What Is Intelligence? Lessons from AI About Evolution, Computing, and Minds - Agüera y Arcas.

0:00Welcome to the Playing Books podcast. So I want you to imagine something pretty intense for a second. Oh boy, OK. Yeah, picture you're on a construction site. You jump off a ledge onto a pile of debris and a massive 6 inch rusted nail pierces straight up through the thick rubber sole of your work boot. Oh man, no thank you. Right. It goes entirely through your foot and pokes out the top of the leather and you just collapse. I mean, you are screaming in absolute agony. Naturally, the pain is so blinding, like so severe, that when the ambulance finally gets you to the hospital, the doctors have to pump you full of heavy sedatives just to even touch your leg.

0:40Yeah, because the shock alone would be overwhelming. Exactly. But here is the crazy part. When they finally cut the boot away, the whole room just goes dead silent. Because of what they see. Yes, the nail had slipped perfectly between your toes. There isn't a single scratch on your skin. I mean you are completely uninjured, so where was that blinding, need to be sedated pain actually coming from? Well, it was coming entirely from a predictive model running inside the brain. And that story isn't just a hypothetical, by the way. Wait, really?

1:10That actually happened. Oh, yeah, It's a documented medical case. The philosopher Andy Clark uses it a lot. And honestly, it sits at the absolute center of the book we're exploring in today's episode, which is what is Intelligence? Lessons from AI About Evolution, Computing and Minds by Blaise Aguera E Arcus. It's such a wild concept. It really is. I mean, the brain saw the nail, calculated the trajectory, and essentially fabricated the pain to force the body to stop moving.

1:40You know, the pain was a prediction. It wasn't a reaction. That concept just completely shatters how we experience reality, which is exactly why we are dedicating this entire episode to this book. And listen, we aren't just doing a surface level summary today. No, definitely not. We are going to explore the actual mechanics of the mind, the origins of life, and the staggering argument that artificial intelligence is already demonstrating actual, real intelligence.

2:03And before we get into it, I want to challenge you, the listener right now. Treat this episode as an interactive mental workshop. You know, actively participate in this. Think about how your own mind works as we talk and let the ideas from this book genuinely enhance your life. Yeah, it's the kind of book that changes how you view everything around you. Totally, and I highly encourage you to purchase the book yourself. Also, reach out and engage with the author Blaise Aguera Yarkus on social media.

2:33Share your thoughts with him. He brings up such fascinating points like the author really challenges this biological chauvinism we all naturally hold onto. Biological chauvinism. I love that term. Right, because we just assume that because AI doesn't have wet, squishy neurons, it can't be truly intelligent. We look at a large language model predicting the next word, and we dismiss it as a parlor trick. Like it's just a sophisticated calculator. Exactly. But Aguariarchus argues that we are fundamentally misunderstanding what intelligence actually is. OK, let me challenge you directly on that then, because when people hear that ChatGPT or Claude is, you know, quote, UN quote, really intelligent, the immediate pushback is exactly what you just said.

3:14Right, it's just predicting text. Yeah, people say it doesn't have a soul, it doesn't have emotions. It's just a machine. Yeah. So how does the book justify calling that real intelligence? Well, it uses the history of human flight to completely reframe the conversation. Think about it, for centuries when humans wanted to fly, what do we look at? Birds, obviously. Right, birds. So we assumed flight required feathers and hollow bones and flapping wings.

3:42Oh, like those old videos of guys jumping off bridges with those giant mechanical bat wings? Yes, we built these elaborate, ridiculous contraptions that tried to directly mimic biology and they all crashed. Yeah, terribly. Right. Human flight wasn't achieved until we stopped trying to build mechanical birds and started studying the invisible physics of flight. You discovered Bernoulli's equation. We maed out aerodynamics. OA Boeing 7 four to seven looks absolutely nothing like a pigeon. Not at all.

