Founder's Story - Everyone Is Asking The Wrong Question About AI | Ep. 422 with Rana Gujral CEO of Behavioral Signals

Episode Date: July 20, 2026

Daniel and Rana Gujral, CEO of Behavioral Signals, begin with the biggest misconception in AI: that the real debate is about capability. Rana argues that the more important question is not whether AI can write, reason, analyze, or outperform humans on benchmarks, but whether it is strengthening human instinct or quietly replacing it. From there, the conversation explores why enterprise AI often fails when companies use it as a headcount-reduction shortcut, why workers resist tools they fear will train their replacement, and why AI has to be built into redesigned workflows rather than bolted onto old processes. Rana also breaks down voice deepfakes, machine consciousness, artificial general experience, trusting intuition, the role of failure, and why being human is about creating meaning under constraint. Key Discussion Points Rana says the public AI conversation is focused on the wrong axis: instead of asking what AI can do, we should ask what using AI does to human attention, judgment, and instinct over time. He explains that AI harm may not arrive as one dramatic rupture, but through quiet drift: defaults, recommendations, attention systems, and convenience slowly reshaping how people think. Rana argues that many enterprise AI rollouts failed because companies believed in a “fantasy of substitution,” assuming they could drop a model into a workflow, remove people, and instantly book savings. He says real work is full of exceptions, judgment calls, relationships, and context, and that AI often handles the middle of the workflow but fails at the edges where the real value lives. Rana explains that employees may resist AI not because they are illiterate, but because nobody has answered what happens if the tool makes them more productive: more meaningful work, more workload, or replacement. The conversation explores machine consciousness, with Rana warning that fluent language, empathy, memory, and personality can make systems feel conscious even when that may be human projection rather than evidence. Rana introduces the idea of artificial general experience, arguing that the more practical question is whether machines develop stakes, preferences, and something that functions like caring about outcomes. He says we are entering an era where “hearing is no longer believing,” because voice cloning tools can replicate someone’s voice from only a few seconds of audio. Rana explains that older deepfake detection methods looked for imperfections in synthetic speech, but newer models are learning to patch those tells, making behavioral and temporal patterns more important. He shares that Behavioral Signals focuses on how a specific person speaks over time, including cadence, articulation, co-articulation, and prosody patterns that are harder to fake consistently. Rana reflects on leaving India after undergrad and walking into uncertainty, saying the biggest lesson was that life does not follow a clean formula and the future is far more unpredictable than we are taught. He says one thing he wishes he had done earlier was trust his instincts, because intuition is not magic; it is accumulated experience compressed into a signal. Rana explains that failure is not a detour from success but the road itself, because suffering and breakdowns reveal what someone values, what needs protection, and where their understanding ends. He argues that a smart machine gives the right answer, but a machine that understands can explain why that answer holds, where it breaks, and what would have to be true for it to be wrong. Rana shares his turnaround philosophy: the secret unlock is not a clever pivot, but radical honesty—naming the real problem in the room and giving people a concrete next action. Takeaways The biggest AI risk may not be replacement overnight. It may be the slow erosion of human judgment as people outsource thinking, framing, and decision-making to systems that feel helpful. AI works best when companies redesign the workflow around human-machine collaboration instead of inserting a chatbot into old processes and expecting transformation. Voice deepfakes are becoming a trust crisis, and Rana believes society will need to normalize verification, including callbacks, family code words, and skepticism under emotional pressure. Human intuition should not automatically lose to spreadsheets. Rana sees intuition as pattern recognition built from experience, and analysis as a check—not a replacement. Machines may become more intelligent, but understanding requires consequence, transformation, and the weight of experience—not just eloquent answers. Closing Thoughts Rana Gujral’s conversation is less about AI hype and more about what AI forces us to confront in ourselves. As machines become more fluent, more persuasive, and more integrated into our decisions, Rana argues that the real question is not whether they can think like humans, but whether humans will keep building judgment, meaning, and instinct of their own. This episode captures one of the deepest AI conversations on Founder’s Story: a warning about convenience, a framework for trust, and a reminder that being human means building meaning under constraint. 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We're officially in the era where hearing is no longer believing. Wall Street Journal ran a story about a mother who got a call from someone who sounded exactly like her daughter. Panicked, crying, begging for help. It was just not her. This is Rana Goushrel, Inc. Magazine named him an entrepreneur to watch. He's the CEO of Behavioral Signals. And after years studying how machines read human behavior, he's landed on something uncomfortable. What you're about to hear might change the way you think about AI. The important question isn't how clever it makes you sound in the moment.

