Mr. Musk had largely come to peace with a price of $100 a year for Blue. But during one meeting to discuss pricing, his top assistant, Jehn Balajadia, felt compelled to speak up. “There’s a lot of people who can’t even buy gas right now,” she said, according to two people in attendance. It was hard to see how any of those people would pony up $100 on the spot for a social media status symbol. Mr. Musk paused to think. “You know, like, what do people pay for Starbucks?” he asked. “Like $8?” Before anyone could raise objections, he whipped out his phone to set his word in stone. “Twitter’s current lords & peasants system for who has or doesn’t have a blue checkmark is bullshit,” he tweeted on Nov. 1. “Power to the people! Blue for $8/month.”

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.

The “hard tech” in AI are the LLMs available for rent from OpenAI, Anthropic, Cohere, and others, or available as open source with Llama, Bloom, Mistral and others. The hard-tech is a level playing field; startups do not have an advantage over incumbents.

There can be differentiation in prompt engineering, problem break-down, use of vector databases, and more. However, this isn’t something where startups have an edge, such as being willing to take more risks or be more creative. At best, it is neutral; certainly not an advantage.

This doesn’t mean it’s impossible for a startup to succeed; surely many will. It means that you need a strategy that creates differentiation and distribution, even more quickly and dramatically than is normally required

Whether you’re training existing models, developing models from scratch, or simply testing theories, high-quality data is crucial. Incumbents have the data because they have the customers. They can immediately leverage customers’ data to train models and tune algorithms, so long as they maintain secrecy and privacy.

Intercom’s AI strategy is built on the foundation of hundreds of millions of customer interactions. This gives them an advantage over a newcomer developing a chatbot from scratch. Similarly, Google has an advantage in AI video because they own the entire YouTube library. GitHub has an advantage with Copilot because they trained their AI on their vast code repository (including changes, with human-written explanations of the changes).

While there will always be individuals preferring the startup environment, the allure of working on AI at an incumbent is equally strong for many, especially pure computer and data scientsts who, more than anything else, want to work on interesting AI projects. They get to work in the code, with a large budget, with all the data, with above-market compensation, and a built-in large customer base that will enjoy the fruits of their labor, all without having to do sales, marketing, tech support, accounting, raising money, or anything else that isn’t the pure joy of writing interesting code. This is heaven for many.

A chatbot is in the chatbot market, and an SEO tool is in the SEO market. Adding AI to those tools is obviously a good idea; indeed companies who fail to add AI will likely become irrelevant in the long run. Thus we see that “AI” is a new tool for developing within existing markets, not itself a new market (except for actual hard-tech AI companies).

AI is in the solution-space, not the problem-space, as we say in product management. The customer problem you’re solving is still the same as ever. The problem a chatbot is solving is the same as ever: Talk to customers 24/7 in any language. AI enables completely new solutions that none of us were imagining a few years ago; that’s what’s so exciting and truly transformative. However, the customer problems remain the same, even though the solutions are different

Companies will pay more for chatbots where the AI is excellent, more support contacts are deferred from reaching a human, more languages are supported, and more kinds of questions can be answered, so existing chatbot customers might pay more, which grows the market. Furthermore, some companies who previously (rightly) saw chatbots as a terrible customer experience, will change their mind with sufficiently good AI, and will enter the chatbot market, which again grows that market.

the right way to analyze this is not to say “the AI market is big and growing” but rather: “Here is how AI will transform this existing market.” And then: “Here’s how we fit into that growth.”

Despite its direct affiliation, Stripe Press provokes a distinctive, emotional feeling. It’s an example of how form affects soft power. By focusing on actual, physical books — and giving them a loving, literary treatment — Stripe shows this project is firmly outside the world of “marketing.” Rather, this is a place for Stripe to demonstrate its ideological affinities and reinforce its philosophical positioning. The affection this project has earned suggests it has found distribution.

Most obviously, they can invest in it via in-house initiatives. Even moderately sized tech companies have large marketing teams capable of running interesting experiments, especially if augmented with external talent. Business banking platform Mercury has made strides in this area over the past couple of years, launching a glossy, thoughtful publication named Meridian.

We still believe that there will be a separation between the “application layer” companies and foundation model providers, with model companies specializing in scale and research and application layer companies specializing in product and UI. In reality, that separation hasn’t cleanly happened yet. In fact, the most successful user-facing applications out of the gate have been vertically integrated.

We predicted that the best generative AI companies could generate a sustainable competitive advantage through a data flywheel: more usage → more data → better model → more usage. While this is still somewhat true, especially in domains with very specialized and hard-to-get data, the “data moats” are on shaky ground: the data that application companies generate does not create an insurmountable moat, and the next generations of foundation models may very well obliterate any data moats that startups generate. Rather, workflows and user networks seem to be creating more durable sources of competitive advantage.