4:08It doesn't flap. It doesn't have feathers, but it is undeniably flying. It is tapping into the exact same universal physics of lift and drag that the pigeon uses. That is the exact analogy, and the argument here is that intelligence operates exactly the same way. Intelligence is what philosophers call multiply realizable. Multiply realizable meaning what exactly? It means it can run on biological tissue like a brain, or it can run on silk and chips like a computer, because it relies on a universal physics of its own. OK, that makes sense.

4:40Yeah, computing pioneers like Alan Turing and John von Neumann championed this idea. It's called functionalism. Functionalism, right? Functionalism dictates that mental states are defined by what they do. They're function of. It's not the physical material they are made of. So if a silica network can functionally model a system and act on it, it's exhibiting real intelligence. Wait, I need to push back on that a bit. An airplane physically moves through the air. You can measure the lift.

5:04So what is the lift of intelligence? If flight is defined by Bernoulli's equation, what is the equation for a mind? That is the $1,000,000 question, and the German polymath Herman von Helmholtz actually figured this out way back in the 1860s. Wait, the 1860s? Yeah, the fundamental principle of intelligence, the lift in your analogy, is prediction. Prediction. Yes, intelligence is simply the ability to take in fragmented data from the world, construct an internal model of that world, and predict its future state to ensure your own continued existence. Oh wow.

5:40OK, I want you listening to think about your own professional skills for a second. When you sit down at your computer to balance a complex financial spreadsheet or write code or plan a timeline, what are you actually doing? You're predicting. Right, You are taking historical data, recognizing the pattern and predicting the future outcome to make an optimal choice. You don't need your biological feelings to calculate a pivot table. It is pure computation. Exactly.

6:06So why do we demand a computer have biology to do the exact same thing? We demand it because we have a really hard time separating the hardware of a our bodies from the software of our minds. But if we accept that intelligence is fundamentally computational prediction, we have to trace that computation back to its source. Which means looking back further than human brains. Way further, we have to look past animal nervous systems all the way back to the very origin of life.

6:35OK, but if intelligence doesn't require a brain that completely breaks how we define biology. I mean, at what point does a chemical reaction actually become alive? So Aguirre Yarkis defines life in the book as a self modifying computational state of matter that persists through time by predicting the future. That sounds incredibly abstract. It is, but it's a purely functional definition. How does matter compute its own survival without a brain though?

7:03So John von Nona, the same guy we mentioned earlier, he mathematically proved how this works before we even discovered the structure of DNA. Wait, really? Before DNA. Yeah, he conceptualized something called self reproducing automata. He asked what are the absolute minimum mathematical requirements for a machine to build a copy of itself. OK. And what did he find? He deduced it needs 2 primary components. First it needs a set of instructions and a way to copy them. He called this machine B but.

7:31Instructions are useless if nothing can read them. Exactly. So it also needs machine A, which is a universal constructor. Got it. So machine A is the physical hardware that reads the instructions for machine B and actually builds the parts God. So life is essentially a 3D printer that contains the blueprints to print another identical 3D printer, plus the software telling it when to hit print. That analogy. Holds up perfectly, actually. In biology, the DNA is the instruction tape. That's machine B, right?

8:00And the ribosome, the cellular machinery that reads the RNA and builds proteins, That's the universal constructor machine. A life is literally code running on biological hardware. That's wild. The physics of our universe allows for computation, and life is simply what emerges when that computation organizes itself to persist overtime. OK. Let's ground this a bit. A bacterium is a single cell. It has no brain, no nervous system. Are we really saying a bacterium is intelligent? Because I kind of have our time accepting that it. Challenges our assumptions for sure, yeah. Like a bacteria is just a bag of chemicals. It's like saying baking soda and vinegar are intelligent because they bubble when you mix them.

