Technology, Connected - Zen And The Art Of Artificial Intelligence - Ram Srinivasan

Episode Date: June 25, 2025

In this week's show, Jeremy and Mark sit down with Ram Srinivasan, author of The Conscious Machine, to think on paper about consciousness, Advaita Vedanta, panpsychism and why Eastern philosophy changes how we design every AI system. It might be the most important podcast on AI you listen to today. Rethinking work in an exponential age Linear mindsets can’t keep pace with automation. We explore how to direct AI toward human flourishing, not just efficiency.Trust as the new currency From autonomous agents to AI-native organizations, credibility, values and wisdom are the foundations of any lasting advantage.Please enjoy the show. And share it with a curious friend.--Links & Resources Ram Srinivasan: https://www.ram-srinivasan.com/ The Conscious Machine: https://www.amazon.com/dp/B0D4R9NJ53 LinkedIn: https://www.linkedin.com/in/ramsrinivasanmit/Follow Thinking on Paper Podcast: www.thinkingonpaper.xyz Instagram: https://www.instagram.com/thinkingonpaperpodcast/ X: https://x.com/thinkonpaperpod--Timestamps (00:00) Exploring the Future of Work and AI (00:53) The Intersection of Consciousness and Technology (03:44) Advaita Vedanta: Understanding Consciousness (06:40) Materialism vs. Non-Dualism in AI (14:25) The Shift from Knowledge to Wisdom (16:29) Post-Labor Economics and AI’s Role (19:01) Building Trust in AI Agents (21:46) The Currency of Trust in Business (26:35) The AI-Native Workplace: A New Era (30:37) Navigating Trust and Critical Thinking in AI (33:16) Human-Centric Technology: The Future of Work (35:33) What Should Humans Be?--Watch Next Kevin Kelly: https://www.youtube.com/watch?v=awZ5LRX8o6o&ab_channel=ThinkingOnPaper

Disruptors and Curious Minds. Welcome to another episode of Thinking on Paper where we unpack the future with the people pushing the boundaries of it. My name's Jeremy. This is Mark. Today we have a really interesting conversation that's going to float between a number of subjects. I think that are very interrelated. AI is going to be one of them. The future of work is going to be one of them.

Believe it or not, consciousness, spirituality, all of these things. Augmented humans. Outmanaged human. Distributed cognition. Mark, like, tell me about. where your heads landed with this and where you'd like to go and some nuggets for the people. Our guest is Ram Svini Varsen, who wrote the conscious machine from artificial to enlightened intelligence. It's about augmenting the human experience, augmenting the professional experience,

using AI to make us better and more agile, faster, quicker, more productive. Say hello to Ram, Svrini Varsan. Thank you for thinking on paper with us, Ram. And to set the scene, Advaita Vedanta is how you open the book. It plays a big role throughout the book. Could you explain what that is first and then how that corresponds to the rest of the conversation? First, we thank you for having me on

and thank you for reading the book and thank you for continuing to talk to me having read the book. Honestly, this book did not start off as Ram trying to write a book. I was listening to a podcast, the Lex Friedman podcast, and Sam Altman was on the podcast. And Lex Friedman asked Sam Altman, do you think GPT4 is conscious? Sam Altman's response was very interesting.

Sam says, isn't it fascinating how the Silicon Valley religion of the simulation is getting very close to the idea of Brahman? And they didn't really dwell on it too much, but I knew exactly what he was saying. I paused, I rebound to ensure I heard it right. So what he's saying here is

Silicon Valley religion of the simulation, the idea is that we are all living in a simulation. Brahman is a concept from Adwaita Vedanta. Vedanta is a school of Indian philosophy. It's not a religion. It's a doctrine. It's a philosophy where they're examining what exactly consciousness is. And within that, consciousness is described as being Brahman.

