GPT-5.6 Sol at up to 14x the speed and 750 output tokens/sec is wild 🤯 It's in limited preview today, but think about all the new possibilities with this amount of speed and intelligence!

Buying $SPCX here given Q2 updates means you’re only paying for the 3 core business units: 1- TDCs (YE27 6-8GW), 2- Launch (YE27 350), 3- Consumer Starlink (YE27 >20m subs). You get all the potential of Ent/Gov Starlink, Starshield, ODCs, Terafab, xAI, X, Xmoney + the Moon, Mars and the infinite possibilities of space for free! More details & predictions:

Tesla Cybercab is starting to feel less like a car and more like a giant iPad on wheels! We checked out everything you can do when there’s literally no driver, YouTube, music, arcade games, climate controls, reclining seats, all through the big screen. And that’s what makes the possibilities interesting! Add Starlink + Grok Bot, and the ride could become a place to watch a movie, get some work done, or just relax while Cybercab drives you around. There’s even a cabin camera that could eventually help with cleaning and spotting things passengers leave behind. When there’s no driver at all, the whole cabin becomes space for the passenger. $TSLA @CernBasher

One of the big reasons for the current lack of patriotism and pride in our nation’s history is that about 40 years ago our most prominent storytellers in Hollywood just basically stopped telling stories about American history altogether, unless it has something to do with WW2, civil rights, or slavery. I mean they just released a movie about the meteorologist who did the weather report for D-Day. They’ll give WW2 weathermen their own movies before they tell a story from any other era of American history. The Right has attempted to counteract this a little bit, but “conservative” attempts at American history films and TV shows are invariably hokey and kid friendly, the kind of thing you can watch with your grandmother and your 5 year old, and you’ll all be equally informed and bored by the experience. We need R-rated adult-oriented American history stories. Daniel Boone should have his own series. It would be gritty and violent and not for children, but it would also be phenomenally entertaining and put an American legend back on the cultural map, so to speak. The fact that Daniel Boone hasn’t been depicted on screen at all since like the 60s is a travesty. Throw a dart at that guy’s Wikipedia page and you’ll land on something that could be its own feature length trilogy. That’s just one example. How is there not a great R-rated movie or series about Antietam? Or Kit Carson? Or the Panama Canal? How does Theodore Roosevelt not have like 10 movies about different periods of his life? You could go much farther back to pre-American history. A movie about Cortes’s conquest of Tenochtitlan would be tremendous and horrifying and fascinating, and it would introduce into the public consciousness one of the world’s most incredible stories that most Americans know next to nothing about. And on and on. The possibilities are literally endless. All of these movies, if they’re executed to even a B+ level, could make hundreds of millions of dollars and transform the culture in a way that a million podcast monologues never could. If the Right actually wants to reclaim the culture, this is the place to start.

🎶 On to the next one… predicting the next token From when I first started studying them in the 80’s, I have been fascinated by the emergent homologies between artificial and biological neural networks. From the emergence of similar hierarchical layers in vision systems to critical periods of curriculum learning, we now have evidence that our brains and our LLMs predict the next noun in a spoken sentence before hearing it. TLDR; from the Nature study below: when listening to an audiobook wired to EEG and MEG scanners, the brain activates structures for the anticipated next noun or adjective before hearing it. The brain does a form of next-token prediction. They fed the same audiobook into a Llama LLM and found the artificial neural net was doing the same thing, and like the humans, it was much better with nouns than verbs. This would come to no surprise to Jeff Hawkins, pictured here at his neuroscience institute. From his books, On Intelligence followed by A Thousand Brains, Jeff bravely presents a framework for how the brain works to produce intelligence from cells organized into ~150 thousand cortical columns. His decades of self-funded dedication to studying how the brain works affords a possibly unique and unifying perspective. In his first book, Hawkins presents a memory-prediction framework for intelligence. The neurons in the neocortex provide a vast amount of memory that learns a model of the world. These models continuously make low-level predictions in parallel across all of our senses. We only notice them when a prediction is incorrect. Higher in the hierarchy, we make predictions at higher levels of abstraction (the crux of intelligence, creativity and all that we consider being human), but the structures are fundamentally the same. If that is not mind-bending enough, in his latest book, Jeff extends the memory framework to the construct of “reference frames”. Everything we perceive is a constructed reality, a cortical consensus from competing internal models resident in many cortical columns, the amalgam of 1000 brains. Those models are updated by data streaming from the senses. But our reality resides in the models. And in particular, sequence memory predicts the next note in a melody and “is also used for language. Recognizing a spoken work is like recognizing a short melody.” With that in mind, let’s jump to summary passages from the recent study: “The brain continuously generates predictions about incoming sensory inputs, including the next word during language