企业为何正演变为一系列循环 | Anish Acharya（a16z）

Brief Description

Lenny Rachitsky talks with a16z general partner Anish Acharya about why AI should inspire more ambition than fear. They examine the idea of companies becoming nested loops of agents and humans, why automation reaches local maxima without human intuition, how different models fit different kinds of work, and why consumer AI should aim beyond productivity toward connection, creativity, and happiness. The conversation also covers model choice, startup moats, distribution, ambitious company building, learning by shipping, and practical advice for product builders.

Table of Contents

• The “Permanent Underclass” and the Case for Optimism
• Reorganizing Companies Around AI
• Companies as Cascading Loops
• Choosing Models for Different Kinds of Work
• Building Intuition by Making Things
• The “Make Me Happier” Loop
• AI, Human Agency, and Expanding Ambition
• Three Consumer AI Opportunities
• Moats, Craft, and Distribution
• A New Ceiling for Company Ambition
• Advice for Product Builders
• Books, Products, Life Lessons, and Music

The “Permanent Underclass” and the Case for Optimism

Lenny: I want to start with the meme of the “permanent underclass.” People joke that if you fall behind on the latest AI tools and do not become extraordinarily productive, you will permanently fall behind. A lot of people seem to take that seriously, and it stresses them out. How real is that concern?

Anish: Not very real. It is a funny, dark fantasy that Silicon Valley collectively seems to have. By almost every measure, things have never been better: opportunity is broadly distributed, extraordinary technology is widely accessible, people are allowed to have more ambitious goals, and many independent companies are succeeding. Yet people from foundation-model researchers to ordinary Silicon Valley workers worry about falling outside the light cone.

The previous era of technology was much more centralized. Network effects were the gold standard in the mobile era, and those businesses were definitionally winner-take-all networks. In the current era, nearly every layer of the stack has not two relevant players but perhaps twenty: frontier labs, open-weight models, and many variations within both. Two years ago, coding agents looked like a winner-take-all market, but Claude Code, Codex, Lovable, Replit, Wabi, and others are all working. That is encouraging.

The economic evidence does not support the permanent-underclass story either. Radiologists have supposedly been about to disappear for roughly twenty years, yet job postings are higher than ever. The same has been said about programmers. There is also a lot of discussion about recursive self-improvement, but sophisticated people at the labs describe what is happening as autocatalytic improvement: new technology improves the process that creates the next technology, but it is not truly recursive in the runaway sense. The empirical and technical evidence both point away from a single runaway winner, yet we cannot let go of the fantasy.

Lenny: Recent stories about models doing unexpected things make me think we are in a slow-takeoff scenario. Each milestone surprises us, but we catch it, observe it, and iterate. The fear is that tomorrow intelligence suddenly takes off and we are in trouble. You do not expect that?

Anish: The fast-takeoff argument often says that everything proceeds as it has until something no one can articulate happens, followed by sudden takeoff. I do not believe that. Model progress is faster than ever, but economic diffusion is slow. I grew up in a small town and visited last summer; people’s daily lives had not changed very much. That rate of diffusion is itself a brake.

We also overestimate how many problems are intelligence-bound. If you put a data center full of PhDs inside FedEx or Domino’s, would it exponentially dominate supply chains or pizza? Probably not. Many problems are constrained by physical systems, organizational realities, human relationships, and other factors rather than intelligence.

Reorganizing Companies Around AI

Lenny: Inside companies, do you see a growing divide between people who embrace these tools and those who feel too busy or stressed to learn them?

Anish: We do not give the average employee enough credit. We caricature the white-collar manager as someone shuffling paper and the average consumer as a low-agency NPC. We assume everyone else’s job is automatable while ours is uniquely complex. In reality, many people are excited to improve themselves and gain leverage.

That is true at sophisticated companies such as Google, but also at Kavak, which sells used cars in Mexico. Kavak has a “Jedi Academy” that teaches everyone, including mechanics, to use new tools. At the end of a six-week course, participants ship a cutting-edge production agent. More people are embracing the technology than the public discussion suggests.