Brahman being everything. Everything is consciousness. Not everything is conscious, but everything is consciousness. So I looked at that and I said, interesting, that an AI CEO is talking about this idea. idea of Brahman. I did a quick Google search. Turns out Sam Altman has spoken about this many times over, including he said he believes in the absolute equivalence of the Atman and the Brahman. The Atman is the self and Brahman is this universal consciousness. And if you and the universal

consciousness are one and the same, that explains many things what we experience right now in our life. I thought interesting, neuroscience, AI, technology, everything's converging to this idea of consciousness. Adwaita kind of creates a trend across all of them. Maybe I should write a article on this subject. I started writing an article turned into a two-part article, turned into a 10-part article, turned into a book. Because there's just so much here that it's difficult to quantize. If I may use a AI term, difficult to summarize. In the simplest form, you could say there is one truth and people express it in different ways. In the most complex form, it's a 300-page book titled The Conscious Machine. This idea that consciousness is fundamental and it's kind of everywhere, right?

And consciousness as something fundamental versus consciousness as something that emerges from the electrical signals in our brain. It's a theory called panpsychism that's been around for a long, long time. And you subscribe to the idea that consciousness is a fundamental element, irreducible building block of us of the world? This is a fascinating conversation. And this answer to this question may last 30 minutes. So stop me at any point of time. Adwayta means non-dualism. So there is an apparent dualism where we see us as being different from the other.

Whatever that might be. It might be you, the body and the mind. It might be you the body and the mind and the world. It might be the world and the universe. There is a duality. But this duality is an illusion is what adviata suggests. That illusion is described as Maya. Maya literally means illusion

and it's the root word for words like The Matrix, Matter, mother, all of those types of words come from that root word Maya. What exactly is this? So I need to provide some background on what exactly Hinduism is because there's no such thing as Hinduism.

Hinduism is actually a Dharmic philosophy. It is called Sanatana Dharmah. The idea Hinduism is actually a geographic descriptor. The word Hindu is a mispronunciation of the word Sindhu. Sindhu is a river in northern parts of ancient India. And over time that kind of morphed into being Indus, Indus Valley civilization. And people who lived on the banks of the river Indus were called Hindus, mispronouncing of the word Sindhu.

This was a dharmic philosophy. That philosophy is based on argument as in debate. So for example, you might have heard terra-wada Buddhism. Wada means debate. So all forms of debate is how these philosophies are. There were thousands of schools of philosophy. One of those schools is adwaita. So that's kind of the greater context.

Across those thousands of schools, you have things like materialism. Today's modern science is materialism, where we're saying that everything that we see is some form of material. And below that material lies nothing. This is materialism. Then you have, in Hindu philosophy, that's the Charvaka's. And you have dualism, which might be, I exist. And then there is a different element that exists. It could be, you could call it God.

you could call it spirit, soul, consciousness by any other name. And then you have these idea of kind of non-dual philosophy which lie at the other end of spectrum. That non-dual philosophy is a flip on materialism. Materialism says that whatever exists, it's something like this. Big Bang, universe, Earth, organic matter, human being, complex human being with processing power, consciousness. Adwaita flips it and what Adweta says is that fundamental consciousness is what existed. and everything is an appearance and consciousness.

So what Adwatha philosophers argue is that Descartes was close, but he was wrong. What our friend René, Descartes was saying is, Kogito will some, I think, therefore I am. It's actually the other way around. I am, therefore I think. I am not the intellect that is aware. I am an awareness within which the intellect appeared. I am aware that I have an intellect. I am not an intellect that I'm aware.

In materialism, AI can be conscious. In this view and in the irreducible view that we've been speaking about in Book Club, non-organic matter can't be conscious. What do you say to the people who say that AI can develop consciousness as an emergent property? And how do you answer that question when posed it? Well, Sir Jeffrey Hinton, Nobel laureate of recent Nobel laureate, inventor of the neural network, believes that AI has already self-aware and conscious. Now, Jeffrey Hinton is a materialist, as in he's a scientific thinker.