It's whether the terrifying part is that it... What is the biggest misconception that you think people have right now around AI? Yeah, I think the biggest misconception, honestly, is that the debate is about capability. Like, can it do this task? Can it do that task? will it pass this benchmark, when will it beat humans at X? That is the entire public conversation. And I think it's the wrong axis. The real story isn't what AI can do.

It's what using AI does to us over time. As a shift, I keep trying to get people to see because once the system sits inside your loop of attention and judgment, the important question isn't how clever it makes you sound in the moment. it's whether it's building your instincts or quietly just replacing them. So I'll give you the version I noticed in myself, right? So at some point, I stopped using AI the way I use, say, a calculator. Like something you pick up and put down, I started using it as the way in the way I use my own mind,

like to draft touch, to test arguments, to frame decisions. You know, that felt like productivity. And it is. but there's a second thing happening underneath that nobody talks about. Like, am I accumulating judgment or am I generating a convincing stream of outputs that feel like thinking without actually being it? And I think that distinction matters. Almost no one is asking it because we're all fixated on capability benchmarks.

And the other big misconception related to that is people imagine harm from AI as something dramatic, like, you know, a rogue system, a job apocalypse, some rupture moment, you know, that's going to happen. And what I actually worry about is something more dangerous. It's the drift. The most consequential changes AI brings don't arrive as ruptures. They arrive as defaults. Like, you know, decision pipelines that surface a recommendation before you formed an opinion, like attention systems that learn your psychology with unsettling precision. And none of that is coercive. It's just convenient.

And the convenience is more than enough to reshape a life or society without anyone noticing until it's done. So I think what I would correct, one thing in the public conversation, it would be this. It's like stop asking what AI can do. I mean, start asking what it's doing to shape the human thought. I think that's the real frontier. Well, it seemed like we were so excited about AI, specifically, regenerative AI, being able to replace so many tasks, being able to free us up. But a lot of the research is showing that many companies are not getting any better. So employees are not really

adopting it at a corporate level. And they're not really finding that it's really great at replacing people even. Many companies are hiring people back. What are you seeing in terms of this being our savior. I built Founder Story from a $50 microphone, and the most important thing is I didn't do it alone. For years, I've been using Upwork to hire marketing, editing, branding, you name it. In fact, the editor who cut this very episode and the team behind all of Founder's Story branding found them on Upwork. The quality of people is top-notch and paying people is simple. Upwork is a one-stop platform to find hire and pay expert freelancers across development, data, marketing, operations, and more. With Business Plus, you can access the top 1% of talent on Upwork, and with AI-powered shortlisting, you'll get matched to the right freelancer in under six hours.

No endless searching required. It's free to sign up and posting a job is easy. Visit Upwork.com right now and post your job for free. That is upwork.com to connect with top talent, ready to help your business grow. That's upw-w-org.com. Upwork.com. Yeah, I mean, the gap between the hype and what's actually landing inside companies is very real. And I think it's healthy that we're finally talking about it, honestly.

Here's what I see. The initial wave of enterprise AI adoption was driven by, you know, what I would say is a fantasy of substitution, right? Take a workflow, drop in a model, remove headcount, book the savings. And it turns out that almost, you know, that's almost never how work actually flows. I mean, real work is full of exceptions, judgment calls, tacit contacts, relationships, et cetera. And the model hands the middle 60% nicely and then falls off a cliff on the edges.

