‘Nothing Ever Happens’ Is Over

Summary

Naval Ravikant and Nivi open with how AI is reshaping company structure. At Naval’s new startup Impossible, there’s no Slack, no project management tool, no explicit intranet — just a “fully interconnected graph” of highly intelligent generalists routed through a hub-and-spoke co-founder. Naval argues that hierarchy is “a requirement of size” and a tax on talent, and that AI now makes the interconnected-graph model viable: agents read code, summarize papers, identify subject-matter experts, and generate dashboards on demand, so companies no longer need the legacy scaffolding of middle management and BI tools. Hardware engineers can write 20–30% of the software they need; AI engineers can build their own harnesses — everyone becomes a generalist.

The conversation pivots to the geopolitical and technological phase shift Naval senses post-COVID. The “nothing ever happens” meme on X, he says, is over. VCs are funding rockets, drones, and sci-fi tech. Drones in particular bring “the logic of mutually assured destruction down to the individual level” — a shift on the scale of the rifle (which ended feudalism and birthed the nation-state) and nuclear weapons (which created the seven-to-nine sovereign-state world order). Bioweapons follow the same democratization curve: AI lowers the activation energy for both attack and defense, but the defensive side is choked by medical regulation, data silos, and bioethicists who block volunteer “right to try” trials.

The episode closes with a defense of irrational optimism. Doom scenarios are “more legible to our minds” — easier to imagine than the creative leaps that produce new jobs, new industries, and new defenses. Naval argues that hardware is entering a renaissance because AI finally makes the software layer trivial: security cameras, programmable lamps, and toys no longer need bespoke apps when each user’s personal agent can drive them directly. China and Nvidia push open-source because it commoditizes their complement (hardware). The only path through the “interesting times” curse, Naval argues, is to be irrationally optimistic — the doomer pulling the optimist back down “is not the person you want to be in a foxhole with.”

Highlights

”I hate large groups… I just prefer keeping groups small”

“I actually hate organizational management because I hate organizations. I hate large groups. I think it’s just so hard to get things done and you’re not dealing with the best and the brightest and there’s always politics. So I just prefer keeping groups small, and we count on people to just operate independently and communicate with each other as needed. Like I said, we don’t even use Slack, we don’t use any project management software. I think it’s just GitHub, and then when people want to talk to each other, they just text each other, literally, they talk one-on-one.” — Naval Ravikant, 0:20

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”You don’t need the explicit intranet as much anymore”

“If you’re reading code that was written by somebody else that’s very complicated, you can just have the AI read it for you and give you a summary. Papers, they can read other people’s papers and give you a summary. It can actually go through the codebase and tell you who in the organization is likely to be an expert on what topic and guide you to them. So AI can do a lot of that digging for you. You don’t need the explicit intranet as much anymore.” — Naval Ravikant, 4:14

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”Nothing ever happens is over”

“The famous meme, I think, on X was like, ‘nothing ever happens’, right? I think that’s over. I haven’t quite been able to put my finger on why, but I think anyone who is paying attention would tell you that post-COVID, the world is changing a lot faster… We are living within that Chinese curse of ‘may you live in interesting times’.” — Naval Ravikant, 9:34

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”Drones bring the logic of mutually assured destruction down to the individual level”

“Drones bring the logic of mutually assured destruction down to the individual level. If you really hate somebody in the future, a drone will be able to get them. That’s a weird form of violence coming up that’s going to basically restructure society as we know it.” — Naval Ravikant, 12:15

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”The same way vibe coding is democratized… bioweapons access is democratized”

“Now that power is going to be democratized just like vibe coding is democratized. Now the number of people who can vibe code is hundreds of thousands of times greater than the number of people who were coding. And so the same way, the number of people who can get access to biological weapons or viruses is hundreds of thousands of times what could have gotten access to them before. So that’s a pretty scary thought.” — Naval Ravikant, 13:03

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”Optimism requires creativity”

“I don’t get worked up about it because I think it’s just so much easier to imagine doom scenarios than it is to imagine positive scenarios, because optimism requires creativity. For example, the job loss thing is a clear example. It’s very easy to look at existing jobs and see how they will go away, but it’s very hard to predict what the next job will be.” — Nivi, 17:44