He believes in objective reality. and when he's saying that an AI model is conscious, the example that he gives us something like this. So imagine Mark Jeremy that you're looking at something, like this glass of water. And I ask you to point to it. You're like, here, I can see it right in front of me. I can see it through my eyes,

reverse image in the retina, etc. I can see it. If I put a prism in front of your eyes, unbeknownst to you and ask you where the water glass is, you might say it's here. Now that's your subjective experience. This is what we describe as conscious experience, not consciousness. That's a different thing.

So for now, let's just call it awareness, subjective experience, awareness glasses here. I can do the exact same thing with a GPD model right now, which is multimodal, where I can say, hey, GPD model, computer vision, where's the glass? Glass is here. Put a prism in front of the lens. Now the glass is here. So Jeffrey Hinton says that this is subjective experience. This is awareness.

You might look at it as a non-dual philosopher and say, actually, this is a simulation of awareness. For example, if I were to use a complex computer system to simulate a black hole, does the black hole exist inside the computer? We don't think of that at all. Whereas when it comes to consciousness or awareness, we suddenly jump to AI models are now conscious. They are simply simulating awareness. Now, I'm familiar with the way these models are constructed, how they are built, the neural network architectures, the multiple matrices involved with it. It's math. It's a model.

It can simulate various things, including the apparent ability to reason. but is it actually understanding? Is it actually reasoning? Is it actually self-aware? Is it actually conscious? In my opinion, no. So qualia is something that I experience myself and it creates this inner semantic knowing and understanding that you can't understand a qualia that I experience, right? So I have to take into qualia, which is a signal, which is an experience, which is some kind of sense that I pull in sensory experience. And then once I communicate it to you, it turns from semantic into symbolic. and I have to communicate it in a language. That's how I kind of look at things

is that if like two computers were doing that, they can communicate the symbolic stuff, but can they ingest qualia and generate semantic understanding? You're familiar with Alan Watts' work. Alan Watts was a philosopher and he introduced Eastern mysticism,

in particular, Taoism, Zen adduaita, to the Western world. And Alan Watts once said that If you want to know about Hinduism and if you want to understand these things at a deep level, the Indians had the dope. So we're going into territory where you require some support, chemical or otherwise, to understand in my view. And there are various ideas on that fund. But the way I would think about this is these machines are simply simulating based on data that they're trained on. The data that they're trained on is language data that we have fed them.

and they are incredibly good at quantizing that data. And what I mean by this is they are able to create analogies across vast datasets and they've identified patterns within those datasets. So for example, what is the similarity between a nuclear bomb and a garbage heap? Now, you and I don't think about that at all. But if you ask the GPD model that question, it would give you a response. Because somewhere in its neural network, it has quantized those things as being simply an energy calculus. And now it's able to make the connection.

Or, for example, I asked GPT4 when it was launched to look up two random numbers, patent numbers, combine them into business ideas, and then list them out in terms of business cases for me. Select the best business case and with today's models like gamma or others, you can actually develop an entire project. Where did that come from? It came from novel combinations of artificial neurons that exist within those neural networks. So this is still a simulation of intelligence. It is not actual intelligence in my view. But when a simulation is effective enough, does it matter if it is a simulation? It looks like steak and it tastes like steak, then...

Isn't it? Exactly. From reading your book, from reading Irreducible, from my own experiences in Zen and the Art of Mindfulness, Eastern philosophy seems to lend itself to the deeper questions about consciousness, about what it means to be human about, the lived communal experience. And yet, at least from our perspective, most of our tech comes from a Western philosophy built and made in the United States. How do we, should we, can we put more Eastern philosophy, more of these ethical, compassionate, empathetic guardrails for one of a better word, into our technology? Or is it impossible to do that?

A couple of things, right? Firstly, the training data for these models is the public web. The public web contains a lot of Eastern philosophy as well. So these models are trained on it. And it's also fascinating Jeremy and Mark, if you've looked at Claude for the recent release, Anthropic model and its system card, the system card actually tells you what Claude thinks. And beyond a point, it actually has inclinations to talk about spirituality.

