OpenAI understands something special and rare, hiding in plain sight. Which is, as Peter Drucker put it a half century ago, the purpose of a business is to create a customer.

OpenAI is not going to win because they have the best models, or the most advanced chips, or the best cost-performance curve, or even the best product, although we think they do have those things. We think that OpenAI will win because they are so good at creating new kinds of customers, and because they have the most durable distribution strategy.

As we write this in September 2026, the intelligence-as-a-service product rankings aren’t the most important question anymore. Intelligence is everywhere. Anyone can do nearly anything; but most people aren’t, yet. Can you awaken the new behavior, and can you win at distribution, are what matters.

The case for OpenAI comes down to the following argument. To build a durable business at AI frontier scale, there are four levers you can pull:

1. Create new kinds of behavior

2. Distribute to lots of users

3. Price your product in a way that captures value

4. Have high switching costs

4 is basically out. The AI Frontier is a Red Queen’s Race, at least at the model layer. It’s so easy to swap between models, down to task-by-task switching, and everyone’s getting better all the time.

3 is what everyone hopes to achieve. But again, it’s a very competitive world out there & OpenAI (and others) are determined to lead at the cost-compute frontier, and pursue it via economics of scale, vertical integration, and other strategies to get token costs down. Whoever ends up leading on price-to-performance is going to be whoever’s winning at scale anyway.

Which means it comes down to 1 and 2. We think OpenAI is clearly the best at 1:

• They have a well-established track record in AI of finding the simple, obvious-in-hindsight breakthroughs that unlock new kinds of consumption behavior

And they’re also the best at 2:

• Every AI lab right now is trying to speedrun distribution, beyond their first-party products. And there are two ways you can do that: as a platform, or through partnerships. Doing it as a true platform is long-term preferable: the revenue comes a bit slower, but it’s much more durable and valuable when you get it.

There is an underlying reason why OpenAI is prevailing at 1 and 2, which is that they have some of the broadest and most well-rounded general intelligence, not only in their models but in their company and in their customer base. They are exposed to the broadest user base (across consumers, prosumers and enterprise), they have the deepest self-improving tech stack (down to their proprietary chips), and that’s why they consistently find the new patterns and primitives that work. They are taking the long view for how to build the winning AI platform, and it’s working.

OpenAI is really good at discovering the next thing

OpenAI did not set out to be a consumer company. They kind of fell into it by chance, with the release of ChatGPT. It’s a happy accident they did, because that moment is one of the turning points in technological history. Had they not done this, the world would look very different. AI would probably still be guarded in the hands of labs and close customers, it wouldn’t be improving at nearly the rate that it is, and we’d be in a “default closed” timeline, and not the infinitely richer one we have today.

Sam Altman recently told a story about the weeks after the release of ChatGPT, when most people at OpenAI had the attitude, “Well this was fun, but we should probably move on to building some real products. A chatbot can’t be it, can it?” It was specifically Peter Thiel who counseled Sam, no, this is it. You have created a new kind of customer. Nothing else is as important as this.

It’s not just ChatGPT, either: OpenAI deserves credit for being generally good at finding the breakthrough patterns and primitives that really matter, and awakening new kinds of user behavior. Ben Hylak articulated it well in a piece the other day: “OpenAI is the (mostly) undefeated king of finding the ‘next thing’. It’s always a lot simpler than you’d think and very obvious in hindsight.” The GPT chat interface, reasoning, tool calling, and Computer Use were all big breakthroughs that simplified and focused the direction of the technology, and made it usable. The one major exception where OpenAI was not the breakthrough leader was coding, and they caught up just fine.

Why are they so good at this? Perhaps in the early years they just had that special sauce; no one can exactly articulate the founding energy and magic that creates something like this. But over time, this has clearly become a skill they have cultivated. There are three critical inputs here:

• They think and act like a company that wants to be a platform. “Platform” is a loaded term and we’ll get into it in detail in a second, but this is a company that actually believes its users are smart, and can make their own choices, within carefully crafted constraints.

• They’re doubling down on technical depth, meaning they’re trying to own or control every layer deep into their stack, and continually refine the platform primitives their users need as building blocks.

• Maybe most of all, they have tremendous consumer breadth. They remain the leader in sheer variety of kinds of consumers and use cases across people’s personal and work lives.

The breadth of people using them is the most underrated factor here. As Hylak puts it simply: “Computer use” [for example] is a very generic, and unspecific thing. To be better at it, you need to increase your intelligence in a general way. Across many domains, across many workflows.” People consistently underestimate how difficult it is to go from a specific solution to a general solution. But you usually only discover what’s simple and obvious (in hindsight), but generic and unspecified (as problems to tackle), by going after that general solution.

Why don’t more companies go after “general solutions”? Because it’s actually quite costly to do so, and goes against most of the best practices of running a focused product organization. You need to gather inputs from a huge range of customers and behaviors and actions, letting them do essentially anything they want to do, to make general progress on a domain, as compared to specific progress on a product.

That means that the ability to build “general knowledge” into your product is actually dependent on your distribution strategy. This is non-obvious but important, and OpenAI understands it.

How do you serve “exactly what I want” (which can be a very custom, specific, finicky thing) at massive scale, to everybody?

There are basically three ways. You can deliver it in a standalone product, you can deliver it through partnerships, or you can provide it as a platform.

• Standalone Products have an advantageous constraint, which is that your product must, inherently, let the user “do whatever they want” (i.e. ask ChatGPT anything), so you are genuinely creating the customer experience, from getting it into their hands to watching them use it daily.

• Distributing through partnerships has the advantage that partners give you a lot of reach, quickly, around a lot of use cases. But it is ultimately the partner that owns and directs the customer’s “anything I want” instinct. And therefore, over the long run, not only will they likely capture the attractive economics, they are also in control over what the user ultimately gets to do, and what gets learned. Furthermore, your customers may not be aware you are even powering it, so you don’t earn any trust or goodwill from them.

• Distributing as a platform means people build whatever they want on top of you. This is slow, because you have to make all the new products supporting “anything I want” from scratch. But it’s very powerful once established, because even though revenue may come slower or at lower take rates, you have established a really powerful learning loop about the world, as users employ your primitives and patterns, that teach you about the world and about what people want from you. Not in the “training on customer data” sense, but in the, “the primitives become a better map of the territory” sense.

Over a long enough time, products come and go, partnership dynamics change, and their economics may disappoint relative to expectations. But OpenAI keeps coming back again and again with their drive to build the enduring platform. And what they’re doing is working.

True platforms are rare but worth it

Platforms are hard to build, and they’re hard to reason about. They’re even hard to define!

Generally we have a broad, fuzzy concept of platform that goes, “platforms let people build the thing for their specific need.”

In software, there are a few ways you can do that. Nineteen years ago, Marc wrote an essay called “Three kinds of platforms you meet on the internet” that remains useful today. Marc’s definition is: “A ‘platform’ is a system that can be programmed and therefore customized by outside developers—users—and in that way, adapted to countless needs and niches that the platform’s original developers could not have possibly contemplated, much less had time to accommodate.”

The “three kinds” correspond to three different ways you can arrange computers so that a user gets a custom outcome that they want. They are progressively harder to build (as you ascend from 1 to 3), but more magical in terms of what kind of behavior and building they unlock.

• Type 1 is what we’d call “Headless” today - give users access to something useful through an API (like Flickr back then, or even some companies as valuable as Stripe would fall into this.)

• Type 2 uses what you’d call “Plugins”: like the old Facebook platform, or most Shopify or HubSpot Apps today. You have a core service that provides the first 80% of users’ needs, and then custom apps that run on their own server, and probably managed through some kind of app store, that give users the custom experiences they want.

• Type 3 is the critical one: it’s a genuine runtime environment that people can program to create whatever they want. These are really, really hard to build, but they are true magic when they work. iOS, AWS, Ethereum, and recently Cloudflare are some examples.

What’s interesting about AI today is that all model companies and inference providers, in one sense, have a claim to be contributing “Type 3 platform” work. (Models are a kind of runtime, prompted with custom instructions!) But models alone do not unlock new behavior, nor are they all that defensible. Like we saw with coding, where the harness matters just as much, it’s the superstructure of constraints and primitives that actually matter here. We’ll give you a theoretical reason and a practical reason why.

The theoretical reason is that, if you want to discover the obvious-in-hindsight patterns and primitives that matter, you need exposure to as wide as possible range of use and tacit knowledge and trial-and-error from your users, so that you can evolve from a specific product (a local breakthrough) to a general product (a global breakthrough). And the purest way to do that is to let them write arbitrary instructions (i.e. code) for what they want to do. Getting the security right and the access and data structures and constraints right is a massive undertaking that “product-oriented” companies have a hard time prioritizing; only the truly committed will reach the promised land.

The practical reason, for AI right now, is that for agents to be useful, they need to write code. This is something that is perhaps non-obvious, but that we’ve learned in the past year. Given how amazing AI models are at writing code, they’re surprisingly bad at calling tools and carrying out instructions in everyday knowledge work - unless you flip the script around and say, “write a program that does these tasks”, and then they’re great at it. An open question in enterprise AI adoption right now is “how are people and organizations going to support this, safely?” and you should probably bet on the platform-minded people to get it right.

One of the big open questions right now is what kind of network effects, switching costs and other moats will emerge at the various layers of the AI stack. As of now, the model layer remains shockingly substitutable. There’s a general sense that “multiplayer mode” (e.g. collaboration on projects, agents being trusted by friends’ agents) has some good ingredients for business model defensibility, but it hasn’t yet really impacted the overall brawl around “usage up, costs down” that we see with token spend. The primary question that matters is not “is your model sticky”, it’s “are you creating new behavior?” As Steve Hou put it well: “The elasticity that matters is not the elasticity of substitution between models but the elasticity of aggregate demand for AI.”

Pattern-matching is never perfect, but if the past four years of AI is any indication, the next big breakthroughs in defensible, multiplayer usability of AI by regular people are going to emerge from epiphany moments where we find optimal layering of intelligent models, computing primitives, and behavior patterns, and something simple and obvious emerges. It could be from startups building on the model companies as platforms; it could be from the model companies themselves. Either way, OpenAI obviously wants to power it, and deliver it to users.

In a world of abundance, the causality goes backwards

Old habits die hard. In the regular ways that you might analyze an AI Frontier lab as a business, you might look at all kinds of obvious things to assess competitive advantages: who has the best models? Who has the best chips? Who has the best cost-performance? Who has the best products?

Obviously these all still matter. But in a world of abundant intelligence, on-demand anywhere, we’re probably getting the causality backwards if we think that having all of those things means you win. We think that, in the future, OpenAI will have the best models, chips and products because they have the best distribution, not the other way around. They’ve made great decisions thus far around securing enough compute, training models really effectively, developing Jalapeno as an optimized chip, and other practical things that can make a competitive difference within the set of AI options. But in the future, we think they will keep winning because they will stay at the frontier of gathering information about the world, from their own newly-awakened users, and self-improving from there.

New product and model offerings can grab everyone’s attention for a week or two, but over the long run we don’t really see OpenAI relinquishing their leadership position across what matters:

• They have retained the dominant consumer brand and a wide user breadth

• They have awakened new kinds of user behavior, that are obvious in hindsight and change the game of what’s possible

• They have pushed the frontier of “best models, best price” in their model offerings

• Their “flywheel” never lets up, and everything improves together

Sam describes the future product state of OpenAI as having only two offerings: either you can make anything you want, or else you can just talk to ChatGPT. This is what real abundance looks like in a product. You can either describe exactly what you want that will help you and get it, or else you can fall back to, “or I can just talk to this text box and figure out wherever I’m going.”

That abundance is generally available; today. There is a pretty small group of people that are using AI rampantly for everything in their lives; there is a somewhat larger and broader group of people that are using ChatGPT on a regular basis. They haven’t been awakened as customers yet. Who will do it?

We think OpenAI understands how.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

Today’s article shines a little light on why a16z is launching Cosign. To learn more about the product, watch the a16z Show episode with , , , and here.

One of Silicon Valley’s great talents is turning early belief into consensus.

Belief is private conviction. When you believe in someone, you can invest something based on that belief. Time, attention, access, reputation, and, of course, money. Affiliation is what makes those investments legible to everyone else. What changes as people read all these signals is status.

It is a well-known trope that VCs all want to be non-consensus and right about companies because that’s the most lucrative bet. But really, being “early” matters far more than being “non-consensus.” Because if you’re early enough to something, there’s little to agree or disagree with yet. So someone can be captivated by a little signal, give it some attention or resources, see some lift, and double down. Soon others see this and follow.

Silicon Valley knows people by watching other people know them. One person’s visible belief becomes a signal to someone else, and the network updates its priors. It’s a kind of live, recursive price discovery for people. A more romantic way to put it is that all of these signals build a person’s lore.

People used to earn status through rites of passage like graduating college and landing prestigious jobs. It still doesn’t hurt to get into Stanford, drop out, become a Thiel Fellow, join YC. And yes, it helps to know people who know people. But even these stamps and side doors matter less than they used to. People collect status in bits now, 24/7, often outside any formal system. And unlike the old stamps, a lot of these are earned in public.

Showing your work in public has become the new “warm intro” (and, in turn, gets you plenty of those). Someone unknown makes something good and puts it out there. A few people notice. They follow, reply, DM. Others notice who’s noticing them and tell a friend. Soon they’re invited to an IRL thing, then onto a podcast, and maybe another.

You are built by your achievements and by others’ belief in you. I’ve written that ascendancy is a game of two moves: self-made wins and high-status vouches. The earlier someone is in their trajectory, the less proof there is to go on, and the more meaningful the vouch. Belief compounds.

Being an early believer is a stamp of skill and taste. Everyone wants to be the guy who discovered the band when it was still underground. Pharrell’s viral reaction to hearing Maggie Rogers play “Alaska” was career-making. Paul Graham included Sam Altman in his Five Founders essay, in 2009.

“Non-consensus” people are just unknown, until they aren’t.

Vouches take many forms. A tweet, an invite, an introduction, an angel check. These are all their own versions of a co-sign. Being undeniable and getting credible people to say so is how you become somebody in Silicon Valley.

Twitter is, of course, where so much seeing and associating happens online.

Tech people want to just hang out there, launch products, publish essays, monitor the situation, opine on the current thing; audition for a theoretical job and collect job offers in the DMs; win friends and influence people and maybe find romantic partners through it all. Twitter owns the live game because so much of the Valley’s social life has been bundled onto the same graph, and Twitter wants to bundle more media and behavior into it still.

The graph tells on everyone. Who follows whom, who’s mutuals with who. And if the graph doesn’t, the anonymous “watch” accounts do.

The game of Tech Twitter is a bit like if MySpace Top 8 was back, but you didn’t have to lock in your few favorites. Tech Twitter is Top 8 by soft, constant, often-public affiliation. You follow someone, reply to them an earnest but suspicious amount, quote-sweet their latest launch, invite them onto your podcast and go on theirs, post a photo together, and confess that you think they’re one of the smartest and coolest people you know.

These affiliations spill into broader new media. Once someone gets noticed enough, they start showing up in newsletters and profiles, on podcasts and shows. The media itself becomes part of the affiliation graph. Hosts and guests vouch for each other. Then the quotes and clips get fed back into the timeline as social proof and discourse prompts. And on we go. Insider media is the Valley’s breaking news and reality TV, covering the action and entertaining us while making its people legible even faster.

Not all vouches are the same, obviously. If Marc Andreessen tweeted tomorrow that I was one of the best writers of our time, that’d be a strong signal. If you tweeted it, it might mean considerably less (No offense, I mean the average you. And Marc, if you’re reading this, this experiment remains available.) Of course, some of the highest signal vouches go unseen. In backchannels and group chats, the question is still: Are they good? How good? Top 10%? Top 5%? Top 1%? I’d-regret-it-forever-if-I-passed-good?

If likes are pennies now, IRL time is expensive. IRL signal is stronger because seats are scarce, an invite puts the host’s reputation on the line, and you can’t split-screen the conversation with Subway Surfers. But even offline vouches make their way back online. If you attend a private dinner, there’s a good chance it’ll show up on the timeline soon. The timeline is the tabloid multiplier.

All of which makes the timeline a very efficient place to watch people watch each other. Silicon Valley’s bankable social graph is much less about who worked where and with whom, or even who knows whom, than who chooses to be associated with whom, how and when. The nice thing is that belief isn’t tied to where you started. It’s much more about what you’re up to and where you’re headed.

The affiliation graph is the visible layer of the belief graph.

The social capital machine is part of the magic that makes this place work.

We say we don’t care about legibility (we do). We say we don’t care about comparisons and rankings and lists (we do). We say we don’t care about status (we really do, because everything else feeds into this). And, in the right dose, the game is fun! To be clear, this is all very human. We just get to watch more of it play out in public now.

It’s 2026, and every game feels like it’s gotten faster and more competitive. AI has made even the most complex and technical things radically easier to attempt. As output gets abundant and attention stays scarce, it gets harder to tell what and who stands out. Which makes the human layer matter more. Who noticed? Who believes? We start reading the second-order signals closer.

Being illegible can be charming, but not for long. You still have to be good. It just helps enormously if people can watch you be good. You can bid for legibility with your thoughts, with experiments, by building things, sharing art, or anything that helps people watch you play the game. What’s new is that reach and virality don’t bank permanent social capital anymore. Having 10K followers isn’t as exciting as seeing someone get them in a week.

That last bit is all about momentum. Companies are told to fundraise with killer metrics or no metrics at all; for people, the no man’s land of status is being legible but moving at a pace that inspires neither love nor fear. We’re in a continuous-proof-of-potential world now. Up and to the right, if you can.

And maybe it’s fine that we notice all of this.

Because in this AI-pilled, attention-first era of Silicon Valley, it’s all still part of the machine that helps outsiders become insiders and insiders become undeniable as fast as code ships and word travels.

The game only works if new people can win.

The thing about praising people on Twitter or anywhere is that the generous and strategic motivations are hard to separate. You might genuinely want someone to win. You might also like what believing in them says about your own taste. And if you’re early and right about them, you get some status for it. The nice thing is that all three incentives can work together for some greater good: You get credit for seeing someone early, and they get seen.

Alex Danco has written about this very thing as the “social subsidy” of angel investing, and more broadly written on gift culture – that generous and useful behavior can be self-interested and still be helpful to others. In Social Capital in Silicon Valley, he calls some of this useful ambiguity around status the “Social Fog of War,” which helps keep the network open.

Another game rule I’ve internalized from the reality TV show Survivor (yes, people still watch it): “Treat everyone like they could be on top tomorrow” (i.e. whoever’s on the bottom of the tribe today could hold all the power tomorrow). And Danco’s “Golden Rule” of Silicon Valley is to treat people as if they might be the next great founder. Just like VC funds have anti-portfolios of companies missed, plenty of us have anti-portfolios of people we saw early, believed in fast, and wish we’d vouched for sooner.

It’s fine if we know who has status, as long as we don’t know who will next.

More people are finding the game now, and there are more ways to play it. We see the work, the network forming around the person, and belief compounding. We watch private belief become public consensus in real time. And everyone benefits when good people get pulled into the game faster.