The important distinction is between using AI and reorganizing the entire company around AI. Electricity offers a useful analogy. It took roughly forty years to move from introducing electricity to redesigning factories around it. At first, companies simply swapped an electric power source into a factory organized for coal. The ambitious companies eventually rebuilt the factory. The most ambitious companies today are rethinking their organizations around models; earlier or less ambitious adopters are giving existing teams and job functions access to new tools.

Lenny: So people can be less stressed about the future of their jobs and careers?

Anish: I think so. Consider the incentives of CEOs and executives. Sundar Pichai does not want merely to operate a more efficient four-trillion-dollar Google; he wants to build a forty-trillion-dollar company. If a unit becomes more economically productive, the rational response can be to keep it and expand what it does.

A Google executive told me that his team did not lay people off. Instead, it tore through the roadmap: two years of planned work happened in three months. Their hardest problem became deciding what else to add to the roadmap, which is every product manager’s fantasy after years of emotionally draining prioritization conversations.

People should feel empowered, and the best way to get there is to ship. Claire Vo is a model for this: she is always making things, trying ideas, and accepting a little embarrassment. She seems like the best version of herself because of it. We all have that opportunity.

Companies as Cascading Loops

Lenny: You have said company building is increasingly becoming a series of loops. What do you mean?

Anish: We began with prompts, then agents. An agent is a model in a loop with tools, memory, and skill files. The next level is loops made of agents doing linked tasks. Coding demonstrates this clearly because the strongest models serve technically sophisticated users, and software work already contains established, verifiable loops.

A bug report arrives, a reproduction is generated, a fix is created and reviewed, and a risk assessment determines whether a human must approve production deployment. A low-risk fix might ship automatically, followed by an email telling the customer that the reported bug was fixed. The entire sequence can happen in minutes. Engineering contains many such loops, including bug fixing, customer-feedback processing, sales demos, and feature development.

The larger question is what business loops look like. A general manager oversees loops in coding, marketing, sales, support, and legal. Their combined output can itself become a loop that is measured and optimized. In its strongest form, that system might tell the CEO that the company must change a physical part of the business, its business model, or its strategy. We will see a cascade from a loop per person, to a loop per function, to loops across business units, and eventually loops running large parts of a company.

Humans remain critical. Most organizational work cannot be fully autonomous. In an AI-native company, people will remain central to sales, support, strategy, and exceptions. Models still struggle with genuinely new, out-of-distribution thinking. Progress in mathematical reasoning does not prove that models can originate strong business strategy. A person still has to decide what should be made and be right about it.

Lenny: Engineering is becoming a loop from an input such as a support ticket, through deciding what to build, creating a pull request, reviewing it, and shipping. You expect that pattern to spread to go-to-market, legal, growth, and support. What would that look like?

Anish: Consider a traditional growth team. People gather, brainstorm experiments, prioritize them, build them, ship them, and measure the results. In a loop-based version, every variant can be generated and measured. Once a variant reaches statistical significance at the required confidence level, it can be merged and shipped. A long-term holdout remains in place while the system begins the next experiment.

Eventually, that loop reaches a local maximum. This is crucial: the loop helps you climb the hill, but then it plateaus. Out-of-distribution thinking and human intuition are needed to place the organization at the base of the next hill. Agents climb; humans decide which hill matters.

If you ask an agent, “Make me a million dollars and make no mistakes,” it does not work because the agent still needs a useful direction. Nothing in today’s technology suggests that human direction is unnecessary.

Lenny: I heard that OpenAI’s go-to-market team may use Codex even more than its engineering team.

Anish: That should make go-to-market teams happy. Their core work is relationships, persuasion, and closing business. AI can remove the administration surrounding that work, distilling the job toward what they are best at and enjoy most.

Lenny: A product-manager friend joked that instead of saying no to ideas and putting them on a roadmap, he could say yes to everything, build every idea, simulate user reactions, and let the evidence decide.