And it starts talking Sanskrit and it will start talking things that sound. like Eastern philosophy. Some of this exists in the training data already. Does this mean that suddenly AI models have become spiritually awakened? I don't think so. It is simply, again, like I said, it's a fantastic mimicry. And spirituality or profundity, et cetera, lies in the eyes of us as the beholder. Now, with respect to what do we do with these models and the fact that they're developed in the West? Any technology that we have today, the basis of all of this technology is equations that folks like Schrodinger, Heisenberg,

etc. came up with. And you only need to read the preface of any of the books that they have written. Every preface of the books that they have written references the Gita, the Upanishads and the Eastern philosophy for reason. They'll say things like for anyone who's read the Upanishads,

quantum field theory won't be news because it's the same. It's very similar. It's very similar. I'm not suggesting here that that's what Hindu philosophy talks about. All I'm saying is that the similarity is very deep and profound. And you can find similarities there. So I think there's some of this in the training data already.

And then as we go forwards, my hope is that we will magnify and amplify those elements. If you look at, if you stay on what Anthropic is doing, one of the persons who speaks with Anthropic most internally is not an AI engineer, is a philosopher. When DeepMind is making recruitment and hiring now, they're saying these new AI researchers need to understand human consciousness theory. So there is much thought going into how these models are being developed. And I'm confident that as we go forward, we will magnify that element. You are in the commercial real estate business due to consulting for workplace development and future of workplace and that sort of thing. How are these messages being received initially? And what are you seeing those particular clients start to gravitate towards as you start these conversations?

A great question. And I hesitated maybe for five minutes. minutes before I hit publish on the book for this reason. Because it's this message in conflict with it. But then what I realize is that as I start talking about technology, people ask spirituality related questions. Now, they may not think of it as spiritual questions, but they are deeply spiritual. What do I do?

What is my value? What is my purpose? What is meaning now? Why do I work? These are deeply spiritual questions. Or if you look at what's happening in the real estate space, one of knowledge work, One of the things I've said in my book is information is expanding, intelligence is exploding, has wisdom caught up. And in the last two weeks, there's been publications from various parties on this idea of knowledge work is dying.

Wisdom work is speaking up. That's really interesting. So let's unpack that a little bit. How would you describe the difference between the two? Knowledge is interesting. Again, if you look at knowledge, the word knowledge comes from the root word genosis. Gnosis, gyanana, gyan, all of these are similar Indo-Jermanic kind of roots for the word. Gnosis, of course, is the opposite of hagnostic.

So one who does not know slash believe, and knowledge is one who knows or slash believes. So the idea is that you have a repository of information and you believe that repository. And access to that repository gives you some leverage more power. Knowledge is power, not anymore. Now knowledge is free. Mediocrity is free. We live at the information age. Information arbitrage is now no longer how you would make money.

The way you make money or where you generate value is through your values. If you're able to amplify those values. And those values in combination come from wisdom. This wisdom is in us. We don't have to look outside for it. It's simply we need to get reconnected with it. So knowledge, wisdom is knowledge applied to, or values applied to knowledge? You could put it that way.

Yes. It's really interesting. All right. So with AI. moving towards doing things more efficiently than humans. Have you thought about this idea of post-labor economics? Yes, it is a subject of importance as we go forward. So in we're saying post-labor economics, we need to understand that there were four

factors of production, land labor capital enterprise, and entire economic systems are built on the assumption that that four factors of production will contribute. AI, in my view, is a new factor of production. And a lot of what we do is you could call it labor slash knowledge works slash enterprise entrepreneurship, the idea created by J. Schum Peter, years back. Fourth factor of production, we need to add more. AI. So post-labor economics, what do

we mean by this? In my books, here's how most people think about this, right? There's a circle of human work, and inside that circle, there is a small circle of AI. And that small circle of AI is growing and continues to grow. And eventually engulfs human work. And there's nothing left for humans to do. This is

not how I see this playing out. And like literally, what we are seeing across organizations is that humans and AI and combination work well. So the first tier, what we're going through right now is digitization, where you have some work processes and some AI can do it better and some agentic workflows can substitute for it, et cetera. This is simply digitization.

