality. Meanwhile, people who love to manage, communicate, and work as a team are leveraging these tools more swiftly. They already know how to review imperfect work and give feedback, which is exactly what thriving with AI looks like.

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I am really glad to see good engineering practices such as linting, proper code architecture, and no network calls in tests, etc. are now paying off by letting agents move faster and more precisely through more automated self-healing loops and more guardrails.

It is also interesting to see that some things that were initially done to simplify human readability of code, such as default imports, may now have a negative impact. I wonder if this could also extend other things such as adding explicit type annotations everywhere instead of relying on implicit ones, or using full variable names instead of `x`, `i` or abbreviations, which are all currently written to optimize human consumption.

I am also curious to learn what the sweet spot is between enforcing rules for humans versus code written for agents. My gut feeling is that it is now time to start optimizing for agents.

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