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Key Points

• Hub-and-spoke at Impossible (0:20) - Co-founder is the CEO and one product manager who keeps the whole org in his head; everyone interfaces through him; no Slack, no PM software, just GitHub and texts.
• AI as an implicit force multiplier (0:20) - Not used for explicit communication, but everyone benefits because AI handles the digging.
• Networks and hierarchy (0:20) - Tree-structured hierarchy is the traditional answer to communication overhead, but it’s stifling and politicized; Naval prefers the fully interconnected graph.
• Fully interconnected graph requires intelligent nodes (0:20) - Hire highly intelligent people who can navigate to the person they need; if they can’t, they belong in a hierarchical org.
• AI reads code, papers, and finds experts (4:14) - Get summaries on demand; AI can identify who in the org is the expert on a given topic.
• Reports and dashboards on demand (5:00) - Unleash AI on codebase, designs, supplier database, or email to generate gantt charts and resource analyses on the fly — no BI integration needed.
• Specialists become 20–30% generalists (5:44) - Hardware, software, and AI people can now do 20–30% of each other’s work; AI people build their own software harnesses; hardware people bring up new devices without waiting.
• Better touchpoints, no explicit APIs (6:00) - Generalists interface naturally; AI can discover or generate an API, or bypass it and connect directly at the database or codebase level.
• The two-to-four AI labs question (7:13) - Is the dominant-labs configuration stable? Does it commoditize, consolidate to Mag 1, or fragment? Does open source have a chance?
• People always want the smartest model (7:13) - Users will trade privacy and open source for the smartest cloud model.
• AGI belief is concentrated inside the labs (7:13) - The frontier-lab insiders believe value will disappear into the AI labs.
• World models are not video models (7:13) - A real world model has an agent that predicts consequences and adjusts behavior — a reinforcement-learning loop. Generating “a world you can wander in” is not a world model.
• Yann LeCun’s JEPA (9:00) - Cited as an example of an emerging world-model approach.
• “Nothing ever happens” is over (9:34) - Post-COVID, geopolitics, economics, and technology are accelerating; VCs being forced into rockets, drones, AI, sci-fi tech.
• Sci-fi engineers in low supply (9:34) - Sci-fi authors, scientists, and engineers are scarce relative to the new demand.
• The rifle and nation-state analogy (10:44) - The rifle let peasants take down knights, required factories and trained musket men; nation-states arose as the right structure to do that. Drones are now the new logical violence.
• Seven-to-nine sovereign nations (10:44) - Post-1945 nuclear umbrella means only 7–9 truly sovereign states; everyone else lives under one.
• Drone attack vs defense asymmetry (10:44) - Attackers get kinetic energy from gravity plus the advantage of surprise/massing; defenders are spread thin but have a shorter range to traverse.
• MAD at the individual level (12:15) - The terminal point of drone democratization is personal mutually assured destruction.
• COVID and the Wuhan bioweapons lab (12:43) - Naval notes the coronavirus “coincidentally got unleashed right next to the bioweapons lab in Wuhan.”
• Vibe-coding analogy for bioweapons (13:03) - AI democratizes biothreats just as it democratized coding — hundreds of thousands of times more potential actors.
• AI for medical research is data-gated (13:43) - Good-guy research needs everyone’s data anonymized and unleashed for “right to try” — but it’s locked behind silos and the worst regulations of any industry.
• COVID vaccines were too slow (14:22) - Even in an emergency we didn’t allow volunteer “give me COVID, I’ll take one for the team” trials; bioethicists blocked it.
• Hardware renaissance (15:10) - Historically hardware was bottlenecked by bad software; AI removes that constraint. Apple has both; Google has cloud and AI but weak hardware/consumer software.
• AI agents replace device software (15:10) - Security cameras controlled by each person’s agent don’t need custom apps; programmable lamps, kids’ toys all benefit.
• Why China loves open source (15:10) - Open source commoditizes their complement (hardware); same for Nvidia, same for hyperscalers — all push for open AI models.
• Optimism requires creativity (17:44) - Doom is easier to imagine than the next jobs and technologies that will emerge.
• 200 years ago no one could have imagined today’s economy (18:11) - Couldn’t have imagined 10% of today’s jobs back when everyone was farming.
• Irrational optimism is the only way out (19:14) - We have to nurture and reward optimism; the crab-in-a-bucket doomer is not who you want in a foxhole.

