inside look at how Perplexity builds product—which to me feels like what the future of product development will look like for many companies:AI-first: They’ve been asking AI questions about every step of the company-building process, including “How do I launch a product?” Employees are encouraged to ask AI before bothering colleagues.Organized like slime mold: They optimize for minimizing coordination costs by parallelizing as much of each project as possible.Small teams: Their typical team is two to three people. Their AI-generated (highly rated) podcast was built and is run by just one person.Few managers: They hire self-driven ICs and actively avoid hiring people who are strongest at guiding other people’s work.A prediction for the future: Johnny said, “If I had to guess, technical PMs or engineers with product taste will become the most valuable people at a company over time.”

Typical projects we work on only have one or two people on it. The hardest projects have three or four people, max. For example, our podcast is built by one person end to end. He’s a brand designer, but he does audio engineering and he’s doing all kinds of research to figure out how to build the most interactive and interesting podcast. I don’t think a PM has stepped into that process at any point.

We leverage product management most when there’s a really difficult decision that branches into many directions, and for more involved projects.

The hardest, and most important, part of the PM’s job is having taste around use cases. With AI, there are way too many possible use cases that you could work on. So the PM has to step in and make a branching qualitative decision based on the data, user research, and so on.

a big problem with AI is how you prioritize between more productivity-based use cases versus the engaging chatbot-type use cases.

we look foremost for flexibility and initiative. The ability to build constructively in a limited-resource environment (potentially having to wear several hats) is the most important to us.

We look for strong ICs with clear quantitative impacts on users rather than within their company. If I see the terms “Agile expert” or “scrum master” in the resume, it’s probably not going to be a great fit.

My goal is to structure teams around minimizing “coordination headwind,” as described by Alex Komoroske in this deck on seeing organizations as slime mold. The rough idea is that coordination costs (caused by uncertainty and disagreements) increase with scale, and adding managers doesn’t improve things. People’s incentives become misaligned. People tend to lie to their manager, who lies to their manager. And if you want to talk to someone in another part of the org, you have to go up two levels and down two levels, asking everyone along the way.

Instead, what you want to do is keep the overall goals aligned, and parallelize projects that point toward this goal by sharing reusable guides and processes.

Perplexity has existed for less than two years, and things are changing so quickly in AI that it’s hard to commit beyond that. We create quarterly plans. Within quarters, we try to keep plans stable within a product roadmap. The roadmap has a few large projects that everyone is aware of, along with small tasks that we shift around as priorities change.

Each week we have a kickoff meeting where everyone sets high-level expectations for their week. We have a culture of setting 75% weekly goals: everyone identifies their top priority for the week and tries to hit 75% of that by the end of the week. Just a few bullet points to make sure priorities are clear during the week.

All objectives are measurable, either in terms of quantifiable thresholds or Boolean “was X completed or not.” Our objectives are very aggressive, and often at the end of the quarter we only end up completing 70% in one direction or another. The remaining 30% helps identify gaps in prioritization and staffing.

At the beginning of each project, there is a quick kickoff for alignment, and afterward, iteration occurs in an asynchronous fashion, without constraints or review processes. When individuals feel ready for feedback on designs, implementation, or final product, they share it in Slack, and other members of the team give honest and constructive feedback. Iteration happens organically as needed, and the product doesn’t get launched until it gains internal traction via dogfooding.

all teams share common top-level metrics while A/B testing within their layer of the stack. Because the product can shift so quickly, we want to avoid political issues where anyone’s identity is bound to any given component of the product.

We’ve found that when teams don’t have a PM, team members take on the PM responsibilities, like adjusting scope, making user-facing decisions, and trusting their own taste.

What’s your primary tool for task management, and bug tracking?Linear. For AI products, the line between tasks, bugs, and projects becomes blurred, but we’ve found many concepts in Linear, like Leads, Triage, Sizing, etc., to be extremely important. A favorite feature of mine is auto-archiving—if a task hasn’t been mentioned in a while, chances are it’s not actually important.The primary tool we use to store sources of truth like roadmaps and milestone planning is Notion. We use Notion during development for design docs and RFCs, and afterward for documentation, postmortems, and historical records. Putting thoughts on paper (documenting chain-of-thought) leads to much clearer decision-making, and makes it easier to align async and avoid meetings.Unwrap.ai is a tool we’ve also recently introduced to consolidate, document, and quantify qualitative feedback. Because of the nature of AI, many issues are not always deterministic enough to classify as bugs. Unwrap groups individual pieces of feedback into more concrete themes and areas of improvement.

High-level objectives and directions come top-down, but a large amount of new ideas are floated bottom-up. We believe strongly that engineering and design should have ownership over ideas and details, especially for an AI product where the constraints are not known until ideas are turned into code and mock-ups.

Big challenges today revolve around scaling from our current size to the next level, both on the hiring side and in execution and planning. We don’t want to lose our core identity of working in a very flat and collaborative environment. Even small decisions, like how to organize Slack and Linear, can be tough to scale. Trying to stay transparent and scale the number of channels and projects without causing notifications to explode is something we’re currently trying to figure out.

If you’d looked at Skype in 2004 and argued that it would own ‘voice’ on ‘computers’, that would not have been the right mental model. I think this is where we’ll go with video - there will continue to be hard engineering, but video itself will be a commodity and the question will be how you wrap it. There will be video in everything, just as there is voice in everything, and there will be a great deal of proliferation into industry verticals on one hand and into unbundling pieces of the tech stack on the other. On one hand video in healthcare, education or insurance is about the workflow, the data model and the route to market, and lots more interesting companies will be created, and on the other hand Slack is deploying video on top of Amazon’s building blocks, and lots of interesting companies will be created here as well. There’s lots of bundling and unbundling coming, as always. Everything will be ‘video’ and then it will disappear inside.

the calendar is often the aggregation layer - you don’t need to know what service the next call uses, just when it is. Skype needed both an account and an app, so had a network effect (and lost even so). WhatsApp uses the telephone numbering system as an address and so piggybacked on your phone’s contact list - effectively, it used the PSTN as the social graph rather than having to build its own. But a group video call is a URL and a calendar invitation - it has no graph of its own.

one of the ways that this all feels very 1.0 is the rather artificial distinction between calls that are based on a ‘room’, where the addressing system is a URL and anyone can join without an account, and calls that are based on ‘people’, where everyone joining needs their own address, whether it’s a phone number, an account or something else. Hence Google has both Meet (URLs) and Duo (people) - Apple’s FaceTime is only people (no URLs).

When Snap launched, there were already infinite ways to share images, but Snap asked a bunch of weird questions that no-one had really asked before. Why do you have to press the camera button - why doesn’t the app open in the camera? Why are you saving your messages - isn’t that like saving all your phone calls? Fundamentally, Snap asked ‘why, exactly, are you sending a picture? What is the underlying social purpose?’ You’re not really sending someone a sheet of pixels - you’re communicating.

That’s the question Zoom and all its competitors haven’t really asked. Zoom has done a good job of asking why it was hard to get into a call, but it hasn’t asked why you’re in the call in the first place. Why, exactly, are you sending someone a video stream and watching another one? Why am I looking at a grid of little thumbnails of faces? Is that the purpose of this moment? What is the ‘mute’ button for - background noise, or so I can talk to someone else, or is it so I can turn it off to raise my hand? What social purpose is ‘mute’ actually serving? What is screen-sharing for? What other questions could one ask? And so if Zoom is the Dropbox or Skype of video, we are waiting for the Snap, Clubhouse and Yo.