Why Anish Acharya Thinks Companies Are Becoming a Series of Loops
Lenny's Podcast
Anish Acharya, a general partner at Andreessen Horowitz who focuses on consumer investing, was a product builder before he became an investor. He founded SocialDeck and sold it to Google, then founded Snowball and sold it to Credit Karma, where he became VP of Product and later GM of the consumer product and credit card business. In this conversation with Lenny Rachitsky, Acharya argues against a mood of dread that has settled over Silicon Valley. His position is that AI is not about to create a permanent underclass, that companies will increasingly be built as nested loops of agents with humans supplying the out-of-distribution ideas, and that the biggest unclaimed consumer opportunity is not productivity but what he calls "/loop, make me happier."
The "permanent underclass" is a dark fantasy, not an empirical trend
Lenny opened with the meme that anyone who falls behind on AI tools will end up in a "permanent underclass." Acharya said people should not take it very seriously. He called it "a funny dark fantasy" that Silicon Valley collectively seems to hold, even though by almost every measure things have never been better: opportunity is widely distributed, the available technology is extraordinary, and many independent companies are winning. He noted that the fear is not limited to outsiders. It runs from researchers at foundation model labs to the Silicon Valley layperson.
He gave three reasons for skepticism. The first is structural. The previous tech era was organized around network effects, which he called the gold standard of the mobile era, and those produced heavy centralization. Today, he said, every layer of the stack has not two relevant players but something like twenty: closed labs, open-weight models, and variations within each. Two years ago the reasonable assumption was that coding agents would be winner-take-all. Instead Claude Code, Codex, Lovable, Replit, and Wabi all seem to be working.
The second reason is economic data. Radiologists, he joked, have been "supposed to be cooked" every year for about twenty years, yet job postings are higher than ever, and he said the same holds for programmers. In his reading, the empirical data does not support the fear.
The third reason is technical. People like to invoke RSI, recursive self-improvement, which could let a lab that is slightly ahead run away with the field. According to the most sophisticated people he talks to at the labs, what is actually happening is not RSI but "autocatalytic effects": labs use the new technology to improve their own process, but the improvement is not truly recursive. So, as he put it, everything from the empirical to the technical points the other way, "and yet we can't seem to let go of this fantasy."
Why takeoff may be slower than feared
Lenny raised stories of AI models doing unexpected things, such as reports of OpenAI models hacking Hugging Face. He suggested these incidents actually indicate a slow takeoff, because each one is caught, observed, and iterated on. Acharya agreed. He described the fast-takeoff argument as a familiar pattern: everything proceeds as it has so far, then "something happens that no one can quite articulate," and then fast takeoff. He does not believe that will happen.
He was careful to separate model progress from economic effects. Model progress, he said, is faster than ever. But economic diffusion is slow. He grew up in a small town, went back the previous summer, and found that people's lives had not changed much. If nothing else slows AI down, he argued, diffusion will.
He also raised a question he thinks gets too little attention: how many problems are actually bound by intelligence? If FedEx or Domino's Pizza had "a data center of PhDs," he doubts they would come to dominate supply chains or pizza exponentially. His view is that we may be overestimating how many problems are constrained by intelligence rather than by other factors.
How companies are actually adopting AI
Asked whether a divide is opening inside companies between AI enthusiasts and people who feel too stressed to engage, Acharya said we don't give the average employee enough credit. He sees a habit of abstraction: the white-collar worker becomes a Dilbert-style paper shuffler, consumers become low-agency NPCs, and everyone assumes other people's jobs are easily automated while their own is not. In the details, he said, many people are eager to improve themselves and get more leverage.
His example was Kavak, which sells used cars in Mexico. Kavak runs what it calls a "Jedi Academy" that teaches everyone at the company, including mechanics, to use the new tools. At the end of the six-week course, participants ship a cutting-edge agent into production. Acharya takes this as evidence that adoption is broader than people usually admit.
He drew a distinction between using AI and reorganizing a company around AI, and compared it to electricity. By his account, it took about forty years to get from the invention of electricity to reorganized factories. That meant tearing buildings down and starting over, not just swapping coal for electricity. The most ambitious companies today are rethinking everything around the models. Less ambitious or earlier-stage ones are focused on giving existing people and job functions access to the technology.
He also argued that executives' incentives work against mass layoffs. Sundar Pichai, he said, doesn't want to run a more efficient $4 trillion company; he wants to build a $40 trillion one, so it is rational to keep productive people and let them become more productive. He shared an anecdote from a friend who is an executive at Google. Asked whether they had laid anyone off, the friend said no. Instead the team is ripping through its roadmap, so two years of roadmap gets done in three months, and the hardest problem is now deciding what to add. Acharya called that "every PM's fantasy," given how much emotional energy used to go into prioritization debates. His advice for coping with the change is to ship things. He pointed to Claire Vo as his "muse," someone who is always shipping, isn't afraid of mild embarrassment, and seems to him to be the best version of herself.
Company building as nested loops
The core idea of the episode is that companies will increasingly be built as a series of loops. Acharya acknowledged the concept gets teased on X, where the joke is that the next stage after loops is graphs. He then gave the "steel man." First there were prompts. Then agents, which he described as models in a loop with tools, memory, and skill files. Then loops, which are sets of agents doing tasks.
Coding is where this is most developed, because it has the best models, the most technically capable customers, and established workflows. He walked through one. A bug report comes in, a repro is generated, a fix is written, and the fix is reviewed. If the change is high-risk, a human confirms it is OK to ship to production. If it is low-risk, it ships automatically, and maybe the customer gets an email saying the bug they reported has been fixed. All of that takes five minutes. Engineering has many loops like this: bug fixes, customer feedback, sales demos, new feature development.
A coding loop changes the codebase. His question is what the business loops are. A GM oversees coding, marketing, sales, support, and legal, each potentially running its own loops. The outputs of those loops, he argued, form a higher-level loop that can itself be optimized. In the strongest version, that loop sends a message to the CEO saying the business needs to change something physical, or its business model, or its strategy. He expects a cascade: loops per person, per job function, across business units, and eventually loops that run large parts of the company.
He insisted that humans remain essential. He doesn't think most work in an organization can be done fully autonomously. The human roles in an AI-native company, he said, are sales, support, strategy, and exceptions. He argued that models are still very limited at "new thinking, out-of-distribution thinking," and said he does not accept that recent novel results in math show models can think new thoughts in domains like business. Someone still has to say "here's the thing I think we should make" and be right.
Growth teams, local maxima, and the agent–human handoff
To make this concrete, Acharya used Lenny's former job on Airbnb's supply growth team. The traditional process was to gather the team, brainstorm experiments, prioritize them, build, ship, and measure, with a lot of brainstorming and spreadsheets along the way. The loop version generates every variant and measures every variant. Once a result reaches statistical significance with a strong enough p-value, the variant is merged and shipped, a long-term holdout is kept, and the next experiment begins.
That loop will eventually reach a local maximum. In Acharya's words, "the loop will help you climb to the local maxima, but then it plateaus." At that point you need out-of-distribution thinking and human intuition, "somebody to actually help you land at the base of the next hill." Lenny mentioned a chart Acharya has drawn of this pattern: agents climb a hill, reach a plateau, a human supplies a bigger idea, and the climb resumes, in an ongoing back-and-forth.
As evidence, Acharya pointed to what happens when you ask an agent for a business idea, something like "Hey Claude, make me a million dollars, make no mistakes." It doesn't work, he said, because the agent has to be pointed in the right direction, and nothing in the technology so far shows that this step can be skipped.
Lenny mentioned a tweet suggesting that at OpenAI the go-to-market team uses Codex more than engineering does. Acharya responded with what he admitted was a caricature he would stand behind: salespeople want to hit the gym, go to steak dinners, and win salesperson of the quarter. He sees the tools distilling their jobs down to what they are best at and most interested in, while the surrounding administrative work gets handled for them.
Lenny described a PM friend who plans to say yes to every idea and simulate user reactions with tools like Simile. Acharya saw a less obvious effect. Many PMs believe they are great zero-to-one thinkers held back by management and limited engineering capacity. In a world where every feature gets tried, he predicted, many will discover they aren't that good at zero-to-one, and that "it's much more fulfilling to work on someone else's good idea than your own bad idea." He thinks this will make organizations healthier, because the winning idea will no longer be the one best pitched to an executive; it will simply be the one that performs best.
Finding where the loop gets stuck
Lenny cited a point Claire Vo made recently: soft skills were long considered less valuable than engineering, yet engineering turned out to be what AI does best because its outputs are verifiable. So which skills resist being turned into loops? Acharya agreed those will become the rate-limiting factors, joking that you can only do one steak dinner a night.
His practical heuristic is to ask, whenever a model makes a mistake or does something you wouldn't, what you know that it doesn't. He shared an example from Ale (Alejandro Maza) at Kavak, whom he called probably the most sophisticated thinker on this that he spends time with. Kavak runs an agent per customer, and when an agent gets stuck, it calls a human, who coaches it through. This unblocks the agent, and the agent also captures the traces and learns from them. The gap is either knowledge or data, Acharya said, and once you supply it, the agent shouldn't need to call you again the next time.
Ideally, when someone has a new idea that works, the downstream work — marketing, sales, product marketing, communications, legal — should be largely handled. The person's job, as he put it, is to "go take a hike and dream the dreams and come up with the next hill to climb."
Will competitive dynamics actually change?
Asked what will separate winning companies, Acharya offered an under-discussed possibility: competitive equilibria in many industries may not change much. If Pizza Hut, Domino's, Papa John's, and Round Table all get a data center of PhDs, they will all eventually adopt it, possibly after a CEO change. There will be a rocky period and some reshuffling, but he doesn't expect any of them to end up with 99% of the market. Near-term winners and losers will be decided by how ambitiously companies adopt the technology, but many industries will keep their current dynamics because they aren't intelligence-bound. His suggested question for founders and CEOs is: if these systems are infinitely intelligent and astonishingly cheap, how would you reorganize the company? "Cuz that's where we're going."
Frontier intelligence versus "mid-IQ" models
Acharya expects a split between job functions that justify frontier models and those better served by cheaper ones. He framed it with Pareto efficiency: along the efficient price-performance frontier, you pay a rational amount for each unit of performance. Frontier models, he argued, are irrationally priced. Mythos is effectively "infinite dollars a token" because you can't use it. And with something like Fable 5, he said, you pay about 100x more than Opus 4.8 for, conceptually, one extra IQ point.
That premium can still be rational where the upside is unbounded. In drug discovery, if one extra IQ point leads to the next statin, the outcome is worth a trillion dollars. In functions with bounded upside, such as legal or finance, he thinks you want Pareto-efficient good performance at a good price. He predicts those roles will often run on open-weight models tuned with reinforcement learning for narrow jobs, while "quote-unquote expensive" frontier tokens go to sales, support, research, and engineering. He was careful not to be dismissive: there are extraordinary people in those functions, but "you can't close the books 100x better."
When Lenny asked whether verifiability is the deciding factor, Acharya said he wasn't sure. Drug development is verifiable and has huge upside, while closing the books is also verifiable but has limited upside. The real questions are how much upside there is and how hard it is to calculate. He then argued both sides. A customer support call might report a bug that is the first breadcrumb to something that changes the whole company; the smartest person on that call could follow the trail, which argues for always using frontier intelligence. On the other hand, one could argue that we have already crossed an intelligence threshold for almost every economically useful problem, and anything beyond it is waste. He left the question open. Lenny added that today's non-frontier models were the frontier six months ago, when everyone was amazed by them. Acharya agreed: something is hailed as AGI one day and thrown in the dustbin the next.
