Everyday I find myself grappling with the rapid adoption of AI and what it means for human ownership over the work we deliver and accountability for that work. As I think about how we’ve been approaching this at Monarch so far, I wanted to share my thoughts and hear from others.
My father’s family is from the Egyptian town of Ismailia, which sits right in the middle of the Suez Canal. Growing up, we would watch as the massive ships navigated the narrow waterway while small, more agile boats sped up alongside them. As the vessels matched each other’s speed, a local Egyptian pilot would climb aboard and assist the ship in navigating the canal. Every ship navigating the Suez Canal would take on one of these local pilots.
If the ships already had their own captains and crew, why did they need local pilots? While the captain and his crew continue to own the ship’s ultimate goal — getting the ship, crew, and cargo to its destination — the local guides have two goals:
- To use their superior knowledge of specific technical and local issues to help the ship navigate the Canal.
- To maintain, through care and coordination, the health and function of the overall Canal, for many ships across multiple years.
At Monarch, we’ve always leveraged a system of Project DRIs and Domain Owners that in many ways mimics what I witnessed growing up. In practice, this looks like:
- Project DRIs that focus on execution of a particular project. Their goal is a successful outcome. They and their team are the ship’s captain and crew. We’ve always aimed to give Project DRIs as much autonomy as possible, and with AI, those Project DRIs can now execute a lot faster and more flexibly. Imagine our ship’s crew above, but suddenly, that ship’s speed has quadrupled and it has gained the ability to traverse not just water, but land and air.
- Domain Owners that have a deep understanding of a particular “domain” just like the local guides. Domain boundaries aren’t strict, and can be drawn in different shapes and sizes. For example, a domain could be our product’s overall visual design, our budgeting code, or a part of our technical infrastructure.
Like the local pilots, domain owners maintain a deep understanding of their area coupled with a strong sense of ownership and accountability for it. They help others navigate their domain while ensuring that domain is healthy over the long run. It’s the ownership and accountability that ultimately results in good work.
Can AI be a domain owner?
AI does not have the capacity to be a domain owner. At least not yet. To do that, it would need the right knowledge — both domain expertise and context — and the right accountability loop.
As LLMs get better, they will have better knowledge and domain expertise. Their “jagged edges” will smooth themselves out. And as we get better at handling the “mismanaged genius,” we’ll find the best ways to give them the context they need. It may take some time and effort; because, unlike AI, humans have the ability to recall things that are important to them while ignoring things that are not, and to fill in missing or bad context with expertise or common sense.
The one thing LLMs won’t likely have is the essential degree of ownership and accountability. If you’ve been part of a high-performing team you know that so much of the work succeeds because of human trust, accountability, and initiative.
I want to be clear that accountability for high-performing teams is not of the “who’s neck do I wring if something bad happens” variety. It’s the confidence you have in work that has someone you trust’s name on it. Someone who is competent and conscientious and has some sort of (material and/or reputational) skin-in-the-game. For example:
- “I trust that my colleague understands their work and will do it well, and if they don’t, given feedback, they’ll correct it, because their name is on it.”
- “I trust that if I ask my colleague to do something that doesn’t make sense, they’ll push back in correlation with their level of conviction (rather than pander to me).”
- “I trust that my colleagues will take initiative and come up with creative solutions to the problems we have.”
If we introduce AI thoughtlessly and haphazardly, we risk undermining the very parts of human ownership that actually result in great work and differentiate great work from work that is just okay. No matter how much knowledge or context AI has, it may never replace a well-informed, well-intentioned human with a sense of ownership.
Avoiding the slop trap
Throwing copious amounts of information (code, text, design, etc.) at someone, without the ability to judge the quality of that information, is bad citizenship. It just so happens that LLMs make it easier to produce copious amounts of information that looks good at first blush, but puts the onus on the reviewer. This is where ownership and accountability matters most.
We still empower domain owners to use AI in the same way we empower execution teams. For instance, they might leverage AI to make reviews easier, or to auto-review smaller changes. They might one day decide they will automate the entire review process. But their name is still on the review.
This has driven a dedicated anti-slop practice at Monarch. We call it out when we see it, sometimes candidly, usually very diplomatically: “Did you review this before sending it? Or was it just one-shotted in Claude?”
But how can we allow for essential “lane-switching” without the potential of slop? On the surface, it might look like allowing someone (a product manager for example) to produce work in a domain with which they are less familiar (say, code) would result in endless slop, right? The answer is to prime people to generate work that in quantity that correlates inversely in size to their ability to judge the quality of that work. Using AI in an area you’re very familiar with? Produce away because you can judge the quality. Venturing outside your comfort zone? Work in smaller chunks.
Keeping the work human
Our team recently gave us some feedback that as we’ve rushed to adapt to AI, the work has started feeling less “human”. This feedback was tough to hear, but it was a great reminder.
Even before AI, we’ve always believed that there are many ways to build a successful company. You can do it in a way that’s exploitive, or in a way that’s more mindful and human. You can build on trust, or you can build on transactions. This applies both to how you view and treat your team as well as your customers.
Pride in our work is the fuel that keeps us going. Trust in our colleagues is the glue that keeps it all together.
With AI, this dynamic becomes even more important. Is AI replacing our toil, or our taste? Is it disconnecting us from doing the work we love, or allowing us to do more of it? Is it undermining our craft, or elevating it? We’ve agreed, as a company, to give ourselves space to make that an ongoing conversation—and to raise a flag when we think things are going off the rails.
Ownership = Integrity + Execution
At Monarch, we’ve always believed in encouraging a sense of ownership and accountability, while avoiding gate-keeping. This has become even more critical in the age of AI and we believe it’s the answer to leveraging AI productivity boosts without losing the human aspect that makes the work special.
Ownership drives the balance of two essential qualities of great work:
- Execution: short-term, high-quality work.
- Integrity: long-term health of the system (to enable execution over a longer-period of time and across a larger number of people).
If you optimize only for execution, over the long-run, you lose your ability to execute well. If you optimize only for integrity, you’d never get any work done in the short-term. At Monarch, we’ve always strived to find this balance. Sometimes, you have to choose between one of those goals or the other, but you can almost always find ways that enable both.
This post was written with human hands. AI was used via chat as a thought partner to help with expressing myself. Emdashes are my own—I’ve been using them since college and have the keyboard shortcut for them memorized.
Somehow Manage 