Some of the best consumer companies have 60-65% DAU/MAU; WhatsApp’s is 85%. By contrast, generative AI apps have a median of 14% (with the notable exception of Character and the “AI companionship” category). This means that users are not finding enough value in Generative AI products to use them every day yet.

generative AI’s biggest problem is not finding use cases or demand or distribution, it is proving value. As our colleague David Cahn writes, “the $200B question is: What are you going to use all this infrastructure to do? How is it going to change people’s lives?”

There is little precedent for a civilian’s becoming the arbiter of a war between nations in such a granular way, or for the degree of dependency that the U.S. now has on Musk in a variety of fields, from the future of energy and transportation to the exploration of space. SpaceX is currently the sole means by which NASA transports crew from U.S. soil into space, a situation that will persist for at least another year. The government’s plan to move the auto industry toward electric cars requires increasing access to charging stations along America’s highways. But this rests on the actions of another Musk enterprise, Tesla. The automaker has seeded so much of the country with its proprietary charging stations that the Biden Administration relaxed an early push for a universal charging standard disliked by Musk. His stations are eligible for billions of dollars in subsidies, so long as Tesla makes them compatible with the other charging standard.

In the past twenty years, against a backdrop of crumbling infrastructure and declining trust in institutions, Musk has sought out business opportunities in crucial areas where, after decades of privatization, the state has receded. The government is now reliant on him, but struggles to respond to his risk-taking, brinkmanship, and caprice

Current and former officials from NASA, the Department of Defense, the Department of Transportation, the Federal Aviation Administration, and the Occupational Safety and Health Administration told me that Musk’s influence had become inescapable in their work, and several of them said that they now treat him like a sort of unelected official

Sam Altman, the C.E.O. of OpenAI, with whom Musk has both worked and sparred, told me, “Elon desperately wants the world to be saved. But only if he can be the one to save it.

later. “He had grown up in the male-dominated culture of South Africa,” Justine wrote. “The will to compete and dominate that made him so successful in business did not magically shut off when he came home.”

There are competitors in the field, including Jeff Bezos’s Blue Origin and Richard Branson’s Virgin Galactic, but none yet rival SpaceX. The new space race has the potential to shape the global balance of power. Satellites enable the navigation of drones and missiles and generate imagery used for intelligence, and they are mostly under the control of private companies.

A number of officials suggested to me that, despite the tensions related to the company, it has made government bureaucracies nimbler. “When SpaceX and NASA work together, we work closer to optimal speed,” Kenneth Bowersox, NASA’s associate administrator for space operations, told me. Still, some figures in the aerospace world, even ones who think that Musk’s rockets are basically safe, fear that concentrating so much power in private companies, with so few restraints, invites tragedy.

Tesla for a time included in its vehicles the ability to replace the humming noises that electric cars must emit—since their engines make little sound—with goat bleats, farting, or a sound of the owner’s choice. “We’re, like, ‘No, that’s not compliant with the regulations, don’t be stupid,’ ” Cliff told me. Tesla argued with regulators for more than a year, according to an N.H.T.S.A. safety report

Musk’s personal wealth dwarfs the entire budget of OSHA, which is tasked with monitoring the conditions in his workplaces. “You add on the fact that he considers himself to be a master of the universe and these rules just don’t apply to people like him,” Jordan Barab, a former Deputy Assistant Secretary of Labor at OSHA, told me. “There’s a lot of underreporting in industry in general. And Elon Musk kind of seems to raise that to an art form.”

Some people who know Musk well still struggle to make sense of his political shift. “There was nothing political about him ever,” a close associate told me. “I’ve been around him for a long time, and had lots of deep conversations with the man, at all hours of the day—never heard a fucking word about this.”

the cuts that Musk had instituted quickly took a toll on the company. Employees had been informed of their termination via brusque, impersonal e-mails—Musk is now being sued for hundreds of millions of dollars by employees who say that they are owed additional severance pay—and the remaining staffers were abruptly ordered to return to work in person. Twitter’s business model was also in question, since Musk had alienated advertisers and invited a flood of fake accounts by reinventing the platform’s verification process

Musk’s trolling has increasingly taken on the vernacular of hard-right social media, in which grooming, pedophilia, and human trafficking are associated with liberalism

It is difficult to say whether Musk’s interest in A.I. is driven by scientific wonder and altruism or by a desire to dominate a new and potentially powerful industry.