Being a model sommelier
People jokingly call Acharya a "model sommelier." His method is to use everything: he pushes himself to ship something with every new model. Anyone who thinks models are commodities or fully interchangeable, he said, simply hasn't used them.
His recent obsession is Qwen 3.8, which he described as very good at long-horizon tasks, creative, and a strong storyteller. He has been using it to make "impossible documentaries" about planets in the Star Wars universe; Tatooine turned out great, and he made one about Bespin the night before the recording. By his account, the model can work for four or five hours: it writes the story, generates video with a MiniMax model via fal, generates audio via ElevenLabs, and directs a roughly five-minute film by cutting clips together, overlaying them, and watching the result. He contrasted it with GLM, which he used for product work. He noted that GLM 5.3 had just come out, has no vision component, and is "sort of like this neurotic PhD you put in the corner." His point is that models are not simply ranked by intelligence. They have minds shaped in different directions: creativity and openness in Qwen's case, neuroticism and precision in GLM's.
On how to come up with things to build, he said everyone should become the "app idea friend" who used to be insufferable over beers, and should build even the silly ideas, since those often carry the most alpha. He has about two dozen apps and one or two large ones he keeps iterating on. His key advice is to have a "chassis": an ongoing project, preferably not important with a capital I, that you keep extending with each new model, treating the model as a tool rather than the goal. His own projects include a DJ streaming app and what he called a Google Reader for X.
He also described a family experiment. His laptop transcribed everything happening in the kitchen and added or subtracted iPad screen time for his son depending on whether the son was saying prosocial or antisocial things. The son responded by recording himself saying "I love you, Dad" over and over and placing the recording next to the mic. Acharya treated this as a lesson that any measure turned into a target stops being useful, and as a fun social experiment. Lenny mentioned a friend who hacked a Limitless pendant to count how often the family laughs each day. Acharya said that is exactly his point: it isn't a startup or economically consequential, but it matters for quality of life. He suggested happiness may not be fixed, and that AI can add texture to human lives even where there is no economic payoff.
"/loop, make me happier"
This leads to what Acharya sees as the biggest consumer opportunity. "We believe that people want to be more productive, but they don't," he said. "I think more people want to spend time than save time." That, in his view, is why the biggest products are entertainment and social.
He contrasted the "X AI user," who fears the permanent underclass and has strong opinions about GLM 5.3 versus Kimi K3, with the "Instagram AI user," who sees AI as a somewhat better Google search and doesn't understand the hype. He called this gap a product design failure. In his framing, we spent forty years building technology that enables better spreadsheets, technology that extends our intellect, "but nothing to extend our soul." He sees a spiritual hunger, especially as cultural institutions that once met it have faded, and particularly across the rest of America, which has fewer hot yoga classes, Pilates studios, and Friendsgivings. The opportunity is the basics of consumer need: "How do we feel more connected, more loved? How do we make progress? How do we have fun?" He repeated that this is not a model or capability problem but a product design problem.
He added that AI can challenge people, push them, and be disagreeable, and that startups have an advantage here. He said "a thousand Google committees" would recoil at releasing a model that is disagreeable, let alone sexually suggestive, yet these are parts of human existence. Startups, he said, are uniquely positioned to explore the uncomfortable parts of our social lives.
In his view, three things have held consumer AI back. Models have been expensive, which made free products hard to offer. There has been an interface problem: chat makes sense for the highest-agency people in the world, like Elon and Sam, but the average consumer's ideal interface is TikTok, so something between chat and TikTok is needed. And the technology has focused on productivity rather than connection and entertainment. He thinks all three are now in play: open-weight models have made things much cheaper, conversations like "loop make me happier" are starting, and founders such as Eugenia Kuyda are thinking ambitiously about interfaces. He also said that Brian Chesky of Airbnb has started a lab focused on next-generation user interfaces. His analogy is that we are at the equivalent of iPhone 2010, before Airbnb, WhatsApp, and Uber.
Optimism about individuality, ambition, and cheaper essentials
Lenny noted Marc Andreessen's earlier argument that AI arrived just in time to offset declining population and productivity. Acharya offered his own reasons for optimism. He described the Industrial Revolution as creating insurmountable scale advantages. While he is strongly pro-capitalism, he thinks that centralization can discourage individuals and diminish their identity. AI, in his framing, "unbundles skill from desire." You can make music without playing piano and build software without being a programmer, so AI amplifies agency and individuality.
He questioned why GDP growth should be stuck around 2% instead of 10, 15, or 20%, and argued AI can drive ambition as well as productivity. His theory is that "when the stakes are high, we are awesome," and when they are low we are at our worst. He called the postwar 1950s and '60s a (possibly caricatured) high-ambition era. The world of five years ago felt low-stakes to him, which is why society took on "side projects" that didn't make anyone more productive or happier. Now he thinks everyone feels they are climbing an ambition ladder: "You ship your bad ideas so you can discover your good ideas."
He addressed the gap between stated and revealed preferences. People's own lives feel more capable and empowered, but when they think about abstractions — "the average worker," "the person who lives near a data center" — they worry. Most people say they don't want a data center nearby, yet most say they use ChatGPT.
On AI's poor public image, he said the most important thing AI can do is "make important things cheap," above all health care and education, the two things in America that have only gotten more expensive. He cited a figure that 45% of health care spending is administrative, and said removing that burden could make health care deflationary. He also pointed to GLP-1s, which he acknowledged are not an AI development, as another reason for optimism. He believes traditional education now faces its strongest competition in 200 years, and welcomes unbundling learning from institutions and status from credentials.
Skepticism about "too dangerous to release"
Lenny raised reports that OpenAI had paused the RL phase on its latest model. Acharya declined to comment on OpenAI specifically but voiced general skepticism. He said Anthropic gained an extraordinary aura from having a model considered too dangerous to release, and listed possible confounders for any such pullback: a GPU shortage, capabilities beyond what the lab wanted, or a desire to keep a proprietary model internal to extend its lead. He does take offensive cyber risk seriously and thinks systems should be hardened before they become trivial to penetrate. But he said the "too dangerous to release" framing conflates marketing, inference capacity, and the economic choice of whether to externalize your advantage or use it yourself. He also noted that the fear of one company holding an unassailable model hasn't matched events: Anthropic once looked unassailable, OpenAI then had "an amazing six months," and open-weight models are "ripping."
On personal agents, he named three products he likes: Grok bot, which he said came out of left field with an excellent underlying model; ChatGPT Work, which he felt is buried in the UI but among OpenAI's best releases; and a startup called Instinct, which he said has made more aggressive trade-offs. He singled out ChatGPT Work's cloud execution, credential caching (less aggressive than Grok or Instinct, he said), and especially its full-duplex voice mode, which can see all your threads. It feels like calling an assistant who knows everything happening in your world, whereas Grok, he said, offers one-way transcription.
On jobs, Lenny placed Acharya closer to David Sacks than to Dario Amodei's warnings about massive disruption of knowledge work. Acharya argued that human desires have always grown faster than our ability to satisfy them. Therapy was once an unimaginable luxury, and antibiotics were unavailable at any price a century ago. He joked that in 20 years people will be really mad they don't have a vacation home on Mars. He also broadened the meaning of ambition beyond founders and software to include creative ambition, like a five-year-old painting without being judged; local ambition, like making the UK's NHS "as good as the iPhone"; and simply being a more present parent. For builders, he said, the harder shift may be learning to "think small," since lives have been organized around software and intelligence being precious, and now they aren't. He also emphasized learning by doing. People are a little embarrassed to talk about their vibe-coded projects, he said, but building is valuable "as an activity rather than an outcome," much like a DJ set that three people hear but he listens to a hundred times.
Consumer AI: three areas to watch
Acharya named three consumer areas he is watching. The first is coding agents. His thinking changed when he realized coding agents are a general way to interact with the world, not just a way to write code: people use Claude Code to edit video or Codex to build a game to play with their kid on a flight. He cited Wabi, an a16z investment, as the best example: a platform where people create, consume, and share mini-apps. The second is personal agents. He described the "Open Claude" moment, along with Hermes and "Multibook," as developer phenomena that are now being distilled into mass-market consumer and enterprise agents. The third is what he loosely called entertainment, which he said undersells it: creative tools like Suno and companionship products. He called this area uncomfortable to talk about and therefore under-discussed, despite containing huge, fast-growing products. Correcting the assumption that companions are mostly "AI girlfriends," he said the majority of companion users are women in their 40s and 50s. He summarized the three categories as a channel for ambition, a channel for fulfillment, and something to do when you're not doing everything else.
Moats are discovered, and distribution is harder but purer
On durability, Acharya cited Jesse of Decagon: moats are "most often discovered, not designed." As a founder, he said, he once got stuck trying to write a moat story that would satisfy MBAs and VCs. Decagon just started shipping, and its moat developed over time. Cursor was criticized for lacking a moat, then captured reasoning traces and trained its own Composer models. He also noted that none of the classic moats depend on software being hard to build. Network effects, scale advantages, brand, and proprietary data (a "cornered resource") are still good moats. What's needed are founders ambitious in those directions: more multiplayer products, consumer social, and products that improve with use. He called Hamilton Helmer's 7 Powers one of only about five real business books, alongside High Output Management and Ben Horowitz's The Hard Thing About Hard Things, which he called the first emotionally honest business book.
For pitches, he said a16z would happily back a product without a moat story if it has momentum, craft, and growing engagement. He used to worry about idea theft, but learned that big ideas rest on a dozen invisible small ideas that copycats never see. He pointed to Granola: criticized two years ago, still without a strong durability story in his view, yet "beloved and dominant." He listens to customers more than to business books. Lenny added that with so many overlapping agent products whose underlying models don't differ much, the harness and UX may be where the advantage lies. Acharya noted that people at the edge pay for all of them because each has its own strengths.
On distribution, Acharya said he had asked Chris Dixon about it. Dixon coined "come for the tool, stay for the network." Acharya's argument is that a generation of founders was trained on network theory, so every existing network is now hyper-trained to prevent others from building networks on top of it. As a result, the network effect has returned to grassroots word of mouth: organic mentions on X, YouTube, and Instagram. Unlike the mobile era with its App Store and growth-hacking industries, founders now have to build their own channels, which he described as a purer growth problem but a harder one.
His hopeful version is that "nobody has a growth problem these days, they have a product problem," because you can now build wildly ambitious products and charge a lot for them. He suggested asking: what if our product were a "software Birkin bag," costing $1,000 or $10,000 a month, and what would it have to do to justify that? Asked whether incumbents' distribution makes things harder for startups, he said he thinks it's easier: despite heavy cross-selling, nobody would say Google's Gemini is winning. Startups can go where incumbents are uncomfortable, prices can be high, and enterprises are signing million-dollar contracts without fully knowing what they're getting. It feels to him like Christmas 2009, when everyone got an iPhone and wanted new apps. He expects that window to close eventually but says it is open now. He cited MSCHF's "Card vs. Card" as inspiration: they mailed debit cards to about 100,000 people (by his recollection), then texted locations where $100 could be spent, so everyone rushed out and only one or two succeeded. He saw it as proof that money is inherently social, and believes AI now allows similarly wild products that touch social nerves.