Poetically enough, technology itself helps keep Silicon Valley’s order from being set in stone. Every new technical era reshuffles the order - who the promising unknowns are, who sees them first and how, who’s best fit to play the newest game of legibility. And yes, we’re in one of those moments now.

If you believe in someone early, say so.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

Today, we’re introducing the a16z Ops Engineering Fellowship, a six-week cohort for the innovators redefining how organizations operate with AI.

AI is no longer just an individual productivity tool. The fastest growing companies in tech are now deploying it at the systems level: rolling out agents to handle cross-team tasks, redesigning workflows to eliminate manual handoffs, and building durable infrastructure that enables a company to up-level its capabilities.

The people leading this shift are cross-functional leaders who sit at the intersection of operations and engineering. They might be AI-native operations leaders, scrappy engineers, or technical founders who are transforming the infrastructure of their organization and operational stack. They identify how to leverage AI internally, then build and implement the systems that empower each team member.

We’re launching this fellowship to bring these builders together to share what’s working, level up, and define the frontier of internal AI playbooks.

Apply Here

How it works

• Six weeks

• Starts in October 2026

• Flexible time commitment

What you’ll get

• Tight network of AI-pilled leaders at the fastest growing companies in the world

• In-person dinner series in San Francisco (additional cities based on demand)

• Candid, tangible learnings from those building and implementing internal AI

Who you’ll be joining

You’ll break bread with founding fellows and industry leaders such as:

• Seb Goddijn - Builder of Glass @ Ramp

• Claire Vo - Founder @ ChatPRD and CXO.dev

• Matt Portnov - Internal AI Lead @ Decagon

• Ann Miura-Ko - General Partner @ Floodgate

• Brandon Gell - COO @ Every

• TK Kong - Co-founder @ Concept and Prev. Lead @ Ramp Labs

• Owen Williams - Design Manager, Developer AI @ Stripe

…and many more.

Apply Here

The rising need for AI Operations

Every company is asking the same question: How do we leverage AI to transform how our team operates?

Granting teams access to AI tools is only the beginning. The fastest-growing companies in tech are turning this patchwork of siloed team-wide productivity gains into one shared system. The leaders of Internal AI understand existing operational systems, see how AI drives productivity team by team, and translate those gains into infrastructure that scales across the company.

Operations is now an engineering problem, and this group is solving it.

We’re looking for fellows who

• Built or deployed AI agents and systems that redesign workflows across functions (operations, product, go-to-market, etc.)

• Turned AI from an individual productivity tool into durable organizational infrastructure

• Think in systems rather than isolated features or one-off automations

• Can navigate both the technical and organizational realities of deployment

• Care deeply about measurable outcomes, internal adoption, and the craft of building better companies

You might call yourself an Internal AI Lead, Chief of Staff, Internal Tools Engineer, Platform Lead, or something else entirely. Titles matter less than what you’ve built, the work speaks for itself.

Questions? Contact Brooke Martin - bmartin@a16z.com. Thank you!

Apply Here

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

Hello everyone! This post is written by and together. To celebrate the launch of HAA, we decided to throw blogging convention out the window and write a joint post, reflecting on how we’re thinking about HAA now that we’re parents. We hope you enjoy it! We always have a great time getting to write together. -AD

Yesterday, the Horowitz Andreessen Academy officially launched; and if you haven’t seen it, please go check that out. Gagan Biyani (who founded Udemy) is a co-founder, and will be the CEO, and he wrote a manifesto here on what they’re doing and why. Tldr, it’s a program for young college-aged people (it’s not a college or university; it’s an alternative) to come to San Francisco, learn how to build things and be useful with a like-minded peer group of other ambitious kids, living in a residential setting in SF.

Now, in an early era of our lives as tech bloggers, the obvious way to write about this would be, “What an interesting idea; is this unbundling the university? Is this disruption? Is it some other mental model? And it would make for a great think piece. You know the type.

But the thing is, now we’re both older, and we’re both parents. So this hits different. It’s not just a theoretically interesting exercise for us; there’s a really literal question, “could we imagine our kids going to something like this?” (Gasp!)

Speaking for both of us, we both had a really good time in college. Danco went to McGill, up in Montreal (a great college city, arguably the best); Packy went to Duke, down in Durham, NC (maybe the greatest American city outside of New York). We both had really good experiences, and I think it’s reasonable to say that we both ended up as curious life-long learners that know how to pick up books and read them, and that appreciate whatever “soft” or “social” skills you pick up in a university setting that aren’t strictly utilitarian, but seem important.

Their plan for the Academy is interesting: they clearly aren’t just trying to make it an apprenticeship program. There’s definitely a “bundle of life experience” that’s coming together, in a totally different way than it comes together for most people in traditional college, but definitely valid. I’m not sure you would’ve arrived at something like this if a bunch of experienced professionals sat around and thought hard about “the future of the University” or something. It’s like that Paul Graham essay: “The way to find a good startup idea is definitively not to sit around thinking of startup ideas. It’s to solve a problem that you personally have.” In this case, the good folks at a16z have seen some pretty impressive young people become phenomenally useful (and interesting, and happy) jumping right into building, and the plan for HAA seems clearly to be, “get more people launched on that trajectory”, rather than something more abstract.

A large part of my appreciation for traditional college - but also, a reason why I might like HAA too! - comes from my (Danco’s) experience parenting. The thing about parenting is, before you become a parent you imagine a lot of the “hard parts” of being a parent (especially in the younger years) and you picture all of the typical stuff like changing diapers, teaching them skills, driving them to soccer practice, whatever.

Once you actually have bigger kids, you realize those aren’t the hard parts at all. The hard parts are like, “are your kids resilient? Are they kind? Do they take initiative? Do they take responsibility?” and when you think back on it, the real purpose of all these classic rituals and institutions around growing up (e.g. playing team sports) is like, yeah it’s kind of about the sport, but it’s also about the life experience.

Some of the hardest parts aren’t about the kids at all. They’re about your own psychology as a parent. Parents in cities like New York and Toronto and San Francisco are competitive and worried. Kids like certain things, but are they the right things? Will they get our kids into good schools? Will they set them up for success? I (Packy) live in a neighborhood in Brooklyn where parents (not us) pay $65k per year for every year from Kindergarten onwards, because those grades are attached to a good high school, and if you get into Kindergarten, your kids are practically guaranteed admission to that high school. You’re basically paying $650k to make sure your kid is admitted to a good high school, which should, but certainly isn’t guaranteed to, help them get into a really good college, at which point they’ll probably get a good job, at which point, they’re set for life. You don’t want to screw up your kids’ futures, right?

The challenge with that, though, is that who knows what a good job will be in fifteen years? Our parents didn’t send us to McGill and Duke to become bloggers.

But one of the things you learn raising kids is that it’s a lot easier to move with them than against them. If they can find something they love, your effort will go to getting them to stop doing it instead of getting them to start. My (Packy) son is obsessed with chess. Obsessed. If there’s no one to play with, he’ll set up puzzles and play against himself. We finally gave in and started letting him play on chess.com for an hour a day, and we have to pry his little fingers from the keyboard when the clock strikes 60. Our daughter? Couldn’t care less about chess. Absolutely loves gymnastics and music. If we give her a few minutes of YouTube, she’ll ask for the Michael Jackson Name That Song game and breeze through the back catalog. We’re starting puppeteering club at school soon, too, because she talks as her stuffed animals all the time anyway and she’s legit funny.

Do I think my daughter will be a professional song guesser or puppeteer? I don’t know man, the world is getting weirder, anything is possible, but I doubt it! Do I think that being really good at those things will develop her confidence and sense of self and her memory and performance skills and her trust in her own gut? I do!

Do I think my son will become a professional chess player? Unless his brain is 99% his mom’s and only 1% mine, I’m afraid he’ll hit a ceiling, just as a son of mine probably isn’t going to play in the NBA. But during his first chess tournament last year, he came into the auditorium where I was waiting after his first match and I couldn’t tell if he’d won or lost because he looked so happy. (An eight-year-old kicked his five-year-old ass, it turns out.) He’s learning that if he works really hard, he can get better at stuff that seems really hard. It’s right there in his Elo. Resilience and relentlessness seem like good traits for him to hone.

It’s funny writing this now, now that I know the kids. In 2022, when Dev turned 2 and Maya was just 2 months old, I wrote an essay called How Do I Teach These Kids?! about how we were thinking about raising those two little blobs given how fast everything was changing.

Things are changing even faster now, and parents are rightfully worried and confused about what exactly they’re supposed to do to give their kids a good life. Pushing them to get really good at math seems silly, when AI is currently tornadoing through all of the field’s hardest open problems. Math is useful in investment banking too, but are the kids going to be better modelers than 2040 ChatGPT? Should you push them to become a lawyer? That seems risky! Even if senior partners and their relationships remain valuable, are they going to need armies of highly-paid whippersnappers drafting docs and finding precedent? Doctor? Probably your best bet, given licensing requirements. But do you dare risk their future on such a specific path? And what if they don’t want to become a doctor?

Continue through this maze, and you’ll keep hitting dead ends. There is no safe career path today, let alone one that will remain safe for twenty years, which means that there is no set of schools and degrees that can guarantee a safe career into the 2040s and 2050s.

This seems really scary. It is freeing!

We can stop pretending that we can plan our kids’ lives and follow them into whatever lights them up.

In that 2022 piece, when I knew even less than I do now, I synthesized all of the research and conversations I’d had into a paragraph:

Expose your kids to a lot of things early, and then help them go as deep as they’re able to go on the things that excite them the most. It doesn’t matter what those things are; cultivating curiosity and depth is the point.

I’m surprised at how well that’s seemed to work in practice. Our kids are a lot more motivated to learn skills in service of some other thing they really want to do than because we tell them they have to learn something.

Tyler Cowen points out, recently, that the most valuable thing you get from college nowadays is being more likely to get married and stay married. A strong group of friends that gets established together, witnesses life events together, and follows each other through the ups and downs of adulthood is just one of the most valuable things you can have, even if for no other reason than making you accountable to yourself. College friends make great witnesses.

One thing that’s definitely true, though, is that you forge your closest friends and most valuable peer groups by going through real, shared experiences. College is certainly designed as one, although in recent years, some of it has gotten a bit too cushy and resort-y for our taste. Esprit de corps is best forged in non-ideal physical environments, not in luxury accommodations.

To that end, working on real things is a great way to forge those kinds of connections, experiences, and appreciation for the best things in life. One of Gagan’s points in his essay, which is absolutely true, is that most kinds of “real work” mean making things, and college traditionally comes up pretty light in that department.

Kids come with this unbelievable gift that they don’t know certain things are impossible, and so they try to figure out how to do fantastical things. The longer you can keep that alive, the better. I (Danco) have had the pleasure of working alongside some 19-20 year olds on the a16z New Media team who have been unbelievably impressive in terms of the responsibility they’ve taken on, their maturity, and their growth as interesting people. “There’s no way I could’ve ever handled responsibility like that when I was that age”, is the obvious thought. But on the other hand, people have a way of rising to the occasion. Maybe more young people should simply be given the opportunity to try hard things? That’s how we’ve done it, through most of human history.

When I (Packy) wrote about Somos and Panandina founder Forrest Heath last year, we talked about this. He said:

I’d say the thing I’m grateful for, and I don’t really have the ability to take much credit for it, is just somehow I never lost my innate childhood curiosity.

And I think this is something that I like, when I met your kids and they’re just playing around and imagining stuff on the playground in Brooklyn, it’s like, everyone was kind of like that at some point, right? Like, almost all of us had some way we were playing with imaginary forts and imagining a future that didn’t exist or imagining a world that didn’t exist and kind of putting our headspace into it.

And I think for some weird reason—both me being kind of stubborn and a bit skeptical of a bunch of different things, and honestly, I think just random chance—I never got that beaten out of me. And I kind of was always, like, finding my own weird path to sort of come at stuff.

Tangentially, that meant that I wasn’t really competing against the system of, ‘Get higher SAT stores, get into the right college and, check all the right checkboxes.’ And I think there’s this sort of, like, innate builder kind of curiosity in me that I never lost. If anything, I’ve kind of compounded it.

In a recent conversation with David Senra, Ribbit Capital’s Micky Malka talked about this, too, about what young people will do given naivete, a desire to do something, and time to figure it out:

But the more time I spend with them [young founders], the biggest thing and the biggest question that I find asking them is because the oldest of the generation are probably 25, 26, the younger are 18. You ask them what were you doing during COVID? And that is the most fascinating question.

The most successful ones right now were the ones that during COVID were learning and were on YouTube sucking information about anything and learning about everything in life. So when they go and build companies, they find that first they’re naive, so they think anything is possible.

We’re talking about founders here, but you can find the same pattern in exceptional outcomes in any field.

Taylor Swift’s dad was a stockbroker and her mom worked in marketing at a mutual fund; they had no grand musical ambitions for their daughter. But she got obsessed with musical theater, then with country music, and started lobbying her parents to take her to Nashville. When they did, on Spring Break at age 11, she walked into record label offices by herself to give them demos of her singing and try to get signed. They didn’t sign her. A lot of people in Nashville could sing. She needed more skills: guitar and songwriting. 12-year-old Taylor learned a few chords from a computer repairman, and she wrote her first song shortly after. Story goes she practiced guitar so many hours a day that her fingers bled and cracked and her mom had to tape them up. Her parents drove her to New York for voice lessons. They got her a slot singing the national anthem at a 76ers game. At 14, the family moved to Nashville so she could go for it. Sony/ATV signed her to a publishing deal that year, the youngest songwriter they’d ever signed. Two years later, in 2006, she released her first single, Tim McGraw.

If you’re a parent, you know that it’s impossible to get your kids to practice anything until their fingers bleed if they don’t want to themselves. You also know how hard it is to get them to stop if they do.

There has never been a better time to be a kid who wants to figure something out. I think the best we can do as parents is help them figure out what that something is, and then give them every opportunity to pursue it. At some point, that means letting them outgrow what you can do for them finding other people who are as obsessed as they are.

That’s how I read what Gagan and the team at HAA are doing: creating a place for young people who are weirdly obsessed with building something to surround themselves with other young people who are, too, and an environment in which they can more easily access the skills and experienced people they’ll need to do it. A Nashville for Nerds. It’s taking kids seriously, and adding real stakes early.

So could I imagine sending Dev or Maya to HAA?

No idea. It’s not going to be up to me. Personally, I’d love for them to go to Duke, to have the experience that I had, but they’re their own people. If I need to pry their fingers off of making things that the world needs, it would be awesome to have a place where they can go as hard as they want surrounded by other people their age who push them to go even harder, and I’m glad Gagan and team will have twelve or fourteen years to make it great by then.

But I also hope that there are dozens of options like HAA at that point, for all manner of passions. For songwriting, puppeteering, engineering, mountaineering, imagineering, and for pursuits that we can’t imagine today. I hope today’s great universities redouble their commitment to what makes them great, to support those kids, like me, who haven’t figured out what they want to be when they grow up by the time they’re eighteen. Some people, like Jamie Beaton, might just want to keep learning forever, and I hope that we’ve created enough abundance that they can.

A good future is a world in which, in the words of Isaac Asimov, kids have the agency, curiosity, and support to pursue Whatever They Wish. So, maybe the right way to think about it is that, a dozen years from now when our oldest kids start making those kinds of choices, it really won’t be up to us at all. If we’ve done our jobs right, wherever they go to college - or HAA or something like it, or something else entirely - will be whatever they make of it. And we’ll be delighted to send them off.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

To learn more about The Horowitz Andreessen Academy, watch the a16z Show episode with Gagan Biyani, Ben Horowitz, and Erik Torenberg here and check out the Academy’s website here.

The internet made it possible to know anything. AI makes it possible to do anything.

We live in an age of infinite knowledge, and we are entering an age of infinite doing. One in which we are constrained only by our creativity. That changes what it means to be educated. It changes what it means to be young. And it demands a new kind of institution.

We are thrilled to announce The Horowitz Andreessen Academy. HAA is a private, full-time, in-person school in San Francisco for young people coming out of high school. Our mission is to nurture the best young people in the world, and equip them with the tools to shape it.

The campus will be humming. Jensen Huang, Satya Nadella, Travis Kalanick, Mira Murati, Ali Ghodsi, Patrick Collison and countless other luminaries in tech will visit and give lectures. Students will be excitedly sharing their projects, workshopping ideas with their mentors, and constantly pushing their peers.

Student experience at The Academy

The Academy is for students to both explore and go deep. This materializes in 3 principal ways: Co-ops, Courses, and Pursuits.

Co-ops are the opportunity to work during the year (not just over the summer). It can be a rotation program across countless different roles and companies. We want you to have so many work opportunities that you’ve tried everything and directionally know what you like and what you don’t. Or if you know what you like on the first one, you keep doing it. Students may have opportunities to work at our founding partners Anduril, Anthropic, Coinbase, Google, Meta, NVIDIA, OpenAI, Palantir, Replit, and Stripe or any of our hiring partners here.

Courses are taught by legendary people. Students will study the complete work of individuals and then be speaking with them directly. Our instructors have founded unicorn companies. We also have the investors who invested in those unicorn companies. There will be people who write textbooks. As well as the people the textbooks will be written about.

And most importantly, it’s about Pursuits. It’s about personal projects, quests, startup ideas, rabbit holes that define the student experience. At any given point, students will be working on one or multiple Pursuits. It’s what they spend their free time pursuing, what they workshop with their coaches and mentors, and what they talk to guests who host sessions. A pursuit can also be three weeks in a different country, where the Academy will connect you to the right people when you arrive.

Training for the AI Era

What are the genuinely hard challenges, assignments, and projects of this era akin to the challenges that once pushed us to our limits? Some of the answers are already visible. The best assignments now are problems so hard they cannot be solved without AI or the opportunity to generate problems that AI cannot yet solve (and their accompanying solutions) to create data sets.

The Academy courses follow the same logic and will be taught by world-class leaders. Here’s a sampling:

• Frontier Systems

• Origins and Innovations in Digital Computing 1940-2025

• The Craft of Selling

• The Modern Software Developer

• AI Inference Engineering

Networks and access matter. Life skills matter. Ask any GSB student what are the most over-subscribed classes, and they’ll report Interpersonal Dynamics “Touchy Feely” and Paths to Power with Jeffrey Pfeffer. HBS students will talk about Negotiations with Deepak Malhotra and real-world exercises. Classes like these will be central to the HAA curriculum in developing core life skills.

SF is where highly-open, smart, energetic, and optimistic people convene

San Francisco is meritocratic and accessible in a way that is hard to explain. The doors are literally open. People read cold emails. They read replies and cold DMs. If you can find the address to an event and show up, you will seldom be turned away. The city loves hustlers and rule breakers. And the leaders at the highest level of the tech industry show up every day at every day events. In what other place or what other industry, would you be able to walk up to the equivalents of Jamie Dimon or David Solomon at an event and have a great conversation?