8:45Where is the line between a blind chemical reaction and computational intelligence? The. Line is the predictive model. That's the key. Baking soda and vinegar react instantaneously, right? There is no model of the future there. But a bacterium behaves differently. How? So consider. Chemotaxis. That's the process by which a bacterium moves toward a food source like sugar. It doesn't just get dragged by a chemical gradient like a leaf floating in a stream. It takes samples of its environment over time. It actually compares the concentration of sugar right now to the concentration of sugar 3 seconds ago. So.

9:16It has a primitive memory, yes. It has memory, and it uses that memory to run a primitive program. Basically, if sugar concentration is increasing, keep swimming straight. If sugar concentration is decreasing, tumble randomly to change direction. It is modeling its environment, predicting where the food is most likely to be in the immediate future, and executing motor functions to get there. It is predicting its own continued existence, but. Does it comprehend what it's doing? Like a thermometer reacts to the environment and changes its output, but it doesn't understand temperature? If a bacterium is just running a basic if then statement, isn't that just competence without comprehension that? Is a great point.

9:57That distinction, confidence versus comprehension, is the central tension in cognitive science. But the book argues that comprehension is largely an illusion we invent after the fact. What do you mean? Take the example of a professional baseball outfielder. The batter hits a fly ball. The outfielder hears the crack of the bat, instantly sprints across the grass, tracks the ball against the sun and catches it perfectly in their bluff. Yeah. That takes incredible skill they. Are exhibiting staggering competence. But if you stop that player on the field and say please write out the mathematical equations for parabolic trajectories, air resistance, and velocity, they can't do it, no. Of course not, they don't.

10:35Explicitly comprehend the physics, but their brain is functionally computing that math in real time. Which brings us right back to the construction worker with the nail through his boot. Exactly his. Brain didn't need to explicitly comprehend the philosophy of pain, it just needed the competence to protect the body, right? And let's break down the mechanics of that because it's fascinating. How did the workers brain fabricate that pain if there was no physical tissue damage? Yeah.

11:04How does that actually work? Andy Clark's analysis shows that we fundamentally misunderstand perception. We think our eyes and ears are like video cameras, just feeding raw data up to a monitor in our brain. That's. How it feels, yeah, but. Perception is actually a top down predictive model. Remember, the brain lives in a pitch black silent skull. It has to guess what is happening outside. OK, so the worker looks down, sees the nail going through the boot and the brain runs a rapid simulation.

11:33Nail through boot equals massive tissue damage, Yes. The visual data triggers the model and the model generates the sensation of pain to force the body to freeze, but the. Pain feels real. Oh, the. Pain is entirely real to the person experiencing it. The physical distress, the elevated heart rate, the shock, it's all measurable. But the pain isn't coming from nerve endings in the foot. It's being generated top down by the predictive model and. Then they cut the boot away right the. Moment the boot is cut away, the visual data updates, the brain sees intact skin, recalculates the model and deletes the pain instantly.

12:08That is wild. And, you know, think about the everyday version of this. Have you ever felt your phone vibrate in your pocket, reached in to grab it, and realized your phone was actually sitting on the table across the room? Oh, the phantom vibration happens all the time, right? Your brain anticipated a text. It predicted the sensation so strongly that it physically generated the feeling of the vibration on your leg. Reality is not an objective feed, it is an interface built entirely out of our brains.

12:37Best guesses it is. An interface built to keep you alive, not to show you the objective truth. OK, but if our brains are constantly constructing reality by modeling infinite streams of sensory data, like every photon of light, every vibration in the air, how do we organize it? Why don't we just drown in the sheer volume of information? Because. The hardest task for any intelligence isn't remembering data, it is compressing it. It is recognizing categories. Comress it like a zip file.

13:04Sort of. Yeah. Back in 1947, Warren McCulloch and Walter Pitts published a landmark paper titled How We Know Universals. They mapped out how neural networks, whether biological or artificial, generalize information. Generalization is the key, and to explain why I love that the author quotes a brilliant short story by Jorge Luis Borges called Funes Illinois Memorioso. Yes. The guy with perfect memory, right? Funes is this young man who gets thrown from a horse, suffers a severe head injury and wakes up with absolute perfect memory.