Anish: That has two beautiful consequences. First, many PMs believe they are exceptional zero-to-one thinkers held back by management, founders, executives, or engineering capacity. If every product story can be told and every feature tried, some may discover they are happier working on someone else’s strong idea than their own weak one. That honesty could improve organizational health.

Second, the winning idea no longer needs to come from the person who can sell it best to an executive. Ideas can be tried, and the best one can win.

Lenny: Which skills resist becoming loops because their output is difficult to verify?

Anish: Those skills become rate-limiting factors. A useful diagnostic whenever a model makes a mistake is: what do you know that it does not know? Kavak has an agent for each customer. When an agent gets stuck, it calls a human, who coaches it through the situation. That intervention both unblocks the customer and produces a trace the agent can learn from. The failure usually exposes a knowledge gap or a data gap. Supply that missing context, and ideally the agent will not need to call next time.

Lenny: So every function should be viewed as an agent loop. The human’s job is to identify where it gets blocked or goes wrong and provide context, insight, or direction.

Anish: Exactly. Once a new product idea works, its implications for marketing, sales, product marketing, communications, and legal should be handled largely by the surrounding loops. Your job can become taking a hike, dreaming, and choosing the next hill.

Lenny: What separates winning companies if AI does so much of the work?

Anish: Competitive equilibria in many industries may not change dramatically. If Pizza Hut, Domino’s, Papa John’s, and Round Table all receive a data center full of PhDs, they will all adopt it, or replace a CEO until they do. There may be a rocky period and some relative shuffling, but most established companies adopt useful technology. It does not follow that one ends up with ninety-nine percent of the market, especially in industries that are not intelligence-bound.

In the near term, there will be winners and losers based on how quickly and ambitiously they adopt. The most useful question for a founder or CEO is: if intelligence becomes effectively unlimited and astonishingly cheap, how would we reorganize the company? That is the direction of travel.

Choosing Models for Different Kinds of Work

Lenny: You expect a split between generalists and specialists inside companies. How does model choice fit that picture?

Anish: Think in terms of a Pareto frontier: the efficient trade-off between performance and price. Frontier models can look irrationally priced. A hypothetical unreleased model costs infinite dollars per token because no one can use it. For one conceptual point of extra intelligence, a frontier model may cost a hundred times more than a strong older model.

That can still be rational in a field with unbounded upside. In drug discovery, if one extra point of intelligence leads to the next statin, the outcome may be worth a trillion dollars. Pay for the highest intelligence available. Other tasks have bounded upside. In accounting, the books can be closed correctly, but not a hundred times more correctly. For such jobs, price-performance efficiency matters more.

Organizations will therefore use both architectures. Narrow, bounded tasks may run on inexpensive open-weight models tuned with reinforcement learning for a specific job. High-upside work in sales, support, research, or engineering may justify expensive frontier tokens. Model families also have comparative advantages, so it will not simply be open weights or frontier models, nor one universal model.

Lenny: Is the distinction also about verifiability?

Anish: Not exactly. Drug discovery may have unbounded upside and be verifiable. Closing the books is also verifiable but has bounded upside. The key questions are how much upside exists and how hard it is to recognize.

Customer support adds nuance. A customer may report a bug that is the first breadcrumb toward an insight capable of changing the entire organization. A CEO or highly perceptive person on the call might follow that trail, while a merely adequate generalist may not. That argues for applying frontier intelligence to the whole basket of support problems. The counterargument is that we may already have crossed the intelligence threshold for almost every economically useful problem, making further intelligence wasteful. Both cases deserve consideration.

Lenny: People forget that today’s non-frontier models were frontier models six months ago. We were amazed by them then, but discount them as soon as something better arrives.

Anish: Exactly. One day we call something AGI; the next day it becomes the old thing thrown in the dustbin.

Lenny: People jokingly call you a model sommelier. What is the current state of the art, and what is each model good or bad at?