Patrick Collison’s advice to his younger self and to those who are 10-20 is to figure out a way to travel to San Francisco and to meet other people who’ve moved there in pursuit of their dreams. It’s WeirdHQ. And that’s why our in-person program cannot be centered anywhere else.

The Academy

Higher education’s most underrated function is that it is a low-stakes environment to test not just ideas but collaborators. Group projects are how you discover who you work well with and countless great founding teams of the last two decades came out of exactly those networks.

Think about what college actually compresses. In four years you meet more people, in more contexts, than most people meet in the following twenty. You trade music with the person down the hall and discover a shared obsession. You join a club on a whim and end up running it together. You suffer through difficult homework problems with the same three people until you know exactly who shows up, who quits, and who gets funnier as things get harder.

The most talented high school students today might miss this opportunity if they don’t pursue college. When they’re in high school, they rarely have dozens of their sharpest peers within walking distance. Their network is online. In Discord servers, group chats, and hackathon circuits. Those networks are real, but they’re thin. That’s why a physical space to convene after high school matters so much.

The next generation will not be taught the way the last one was. They will not work the way the last one did. They will not live in the same world either. The Academy is built for that world. And it begins by bringing the most talented young people from around the world together to San Francisco.

If you’re young, hungry and love to build, apply here for the Founding Class Fellowship. The Horowitz Andreessen Academy is excited to meet you.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

To learn more about The Horowitz Andreessen Academy, watch the a16z Show episode with , , and here and check out the Academy’s website here.

We’re launching The Horowitz Andreessen Academy. It’s a highly selective school in San Francisco for the most ambitious young builders after high school (or the first or second years of college) - giving them the skills, network, and real-world experiences to excel in a world being fundamentally re-shaped by AI.

Today’s students are growing up in a completely different world with new capabilities. The kind of world where teenagers can learn from intelligent machines how to train a robotics model from terabytes of newly open-sourced egocentric video or envision a new business in the morning and earn revenue by sundown.

The focus of The Academy is on building - which we believe is the most valuable thing humans can do to learn to become a productive, curious and happy adult. Our goal is to help high school graduates thrive by cultivating this instinct. Building is an underlying skill that helps you create lifelong friendships, be indispensable at work, create jobs by providing value to society, and discover your inner joy. That’s why we’re doing this.

There will be no traditional grades, tests, or homework. Instead, you’ll get to learn from Silicon Valley’s most storied founders and operators in short, high-impact classes and spend the rest of your time working and exploring. And by “working,” we mean - starting companies, operating at companies, and tinkering on projects that will make you an indispensable addition to any fast-growing startup in the Valley. We also believe students will enjoy a robust social life in one of the most vibrant cities in the world–San Francisco, California.

We created The Academy to be a network: we’re bringing together a wide array of companies, people, and opportunities to help our students thrive. We’re launching with 10 of the world’s most innovative companies as our Founding Partners: Anduril, Anthropic, Coinbase, Google, Meta, NVIDIA, OpenAI, Palantir, Replit, and Stripe. Depending on the partner, these companies will offer access to compute and hardware, co-create the curriculum, and provide hands-on learning and co-op opportunities. They’ll also be joined by over 40 additional hiring partners that will be considering students for co-op and full-time opportunities.

This network is overlaid on top of the three main aspects of The Academy’s educational philosophy:

• Pursuits

• Courses

• Co-Ops

Pursuits are the core unit of education at The Academy. They are personal projects, quests, startup ideas, and rabbit holes that define the student experience. At any given point, students will be working on one or multiple Pursuits, and personally choose what they work on. Our goal isn’t to tell students what to build - it’s to inspire them to set audacious goals and exercise extreme ownership and agency over the outcome. If a Pursuit involves a three-week trip to Japan, The Academy will help pay for the trip and connect you to the right people when you land.

Courses are short, high-impact programs ranging from two days to four weeks long - all taught by legendary tech founders and operators. Our faculty and guest speakers will include Sam Altman, Brian Armstrong, Dylan Field, Amjad Masad, Naval Ravikant, Marc Andreessen, and hundreds of additional industry leaders that will walk The Academy’s halls to give you direction, introductions, and inspiration. The idea is simple - instead of learning from the people who write your textbooks, you’ll learn from the people that your textbooks are written about.

Co-ops are immersive learning experiences at real-world companies like Stripe and Google, so students can “learn by doing.” These opportunities are abundant and right next door. The Academy already has dozens of world-class hiring partners eager to hire and connect with students - not to mention, the a16z portfolio alone has 19,000+ open roles across 850+ companies. You could dig deep with one company throughout your time at The Academy - or you could create your own mini rotational program, and try different roles at companies at different stages to see what it’s like.

When you graduate, you could either start your own venture - or join a fast-growing tech company. Some of the world’s most impactful builders today work inside our greatest companies - crafting Claude Code at Anthropic, or reimagining our military at Anduril. They’re the product, finance, marketing, and technical leaders that are the cornerstone of incredible organizations and founding teams. The network we’re building will help students discover the right path for them and find the opportunities they need to achieve their potential.

It’s San Francisco or nowhere

The Academy is located in the heart of San Francisco. This city has the highest density of tech talent on Earth. It’s where highly open, smart, energetic, and optimistic people convene. Proximity to that density isn’t a nice-to-have - it’s critical.

Our physical campus for The Academy is designed to be a honeypot for ambitious builders, founders, and operators to gather, co-work, and draw inspiration from each other. The kind of place where you might run into the next billion-dollar entrepreneur over lunch or dinner.

We also believe that post-high school is a critical time for young people to personally mature and grow - social life matters. We’re not just creating opportunities for late night 3am conversations about philosophy and early morning jam sessions on math and science. There will be family dinners, book clubs, nights out, museum visits, weekend trips, and more. The Academy will be a place to create lifelong friendships and build a community that’s behind you for life.

A final note, for the parents

You’ve known your child was exceptional for years. You’ve watched them do things most adults couldn’t, teach themselves skills nobody assigned, and outgrow every room they’ve been put in. You’ve seen their spark, and you know it needs a place to flourish.

With exceptional operators teaching them, access to the a16z network, and alongside some of the most exceptional young builders on the planet, they’ll be in the place most optimized for their special kind of magic.

You were always right about how special your child is. We built The Academy to prove it.

Applications are officially open for the Founding Class Fellowship, our inaugural one-year program, which will begin in Fall of 2027. Notably, our Founding Class Fellowship will be tuition-free. We’ll accept applications from students currently attending four-year universities and from international applicants, though priority will go to graduating high school seniors. Some students may treat the Founding Class Fellowship as a gap year or a deferral of college admission.

Separately, The Academy intends to seek regulatory approvals for a two-year program that we hope to begin in Fall 2028. That program is not currently offered or enrolling students. We’ll be filing an application for this soon and, if approved, expect to charge an annual tuition similar to that of an elite private university.

Founding Class Fellowship students will have access to $50,000+ of compute credits, a $5,000 travel and research budget, and a network of the most inspiring peers on the planet. We plan to announce more offers in the coming months leading up to our September 2027 start.

It’s time to build

When I co-founded Udemy and Maven, I imagined a future where learning would be a lifelong pursuit. The Horowitz Andreessen Academy is the next evolution of that vision.

To make this possible, we’ve raised a total of $42M in funding, led by a16z - with Marc Andreessen and Erik Torenberg joining the board. We also have a great group of angels such as Adam D’Angelo (co-founder, Quora), Tobi Lütke (CEO, Shopify), Tony Xu (CEO, DoorDash), Fidji Simo (recently CEO of Applications, OpenAI), Garry Tan (CEO, Y Combinator), Joe Liemandt (Principal, Alpha School), and Shyam Sankar (CTO, Palantir).

We are thrilled to partner with incredible mentors, guest lecturers, founding partners, and hiring partners. We’ll see you on campus next year.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

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So Many Apps, So Little Time

Charts (and others) has observed that one effect of AI code gen tools has been the proliferation of code. More code has, among other things, also led to a pronounced spike in the number of apps. Everyone’s a vibecoder now, which means everyone’s an app publisher now, too.

Of course, just because there are now more apps to choose from, doesn’t mean that those apps are particularly compelling, or that people are suddenly spending more time in the app store. Supply may increase, but demand may have a life of its own.

As it happens, a recent paper sheds some light on the demand-side of the equation, and the results are pretty clear: the supply of apps has, indeed, surged . . . but, for the most part, no one is really using them:

• Across iOS, Android and Chrome, total new apps per month have variously doubled or quadrupled.

• On the other hand, the number of downloads (or ratings, in the case of iOS) has been pretty much stagnant.

• And, in case you’re wondering whether there’s been any share-shift, such that aggregate downloads are flat, but within that universe are still some winners—well, the researchers cover that one, as well. Alas, the share of apps with any indication of escape velocity—10+ ratings, 100+ downloads (for Android), and/or 10+ downloads for Chrome—has plummeted.

In other words, yes, the data suggests that App-Slop is the story, for now. AI has led to a lot more apps, but they are not successful apps, in any meaningful sense.

That’s not really surprising. Building successful apps (or software) has always been more than just generating the code. Plus, it’s still very early, and one would expect that all the amateur tinkering would produce supply ahead of demand.

For what it’s worth, SensorTower’s data corroborates the story, for the most part. In the US, total app revenue has been basically flat since the beginning of last year, with a modest increase in total time-spent:

SensorTower’s estimates indicate a ~2% increase in app revenues, and a 7% increase in usage time, over the past year and a half. Once again, more apps has not led to a boom in the app economy.

Beneath the surface, however, it is nonetheless clear that AI is making a difference:

The category with the fastest growing time-spent (and the second fastest growing revenue, after the relatively tiny Food & Drink category) is Productivity, driven by none other than ChatGPT, Claude, Gemini and Grok. Another standout category, albeit a tiny one, is Dev Tools, where Replit is by far the category leader for growth.

AI apps are plainly very popular, but the apps made by AI, not so much.

Consider this one small footnote in the broader question of “when will AI adoption show up in GDP?” (and the answer, in this case, is when the apps get much better).

In the bigger scheme of things, paid consumer AI adoption remains quite low, even as it grows:

Consumer Edge indicates that, as of Q1, paid consumer AI adoption was around ~3%, with younger folks out-adopting older folks by ~4x.

So, yes, as cringey as it is to say, it’s nonetheless true: we’re still so early.

New Breed of Unicorn

On the subject of AI’s early ripple effects through the economy, one area where they are clearly visible is in the landscape for venture-backed companies.

Not only is AI driving historically large value creation in a historically small share of companies, there’s some data to suggest that AI is minting an entirely new breed of unicorn—one that is younger, and faster-growing than before.

According to data from SVB, while the entire universe of venture-backed $1B+ companies has gotten steadily older, the recent vintage has gotten progressively younger:

The “full herd” has gotten ~7% older, but the median age for new unicorns is now just a shade over 4 years, reflecting a 37% decline since 2023.

It’s not just that unicorns are being minted earlier in their lifecycle than before. Across all the companies in SVB’s data, median revenue growth for the post-GPT generation is operating on a noticeably different curve:

For the 2022 cohort, the revenue jump from year 3 to 4 at the median is ~3x larger than earlier cohorts. Put another way, instead of going from $2M to $3M (as per the pre-GPT cohorts), the ‘22 batch went from $2.8M all the way to $5.6M.

Is that AI? It’s possible, but we’ll have to wait a bit to see if the ‘23 and ‘24 batches follow a similar trend.

Truthfully, the reality is a bit more complicated. AI has changed the venture landscape, but one feature of that shift has been greater allocations to non-software businesses, which one would expect to have very different growth curves. By contrast, one could easily imagine that the ‘22 batch is full of fast-growing “AI wrappers” that may or may not be able to keep it up.

Growth-curves aside, it is undoubtedly the case that AI has changed the game for pre-AI techcos. For one thing, the combination of AI-driven shift in VC strategy plus higher interest rates has led to a noticeable change in operating priorities: beginning in Q1’22, techcos in all verticals tracked by SVB, began trading growth for profitability:

Growth went from 40%-70% at the median all the way down to 15%-30%, while margins did the opposite, running from deeply unprofitable to nearly breakeven. By contrast, the companies getting funded most recently have growth/profitability profiles not unlike the ‘22 standard.

Now, to be fair, ‘22 was something of a pandemic-reopening-ZIRP high watermark for growth, and then later, when interest rates went up, everyone battened down the hatches—public companies traded growth for profitability, too. Plus, growth does appear to have reaccelerated recently.

That all being said, companies minted during the previous cycle are confronting a decidedly new-normal. An historic number of businesses were founded, and now an historic number of businesses are shutting down:

Unsurprisingly, the largest share of shutdowns comes from the peak-ZIRP vintages.

Likewise, SaaSpocalypse appears to have hit startup land, as well:

According to data from SimpleClosure, while AI startups are an increasingly small share of shutdowns, B2B SaaS represents more than a quarter of H1 ‘26 shutdowns.

Again, the denominator was high—B2B SaaS was a very popular category—and, beyond that, it’s hard to say how much of this is AI-driven disruption vs. a shift in thematic winds, but either way, the new cycle is not like the old cycle, and that’s not fun for everyone.

Citrini Called the Bottom

Speaking of SaaSpocalypse, here’s a fun one. Remember that Citrini doompost that foretold the end of software (and pretty much everything)?

Well, for now at least, Citrini seems to have ticked the bottom of the software selloff:

Software performance for all companies big and small has climbed unsteadily upwards since April . . . so Citrini didn’t quite tick the bottom, but came awfully close (at least for now).

Charts has gone over software’s selective selloff a few times, so no need to rehash it all here, but suffice it to say that fundamental performance for SaaS has more or less held up. That doesn’t mean it will continue to hold up, of course, but a rough summary of the headlines shows that software companies have generally beat or met expectations for the first half of the year:

Notably, the share of software companies with disappointing guidance increased in the most recent quarter, but meet or beat was still the predominant story.

Take this data with a grain of salt because it’s not comprehensive, and it says nothing about actual performance—it’s of course easy to exceed low expectations, and similarly easy to disappoint high expectations, but in both cases, actual performance can be variously strong or weak. In all events, whatever SaaSpocalypse may be in the offing, it hasn’t arrived yet.

MA and CA Make it Very Expensive to Start a Business

Apropos of nothing (except a passing interest in jurisdictional competition for startups), it turns out that there is a very not-normal distribution of startup costs, by state:

LLC costs in Massachusetts run ~$1,000, or ~5x the nationwide median.

To be fair, $1,000 is a rounding error in the big scheme of business costs, but these things do add up, and may be symptomatic of a broader policy posture. MA was ranked 49th in the nation for the cost of doing business, and despite having some of the best research institutions in the world (and some of the best talent), Massachusetts has been hemorrhaging businesses over the last few years:

Net business formation has been trending downwards since 2017, and has been sharply negative since the pandemic reopening, leading to the worst ratio in the country.

Approximately 17,500 MA businesses were lost (on net) over a 9-quarter period. To be fair, the data lags (because a business has to report four quarters of zero employees to be considered a “death”), so it’s possible things have improved . . . but for now, it seems that Massachusetts is a pretty unfriendly place to open up shop.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

In March, Senate Armed Services Chairman Roger Wicker pressed a panel of four Pentagon officials during a hearing on this potentially existential issue—munitions magazine depth. “We cannot be the Arsenal of Democracy,” Wicker reminded the hearing, “if we cannot get weapons out the door.” There has been ample concerning coverage in recent months on the challenges we face in maintaining our national munitions stockpiles. Whether our next conflict is a continental land war like Ukraine, or a battle for sea control in the Pacific, we will need a spectrum of munitions—defensive and offensive, traditional and new—in the arsenal.

At its core, the deterrent quality of our magazine is a function of two factors: the disposition of munitions on hand, and the capacity to produce more. Production carries vastly more weight in any rational actor’s calculation than inventory alone. For the U.S., maintaining a warm production line of deterrent capacity has been an affordability challenge. These legacy munitions have been intricately designed and built with bespoke aerospace components, to fulfill erratic and inconsistent orders from the military. This has made our current arsenal of munitions incredibly expensive at unit cost.

New entrants to this space have recognized two opportunities to deliver quality munitions at greater volumes in less time. First, they are designing new munitions from the ground up for manufacture at high rate, drawing on readily available automobile, civil aviation and consumer electronics supply chains. Second, they have conviction in the long-term need, and are funding production with private capital now to overcome jittery government demand.

According to Wicker’s hearing, “less than three percent” of the department’s munitions plan funds low-cost munitions. The plan, assembled by the Munitions Acceleration Council, assigns 97 percent of funding to legacy munitions; the remaining three percent goes to new entrants building lower-cost, highly capable products that use modernized design and manufacturing.

This seems like the wrong ratio to us, and the Pentagon is coming around to it. We’re not advocating for completely pivoting the US Arsenal towards new entrants, but we are arguing, if we shift that spend into new options that are both privately funded and low-cost supply chains, by changing how we buy, we’ll get more for our money and be in a stronger place to defend America and her allies.

Take cruise missiles as example A: Tomahawks run about $2-4 million, on the high end. Versus a new class of commercially built strike weapons—Covenant’s Anthem or Anduril’s Barracuda family—that are only about 1/5 to 1/10 the cost.

Then examine close and short range ballistic missiles as example B: The “legacy” (brand new) Lockheed Martin PrSM Increment 1 missile—initially priced at $2.7m (now $1.7 million with scale)—is less performant than Castelion’s Hypersonic Blackbeard GL, entering with a cost under $500,000.

So each incremental legacy missile costs about five to ten times as much as its new entrant alternative. In practice, the gap might be closer to twelve to fifteen times, because the flyaway price on a legacy munition the government pays today doesn’t account for the RDT&E the government committed to designing it—roughly 25% of the overall cost of legacy munitions.1 The new generation of startups develops their technology from their own balance sheets, often with capital from investors like a16z. Market competition sets the ultimate price.

Critics will say “well, a $300,000 missile is not a $3 million missile.” We’re happy to concede that our fleet of munitions includes some true feats of engineering. But the American taxpayer should never be paying for superfluous capability. Certainly not in a world where we are $30 trillion in debt! We need to produce the required capability to win, as fast and inexpensively as possible. Ultimately, though, our claim is that the volume of effects a military force can deliver and replace across a long fight is what influences adversary decision making. The good news for the U.S. and our allies is that high-quality munitions at lower cost are an option today.

Munitions come in a variety of shapes and sizes, from mortars to intercontinental ballistic missiles. When we talk about munitions in this article, we are by and large discussing short- to intermediate-range precision strike munitions.2 There are similar magazine-depth dynamics around ground-based interceptors; we touch on those below and may return to them properly in a Part II.

Most of the shouting in the munitions-affordability debate comes from people answering different questions, because “munitions affordability” conflates several issues. Do we have enough rounds (depth question)? Are precision weapons too expensive per shot (unit cost)? Can industry build and replace them fast enough (industrial capacity)? Is it sustainable to fire a $4 million interceptor at a $20,000 drone (cost-exchange)? And is the cure buying more of the same, building munitions differently, or, everyone’s favorite boogieman, reforming acquisition (system design)? The five sections that follow take those questions in turn:

• Munition magazine depth is the critical variable;

• unit cost of legacy munitions is what makes depth unaffordable;

• cost-exchange dilemma is best fixed by stocking up on affordable mass;

• manufacturing throughput is—at least partially—a function of design;

• and the remedy is to buy differently within the money we have.