Mentions

Companies

• Impossible (0:20) - Naval’s current startup, run as a flat hub-and-spoke; no Slack, no PM tools.
• Square (0:20) - Jack Dorsey reorganized Square around AI.
• Shopify (0:20) - Tobi Lütke is doing AI-driven organizational experiments.
• USVC / AngelList (2:57) - Episode sponsor; venture fund every American can invest in; includes OpenAI, Anthropic, xAI, and Vercel; Naval is chairman of the investment committee.
• OpenAI / Anthropic / xAI / Vercel (3:00) - Holdings in the USVC fund.
• Varda (5:44) - Cited as a company with separate hardware, software, and AI teams that AI now glues together.
• NVIDIA (7:13, 15:10) - Part of the “Mag” count if you include hardware; commoditizes its complement by pushing open-source AI models.
• Apple (15:10) - The exemplar of integrating great hardware with great software; weaker at cloud and AI.
• Google (15:10) - Strong in cloud and AI; weak hardware and consumer software.

Products & Technologies

• GitHub (0:20) - The only tool Impossible standardizes on.
• Slack (0:20) - Pointedly NOT used at Impossible.
• Claude Code (15:10) - “Some bright kid with Claude code” can write all the hardware-driver software you need.
• JEPA (9:00) - Yann LeCun’s recent world-model architecture.
• Drones (10:44) - The new logical violence; restructure militaries and states.
• Bioweapons / vaccines (12:43) - Democratized by AI on attack side; constrained by regulation on defense side.

People

• Naval Ravikant (0:00) - Host.
• Nivi (Babak Nivi) (0:00) - Co-host.
• Jack Dorsey (0:20) - Reorganized Square around AI.
• Tobi (Tobias Lütke) (0:20) - Doing AI organizational experiments at Shopify.
• Elon Musk (0:20) - Cited as an example of “founder mode” CEOs who break through hierarchy.
• Brian Chesky (0:20) - Same — celebrated for going “founder mode.”
• Yann LeCun (9:00) - Recently published JEPA, a new kind of world model.

Themes & Concepts

• Fully interconnected graph (0:20) - Naval’s preferred organizational topology.
• Hub-and-spoke architecture (0:20) - One product manager keeping everything in their head.
• Founder mode (0:20) - The CEO finally getting permission to talk to engineers — said sarcastically.
• Jagged intelligence (7:13) - Current AI is uneven, with weak multimodal reasoning.
• “May you live in interesting times” (9:34) - The Chinese curse Naval invokes for the current moment.
• Logic of violence (10:44) - Naval’s framing for how dominant weapons technology restructures political organization.
• Commoditize your complement (15:10) - Why China and Nvidia push open-source AI.
• Crabs in a bucket (19:29) - The metaphor for doomers pulling optimists down.

Surprising Quotes

“I actually hate organizational management because I hate organizations. I hate large groups.” — Naval Ravikant, 0:20

“Like, I just think that’s a terrible way to operate, but it’s a requirement of size. And we’re just not at that size. So I don’t like it. Instead, I like the fully interconnected graph.” — Naval Ravikant, 0:20

“The coronavirus that coincidentally got unleashed right next to the bioweapons lab in Wuhan figured it out.” — Naval Ravikant, 12:43

“In the old days, like you would have had a bunch of healthy young volunteers who would have said, ‘Sure, give me this vaccine and then give me COVID, I’ll take one for the team.’ But now because of, quote-unquote, bioethicists, we don’t even allow that.” — Naval Ravikant, 14:44

“We have to be irrationally optimistic because that’s the only way out of this anyway.” — Naval Ravikant, 19:14

“Whenever people sort of do the crabs in a bucket thing where they try to pull the optimist back down and they keep saying doom doom doom they might be right but it’s certainly not helping matters that’s not the person you want to be in a foxhole with.” — Naval Ravikant, 19:29

Transcript

Nivi: 0:00 You’re listening to the Naval podcast, this is Nivi. There’s no set topic for this episode; it will be a potpourri. Naval, how are you using AI at Impossible, your current company, to change how you manage the business, or are you guys just too small and a bunch of brilliant independent contributors where it’s not having an effect on how you actually run the company?