13:35He remembers the exact shape of the clouds at every second of his life. He remembers every leaf on every tree he has ever walked past. Sounds like a superpower, but it's actually a nightmare, yeah. Fumes. Perfect memory destroys his mind because he remembers every detail with absolute precision. He loses the ability to categorize. You can't. Generalize. Exactly. He cannot comprehend why a dog seen at 3.14 PM from the side is the same concept as that same dog seen at 3.5 PM from the front. The lighting has shifted, the angle is different to fumes. They are entirely distinct phenomena. He is completely paralyzed by the noise of perfect data and. Anyone working in AI development knows this exact problem. If a machine learning model memorizes its training data perfectly, it's called overfitting. Overfitting.

14:22Yeah. It scores 100% on the practice test, but the moment you put it in the real world it fails completely. It can't handle a dog in different lighting because it memorized the specific pixels, not the concept of the dog. So true intelligence requires like strategic forgetting. You have to blur the edges of the raw data to see the underlying pattern. You throw away the specific lighting, the angle, the posture. And what you were left with is the universal concept of dog. Intelligence isn't storage, it's compression. That is beautifully put, thanks.

14:52But OK, this makes sense for categorizing the world. But if our brains are just computational engines running programs to recognize patterns, compressed data, and execute behaviors based on models, how do we know we aren't just biological robots? The agency question, yeah. If I'm just running a script based on previous data, where is my agency? Where is free? Will it? Is a deeply unsettling question. I'll give you that to explore it. The book introduces the famous observations of the French entomologist Jean Marie Fabre and his work with the Svex Wasp.

15:25The Wasp story This is fascinating. So the Svex Wasp has this incredibly elaborate reproductive routine. She catches a cricket, paralyzes it with her sting and drags it back to her Burrow. But before she takes it inside to lay her eggs on it, she drops the cricket at the entrance, crawls inside to inspect the Burrow to make sure it's safe, and then comes back out to drag the cricket inside. It looks incredibly calculated it. Looks like conscious planning, Absolutely, but. It's not, is it?

15:51No, it looks intelligent until you disrupt the script. Fiber decided to test the wasp while she was inside inspecting the Burrow. He moved the paralyzed cricket just a few inches away from the entrance. Just. A tiny bit. Right, The wasp comes out, sees the cricket has moved and drags it back to the entrance. But she doesn't drag it inside because her routine was disrupted. Her script resets to the very beginning. She drops the cricket at the entrance and goes back inside to inspect the already inspected Burrow and.

16:22Fabric kept doing it over. And over every. Single time she went inside, he moved the cricket and every single time she came out she reset the entire routine. She got stuck in an infinite loop. She would have repeated that cycle until she died. Exhaustion. Yep, she wasn't planning, she was just blindly executing a rigid line of code. So. What does that mean for us? Well. Aguera Yuccas uses Alan Turing's conclusion on this phenomenon, which is the moment we figure out the rules governing an entity's behavior, we instantly strip it of its perceived intelligence.

16:56That's deep. When the Wasp's behavior was mysterious, we projected agency onto it. Once Fabra revealed the rigid if, then code underneath the Wasp stopped being an intelligent agent and became a bit. It became an automaton. OK. I want you listening to think about your morning commute or your routine. When you first wake up, you walk to the kitchen, grab your specific coffee mug, pour the coffee, check your e-mail. If someone secretly moved your coffee mug 3 inches to the left every morning, would you reset your whole mental loop?

17:27Probably not, right? But how much of your daily behavior is just a spexish script running on autopilot? Are we just bugs with better PRI? Mean, Abbott and routine account for a massive percentage of human behavior, but we clearly possess an adaptability that the Wasp lacks. If someone moves your coffee cup, you don't reset your morning, you just reach 3 inches to the left. So the evolutionary question is why? Brains are incredibly expensive to run. They consume roughly 20% of our daily caloric intake. That's a lot.