1. Adversaries Count Rounds

One assumes adversary war planners in Beijing see things the same way we do. They’re asking how many rounds the United States can fire, for how many weeks, and how fast the production lines can replace what is expended. Deterrence means persuading an adversary that the operation cannot succeed at acceptable cost. Initial magazine depth, and the production capacity behind it, are a critical factor.

There is not much disagreement that our stockpiles are insufficient. One of the most widely cited set of wargames said “the U.S. will run out of preferred munitions in the early days,” and once the shooting starts, “resupply is not an option.”

Across the first thirty-nine days of Operation Epic Fury, by independent tallies, the Navy fired more than 1,000 Tomahawks from a stockpile of roughly 3,100, and the Air Force expended about 80 percent of its JASSM-ER inventory, while Patriot batteries fired 943 interceptors in the first four days — about eighteen months of output at current throughput capacity.

The counterargument is that drawing down stocks does not meaningfully erode deterrence with China, whose calculation to go to war revolves around a wider set of factors, and that the munitions spent over the Red Sea or gifted to Kyiv are not exclusively those required for a Pacific war — which would also demand torpedoes, anti-ship missiles, and guided bombs.

Those people are kidding themselves. Deterrence is weaker when the United States has fewer of the weapons needed to win a conflict, even if that weakness alone would not cause Beijing to launch. The same long-range strike weapons and missile-defense interceptors support multiple war plans, and firing them in one theater leaves every other plan with fewer rounds and less margin for error.

Operation Epic Fury consumed munition stockpiles relevant to a Pacific contingency—Tomahawk, JASSM, Patriot, THAAD, SM-2, and SM-6. Rebuilding those inventories is, per follow-on analysis, a multiyear project: at historical production rates, replacing major-program inventories takes eight to fourteen years. A bipartisan commission chartered by Congress to review defense strategy stated flatly that the defense industrial base “is unable to meet” U.S. and allied munitions needs. One school of analysts said it best—“production is deterrence.” A hot production line, capable of surging, is in some ways even more convincing to an adversary than warehouses full of missiles.

Senator Wicker closed his hearing statement with the observation that the adversary fields large numbers of low-cost systems “on purpose,” intending “to overwhelm our defenses, aware that our magazine is full of expensive weapons that we will use to defend against their low-cost systems.” Every round expended against Iran, or some other adversary, reduces the magazine available to deter China and Russia, allowing one war to consume the munitions meant to prevent another.

2. The Cost of a Legacy Magazine

We’ve found that cost data is generally piecemeal, but the comptroller’s 2027 weapons-price exhibit consolidates some of it. As mentioned, a Tomahawk, the military’s heavy warhead, deep strike cruise missile, costs about $2-4 million. LRASM, an air-launched missile designed to destroy ships at long range costs $3.2 million. At the top of the ladder sit the hypersonics: the Army’s ground-launched Dark Eagle and the Navy’s sea-launched Conventional Prompt Strike, conventionally armed weapons built to strike distant targets within minutes, cost between $41 million and $70 million per missile.

The defensive interceptors are also steep: a PAC-3 MSE, the ground-to-air interceptor used against aircraft, cruise missiles, and short-range ballistic missiles, costs $3.9 million; a THAAD round, designed to intercept ballistic missiles in their terminal phase of flight, costs between $12 million and $22 million; and an SM-3 Block IIA, the Aegis interceptor that destroys ballistic missiles outside the atmosphere, costs $28 million and up.

The publicly conducted wargames call for thousands of long-range land attack munitions in the opening weeks. Epic Fury consumed a thousand Tomahawks in thirty-nine days against a regional power; a Taiwan scenario would demand a multiple of that, sustained for months if not longer, with much greater constraints on the ability to deliver resupply. The same is true for anti-ship munitions: giving the joint force even four thousand LRASM-class weapons – modest for a Taiwan scenario – is a $13 billion magazine at current prices, procured at a production line throughput capacity that just reached about 1,100 missiles a year (shared with JASSM). Even the largest defense topline in American history cannot procure deterrence-sized depth at seven and eight figures a round.

There is concern that new products advertised as affordable now will get far more expensive before delivery—a phenomenon Augustine’s Law warns about. Studies of weapons cost growth have updated this for the age of autonomous weapons: the Collaborative Combat Aircraft—intended, I’ll remind you, to complement a fighter, not substitute for one—has exceeded its $3 million target to $25–30 million.

Cost inflation is a concern, but Augustine’s Law describes what happens when requirements float free under cost-plus contracts with a monopoly supplier. The new programs are built around competition and aggressive cost strategies: the Navy’s MACE program caps its hypersonic round at a hard $300,000 ceiling, the Air Force’s affordable-mass cruise missile line at about $400,000, and Senator Wicker counted several new companies now competing for the government’s business in the $300,000-to-$600,000 band.

Overall, there is strong evidence that commercially developed technology comes in far cheaper than the same capability developed under a government R&D program; for example the Falcon 9 program.

3. Measure the Cost of Effects

“For too long,” Wicker told his committee in March, “we have equated the cost of a munition with the capability of that munition.” Most of a modern target set (trucks, radars, small ships, drones, buildings, massed infantry) does not require a $3 million munition.

The now infamous cost-exchange stat from Epic Fury is the $4 million Patriots against $20,000 drones, though cost-exchange pressure of this kind has been a feature of the modern battlefield for some time. Skeptics call the ratio misleading because it ignores the value of the defended asset. When defending an asset worth hundreds of times its price, a $4 million interceptor protecting a $2 billion destroyer is a bargain. What Epic Fury exposed is a magazine without optionality: every cheap drone got the $4 million Patriot because that was the only answer in inventory. Whether that gap can be closed affordably remains an unsolved problem.

On the strike side, the inexpensive, effective option now exists as a production item. Castelion’s Blackbeard, a hypersonic strike missile targeted below $500,000, has a signed framework for 500 or more missiles per year and a Navy program of record planning 4,510 rounds across FY27–31. The Air Force’s Family of Affordable Mass Munitions plans nearly 28,000 weapons for $12.6 billion with production starting this September. Anduril will supply the Army at least 3,000 of its surface-launched Barracuda-500Ms, with first deliveries in mid-2027. Thousands of rounds are now on contract at prices an order of magnitude below the incumbents’. The Pentagon recognizes the shift, and we think it’s time to pour gas on the fire so that planners can tailor munitions to required effects.

Some critics argue that mass production should focus on ordnance rather than other classes of unmanned systems. The Pacific is a theater of brutal distances, and regardless of the ability of the drone to create an effect on target, they argue that the, now prolific, quadcopter is incapable of relevant operational reach in the Pacific. Drone-warfare studies reach a similar conclusion: distance forces you back toward more expensive, long-range systems with rocket motors and sophisticated guidance. But the low-cost class now includes cruise missiles — Barracuda-500, RAACM-ER, and Covenant’s newly launched Anthem — that compete with Tomahawk on range, on quality, and increasingly on warhead size—capable of delivering commensurate effects at lower cost per effect than the legacy munition. None of this makes the Tomahawk obsolete: it means we can launch a heterogeneous salvo from multiple directions, ultimately improving air defense penetration.

Analysts have warned against a race to the bottom on production cost because mass for its own sake is not the goal. We must remain focused on cost per effect. When we reframe targeting to consider the weapon’s end effect as the priority instead of its raw capability, the round of choice will skew overwhelmingly towards the cheaper option—preserving our exquisite legacy rounds for the most hardened, heavily defended targets. This decision becomes even more imperative when a single Tomahawk replenishment becomes a multiyear proposition.

Simply put, new entrant rounds will cover the bulk of targets based on the cost per effect model.

4. The Factory Is the Product

The most useful definition of affordable mass treats it as a property of the production system rather than the price tag. Affordable mass is the condition in which a force replaces munitions as fast as it expends them with the caveat that “a $200K weapon with half the reliability is really a $400K solution.”

By that standard, the new class was designed for the right war. Anduril’s Barracuda assembles in about 30 labor-hours from roughly 70 percent commercial components. The supply chain the production line draws from is the small-aviation and automotive industries rather than aerospace sub-tiers. Castelion runs flight tests monthly and makes supply chain and design choices for manufacturability from the get-go. The LUCAS drone went from obscurity to combat employment in Epic Fury at $35,000 a copy. It’s harder and more expensive to scale the legacy production lines—but still necessary. PAC-3 MSE is headed from about 600 interceptors a year toward 2,000 under a seven-year framework, THAAD from 96 toward 400, SM-6 from 125 toward more than 500. Unfortunately, the American people are going to have to shoulder much of the burden for those capex projects.

The greatest challenge to maintaining warm production capacity is unreliable government demand. One study of two decades of munitions budgets found funding swinging more than 50 percent year-over-year across eighty programs, and survey work on defense entrants identifies those inconsistent signals as a principal driver of perceived risk.

The government’s capricious buying decisions drive unit cost. When the Army bought only 23 JAGM missiles in FY25, the unit price hit $360,000 against a program-life average of $212,000. Jittery buying is itself part of what makes munitions expensive.

When the Deputy Secretary of War began calling missile-industry CEOs weekly to push for increased output, the executives’ answer was the same one they have given for a decade: the targets are not realistic without contracts to finance them. The new entrants are funded largely on private capital precisely to avoid such constraints.

Critics counter that at this point, money alone cannot fix the production lines: “the problem today isn’t money; it’s time.” Complex missiles take two years to build, plant expansions take three, and no appropriation compresses the queue for solid rocket motors, seekers, and energetics. But that two-year build time only applies to old-style missiles—custom-designed, built from parts that come from a single specialized supplier, and assembled by high skill technicians at a workbench. There are fewer (but admittedly perhaps still some) bottlenecks for a missile built from plentiful automotive, consumer electronics and civil aviation supply chains, on lines built and paid for ahead of need by private capital.

5. Allocation Is the Only Decision Left

The FY27 NDAA that cleared the Senate Armed Services Committee in June authorizes $1.15 trillion for national defense, the largest topline in history. The administration asked for a 188 percent increase in missile procurement.

The Munitions Acceleration Council’s priority list is over $47 billion, and official budget projections show missiles and munitions climbing toward 41 percent of Army procurement. Congress has already granted multiyear procurement authorities. The department spent the 2010s explaining that it lacked the money, the demand signal, and the authorities to buy munitions at scale. As of this budget, it has all three.

Yet under the current plan, the vast, vast majority of spending goes to legacy technology. That is how a historic topline can still produce an insufficiently deep magazine. It would be a generational mistake to take the largest pool of resources the Pentagon has ever been handed and simply stock up on 1970s technology.

The committee, to its credit, has proposed a remedy. The markup it advanced 18–9 on June 11 requires the department to accelerate the adoption and purchase of low-cost munitions, directs accelerated acquisition programs for low-cost alternatives in eight critical categories, authorizes multiyear munitions procurement, and requires second sources for the solid rocket motors that bottleneck every line (though we believe liquid propulsion technologies as a complement to solid motors are the only way to truly unstick this logjam).

The fact that our sovereign demand outpaces supply has a knock-on effect on foreign military sales. Our allies and partners face five- to seven-year backlogs for some traditional munitions—and are growing weary of the wait. Facing existential threats of their own, our allies cannot afford—in both time and money—to wait for US legacy rounds and are now turning to non-US component manufacturers to bridge the gap.

“We cannot be the Arsenal of Democracy,” Wicker reminded the hearing, “if we cannot get weapons out the door.” A $300,000 missile built from commercial components is one an ally can actually afford in quantity and, where policy allows, license and co-produce. The bill’s directed U.S.–German co-production program and its war-reserve stockpile authority for Taiwan represent progress, so long as the underlying designs are manufacturable affordably at scale.

The authorization addresses some key cost levers such as hard unit-cost ceilings, fixed-price awards, two or more vendors, commercial components unless waived, and multiyear orders big enough to justify a factory. Over the last two years, price has followed the size and steadiness of the order.

CBO puts the Golden Dome homeland-defense architecture at $1.2 trillion over twenty years if it is built the way we build interceptors today; the administration’s proposed budget is a fraction of that, and General Guetlein has been emphatic that we cannot bankrupt the country to build it. Whether interceptors can follow strike weapons down the cost curve is an open question — the engineering problem is harder, and no one has demonstrated the answer yet.

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So, back to the opening question: is 97-to-3 right? We’re skeptical. Whatever minimum number of legacy rounds the war plans demand, the current plan buys them and little else. Depth has been affordable at current prices for a while now. We know which magazine we’d rather take to war.

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Anduril, Covenant, and Castelion, discussed throughout, are portfolio companies of Andreessen Horowitz. All figures cited are from public budget documents, government statements, and press reporting. This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

Product management is all about telling stories. So let me share how I think about product management, and how AI has changed it, by telling you one.

The interview question I’ll never forget

I started my career as an engineer at RealNetworks, and in a few years I was responsible for the product and engineering team for RealPlayer. It was a pretty meaningful consumer product at the time - it had hundreds of millions of users, and helped bring audio and video to the early internet. I’d sit in meetings with people over on the business side, who’d suggest ideas like, “We should show an ad every time the player starts.” I knew this wasn’t the right thing to do, but I had no way to argue with their Excel spreadsheets showing how much money we’d make. I wanted to become a real product manager, and concluded I should probably go to business school.

I started business school at Berkeley, and soon after arriving I saw on my former college email list that LinkedIn was hiring. I applied, and found myself in an interview with Reid Hoffman. He sat down and asked me an interview question I’ll never forget:

“So, you want to be a product manager. What is the artifact that a product manager produces?”

He elaborated. Engineers have an artifact: code. Business development has an artifact: signed contracts. Designers make the visual look and the graphics. The CEO has the org chart, the funding plan, and the vision that keeps everyone together. What about a product manager?

I told him that I wasn’t sure product managers really had “artifacts” like that, but that the fundamental thing we do is gather up everything that’s going on and write it down in a spec. The spec is the blueprint. It’s where we define the requirements, and everything we’re going to do, and it becomes one of the most important documents in the company because it unlocks every team to go build from there.

I was clearly nervous. I think he noticed, because he reassured me that it was a fine answer. I ended up getting the job, dropped out of business school to join LinkedIn, and I’ve been thinking about that question ever since.

The evolution from spec to story

Because I gave the wrong answer.

This was still the Waterfall Era of building software. At LinkedIn we were trying to reimagine a jobs platform within a social platform - where hiring managers could see job applications in the context of mutual connections, candidates could see job listings and find ways through their network to get to the front door. During this discovery process, I wrote a 120-page spec defining the entire experience, and what the requirements were.

I’ve thought a lot about it in the years since. Because that spec, obviously, is not the most important artifact. The story is.

The spec describes a system, what it must do, and what boxes must be checked off before it is done. That is not the art of product management. Product management is about telling the story of people who are going to use the product, and why it will matter in their lives. It has to be immediately understandable, by whoever you’re talking to. And it has to be repeatable - people need to be able to pass it around faithfully, without you being in the room.

That is a completely different document, and a completely different job.

Ten years ago I gave a talk about product management, and people still send it to me; which is either flattering, or a sign that the field hasn’t moved. I’ll consider both possibilities. Anyway, the whole talk came down to one sentence: “A product manager helps their team (and company) ship the right product to their users.” I spent most of the talk breaking down that sentence, word by word.

• Helps their team. You’re not the leader. A lot of people think the product manager is the leader. You’re the person who helps make the thing happen. Which means you must …

• Understand your team and your company. Your team is your domain: you have to understand it! And you have to understand how it sits inside the bigger picture, so you can serve the company’s goals and not just yours.

• Ship. We can talk all we want, but at the end of the day, all that matters is putting the product in front of customers.

• The right product for your users. We’ve finally arrived at the job: honing in on what “right” actually means.

How much of that changes in a world of AI?

What is changing

Obviously something has changed, a few things actually. On the one hand, it’s changing how we code, and how fast we can get from an idea to something running. On the other hand, it’s changing what users expect a product to be. I think we’ve barely tapped this, especially in consumer. Being able to describe what you need and have the product deliver it, maybe with agents running in the background, without you having to learn and interface.

There’s no question that the cost of making stuff has collapsed. It’s no longer all that difficult to scope something out and try it; that gives you enormous flexibility. But the cost of judgment hasn’t changed at all. Figuring out what to build is more important now than ever.

Product development is a loop. It used to be that someone had an idea - and it doesn’t have to be you; in a good company it can come from anywhere. You try it. You write a spec, or a product brief, or whatever your name for that document is. There’s some up front cost to it: scoping, designing, arguing - everything that must happen before you spend precious engineering time. These are all rituals that we invented in order to protect engineering time from bad decisions. Because you only got six or eight turns around that loop a year.

Then making things became absurdly cheap. Not somewhat cheaper; a different order of magnitude. And what has happened is really interesting. That old loop is still very much there - just rearranged into a new order.

The old loop went: idea, spec, costing, scoping, everything else, then building. Now:

• First, take the idea and you build it quickly with AI, just to see how it works and how it feels.

• You get to play with it, and figure out how it feels and fits into the overall picture. Prototypes beat “what-ifs”, every time.

• Then you design it. Now that you’ve played with it, you know what it is, and you can actually talk about what it’ll take for this to be more than a prototype. I mean design here in both senses: visual and UX design, and also engineering design.

• Then you ship and learn.

It completely inverts: from spec-and-scope, to build-and-play. I think that changes product management more than anything else happening right now.

This finally means that the spec is no longer the deliverable; for real. You don’t have to start by writing a long document and getting everything right on paper. This used to be true in an aspirational sense, but now it’s just obviously true, in a literal sense.

But I want to be careful, because there’s an equal and opposite mistake you can make.

Demos are almost free now. Working products are not. I keep seeing the flip side of this new approach: “That’s great, just ship it.” That’s still not how it works. We all still have to respect that the distance from a prototype to something real still takes time to cross.

There’s a bit of a trope about product managers, that their job is mostly about asking, “Does it fit in the schedule?” Throw that idea away entirely. The most important question is: Does it fit in the product?

We all have great ideas, and now we all have agents that can code for us. Deciding what to build is officially not a resourcing debate. It is an impact debate. “This or that”, not “this or nothing.” Taste and curation matter a lot here, when you have a vision and you really know what you’re trying to do for the world. But the system you build still has to feel complete.

My biggest worry about AI is that it lets us go faster, and therefore just cram everything in. We talk about “AI slop” in content; this is what AI slop means for product. I’ve seen it happen in a few places already, and I think we’re all a bit worried about it. When anyone can build anything, deciding what to build is the whole job. And that’s a story problem. What story do you want to tell? What story do you want your customers to understand? What story do you want living in their heads?