Naval Ravikant: 0:20 It’s more the latter. We’re a hub and spoke architecture. My co-founder is the CEO, and everyone kind of reports into him. He’s just kind of the one product manager who runs around with everything in his head to try to bring this whole impossible task together, and everybody’s interfaced through him. And people are pretty smart. We keep a very flat structure. We try to push people to communicate with each other directly. We don’t even use Slack, if that gives you a sense. So we’re not using AI as a communication method explicitly inside, but implicitly AI is still very helpful. So we’re not like Square, I know Jack Dorsey reorganized Square around AI, and maybe Toby at Shopify is doing that. You know, there are some guys who are very good at organizational management and they do these kinds of experiments. I’ve never been good at organizational management. I actually hate organizational management because I hate organizations. I hate large groups. I think it’s just so hard to get things done and you’re not dealing with the best and the brightest and there’s always politics. So I just prefer keeping groups small, and we count on people to just operate independently and communicate with each other as needed. Like I said, we don’t even use Slack, we don’t use any project management software. I think it’s just GitHub, and then when people want to talk to each other, they just text each other, literally, they talk one-on-one. And sometimes it’s chaotic and they have to figure out how to navigate their way through towards, but that’s part of the skill set. It’s sort of like in computer networks, how do you organize a network for efficiency because at some point the communication overhead gets very high. The traditional answer is hierarchy, it’s a tree system. It’s like there’s one person at the top, the CEO, then a bunch of VP’s or SVP’s report to them, then a bunch of VP’s below that, and then middle managers and so on. And that keeps things organized and marching in one direction, but it’s stifling. There’s a lot of politics. You can’t talk to people two or three levels below you unless you go founder mode, like Elon or Brian Chesky is celebrated for some wonderful achievement that all of a sudden the CEO’s allowed to talk to an engineer. You can tell I’m being sarcastic there. Like, I just think that’s a terrible way to operate, but it’s a requirement of size. And we’re just not at that size. So I don’t like it. Instead, I like the fully interconnected graph. And that’s insane. Fully interconnected graph is everyone talking to anyone with a light hub and spoke with one person in the middle who’s trying to keep everything in their heads. The thing about a fully interconnected graph in networking is that every node has to be highly intelligent. So that’s what you do, you hire highly intelligent people who can operate in a fully interconnected graph. And if they can’t navigate their way to the person they need to talk to to solve a specific problem, or if they can’t cooperate or communicate with other people, then they don’t belong in this kind of an organization and they should just go and find a hierarchical organization where they’re going to be more comfortable. So we don’t really rely on any tools.

Nivi: 2:57 This episode is presented by USVC, a public USVC is a venture fund that every American can invest in. Venture capital has been unreachable for most investors. You’ve got your nose up to the glass watching companies compound to trillion dollar valuations in the private markets while you wait for them to go public. USVC was built to change that. It’s a single basket of high growth venture capital across stages, SEC registered, professionally managed, with no accreditation requirement and a low $500 minimum. It’s for US investors only for now. The fund includes OpenAI, Anthropic, xAI, and Vercel. As USVC adds companies, investors will own a piece of those too. And Naval is chairman of the investment committee. Venture is not for the money you need tomorrow, it’s risky and illiquid. Don’t invest anything you can’t afford to lose, but if you have the appetite, there may be no harder working way to deploy a dollar than true venture capital. The smartest young people in the world working insane hours to build the future. Investing is easy, go to usvc.com/podcast. Before investing, carefully read the objectives, risks, fees and expenses in the fund’s prospectus at usvc.com. Investing in USVC is speculative, high risk and shares are illiquid with no public trading market. The fund is new with limited operating history and financial information. It may not achieve its investment objectives or provide distributions and investors may lose all or a substantial portion of their investment. Past performance does not guarantee returns. Distributed by Alps Distributors, Inc.

Naval Ravikant: 4:14 Now AI is implicitly still a very helpful tool within the organization and I can give you two examples although there are more. One is just if you’re reading code that was written by somebody else that’s very complicated, you can just have the AI read it for you and give you a summary. Papers, they can read other people’s papers and give you a summary. It can actually go through the codebase and tell you who in the organization is likely to be an expert on what topic and guide you to them. So AI can do a lot of that digging for you. You don’t need the explicit intranet as much anymore. You don’t need the explicit marking down of things because AI can figure out where you are.