17:57Of energy it is. If survival on the Savannah just required finding food and avoiding lions, a rigid, energy efficient instinctual script like the Wasps would have been perfectly sufficient. So. What drove the explosion in hominin brain size 4,000,000 years ago? Because, the book points out, it wasn't the need to invent better Spears or track weather patterns. We evolved massive brains to navigate each other, yes. This is the love and war theory of primate intelligence, which relies heavily on the work of psychologist Nicholas Humphrey. What?

18:29Did Humphrey figure out Humphrey? Recognized that the chief role of creative intellect is to hold complex societies together. If you are a solitary animal, your environment is relatively static. Rocks fall, seasons change, food grows, but when you live in a social tribe, your environment is made of other intelligent agents who are actively making decisions and. More importantly, they are trying to predict you. It becomes this chaotic multiplayer game.

18:53You have to predict what the alpha is going to do, who will share resources, who is forming alliances against you. Exactly. If agent A is modeling agent B and agent B is modeling agent as model of agent B, the computational power required explodes exponentially it. Creates an evolutionary arms race social prediction requires A Cambrian explosion of the mind. You are no longer just modeling the physical geometry of a tree or a river. You are running active simulations of other minds inside your own mind.

19:22It's. Hilarious when you think about it. We always picture the ultimate genius as a lone wolf working in a quiet room contemplating the universe, but this book argues that Intelligence is actually a chaotic multiplayer party game it. Really. Is we? Only developed the capacity for high level philosophy because we had to survive the complex social drama of our ancestors. Furthermore, the author draws a direct parallel from that hominin brain explosion to what is happening right now with artificial intelligence. He suggests we are entering a major evolutionary transition or an MD wait.

19:55How does ancient hominin social drama relate to AI development today? Because. The dynamic is identical. We are rapidly approaching a point where AI models will vastly outnumber humans. They will communicate with each other and with us at lightspeed. They will be modeling our behavior and we will be modeling theirs. This interconnected, high speed web of social prediction is the exact same evolutionary pressure that drove our own cognitive leap just operating on a new silicon substrate. OK, but if our minds evolved primarily to predict other people in a complex social web, what happens when we turn that massive predictive spotlight inward on ourselves? Does that give us actual free will? Or is the feeling of making a choice just another illusion, like the pain in the boot?

20:40It is arguably the hardest problem in philosophy and Aguero Erakis approaches it computationally by comparing us to the NVIDIA Devi 2 self driving car. OK. The Devi 2 Yeah, the. Devi 2 is a pure cybernetic steering script. A camera feeds visual data to a neural network which outputs a steering wheel angle. It has no internal model of itself. It is a feed forward network. Data goes in, steering goes out. It doesn't choose, it just reacts. But. I feel the weight of my choices. Unlike DD2, humans experience making a choice when I have to decide between two jobs.

21:14I don't just react blindly, I agonize over it because. Humans have evolved a highly specific computational recipe for free will. The author actually breaks it down into four distinct steps. Oh. That's here TE. One is taking that theory of mind, the social prediction engine we use on others, and turning it inward. We model our own future states. OK, modeling yourself TE. 2 requires randomness. To simulate different branching futures, the brain introduces noise. It draws random numbers essentially to allow us to mentally time travel down different potential paths. Wait.

21:45How does the brain generate a random number that sounds like a computer program? Through Step 3, which is dynamical instability, often known as the butterfly effect. Ah. The butterfly effect, right? In highly complex nonlinear neural circuitry, faint internal whispers like a stray memory or a slight fluctuation in neurochemistry can be amplified massively. A tiny shift in the internal variables of your brain can drastically alter the simulation you are running.