Your job as a PM is not to write a spec of what the product will do. It is about creating a shared understanding - a shared picture of what we’re doing and why. Why is the user here? What do they feel at each step, and why does that matter? Where is it impressive, and where is it boring? It’s okay for a product to be boring occasionally, as long as you know where. But if you can’t write a good script, the product is going to be dull.

The gift that AI gives you is now you can find this out for free, right up front. You can build it quickly, get a feel for it, play with it, and figure out that one sentence: what does this product do for somebody in their life? Because if you can answer that, you can answer my question: “Are people actually using it?” Because now you’ve said what it does, and you’re asking whether they do it.

What isn’t changing

What does it mean to have a “vision” for your product?

When I say vision, I don’t mean a mission statement. Those matter, but they are not a vision. A vision is the end-to-end reason for the product to exist for users. I have a simple framework for this:

• Purpose. Why is someone picking up your product and putting it in their life?

• Core actions. When they pick it up, what are they actually doing? There can be more than one thing, you have to understand them all.

• Cycle. What is the expected frequency of each of those core actions?

My whole career, as I’ve met with founders and other product people, I ask them: are people using your product? And they almost always jump straight to user data. “We have a 50% DAU/MAU ratio. We crossed 10,000 signups. We’ve got a million people on the waitlist. Our ARR is a million. We’re doing four billion tokens a day. We hit #3 on the App Store.”

Are any of those an answer to the question I asked?

Sometimes I ask the question again, but I add one more word: are people really using your product? And then, sometimes, they clue into what I’m asking about.

LinkedIn’s purpose was to find and be found. Maybe the core action, for some people, was just responding when somebody reached out. For most people that’s not a daily thing; it might be once or twice a year.

Look at that cycle - once or twice a year. Understanding this was critical to LinkedIn working, because the network needed a very large number of people willing to be found, and at least some people doing the finding.

LinkedIn was a social network, after all, so you might be tempted to prod users into taking actions every day. We didn’t do that. Instead, we spent enormous time in the early days on making sure people kept their profiles accurate. It was completely fine if you were only found once or twice a year, as long as when it did happen, you clicked through and understood, “someone’s reaching out to me, that’s great.”

When you’re measuring whether your product is working, those core actions are what matters. Focus on direct traffic: find the people who literally came to you. They have the app installed and hit the icon, or they typed your domain in by hand; they went to you, of their own volition. That’s the traffic that matters, as opposed to all of the other ways you can get somebody back in the moment.

And then only really count the people who perform the core actions. Not “briefly opened the app”, but actually engaged with it. On Discord it would be, “got into a live session. Actually read and sent messages.”

If you can’t define what those core actions are, then you do not have a product, because you don’t have something that you understand.

Now, one thing that’s new, and that I love, is that in AI products where the user is talking to the product, or prompting it in some way, you now have a literal transcript of your user journey. You can see what people are saying in their own words. You can see the exact moment someone gave up and rephrased. You can see what they expected the product to do that it didn’t. READ THESE! AI is great for surfacing things you wouldn’t have seen before, but you can’t have it summarize everything, and you can’t have it form your opinion for you. Forming your opinion - figuring out what the story actually is - is the job and the art of product management.

Onboarding

Onboarding is the single most important moment you have to tell your story to a customer. They’ve discovered your product - maybe through an ad, a viral invite, an article, whatever. They know you exist; they’re curious and want to try it. You will never get this much attention from them ever again.

You have to remember, at this point, that not everyone shows up to your product with the same motivation. There are the eagers. They want in so badly. They’re ready to go. And, just so we’re clear, if you work at the company, you live in eager-land. Everyone internal to your company should be treated like an eager; they’re already steeped in the product every day. When they onboard to the product they think, “I know what I’m doing, this is boring, why is this step here?”

On the other hand there are the fly-bys. They’re just not that into you. They heard about it, checked it out, but the message didn’t land, and they’re going to bail.

Those two kinds of users are the edges of the distribution. In between is a big fuzzy middle. These are people who showed up for a reason: they’re curious! They want to learn more! And you genuinely can convert them into core users of your product. These are the people you need to build around. You’ll get the eagers anyway. The middle is who you need to understand.

Assume your users are motivated and curious. Take the time to introduce the product, step by step. More simple steps beat fewer complex ones. I’ve proven this in A/B tests at multiple companies over the years. If every step is discrete and simple, and it’s clear what you’re asking and what you’re teaching, that beats single large screens, or complex choices to keep the number of steps low. Every time.

So how do you actually build that?

Start by repeating the core message: Here’s what this is for. State the context, inside the product. It’s fine to ask for the basics - email, password, phone. For everything else, explain why you’re asking, and how it relates. Then break your product down into its key concepts, each with a clear action for the user to take.

AI products have made this harder, not easier. You get the blank prompt box. In some ways it’s the worst onboarding screen ever designed. It’s a magic box. It can do anything. So… what do you want to do?

A lot of products nowadays start with, “Hi, I’m here to help, ask me anything!” Speaking for myself, I am not the most articulate or creative person, in that moment. You have to teach capabilities concept by concept. “If you ask something like this, I can do it.” And then let the product do it. Get the user to at least one valuable use case quickly, ideally with their own data, so it’s actually valuable to them.

People ask me sometimes: with a longer flow, won’t more people drop off? Yes! But the ones who get through are far, far more likely to actually use your product. If you’re A/B testing two different onboarding flows, do NOT look at how many people made it to the end of the flow. Look at how many people come back the next day, or the next week, and how many took a core action. If you ask them, at that moment, “What is this product?” they should give you more or less the right answer. Your retention data, from this point forward, is your report card.

A story from Twitter

I’m going to wrap all of this together by telling you a story from Twitter.

I joined Twitter in late 2009. We had a growth problem - except it wasn’t really a growth problem. Twitter was in the news constantly. People were blogging about it, the media was talking about it, and plenty of people were asking: “What is this Twitter thing? I need to go figure it out and sign up.” And then millions of them did. But they never came back.

The problem was that no one could tell you what Twitter was. I can actually prove this:

Caption: “we eventually got to number one.”

The way we were doing onboarding was, people would sign up, and see options to “Find your friends” or “Follow 20 random people”. Most people skipped it, and then landed on a page that looked like this:

This is pretty terrible! It’s a big empty box. People would look at it and think, “...I don’t have anything to say.” And then they’d leave. If you asked them at that moment, “What is Twitter?” they’d say, “I think it’s about saying something to the world? Or finding my friends? I don’t know.”

So we rebuilt onboarding over a couple of years, and found the thing that worked, which was the Learn Flow. We taught them Twitter, one concept at a time, as a story. And it moved retention more than anything else we shipped that year.

The Learn Flow, screen by screen

First, the new home page: “Welcome to Twitter.” We didn’t try to put content there, just: “Find out what’s happening right now with the people and organizations you care about.” That’s a pretty good description of Twitter, honestly.

Then: This is a tweet. It’s a short message, up to 140 characters, and it can contain links. Now you know that tweets are the unit of this thing.

Next you have to build your timeline. So we showed you a timeline. We made you click “follow” on people on the left. And when they click follow, their tweets appeared on the right. So you get the whole idea, in one motion: I click follow, tweets show up, that’s my timeline. Which is the actual concept of Twitter - tweets, following, and a timeline.

And then, finally, your timeline. You’d recognize every account on it, because you had actually followed them yourself.

Onboarding is your story.

The right product for your users

Your job as a product manager is to help your team and your company ship the right product to your users. In a world of AI, the shipping is less of the problem than it used to be. Figuring out the right product, and who your users are, is just as important as it has ever been. If not more.

Always ask whether people are really using your product. Understand what that means. Think about the purpose, the core actions, the cycle. Spend more time on onboarding than feels reasonable. It’s where you convert the fuzzy middle, and it’s where you actually tell the story of your product.

Use AI to go faster on the prototypes — but don’t speed up your judgment. Don’t give up your judgment. Don’t just say, “Well, let’s test it and see.” That’s how you end up with a sloppy product. Keep your judgment everywhere. The hardest part of the job is still balancing all of our creativity as product managers against all the data we now have access to.

Good luck!

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

September 11, 2001 was 25 years ago, today. Never Forget.

The brain of a venture capitalist can be succinctly summarized as:

In venture, no one talks about the mistakes of commission: investing in companies that didn’t return as promised. There’s no scoreboard for how many of your checks get buried behind the back of the shed - it’s part of the job. In fact, our losses on investments equate to just 15 cents for every dollar invested across the funds we manage. But a single winner can have an outsized impact. Our stake in Databricks alone represents 20% of our total AUM.1

There are other kinds of mistakes that we do consider fatal. Investing in the right category, but in the wrong company. Worse yet, choosing to sit out on a company like a SpaceX, an OpenAI, or a Stripe. As my partner Marc Andreessen reminds us: if you invest in a company that unfortunately goes belly up, at least, at a certain point, you stop hearing about it. If you pass on a company and the company succeeds, you never stop hearing about it–you see it constantly in the press, on X, and in your nightmares. It haunts you every single day and every VC can recount those misses more than the successes. Our brains are wired for it.

The irony, however, is that the mistake LPs may be making today is one of commission, driven by a seemingly prudent question: Is my portfolio appropriately balanced across asset classes?

A “cottage” no more

Every week, I come across an LP who has nearly zero exposure to the three current frontier models (SpaceX, Anthropic and OpenAI). And they don’t have exposure to the leading open source ones. So how did so many LPs miss this initial wave of AI?

A few hypotheses. To start, venture allocations were historically “right-sized” to reflect the “cottage industry” nature of the business. Venture is typically a subset of a private alternatives allocation and targeted to be 5-10% of an overall portfolio. For endowment LPs, historically the most long-dated and aggressive allocators to venture, their exposure can be somewhere between 25-40%. And for some, that’s mostly through NAV appreciation vs active primary $s deployed to that percentage.

But the day SpaceX went public, historical asset allocation went out the window. Technology is the dog that caught the bus and at ~$2T market cap today, SpaceX is the largest venture-backed IPO by a factor of 10x2 and right around the corner is Anthropic (valued at $965B, rumored to be going public shortly at $2T) and OpenAI (most recently valued at $852B). That’s roughly $3.8 - $5T of combined equity value cultivated largely in the private markets and for those LPs who don’t have exposure to it, they have a lot of ‘splaining to do to their boards and investment committees.

Moreover, the biggest companies in the world are technology companies and they are getting bigger, more quickly than ever before. What was once success in venture (a unicorn) is now your average Wednesday and private companies are generating $1B+ in revenue on a timeline measurable in quarters.

While this is happening, the asset allocation frameworks LPs have relied on for decades have started to fracture. I’m reminded of something Robert Smith, CEO and founder of Vista Equity Partners, one of the great software buyout funds, famously once said: “Software contracts are better than first-lien debt”.

It turns out, the next wave of disruption can make recurring revenue less durable than expected. And we may be seeing this reflected in buyout returns, where average and top quartile returns are near 15-year lows.

For prudent institutional investors, who have historically “right-sized” their venture exposure to a small piece of their overall alternatives pie, the landscape has changed, and this is a call to resize again.

Power Law is now the shape of our economy

It’s hard to overstate how a small number of companies are now capturing a monstrous share of everything. NVIDIA chips are now even being used to broker peace deals. Take a look at the evolution of the S&P 500 below, where 8 out of the top 10 most valuable S&P 500 companies are tech companies (and turnover in the top 10 is more dynamic than ever before):

Even in credit markets, tech is the main event now:

So, tech is getting bigger, and more central to the economy, and as everyone knows by now, companies are staying private longer.

~1/3rd of technology companies valued $150B+ are privately-held and for the second-largest public tech co (SpaceX), the lion’s share of enterprise value creation happened on the private side.

Further down, the companies with real momentum are moving up at an accelerating pace: median time between rounds for actively-raising unicorns compressed to just one year in Q1 2026, down from 1.5 years in 2024, while over half the private companies that once cleared unicorn status (51.2%, per PitchBook) haven’t raised in more than two years.

The fact is that tech companies are getting larger, especially at the tail, and that growth is happening in private markets:

In the good ol’ days (2004), Google went public with a ~$1.67B offering and closed its first day at a “bubbly” $27B market cap. Adjusted for inflation, that’s roughly $3B and $48B, respectively. Today, that offering wouldn’t crack the 20 largest completed VC financings recorded by PitchBook since January 2025, and its first-day market cap would amount to just a third of those companies’ $75B median reported post-money valuation. In other words, this would be a rounding error in the news cycle.

When venture eats the world

PE has historically captured a much larger share of alts allocation, but venture outcomes are simply changing the math:

Private equity and venture capital have maintained roughly equivalent exit counts for a long time, but exit value for VC-backed companies just tripled PE’s high watermark.

The largest, majority-PE-owned company to go public was Medline in December 2025.3 $54 billion based on first-day closing market cap. SpaceX, the first of the new AI generation, private juggernauts stepping onto the public markets, closed its debut at approximately $2.1 trillion—roughly 39 times as much.

What about M&A? In July 2026, Aligned Data Centers became the largest, majority-owned, private equity backed M&A exit by enterprise value at $40B (according to Pitchbook). The biggest venture-backed M&A exit? Cursor to SpaceX at $60B.

A single venture IPO (SpaceX) can be roughly ~39x the largest PE outcome on record. Multiply that by 2 more $1T+ potential IPOs in the pipeline, and you can see where I’m going with this.

Case in point: consider CalPERS. In late 2022, the largest public pension fund in the U.S., reworked its private equity book. Buyouts fell from 91% of new commitments in FY2020-21 to 58% in FY2023-24, while growth equity and venture together rose from 9% to 43%. Its PE program went from ranked 30th to ranked 1st among the 30 largest US pension PE programs in just three years, and their growth equity and venture program is now its single strongest-performing segment.4

Other public pension plans are starting to follow suit, including MassPRIM and North Carolina Treasurer, just to name a few.

Machine intelligence compounds the trend

If nothing else, we can all agree that the train has left the station on machine intelligence. We have already seen exponential improvements in capability, autonomy and cost—and this is all before robotics really arrives. Innovation will likely continue to expand faster than any of us, investors or LPs, can map in advance.

Fortunately, this is the kind of ambiguity venture is designed to thrive in. Unlike private equity, the power law in venture absolves all sins. We don’t need to save every portfolio company to generate great returns and in fact, while we don’t invest with this expectation at the outset, the majority of the portfolio can go to zero and we can still generate outperformance from a small handful of power law winners.

The legitimate case against venture

So if the case is so clear for venture, why isn’t everyone making a drastic change to their portfolio?

Show me the money

The critique of venture that hits hardest is, of course, liquidity (even if that’s not unique to venture). As one of our partners once said to me, “I’m knee deep in TVPI, but where is DPI?”

For some managers, secondaries may be the only answer to post up liquidity and stay in the game between fundraises. It’s a fair strategy, but remember, VC firms typically generate their returns from a small batch of winners, so you might be pruning your roses to water your weeds. Even still, secondary capital is still a rounding error in the scheme of things and the vast majority is targeting a very small subset of companies. And there is absolutely no discount for the assets you actually want:

In my experience, most LPs would prefer to hold compounding assets. An example I’ve shared publicly with Packy McCormick before: a few years ago, we offered our LPs liquidity in our older vintages. Fund I had a seed position in Stripe and Fund III invested in the first round of Databricks.5 We offered various structures to give LPs liquidity on those names, and do you know what happened? Not a single LP chose to sell. Ironically, receiving money back is sometimes a burden for LPs because then they have to reinvest the capital. Can they find a comparable asset that can continue to compound at that expected rate? Even if they can, why pay interim taxes instead of letting it grow?

Returns over marks

Here’s another one we hear all the time: venture valuations are too opaque. And how do LPs calibrate to a legacy portfolio of companies from the last technology cycle that may be worth less than what their last round valuation was?

Somewhat fair, but let’s unpack. Venture marks are kept close to the vest because, rightly or wrongly, they’re often viewed as a vote of confidence (or not) in one company over another. The mark is a signal that affects both relationships with founders, and founders’ relationships with their employees and prospective hires. The reality is that the valuation process for all private assets is complex. Almost every single venture manager holds the same company at different valuation, which makes it even more confusing. As this oldie, but goodie from Scott Kupor posits, when is a “mark” not a mark? At the end of the day, the only true mark is cash in the bank account after an exit. DPI speaks for itself.

Plus, this phenomenon isn’t unique to venture. Private equity and private credit aren’t “mark-to-market” either, and now that AI is re-writing the leaderboard for enterprise value, there may be a whole lot of pain in PE.

Similarly, private credit was the coupon-like antidote to PE’s illiquidity. ‘Equity-like returns, with debt-like downside,’ they said. There’s nothing wrong with that, and credit where it’s due (pun intended), the fundamentals have largely held, and the asset class is evolving in real time, moving up the stack to underwrite the AI infrastructure buildout itself.

But for those earlier cohorts of loans, especially to supposedly uncorrelated PE-backed software companies, where AI has compressed multiples, growth has slowed, rates have moved higher, and refinancing is around the corner? There likely will be (and likely already have been) write-offs, and likely no power law of outlier upside to make up the difference, either.

Platform shifts may keep the other guys up at night, but for venture, it’s where the opportunity is.

Proximity is not participation

My last point is crucial because it’s often a trap for allocators who are feeling the weight of the argument so far. Too often, the folks I speak to feel like they are already overexposed in venture, with decades of legacy portfolios sitting with managers who have zero incentive to sell or re-value their book.

But, in continuing the theme of power law, it’s not just a smaller number of companies generating the returns. It’s a smaller number of funds with exposure to those companies:

Once again, the fat-tail of outcomes is happening within a small group of companies held by a small group of funds.

Top-quartile 5-year IRR is now double the median for the most recent vintage:

Even more recently, the top decile funds by IRR have never been farther away from the top quartile:

Manager dispersion is becoming even more pronounced.

And it’s not just paper returns–top decile’s got your DPI, too:

Top decile DPI for nearly 10 year-old funds is 2-3X larger than the top quartile (and 4-10X larger than the median). Median just isn’t good enough. Top-quartile ain’t it either.

It shouldn’t be any surprise that a small handful of funds are capturing an outsized share. After all, winning begets winning: a fund that lands one behemoth gets the follow-on rights, the founder referrals, and the information edge that make it more likely to land the next one. Access compounds the same way outcomes do.

In the bigger scheme of things, diversification is something we’ve all been taught to be prudent. But this is perhaps one of the few times where that logic doesn’t land. In a recent podcast, Aram Verdiyan from Accolade Partners revealed that based on Accolade data, 3,000 venture capital firms in the US, and only 20 have achieved consistent 3X net returns over the last two decades. Furthermore, a September 2026 PitchBook screen of 2,143 global VC funds with 2000–2018 vintages and reported DPI revealed that, only 365—or 17.0%—had returned at least 2x invested capital. Just 143 funds, 6.7%, reached 3x, and only 51, 2.4%, reached 5x. Put differently, 83% of funds failed to return 2x and nearly 98% fell short of 5x. So it’s worth saying out-loud that only the best funds have the access, the power, or the muscle to repeatedly find, nurture and exit a behemoth.