And the edges are where the values live, right? So the company's deployed, measured, and found that production. Activity gains didn't materialize the way the deck promised. And some of them are now hiring back because they cut into the muscle, not fat. The second thing I point to is that most enterprises tried to bolt AI into processes that were designed for humans doing the whole task. That's like buying a Formula One engine and putting it in a mini-wad, right? So you have to redesign the vehicle, right?

I mean, the companies I see actually getting value are the ones rebuilding the workflow around the collaboration, not just inserting a chatbot into an existing pipeline. And I think then there's the adoption piece, right? Which is, I find the most interesting, to be honest. I mean, employees aren't refusing to use these tools because they're litalities, right? I mean, they're refusing because nobody's

answered the basic question of what's in it for them. If I use this thing and I become twice as productive, does this mean that I get to do more meaningful work or does it mean I get more work piled on? What does it mean, you know, or does it actually mean I'm training replacement. And until that question's answered, I mean, you're going to get quite resistance. And it's you're getting it. You're not necessarily seeing it on the dashboard yet.

So, I mean, to your question, I mean, is AI our savior? I don't think so. It was never going to be. It is just a powerful capability that reshapes what's possible. But the returns show up when you rethink the work, invest in pupil and stop treating it as a cost-cutting shortcut. I mean, the ones that are doing that are winning and the ones that are chasing substitution, I think, you know, they're the ones that are writing the disappointed headlines. Well, I think it's really funny that we told people basically use this tool that we already knew will probably replace you at some point. And then we're shocked that they didn't want to use the tool.

It's really funny. As we know, there's always a disconnect from the top executive level down, right? Something you said, though, that stuck with me is that you're worried about. AI developing consciousness beyond whom in control. How real is that? I mean, I'd say, let's be precise about what we're actually talking about, right? So because consciousness is doing enormous work in that sentence and most people using that word haven't defined it even for themselves here's what I think is happening. So we're seeing systems that are extraordinarily good at producing outputs that feel conscious. Stuff like fluid,

language, empathy, memory, personality. And the human brain is exquisitely tuned to attribute mind where it sees those signals. It's the same instinct that makes us name our cars, right? And when a model responds with warmth and nuance, our wiring say, is there something, someone in there? Like, that's a projection, not really evident. So the harder truth is that we don't really actually have a consensus on what generates consciousness in us. The neuroscientists are still trying to argue about whether it emerges from specific network dynamics or whether it's a global workspace phenomena, whether the brain generates thoughts or receives it.

And if you can't explain the one working example we have, that one working example, I mean, claiming about how to replicate it in silicon is at minimum, I think, just premature. So I don't, I mean, I want to be careful. I'm not in the camp that says machine consciousness is impossible. Dehan and others have made a reasonable case that if you replicate the right computational signatures, denying subjectivity to that system becomes just as arbitrary as denying it to other human.

That day may come, but there's a big difference between, you know, may come and around the corner. And so what I, you know, I rather focus people on is something I call artificial general experience, you know, not whether the machine is conscious in some metaphysical sense, but whether it starts to have stakes, like, you know, preferences that shape behavior, something that functions like caring about outcomes. And as a threshold that actually matters, right, you know, for how we treat these systems. I mean, and it's... Quick break to talk about something I get asked about all the time on this show, Bitcoin. For years,

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see the Bitcoin Disclosures at cash.app slash legal slash podcast. Much more of our tractable question than the hard problem. What are you seeing in terms of voice deep fakes and being able to stop a lot of this because I think we're starting to already see people are calling you or they're calling companies acting like different people

and people don't realize it. I've been called a few times and I'm wondering if this is a human. The funny thing is every time someone calls me, I ask, are you human or are you AI? Yeah, I mean, I think you're touching onto something that does keep me up at nights, honestly. I mean, we're officially in the era

where hearing is no longer believing. You know, that phrase is dramatic, but it's just true now. I mean, you know, you've seen examples like, you know, earlier this year, Wall Street Journal ran a story about a mother who got a call from someone who sounded exactly like her daughter, panicked, crying, begging for help. It was just not her. I mean, it's synthetic waste generated by AI. And the terrifying part is that it isn't that it just happened once.