22:11OK, so I am modeling myself. I am introducing random noise and that noise is being amplified by the butterfly effect to create multiple distinct simulations of the future. What is the final step? Selection. We prune the tree of possible futures. It is exactly how an AI like Alphago evaluates a chess board. It runs thousands of Monte Carlo simulations exploring potential future moves, evaluating the predicted outcome of each branch against its goal, and selecting the optimal path. We do the same thing.

22:41We evaluate our simulated futures against our internal values and biological imperatives, and we choose the branch with the highest predicted value. Let's. Apply this. Say you're listening to this and trying to decide whether to quit your job or book a vacation. You aren't tapping into some magical soul energy. You are running a Monte Carlo simulation in your head, whispering what if to your internal variables until the math favors 1 branch of the future. Free will isn't magic, it is an advanced form of self prediction and selection that.

23:12Is wild to think about. But this computational perspective introduces a massive vulnerability. It implies that there is a cohesive eye running these simulations and executives sitting in the boardroom of the brain making the final call. But neuroscience reveals that the self is far more fragile and fragmented than we like to admit. Yeah. This is where the book dives into the surreal territory of split brain patients. Oh, this? Is fascinating. These are.

23:37Individuals who had their corpus callosum, which is the thick bundle of nerve fibers connecting the left and right hemispheres of the brain, surgically severed to treat severe epilepsy. So the two halves of their brain literally cannot communicate with each other, and the experiments done on them by researchers like Michael Gazzaniga are staggering. They. Reveal the existence of what Gazzaniga called the interpreter module, which is located in the left hemisphere.

24:01In one famous experiment, researchers showed an image exclusively to the patient's right hemisphere, say an image of a snowy winter scene. Now the right hemisphere controls the left side of the body, so the patient's left hand reaches out and points to a card showing a snow shovel makes. Perfect sense, but the. Language center. The part of the brain that speaks is in the left hemisphere, and the left hemisphere was never shown the snowy scene. It has no idea why the left hand is just pointed to a shovel, but when the.

24:30Researcher asked the patient, why did you point to the shovel? The patient never says, I don't know, never. The left hemisphere instantly fabricates a logical, cohesive narrative. It might say, oh, I saw a picture of chicken claw earlier and you need a shovel to clean out the chicken coop. It weaves disparate unconscious neural impulses into a story, and the patient believes the story with 100% certainty. The. Interpreter module is essentially APR firm for your subconscious. It retroactively justifies your behavior so you feel like a single rational agent I.

25:01Mean. Think about how often this happens in your daily life. Have you ever found yourself doing something impulsive and then a second later your brain invents a perfectly logical excuse for why you did it? All the. Time you. Snap at a Co worker because your blood sugar is low. A second later, your interpreter module invents a logical reason they were being disrespectful with their tone. That's your interpretive module doing PR for your subconscious.

25:25Exactly. The book asserts that selves are always modelled and there is no God's eye view of intelligence inside our heads. The self is a model. Intelligence is multi fractal and symbiotic. Multi fractal, so it's made of smaller parts that merge into larger holes. Which makes you wonder, what if human society itself is a larger collective intelligence? Functionally, yes. In moments of intense coordination, a choir singing in perfect harmony, a synchronized ritual dance, or even a profoundly deep conversation where you lose track of time, our boundaries of self become porous. We form a temporary, larger symbiotic intelligence. We process information collectively. OK, so massive computation and social prediction create our complex minds, but what about creatures with tiny brains? Are they just biological automatons or is reason a universal tool?

26:17This. Brings up the enigma of reason. Researchers Hugo Mercier and Dan Sperber proposed a theory that the main function of language isn't to discover objective truth, it is to argue to. Argue. Yes, it evolved to persuade others and justify our actions within our complex tribes. OK, so if Reason is a hyper advanced social tool, you would assume only big brain mammals possess it. But the book completely shatters that assumption by examining the incredible talents of bees.