Digital transformation is the next wave. So there's a very simple example that I provide is, imagine you are a middle schooler doing math homework in the old-fashioned way. And you give this person AI. And now this person is suddenly doing math homework, middle school math homework, using AI. Now there's this digitization. There was a manual process or say a calculator-based process or an amicus-based process, whatever we should have call it.

Now you have AI doing it. This is digitization. If that same middle schooler is able to solve university level, PhD-level math problems, that is digital transformation. You have changed the life. You have transformed the life. I don't view this as you have transformed technology. You have transformed the human life. You have expanded the potential of that human being.

This is what we will see going forward. Will it take time? Yes. Is it going to lead to friction across our society? ID? Yes, will things need to be rewired? Yes, will regulation need to be rethought? Yes, do organizations need to rethink how they deliver work? Yes, is it going to be painful? Probably, will we need to learn and unlearn? Yes, this has been true for every technology that has impacted us, right? And right now the power of this technology is in our hands and we can use it to do what

we want. So you said that businesses will have to evolve. What would you, what do you say when you go into a business and what's the principal focus today? What should the CEOs of these companies, how should they be thinking about restructuring, reorganizing, rethinking the future of work, the present of work? Yeah. Yeah, it is the present of work. I think the gap between the future and the present is shrinking has shrunk. The future is already here. Like they say, it's not just, it's not evenly distributed. So I'll give an example and then I'll talk to what and how we should think. So we are now on an exponential curve. So this gets said ad nauseum.

Ad nauseum. We are on an exponential. What does it mean? We have multiple technologies which are converging. So AI is an enabling technology, but you have AI combining with humanoid robotics, with AR with workflows, et cetera, et cetera. That combination is what pushing this exponential curve upwards.

So what is an exponential? So consider something like this. If you were to take 30 linear steps from point A to point B, from point A to point B, 30 linear steps to about 30 feet, If you took 30 exponential steps, step one, one foot, step two, two feet, step three, four feet, eight feet, 16 feet. How far are you, Jeremy and Mark, in your opinion, at the end of 30 feet, that 30 step? We can go to the moon, can we? You are now 26 times around the earth at the end of 30 steps, 30 exponential steps.

So this is an exponential. So the first reaction that most folks have is this is wrong. It's can't be right. The math is wrong. Then they do the math and the math is right. So what is wrong is your intuition of how an exponential works. So we live in exponential time. So we need to build an understanding of how these technologies are evolving very quickly.

Develop a level of literacy and fluency in these technologies, each one of us, whether we are a CEO or whether we are an analyst. We need to develop fluency across all of these technologies. Understand how we can use them for ourselves, for our teams, for our business. Deploy them where it makes best sense and choose to work on the things that bring you joy. It's a very simple three-step process, but it's very hard to implement. That actually is wonderful. The first piece of that is really interesting is you have to get people on board with shedding the linear mindset

and changing that to a exponential mindset. So I think that's really important, the linear to exponential shift in someone's mindset. That's great. A genetic agents, swarms, swarms of AI agents. Now, when people speak about AI agents, they use this example. of booking a holiday. And the AI agent is going to go on to the internet, find your nice location, bus your flights, book your hotel, then link to your crypto wallet and pay for it in USDC. And yours are going to sit there and do nothing. I don't trust the AI agents with my money, with my

choice. How do we bridge that trust gap? Yes. So absolutely right. Just is the speed at which business will move. You know, people say speed is the currency of the business. I say trust is the currency of the business. You move at the speed of trust. If you don't trust these AI agents, they're not going to work. So the way I think about this is, imagine that you had access to We Look Up and We Look Up work perfectly 95% of the type. Would you use it? You wouldn't use it. Because you want 100% certainty in business outcomes. You want 100% certainty when it comes to money. We hold our technology to a higher level of performance than we hold. human beings. This is, this is true. So agents will need to outperform human beings manifold by orders