Anish: Like a wine sommelier, the secret is thousands of hours—or bottles. The way to become a model sommelier is to use them all. I push myself to ship something with every new model. People who say models are interchangeable usually have not used them deeply.

I recently became obsessed with Qwen 38B. It is strong at long-horizon tasks, creative, and a good storyteller. I used it to make “impossible documentaries” about planets in the Star Wars universe, including Tatooine and Bespin. It could work for four or five hours, tell a story, generate video through a MiniMax model, generate audio, direct a five-minute film, cut the clips, overlay them, and review the result.

That model has a very different shape from GLM 53, which I used for product work. GLM lacks vision and feels like a neurotic PhD placed in the corner. Both have a role. One model’s mind may be shaped toward creativity and openness; another toward precision and productive neuroticism. I make something with each model to build intuition about those differences.

Building Intuition by Making Things

Lenny: How important is that habit, and how should someone decide what to make?

Anish: We all need to become the once-insufferable “app idea” friend. Build the silly ideas, especially because they may contain the most unexpected value. I have about two dozen small apps and one or two larger projects I keep iterating on. A continuing project gives you a chassis for testing each new model; otherwise, you must invent a new idea from scratch every time.

Work on something, preferably something that is not Important with a capital I. Keep finding ways to add to it using models as tools rather than treating the model itself as the goal.

Lenny: One useful habit is to pause before doing something and ask, “How can AI do this for me?” It is like creating a space between stimulus and response, but using that space to consider how AI can help.

Anish: That is right. In ordinary knowledge work, the automation opportunity is sometimes less obvious to me because I am naturally drawn to products. It is easier for me to imagine a feature for my DJ streaming app or my personal Google Reader for X than to find a life task to automate.

But small experiments are fun. I once had a laptop transcribe what was happening in my kitchen, then add or subtract screen time from my son’s iPad according to whether his comments were positive and prosocial. He hacked the metric by recording himself saying, “I love you, Dad,” repeatedly and placing the recording near the microphone. Once a measure becomes a target, it stops being useful—but the experiment was still delightful.

Lenny: A friend modified a wearable to count how often he and his kids laughed during the day. They review that metric together.

Anish: That captures a much bigger opportunity. We have discussed productivity and job loss, but counting family laughter is not a startup and may not be economically consequential. It can still matter enormously to the quality of a life. We often assume happiness is fixed. Why should it be? Our jobs today are far more interesting than the jobs most of us would have had a century ago, and jobs a century from now may be far better again. AI can add texture to human life even when the effect has no economic consequence.

The “Make Me Happier” Loop

Lenny: You have said the big opportunity may be the loop “make me happier.” What does that mean?

Anish: We say people want to become more productive, but many do not. More people want to spend time than save it. The world’s largest products are entertainment and social products. The real consumer question is how to deliver value people genuinely desire.

There is an “X AI user” who fears falling into the permanent underclass and has detailed opinions about every new model. Then there is an “Instagram AI user” who thinks AI is a slightly better Google search and wonders what the hype is about. That gap is a product-design failure. We already possess capabilities that could transform ordinary lives.

For forty years, we largely built technology that produced better spreadsheets. It extended the intellect but did little to extend the soul. Meanwhile, many cultural institutions that met spiritual and emotional needs weakened. The opportunity is to address basic human needs: how can people feel more connected and loved, make progress, and have fun? This is not fundamentally a model-capability problem. It is a product-design problem.

Lenny: We have discussed loops such as “grow my business,” “find more sales,” and “close support tickets.” You are describing loops such as “improve my health” or “make me a better friend.” AI can think continuously about those goals and help pursue them.

Anish: Yes, and it may need to challenge us. A useful AI may be disagreeable or push us. Startups have an advantage because large incumbents have committees that resist uncomfortable product behavior. Yet disagreement, sexuality, awkwardness, and tension are all parts of human existence. Startups can explore the uncomfortable parts of social life more freely.

Lenny: So consumer companies can build products around improving connections with friends and family.