Naval Ravikant: 5:00 You could even unleash the AI on the codebase and on the designs, let’s say you have hardware designs, you could unleash them on designs. If you have suppliers and vendors, you can release them on the database or the file folder in which all the documents with suppliers and vendors are kept. You could even unleash it on the company email if you wanted to and just say where are we, how far are we actually from shipping, draw me a gantt chart based on where you think we actually are in terms of the estimates and the timelines and who’s behind and who’s ahead, which division is lacking resources. AI can constantly be doing this data analysis and digging and reporting for you. Reports on demand. You don’t need specific charts and dashboards and business integration systems, you can just have AI literally recreate on the fly. Maybe you don’t want to be doing it every time because it might be too slow, but you can have it build these dashboards on demand and you can have it update them on demand. So that’s one huge thing.

Naval Ravikant: 5:44 The other is that traditionally in a company you would have the hardware people, in a company like Varda you have the hardware people, you have the software people, you have the AI people, and they kind of wouldn’t be doing each other’s work. But now with AI, they can at least get to 20%, 30% each other’s work. So it makes the gluing between them a little easier. The AI people, for example, can create their own software harnesses if they need to test something. It may not be good for production deployment, but it’s better than having to sit around and wait for a software person to come by and write you some custom code. In the same way, the hardware people can also write a little bit of software to bring up a new hardware device, where otherwise they might have needed to wait for software people. So having AI just lets everybody do a little bit of everything. It makes them more generalists, and by being more generalist, it means that you have better touchpoints to interface with other people. You don’t necessarily need to have someone write you an explicit API to work with their code. You can actually just have the AI go and discover an API or create its own API or just bypass the AI and connect directly at whatever level it wants to, whether in the database or within the codebase. So, it’s naturally a force multiplier, but we haven’t done anything explicit with it.

Nivi: 6:56 What are you trying to figure out right now? The reason I ask is because you rarely get to see work product from smart people while it’s in motion. One of my obsessions is trying to excavate the secrets and inner thoughts of smart people.

Naval Ravikant: 7:13 The world is very different than it was a few years ago. There are two, maybe four companies that are dominant in AI, or five if you count hardware with NVIDIA. And the question is, is that the stable situation? Is this going to be a commodity business, or is this going to be a monopoly business, or is it going to be an oligopoly business? Does it top out at some point? Do they run out of data and the models stop improving, or do we go all the way to AGI? So many of the people inside the labs are believers in AGI and think that all value is going to disappear into the AI labs. Does this end up even more consolidated than the Mag 7 world where there’s just Mag 2 or Mag 1, or does it somehow fragment? Does open source really have a chance, or do people just always want the smartest model, and so for that, they’ll give up privacy, they’ll give up open source, and they’ll just pay up in the cloud? So, I think these are huge questions, huge. These are world-shattering questions. But I don’t know the answer to this. Can you train AI in a distributed way? Is distributed training possible? Or are these things going to centralize more and more and more? I think now the conventional wisdom is going centralized training, two to four companies dominating, data centers and power are the limits, and everyone is rushing towards that. But what if that’s wrong? That would be an interesting contrarian bet, but I don’t yet see the evidence. I mean, I think the emerging conventional wisdom for that part in AI is right. As for AGI, I don’t know. I don’t want to be in the futurist business. Certainly the people in the frontier labs believe it, they’ve believed it for quite a while. The AI that I am seeing has jagged intelligence, it’s also pretty bad at multimodal reasoning. I don’t think it has a good model of the world, although there are all these world model companies coming up. Although I think they confuse something that looks like a world that you navigate in, which people are like, ‘Oh, that’s a world model because it looks like you’re generating something that looks like a world and I can wander around in it.’ That’s not a world model.

Naval Ravikant: 9:00 A world model is when you have an agent that has a model of the world inside its head, which allows it to take actions and then predict the consequences of actions and then adjust its own behavior based on what happened, whether it learned or not. So, have like a reinforcement learning loop. That’s a world model. And so we’re seeing world model companies emerging. I think Yann LeCun famously did one recently with JEPA. And so we are going to see new kinds of models, new kinds of agents, new kinds of intelligence.

Nivi: 9:30 Are we going to get to AGI? I don’t know.