Ripping it to studs and starting over again

Institutional money isn’t missing the argument for venture. Many LPs are making this exact argument internally to their boards and investment committees themselves. Tech will drive an outsized share of value-creation, before maturing into some of the largest businesses the world has ever known. As Marc says, “technology is the dog that caught the bus”. But if left unchanged, “prudent” asset allocation to a “cottage” industry that has outgrown the cottage, may miss the bus altogether.

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The above reflects the subjective views and beliefs of a16z Capital Management, L.L.C. (“a16z”) or its respective affiliates, and is not, and is not intended to be, an offer to sell -- or a solicitation of an offer to buy -- any securities, and may not be used or relied upon in evaluating the merits of any investment. It should not be construed as or relied upon in any manner as investment, legal, tax, or other advice. Any projections, estimates, forecasts, targets, prospects and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others. Any charts provided here or on a16z content distribution outlets are for informational purposes only, and should not be relied upon when making any investment decision. Certain information contained in here has been obtained from third-party sources. While taken from sources believed to be reliable, a16z has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation. All content speaks only as of the date indicated. There can be no assurances that a16z’s investment objectives will be achieved or investment strategies will be successful. Any investment in a vehicle managed by a16z involves a high degree of risk including the risk that the entire amount invested is lost. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures.

America | Tech | Opinion | Culture | Charts

Your company chooses your health insurance, and you have to live with it. For the first time in decades, one of healthcare’s stickiest markets is opening up as premiums keep rising, consumers expect more, and AI makes a different kind of health plan possible.

Employer-sponsored (AKA “commercial”) healthcare is peculiar - the buyer and user are different people. The company you work for chooses one or two health insurance options and subsidizes most or all of the premiums. Healthcare is typically the company’s second largest expense after payroll, while you cover co-pays, deductibles, co-insurance, and other out-of-pocket costs. And because you change insurance plans when you change jobs every few years, the incentives aren’t long-term aligned. The plan paying for your care today may not benefit much from keeping you healthy years from now. It’s one reason employer-sponsored insurance has been called healthcare’s original sin.

But it’s a massive market, with $1T worth of spend across 150M+ Americans and highly sticky products. Employers have kept renewing contracts with legacy insurance providers, even as premiums rise by 10% or more every year. Much of that price inflation has come from the rising costs of healthcare itself, including specialty drugs and administrative bloat, and a market with historically high barriers to entry. At the same time, the consumer experience continues to be expensive, opaque, and full of surprise bills.

That has been the status quo for decades. But now, three things are happening at the same time:

• First, employer healthcare economics have risen to the point that the majority of employers are shopping for alternatives, either to switch to lower cost health plans or drop traditional health insurance entirely.

• Second, consumers have an increasingly high standard for care when it comes to access, cost, experience, personalization, and quality. This is partly the result of abundant DTC and now AI-native healthcare services that have normalized consumers seeking care directly and paying out of pocket, then asking insurers and employers if they’ll cover or reimburse it.

• Third, AI is lowering the fixed-cost barrier to building and operating a health plan. Supporting plan members, helping with care navigation, underwriting risk, reviewing claims, remitting payments - all of these functions historically required large teams to execute, but AI is now the leverage to do all of these more efficiently.

These shifts are spurring a generational replacement cycle in commercial health insurance, in which employers are increasingly looking to move to Alternative Health Plans, or AHPs.

A new wave of challenger health plans, challenger PBMs, and modern infrastructure platforms have emerged to meet the demand for better employer-sponsored healthcare. Their challenge and opportunity is to serve their two audiences at once - improving employer economics and risk while meeting the higher standard employees now have for their healthcare experience. Many of these emerging companies have the opportunity to be both AI-native and AI-proof.

And the plans that replace the traditional options might look pretty different. If we look at the various ways challenger health plans are trying to compete, a common theme is that plans are becoming more upstream, personalized, and proactive. They’re becoming more involved in how consumers actually navigate seeking and receiving care instead of just financing and administering it. AI helps enable the evolution from passive to active across these dimensions.

So this is a generational moment to rebuild the commercial health plan. But in an increasingly crowded market of startups, the question is not whether there’s a compelling “why now” - it’s which of these levers, or combination of them, can become a real, durable advantage.

How Challenger Health Plans Can Compete

A challenger health plan might get attention with a superior customer experience or lower admin fees, but the ultimate test is whether it can keep its members healthier and control the total cost of care. Across the health insurance plan ‘stack,’ there are a few places where an upstart can build a meaningful advantage:

And of course, a company can integrate several of these components into a full-stack health plan offering. Given the massive market opportunity, any one of these components could support a standalone company, or become a superpower within a full-stack health plan. And each segment of the employer market, from SMB to jumbo, is a big enough prospect base for a company to focus there for years and still grow significantly before expanding beyond.

Rising costs are forcing employers to shop, just as AI is making health plans smarter and cheaper to build, and consumers are expecting more from them. Health plans have to compete again, and a previously impenetrable market category is now addressable for startups.

Commercial health insurance is one of the largest markets in healthtech. It’s a control vector for how healthcare is priced, accessed, and experienced, and it’s one of the hardest layers to rebuild. For the first time in decades, employers are ready to buy something fundamentally different - and builders have better tools to deliver it.

If you are building a challenger health plan, we’d love to hear from you. Let us know the one or two levers where your approach is structurally different, and why that difference compounds as you scale.

Special thanks to Colin Morelli, Seth Cohen, and Eva Steinman for their contributions to this piece.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

Every consumer brand shares a quiet, expensive problem: they make more product than they sell. This is built into the model - brands forecast optimistically to hedge against stockouts, and end up sitting on trillions of dollars of perfectly good inventory. Even the most in-demand brand inevitably ends up with a color or SKU it can’t fully move.

What happens to that inventory is one of the least glamorous stories in consumer goods. It gets stranded in warehouses, quietly written off, or dumped into off-price and liquidation channels. Aside from the hit to profit, this erodes the very brand equity these companies spend years and fortunes building. And, it forfeits opportunities to reach new customers, on channels that could actually drive net new sales and loyalty.

For decades, the only options were bad ones. Sell product through discounters and jobbers and watch it resurface somewhere that cheapens the brand, or destroy it outright. Many of the best brands chose destruction rather than see their inventory end up in a discount bin. No one had built a brand-safe way to move excess inventory - one that treats it as an asset to recover value from, rather than a problem to bury.

There’s a reason for this. Excess inventory is the opposite of a clean commodity: it’s messy, heterogeneous, one-off lots with no comps, no standard pricing, and a different right buyer for every batch. Pricing and matching that supply at scale was a problem you could only throw humans at. This is why the category defaulted to relationship-driven liquidators and race-to-the-bottom auctions. That’s exactly the kind of unstructured problem AI is now good at.

We believe the winner in this market will be the platform that is AI-native and brand-safe from day one. Highstock is building exactly that, and today we are thrilled to announce that a16z is leading Highstock’s $30 million Series A. Olivia will be joining the Board, and a16z marketplace legend Jeff Jordan is also participating as an advisor.

Highstock is an AI-powered B2B marketplace that connects brands sitting on excess inventory with a vetted network of global wholesale buyers. Brands move overstock discreetly, without the fire-sale optics, while Highstock handles the hard parts: matching, logistics, compliance, payments, and even managing order paperwork. Its models do the heavy lifting so both sides can browse and transact with confidence.

Highstock is hard to replicate because of its team. CEO Camille van Horne and CTO Sean Cashin both came from Instacart, where Camille spent six years as a Director of Product Management leading the end-to-end fulfillment experience. Running fulfillment at Instacart, especially during COVID, was marketplace operations at its most unforgiving - matching supply to demand under messy, real-world constraints, at scale, where every failure is visible to the customer. Camille and Sean bring this level of precision and excellence to Highstock.

In a little over a year, that approach has produced real scale. Highstock now has more than $1 billion in inventory listed on the platform and works with over 100 major brands. Roughly 60% of transaction volume comes from brands and conglomerates that do more than $500 million in annual revenue. On the other side of the marketplace, more than 100 business buyers reach that supply through channels most brands could never access on their own, from live sellers to international e-commerce players. Along the way, Highstock has kept more than 10 million pounds of product out of landfills.

Beauty and personal care is the beachhead, but the opportunity is much larger. The same excess-inventory problem exists in apparel (which Highstock is launching now) and across consumer goods. As Highstock expands category by category, the data and the relationships compound with it.

We could not be more excited to partner with Camille, Sean, and the entire Highstock team as they give excess inventory a second life. If you are a brand sitting on excess inventory, or a buyer looking for supply, check out Highstock at highstock.com - and if you want to help build it, they’re hiring.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

Fifteen years ago, I wrote that software is eating the world - at the time, a controversial claim. The main prediction of the essay has come true. As a simple measure, technology’s share of the market capitalization among the ten largest businesses in the world has gone from 31.5% to 94.4% over that period. Amazingly, this happened even though only 1 in 300 people on earth wrote software, and every line of it had to be written by hand. Software has been eating the world at the speed of human hands. It is about to eat the world at the speed of compute.

Three years ago, when Scott Wu and his team began building Devin, the term “coding agent” barely existed. Today, if you talk to any software engineer at a leading company, they’ll tell you that agents write almost all of their code. In the past year, Devin has gone from writing 13% of Cognition’s production code to more than 90%.

When agents write 90% of the code, engineers can literally do ten times as much. The 10x engineer becomes the 100x engineer, as they shift from writing artisanal code to operating as the CTO of a fleet of agents. The 1000x engineer isn’t too far behind. As Scott puts it: “Within our lifetime, engineers will go from bricklayers to architects, focusing on the creativity of designing systems rather than the manual labor of putting them together.”

At Mercedes Benz, engineers turned what would have been an eight month long COBOL migration into 8 days of work with Devin. Rivian teams increased their test generation velocity by 10x. Devin triages and patches vulnerabilities across thousands of repos at some of the world’s largest financial institutions like Itau, where 70% of security vulnerabilities are automatically remediated by Devin.

Every time programmers get more leverage, doomers predict the end of software engineering. Compilers were supposed to shrink the profession. So was open source. So was the cloud. Instead, each leap made software cheaper to build, and demand for software, and engineers, exploded. It keeps happening because, as Milton Friedman observed, human wants and needs are infinite, so economic demand is infinite, and job growth can continue forever (see The Techno-Optimist Manifesto). Technology is how we unlock that demand. Technology gives us leverage, lowers the cost to produce, and makes previously uneconomical use cases economical. For software, Devin provides that leverage.

It is tempting to think of this as just another application of AI - one vertical among many. But if software continues to eat the world, the automation of software itself accelerates every industry.

This future is core to how we at a16z see the world. We have always believed that software expands markets, that technical founders with seemingly unreasonable ambition create the future, and that small teams armed with powerful tools can change entire industries. Cognition is a powerful expression of those beliefs: a deeply technical company taking on an enormous problem, moving at extraordinary speed, and building for a market whose ultimate size is difficult to comprehend.

Our conviction in Cognition comes from personally knowing Scott for over seven years. We first backed him at Lunchclub, where he and his team built a system for bringing people together and created relationships that have lasted through the years since. Scott is one of the most accomplished competitive programmers of his generation: a three-time gold medalist at the International Olympiad in Informatics and a world champion at 17. But after working with him for many years, we know that his technical brilliance is only part of what makes him exceptional. Scott has an unusual combination of speed, judgment, competitiveness, and leadership. He sees important technical shifts early. He holds himself and everyone around him to an extraordinarily high standard. He cares deeply about his customers, recruits exceptional people, and has built a culture that moves with the urgency of the opportunity. One of Cognition’s values is “every second counts.” They mean it.

An infinite market demands infinite ambition. Scott’s ambition and vision match the scale of the market and we are proud to back him for the second time. Scott, Steven, Walden, Russell and the entire Cognition team are working toward a world in which anyone can turn an idea into software.

I ended my 2011 essay with one line: “I know where I’m putting my money.” Fifteen years later, I still do.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

Every company runs on a CRM, and almost no one likes the one they have. It’s the system of record for how you find, win, and keep customers—the most important software a company owns, and somehow the most dreaded. CRMs are supposed to help drive productivity and efficiency for sales and support teams, while helping deliver insights on how to improve. It turns out they often do the opposite, and the promised “insights” are a stack of dashboards you have to build yourself.

Enter Lightfield, the CRM for the agentic era. That means a fundamentally different data architecture: a flexible data model that needs no configuration, builds itself from your emails and calls in minutes, and fits your business rather than the other way around. The feat underneath is a temporal context graph that captures the why behind every deal. The advantages compound from there: agents and humans work from the same platform through the same API, and the data model scales to track complex, high-touch, multi-threaded enterprise deals that play out over months without a single schema change. The classic CRM headaches, the stale fields, the weeks of configuration, the reports nobody trusts, go away because the data model is finally right.

More than that, Lightfield comes with agents that do the work that piles up around the record, the updates, the follow-ups, the handoffs, the reporting, and pricing follows the work done and value delivered instead of static seats you buy. And because it holds every customer conversation, Lightfield becomes the business world model, shaping what you build next and for whom. In a world where context is the rate limiter, Lightfield delivers a compounding context moat.

Customers have noticed. Since launching late last year, thousands of companies have adopted Lightfield, and teams are ripping out Salesforce, HubSpot, and Attio to switch. As one operator put it after moving off the largest incumbent: “If you’ve only ever ridden a horse, the only thing you ever want is a faster horse. Well, this is a car.”

Special products are built by special people. We’ve known Keith Peiris and Henri Liriani for a long time. They previously built Tome, an AI presentation tool that grew to 25 million users, and when they decided that wasn’t the right strategy to build a massive company, they did the hardest thing founders can do: they pivoted. They shrank the company to a core group of engineers and rebuilt around a problem they had experienced firsthand scaling Tome—there was no AI-native CRM for growing companies.

History says this is how generational systems of record are born. Siebel was born on client-server, Salesforce on cloud, HubSpot on inbound SMB. Each won a greenfield generation of companies the incumbent couldn’t see, and the AI-native generation is being founded right now, choosing its system of record for the first time.

We could not be more thrilled to lead Lightfield’s $47M Series A. If your company is growing fast and the tooling that got you here is starting to break, check them out. And if you want to help build the system of record that humans and agents actually love, they’re hiring!

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

The U.S. needs more long-range missiles.

In the first five weeks of the war against Iran, the United States fired more than a thousand Tomahawk cruise missiles. It did this against Iran: a mid-sized regional power.

The West needs to be prepared for wars against countries such as Russia and China: vast territories with thousands of hardened targets, layered air defenses, and the will and capability to fight for years.

The real challenge is not replacing the weapons that have already been consumed. It is building a new arsenal capable of overwhelming scale.

Europe is in no better position.

Europe’s long-range strike gap is even more pressing. In May 2025, Germany and the UK committed to develop a deep precision strike weapon under the Trinity House Agreement. France, Germany, Italy, Poland, Sweden, and the UK have banded together in ELSA, the European Long-Range Strike Approach, to field a long-range ground-launched missile. And in July of this year, a host of European countries pledged more than $50 billion to build deep precision strike capabilities.

These initiatives are excellent, but they are years away from results.

Enormous distances separate European soil from war-winning targets in Russia, and today, Europe has almost no sovereign way to reach them. If Europe wants to deter, it needs the ability to strike.

Cheap mass transformed the tactical fight but left the strategic one untouched.

When Deputy Secretary of Defense Kathleen Hicks launched the Replicator initiative in 2023, she made affordable mass a mission: thousands of small, cheap, autonomous systems to offset China’s greatest advantage – its sheer numbers. Arming the West with low-cost drones and munitions is one of the most important defense shifts in a generation.

But affordable mass has mostly reached the small end, and while small, tactical drones are deadly, they are not war-winning: Russia and Ukraine have traded them across the front for years, and the line has barely moved.

Even the new class of affordable American missiles is not enough. A hundred-pound warhead can destroy a truck, but it cannot close a port, crater a runway, or bring down a hardened weapons plant. Our adversaries have thousands of these targets, often tucked hundreds of miles behind enemy lines, and each of them needs heavy payloads to be destroyed.

For the West to reach these targets, strategic weapons need the same revolution as tactical ones. We cannot deter with exquisite, expensive weapons built in the dozens. We need greater reach, heavier payloads, and tremendous mass.

Introducing Anthem.

Today, Covenant is announcing Anthem: a heavy-payload deep precision strike weapon system meant to fill the biggest gap in the arsenal of the West.

Anthem delivers multiples of the range and payload of anything in its price class.

Crucially, Anthem is more than just its specs; it was designed to be affordably stored and fielded at scale. The reality of many weapons programs is that the sticker price is often a fraction of the lifecycle cost: bunkers and launchers can cost more than the effectors themselves and are often available in quantities too small for mass fielding. We’ve put as much thought into the storage and handling of Anthem as we have into its performance.

Beyond delivering end-to-end hardware solutions, Covenant is also delivering the mission-planning and command-and-control software to make mass strike missions a reality.

The result is a deep precision strike capability which can be affordably stored, launched, and controlled in the thousands.

Since Anthem’s maiden flight in August 2025, Covenant has flown Anthem in over 200 tests, including multiple flight tests with the U.S. military both on U.S. soil and abroad.

Local Production, Local Supply Chains

In just under two years since our founding, we have raised $250 million, hired hundreds of the most talented engineers and operators, and opened factories to produce Anthem in 3 countries – the US, Germany, and Israel. Our priority is getting thousands of Anthems into the field, fast.

Anthem was designed to be produced quickly and at scale. Most of Anthem’s parts are commercial off the shelf, and where they are not, we did not wait for a supplier to scale up on their own: we either brought the part in house or partnered with the supplier to stand up an entirely new production line to meet our demand.

Covenant is producing Anthems locally for our customers so that they will be ready to launch when each country needs them.

Today, we cut the ribbon on our new 105,000 square foot production facility in Dallas, Texas, home to our American production line and supply chain. Later this year, we will unveil two more production facilities: 110,000 square feet in Germany, which will feature an almost entirely European supply chain, and 30,000 square feet in Israel.

Beginning in 2027, we will deliver thousands of Anthems per year.

We believe Covenant is building the best missile engineering team in the world.

Building an excellent affordable strategic weapon is hard. At our core are some of the most experienced missile engineers in the world, led by my co-founder Ofir Dayan. Ofir understands our customers’ problems as well as they do and has the tenacity to fight requirements that stand in the way of the best solutions. He leads our band of renegades – the engineers who ran as if with ankle weights inside the American, Israeli, and German primes, and still managed to deliver the most advanced offensive and defensive weapons in service. At Covenant they run free.

No less important are the Covenant operations and flight test teams that support them. Flight-testing large missiles is intense and dangerous work, but it’s the way to get a missile into service quickly. Covenant has staged flight tests nearly every week since our founding.

Covenant’s relationship with our customers is led by Abby Denburg, our President and Chief Growth Officer. Abby’s job is not just to sell, though she does that as well as anyone. She is the force converting our customers’ urgency into Covenant’s speed.

This is Covenant.

We’re proud of what we’ve built, but the hardest work — producing at scale, delivering reliably, and servicing our customers — is still ahead.