I mean, the tools do it consistently. 11 labs, meta voice, open voice, they're openly available. I mean, there's no barrier to entry anymore. I mean, all you know, all you need is like three seconds of your voice from a social media clip. And, you know, you can clone. So what on the technology side, right, on the defense side, most detection has traditionally leaned on what we call vocal biomarkers, right? So the little imperfections in real speech, stuff like microposas, pituitaryation.

throat resonance, tiny jitters caused by breathing or emotion. And the assumption was synthetic voices couldn't fake those consistently. That assumption is dying fast. Models like hi-fi-gan and natural speech too are patching those tails one by one. So, like, I mean, I actually compare this to the diamond industry. Like lab-grown diamonds used to be spotted by their lack of natural inclusions. Because chemically, they're still a diamond. Now, manufacturers add synthetic inclusions during the growing process, and you can't tell anymore.

I mean, as the same story with voice. So, you know, so I think what we've pushed in the last several years on our end is we pushed on behavioral mapping and temporal modeling. So instead of asking, does this audio clip look real, we ask how does this specific person actually speak over time? You know, this is our technology at behavioral signals, right? And so, for example, focused on the cadence, their articulation, core articulation patterns, the rhythms of how their prosody moves across sentences.

I mean, those temporal signatures are much harder to fake, even when a window sounds perfect. But, you know, this is an arms race. Every detection gets studied by the generation side and vice versa. The real answer isn't purely technical. I mean, that's a cultural question at this point. We need to normalize verification. I mean, like, you know, maybe at some point we'll need a code word with your family, you know, a call back to a no number, skepticism as default when emotional pressures are high.

I think we're kind of headed there. You have a great story. You grew up in India. You left after undergrad without anything ahead of you, essentially an unknown. You walked into this unknown. You took a big leap. What did you learn from that? You know, so much, right?

I mean, back to your sort of previous question, this, my interactions with model, you know, and it's like there's so many different things sort of that have come out in terms of sort of my interactions with, my own interactions where they are, like, you know, the back and forth, the model surfaced an angle that I hadn't considered, not a solution, an angle, or reframing. In my head, this is the only way I can describe it, like like a lock turning and a door open. And I think to me, that's been the epiphany. I think on my own journey, as you sort of said, coming into this new culture, new country, the biggest things that if I've learned is that, you know, future is very unpredictable. You are naive, and I see a lot of young people do this.

you're naive if you feel that, you know, success or success is a heavy word. Let's say the outcomes you want. Let's go with that. The outcomes you want are driven by a formula. Like you do X, then you do Y, and then you do Z. That results in number 45. And, but that's what we have been culturally taught, like, you know, by our parents, by our society. you know, like, for example, you have to go to school,

and I have to get good grades, then you have to get a job, then you have to get a, you know, progress in the job, you have to be a good employee, you have to get along with your colleagues, you have to be, you know, you know, all of those things, and then you get that.

