How to be a human CEO in a world of AI agents

The art of problem solving is about intelligently breaking down complex problems into simpler ones. I fondly recall reading Jeffrey Dean’s classic MapReduce paper in a graduate-level algorithm course at Stanford as a classic example. MapReduces takes a large task, chops it into smaller pieces, processes them in parallel, and combines the results. Simple enough, but that concept unleashed Google’s ability to handle web-scale data.

Today, I see a similar opportunity with AI agents in business. The idea is to take what MapReduce did for data and apply it to orchestrating AI: break down business challenges into smaller, specialized tasks, parallelize their execution, then synthesize a final result. It’s a pattern that has proven itself in computing, and this analogy will reshape how businesses will soon operate.

The practical question is then: how you, as a human, should prepare to compete in this new paradigm.

MapReduce 101

MapReduce is built on two core operations: “map” and “reduce.” You start with a large dataset—say, logs of every web search for the past 24 hours. Instead of having one system process this all at once, you split it into chunks (map). Each chunk can be processed in parallel, often on multiple machines. After this, you gather all the partial answers and merge them into a single output (reduce).

This approach solves two major headaches: performance and scalability. By distributing the workload, you dramatically cut down on processing time. By modularizing tasks, you make it easier to add new nodes and scale horizontally as your data grows. The result is a more flexible, fault-tolerant system.

Applying MapReduce to your org chart

Right now, your business is structured as some org chart based on technical function or business line. A future org chart model may be a monolithic AI tasked with everything. But this aesthetically doesn’t feel correct. It feels like an intelligent orchestration of multiple parallel, fault tolerant, specialized AI agents is the correct mental model versus a God AI.

Now let’s consider the future where you’re able to spin up this ensemble of AI agents. The concept of MapReduce is useful — instead of splitting a dataset, we’re splitting a business problem into separate chained workflows that leverages the unique strengths of individual AI models. This is well understood by state-of-the-art practitioners, but what I haven’t seen expressed is the next step: build your company org chart to map to AI agents and how to be a good orchestrator of such an organization.

How to structure your business with AI agents and how to be a useful human

The MapReduce paper articulated a key insight for the era of big data: do a bunch of simple things in parallel, then bring it all together. Today, we’re on the cusp of a similar architecture shift. The shift is not on the computer; it’s instead on how to structure your org chart. You need to design your company architecture to map to a world of autonomous intelligent agents. Today, your business “agents” are humans. Tomorrow, some of those agents will be AI. In the future, all of them will be AI.

The most important attribute in this new paradigm is the overarching merge functions i.e. how do you adjudicate the differing priorities and objective functions of local AI agents? Most of these decisions will be automatable because the merge is well-understood. However, the most valuable adjudications will be the non-linear decisions around taste. So as a human, your highest objective should be to develop this taste.

Most people think they have taste, but they do not. Your taste is derived and remixed from your favorite podcaster or X shitposter. Your supposed taste is literally nothing against the entire corpus of human knowledge, which is already indexed and smart people are generating even more synthetic data that you will not be able to process as a human.

To develop useful taste, you will have to have access to unique proprietary data or insights that no one else has. This means you’ll have to do stuff no one else has done and win hard-earned insights that no one else has thought about to develop actual taste, and you’ll have to stack a lot of these insights and lessons to get an edge over a general AI CEO.

Your key objective now should be to optimize your life to develop this type of taste, or else another human will automate you away.

Thank you to Michael Brandt and Tony Wu for reading early drafts and providing feedback.