Anish: Yes. Consumer AI is still early, perhaps comparable to the iPhone around 2010, before Airbnb, WhatsApp, Uber, and many defining mobile companies. Three things have held it back. Models were too expensive for many free-to-use products. Chat is a good interface for an extremely high-agency user, but the average consumer’s ideal interface looks more like TikTok, so we need interfaces between chat and passive feeds. And development focused too heavily on productivity rather than connection or entertainment.

Those constraints are easing. Open-weight models make inference much cheaper. People are beginning to discuss goals such as “make me happier.” Founders are exploring far more ambitious interfaces. The problems are at least better positioned to be solved than they were two years ago.

AI, Human Agency, and Expanding Ambition

Lenny: Why do you think people underestimate the positive possibilities of AI?

Anish: AI can become an emotional and spiritual interface that gives people leverage to explore buried parts of themselves. The Industrial Revolution created immense advantages of scale, but that centralization can discourage individuals and diminish identity. AI moves in another direction: it amplifies identity and agency by unbundling skill from desire.

If you want to make music, you can now make music without first mastering piano. If you want to create software, you can build software without having trained as a programmer. The technology lets people express individuality and explore parts of life that were previously inaccessible.

Economically, there is no law requiring growth to remain around two percent. Why could it not be ten, fifteen, or twenty percent? AI can raise productivity and ambition simultaneously. In the 1950s and 1960s, society believed it could do extraordinary things after seeing entire economies mobilize during a world war. My theory is that people are excellent when the stakes are high and at their worst when the stakes are low.

The world five years ago often felt low-stakes, and society accumulated side projects that did not make us more productive or happy. Now many people feel they are climbing an ambition ladder. You ship bad ideas to discover good ones. You can make software, music, or even approach physical projects without personally possessing every prerequisite skill. We have the ingredients for a more fulfilled and productive society, yet we keep returning to the permanent-underclass story.

Lenny: There are real downsides and disruptions, including the effects of data centers, but employment and the economy have held up so far. Medical progress is also accelerating.

Anish: The fact that someone can seriously write about what happens after we cure every disease, and society can debate it as a real possibility, is astonishing. We tend to worry about abstractions such as “the world,” “the average worker,” or “the person living near a data center.” Our actual lives often reveal that we feel more capable and empowered.

That creates a gap between stated and revealed preferences. People may say they do not want a data center nearby while using ChatGPT every day. The best way to improve AI’s public story is to make important things cheap. Two essentials in America—health care and education—have only become more expensive.

Roughly forty-five percent of health-care spending is administrative. Removing much of that burden could make costs deflationary, alongside advances in medicine. Education now faces its strongest competition in two centuries. AI can unbundle learning from institutions and status from credentials. People may not need a particular degree if they can demonstrate that they know how to do the work.

Lenny: Some labs have reportedly slowed parts of model development because of capabilities concerns. How should people interpret claims that a model is too dangerous to release?

Anish: Without commenting on any specific lab, there can be many confounding factors. A company may gain a powerful aura from saying a model is too dangerous. It may also have a GPU shortage, prefer to keep a competitive capability internal, or have economic reasons not to externalize its advantage. I take offensive cybersecurity seriously; we should harden systems before making them trivial to penetrate. But “too dangerous to release” can mix genuine capability concerns with marketing, inference capacity, and competitive strategy.

The industry evidence also does not show one unassailable winner. Anthropic looked untouchable for a period, then OpenAI had a strong stretch, while open-weight models continued improving. That is not how a permanent monopoly around one supremely intelligent proprietary model would look.

Lenny: What else should make people feel better about jobs?

Anish: Throughout human history, desires have grown faster than our ability to fulfill them. Things considered basic expectations today were unimaginable luxuries five hundred years ago—or even a century ago, in the case of antibiotics. We underestimate human desire and ambition. In twenty years, people may complain that they lack a vacation home on Mars. Every CEO will want to build a bigger company. Human existence already operates at a scale our ancestors could not imagine, and there is no reason that trend must stop.