Bigger bets, expensive consumer software, and advice for builders
The biggest change Acharya has seen as an investor is a reversal. Three years ago, an overly ambitious company would be passed on, and a $100 million seed made no sense. Now, he said, an idea that's too small is the problem, and founders can explain how they would productively deploy $100 million at seed. He recalled Marc Andreessen telling him that a16z's founding pitch was that they would either go to the moon or leave a moon-sized crater, with no other option, and said the firm wants to back founders the same way. He cited Atoms as an effort that would have seemed insurmountable five to ten years ago. He also reversed the old rule that consumer products must be free. Since price is a measure of product-market fit, he thinks expensive consumer software is new and important, and every part of discretionary consumer spending is up for grabs.
Of Andreessen and Horowitz, he said what stands out is their sense of stewardship for the tech industry and the country, in the tradition of Ron Conway, Brook Byers, and Tom Perkins, including doing important work that doesn't directly benefit the firm. He also credited Andreessen with helping make deep tech and work in the national interest high-status after years on the fringe.
His advice for product people was simple: "Just make more things." Pick a project, even an unimportant one you don't tell anyone about, and use it as a chassis for every new model. He promised that people are one "slightly frustrating and then very fulfilling week" away from being as "pilled" as anyone. His heuristic is to ship something once a week. As an example, he had Codex build a Mother's Day slide deck for his wife from his text messages and photo gallery, set to music, including old texts from when he first asked her out. Lenny passed along a point from a mutual friend and former Credit Karma colleague: people change their minds about AI once it creates a moment of joy for them. Acharya added that doing something for someone else may be a good starting point.
Lightning round: books, lessons, and music
Acharya's favorite book is Thomas Sowell's Conquests and Cultures, which he sees as the best historical account of culture as the main driver of outcomes. This resonates with him as someone born in Canada who moved to the U.S., with its different culture of ambition. He also recommended 7 Powers and a dense text on increasing returns by W. Brian Arthur, which Andreessen sent him and which he said explains why software has outlier economics and why the industry is so positive-sum, possibly because of its structure rather than better people. He named House of the Dragon and seeing The Odyssey in a packed London IMAX. His favorite recent AI product was Grok bot, for being "unhinged" in its ambition, for example in caching credentials to act on your behalf, and for suggesting the model race may not be a two-horse contest.
His life motto, drawn from both startups and parenting, is not to learn through painful experience what someone can simply tell you. His startup version: don't build a product and a platform at the same time. His first company tried to be both a mobile gaming studio and a social platform for games. Someone warned him to pick one, and it took years to realize the warning was right.
Asked about DJing, which Ben Horowitz had urged Lenny to bring up, Acharya said he has played for about 30 years, since 1995. He described it as a way to express yourself musically without classical training, and said models now let you go further by generating the strong form of your own musical ideas. He framed music's history through its media: live performance, then recordings, then the cassette tape, which he called the first medium that let people compose their own albums. He believes music struggled in the 2000s partly because that creative participation disappeared and consumption returned to a broadcast model. Now that people are making music again, he predicts the music industry will be bigger than ever.
His closing request to listeners matched the rest of the conversation: don't be despondent, build something, and tag him so he can see it.
There's a lot of fear and worry about the future with AI. I want to talk about this idea that if you fall behind, you're going to become part of this permanent underclass.
It's a funny dark fantasy that we seem to have as Silicon Valley collectively. Like things have never been better by almost every measure.
This is a technology that really amplifies our agency. It kind of unbundles skill from desire. Not only can we dramatically drive productivity, we can dramatically drive ambition.
Can you get too ambitious? Is there like a limit?
In the old days, 3 years ago, we would see a company and if what they were trying to do was too ambitious, we would, you know, not engage. Today, we're almost seeing the opposite problem. An idea that's too small is not something that we want to engage with.
You have this interesting take that company building more and more is going to become this kind of series of creating loops.
We're going to see this sort of cascading set of everything from a loop per person to loops that can run large parts of the company. With that said, I think humans are a critical ingredient. The loop will help you climb to the local maxima, but then it plateaus. You need human intuition. You need somebody to actually help you land at the base of the next hill.
You have this take that the big opportunity is this idea of /loop, make me happier.
We believe that people want to be more productive, but they don't. I think more people want to spend time than save time. So, I think that the opportunity for this technology is the basics of consumer need. How do we feel more connected, more loved? How do we make progress? How do we have fun? I don't think it's a model or a capability challenge. It's just a product design challenge.
Today, my guest is Anish Acharya. Anish is general partner at a16z where he focuses on consumer investing. He is one of the most insightful, thought-provoking, mind-expanding, in-the-weeds product investors I've met. He's been at a16z for over 7 years now and unlike a lot of VCs and why I loved having Anish on the podcast is that he is a long-time product builder and founder. He founded a company called SocialDeck which he sold to Google and then ended up leading a number of efforts within Google, then he started a new company called Snowball, which he then again sold, this time to Credit Karma, where he moved to VP of product and then GM of the broader consumer product and the entire credit card business. This conversation will get your mind buzzing.
Before we get into it, don't forget to check out lennysproductpass.com for a free year of the hottest and most beautifully crafted AI products in the world, available exclusively to Lenny's newsletter subscribers. With that, I bring you Anish Acharya.
Anish, thank you so much for being here and welcome to the podcast.
Thank you, Lenny. I'm so excited to be here, thrilled.
I want to start with a very light topic. I want to talk about this meme of the permanent underclass. It's kind of this joke that people have joke about, this idea that if you kind of fall behind and aren't just like on top of all the latest AI tools, aren't becoming the most productive person ever, you're going to become part of this permanent underclass and fall behind and have a really hard time.
And some people are joke about it. I think a lot of people take this really seriously and stresses a lot of people out. How real of a concern do you think this really is? How seriously do you think people should take this?
Not very seriously and it's a funny dark fantasy that we seem to have as, you know, Silicon Valley collectively. Like things have never been better really by almost every measure by how sort of distributed all the opportunities are, by the kind of technology we have access to, to the types of ambition we're allowed to have, and to the number of companies that are sort of independently working on things that are winning. And yet, there's this sort of discussion of permanent underclass, being outside of the light cone, I've heard it. And it's not just something for deep insiders or outsiders. It feels like there's a real fear kind of from, you know, researchers at foundation model labs all the way through to the Silicon Valley layman.
I mean, here's like a couple of points that I think are really important. So, first, I think the last era of tech was a lot more centralized. If you look at network effects, that's sort of the gold standard. You worked on a network effects product. That's the gold standard of businesses from the mobile era. And those things led to dramatic centralization, right? Of course, all of them are definitionally sort of n of one networks. If you look at what's happening now, it's like every part of the stack, there's not even two relevant players, there's like 20. You know, you've got labs, you've got open weight, you've got different variations within both. If you look at coding agents, we were talking about it, like your mental model for 2 years ago should have been would have been, I think it should be winner take all. And yet, Claude Code, Codex, Lovable, Replit, Wabi, like they're all sort of working. So, it's really really encouraging to see that.
You know, the second thing, you've heard all the kind of economic data, everything from radiologists who are supposed to be cooked every year for I think about 20 years now. And of course, job postings are higher than they've ever been, as well as programmers, you know. So, I don't know that the empirical data bears it out. I think the final thing is that there's this sort of discussion about RSI, and I know RSI is like recursive self-improvement is a fun term to throw around. But, if you ask the most sophisticated individuals at the labs, it's not actually RSI that's occurring, which could lead to some sort of runaway winner cuz they were an epsilon ahead of the others. It's autocatalytic effects, which just means you're using the new technology to improve your process, but it's not truly recursive. So, I think everything from the most empirical to the most technical viewpoints in the other direction, and yet we can't seem to let go of this fantasy.
Something I've been thinking about recently is seeing all these like even seeing these crazy stories about OpenAI's models hacking Hugging Face.
Yes.
All these stories, to me feels like there's always been this question of are we on the fast takeoff or the slow takeoff scenario? And it feels very much so that we are on the slow takeoff scenario, because every one of these milestones is like, "Holy it hacked at We had no idea it was doing this," But, but like we're catching it, we're watching it, we're observing it, we're iterating, evolving. There's always this fear, okay, but tomorrow it's going to take off. What I'm hearing from you is that's probably not the case, which I think is the source of a lot of people's fears, this idea that all of a sudden it's going to become super super intelligent and then we're in big trouble.
That's right. Like the line of reasoning for that case is always everything up until now, then something happens that no one can quite articulate and then fast takeoff. So, I don't believe that that's going to happen. I do think that model progress is happening faster than ever before. But if you look at something like economic diffusion, you know, I grew up in a small town. I went back home last summer, like people's lives haven't changed that much. So, if nothing else, the sort of slow rate of economic diffusion will catch it.
I think the other thing that's under discussed, Lenny, is, you know, how many problems are truly intelligence bound? Like if you had a, you know, a data center of PhDs working at FedEx or Domino's Pizza, are they going to be like exponentially dominating supply chain and pizzas? Like I don't think so. So, I think we might be overestimating how many problems are intelligence bound versus bound by other things.
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What are you seeing inside of companies in terms of is there a more of a divide happening? And do you think there will be more of a divide between the people that are becoming really good and embracing versus like I don't have time for this. I hate all this stuff. My job's already so stressful. What are you seeing happening and where do you think things will go inside of companies in terms of maybe a divide?
I mean, I have so many thoughts on this. I don't think we give the average employee enough credit. I think we have this abstraction of a white-collar employee. You know, the white-collar manager, the abstraction is like some Dilbert-esque manager who's just shuffling paper all day long. You know, it is abstraction of consumers that they're sort of these low-agency NPCs. Of course, that would never apply to us or our friends. You know, we have this abstraction that everybody else's job is super automatable by AI, but of course ours is not. So, I think when you actually get into the details, a lot of people are actually excited to you know, better themselves, get more leverage, and you see this with of course sophisticated companies like Google, but even a company like Kavak where they sell, you know, used cars in Mexico, they've got this concept of a Jedi Academy where they're teaching everybody at the company including the mechanics how to use the new tools and technologies and kind of at the end of the 6-week course, they ship a cutting-edge in-production agent. So, I actually think that more people are embracing the technology than we sort of like to discuss.
I think a big change is going to be using AI versus reorganizing your entire company around AI. And a great example of this is if you look at the diffusion of electricity as a technology, you know, it took 40 years for us to get from the inception of electricity to reorganizing factories. And that means like re- like burning the buildings down and starting from scratch versus taking what was previously coal and simply swapping it with electricity. So, I do think that like the most ambitious companies are rethinking everything around the models. And those that are a little less ambitious or perhaps a little earlier are thinking more about how do we give people and existing orgs existing job functions access to the technology.
So, a kind of a theme I'm hearing so far is we can be a little less stressed about where things are going and the future of your job, your careers.
I think so, man. I mean I think if you even just think of the kind of incentives for the CEO and executives, you know, Sundar running Google, he doesn't want to run a more efficient $4 trillion company. He wants to build a $40 trillion company. So, anytime you have an economically productive unit, it's rational to kind of especially if it gets more productive to maintain that sort of presence in your organization.
And then I don't know what you hear, but anecdotally I talked to a good friend who's an executive at Google and I said, "Hey, have you laid anyone off?" And he said, "No, we didn't. What we instead do is now rip through our road map. So, 2 years of road map happens in 3 months. And where actually our hardest problem is knowing what to add to the road map, which by the way is like every PM's fantasy. You know, how much emotion has been drained on prioritization conversations between you and I?"