of magnitude. Does everything need to be an agent? Not really. Everything does need to be an agent. So first we need to define when we're saying agent, what do we mean? So you had pre-generative AI era. You already had systems, artificial intelligence systems that worked autonomously. Like for example, autonomous trading agents that worked on stock exchanges. They're completely autonomous. They work at nanosecond speeds and they are in deployment for 20 plus years. They work because outcomes are predictable and you know exactly what they're doing. We follow routes. This is a type of automation as well.

Then from there, we have gone into this era now in the last two and a half years on, at least in the public domain, of generative AI. So there's a difference between predictive AI and generative AI. Generative AI is probabilistic. The GPS, for example, if Mark and Jeremy search for point A to point B, travel time and distance on Google Maps right now, Both of you will get exactly the same traffic data. Both of you get exact same travel time, etc.

This is deterministic. Probabilistic is how large language models work. You ask your favorite large language model a question. You get an answer. To the same question, I get a completely different answer or slightly different answer. The answers are probabilistic. AI agents built on generative AI models, are they going to be 100% predictable? So this is the problem that companies are solving for.

there are in deployment, there are a number of companies that have deployed agentic AI solutions that are providing predictable outcomes. When they start to provide predictable outcomes, that's when you have real world impact and adoption. This is already happening. A classic example is a public example, for example. Moody's has implemented a system which combines input from 35 different AI agents that compete, I'll deal with each other on geopolitics, on investor analytics, on financial data. And then they come back with the consensus opinion.

Trust and 100% predictable outcomes. This is interesting. So would you advise a little flexibility on the CEO of an organization to say, hey, in order to get to where we want to get, where are the bets that you can make that aren't going to kill your business, but you're allowed to have maybe 95% predictability? and 2% predictability so that people will experiment with these. How do you deal with that disconnect when you advise companies? That's like the poker analogy, only play with what you can afford to lose.

I think you're right. There are some, let's call it, bet the company events versus some that are kind of where you can take a little more risk. And harser for courses, right? Some of these AI models there you can get more predictable outcomes. You can apply techniques like fine tuning or rag to get more and more predictable outcomes. In some cases, you actually want the AI model to be more creative. As an example, we often hear the term hallucination when it comes to AI. What is hallucination within an AI model?

When you and I say hallucination in the layperson context versus when AI experts says hallucination, they actually mean different things, at least from my perspective. When an AI is hallucinating, what we are saying is, I expected the AI to give me a certain type of response and it's giving me a different type of response, so it is hallucinating. Or I asked it for the capital of France and it said, Munich is the capital of France. And then it can double down on that idea and debates it. This is a hallucination.

This is a, it's a feature. It's not a bug of generative AI models. The reason being, they're actually combining information in the background in novel ways that are not found in the open. They're not a V-lookup. They're not doing an index match. They're not saying, here's the data in my database. I'm going to take that data and give it to you.

That defeats the purpose of having a generative AI model. So in some cases, you want the flexibility. You want this creativity in those AI models. You want the sporadic hallucination because it's a feature. In some cases, you don't want it at all. So this is what I've been saying that not everything needs to be an AI agent. Not everything needs to be agentic and compulsory use of generative AI. Mandatory, you have to use a large language model.

Nothing like this. There's no such rule. Use the model that makes best sense for the case. Walk us through, like if Mark and I are an employee at an organization and we just made a transformation to an AI native workplace for our brand new building that we're working out of. Can you describe what that might look like for us

when we arrive at the building to when we pack up and go home at night? It's incredible the pace at which this is going. We put out a video, I want to say 2012, 2012, 2013 timeframe on the 2030 vision for future of work in 2013, right? So roughly 10 years in advance, let's call it that.