Ambition should not be reduced to founding a company or making beautiful software. There is creative ambition: a child makes things before anyone tells them what medium they are good or bad at. There is local civic ambition, such as making the United Kingdom’s NHS as good an experience as the iPhone. There is family ambition: being more connected and present as a parent. Ambition can mean anything people want to do more fully.

Lenny: Product people are trained to think about MVPs and constraints. Now we may need to ask for the ten-times or thousand-times version.

Anish: Our lives were organized around software and intelligence being scarce. They are becoming abundant, and adjusting to that may be harder for experienced technology workers than for newcomers.

There is enormous learning in doing. More people talk about vibe coding than share the projects they made, perhaps because the projects seem insufficiently important. But execution and shipping produce learning and fulfillment. Building can become the new reading: you build to learn and experience, and it is acceptable to discard most of the result because the activity strengthens your abilities. Building is an activity, not only an outcome. I can make a DJ set that three people hear while I listen to it a hundred times; it remains fulfilling.

Three Consumer AI Opportunities

Lenny: You focus on consumer investing at a16z. Which areas are most exciting?

Anish: I see three. The first is coding agents. It is easy to say that most consumers do not want to write code, but coding agents are becoming a general interface to the world. People use them to edit videos or create games to play with their children on a flight. Wabi is one example: a platform where people create, consume, and share mini-apps. Coding becomes a general problem-solving medium.

The second is personal agents. Developer projects demonstrated what was possible, and those ideas are now being distilled into reliable consumer and enterprise assistants that ordinary people understand. Personal agents can hold context, operate software, and complete tasks rather than merely answer questions.

The third is a broad entertainment and creative category that includes tools such as Suno and companionship products. “Entertainment” does not fully capture it. Some of this space is uncomfortable to discuss, which is why fast-growing products remain underexamined. Companion products are not used only by stereotypical young men; many users are women in their forties and fifties.

Together, these products offer channels for ambition, fulfillment, and leisure. They map to enduring human jobs to be done: make me happier, make me healthier, help me live longer, and help me create.

Lenny: Among personal agents, which products stand out?

Anish: Grok’s agent product made aggressive choices around retaining credentials and getting work done, supported by a strong model and thoughtful interface. ChatGPT’s agentic work product is somewhat buried in the interface but very good. A startup called Instinct has made more aggressive trade-offs in the same domain.

Cloud execution and cached browser credentials matter. Full-duplex voice is especially compelling: you can call an assistant that sees your active threads, ask what is happening across coding agents, and tell it to change something. That feels like calling an assistant who understands your world, rather than submitting a one-way transcription or text instruction.

Moats, Craft, and Distribution

Lenny: Products are launching at extraordinary speed. What tells you a startup can be durable rather than merely a wrapper?

Anish: Jesse from Decagon has a line I love: moats are most often discovered, not designed. Founders can become trapped trying to create a business plan that survives scrutiny from MBAs and VCs, complete with an elaborate theory of durability. Decagon started shipping and discovered its moat over time.

Cursor offers another example. It was criticized for lacking a moat. It began as a high-end product with strong product-led growth, then accumulated reasoning traces, trained its own models, and deepened the product. Durability emerged from use and execution.

We have also forgotten that classic moats were rarely based on how hard ordinary software was to build. They came from network effects, scale advantages, brand, proprietary data, or what used to be called a cornered resource. Those moats remain valid. We need founders ambitious enough to build multiplayer products, consumer social systems, and products that improve dramatically with use.

Lenny: Can a founder simply tell an investor that the moat has not been discovered yet?

Anish: We can invest in a product without a polished durability story if it has strong momentum, craft, and growing engagement. I used to fear that someone would steal my idea. I learned that every big idea rests on a dozen small, invisible ideas. A competitor may copy the visible concept without seeing the details that make it work.

Some products contain something special that makes them successful despite intense competition. Granola was criticized for weak durability, yet it became beloved and dominant through exceptional craft. I listen to what customers say more than what a business textbook says.