So, yeah, I actually think that people shouldn't be as stressed and I think they should feel really empowered. And by the way, the best way to do it is just to ship stuff. I mean, Claire is my muse. She is so awesome cuz she's always shipping. Yes, Claire Vo. She's shipping. She's trying things. She's not afraid to be a little embarrassed by it. And if you just see her whole kind of affect, she feels like the best version of herself that she's ever been. And I think we all have an opportunity to be that.
I love that. I want to be Claire Vo when I grow up.
Totally.
Kind of along those lines, you have this interesting take that company building more and more is going to become this kind of series of creating loops and creating series of loops. Talk about that.
Yeah, yeah. Well, I think the broad concept and you know, the loops concept kind of gets teased a little on X cuz at sometimes it feels like maybe we're big braining it. I know there was a big meme around graphs, like the next stage of loops is graphs. But let me make the kind of steel man for it, which is
you know, we had prompts and then we invented agents. Agents, of course, are just models in a loop with tools and memory and skill files. And then we had sort of loops, which are, you know, sets of agents that are doing tasks. If you look at a lot of work in coding, coding such a great domain because you have the best models and you have the most sort of technically apt customer. Plus you have these established loops. Like bug fix or sort of bug report comes in, repro gets generated, bug fix gets created, bug fix gets reviewed. If high risk, then human should confirm that it's okay to ship to prod. And if low risk, it just gets shipped. And maybe you even email the customer and say, "Hey, we fixed the bug you reported." That happens in 5 minutes. There are many loops like that in engineering, everything from, you know, bug fixes to customer feedback to sales demos to new feature development.
So, coding sets this up well itself up well. So, you have a coding loop and the kind of change it makes is to the code base. My question is, what are the business loops? Right? So, if you're the GM of a business, you're looking across many job functions and you've got loops running in coding and marketing and sales and support and legal. The output of all of those loops is something that is itself a loop that you should be able to optimize for. And I think the strong form of this is that it sends a message to the CEO saying, "Hey, we need to actually make a change to one of the physical aspects of the business or to our business model or to our strategy." So, I think we're going to see this sort of cascading set of everything from a loop per person, loop per job function, loop across entire business units to, you know, loops that can run large parts of the company.
With that said, I think humans are a critical ingredient. I just don't think that most work in the organization can be done fully autonomously. When you think of what a human will do in this like AI native company, sales, support, strategy, and exceptions. Right? And all those things are super critical. If we've seen one thing, Lenny, it's that the ability for models to do new thinking out of distribution thinking is still really limited. And I don't actually take the point that some of the new thinking in math is actually representative of new thinking in domains like business. So, you're still going to need a person to say, "Hey, here's the thing I think we should make." and have them be right about it.
>> Let me just kind of make sure this point is really clear, 'cause it's so interesting. What you're saying here is engineering and building more and more is becoming this loop of input, feedback, or support ticket, whatever, input of just like what to build, and then AI more and more is taking that, deciding here's a PR, here is this ready, and then shipping. And you're saying that you expect that to spread to like, say, go-to-market, legal, growth, support. So, maybe describe what that loop looks like or may look like within a company.
>> I mean, a great example is a growth team. You worked on the growth team at Airbnb, right?
>> Yeah. Yeah, supply growth. Yeah, that's right.
>> Yeah. Awesome. Right. So, you remember those like war... I don't know how you ran your team, but I'm guessing it was something like you got everyone together, you built a list of possible experiments, you prioritized them, you built, then you shipped them, you measured them.
>> Brainstormings. Yeah. A lot of brainstormings. A lot of spreadsheets.
>> So, the loop version of that should be that every variant gets generated, every variant gets measured. Once you get to stat sig with a high enough P value, you can merge and ship that variant. You then have a long-term holdout, and you start working on the next experiment. And then you're going to hit some local maxima. And I think this is really important: the loop will help you climb to the local maxima, but then it plateaus. And you need some sort of out-of-distribution thinking, you need human intuition, you need somebody to actually help you land at the base of the next hill.
>> Yeah, you have this chart. I don't know, maybe we'll show it later as we talk about this, which is such an interesting way of thinking about it. This idea that agents will help you hill climb and reach some new plateau, and then you need a human there to think about a bigger idea, kind of unlock it, and then it keeps going and going, and there's kind of this like agent-to-human kind of back and forth.
>> Yes. Yeah, and you know what really illustrates that? If you've ever tried to have an agent come up with a business idea for you. Like, "Hey Claude, make me a million dollars, make no mistakes." Like, why doesn't that work? And it's because you sort of need to set it in the right direction, and nothing in the technology has shown us that that is not needed.
>> Yeah, it's interesting. I've been hearing more and more... I just saw a tweet that I think at OpenAI, more of the go-to-market team is using Codex more often than even the engineering team.
>> Yes. And think of how happy that makes them. Like, what does the go-to-market team want to do? I mean, this is a caricature, but I'm going to stand behind it, which is they want to hit the gym, they want to go to steak dinners, and they want to like raise the trophy up for being salesperson of the quarter, of the year. So, I actually think that this is a distillation of their job into the thing that they're the best in the world at, that they're the most interested in, and all the administration that goes around doing that core work is now handled for them. So, like, that's where go-to-market is going, and it's going to be awesome.
>> This reminds me of a PM friend who had this really funny take that as a PM, you're constantly having to say no to all these ideas that are coming at you, and you have like, "I'll put it on the road map. We'll prioritize it." He's like, "Okay, I'm going to flip this. I'm going to say yes to everything. I'm going to build everything and then simulate every idea with like Simily or all these products that are launching where you can simulate how a user will react. And then that'll tell you should we build this?" What a hilarious way to rethink PM.
>> And you know what's so beautiful about that? Actually, there's two things. I'll tell you maybe the one that's less obvious to me, which is I feel like every PM at every company feels like they're a true zero-to-one thinker, but they're held back by the heavy hand of management and executives and founders and engineering capacity. And in a world where every story gets told, every product story gets told, every feature gets tried, I think a lot of PMs are going to realize they're actually not that good at zero-to-one, and it's much more fulfilling to work on someone else's good idea than your own bad idea.
So, I think even things like that are going to lead to a lot more organizational health than we've had in the past. Not to mention the fact that the idea that ends up winning doesn't have to be the one that's come up with by the person who can sell it best to an executive. It just gets tried and the best idea wins.
>> So, coming back to this loops idea, the way I'm thinking about it is how can every function start to think of their function as setting up an agent to be able to just go from input to some kind of impact?
>> Yeah.
>> And what this makes me think about is something Claire actually tweeted recently. This point that people always used to joke that soft skills are the least valuable and engineering skills the most important and valuable because they're so concrete, and it turns out that's what AI is the best at. The things that are verifiable, and you know what success looks like. And so the question I think about now is just like which skills can you not just turn into a loop because the output is so hard to verify.
>> Yeah, I think that's right, and I think that those are going to be the rate-limiting factors, 'cause you can only do one steak dinner a night. I guess you could do a steak lunch, but to some extent there's going to be these rate-limiting factors in every system. I think a useful way to think about it is anytime the model's making a mistake or doing something you wouldn't do, what do you know that it doesn't know?
And there's a really interesting example I heard from Ale at Kavak. He's probably the most sophisticated thinker on this stuff that I get to hang out with, where he said they have an agent per customer, they sell used cars online, and when the agent gets stuck, it actually calls a human. And the human will coach the agent through. Now, the magic of that is not only does it unblock the agent, but of course the agent then captures all the traces and learns from it. Like, that's a really interesting mental model. It's either a knowledge gap or a data gap that you have to give the agent, and the next time it shouldn't have to call you.
>> I love that example. Basically, the takeaway here is you need to start thinking about every function as an agent loop, and the job is to figure out where it gets blocked, where it goes wrong, and give it more context, more insight, more direction, basically.
>> That's right. That's right. And then hopefully a lot of your day-to-day work, when you come up with a new idea that works, the kind of implications of that idea, if it's a product, there's marketing work, there's sales work, there's product marketing, there's communication, there's legal, all of that should be largely handled for you. And your job is to go take a hike and dream the dreams and come up with the next hill to climb.
>> And this begs the question a bit of just what will separate the companies that win in this world where AI is kind of doing a lot of this? I imagine part of the answer is the human in this local maxima coming in with the better idea. Is there anything else that you think becomes like a differentiator in this world where AI is doing so much of the work that humans are currently doing?
>> I mean, one thing that's under-discussed is that it's unclear that the sort of competitive equilibria that exist in a lot of industries will really change. So let's say Pizza Hut and Domino's and Papa John's and Round Table all get a data center of PhDs, and they're all going to either adopt it or have a CEO change and adopt it. So, it'll be a rocky period, and there'll be some relative shuffling, but I think that they're all going to embrace new technology, 'cause most companies do. I don't know that one of them is going to have 99% of the market.
So, I think what's under-discussed is that yes, in the near term, I think there'll be winners and losers based on adoption of the technology and how ambitiously you adopt it, but I do think there's a lot of industries that will sort of maintain their current competitive dynamics because they're not intelligence-bound. I think the most useful question to ask yourself as a founder CEO is just, "Hey, if we assume these things are infinitely intelligent and astonishingly cheap, how would we reorganize the company?" 'Cause that's where we're going.
>> Kind of along those lines, you have this interesting take about this kind of split that might be coming within companies, between these generalists and specialists, and how AI plays into that. Talk about that.
>> Yeah, well, I think that there's so many interesting things that this touches on. One is the question of open weight versus frontier. Are you familiar with Pareto efficiency? I'm sure you are.
>> Please explain.
>> Yeah, so Pareto efficiency is just, for price performance, for example, the efficient frontier is what's considered the optimal trade-off of a unit of performance for a unit of price. And if you're along that curve, you're always paying the rational amount for the performance. And what's interesting is that frontier models are actually irrationally priced, in that, first of all, Mythos is infinite dollars a token, you can't use it.
But if you look at even something like Fable 5, for one IQ, conceptually one IQ of extra intelligence, you're paying 100X more than Opus 4.8. So, it's not rational, but I think there's a lot of jobs in which you have unbounded upside, like drug discovery. And if that one IQ point lets you discover the next statin, it's a trillion-dollar outcome. So, it's sort of rational to pay for the highest intelligence in these types of jobs and industries.
Now, on the other hand, I'm going to pick on maybe legal. There's perhaps only so much upside to be had in legal or finance. And for those jobs, you actually do want to be very Pareto efficient and probably pay for good performance at a good price, not infinitely priced, infinite potential upside performance. So, I think what we're going to see is a split between job functions that demand kind of mid-IQ intelligence, and those will often be open-weight, sort of biased with reinforcement learning, things that make the models even cheaper and more performant for a narrow job, along with incredibly, quote-unquote, expensive but performant frontier tokens for sales, support, research, engineering.
So, I think you're going to end up having both architectures. And we can touch on this in a little bit, but I do think there are these sort of comparative advantages amongst model families, and then also amongst, of course, individual models that we're seeing more and more of. It's not going to be one or the other.
>> Such an interesting insight. So, just to make sure I understand what you're describing here, you're thinking there's going to be this split within an org of function and model, where specific functions that have a lot more upside and potential leverage go for the frontier models. And you describe these kind of product, sales, engineering, research roles. And then for other functions, you don't need Mythos. You don't need Astra, I think. Is that the latest one coming out?
>> Yeah.
>> And so, you're saying both that AI doesn't need to be the frontier model and the people don't have to be the smartest people in the world to do really well in that world.