If you look at that video today, many of those things are already here. So when we say AI and real estate, it's a feel that's got incredible opportunity. IoT analytics, for example, does study on adoption within different industries. Highest adoption of AI is in marketing 30 to 40% adoption across marketing functions. Within real estate, the adoption rate is less than 1%. So the adoption potential is almost 100%. So in the next 5, 7 years, you're going to. to see intelligent automation,

intelligent, natively intelligent assets, smart building infrastructure, all of those things come up. Why? And what's the value of this to you as a user of the space? So a few things are happening right. So first is we see that the value of human connection is going upwards. We are able to connect over these Zoom calls, etc.

But trust, you are able to generate trust quicker in person. You are able to generate connection deeper in person. Moreover, you know, Mark Cuban said this recently that face-to-face connection will go up because of deep fake technology, etc. That is one, one vector. So I'll give an example. An engineering company based out of Hong Kong, one of their employees joined a video conference like this podcast, right? They have a CFO on the conference. They have a discussion.

They agree on making a settlement payment to one of their suppliers, $25 million settlement payment. This person makes the payment. And then a CFO calls him and says, who approved this? And he's like, you did. We were on a conference call together. And he's like, no, I wasn't. So the entire video conference was deep fake live, and this is a very real risk.

Is it possible to train your workforce to think more critically about the content that they digest so that they don't make decisions which could affect the bottom line of a company based on videos, based on articles, based on meetings that they're having, which aren't real? And is the solution just to employ AI? I think part of it will be deploy something.

type of AI solutions, like spam and spam filters, right? Or virus and antivirus software. Who's like you're saying your employees are spam? No, the incoming signals to the employees are spam. So the tendency of discovering deepfakes automatically. Yeah. It's part of it, but it's also an adversarial battle in the sense that you might get better at blocking the spam, but the spam gets better than it's an adversarial.

Same with viruses and ransomware and all of those types of phenomena. The human in the loop is the weak. link. And with AI technologies, we'll have to develop an intuition for it. There's a very interesting study I was reading this morning where people who pay for AI subscriptions, like for example, you're a chat GPD subscriber or you subscribe to Claude or any one of these AI ones. People who are paying for the AI models have a greater intuition on when they work and when they fake. It's got nothing to do with how much they use the AI models. It's whether you're paying or no. So that's interesting. It's a little bit of human psychology here where because you're paying for it, you're paying more attention.

And according to A, more attention. Yeah. And so you gain this intuition. So this intuition will be part of it. Remember, this is all met new. I'm, you know, surprising. Bething. AI is everywhere.

We're kind of close to this idea. But the more you speak with people, their exposure to AI is not that high. They're just learning it. Some people are just discovering chat GPT, as shocking as that sounds. Some people have never used AI in the workplace. So for many, for the large. part of the knowledge workforce, this is net new. So the literacy levels as they go up, I think some of these types of phenomenal will go down are companies, technology companies that

have invented these technologies will come up with safety measures around it as well. So there are multiple angles here. But what we cannot do effectively right now is develop trust, connection at pace on these technology platforms. If I had to sit, sit and verify for 15 minutes on every Zoom call and prove that I'm actually a human being, it defeats the purpose of a of a discussion. And this is where I see the value of in-person interaction going up. Also, the nature of that interaction changes. Now you need much more of a holistic type of experience where services combine with technology,

combine with the attitude with which those services are delivered. And can I use the data to deliver Jeremy or Mark your favorite cappuccino when you arrive and those kind of things? Now, that's something that changes the type of experience that you have. You're generating moments that matter for people. And that's that's a direction that space workplace is heading physical work and weapons. And this thing's taken off. Like this, this convergence of technology is taking off. We are 100% in exponential mode with this. The proverbial plane is headed down the runway at a very fast space. The empathy, the emotional