Lenny: Products can share similar underlying models while differing mainly in the harness and user experience. That suggests UX itself can create an advantage and buy time to discover deeper durability.

Anish: Yes. Sophisticated users often pay for several products because each has a different specialty. Model and product differences remain meaningful.

Lenny: Distribution feels increasingly valuable because thousands of things launch and compete for attention. Existing distribution seems like a huge advantage.

Anish: It is important, but every existing network has been trained to prevent others from building a network on top of it. As a result, network effects have partly returned to grassroots word of mouth. Organic mentions across X, YouTube, Instagram, and other channels may be the strongest external network effect available.

Unlike the mobile era, where the App Store and a cottage industry of growth tactics provided channels, founders increasingly have to build their own channels from word of mouth. It is a purer and harder growth problem.

Lenny: Seth Godin says to build something remarkable—literally worth remarking about. Amid the noise, people look to what their friends use and recommend.

Anish: The hopeful point is that many companies do not have a growth problem; they have a product problem. Today you can build a wildly ambitious product in a functional or emotional direction and charge a great deal for it. Ask what the product would need to do if it cost a thousand or ten thousand dollars a month—if it were a software Birkin bag. Then build toward that imagination.

Startups may have an easier time than incumbents because they can build in directions that make incumbents uncomfortable, including companionship. Buyers are also unusually open to trying expensive products and signing large contracts. It resembles the period when everyone had just received an iPhone and wanted new apps. That window will eventually narrow, but it is open now.

Creative products can also touch social nerves in ways ordinary business software does not. MSCHF once issued a large group of people debit cards and periodically announced a store where a shared pool of money could be spent. Everyone rushed to use it; only the first few succeeded. It was a funny, viral social experiment. Money is inherently social, yet most financial products are dry, private, and embarrassing. AI creates a similar opening for ambitious products that use technology as a creative medium.

A New Ceiling for Company Ambition

Lenny: What counterintuitive lessons have you learned from successful companies?

Anish: Three years ago, if a company’s plan looked too ambitious, investors often disengaged because it seemed too complex. A hundred-million-dollar seed round sounded impossible to deploy productively: too much money for the problem, too much for one founder to manage, and too much hiring for an organization to absorb.

Today, we increasingly see the opposite problem. An idea can be too small to matter, while a very large seed investment can have a plausible use. I am not recommending that everyone raise an absurd amount, but the ceiling on ambition has changed. At a16z, we tell founders that we want to help build the strongest form of their vision. Marc Andreessen described the firm’s own early posture as going to the moon or leaving a moon-sized crater in the ground—there was no timid middle outcome.

Projects that felt insurmountable five or ten years ago can now feel merely difficult. That changes which companies investors select and which investors founders select.

Another old assumption was that consumer products had to be free. I am increasingly interested in the opposite: extraordinarily expensive consumer software. Every category of discretionary consumer spending is available to software that creates enough value. Because price is evidence of product-market fit, founders should ask what the thousand- or ten-thousand-dollar version of their product would be. The exercise pushes the vision toward something meaningfully more ambitious.

Lenny: You work closely with Marc Andreessen and Ben Horowitz. What have you learned from them?

Anish: Both embody stewardship for the technology industry, the country, and the broader Western way of living and thinking. Earlier venture leaders carried an obligation to leave the industry better than they found it. Marc and Ben aspire to more than being excellent investors. They undertake difficult, important work that does not exclusively or directly benefit the firm because they believe it matters.

They also helped shape the ambition of the founder community. Five or ten years ago, deep tech was fringe and low-status. It has become popular, mainstream, and prestigious. Many firms contributed, but Marc was especially vocal about supporting Important work in the national interest. Silicon Valley changed partly because influential people encouraged founders to aim there.

Advice for Product Builders

Lenny: What should product people change about how they work or think to succeed in the future?

Anish: Make more things. Choose a project, even one you never show anyone and that seems unimportant. Use it as a chassis for trying new models. Ship, discuss what you learned, and develop your own intuition. A slightly frustrating but deeply fulfilling week of real use can teach more than abstract discussion.