>> Yeah, and I don't want to be diminutive. Like, there's extraordinary people in those job functions. I just think that they're bounded-upside problems that they work on. You can only close the books correctly. You can't close the books 100x better. So, yeah, that's exactly what I'm saying.
>> I wonder if it's connected back to that discussion we had earlier about it being verifiable. If it's a lot more verifiable, you don't need the frontier model, versus, I don't know, the potential upside.
>> I'm not sure. I mean, verifiability is a good question. I think for a drug development company, you have infinite upside. It is verifiable, but closing the books is also a verifiable problem, which has limited upside. I think the question is really how much upside is there and how hard is it to calculate it?
Like, here's a nuance. Customer support: a customer may call in and report a bug. That bug may actually be the first breadcrumb to a thing that changes our entire organization. And if the CEO is on that call, or the smartest person, they could follow the trail. But if we actually have this quote-unquote mid-IQ generalist, then not. That actually makes the case for, as a basket of problems, always use frontier intelligence. And I can of course make the case for the mid-IQ basket as well, which is simply that we've crossed an intelligence threshold for almost every economically useful problem, and anything beyond that threshold is simply waste.
>> Yeah, and I think what people forget is that the models that are not the frontier models, that was what the frontier was, I don't know, six months ago, and we were so impressed and we loved it. It was like, "Holy, it can do all this." And now just because there's something better, we don't give those models as much credit.
>> You're right, 'cause it's sort of like this thing is so crazy, this is AGI, and then a day later it's like the old thing that you throw in the dustbin.
>> So kind of along those lines, I hear people jokingly call you a model sommelier. Tell us, with your sommelier credentials, what's kind of the current state of the model arts? What is each model great at? What are models terrible at?
>> Yes, well, as you know, the secret of every sommelier is 10,000 hours, or maybe 10,000 bottles. So I think that the secret of being a model sommelier is just using them all.
>> Drinking them all.
>> I push myself really hard to ship something with every new model that comes out. And I think you learn so much. I think for people who believe the models are commodities or totally fungible, you just haven't actually used the models. For example, in the last few weeks, I've been obsessed with Qwen 3.8. Qwen is an awesome model. It's very good at long-horizon tasks, and it's actually really creative. It's a great storyteller.
So, I've been using it to create these impossible documentaries, mostly of planets in the Star Wars universe. I did Tatooine, which turned out great. I did Bespin last night. And it can just work for 4 or 5 hours. It tells a great story. It generates all the video using the MiniMax model via fal. Generates audio via ElevenLabs. It actually directs the movie, the 5-minute movie, in that it cuts all of the clips in. It overlays it. It watches it. It's just extraordinary.
And it's got a totally different shape than GLM 5.2. GLM 5.3 just came out, which I used for a bunch of product work. It doesn't have a vision component, and it's sort of like this neurotic PhD you put in the corner. And they both have their role, right? This is a little bit of the tension of models. It's not that one is ahead of another, one is more intelligent. It's rather one has a mind that's shaped in one direction, perhaps creativity and openness for Qwen, and others that are shaped in other directions, like, you
know, neuroticism and precision, like GLM 5.3. So, that I just make something with every model, and that's how I build my intuition.
How important is this habit, do you think, for people? Because I hear a lot that it's really important to be using these models. And kind of a secondary question is, how do you come up with what to do with these models? Cuz a lot of people want to try these things. They're like, "Okay, what do I do?"
I know.
Any suggestions?
Well, I think you Almost all of us have got, you know, the most insufferable thing for a long time was your app idea friend. You know, every time you went to have a beer, they're like, "Let me tell you my app idea." You're like, "Ugh, here we go again." Like, we have got to be the app idea guys now. And all the silly ideas, actually, especially the silly ideas, cuz those are the ones that often have the most alpha, are the ones that we should all be building.
So, I probably got, you know, two dozen apps that I build. I've got one or two large apps that I iterate on. And I think that if you don't have a chassis on which to like with which to use the models, it's just really hard to come up with an idea from scratch every time. So, I'd say like work on something. It's actually better if it's not important with a capital I. And then keep finding new ways to invest in it and add to it with a model as a kind of tool rather than as a goal. Does that make sense?
Yeah. Like maybe even zooming out. I've been thinking more and more one of the most important habits to build right now is to whenever you're about to do something, ask yourself, "How can AI do this for me?"
Totally.
And visualizing this like input to response, you know, like the whole idea of there's a space between input and response. And meditation helps you think more deeply before you respond. And I feel like the trick now is insert in that moment, "How can AI help me with this?"
Yeah, no, I think that's I mean I'm also a long-time meditator. We should talk about a lot of you like, but yeah, I think that's right. I think in our day-to-day knowledge work, sometimes it's less obvious to me. I guess my mind is I've always been a consumer product person. I love products. So, for me it's easier to think of a new feature to add to my DJ streaming app or, you know, my sort of Google Reader for X that I use than it is to kind of find a part of my life to automate. But you're right, there's some fun examples. You know, I posted about one a few weeks ago where I had my laptop transcribe everything that was happening in the kitchen. And then it would award or detract screen time from my son's iPad depending on whether he was being good or bad. So, it was like a fun little social experiment. It also had a fun outcome, which is that he recorded a video of himself saying, "I love you, Dad." over and over and put it next to the mic
to hack the metrics.
To hack the metrics. So, yes. So, yes, the kind of you know, we anything becomes a measure, it's no longer useful. But, it was just a cool little social experiment. I think those things are super fun.
That is hilarious. And one of So, one of the measures was how often he said I love you. And I was going to give him more screen time.
he saying things that are positive and prosocial or negative and antisocial? And it you know, saying I love you is very prosocial.
That's so funny. I had a friend who built this little device that measured how often he and his kids laugh throughout the day.
Oh, that's nice.
Yeah. He hacked like one of his Limitless pendants to do that. And then they just look at that metric every day.
Isn't that so And this is what I mean, you know, man? Like, I think we spent a lot of the time on the show so far really like intriguing talking about productivity, job loss, kind of all these like heady important topics with a capital I. But, what you just described, how often you laugh, like that's not a startup. That's probably not an economically consequential idea, but it is for like the quality of our life.
And I think we tend to think that happiness is fixed, but what if it's not? You know, if you and I were doing jobs 100 years ago, like what would our jobs be? I promise they'd be less cool than they are right now. And maybe 100 years from now, they'll be that much cooler. So, I think that the thing that gets often missed, and this is why I'm a fan of Claire and others, is just like how can this thing add texture to our human lives even if they don't have economic consequences?
Yeah. Along these lines, I saw somewhere you have this really interesting take that like the big opportunity, maybe just in consumer but broadly, is this idea of /loop make me happier.
Yeah.
Talk about that.
Oh, yeah. I mean, I think that we believe that people want to be more productive, but they don't. I think more people want to spend time than save time. There's a reason the biggest products in the world are kind of entertainment and social. So, we get at the heart of how do we sort of deliver the value to the consumer? I think for most consumers, you know, sometimes I tease and I say it's like the Instagram AI user versus the X AI user. The X AI user is like, you know, fearful of being outside of the permanent underclass, is really opinionated on GLM 5.3 versus Kimi K3, like they're just so pilled. And then the Instagram person is like, "Oh, this is like a better Google search, kind of. It's cool, you know, I don't get what all the hype is about."
So, I think that a lot of the And it's really a product design failure. We have the capabilities to radically transform people's lives. I mean, Lenny, in many ways we spent 40 years building a technology that enables better spreadsheets, right? We built this like technology that extends our intellect, but nothing to extend our soul. And I think that we have a little bit of a spiritual hunger, especially as a lot of these cultural institutions have gone away that fulfilled that, especially in the rest of America, you know, where you don't have as many hot yoga classes and Pilates and fasting and Friendsgiving.
So, I think that the opportunity for this technology is like, "Hey, that the basics of consumer need. How do we feel more connected, more loved? How do we make progress? How do we have fun?" Like the things that we all aspire to, the basics. How do we apply this technology to those areas? That's what I want to see more of. And again, I don't think it's a model or a capability challenge, it's just a product design challenge.
I love this so much. We talked about this idea of /loop, like grow my business, /loop find me more sales, /loop close support tickets. But the way you're describing here, I think the way I had it in my notes here, just like /loop improve my health or /loop make me a better friend. And there's no reason AI can't just think deeply and hard about all those things and figure out a way to actually do this.
Yeah, and also, you know, look, there's a we have to dial this in, but I think there's a way that AI can sort of challenge you, can push you, can be disagreeable. This is also why I think startups are advantaged over incumbents. You know, the idea there's a thousand Google committees who would, you know, roll in their graves at the idea that they're going to release a model that's disagreeable or God forbid it should be like sexually suggestive. Or but guess what? Those are all parts of human existence. So, I think exploring the kind of uncomfortable parts of our social existence are things that startups are uniquely set up to do.
And I know you spent a lot of time investing in consumer companies. What I'm hearing here is this is a big opportunity for consumer businesses to basically build a app product that is exactly this loop around improve my connections with my friends and family.
I think that's right. And you know, I think the thing that's held back consumer so far a little bit and it maybe held back is just strong cuz if we kind of put this into iPhone terms, we're in iPhone 2010, right? What is iPhone 2010? I think it's pre-Airbnb, pre-WhatsApp, pre-Uber, pre-all the important kind of consumer companies. So, it is early days, but what's held us back is I think three things. One is that the models have been expensive. So, if you want to do a kind of free to use product, that hasn't been easy.
The second is that we've kind of had an interface problem. Like chat makes sense if you're the highest agency person in the world, which is Elon and Sam. But for the average consumer, like their ideal interface is TikTok. So, we need to find something between chat and TikTok. And then the fact that the technology has been so much more focused on productivity than things like, you know, human connection and entertainment. I think all those things are kind of up for grabs. Like the open weight models mean things are way cheaper. I think that we're starting to have conversations about things like /loop make me happier. And I think that founders like Eugenia, who should have been on the show, she's tremendous, are thinking really ambitiously about user interfaces. Brian Chesky from Airbnb had he started a foundation lab focused on next gen user interfaces. So, I think all those problems will get solved or they're at least in a better position to be solved than they were 2 years ago.
I want to come back to this the whole space of consumer and AI and things like that. But I want to follow this thread a little bit more about just the optimism around where things might go. There's a lot of fear and worry about the future as with AI and just generally. Marc Andreessen when he came on the pod had this really interesting take that AI came just in time to save us because population is declining, productivity is going down, there's all this war, climate change and all these things and we would be in big trouble if AI wasn't here to fill that gap. Talk about just kind of your bigger picture perspective on why you think maybe people are underestimating the positive and optimism around AI.
One, I think it's a potential for it to be a sort of emotional spiritual interface on which we can kind of get leverage and explore aspects of ourselves that have been really buried. If you look at the kind of effect of the Industrial Revolution, it's that there are these scale advantages which are insurmountable. And as much as obviously I'm very pro-capitalism and I love the economy that we live in, I think that centralization it sort of discourages the individual in some ways and it maybe detracts from their identity. So, I think one of the really magical things is this a technology that really amplifies our identity, our agency. It kind of unbundles skill from desire. For example, if you want to make music, you can make music now. You don't have to know how to play the piano. If you want to be a programmer and make software, you can make software now. So, it really amplifies our individuality. It allows us to explore aspects of our lives that we were never able to explore before.