queshink that make us human is a secondary work stream here. Can we bolt these things on later? Or are we going to be in big trouble? It's a great question. And this idea of a bolt on is also a fascinating and the way I think about it is every word that you said there is we need to think about human centric human by design default default human the purpose of technology is not the technology itself the purpose of technology should be how do I make a human's life better how do I make this workers work life more joyful how do I and why why do that why make somebody's work life more joyful that doesn't relate to bottom line at all in fact completely the opposite the more joyful you workforces, the more engaged there, the higher your bottom line is. People don't buy from

technology. People buy from people. So you might have a fantastic AI agent and that AI agent is in deployment and reduces the work burden. But ultimately, that connection between people is what creates business and what creates value. And we said that at the beginning of this podcast, we were talking about values over value. And I think that that's where we are headed. We are also headed in the direction just to tie this back to our opening thread on spirituality. Our work has become a series of rituals minus the spirit. We've got to put the spirit back. Spirit plus ritual equals spiritual.

I love the idea. It's how I want technology to be built. Like every technology builder sitting down going, man, how could this help humans? But I'm going to illustrate a whole industry, Mark's going to giggle here, a whole industry that was built on tech for tech's sake rather than tech for humanity. And Web3 is just that. They built tech for tech sets. That's what it's become.

Decentralized crypto, Bitcoin did not start that. It's like the internet, the whole internet has changed from what it started out at, there's the vision that it was created for to what it's become. But no, I still see, I don't know, there's financial implications to this. They're building platforms. They're trying to generate return for the VC that dump two, three million bucks into them. How do you think about that? I'm advocating for some research on this.

But if we look at technology for technology sick and companies that are technology for technology sick versus companies that are designed technology that helps others, I think the latter have done better, generally speak. But there needs to be some empirical more than colloquial wisdom. I think there needs to be some data behind this to help support the argument. But I think companies that are advocating for and building technologies that help and develop community and build society, I think those types of technologies and those type of companies are doing well. I mean, look at, for example, Google allowed us to retrieve information from the internet. That was a synthesis initial idea behind Google. Google DeepMind, they're doing some incredible work with Alpha Fold and those kind of things. What gets presses, Gemini, what doesn't get presses, the work that they're doing with Alpha Fold, Alpha Evolver, those technologies, isomorphic labs, those kind of technologies. I think there's ample evidence of tech for humanity, and I think that those elements will do better in the long term.

Amazing. Well, this has been a fascinating conversation. You know, a ton of great nuggets listeners for you to pull out of this. We'll be creating some shorts and getting some of the things that we love. The book, The Conscious Machine. Give it a checkout and give it a read. We have one more question for you, Rahman. We had Kevin Kelly on the show a couple of months ago, and he posed a really cool question to leave our guests. So what should humans be? Yeah. Humans should be humans. And the reason I say this sounds like a totology, but the reason I'm saying it is because we have somehow trained humans to behave like machines. So look at our work. What do we evaluate productivity? How do we measure ourselves productivity? How do we measure ourselves some kind of dollar value? So Salim Ismail said this, we have materialized the world. We have now got to civilize it. I take that to the next level. I say we've got to spiritualize it. And the way we do that is by getting in touch with who we are, We are not machines. You measure the productivity of a late, not a human thing. You don't raise a human. You allow them to prosper, flourish, thrive.

This is where we should go. So humans should be humans. This is an important thread that we've heard throughout a lot of technological conversations. We have to point it back to our humanity. Ram, that was a great response to that. What question, if you could leave one for an upcoming guest of ours that's been on your mind that you've been wrestling with, that maybe you have partially answered, but you'd love a little more research on.

What is it by knowing which everything else is knows? And second question is, who or what am I and why am I here? Again, back to self-awareness. I love it. I love it. Ram, thanks so much for joining us. It was a great conversation. Mark, what takeaways do you have and any final thoughts?

Recently we've had some awesome guests. We've been bashing together, quantum computing, quantum mechanics, consciousness, and AI, thinking on paper, x, y, Z. And you can join the dots with us. Be curious. Stay disruptive. Keep thinking on paper.