Netflix could buy 10 top quality screenplays per year with the cash they’ll spend on that one job.  They must have big plans for AI.There are also a half dozen AI job openings at Disney. And the tech-based streamers (Apple, Amazon) already have made big investments in AI. Sony launched an AI business unit in April 2020—in order to “enhance human imagination and creativity, particularly in the realm of entertainment.”

When Spotify launched on the stock exchange in 2018, it was losing around $30 million per month. Now it’s much larger, and is losing money at the pace of more than $100 million per month.

But the real problem at Spotify isn’t just convincing people to pay more. It runs much deeper. Spotify finds itself in the awkward position of asking people to pay more for a lousy interface that degrades the entire user experience.

Boredom is built into the platform, because they lose money if you get too excited about music—you’re like the person at the all-you-can-eat buffet who goes back for a third helping. They make the most money from indifferent, lukewarm fans, and they created their interface with them in mind. In other words, Spotify’s highest aspiration is to be the Applebee’s of music.

They need to prepare for a possible royalty war against record labels and musicians—yes, that could actually happen—and they do that by creating a zombie world of brain dead listeners who don’t even know what artist they’re hearing. I know that sounds extreme, but spend some time on the platform and draw your own conclusions.

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.

In the past several months, I have come across people who do programming, legal work, business, accountancy and finance, fashion design, architecture, graphic design, research, teaching, cooking, travel planning, event management etc., all of whom have started using the same tool, ChatGPT, to solve use cases specific to their domains and problems specific to their personal workflows. This is unlike everyone using the same messaging tool or the same document editor. This is one tool, a single class of technology (LLM), whose multi-dimensionality has achieved widespread adoption across demographics where people are discovering how to solve a multitude of problems with no technical training, in the one way that is most natural to humans—via language and conversations.

I cannot recall the last time a single tool gained such widespread acceptance so swiftly, for so many use cases, across entire demographics.

there is significant substance beneath the hype. And that is what is worrying; the prospect of us starting to depend indiscriminately on poorly understood blackboxes, currently offered by megacorps, that actually work shockingly well.

If a single dumb, stochastic, probabilistic, hallucinating, snake oil LLM with a chat UI offered by one organisation can have such a viral, organic, and widespread adoption—where large disparate populations, people, corporations, and governments are integrating it into their daily lives for use cases that they are discovering themselves—imagine what better, faster, more “intelligent” systems to follow in the wake of what exists today would be capable of doing.

A policy for “AI anxiety” We ended up codifying this into an actual AI policy to bring clarity to the organisation.[10] It states that no one at Zerodha will lose their job if a technology implementation (AI or non-AI) directly renders their existing responsibilities and tasks obsolete. The goal is to prevent unexpected rug-pulls from underneath the feet of humans. Instead, there will be efforts to create avenues and opportunities for people to upskill and switch between roles and responsibilities

To those who believe that new jobs will emerge at meaningful rates to absorb the losses and shocks, what exactly are those new jobs? To those who think that governments will wave magic wands to regulate AI technologies, one just has to look at how well governments have managed to regulate, and how well humanity has managed to self-regulate, human-made climate change and planetary destruction. It is not then a stretch to think that the unraveling of our civilisation and its socio-politico-economic systems that are built on extracting, mass producing, and mass consuming garbage, might be exacerbated. Ted Chiang’s recent essay is a grim, but fascinating exploration of this. Speaking of grim, we can always count on us to ruin nice things! Along the lines of Murphy’s Law,[11] I present: Anything that can be ruined, will be ruined — Grumphy’s law

I asked GPT-4 to summarise this post and write five haikus on it. I have always operated a piece of software, but never asked it anything—that is, until now. Anyway, here is the fifth one. Future’s tangled web, Offloading choices to black boxes, Humanity’s voice fades

software gives you its own set of abstractions and basic vocabulary with which to understand every experience. It sort of smells like mathematics in some ways. But software’s way of looking at the world is more about abstractions modeling underlying complexities in systems; signal vs. noise; scale and orders of magnitude; and information — how much there is, what we can do it with, how we can learn from it and model it. Software’s interpretation of reality is particularly important because software drives the world now, and the people who write the software that runs it see the world through this kind of “software’s worldview” — scaling laws, information theory, abstractions and complexity. I think over time I’ve come to believe that understanding this worldview is more interesting than learning to wield programming tools.