The truth is that it doesn't quite work that way. I mean, life is very, very unpredictable. Now, you know, there's a debate about whether, you know, things are happening in a deterministic fashion or everything is all just pure random and how much of what happens you actually control yourself. But to me, like, whether you're in this camp or that camp, like, you know, whether you're in the deterministic or undeterministic camp, whether you believe there's free will or no free will, it doesn't really matter either way. So if you really ponder on that questions carefully and sort of say, well, am I in control or can I affect

outcomes or things are going to happen. The bottom line is doesn't matter. And so just enjoy the journey. I mean, I think that that's one thing that I've learned is that, you know, and that's the thing to it is whether you're doing things that are going to happen in an unpredictable form and, you know, or it's already destined in both cases, you know, just chill out. I mean, just take it easy and go with the flow and be in the moment. And it took me like decades to get to that realization. I wish I had that realization many decades ago. And if I did, I would have made different choices or have done things differently

or had lived those years differently. But I think that's the biggest learning on my end. What is something when you think back that if you had done differently, it would have made a positive impact on your life? Honestly, I mean, if I had started trusting my own instinct sooner, and I think I mean that in a very specific way, like, you know, long stretch of my career, I treated intuition as a lesser cousin of analysis. If I couldn't defend a decision on spreadsheet, I'd second guess it. I'd gather more data, run one more model, get one more opinion. And there's a lot of times weeks later, I'd land exactly where my gut had pointed me on day one. But that's expensive,

right? Not just in time, but in conviction. So every time you override your instinct, and it turns out that you've been right, you teach yourself to distrust the same signal you should be actually strengthening. And there's a thread in the book about this. I mean, humans have two modes of thought, like the fast, intuitive one and the slow deliberative one. And we built this whole cultural bias towards the slow analytical reasoning as the serious mode. But the intuition isn't magic. It's accumulated experience compressed into a signal. It's your pattern recognition talking. And if you've been in an arena a while, that signal is worth listening to, even when you can't fully articulate why, at least in the moment. So, I mean, the thing I tell my younger self is, you know, run the analysis.

sure, do that, right? But treat it as a check on your instinct, not a replacement. And if their two are not lining up or they disagree, get curious. I mean, don't automatically side with the spreadsheet. So I think that's the big learning. Maybe our intuition is our advanced human brain figuring things out in a very short, quick time. But it's hard for us to fathom that we could come up with that answer because subconsciously we're coming up with something that we didn't really think about. Now, there is a quote. Everything you want is on the other side of failure. What does that quote mean to you?

Yeah, before I get to the quote, I want to just pick up on what you said because you're right. Intuition, you know, isn't magic, it's compression. I mean, it's your brain running an enormous amount of pattern matching in the background faster than conscious thought can actually track. I mean, there's a concept called embodied cognition. the idea that thinking isn't just in your head. Your gut, your nervous system, your hormones, they're all processing signals constantly.

And most of it is actually below awareness. That's why a gut feeling shows up as a physical sensation, actually, before you can articulate why your body knew before you did. So now the quote, like, everything you want is on the other side of the failure. For me, that lands in a very personal space. I think we've been sold to submit that success to a straight line. I mean, you work hard, you're smart, you win. But that's not how any of it actually works.

I mean, every meaningful thing I've built, every insight I've had that mattered, came out of something breaking first. I mean, you know, a company that didn't work, a decision that I got wrong, a moment where I was pretty sure I'd be embarrassed myself beyond recovery. And so what I've come to believe is that suffering, failure, the hard stuff, it carries information. It tells you what you value. It tells you what needs protection. It tells you where the edge of your understanding actually is.

So if you try to numb it out, and I see a lot of people aiming to do that, I mean, if you optimize around it, you will lose the signal. I mean, you lose the thing that was going to make you remarkable. And I write about this in the book as well. It's like, you know, that my own suffering has helped me make who I am. And I suspect a lot of people feel the same way if they're actually honest about it. I mean, a life without contrast collapses into just numbness. I mean, you need the low points, not because pain is virtuous, but because that's where the shape of what you actually want become visible.

So the quote to me means, you know, stop treating failure as a detour. It's not a detour. It is the road. That's the road. Reading your book, the argument that I got that your book has is machines need more than intelligence. they need experience and consequence. What's the difference, though, between a smart machine and one that understands?