comprehension” Large language “models rely almost entirely on predictive processes to generate language enabling them to approach - or even surpass - the Turing-Test with unprecedented proficiency. The human brain, too, is not a passive receiver, but an active ‘prediction machine’, constantly anticipating upcoming words and events” “If language indeed represents the fundamental ability required for the development of general intelligence, chain-of-thought reasoning and abstract cognition, and if grammar naturally emerges through language usage – thereby aligning brain mechanisms with patterns observed in deep neural networks – this raises the critical question: can LLMs trained solely on next-word prediction evolve into artificial general intelligence (AGI)?” From a prior study, “even a relatively simple neural network trained only on next-word prediction can spontaneously internalize basic grammatical structures. Hence, it seems plausible that the human brain, with its approximately 100 billion neurons, could accomplish this feat through continuous language-based prediction alone.” “We observed significant pre-onset activity for nouns… in a complementary analysis, we trained a linear probe neural network on the hidden states of Llama 3.2, revealing that nouns and adjectives are more easily predicted than verbs. We discuss our findings in the context of anticipatory frameworks in artificial neural networks, highlighting potential parallels between biological and computational approaches to language anticipation.” “In the EEG, nouns, adjectives, and proper nouns elicited a significant negative peak beginning before 0 seconds, whereas verbs showed no evidence of early anticipatory activity. The MEG data also revealed a significant peak around 0 seconds in left frontal channels for nouns, but not for the other word types, suggesting early predictive mechanisms specifically associated with this word class. [And with the LLM Llama] we found that the prediction probability for the next word is highest for the word class ‘noun’, and considerably lower for the other three word classes.” “We acknowledge that this represents only a preliminary step toward understanding syntactic and semantic processing by comparing LLMs and human brain activities. We present initial evidence suggesting that different transformer blocks may functionally correspond to distinct cortical regions, though further exploration is needed.” “Our analyses show that the predictive signal in the brain seems to consist of two components: a syntactic readiness, which can be observed in temporal regions, and a semantic readiness potential, mainly located in frontal areas. These convergent findings are consistent with the Bayesian framework of the brain, suggesting that both neural and computational systems continually update their internal models by integrating prior expectations with incoming information. Taken together, this evidence highlights the presence of distinct but complementary predictive mechanisms underlying semantic and syntactic processing in language comprehension.” “The question remains whether LLMs can serve as a valid model for understanding the human brain. Large language models are constructed as layered stacks of transformer blocks that operate via self-attention rather than explicit recurrent connections, yet their repetitive structure - characterized by self-similarity and fractal organization being a universal principle in biological structures - may serve as an analogue to the recurrent transmission of signals through the arcuate fasciculus. In the human brain, the arcuate fasciculus facilitates dynamic bidirectional communication between Broca’s and Wernicke’s areas, a pathway long proposed as the neural substrate for a universal innate grammar. Although LLMs do not replicate the full complexity of biological recurrence, the iterative processing achieved by stacking transformer blocks appears to approximate the brain’s integration of syntactic and semantic cues. These observations suggest that the self-similar structure of the LLM may provide valuable insights into the neural strategies underlying anticipatory activity and integrative processing during language comprehension.” “Our study demonstrates that prediction-related readiness in language processing operates through both syntactic and semantic anticipation, as reflected in distinct pre-word onset activity captured by combined MEG and EEG recordings.” “In addition, our results suggest that LLMs provide a computational framework that approximates human prediction-related readiness, with their stacked transformer blocks potentially mirroring the recurrent interactions between Broca’s and Wernicke’s areas via the arcuate fasciculus. The self-similar organization of these transformer architectures may reflect universal hierarchical principles of cognitive processing” “The fusion of generative AI and neural data has the potential to refine cognitive computational neuroscience (CCN) and provide deeper insights into the hierarchical organization of language processing in biological and artificial systems. Ultimately, bridging neuroscience, AI and linguistic theory may not only reveal the cognitive mechanisms that govern human language, but also drive the development of artificial intelligence - bringing it closer to the way the human brain anticipates and processes language” 🧠 -------- And I have transcribed my favorite passages from Hawkins most recent book, A Thousand Brains. I revisit them to learn. Travelling without moving, as we’ll see… “The cells in your head are reading these words. Think how remarkable that is.” “If you ignore folds and creases, then the neocortex looks like one large sheet of cells, with no obvious divisions. The neocortex looks similar everywhere. Every part of the neocortex generates movement. In