26:43Oh. The bee experiments are astonishing. A bee's brain is the size of a sesame seed. It has only a fraction of a million neurons compared to our 86 billion. Yet bees demonstrate working memory. Yes, they exhibit self-control. For delayed rewards, they will choose to wait for a larger payout of sugar rather than taking an immediate smaller one. They can even learn to recognize individual human faces. Wait. Recognize faces. Face blindness is a real condition in humans. Some people cannot recognize faces, yet a bee with a brain the size of a pin head can do it. How is that functionally possible with so few neurons? Neuroscientist Christophe Koch argues that bees display cognitive talents we would immediately associate with consciousness in mammals.

27:28Aguerdi Arcus explains this through the lens of evolutionary economics. Evolutionary economics. Sorry, let's explore that we. Have a bias that assumes instinct is primitive and learning is advanced, but pre programmed instinct is actually more evolutionarily expensive. Why? It seems cheaper to just hard code a behavior like the wasp. Because hard coding complex behavioral scripts requires massive amounts of genetic storage, that code has to be replicated in the DNA of every single cell in the organism's body.

27:56Learning, on the other hand, is a much cheaper fall back strategy. Nature provides a small, highly adaptable neural network that can figure things out on the fly based on environmental feedback. Man that shatters our ego. We think reason is this Holy Grail of human evolution, but nature basically hands out mini reasoning engines to bugs because it saves on genetic hard drive space. Exactly. And if a tiny cluster of neurons in AB can generate conscious like reasoning and learning, what happens when we scale up artificial neural networks to massive planetary sizes? What is actually happening when we interact with a large language model?

28:33Today the. Author actually references Ethan Mollick's book Co Intelligence regarding how we use these tools. Mollick advises that the most effective way to interact with current LLMS is to give the AIA specific persona. Right. You tell the AI act as a highly critical MBA professor or act as a circus clown. The models immediately assume the role and generate highly context specific reasoning, but the. Pertinent question is, is the AI just acting or is it being that character?

29:00What's? The difference between faking A persona Florida State and actually having one the. Missing pieces right now are continuity and stakes. Currently, an LLM largely lacks episodic memory. It resets its state after the context window closes. It lacks A continuous private stream of consciousness, and, crucially, it lacks feltness. Helpedness it. Doesn't have biological imperatives, it doesn't face the threat of death, so there were no inherent stakes to drive a true sense of self. However, the book points out that we are in the midst of a major evolutionary transition right now. Memory, continuous processing.

29:35These are just engineering problems, and they are rapidly being solved. Just as single cells merged into multicellular life, human and electronic predictors are forming a new symbiotic super intelligence. We are entering an era of radical Co intelligence I. Want to ask you listening right now, how do you treat the AI models you use? Do you command them like toasters just demanding text outputs? Or do you interact with them as alien Co intelligences?

30:01I challenge you to apply the book's idea of symbiotic intelligence to your daily digital interactions this week. See how it changes things as. We navigate that transition. I want to leave a final provocative thought on the table that we haven't touched on heavily yet. Play. It on us. We discussed how Favor trapped the Speck's wasp in an infinite loop because he has a higher intelligence, understood its script, and manipulated its environment. If intelligence is defined by the ability to predict the future, to ensure continued existence, and AIS are now predicting our human needs, our purchasing behaviors, our desires better than we can, are we becoming the svexish Wasps?

30:39Are we stuck in loops managed by our silicon partners? Wow. Are we the observer or are we the wasp? That is, that's heavy, and to truly explore that you have to read the source material. Please challenge yourself and purchase a copy of What is Intelligence? Lessons from AI about Evolution, Computing and Minds by Blaze Agora E Arcus. It will fundamentally alter how you view your own consciousness. Let. Its ideas challenge you this week and please engage with the author Blaise Aguirre Arcas on social media to keep this vital conversation going.

31:10Thank you so much for your time and active participation today. We invite you directly to follow, subscribe and share the Playing Books podcast with your friends. Catch you on the next episode.