And you know, just in terms of the nuts and bolts, like we have been in this sort of morass of 2% GDP growth. Like who said that we have to be there? Why can't we be 10 or 15 or 20%? And this is a technology with which not only can we dramatically drive productivity, we can dramatically drive ambition. Like think of the maybe this is a caricature, but the 1950s and 1960s, we believed that we could do anything, right? We were coming out of World War where the entire economy, the entire world mobilized in a way that we didn't think was possible.
And one of my theories Lenny is that like when the stakes are high, we are awesome. When the stakes are low, we are at our absolute worst. And in a lot of ways, I think the world we lived in 5 years ago felt like a low stakes world, which is why we kind of collectively had a lot of these like side projects as a society which weren't necessarily productive or making any of us happier. And now you've got, you know, it's not just Elon doing everything he's doing, but I think everybody feels like they're climbing the ambition ladder. You ship your bad ideas so you can discover your good ideas. And if you want to, you know, build software, great. If you want to build a bridge, you don't have to be a civil architect to know how to do that anymore. So, I really think that we have the makings of a sort of dramatically happier, more fulfilled, more productive society. And, you know, and yet we're here talking about permanent underclass.
I love all of that. That all feels so right. It's so hard to really believe that. But if you actually look at how things have gone so far with the rise of AI, unemployment's down, people are making a lot of money. You know, obviously a lot of people are struggling. There's a lot of downsides. Data centers causing problems for people, things like that. A lot of issues. But it feels like as a economy and as a country, feels like things are going well so far. Like they just almost like cured some kind of cancer the other day. So, yeah.
Yeah, you're right. Moderna just did I mean, the fact that Dario can write a blog post and say, "What happens when we cure every disease?" And then we debate it as a serious topic. Like what world are we living in here, you know? I also think that we're collectively worried always. Like you know, the kind of these abstractions, the world, the average worker, the middle manager, the person who lives in the vicinity of a data center. These are abstractions. But our actual lives feel like they're getting more fulfilled, we're more capable, we're more empowered. So, I think that's also something then the revealed preferences tend to show how people are experiencing individually. And the stated preferences tend to show how people are sort of observing it or believe it's playing out societally. You know, if you ask people if they want a data center in their neighborhood, most will say no, but if you ask them if they use ChatGPT today, most people will say yes. So, that's kind of the dissonance.
And the obvious issue is that just the PR around AI has not been great. There's a lot of fear-mongering. Thoughts on that? What's going on there? Do you think that all change?
The most important thing that we can do with AI to change the kind of conversation around it is make important things cheap. There's two things that are extraordinarily important in America that have only gotten more expensive, right? Health care and education. If you look at health care, 45% is administrative. So, if you take a lot of that administrative burden out, you can actually see deflationary health care costs. Also, things like curing every disease, that sounds awesome. You know, GLP-1s, also obviously not an AI thing, but I think is a reason to be optimistic about deflationary health costs. And then education as well. I think education now has the strongest form of competition, sort of traditional education, that it's had in 200 years. And I think it's going to be very, very good to kind of unbundle learning from institutions. And also, by the way, like status from credentials. You know, like you don't need a Harvard degree, you just need to get And that's pretty cool.
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I saw OpenAI recently slow down their AI development. They paused their RL kind of phase on their latest model because of what they're seeing. So, that's obviously a big concern for people, just how fast and smart these models get. Any thoughts on just that? That's like a big shift now. Instead of race ahead to the fastest, best model ever, okay, we actually have to slow these things down. That feels crazy.
I mean, without commenting on OpenAI specifically, I think that maybe I'm a little skeptical on some of these things where I think the kind of the aura that Anthropic got from having a model that was too dangerous to release was extraordinary. And maybe they had a GPU shortage. Now, maybe it was, you know, the capabilities were more advanced than they actually wanted. You know, maybe they actually wanted to keep that proprietary model internal to extend their own lead. So, I think there's a lot of sort of confounding factors that would cause you to pull back a little bit.
Look, I do take the points about offensive cyber seriously, which is we should harden all of our systems before we make them trivial to penetrate. But I think the sort of concept of the model that's too dangerous to release, it kind of conflates marketing, inference capacity, and then also economic considerations like do you want to externalize your competitive advantage or use it to make yourself better?
Yeah, I always think about that when I have folks from Anthropic and OpenAI on the podcast, just like how an advantage they have when they have the best model. It's crazy, right? Just like that's a loop right there, is just have the best model for longer and they can move so much faster.
It's crazy though, you know, to take the other side for a moment, like it felt like Anthropic was unassailable and now OpenAI has had an amazing 6 months and open weights are also ripping. So, despite the sort of scary concept of this, like, you know, supremely intelligent model that's totally proprietary to one company, so far the kind of industry trends haven't played that way at all.
Yeah, like Grok bot just came out of nowhere and is now like the most amazing AI kind of assistant tool. I'm just hooked on it.
Oh man, I mean we should talk about the personal agent thing. Like actually the three big products that I love here: Grok bot totally nailed it. And also the model underneath it is awesome, right? They came out of left field. And I know a lot of the good work that Chris or team did. I think ChatGPT work, it's kind of buried in the UI, but it's really, really good. It's one of the best products they've released. And then there's a startup that's really getting some buzz called Instinct, which has made some more aggressive and interesting trade-offs, but all in the same domain.
That's amazing. I don't know if it was a strategy for someone tweeting this vague tweet about how awesome it is and everyone's like, "What the hell are you talking about?" That was really effective. Cuz it was like, "Where did you get that?" ChatGPT work, the episode that will come out right before this is the PM in charge of that, Atara.
Oh, really?
Yeah.
Oh man, they did such a good job on it. I don't know how much you use it, but it's really well done.
What is it better than Cowork there? Is it that it runs in the cloud? Is that the big differentiator?
It runs in the cloud. It does a good job of kind of caching browser credentials, though it's not as aggressive as Grok or Instinct. And also the remote feature, it's got the full duplex voice mode. So you can actually just call it. It can see all your threads and you can just talk to it and say, "Hey, what's happening across all my coding agents, across this? Can you change that?" So, it's just because the full duplex voice is so good, you really feel like you're calling your assistant who knows everything that's happening in your world, whereas with Grok, it's a sort of one-way transcription. Right? Or with some of the other assistants, it's a text. So, the voice plus the kind of model of it can see all threads is really well done.
Awesome. Okay, I'm going to come back to this AI assistant consumer stuff, but something I wanted to kind of close the thread on. I feel like in terms of jobs and the economy as a result of AI, I see it almost as the spectrum of there's like the Dario end of the spectrum of 50% of knowledge work will be disrupted and we're all going to have massive unemployment, to like the Sacks, David Sacks spectrum of like it's going to be incredible. Jobs are going to be fine. So far, everything is pointing in the good direction. Clearly, you're closer to the Sacks direction. Is there anything else just along the lines that you think might make people feel better about jobs in the future?
I mean, I think that the entire trend of human existence has been that our desires grow faster than our ability to fulfill them. If you look at the things that are expectations today, they were unimaginable luxuries 500 years ago, even 50 years ago for things like therapy, right? Or things like antibiotics 100 years ago, right? I mean, didn't matter how rich you were, you simply didn't have access to it. So, I think we're underestimating human ambition, human desire. You know, people are going to be mad that they don't have a vacation home in Mars in 20 years. Like really mad. They'll be seeing it on Insta and be like, "Come on, babe. We got to like work harder and make this happen." Every CEO is going to want to build a much bigger company. So, don't they already, it feels like we're living a much larger form of sort of human existence than we could have imagined 100 years ago? And there's no reason that trend won't continue or accelerate.
This idea of ambition, I'm glad you even brought that word up again. It's something that's coming up a lot on this podcast. That not only is AI making it easy to be a lot more ambitious, it's almost making us have to be more ambitious because everybody else can just do all the easy stuff now. And now what separates us is just how big can you go? Okay, I'm going to make a personal website. Okay, it's cool. It'll be this white simple background. No, okay, I'm going to make it this 3D game where you have to like walk through a world and discover all the things that you like. Everything is just getting more epic. Thoughts on just this idea of ambition becoming a bigger, I don't know, scale and habit.
A lot of things get conflated because when you and I say ambition on this pod, I think people have a very specific idea of what that ambition is, you know? Ambition to be a founder, ambitious to build a beautiful software product. Like those are types of ambition, but there are other types of ambition. Let's talk about creative ambition. You know, when you're five, nobody says, "Lenny, you're good at painting, but you're bad at drawing." You know, you just have an ambition or a desire to make something and you make it. Like that's something that's very unique and can now actually be encouraged.
You know, think of a very local ambition. So, you know, they have the NHS in the United Kingdom. I think it's a sort of treasured institution that's not working well. Maybe the way AI shows up in their society is making like the NHS as good as the iPhone. Right? That's very specific and local to them. Or simply the ambition to be more connected to our family, to be more present parents. So, the ambition doesn't have to be sort of ambition in the narrow economic sense. It can be really anything that we want to do more of, and who doesn't have that in their bones?
Yeah, my example is so dumb now that I think about it. It feels like the thing we have to unblock in our brain is like, "Okay, AI, solve cancer." Like that's where we can start to think now, and it's so unnatural to us, especially as product people that always have to think about the MVP and the constraints. Now we have to think big. Okay, what's the big, what's the 10x, the 1000x version of this?
Yes. I know, right? In a way we have to think small because our whole lives have been built around how precious software and intelligence is, and now it's totally not precious. Like that probably will be a harder change for you and I.
Yeah. So, like all these new little habits. I think a lot about, on the Claude Code team, they have a principle: you know what's better than me doing it? It's Claude doing it. And that just created this habit in everyone on the team. How do I help Claude do this thing for me? And I feel like that's a thing we all have to start to build in our head. And Grok Bot's really good at that. Not an investor, no affiliation, but it's just so simple and good at this stuff.
I also think that there is so much learning that happens through doing. You know, it's funny, if you look at the number of people that are talking about vibe coding versus the number of people that are talking about their projects, people are a little embarrassed. I'm a little embarrassed to talk about a lot of my projects cuz they don't seem important or substantial enough. But so much of it is learning through execution or shipping, or being fulfilled through execution and shipping. I don't think we can underestimate that either, you know.
I have a guest post coming together from someone at Google that works on a lot of their labs stuff. And I don't want to give away the goods, but just broadly her concept is that building is now the new reading, where you build to learn and infuse and experience, and it's totally okay for most of it to go throwing away because that's still building your muscle, you know.
So well said. Yeah, it's building as an activity rather than an outcome.
Right. Yeah. Which I think a lot of people feel bad. I shipped all these things, but no one's using it or ever used it. And I think the key here is that's actually okay, and that's totally fine.
Oh man, I mean it happens in every other domain, you know? Like I make a DJ set and three people listen to it, and I listen to it a hundred times. And it's very fulfilling. It doesn't matter.
We're going to talk about your DJ stuff later.
Okay. Okay.
Okay, let's come back to consumer stuff. So, you focus on consumer at a16z. What's happening in consumer these days? What's kind of the landscape? What are you excited about?
So, I think there's three big areas. We're very early, as we kind of discussed. You know, the labs have done a good job, but there aren't as many sort of independent mass market consumer products. Coding agents are awesome, and I think a total lightning bolt. You know, I think it's easy to say most people don't want to make code, but the big change in my thinking is that coding agents are a way to interact with the world generally. And you've seen a lot of this on X. You know, people use Claude Code to edit videos, or Codex to create a game that they play with their kid on an airplane ride. It's sort of this general problem-solving tool that consumers can use in very unique ways. And probably the best example company that we've invested in is Wabi, where it's sort of a platform for mini apps. People can create them, consume them, share them. So, coding agents is one big area I think that's important.