We open with Anthem.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

We are running out of watts.

AI inference is one of the fastest-growing markets in the history of capitalism, and we are running out of nearly every physical input required to serve it: powered land, turbines, transformers, data centers, GPUs, advanced-node wafers, and high-bandwidth memory. GPU capacity is being leased at record prices, while OpenAI’s and Anthropic’s growth is constrained by how quickly they can expand their compute fleets. You feel that scarcity firsthand: rate limits tighten and responses slow as providers trade latency for throughput to produce more tokens from the same watts.

This is why the largest capital investment cycle in contemporary history is underway. Seven of the ten most valuable companies in the world are hyperscalers or supply them. Five U.S. hyperscalers alone are expected to spend $1 trillion in capex next year. NVIDIA has gone a step further, working with capital partners to mobilize more than $500 billion for AI factories.

But expansion alone will not be enough. Inference demand compounds at software speed; power plants, data centers, and semiconductor fabs do not. Serving the most consequential workload of our lifetime requires squeezing more intelligence from every watt. That starts with recognizing that inference is not one workload.

The End of One-Size-Fits-All Compute

Without the GPU, today’s AI would not exist. Its flexibility enabled the early experiments with transformers. Then enormous clusters built around a single accelerator architecture powered the training runs that proved scaling laws and produced today’s frontier models.

But the diversity of AI applications is exploding. A voice assistant lives or dies on latency; batch processing maximizes throughput; a research agent may prioritize cost per token; a coding agent must balance all three. Even a single model call splits into compute-intensive prefill and memory-bound decode. Agents multiply the variation by routing among small specialized and larger reasoning models, retrieving files, executing code on CPUs, and calling external tools. Inference is no longer a single workload.

At the same time, hardware choices are proliferating. Some architectures optimize arithmetic throughput; others optimize memory capacity, bandwidth, or low-latency connectivity. No single processor can deliver the best combination of cost, throughput, and latency across every application and every step. GPUs will remain foundational, but they will increasingly operate alongside CPUs, memory-optimized systems, and other purpose-built silicon.

The optimal system must match each piece of work to the architecture that handles it best, producing more useful work from the same resources. It must be heterogeneous.

The challenge is making all of this hardware operate as one system. Large-scale infrastructure is designed around homogeneity with standardized servers, networking, power, cooling, and operating models. Introducing multiple architectures creates complexity across every layer. The software must coordinate processors that were never designed to work together, while the physical facility must accommodate different networking topologies, rack densities, power profiles, and cooling requirements. Cerebras and NVIDIA systems, for example, require different inlet-water temperatures. Sometimes heterogeneous compute is a compiler problem; sometimes it is a plumbing problem.

The Multi-Silicon Inference Cloud

Making heterogeneous compute work at scale is key to unlocking what we believe will be the world’s largest market. We believe Gimlet Labs has built the solution: the first multi-silicon inference cloud, designed to produce more intelligence from every watt.

For each workload, Gimlet determines an execution plan that balances latency, throughput, and cost. It can route different models and tools onto different processors, separate prefill from decode, or divide a model at the layer or operation level. Its compiler optimizes each piece for the target hardware, while its runtime coordinates execution across the system. To developers, all of that complexity sits behind a single inference API.

Gimlet extends that orchestration into the physical data center. It integrates GPUs, CPUs, and purpose-built accelerators into one pool of capacity, managing the differences in networking, power, and cooling that make heterogeneous infrastructure difficult to operate. This system is already delivering up to 10X gains in throughput and interactivity on frontier models within the same power envelope.

In a world where scale matters, Gimlet has done this at the largest scale possible, counting both a frontier lab and a hyperscaler as customers. In a market starved for compute, efficiency is net-new capacity and latency is product differentiation.

We believe there is no better team than Zain, Michelle, Natalie, Omid, and James to tackle this problem. They’ve seen this movie before and are one of the few teams that have the range to follow a problem through every layer of abstraction. When kernels and compilers were not enough, they pushed into high-speed networking. When software was not enough, they moved into power, cooling, and data center construction. Wherever the existing stack ends, the Gimlet team starts.

The future of inference is heterogeneous, and Gimlet is building the infrastructure layer that makes it possible. We could not be more excited to partner with the entire Gimlet team.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

Experience > Skills

Charts has already observed that one of the challenges for prospective entry-level hires (other than the fact that entry-level hires have always been a relatively small share of tech hiring) is that existing tech workers appear to be getting older. Why that’s the case is some combination of aging-in-place, plus remote work unlocking a broader pool of talent, plus perhaps some premium for more experienced workers—and while AI may have something to do with it, as well, the evidence for that is pretty weak.

On the experience point, there’s some recent data from Revelio Labs that suggests experience is, indeed, becoming a higher priority for tech hiring:

Since January 2025, tech job postings have seen a marked rise in preferred years of experience, with a concomitant drop in preferred number of skills.

The change is small—~5-10% in both cases–but noticeable, and it’s consistent with the notion that tech is looking for more experienced workers, rather than workers with any particular set of skills, let alone a broad range of skills. That could very well be an AI-driven shift: grappling with a new technology means that extant “skills” are less applicable, and the new set of skills is still a bit of a whitespace. When you’re operating on a new frontier, you don’t yet know what you’ll need, other than the need to be able to figure it out—and that’s where experience goes a long way.

If you look at the types of jobs that require the largest premia to experience, it does seem like AI is the likely driver:

It’s data and systems work (and Java) that dominate the leaderboard for the recent hiring premium to experience.

AI has created new urgency around data wrangling, but the shape of wrangled data (and the process of wrangling) has changed dramatically, with optimal solutions still very much a moving target. If you don’t know what needs to be done, then the skillset hasn’t been defined yet, and someone who can figure it out as they go is the better choice. Eventually, though, as implementation matures, one would expect new skills to be defined, and it’s likely the skills/experience mix will shift again.

Netflix Having a Blockbuster Moment?

A funny thing about social media consumption is that aggregate time spent in the US is still rising (albeit barely), but not for everyone:

According to Apptopia, aggregate time spent on social media apps has risen (after a small drop), but it’s really the 17-25 yos doing all of the work—the folks 36+ appear to have tapered off their social media time since October ‘25.

But that’s not actually the funny thing.

The funny thing is that while social media usage is rising, social media’s share of screentime has actually dropped pretty substantially of late:

Since April of this year, social media’s share of mobile has dropped ~3pp. OK, perhaps “substantially” is a bit of a stretch, but after years of steady growth (including double-digit growth as of last year), the dropoff is meaningful.

So people are spending more time on social media, but even more screen time on other things. What other things? Apps perhaps. AI could also be drawing more attention. But a more likely culprit for social media’s diminished share of mobile is none other than short-form video, particularly via YouTube.

OK, great, so more social media, more YouTube, and more screentime overall–a win-win-win for staying glued to our phones. That’s true so far as it goes, but there does, in fact, appear to be at least one victim of YouTube’s assault on our attention-spans, and that’s Netflix:

Again, according to Apptopia, the number of YouTube users who also use Netflix has declined ~20% since the beginning of the year, while the number of Netflix users who also use YouTube has remained pretty steady.

YouTube viewers seem more inclined to live without Netflix than the other way around.

In general, Netflix’s active users and viewing time in the US have both been in mostly serial decline for the past 3+ years:

Weekly users and time spent for Netflix have dropped ~15% and 30% respectively.

Why that’s the case is anyone’s guess. Among other things, it could be the rise of short-form video, more streaming competition, or streamflation more generally. As a possibly relevant aside, Netflix stands out for having the highest customer LTV by far, at least according to this estimate from Owl & Co.

A Netflix premium customer has an LTV of nearly $1,500 per customer, which is nearly 2x the next best offering.

LTV reflects a combination of higher subscription prices and strong retention—good for Netflix—but one could see why customers might begin to balk at the price. One could also see why Netflix is branching out into live sporting events and clips and shorts of its own.

Bigger picture, though, it’s fair to wonder if Netflix is having its Blockbuster moment, roughly two decades after Netflix gave Blockbuster its Blockbuster moment. It’s too soon to tell, of course, but in a different timeline, where AI was not such a big deal, this would be a much bigger deal.

Cyber Game Done Changed

Have you heard of the ‘Hugging Face Incident’? I mean, you probably have, but if you haven’t, you probably shouldn’t, and then you’ll sleep easier at night. (Old heads might be reminded of the Noodle Incident, but sadly, these are not the same.)

Anyways, HFI aside, there’s plenty of recent data to suggest that the threat landscape for cyber events has changed dramatically. Consider this nightmare fuel from Epoch AI:

The number of reported critical and high severity vulnerabilities from major firms has gone vertical this year, since the beginning of the spring.

Whether there are actually more vulnerabilities, or whether there’s some reporting or definition change, it’s hard to say just by looking at the chart, but it’s likely that at least some (and perhaps a lot) of this parabolic growth is attributable to a major substantive change in either how vulnerabilities are discovered and/or what is still considered secure.

Here’s another bit of nightmare fuel for the cyber-minded:

According to the ZeroDayClock, the Zero-Day Rate (i.e. the percent of vulnerabilities exploited on the day of disclosure (or before)) has basically quadrupled since 2020—including a ~60% increase from last year alone. The Zero-Day Rate is now just under 87%, with a median time to exploit at just one day . . . but projected to reach 1 minute sometime next year.

To repeat, the ZeroDay estimate is that it will take only a minute between discovery and exploitation of a vulnerability (at the median) by sometime next year.

Even now, the “Exploit Survival Curve” falls to 0% at just 1.5 months:

Back in 2022, a full 50% of exploits were still there for the taking after 1.5 months (and at 3 months, too), but now, if you haven’t plugged all the holes, by then, someone else will have already found them.

Clearly AI has changed the game, not only for identifying vulnerabilities, but also exploiting them. The good news is that AI will also change the game for the defense.

Either way, the urgency for cyber has intensified, and as Charts has observed before, investors are way out in front of this one:

Of the $IGV constituent stocks, Cyber has massively outperformed the field, and forward revenue multiples are consistently higher, as well.

Whether and to what extent the incumbents successfully step into the breach, or whether some new cyber platforms emerge to face the challenge, remains to be seen, but there are surely going to be a whole bunch of new sheriffs in town (or else).

Data Center Job Boom Continues Apace

One final aside on the economic driver that is the AI buildout.

BLS released its latest estimate of data center construction spending, and while there was no reason to think otherwise, the pace is blistering:

Data center construction spending jumped by ~$25B in just half a year, which is roughly the same amount it jumped over the past two years.

But, more to the point, alongside the jump in construction spending is a jump in construction job openings, which have otherwise been in steady decline since interest rates went up, and most other construction slowed substantially.

Goldman estimates that ~300,000 construction and specialty trade workers have been added to the workforce since 2022 that are attributable to the AI buildout, with nearly 75,000 of those coming in the past year alone:

Again, while the overall trend for hard hat workers has been directionally negative since rates went up, that trend has reversed to the upside since the middle of last year.

Like ‘em or not, data centers are major jobmakers, bringing a pretty wage premium, as well—and it’s fair to say that without data centers, the employment picture, especially for blue-collar types, would be substantially worse.

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The bullish case for incumbent systems of record is that AI makes systems of record get more important, not less. Why? Because in theory the customer can now pipe the system’s data into Claude or Codex and skip the AI-native app.

As agents do more work through these systems, the data and actions they control become more valuable. An incumbent system of record plus a general agent (or an agent built by the incumbent) therefore might be enough to get the work done. This is what Salesforce just announced in partnership with Anthropic: Claudeforce lets you work with the Salesforce CRM from inside Claude without having to open Salesforce (or so they claim!).

The argument is partly correct. The incumbents aren’t sitting idly.

The incumbents have an incentive to control and charge for access to their data, and now to push their own agents forward too. We’re already seeing incumbents market products that move from simply storing information toward doing more of the work itself. Docusign’s Iris reviews contracts, Atlassian’s Rovo aims to resolve and route requests, and Klaviyo’s Composer builds campaigns and assesses marketing performance. These products move incumbents beyond the “slap on a chatbot” strategy and into taking action.

At the same time, Claude is starting to coordinate work across applications, making it possible for general-purpose agents to act as a layer above the incumbents. Claudeforce shows what that can look like: Claude can become the front door to the job while Salesforce still controls the underlying CRM data and actions. The interface and the system of record look like they’re unbundling, creating an opening for AI-native startups to rebundle around the job.

So where can startups still compete?

The vertical AI-native company has to win through focus. It has to perform a specific cross-system job better than an incumbent’s purpose-built agent or a general-purpose agent like Claude, which will be able to reconstruct it from the underlying tools. Focus can earn the vertical startup deeper access, a deliberate data asset, more specific context, and a learning loop that builds a better understanding over time of what good work looks like for a given job.

The agent hierarchy

To see where incumbents are strongest and where vertical AI companies may have an advantage, it’s helpful to think about the four types of application-level agents, distinguished by how much autonomy and judgment each requires.

Retrieval assistants find information, summarize documents, answer questions, and draft responses. Process agents carry out rule-based work like updating records or routing approvals, using limited judgment to interpret the task, and act. Policy agents apply the rules in an organization’s playbooks, precedents, and thresholds to ambiguous cases. Principal agents make judgment calls about what the organization should do, often with open-ended tradeoffs around strategy, risk, and resources.

A year ago, most incumbents’ AI products were still at the retrieval stage, if that. They functioned mostly as chatbots and analytical tools pulling from existing records. Now, more can take action based on the workflows, permissions, and rules already embedded in their products. A few are beginning to apply narrow, human-defined policy. Judgment gets harder when the decision depends on information outside the record that the incumbent manages.

The broad shift we’re seeing is incumbents moving up the hierarchy, from retrieval and process toward more judgment through policy, like Docusign’s Iris applying a legal team’s playbook. But that judgment is still largely bounded by the record the incumbent owns.

The job is bigger than the record

An incumbent can automate more of the work around its core record, but the customer’s job to be done is bigger than the record. This isn’t a hard technical boundary because incumbents can pull in outside data and add workflows, but their work still expands outward from the record they already own.

A customer’s work gets done through a combination of its people, processes and software. The contract is not the legal matter, the ticket is not the customer’s resolution, and the opportunity is not the sale. The full job crosses applications, teams, and even companies. Different parties may hold different information, and want different outcomes. The incumbent is limited to building a “local system of work” but the larger opportunity is to manage the job across boundaries, including all of the parties, the people, the documents, decisions, revisions, and the final result.

A general purpose agent like Claude or Codex may be able to reach across all these systems, but access alone doesn’t mean it can perform the job well. Even using MCP, pulling info across applications has latency, and each application may describe the same customer, contract, or transaction differently. Even if a general purpose agent can access every app in one company, it doesn’t automatically have access to information held by external parties (e.g. between buyers and suppliers).

Even with these limitations, Claude can still sit above systems of record. Claudeforce makes Claude the front door to the job while Salesforce still owns the CRM data and actions. And Claude can keep gaining context through deeper integrations blessed by the incumbents.

How vertical AI learns

Owning more of the job matters because it lets startups see the decisions and corrections that produced the final result. A model performs better on a specific job via a vertical harness with the right context, tools, workflows and evals. The learning loop is what improves the system - the model + harness - after each job, using the agent’s work, expert feedback, and outcomes themselves to improve the next attempt.

Completed records alone don’t automatically suffice as a training curriculum for the work. A signed contract shows what the parties agreed to, but not every alternative considered or every reason for an exception. A closed ticket shows the resolution, but not every hypothesis the support team tested.

Remembering vs. learning

The labs are also building general-purpose memory, so preserving context alone is not a durable advantage. Remembering and learning are different. Memory can help a general purpose agent recall a customer’s preferences or a decision made last week. But it doesn’t tell the agent whether the work was good or why an expert changed it or what to change next time. Focus lets the company see more examples of the same job, learn faster, and get better at measuring improvement.

Profession and Institution

There are two kinds of learning. Learning how a good professional does the work. A company can begin teaching that with assignments created by experts, realistic examples, and standards for a good result.

Learning how a particular firm or customer does the work, including its templates and precedents, risk thresholds and escalation rules. That can begin with the customer’s existing materials and improve through corrections and exceptions in the training process. Some lessons about the profession can improve the product for every customer, but lessons about one firm may just improve the product only for that firm.

To learn both the profession and the institution, the startup doesn’t need to start with the largest stock of historical data. It can manufacture a curriculum for the profession, then use production to learn the institution. As open-weight models and post-training increase, assembling the right data and evals becomes even more important.

Long running agents especially benefit from learning loops. When an agent does work over hours or days, through a complex logic chain with many intermediate decisions and handoffs, the quality of the learning loop matters even more. The longer horizon task can be unbundled and evaluated through a set of checkpoints.

To be clear, incumbents will also learn from the work performed inside their products. But that learning will cover only their part of the job: the contract review inside Docusign, the ticket resolution inside Jira, or the pipeline update inside Salesforce.

How Harvey manufactured the curriculum

Harvey’s work on Tenet shows what the path for vertical AI can look like. Harvey post-trained an open-weight model using synthetic data, public legal data, and human-expert created data, without using customer data to start. It created roughly 1,750 legal-task environments, each simulating a partner-assigned matter, complete with documents and tools and an expert rubric for a high-quality result. The average assignment had about 50 specific criteria. Instead of waiting years to accumulate enough customer history, Harvey manufactured a curriculum it could use to train and evaluate its model.

Harvey’s broader work also shows how a vertical company can train a model for different parts of the job, including industry-specific capabilities like M&A diligence and understanding a firm’s total knowledge.

The startup’s opportunity is to put it all together. Vertical specificity helps the startup to break the work into specific capabilities, tackle the practical frictions around data rights, systems, and permissioning, and fits the product into the customer’s daily work. Eventually, it can feel comfortable taking responsibility for the result. Deployment and responsibility are key here!

What makes a good vertical AI market?

Using Harvey as one example, here are a few axes for evaluating potential vertical AI markets.

• Can an expert quickly tell what the AI got right or wrong, and explain how to improve it?

• Is the work hard enough that judgment matters (vs. simple rules)?

• Does the work happen often enough for the product to learn?

• Can the startup begin with one assignment and grow into doing an entire job?

The best vertical AI markets tend to meet all four, giving them a strong learning loop.

Many workflows in legal, tax, and accounting meet this test because the work repeats, requires judgment, and experts already review the results. This pattern also shows up in less obvious markets like industrial work. When a manufacturing defect appears, a quality engineer has to gather test results, supplier documents, equipment logs, and the plant rules, then decide what’s acceptable and what needs to change. That information may sit across several systems. One incumbent may store the defect report, but no single product owns the full investigation. A startup could begin with one piece of this work (e.g. drafting the investigation report) and over time own the end-to-end resolution. To do this, it would need to learn both how a good quality engineer investigates a problem and how a particular plant or customer or auditor does it well.