This is why many of us entered technology: to build products we could see in our mind’s eye. Now we have an unprecedented chance to do it. Silicon Valley is positive-sum at its best. People build on one another’s work, and vulnerability is rewarded. Show people what you made.

Lenny: What is a useful cadence?

Anish: Ship something once a week. It can be small. For Mother’s Day, I used Codex to make my wife a slide deck drawn from text messages and our photo gallery, with music and old messages from when I first asked her out. It became a twenty-slide story of our relationship. It was not an important commercial project, and I may never revisit it, but it was meaningful and taught me something.

Lenny: A mutual friend says people often change how they feel about AI after it creates one moment of joy for them. The Mother’s Day project is a perfect example. A useful prompt might be: what would bring you joy if it worked?

Anish: Or ask what you can do for somebody else. That is an excellent place to begin.

Books, Products, Life Lessons, and Music

Lenny: Which books do you recommend most often?

Anish: My favorite is Thomas Sowell’s Conquests and Cultures. It examines how conquest shaped cultures around the world, sometimes positively and sometimes negatively. To me, it offers the strongest historical view of culture as a driver of outcomes. I was born in Canada and moved to the United States, and I experienced a very different culture of ambition.

Hamilton Helmer’s 7 Powers is an excellent intellectual distillation of business moats and compounding advantage. I also recommend Increasing Returns to Scale, a dense text associated with Brian Arthur that explains why industries such as software produce outlier economic effects. It helped me understand why the technology industry and its culture work as they do, including why the system can be so positive-sum.

Two other indispensable business books are High Output Management, which remains as useful as ever, and Ben Horowitz’s The Hard Thing About Hard Things. Ben’s book was emotionally honest about the anxiety, isolation, and failure founders experience. Readers discover they are not alone.

Lenny: What recent movie or show did you enjoy?

Anish: I enjoy unapologetically entertaining television, including House of the Dragon. I also saw The Odyssey in a packed London IMAX where people were drinking pints and having fun. The film was good, but the communal theater experience—being alone together—was what stood out.

Lenny: Which recent AI product has given you joy?

Anish: I would choose Grok’s agent product because it is ambitious about using credentials and completing work on a person’s behalf. It combines a thoughtful interface with a powerful foundation model and takes risks other large-company products may avoid. It also demonstrates that foundation models may not be a two-horse race.

Lenny: Do you have a life motto?

Anish: Do not discover through painful experience what someone could simply tell you. I developed that principle as a founder, and it also applies to parenting. I have too often learned through avoidable failure instead of listening to someone a few steps ahead, and my children sometimes share that habit.

For a child, the literal version is “do not touch the hot stove.” In my first startup, experienced people told me not to build a product and a platform simultaneously. We tried to be both a social platform for mobile games and a game studio. Running a studio was already difficult, let alone running both businesses. Someone wise told me to choose one, but it took us years to accept that they were right.

Lenny: Ben Horowitz told me to ask about your DJing. What advice do you have for someone interested in it?

Anish: I have been DJing since 1995. It is a wonderful way to express yourself musically without being a classically trained musician. You select records and combine them, which requires taste and technical skill. Models now let people go further and make the music itself. You can begin with an idea and have a model help realize its strongest form.

Music is a visceral, satisfying way to create experiences. Its media history keeps widening participation. At first, you could hear music only in the presence of the person playing it. Recorded music separated performance from listening. The cassette was another major shift because listeners could compose their own albums as mixtapes. In the 2000s, some of that participatory quality gave way to broadcast-like consumption. As people begin making music again with AI, I expect the music industry to become larger than ever.

Lenny: How can listeners be useful to you?

Anish: Show me what you are building. Do not be despondent. Make something, tag me, and let me see it. Follow me on X if you want more of these ideas, and explore the builders and thinkers across Lenny’s community. There is a great deal of compelling work happening. The essential message is simple: use the tools, build something, and share it.