The cleanest way I can put it as this. A smart machine gives you the right answer. A machine that understands can tell you why that answer holds, where it breaks, and what would have to be true for it to be wrong. So I'm borrowing here from David Deutsch, who has influenced a lot of money, ideas and I built upon some of his ideas in the book, who also, also I think has the sharpest

lens on this. He argues that knowledge doesn't grow through prediction. It grows through good explanations, like explanations that are hard to vary, that connect across domains, that survive criticism. And by that standard, most of what we call intelligent AI today is doing something quite different. It's doing extraordinarily sophisticated pattern matching. I mean, it's finding the shape of the answer in the training data. That's not nothing. I mean, it's quite miraculous, actually, but it's not understanding, right? I mean, like, so, like, the test I use is, like, take a chess engine that can crush any grandmaster alive. Like, does it understand chess? It doesn't know it's playing a game. It doesn't feel the way of a bad move an hour later. It has no relationship to consequence.

It just has competence. And competence without a relationship to consequence, is exactly what worries me, because we're now embedding those systems inside human decisions where the consequences are real. So the second piece, understanding, in the human sense, isn't just explanatory, right? It's accumulated. You become a different person after you've been wrong. I mean, after you've caused harm, after you've watched a patent play out for years. I mean, your judgment is scar tissue in the best sense. That's what it is, it is, a judgment. And a machine that outputs brilliant answers is identical before and after every interaction. It hasn't learned anything in the way that matters.

It's just been queried. So when I talk about artificial general experience, that's really what I'm pointing at. Like can the system be transformed by what happened to it? Can it carry forward not just data but the weight of having been through something? And until we build for that, we're going to keep confusing eloquence with wisdom. And those are very different things. You had walked into a company that was facing potential bankruptcy, and then you were able to have a huge turnaround, and that company would then go on to have a successful IPO.

What would you say is a secret unlock that you find when you walk into a company like that, that if you make that switch, it changes everything? I mean, I think I'll be honest, right? I mean, most of the stuff is usually worse than what actually is on the paper. You know, like, for example, you know, obviously the cash is an issue, morale is an issue. There's usually a fog over the whole place. And everyone knew something is wrong or was wrong, but nobody can actually name it out loud. You know, so I think what I've seen is that, and I've seen this pattern now play out in multiple

turnarounds is the secret isn't a strategic insight. It isn't a clever pivot. It isn't cutting your way to profitability. Those things matter, but they're downstream. I mean, the unlock is honesty, radical, uncomfortable, name the thing in the room honesty. So when a company's failing, there's almost always a collective agreement not to look directly at the problem. People protect each other. They protect the founder. They protect the their own role. And so the org develops the shared fiction about why things are hard. And everyone quietly just plays long. I mean, it's ridiculous. Like you see this all the time. When slames blame, the sales will blame the product. The product blames the engineering. Engineering blames the

roadmap. And nobody's lying exactly. Everybody's just holding a piece of the truth and refusing to put it on the table. So I think the first thing we did, and I, you know, I would do this now every time is like, you know, you sit with the people. And I, you know, ask them, and tell me what they actually thought, not what they'd say in a board meeting, what they say to their spouses at dinner. And you'd be amazed, right? I mean, once one person names a real problem, the dam breaks, suddenly have like a massive signal instead of noise.

So I think, you know, the second is, I think, you know, this connects to what I sometimes, as I've written about in the book as well, is like you have to give people something to do at every turn. Like when an org is scared, paralysis is the best, is the big thing. It's the enemy, right? So people need the next step. Like, even if the plan isn't perfect, having a clear human size action in front of you changes the temperature of the entire building. So, you know, you've got to get the agency back and agencies contagious. So I think, you know, so I have to compress it is stop managing the story and start facing it and then give