The job is still up for grabs

There are jobs where a system of record plus a general-purpose agent will be enough, but there’s still meaningful opportunity for vertical AI-native startups! The strongest vertical opportunities exist where the work happens often, expert judgment matters, and the learning loop is strong. The incumbent may own the record and the lab may control the front door, but the vertical AI company can still win by becoming the best at doing the job itself.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

Everyday life on Earth depends on space, whether you know it or not.

In the last 24 hours, satellites helped you check the weather, navigate to work, communicate with others, fly across the country, or support those defending our country. Our lives do not just touch space — they rely on it. If today’s satellites stopped working, the world as we know it would grind to a halt.

At Apex, we believe space is already foundational infrastructure for Earth — and this is just the beginning. Space already supports the core systems that modern life depends on. The next era goes further: innovation, economic growth, and defense will move faster because of space, unlocking capabilities beyond what Earth-bound systems alone can achieve.

For most of space history, progress was measured in firsts: first satellite, first human in space, first Moon landing. Those milestones mattered. But the next era will be measured in rate: how fast countries and companies can deploy new capabilities, replenish what is lost, refresh technology, and expand the infrastructure they depend on.

That is the new Space Race: not a race for singular achievements, but a race for resilient infrastructure, industrial capacity, and continuous technology refresh.

To make that future real, the world needs constellations: many satellites working together as systems. That requires two industrial capabilities: launch and spacecraft platforms.

Launch is ready enough. Spacecraft platforms are not.

Apex exists to remove the spacecraft platform bottleneck.

Our mission is to expand humanity’s reach by delivering advanced spacecraft platforms that scale. In practice, that means making constellations rapidly deployable for the markets that need them most.

For most of the space age, launch was rare, expensive, and unforgiving. That scarcity forced missions to be slow, bespoke, expensive, and optimized around one big shot at success. The old model was not broken. It was built for a different constraint.

Then launch became more reliable, more frequent, and more available. Not necessarily cheaper from a customer-price perspective — demand for launch has remained enormous, and prices have risen. But launch capacity, cadence, and reliability changed dramatically.

A ride to orbit every year forces scarcity. A ride to orbit every day creates new models — but only if you have satellites to deploy. As launch became more available, the constraint shifted from getting to orbit to building enough capable spacecraft platforms to use orbit.

That is where Apex plays.

Constellations Are the New Architecture

Constellations are not just more satellites in orbit. They are a fundamentally different way to deliver capability from space.

With more satellites, you cover more of Earth more often. With systems closer to Earth and distributed across orbits, communications move faster. If one satellite is disabled or degraded, the rest of the constellation can help fill the gap. And when satellites are continuously replenished, new technology gets to orbit faster instead of waiting years for the next generation.

This is how space becomes rapidly deployable operational infrastructure: not one-off missions, not isolated assets, but systems that can be deployed, expanded, refreshed, and relied upon.

In defense, that means architectures that are harder to blind, difficult to disable, and faster to replenish. In commercial markets, it means new services can be deployed and improved continuously. For allied nations and emerging space powers, it means access to capabilities that once required decades of bespoke development.

The advantage is no longer only who has the best single satellite.

The advantage goes to whoever can deploy, sustain, and improve their constellations the fastest.

The Platform Gap

Starlink and Amazon Leo are prominent examples of the shift toward constellations: many satellites working together as a system rather than relying on a single asset. More broadly, constellation architectures can offer important advantages over single-satellite architectures, including greater coverage, lower latency, increased resilience, and faster technology refresh.

But these mega-constellations were built for narrow use cases.

Their spacecraft, payloads, network architecture, ground systems, software, and customer experience were designed together around one mission. They are not configurable platforms for the rest of the market.

Most critical space missions need a configurable platform approach. Governments, defense primes, allied nations, and commercial operators often bring their own payloads, ground systems, C2, software, customer relationships, or mission architectures. They need the benefits of constellation-scale deployment without building an entire vertically integrated space company around one use case.

That is the platform gap.

Apex is doing for the rest of the market what vertically integrated constellations did for their own missions: turning repeatability, integration, and scale into deployable capability.

That is the category Apex was built to define.

Build Once. Configure. Deploy Many.

A true platform creates a standard that many missions can build on.

For Apex, that means configurable spacecraft platforms, standard interfaces, clear specifications, integration processes, mission services, and the manufacturing system required to move customers from payload to deployed capability faster.

Apex does not build one-off platforms for every mission. We build an end-to-end, configurable product suite for markets that need constellations.

Apex says yes to missions where scale matter: missions that require many of the same spacecraft platforms, where speed, affordability, quality, and production capacity determine whether the architecture works at all.

Defense, intelligence, communications, sensing, science, commercial infrastructure — the specific mission can vary. The core question is the same: does this market need many satellites, deployed quickly and reliably? If yes, that is where Apex belongs.

Productization eliminates platform-level NRE, improves reliability, accelerates schedules, and creates the Apex flywheel: every platform we build makes the next one faster, more affordable, and higher quality. Volume compounds into learning; learning compounds into better platforms; better platforms win more missions; and more missions spin the loop faster. Every one-off change that breaks the platform resets the learning curve, reinforcing how repeatability becomes a structural advantage, not just an operating philosophy.

The flywheel compounds across product generations. Aries gave Apex flight heritage, production learning, and the foundation for our software and AI orchestrated company operating system, Octopus. Nova builds on that foundation with more capability, stronger price-performance, and a system designed for larger, more demanding customers. Each new vehicle and block upgrade inherits common interfaces, subsystems, software, production processes, and lessons from the last — then feeds improvements back into the platform through block upgrades.

The factory is not separate from the platform: it is the core of the platform. A productized spacecraft, Octopus, and a repeatable production model make it possible to scale beyond a single facility. Over time, Apex extends this model through a global network of Apex-enabled factories — all building from the same standards, software, interfaces, and quality system.

Customers should differentiate on payload, mission, CONOPS, software, and capability — not on custom platform development that slows them down before they ever get to orbit.

We standardize everything that should not be unique, so customers can focus on what must be.

That is how a product becomes a platform. And that is how a platform becomes a standard.

The Apex Industrial System

Moving at constellation speed requires more than a spacecraft platform. It requires an industrial system.

Apex vertically integrates because the logic of the mission demands it: speed, reliability, affordability, quality, and time to market require control over the systems that determine whether customers can deploy on schedule.

Octopus, our proprietary company operating system, connects business development, product, finance, engineering, supply chain, manufacturing, test, and delivery. It gives Apex the control, traceability, and operating rhythm required to build spacecraft platforms repeatedly, reliably, and at speed.

This is the Apex Industrial System: vertically integrated hardware, software-orchestrated operations, and a factory built from day one to scale. Vertical integration controls the parts. Closed-loop industrialization controls the system — and lets the flywheel spin faster.

The Apex Way: Own the Risk

Speed is meaningless if customers pay the price for it. That’s how the satellite industry works. That’s not how Apex works.

Every new spacecraft platform begins as an Apex Testbed vehicle. We move aggressively — designing, building, flying, learning, and iterating as quickly as possible. We “test like you fly.” We also believe in something equally important: learn in flight. Ground testing can only take you so far. Space is the final test environment.

We intentionally take that risk ourselves and launch Apex-owned spacecraft early in every product family to expose issues, learn quickly, and improve the platform. Those are our risks to take — not our customers’. When we encounter anomalies, we solve them, fly again, and keep learning until the platform is ready.

Customers don’t wait to buy the platform. They know that by the time their mission launches, Apex will already have proven it in orbit.

This philosophy is why Apex moves quickly without asking customers to accept unnecessary risk. We move at an unprecedented pace. We absorb the learning. We fly again when necessary. Only after the platform has proven itself do our customers become the first to fly it on their missions.

Design, produce, test, learn, iterate… Apex absorbs the risk, and then passes the proven spacecraft platforms to our customers.

We absorb the risk so our customers inherit proven spacecraft platforms. That’s the Apex Way.

From Platform to Mission

The platform lays the foundation. Mission Integration Engineering takes the foundational platform and turns it into the backbone of a customer mission.

Productization does not mean every mission is the same. Customers bring unique payloads, requirements, interfaces, CONOPS, and constraints. Apex’s Mission Integration Engineers keep the platform sacred, while helping customers connect payloads, resolve interfaces, reduce NRE, and accelerate qualification. By keeping the platform constant and engineering the payload-side compatibility, we help customers get from payload to deployed constellation, fast.

For some customers, that means a spacecraft platform and integration support. For others, it means software, communications, ground, operations, and more of the end-to-end mission stack. Apex meets customers where they are while keeping the platform productized.

As the platform becomes the core of a constellation, there will be missions where Apex should selectively own the entire stack — including prime responsibility — because doing so makes the mission faster, more integrated, and more deployable. We will be selective. The goal is not to chase every mission; it is to own the missions where the scale of the platform is the key to making the constellation real.

Standardize the platform. Integrate the mission. Deploy at constellation speed.

Proof in Orbit

This is not theoretical.

Apex has already proven that a productized spacecraft platform company can move faster than the traditional model without sacrificing quality. The first Aries went from clean-sheet design to orbit in under 12 months and has operated on orbit for over two years. Since then, Apex has achieved 100% customer mission success and delivered over a dozen platforms to customers, while continuing to ramp Factory One production to over 200 platforms per year.

Speed is not the antithesis of quality when speed is achieved through repeatability.

We are not just designing satellites. We are building the platforms, factory, software infrastructure, supplier base, mission integration capability, and operating model required to make constellations rapidly deployable.

Apex’s Strategy

The strategy is simple, but difficult: own the world’s spacecraft platform category.

We do that by empowering exceptional talent to build an end-to-end product suite for markets that need constellations; scale ahead of demand through vertical integration; and win on speed, reliability, and affordability.

We stay focused by refusing to chase every custom opportunity.

By enabling our customers, humanity’s reach expands. Space infrastructure becomes deployable, not theoretical. Constellations become accessible to customers who could not — or should not — build the entire stack themselves.

Space is becoming foundational infrastructure for humanity.

The next era depends on making constellations rapidly deployable.

The world does not need another satellite manufacturer.

It needs the spacecraft platform company that makes constellations possible.

That is Apex.

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This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z; visit https://a16z.com/investment-list/ for a full list of investments. Other important information can be found at a16z.com/disclosures. You’re receiving this newsletter since you opted in earlier; if you would like to opt out of future newsletters you may unsubscribe immediately.

America | Tech | Opinion | Culture | Charts

Today’s guest post is by Ruby Thelot, a professor at NYU and astute tech observer. You can watch his recent appearance on MTS here.

Every year, more people use and spend more time on Big Tech platforms. It is fashionable to hate on social media and to pontificate about how it’s worsened, because the intellectual accoutrement du jour is being a conscientious (™) tech critic. This stated preference does not match the general public’s behaviours.

“Enshittification” describes the progressive deterioration of platforms in service of business goals over user needs. In the writer Cory Doctorow’s original definition from his 2022 blog post, he uses the term to describe how platforms die: “First, they are good to their users; then they abuse their users to make things better for their business customers; finally, they abuse those business customers to claw back all the value for themselves. Then, they die.”

I do not believe, no matter how compelling it is as a theory, that it holds up to scrutiny. Simply put, more people use Instagram every year, they use it on average longer every day, year over year and, finally, their reported satisfaction has also grown over time, indicating no decrease in overall satisfaction, stated or revealed.

For our purposes, let’s focus on Instagram, whose logo was just redesigned to many online commentators’ chagrin. Certain people just hate when platforms change.

People en masse are getting on social media, by choice. From 2016 until now, the number of social media users has grown from 2.5B to close to 6B people. Enshittification isn’t real. It’s three TikToks in a trenchcoat, and, maybe, a book deal. It’s good for discourse but does not describe actual reality and user patterns.

Returning to Instagram. The average daily usage showcases an increase in time spent on Instagram every day as well. Users are coming back for more, in a highly competitive attention arena, year after year.

To be fair, certain people do feel like platforms have gotten worse, this perceived feeling describes the progressive dislocation between the platform’s main features and the user’s needs. Let’s take Facebook for example. Doctorow was probably in his thirties when Facebook started, he probably lived in an urban area, he had a specific set of needs, and that set of needs (or user goals) was met by Facebook, namely adding friends, at some point sharing images, and communicating through the wall.

Instagram has indeed changed. A look at its interface over the years showcases severe and notable differences as it evolved.

In its first five years, icons were altered, their positions were switched.

Its main functionality also changed. Notably, the feed went from chronological to algorithmic, to many’s chagrins, but algorithmic feeds show users the things they are predicted to engage with. The UX has been altered because the function of social media has changed. People now use Instagram as a means to get “entertainment”, it is no longer purely “social” media, as it used to be in yesteryears. Instagram has now taken on the role of social coordination as well. Events, book clubs, concerts all use the platform to broadcast logistical updates and keep community members informed. The “channel” feature is a good demonstration of this need. Nonetheless, the amount of time people spend on social media is increasing every year. The revealed preference is clear.

Facebook is a good example of that shift. If you pull the timeline of features that were dropped on Facebook over time—the Wall (2004-2011), the introduction of News Feed in September 2006, the Like button (2009), the shift to an engagement-based algorithm in 2009, Timeline itself in 2011, Story Bumping in 2013, and the prioritization of friends and family over brand pages in 2015—you can see that at some point, Facebook changes from being a tool that’s used specifically by college students and young urbanites to something much bigger in order to accommodate a larger and more global public. The platform evolves as its user base diversifies.

In other words, platforms start with a pretty constrained set of personas, i.e. a narrow set of user goals, but as the user base grows, as the number of monthly active users increases, there is a multiplication of personas, and therefore, a multiplication of goals. The job of the product managers becomes more difficult as the user goals become more varied, more heteroclite.

There is a third and more pragmatic argument against the enshittification hypothesis. At a company like Meta, platforms are shaped by the emergent outcome of thousands of small, motivated, data-driven product decisions. A large platform is, in practice, an enormous behavioural machine. Growth PMs, designers, researchers, and executives are constantly watching what people actually do. An interesting recurring instance of platform narcissism is that with each rollout, tech critics and certain people on social media do complain. Undeterred, the company rarely rolls things back and lets the numbers play out. Oftentimes, they expose the gap between stated preference (what people say they like and dislike) and revealed preference (how they behave numerically on the platform).

People complained about Stories, Reels, algorithmic feeds, recommendations, the intrusion of shopping and ads, suggested posts, and then, through the blunt and vulgar clarity of revealed preference, they still use them. They do not have to. The platform changes because the quantified flows of user behaviour pull it in that direction. Of course, some of these decisions are balanced with business goals, this tightrope act is quite literally the job of the PM. The point being that “I hate this” is not the same thing as “users hate this,” and “everyone I know hates this” is often just another way of saying “my cohort is no longer the platform’s central desired persona.”

This last segment is the crux of platform narcissism and the dislocation felt by early adopters in the West. This is especially true on large platforms, where you have global growth, and, thus, geographic diversification. Simply put, users in the Philippines don’t want the same things as the users in an urban centre in America want. Even at a smaller scale, users in Missouri similarly don’t want the same thing as a user who lives in Bushwick.

What people call enshittification is the feeling of feature-need dislocation. In this case, it is felt by mostly geriatric millennials. The platforms are more popular than ever. You can see it in the Monthly Active Users (MAU) growth mentioned above, 140% in ten years. The revealed preference is that people are using them more and more. Moreover, satisfaction surveys such as ACSI indicate that satisfaction with social media has remained consistent in the last 8 years, with a slight increase in the last three years, from 2020 to 2025, satisfaction grew from 70 to 75 (out of 100).

Contrary to the enshittification narrative, when broken down by platform, all platforms show increases in satisfaction. Usually, with contentious issues, one expects a divergence between stated and revealed preference, when something is perceived as more virtuous, they may claim that they do it, but in reality, their revealed behaviour showcases that they do not follow their claim. This is notably not the case here, both usage and claimed satisfaction have increased.

A misaligned platform could in theory trap its existing user base, but it would fail to attract new ones. Doctorow claims that platforms favor advertisers (or whomever they get their money from) and often price gouge their users, but, with social media, the big social media platforms are still free. The broad behavioural and satisfaction data do not support the sweeping claim that platforms are simply getting worse for users as a whole. Moreover, we would see decreases in overall use (time spent) and DAU numbers. The claim that the overall quality has decreased is subjective and specific to a narrow demographic of tech critical users such as Doctorow.

Platform Narcissism

Platform narcissism: the belief that the platforms one uses should continue to respond to one’s idiosyncratic user needs.

Perhaps the reason why the platform seems worse is because your life has changed. Over the same period of time, your goals have changed, and they no longer fit with the platform’s new features. Why the hell would a fifty-five year-old be watching Reels? Go take care of your kids.

The platform needs to respond to the needs of new users, who, from a demographic standpoint, often outnumber the original early adopter cohort and are now usually more engaged. Because they have different desires, different wants, executives make a conscious choice to expand in the direction which they think best suits their business goals and aligns with newer cohorts. This is the dislocation which causes the feeling of enshittification: the evolution of the platform away from a group or an initial user base.

Many will claim that platforms have trapped users, that the cost of departure or logging off is too onerous. If that were the case, this would be reflected in the satisfaction scores. Services with treacherous lock-ins tend not to score well in consumer satisfaction. My theory is that the dislocation is most felt by the chronically online, which includes the media class. Due to the nature of their work, they feel trapped. However, for the average Instagram user, who has ~675 followers, this isn’t a real problem. Additionally, as one can see from the per-platform chart, many options exist. People have the power, the autonomy, and the agency to pick the one that best serves them. Anecdotally, when it stopped serving them, Western millennials and Gen Z left Facebook.

The more interesting question, then, is not why platforms change, but why the language of platform decay has become so emotionally satisfying and so virally successful. A person can believe, all at once, that Instagram is worse, that using Instagram is embarrassing, that the platform is culturally corrosive, and that they personally are still using it more than ever. At the heart of the belief in platform decay is a form of cognitive dissonance. Many detractors of platforms are still very active users of the platforms they supposedly hate.

Hypotheses like enshittification provide an absolution for the platform narcissists. It is safe to wager that the platform narcissist is still online, they’re still using the platforms, but they need terms like enshittification to encapsulate their feelings and explain why they are still on the platform. The enshittification narrative resolves the contradiction by relocating agency towards the platform. Often, highlighting that users are trapped on these awful platforms against their will. Crying-worse allows the user to use the platform performatively half-heartedly and outsource the guilt. Critics such as Doctorow offer then a psychic service, a sort of societal exutory for all the amassed angst felt by platform narcissists. They are high priests of the terminally online platform narcissists and their indulgences are views on short form content railing about platforms and book sales.

In sum, the platforms are not worse. There is no such thing as enshittification. It is merely platform narcissism. What is platform narcissism? It is the belief that the platforms you used for the last few years should continue to respond to your specific needs. And frankly, they don’t need to.

Your favourite restaurant can stop serving your favourite dish. Your favourite store can stop selling your favourite brand. It’s just the way it is. These things change. And, for some reason, we think we’re beholden to some kind of static nature of these platforms because we were on them when we were sixteen or twenty-five. No. They’re going to evolve. And if you don’t like it, uninstall them, and move on. Vote with your thumbs, as they say.

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