Naval Ravikant: 9:34 Now, that’s the same thing that everybody’s trying to figure out, right? But this world is changing. The famous meme, I think, on X was like, ‘nothing ever happens’, right? I think that’s over. I haven’t quite been able to put my finger on why, but I think anyone who is paying attention would tell you that post-COVID, the world is changing a lot faster. There was some dislocation around COVID, or perhaps it was just we were in unstable equilibrium and COVID just broke that equilibrium then we had a phase shift. But the world seems to be moving a lot faster now, and that’s true geopolitically, that’s true economically, that’s true technologically. VCs are now being forced to fund more hardware, rockets, drones, AI, you know, sci-fi technologies, if you call it. So I think sci-fi technologies are in high demand. Sci-fi scientists and sci-fi authors are in low supply, sci-fi engineers are in low supply. So we are seeing the world shift, and maybe it’s for the better, maybe it’s for the worse, but things are changing very, very fast now. We are living within that Chinese curse of ‘may you live in interesting times’.

Nivi: 10:40 Is there anything you’re trying to figure out in the world of hardware?

Naval Ravikant: 10:44 I think drones are still under-leveraged even though they’ve come to prominence in the battlefield recently. We still haven’t seen anywhere near the end game of drones. There’s nothing in particular I’m trying to figure out there. I mean, I think drone defense is going to be very difficult because a drone that’s attacking has the advantage of both kinetic energy because it’s coming down on you and it’s got the advantage of surprise where the attacker can mass all the attack drones in one area whereas the defender is always spread thin. The defender has one advantage which is short range, the defender has to traverse a much smaller range going up than the attacking drone probably had to cover coming in. But I think that drone warfare changes the structure of violence in society, so it’s going to actually fundamentally change how militaries and entire states are architected. You could argue that the modern state rose up as a consequence of the rifle because the rifle allowed a former peasant to take down a feudal knight on the battlefield. Then you need a factory to make rifles and you had to drill musket men and arm them and train them. And so nation-states sprung up and became dominant instead of feudal states as the right structure to do that within. And then post-nuclear, there’s only seven to nine really independent sovereign nations and everybody else lives under their umbrella.

Naval Ravikant: 12:00 somebody else’s nuclear umbrella, so those seven to nine call the shots, whether it’s the Security Council or elsewhere. And so nuclear weapons were the new logical violence after 1945. Now the newest logical violence is drones, and that’s going to fundamentally shift the game again. Because drones bring the logic of mutually assured destruction down to the individual level. If you really hate somebody in the future, a drone will be able to get them. That’s a weird form of violence coming up that’s going to basically restructure society as we know it. I don’t know which way it goes. Is it going to be the case that you have a few very large, very powerful countries that control all the drones, or is it that drones get so democratized that any individual can be deadly?

Naval Ravikant: 12:43 Also, I think one of the fears with AI is biological weapons. I don’t want to get people worked up, but in theory, if you were smart in the past, you could have figured out how to make a biological weapon, but the number of people who could have done it, who had both the expertise and had the access, were very low. Although it was still too high because the coronavirus that coincidentally got unleashed right next to the bioweapons lab in Wuhan figured it out.

Naval Ravikant: 13:03 So now that power is going to be democratized just like vibe coding is democratized. Now the number of people who can vibe code is hundreds of thousands of times greater than the number of people who were coding. And so the same way, the number of people who can get access to biological weapons or viruses is hundreds of thousands of times what could have gotten access to them before. So that’s a pretty scary thought. Now, we can also do the opposite, which is hopefully now the same AIs can also research how to create vaccines or how to create things to stop them. But the problem is that all the official research, all the good guy research, is always gated behind regulations. And there are almost no regulations out there as bad as medical regulations.

Naval Ravikant: 13:43 One of the real opportunities out there, I think, is for AI to solve medicine and biology and therapies. But to do that, you need the data. You need to be able to look at everyone’s data set. You need to be able to look at all the outcomes. You want as much data as possible. And this data is hidden behind so many silos and so many regulations and rules. And for good reason, you don’t want to target individuals. But if you could anonymize, clean up, and allow that data set to get out there, and then you could let people test therapies with a right to try, then I think you could have reasonable defenses. But my fear is this will only happen in an emergency situation.

Naval Ravikant: 14:22 Even during COVID, when we had the emergency situation, we took a long time with the vaccines, which turned out not to be that effective anyway. But it took a long time with the vaccines because we just didn’t let people operate under volunteer situations and right to try. It just took way too long. Whereas I think in the old days, like you would have had a bunch of healthy young volunteers who would have said, ‘Sure, give me this vaccine and then give me COVID, I’ll take one for the team.’ But now because of, quote-unquote, bioethicists, we don’t even allow that.