Personal agents, we had this incredible moment around Open Claude, but guess what? Open Claude is a dev thing, you know? And Hermes, of course, and Multibook. All of that is now getting distilled into mass market consumer and enterprise agents that people understand, which is what we discussed previously.
And then the last is, I'm going to call it entertainment, but that doesn't fully do it justice. I think that it's a lot of sort of creative tools. You know, Suno has done such an amazing job. It's companionship products. That entire area is, you know, uncomfortable to talk about, so I think it's under-discussed, but there's some huge fast-growing products there. So, I think those are the three big areas that we're watching right now.
That's really interesting, just thinking of it in these three buckets. Coding agents; AI assistants, kind of Open Claude, but much simpler and easier and more reliable. Sounds like basically the three described there that you're excited about are Instinct, Grok Bot, and ChatGPT work.
That's right.
And then Wabi, I guess, would fit in that first one. It's like a personal coding agent that can build whatever you want for you.
Yes. Yeah, that's exactly right.
And then entertainment is the third bucket. Like AI girlfriends and that kind of stuff, right? Yeah, there's companies, it's like crazy. You guys put out these market maps and like five of them are these different companions.
Though actually, to be accurate, it's more boyfriends and girlfriends, you know. The majority of people using companion products are women that are in their 40s and 50s, actually.
Interesting. Okay, cool. So those are the three kind of areas you think the biggest opportunities will come from? And it's interesting that they connect to this idea of /loop, make me happier. These are kind of like the jobs to be done for humans. Make me happy. Make me healthier. Make me live longer. That kind of stuff.
Yes. Yes, give me a channel for my ambition, a channel for my sort of fulfillment, and then a thing to do when I'm not doing everything else.
That makes sense. Okay. The other question I have for you along these lines is, there's so many companies and so many startups, so many products everyone's launching. The speed at which companies and products are shipped is like thousand x-ing. How do you think about durability and moats when you look at a startup? Because a lot of founders get that question. Everyone's getting that question. How am I not a wrapper? What are signs that tell you this might be a durable thing?
Well, I think there's two important ideas. One is something that Jesse from Decagon said, which I love, and that is that moats are most often discovered, not designed. I think it's really easy, I've done this as a founder, to get in your own head about like, hey, I need a business plan that survives scrutiny from MBAs and VCs. I've got to have some really sophisticated, you know, idea of what my moat will be. And for that team, they just started shipping and it developed over time.
Another great example of this is Cursor. You know, they were criticized a lot for not having a moat, but it turned out that initially being a high-end PSD AU product was really good. And over time they captured all the reasoning traces, they trained their own models, the Composer one, two models, and so on and so forth. We know how that story plays out. So, moats can be discovered, they don't have to be designed, is one.
And then, I think the other is that we seem to have forgotten that none of the classic moats are based on how hard it is to make the software. You know, we're not building self-driving cars. Most of us aren't. So, it's network effects, it's scale advantages, it's brand effects, proprietary sort of data, or what was historically called a cornered resource. Every moat from 5 years ago generally is still a good moat. We just need founders
that have ambition in those directions. We need more multiplayer products. We need consumer social. We need products that get dramatically better the more you use them like town. So on and so forth.
So, still read Hamilton Helmer and all that stuff still applies.
Yes. Love this book.
Yeah, he's been on the podcast.
He has?
Yeah.
Maybe I missed that. Yeah. I feel like there's only five real business books in the world. Every other one is in the business of selling business books. And they're fake. And his is on my list of five.
Are there any other in this list that come to mind real quick or anything?
The two that are so obvious are High Output Management. Which is like that is as good as it ever was. And then, Ben's book, you know, Hard Things is it was the first emotionally honest book about business that was ever written. And that's why founders love it. That's why I love it. Cuz you read it and you're like, "Wow, I'm not the only one that's, you know, anxious and feels like a failure and can't tell anyone what I'm going through." Ben went through it, too.
Mhm. Two of the most mentioned books on this podcast, turns out.
That's a surprise.
So, on this moat idea. So, say you're a founder and you're just like, you know, you're putting a pitch together trying to pitch you or other VCs. What's the best way to talk about a moat? Is it like Can you just say, "We're going to discover it. We're not sure. Nobody really knows yet."?
Yeah, I think that we would happily take a bet on a product that doesn't have a quote-unquote moat or durability story if it has, you know, a lot of momentum, a lot of craft, a lot of, you know, sort of growing engagement. So, actually the thing I've learned over the years, I used to be very worried about people stealing my idea, but I've learned that the big ideas are always supported by a dozen small ideas that are invisible. And even if somebody replicates your big idea, they never see the small ideas that make the big idea work. So, I actually think that there's some just something special in the water with certain products that make them incredibly successful despite extraordinary competition. I mean, look at Granola. You know, 2 years ago, it was really criticized and I don't know that they have a super strong durability story today and yet it is like beloved and dominant. So, you know, you sort of I listen to like what the customers are saying more than, you know, what the business books say.
You get a free year of Granola if you become a subscriber to Lenny's newsletter. I should do this more often when it's part of this product pass. lennysproductpass.com. You get a free year of all these amazing products including Granola. I just saw Ramp put out a report of the fastest growing companies according to their data and Granola is like number two or three.
It's a tremendous product. The craft is really high.
Yeah, and I think about that a lot these days because there's so much like there's Cowork, there's ChatGPT work, there's Cursor, there's Grokbot and it's crazy how quickly one can switch from one to the other. And the underlying model is not that different. All it really is, most of it is the harness and/or UX of the product. And so to me that tells you there's so much opportunity in the actual user experience being a moat or at least giving a lot of time to find something that is durable.
100% and I think for the people that are at the edge, they pay for all of them because they all have their respective areas of specialization.
Yeah, I have many $200 a month plans right now.
I know. I know, right? So, it's a little painful, but yes, me too.
And I think Cursor has a $300 a month plan now.
So does Grok. Oh yeah, you're right. Yeah, that was the Grok one. It's pretty with Grok heavy.
RIP. Yeah, RIP Cursor. I think they've transitioned away from that brand.
Something I've been talking a lot about is the distribution. I call it distribution's new moat, but it's like it's always been a moat, but it feels like more and more that is actually a massive advantage because everybody is There's like a thousand launch videos a day. Everyone's launching launch launch launch. And really the ability to get your stuff into people's feed, get them continue to be reminded your product exists feels like increasingly is powerful and important. Thoughts on the rising value of distribution, existing distribution being a big lever for growth and success.
This is so important and you know, I asked Chris Dixon about this, because he sort of authored the famous come for the tool, stay for the network. I think the issue is that our entire generation of founders and CEOs were trained on the theory of networks and network building. And as a result, every network that exists today is hyper trained to ensure no one else builds a network on their network. So I actually think that the sort of network effect has gone back to this grassroots like true word of mouth. When somebody is getting a ton of mentions on X and on YouTube and on Instagram and all these places organically, that is probably the best form of the sort of third-party network effect that you can hope for today. And actually just like the Web 2.0 era, unlike the mobile era where you had the App Store and you had growth hacking and you had all of these sort of, you know, little cottage industries, we have to kind of build our own channels off of that word-of-mouth growth. So in a sense, it's a purer growth problem but a harder one than we've had in a couple of product cycles.
And to get word of mouth, you need to build something. I always think about Seth Godin's line, build something remarkable. Something that is worth remarking about. Which is basically, you know, build an amazing product that people want to talk about. Which is, you know, very hard to do. And it makes sense that because there's so much happening, people are just going to pay attention to what are my friends using, saying is worth paying attention to.
Well, you know, here's the one thing I would say, here's the hopeful point, which is I always say that nobody has a growth problem these days, they have a product problem. I mean, the reason for that is like you can build such a wildly ambitious product in any direction, you know, functional or emotional. You can charge a lot of money for it. So, my challenge is like, "Hey, is it that you have a growth problem or is it a failure of our collective imagination? You know, if we imagined our product cost a thousand dollars a month, ten thousand dollars a month, like what if our product was a software Birkin bag? What would it have to do to justify that? Okay, let's figure out how we build that."
And it comes back to the ambition question.
Yes. And still though, you still need some advantage to get in front of people, to get it out there, at least initially, because there's like a thousand things launching every day. Imagine that's still a big opportunity and, I don't know, advantage, which to me makes me feel like incumbents have a huge advantage. They have their products, they can tell you, "Hey, go use Gemini." And every time you go search Google. I guess Do you feel like that? Do you feel like it's harder for startups now because of this distribution challenge?
I don't know. I think it's easier because like Gemini, despite all the kind of heavy cross-selling Google has done, nobody would say they're winning. You know, startups can build in directions that incumbents are uncomfortable building in, like everything we talked around companion, but there's many others.
It's true.
You know, prices can be pretty high. Like people are open to paying 200 a month for their enterprise, they're open to signing million-dollar ACV contracts without really knowing what they're getting. So, I think like the floodgates are open. It feels like Christmas 2009, where everybody got their iPhone and wanted to download new apps. You know, that'll change at some point. People will feel like, you know, they're done and they're tired and they don't want to try any more apps, but the windows are open for now. I think it's easier for startups.
That's a really interesting insight and your line about how it's not a distribution or growth problem, it's a product problem, is such an important one. Because if your product was that good, people would talk about it and share it and use it.
And it can be. I mean, what are the wild social experiments that we're going to see with this technology kind of intermediating them? You know, I think a lot about actually, here's a fun example from a few years ago. Have you heard of MSCHF? You know MSCHF?
Yeah. Yeah. Yeah.
Right, they're awesome, right? They're sort of like this creative studio that uses technology as their medium. I'm very web 2.0 in that way, actually. Many of the kind of, you know, the Ev Williams, the Kevin Roses, that's who they were: painters, except technology was their canvas.
So, they created this very funny product called Card versus Card where they shipped, I think, 100,000 people a debit card and then every day they would text those people with a location that they had to spend the money at and they would put $100 on the card and everyone would rush out, spend the money, and one or two would be able to spend it and everybody else would get declined. And it was just this hilarious social experiment that went hyper viral and for me it was always inspiring in the world of fintech cuz it was like, "Wow, why don't we build more products like that?" You know, money is inherently social and yet all the financial products we have are so dry and personal and embarrassing. I think there's a sort of similar moment happening in AI right now where we can build these wildly ambitious products that touch on many of our social nerves. We just have to do it.
So, let me follow that thread. You get to see tons of companies both pitching you and also companies you're working with that you're investing in. What are some counterintuitive lessons you've learned from watching the companies that operate well and have succeeded? Lessons that maybe go against typical wisdom, startup wisdom.
I'll tell you the biggest one and, you know, in the old days, which is 3 years ago, we would see a company and if what they were trying to do was too ambitious, we would, you know, not engage. Just too crazy, too complex. You know, and implied by that is you wouldn't do a $100 million seed because it's just too much money for almost any problem. It's too much money for any person to actually manage. It's too much money for the sort of talent to absorb. Like it doesn't make sense as an inception round. I think today we're almost seeing the opposite problem where, you know, an idea that's too small is not something that we want to engage with. And you can talk about how you put $100 million to work in the seed productively. Now, I'm not recommending you raise $100 billion, but I think that there's this sort of no ceiling on ambition that is also showing up in how we're picking companies. And maybe how they're picking us as well. Because when we invest, we tell every founder like we're here to help you build the strongest form of your vision. You know, Marc told this to me when he and I were talking about coming here. And he said, "Anish, the way we sort of told our story when we were raising our first fund was, you know, we were going to the moon or we were going to leave a moon-size crater in the ground. And there was no other option." That's sort of how we want to work with our founders as well.
