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.

what I cheered as an analyst was Altman’s clear articulation of the company’s priorities: lower price first, speed later. You can certainly debate whether that is the right set of priorities (I think it is, because the biggest need now is for increased experimentation, not optimization), but what I appreciated was the clarity.

The fact that Microsoft is benefiting from OpenAI is obvious; what this makes clear is that OpenAI uniquely benefits from Microsoft as well, in a way they would not from another cloud provider: because Microsoft is also a product company investing in the infrastructure to run OpenAI’s models for said products, it can afford to optimize and invest ahead of usage in a way that OpenAI alone, even with the support of another cloud provider, could not. In this case that is paying off in developers needing to pay less, or, ideally, have more latitude to discover use cases that result in them paying far more because usage is exploding.

You can, in effect, program a GPT, with language, just by talking to it. It’s easy to customize the behavior so that it fits what you want. This makes building them very accessible, and it gives agency to everyone.

Stephen Wolfram explained: For decades there’s been a dichotomy in thinking about AI between “statistical approaches” of the kind ChatGPT uses, and “symbolic approaches” that are in effect the starting point for Wolfram|Alpha. But now—thanks to the success of ChatGPT—as well as all the work we’ve done in making Wolfram|Alpha understand natural language—there’s finally the opportunity to combine these to make something much stronger than either could ever achieve on their own.

This new model somewhat alleviates the problem: now, instead of having to select the correct plug-in (and thus restart your chat), you simply go directly to the GPT in question. In other words, if I want to create a poster, I don’t enable the Canva plugin in ChatGPT, I go to Canva GPT in the sidebar. Notice that this doesn’t actually solve the problem of needing to have selected the right tool; what it does do is make the choice more apparent to the user at a more appropriate stage in the process, and that’s no small thing.

ChatGPT will seamlessly switch between text generation, image generation, and web browsing, without the user needing to change context. What is necessary for the plug-in/GPT idea to ultimately take root is for the same capabilities to be extended broadly: if my conversation involved math, ChatGPT should know to use Wolfram|Alpha on its own, without me adding the plug-in or going to a specialized GPT.

the obvious technical challenges of properly exposing capabilities and training the model to know when to invoke those capabilities are a textbook example of Professor Clayton Christensen’s theory of integration and modularity, wherein integration works better when a product isn’t good enough; it is only when a product exceeds expectation that there is room for standardization and modularity.

To summarize the argument, consumers care about things in ways that are inconsistent with whatever price you might attach to their utility, they prioritize ease-of-use, and they care about the quality of the user experience and are thus especially bothered by the seams inherent in a modular solution. This means that integrated solutions win because nothing is ever “good enough”

the fact of the matter is that a lot of people use ChatGPT for information despite the fact it has a well-documented flaw when it comes to the truth; that flaw is acceptable, because to the customer ease-of-use is worth the loss of accuracy. Or look at plug-ins: the concept as originally implemented has already been abandoned, because the complexity in the user interface was more detrimental than whatever utility might have been possible. It seems likely this pattern will continue: of course customers will say that they want accuracy and 3rd-party tools; their actions will continue to demonstrate that convenience and ease-of-use matter most.

Betting against a first-generation Apple product is a bad trade — from infamous dismissals of the iPhone to disappointment with the original iPad. In fact, this is a reflection of Apple’s strategy: Start with a product that’s more an elegant proof-of-concept than a prime-time hit; rely on early adopters to provide enough runway for its engineers to keep iterating; and trust in unmatched capital, talent, brand equity, and staying power to morph a first-gen toy into a third-gen triumph

We are a long way from making three screens, a glass shield, and an array of supporting hardware light enough to wear for an extended period. Reviewers were (purposefully) allowed to wear the Vision Pro for less than half an hour, and nearly every one said comfort was declining even then. Avatar: The Way of Water is 3 hours and 12 minutes.

Meta’s singular strategic objective is to escape second-tier status and, like Apple and Alphabet, control its distribution. And its path to independence runs through Apple Park. Zuckerberg is spending the GDP of a small country to invent a new world, the metaverse, where Apple doesn’t own the roads or power stations. Vision Pro is insurance against the metaverse evolving into anything more than an incel panic room.

The only product category where VR makes difference is good VR games. Price is not limiting factor, the quality of VR experience is. Beat Saber is good and fun and physical exercise. Half Life: Alyx, is amazing. VR completely supercharges horror games, and scary stalking shooters. Want to fear of your life and get PTSD in the comfort of your home? You can do it. Games can connect people and provide physical exercise. If the 3rd iteration of Vision Pro is good for 2 hours of playing for $2000 Apple will kill the console market. Playstations no more. Apple is not a gaming company, but if Vision Pro becomes better and slightly cheaper, Apple becomes gaming company against its will.