So using that hook of “simplicity”, the best advice for “doing agile” is: Read the manifesto Figure out what makes you happy. And do that.

it is very difficult to figure out what specific effect ATT has because there are so many factors involved

If ATT were so significantly kneecapping revenue, I would think we would see a pronounced skew against North America compared to elsewhere. But that is not the case. Revenue in North America is only slightly off compared to the company total, and it is increasing how much it earns per North American user compared to the rest of the world.

iOS is far more popular in the U.S. and Canada than it is in Europe, but Meta incurred a greater revenue decline — in absolute terms and, especially, in percentage terms — in Europe. Meta was still posting year-over-year gains in both those regions until this most recent quarter, even though ATT rolled out over a year ago.

there are those who believe highly-targeted advertisements are a fair trade-off because they offer businesses a more accurate means of finding their customers, and the behavioural data collected from all of us is valuable only in the aggregate. That is, as I understand it, the view of analysts like Seufert, Benedict Evans, and Ben Thompson. Frequent readers will not be surprised to know I disagree with this premise. Regardless of how many user agreements we sign and privacy policies we read, we cannot know the full extent of the data economy. Personal information about us is being collected, shared, combined, and repackaged. It may only be profitable in aggregate, but it is useful with finer granularity, so it is unsurprising that it is indefinitely warehoused in detail.

Seufert asked, rhetorically, “what happens when ads aren’t personalized?”, answering “digital ads resemble TV ads: jarring distractions from core content experience. Non-personalized is another way of saying irrelevant, or at best, randomly relevant.”

does it make sense to build the internet’s economy on the backs of a few hundred brokers none of us have heard of, trading and merging our personal information in the hope of generating a slightly better click-through rate?

Then there is the much bigger question of whether people should even be able to opt into such widespread tracking. We simply cannot be informed consumers in every aspect of our lives, and we cannot foresee how this information will be used and abused in the full extent of time. It sounds boring, but what is so wrong with requiring data minimization at every turn, permitting only the most relevant personal data to be collected, and restricting the ability for this information to be shared or combined?

Does ATT really “[deprive] consumers of widespread ad relevancy and advertisers and publishers of commercial opportunity”? Even if it does — which I doubt — has that commercial opportunity really existed with meaningful consumer awareness and choice? Or is this entire market illegitimate, artificially inflated by our inability to avoid becoming its subjects?

Conflicts like these are one of many reasons why privacy rights should be established by regulators, not individual companies. Privacy must not be a luxury good, or something you opt into, and it should not be a radical position to say so. We all value different degrees of privacy, but it should not be possible for businesses to be built on whether we have rights at all. The digital economy should not be built on such rickety and obviously flawed foundations.

The game is that every time we create a new technology, we’re creating new possibilities, new choices that didn’t exist before. Those choices themselves—even the choice to do harm—are a good, they’re a plus.

We want an economy that’s growing in the second sense: unlimited betterment, unlimited increase in wisdom, and complexity, and choices. I don’t see any limit there. We don’t want an economy that’s just getting fatter and fatter, and bigger and bigger, in terms of its size. Can we imagine such a system? That’s hard, but I don’t think it’s impossible.

Although Amazon has clocked staggering growth, it generates meager profits, choosing to price below-cost and expand widely instead. Through this strategy, the company has positioned itself at the center of e-commerce and now serves as essential infrastructure for a host of other businesses that depend upon it. Elements of the firm’s structure and conduct pose anticompetitive concerns—yet it has escaped antitrust scrutiny.

This Note argues that the current framework in antitrust—specifically its pegging competition to “consumer welfare,” defined as short-term price effects—is unequipped to capture the architecture of market power in the modern economy. We cannot cognize the potential harms to competition posed by Amazon’s dominance if we measure competition primarily through price and output. Specifically, current doctrine underappreciates the risk of predatory pricing and how integration across distinct business lines may prove anticompetitive.

These concerns are heightened in the context of online platforms for two reasons. First, the economics of platform markets create incentives for a company to pursue growth over profits, a strategy that investors have rewarded. Under these conditions, predatory pricing becomes highly rational—even as existing doctrine treats it as irrational and therefore implausible.

Second, because online platforms serve as critical intermediaries, integrating across business lines positions these platforms to control the essential infrastructure on which their rivals depend. This dual role also enables a platform to exploit information collected on companies using its services to undermine them as competitors.

Every platform has its royalty. On Instagram it's influencers, foodies, and photographers. Twitter belongs to the founders, journalists, celebrities, and comedians. On LinkedIn, it’s hiring managers, recruiters, and business owners who hold power on the platform and have the ear of the people.

On a job site, they’re the provisioners of positions and never miss the chance to regale their audience with their professional deeds: hiring a teenager with no experience, giving a stressed single mother a chance to provide for her family, or seeing past a candidate’s imperfections to give them a once-in-a-lifetime opportunity. These stories are relayed dramatically in what’s now recognizable as LinkedIn-style storytelling, one spaced sentence at a time, told by job-givers with a savior complex.