Naval Ravikant: 15:00 Yeah, there’s just too much bureaucracy in the system. Too many people who can say no to the few people who are trying to get things done. And so for that, I do worry a little bit about the future.

Naval Ravikant: 15:10 What else is interesting in hardware? Hardware, I think, is going to undergo a renaissance because historically, the problem with a lot of hardware is that it’s very hard to write good software. And so you get all this incredible hardware coming out, but the software is terrible, so the device itself doesn’t function well. Apple has done really well because they integrate hardware with high-quality software. You know, most companies do one or two things well. Apple does two things really well: they build great hardware, they build great software. They’re not that good at cloud and AI. Google’s very good at cloud, very good at AI, but they’re not very good at hardware, for example. And software, I would say, they’re good at certain kinds of software. They’re good at cloud software, they’re not good at consumer software. Now, all of a sudden, you have all these companies that are very good at hardware, but not good at software. They can make good enough software, or they don’t even need to make software. My AI agent will interact with the hardware directly and I don’t need the software anymore. So if you’re someone, for example, who was making security cameras, or you were making, like, toys for kids, or you were making programmable lamps, all of a sudden the software for that just got a lot easier. You can have some bright kid with Claude code just get in there and build you all the software that you need. Or maybe you don’t need any software because your security cameras are now controlled by each person’s agent and don’t need custom software any longer. So I think that hardware itself is getting unlocked through software. And this is, I think, one of the reasons why China is so big into open source. Now, they’re behind, so when you’re behind you try to catch up through open source. I think also it’s a little bit of their nationalist pride that we’re in it together, maybe the government’s funding them and encouraging them to do open source. But it also plays well into their hardware dominance. China’s manufacturing most of the consumer electronics goods, and so for them, open source is hugely beneficial because it commoditizes their complement. Same thing for Nvidia. Nvidia just wants to sell as many cards as possible, so they want people to use as many AI models as possible, so they want it all to be open source. So you have a bunch of hardware players, including most of China and Nvidia, whose incentive is, hey, it should all be open source. Hyperscalers also, they want it all open source. So they drive open source on the AI models, and then that commoditizes software and the software unlocks more hardware. So I think we’re going to see more and more interesting, usable hardware because now the software is figured out enough that that hardware becomes unlocked and quite usable.

Naval Ravikant: 17:41 I don’t get scared or worked up about the future, partly because I’m a blind optimist and partly because I live in the first world.

Nivi: 17:44 Yeah, I don’t get worked up about it because I think it’s just so much easier to imagine doom scenarios than it is to imagine positive scenarios, because optimism requires creativity. For example, the job loss thing is a clear example. It’s very easy to look at existing jobs and see how they will go away, but it’s very hard to predict what the next job will be.

Naval Ravikant: 18:00 but yet inevitably there’s always a next job because of that I think people tend to fixate on the doom scenarios it’s much easier to imagine the methods of doom than to imagine the methods of rising up. There is no one no one 200 years ago who could have imagined how we would end up where we are today in terms of technological advancement and capitalism and economics and you know the rise of various societies they just couldn’t have imagined it they couldn’t have imagined 10% of the jobs that exist today because back then everybody was working on a farm but nevertheless here we are so the same way I think the doom scenarios they imagined are actually very similar to the same doom scenarios that we imagine today like even a hundred years ago every decade I’ve been alive there’s been a new environmental catastrophe to come along someone’s talking about the end of the world because of the environment and then every decade there’s a catastrophe coming along because of a war that’s going to end the world and yeah sometimes you get really close COVID was scary if COVID had actually turned out to be a much more nasty virus we could have been in a bad spot if there was a World War III where we started exchanging nukes that would be a very bad scenario so these things are easier to imagine they’re more legible to our minds so we hold them closer to us plus the outcome there is so catastrophic that people obviously fixate on it but I think it’s very hard to imagine creativity it’s very hard to be optimistic and so I think we have to nurture optimism we have to reward optimism we have to be irrationally optimistic because that’s the only way out of this anyway

Naval Ravikant: 19:29 so whenever people sort of do the crabs in a bucket thing where they try to pull the optimist back down and they keep saying doom doom doom they might be right but it’s certainly not helping matters that’s not the person you want to be in a foxhole with