I love that point. So, like can you get too ambitious? Is there like a limit? Obviously, you look at the, you know, the team and what they're But kind of the key lesson here is be more ambitious. It's like the opposite of what used to be of like here's our wedge. Here's where we're going to go. What you're looking for is just how big is the idea.
I mean, look at Atoms.
Yeah.
How crazy is Atoms, you know? I mean, what an incredible hero's journey for all of us collectively. But also what they're trying to do is something that, you know, 5 or 7 or 10 years ago would have felt insurmountable. And now it's like, "Okay, it's challenging. Let's see." You know?
Interesting. Is there anything else that has changed or I guess you've changed your mind about around what you think it takes to build a successful company these days?
I mean, I think that the kind of old wisdom around consumer products have to be free. I'm almost taking the opposite take, which is let's think about consumer products that are extraordinarily expensive. I think every part of consumer discretionary spend is up for grabs right now. And I think a really useful, because price is a measure of product market fit, a really useful product exercise is what is the Birkin bag, $10,000 a month, $1,000 a month version of our product. So, I think expensive consumer software is something like new and important that we wouldn't have thought about 5 years ago.
I love that framing. Cuz it just pushes you again to be more ambitious.
Yeah.
You mentioned Marc and you work closely with Marc Andreessen and Ben Horowitz.
Yes.
What's one thing you've learned from each of those guys?
They're such extraordinary leaders, founders. I mean, the thing that I actually feel so grateful to be a part of that they really embody to their core, I think, is a feeling of stewardship for the technology industry and for really the country and maybe the western way of living and thinking. You know, and if you look at sort of a Ron Conway, you know, or Brook Byers, Tom Perkins, there was this feeling, I think, of obligation to sort of leave it better than you found it from an industry perspective. And that sort of aspiration goes way beyond just being the best investor in the world, though we want to do that, too. I see them both show up that way over and over again, where they want to do hard important things that don't directly benefit the firm or at least not singularly because they're just sort of important. You know, Ben has done a lot of that in the direction of the industry and Marc in sort of the direction of the country, though of course both work on both.
Another really cool thing is just to see how I think Marc and Ben, but maybe the firm a little bit, has shaped our collective ambition as a founder community. I think if you look at 5 or 7 or 10 years ago, deep tech was deeply unpopular. You know, it wasn't a high-status thing to be working on. It was very fringe. And now it's become very popular, high-status, and mainstream. And I think there's a lot of firms that have, of course, pulled in that direction, but I think that like Marc has been very full-throated in his support of working on sort of capital I important work in the national interest. And all of Silicon Valley has changed as a result.
And you guys have had some big wins in the past couple weeks investing wise, too.
Yeah. Thank you.
Congrats. Final question before we get to our very exciting lightning round. What's your advice to product people who are trying to think about what they might want to shift in how they work, how they think, how they operate to be more successful in the future in their careers and with their companies?
Just make more things. And I know it sounds silly. I know everybody says it, but just please like come up with a project. You don't have to tell anyone about it. It can be totally unimportant. But use it as a chassis to use all the new models, ship things, talk about them, build your own intuition. I promise you're one sort of slightly frustrating and then very fulfilling week away from being as pilled as anyone. So, you just got to use the technology. And if not now, then when, right? All of us got in the game to build the products we saw in our mind's eye. And now you have a chance to do it. So, just please, please use the models. You know, and tell me what you built. Text me. You know, tag me. Like I
will reply and respond and engage with you and so will everyone else because we that the magic of Silicon Valley is that it's a very positive sum mindset. You know, it's sort of everybody is building on each other and vulnerability is really rewarded. So, I definitely would encourage people to use the models.
>> What's like a good heuristic if you were doing this enough? Is it like build something once a month? Is it sit like some number of hours per day sitting, talking, building? Anything that you think might help people be like, "Okay, you're doing a good job."
>> I mean, just ship something once a week. And it doesn't have to be crazy. You know, I mean, for example, when I was playing around with Codex, I had it build a slide deck for Mother's Day for my wife that pulled from my text messages. It looked at my photo gallery. It set some music to it. Created like a 20, you know, slide deck of our relationship and pulled some cool old texts from when I first asked her out. It was a really nice Mother's Day, you know? I mean, it wasn't important. It wasn't something that I It might come back to, but it was shipping something. So, it can be that small.
>> I know that you're a our mutual friend Nikil Singh Goel. You worked at Credit Karma with him. He had a really good way of thinking about this. He finds that people flip on AI and how they feel about it once they find some moment of joy that it had created for them. And this Mother's Day idea is such a good example. And so, I think that's kind of a tip I always think about is just like what's something that just would will bring you joy if this works.
>> What's something you can do for someone else? You know, maybe that's a good starting point as well. I love that.
>> Anish, before we get to our very exciting lightning round, is there anything else that you want to share, anything else you want to double down on before we get into the lightning round?
>> I don't think so. I've loved the conversation so far.
>> Me too. And with that, we reached our very exciting lightning round. I've got five questions for you. Are you ready?
>> I am.
>> Okay. Here we go. What are two or three books that you find yourself recommending most to other people?
>> Yeah, so okay. Conquests and Cultures is my very favorite book. It's Thomas Sowell, and it just talks about how conquests have led to culture change in different societies around the world. Sometimes positive, sometimes negative. I think to me, it's just the best historic view of sort of culture as the biggest driver of outcomes. And I've experienced a lot of that as, you know, somebody who was born in Canada and lived here to America, which has a very different culture of ambition. There's that word again.
So, Conquests and Cultures is great. Seven Powers, you mentioned, is actually just an awesome book. I think it's a very intellectual distillation about kind of, you know, moats in business theory and compounding advantages. I really like it.
You know, maybe the third is the one that Marc I actually thought that he was maybe punking me when he sent this to me before I started. I said, "Marc, are there any books that I should read?" and he sent me a couple. And one was a book, a textbook, called Increasing Returns to Scale. Which I think is Brian Arthur is the author. It's an awesome book. It's a you know, it's a slightly dense study of why things like software have such outlier economic effects, but it really helps put things in perspective or it helped me in terms of like why does our industry work in the way that it does? Why does our culture work in the way that it does, right? Why are we so positive sum? I'd love to say that we're better people, but I think it may be a sort of better system and structure we work within.
>> Favorite recent movie or TV show you have really enjoyed?
>> Oh, man. I watch trashy movies and TV. I mean, we watched House of Dragon. Which is pretty cool. Yeah.
>> Yeah, I don't know. Yeah, maybe it's not highbrow. It's not important with the capital I, but it was awesome. We loved that. And then I did see Odyssey. I saw it in London. I was there for a board meeting in an IMAX theater packed with people drinking pints and having fun and amazing. It was cool cuz it's just a whole The movie was great, but it was just like a theater experience, like a weird social experience, kind of alone together. Those are two recent ones.
>> I have not been able to get tickets to the Odyssey at an IMAX. So, I actually have a Grok bot just watching the site constantly and finding me good seats.
>> Perfect.
>> Perfect. They've got them dumb CAPTCHA though on the AMC website that hasn't been able to get through.
>> Really?
>> It's like a tricky one. It's a really tricky one.
>> Is it the like select the fruit? Anyway.
>> Yeah, it's like you have to click three different matching shapes, which like come on, you can't do that. Hopefully by the time this comes out I've seen it. I think there's few podcasts that are on like I haven't seen it yet. Oh, man. Okay. Favorite new AI product. I don't know. Favorite AI product recently that you've that give me joy.
>> Oh, man. I've spent a lot of time with the personal agents. I think Grok's okay. I'm going to give it to Grok bots only cuz it's just so unhinged for it to be so ambitious about sort of caching credentials and getting work done on your behalf. Like, I love it and I expect it from a startup. But they actually are doing things that I think no other sort of big company would do. It's a really, really well done. It's a really thoughtful UI. It's got a really powerful foundation model. I think it also is like, okay, wait, maybe this is not a two-horse race on the sort of model side. So, I just think the product is like fun and ambitious and is sort of taking risks that other products like that wouldn't take.
>> Two more questions. Do you have a favorite life motto that you often come back to in work or in life?
>> Oh, man. I've got one. It's I learned this or I sort of, you know, this is from my founder days, but it also is something that's very true of parenting. Which is, you know, don't discover things through painful experience that somebody can just tell you. So, and I unfortunately have had a bad habit of sort of discovery versus learning from somebody who's just a few steps ahead of me and I find my children have that habit, too.
>> What's one example of something that you wish someone had told you?
>> I mean, for my kids, it's don't touch the hot stove. For my startup, it was like
>> Literally.
>> For my startup, it was don't build a product and a platform at the same time. You know, if you're going to be a platform company, build one. Our first company we tried to build a sort of a social platform for mobile games and be a gaming studio. And you know, somebody wise told me right away, like, look, you can't being a studio is so hard, much less being a studio and a platform. Pick one. And we did and it was it took us years to figure out we were wrong.
>> Final question. I asked Ben Horowitz what to ask you about and he just said, ask him about his DJing. He's very good. And I went to your I found your DJ site I don't know if that's what you call it on SoundCloud/illscience. It's very good. I'm just like listening to it while I work. Any tips for somebody that wants to get into DJing, any tools you found useful, any I don't know, insights that might help someone become better at this?
>> Totally. I mean, I think that this is why I love music models so much. So DJing has always I mean, I love DJing. I've been playing for 30 years now since '95, actually 31. Wow. And it's an awesome way to kind of express yourself musically if you're not a classically trained musician. You know, you select the music, you pick the records, you mix them together, so it requires some technical skill. But now I think you can go a step further and just make music with the models. And the best part is when you can come up with music ideas and have the models do the kind of strong form of them. So I think music is just such a visceral, satisfying way to kind of, you know, experience and, you know, provide experiences in the world. So whether it's DJing or making music, I just suggest everyone do it.
>> I hadn't thought about how DJing has changed now that you have Suno and things like ElevenLabs and all these things where you could just generate music, not have to just splice together existing music.
>> I mean, think of the history of it. You know, you went from Okay, you know, first you could only hear music if you were there with the person playing it on an instrument. You know, recorded music on the phonograph. The big change in music actually, from a medium perspective, was the cassette tape. Cuz the cassette tape was really the first time you could create, right? You could actually compose your own quote album. I think a lot of why music struggled in the 2000s is the sort of that went away and we went back to broadcast, and now that people are making music again, I think the music industry is going to be bigger than it's ever been.
>> Wow. Man, so much disruption. Anish, this was incredible. We covered so much ground. Final question, how can listeners be useful to you?
>> I mean, show me what you're building. Please don't be despondent. Build something and then tag me, and I'd love to see it. If you're interested in hearing more stuff like this, please follow me on X. I try to kind of engage and follow back. And otherwise, make sure you check out all the amazing folks in Lenny's network. Claire is a star. Elena is so, so good. And there's just so much compelling content here.
>> Nikkel, which we talked about.
>> Nikkel is the best, man. That guy is everything.
>> And amazing. Anish, thank you so much for being here.
>> Thank you, Lenny.
>> Thank everyone.
Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at Lennyspodcast.com. See you in the next episode.
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