everyone a concrete move. And I don't think, you know, there's anything genius. here. It's just like, you know, figure out where the oxygen is and unleash that. Sometimes the symbol things are the hardest. I had a recent guest on Oren Barzilli, who's one of the founders of Equity B, and he told me that around 50 to 70% of employees never exercise their equity, which is insane because this could be millions and millions of dollars. I know people that recently, through the equity that they earned or stock options, they were able to get millions. life-changing money. So when you think about how important it is for employee ownership around equity and stock options,

especially in this crazy world of tech, how important is this going forward? Very. I mean, it's wild and it tracks with what I've seen. Yeah, 70% leaving equity on the table. It's a systemic failure of how we communicate ownership. and to be honest, I mean, for the most part, it's because practical matter, they don't have the cash to exercise. It's my take, right? It's one of the most powerful alignment mechanisms we've ever invented in business.

And when done right, it turns employees from labor into partners, changes how people think about decisions, you know. Owner just behaves differently, right? And that's true of houses. It's also true of companies. And I also think like a lot of, you know, many times the reasons people don't exercise is a system is generally opaque. I mean, you get a grant letter with a bunch of numbers. Nobody explains the EMT tax implications. Nobody explains the 10-year expiration window. Nobody explains that a 409 valuation, what it actually means

for you. And then you leave your company and you have 90 days to come up with tens of thousands of dollars to exercise and you don't have that liquidity. So you walk away. And the default is lost. I mean, that's a design problem. So I think this definitely needs to be solved, you know, because the velocity of value creation and tech is essentially ownership. And, you know, and if this is broken, this definitely needs to be fixed. Last question for you is this. You had a forward in your book that compares AI to bamboo popping up everywhere. And I think this sources a question.

probably one of the most important questions that we will think about in our lifetimes. What does it mean to be human? That's the question, right? And I'll tell you, I've been sitting with it for years now, and my answer keeps evolving. I think where I've landed, at least for this chapter of my thinking, is like being human has never been about being the smartest thing in the room. if intelligence were the criteria, we'd have surrendered the title

the moment a calculator beat us at arithmetic. So what makes us human is something stranger and harder to name. I think it's the capacity to assign meaning. It's to care about outcomes that don't may or may not benefit us, to feel the weight of a choice, to love something knowing will lose it.

And I think that becomes clearer, not more confused as the eye gets better. Because for the first time, we have a mirror that reflects back everything we are not. These systems can't produce language. They can reason. They can even simulate empathy. But they don't ache. They don't grieve. They don't sit up at 2 a.m.

wondering if they made the right call with the kid. The texture of experience, the fact that things matter to us in a way that costs us something, that's the thing. I think that's the thing. So in the book, I write about not wanting to engineer away the full range of human emotion. Eliminate disease, eliminate poverty, eliminate violence, yes. But don't numb us into some flat state of engineered happiness because the sadness, the doubt, the longing, those aren't really bugs. They're the substrate meeting grows out of, right?

So my working answer is this. To be human is to be a creature that builds meaning under constraint. you know mortality is a constraint i mean limited attention is also a constraint i guess i mean not knowing how the story ends is a constraint and we make our love and companies and children inside those constraints and i think that's that's the miracle meaning under constraint wow we didn't even get to talk about quantum next time we'll say that because i don't even know what's next what's past quantum and as i I don't even know what the next one is. But I like AGE, what you said.

I think we can adopt that. I'm less scared now than I was 45 minutes ago. So I appreciate that. But Rana Gujaral, serial entrepreneur, investor, friend for, I don't need, maybe 10, 12, 12 years. I'm not even sure. Like, we both had more hair when we started hanging out. We'll look at more here for sure. You're good, although I think you look pretty good if you were bald, Ben.

I think you should just shave it up at some point. It's getting there. So I'm hoping I continue to look good because it's definitely getting there. I think you should. I think you have a good head for it. I think you'd be all right. But, man, always great to talk with you. And I'm super excited that you came on today.

Thank you, Dan. It's always good to talk to you. And thank you for having me.