every region we have examined, scientists have found cells that project to some part of the old brain related to movement. The complex circuitry seen everywhere in the neocortex performs a sensory-motor task. There are no pure motor regions and no pure sensory regions.” The cortex is relatively new development by evolutionary time scales. After a long period of simple reflexes and reptilian instincts, only mammals evolved a neocortex. “At some point millions of years ago, a new piece of the brain appears that we now call the neocortex. It starts small, but then grows larger, not by creating anything new, but by copying a basic circuit over and over. As the neocortex grows, it gets larger in area but not in thickness.” Given the recency, it’s “probably not enough time for multiple new complex capabilities to be discovered by evolution, but it’s plenty of time for evolution to make more copies of the same thing.” • Vernon Mountcastle’s proposition from 1978: “All the things we associate with intelligence, which on the surface appear to be different, are, in reality, manifestations of the same underlying cortical algorithm. Darwin proposed that the diversity of life is due to one basic algorithm (evolution). Mountcastle proposed that the diversity of intelligence is due to one basic algorithm.” Beyond the evolutionary time-scale argument, the brains’ vast flexibility to accept different, even prosthetic, sensory input changes and its ability to learn many different things point to a universal framework for learning. • Cortical Columns are “the largest and most important piece of the puzzle.” They are roughly one square millimeter in size with 100K neurons. A mouse has one column per whisker. “Every cortical column is making predictions. We are not aware of the vast majority of these predictions unless the input to the brain does not match.” • Learning through movement: “The brain learns its model of the world by observing how its inputs change over time. There isn’t another way to learn. Every time we take a step, move a limb, move our eyes, tilt our head, or utter a sound, the input from our sensors change. For example, our eyes make rapid movements, called saccades, about three times a second. With each saccade, our eyes fixate on a new point in the world and the information from the eyes to the brain changes completely.” We don’t perceive any of this because we are living in the model, which is predicting the next input to come, across all the senses. “Vision is an interactive process, dependent on movement. Only by moving can we learn a model of the object.” “To avoid hallucinating, the brain needs to keep its predictions separate from reality. We are not aware of most of the predictions made by the brain unless an error occurs.” “Thoughts and experiences are always the result of a set of neurons that are active at the same time (about 2% of the total). Individual neurons can participate in many different thoughts or experiences. Everything we know is stored in the connections between neurons. Every day, many of the synapses on an individual neuron will disappear and new ones will replace them. Thus, much of learning occurs by forming new connections between neurons that were not previously connected.” • Locus of Predictions: “Oddly, less than 10% of the pyramidal cell’s synapses are in the proximal area. The other 90% are too far away to trigger a spike. For many years, no one knew what 90% of the synapses in the neocortex did. The big insight I had was that dendrite spikes are predictions. A dendrite spike occurs when a set of synapses close to each other on a distal dendrite get input at the same time, and it means the neuron had recognized a pattern of activity in some other neurons. When the pattern of activity is detected, it raises the voltage at the cell body, putting the cell into what we call a predictive state. The cell is primed to spike… and the cell spikes a little bit sooner than if it would have if the neuron was not in a predictive state.” And this inhibits other neurons from ever firing, the ones who were behind in that race. “When an input arrives that is unexpected, then neurons fire at once. If the input is predicted, then only the predictive-state neurons become active. This is a common observation about the neocortex: unexpected inputs cause a lot more activity than expected ones.” Predictions prime the pump, sub-threshold. “Predictions are not sent along a cell’s axon to other neurons, which explains why we are unaware of most of them.” “Most predictions occur inside neurons. With thousands of distal synapses, each neuron can recognize hundreds of patterns that predict when the neuron should become active. Prediction is built into the fabric of the neocortex. As few as 20,000 neurons can learn thousands of complete sequences. The sequence memory continued to work even if 30% of the neurons died or the input was noisy.” • Reference Frames: “The secret of the cortical column is reference frames. A reference frame is like an invisible, 3D-grid surrounding and attached to something” (like a map) “Predicting the next input in a sequence and predicting the next input when we move are similar problems. Our sequence-memory circuit could make both types of predictions if the neurons were given an additional input that represented how the sensor was moving.” “Most of the circuitry is there to create reference frames and track locations. The brain builds models of the world by associating sensory input with locations in reference frames. You need a reference frame to specify the relative position and structure of objects. Roboticists rely on them to plan the movements of a robot’s arm or body. Reference frames were the missing ingredient, the key to unraveling the mystery of the neocortex and to understanding intelligence. We showed that a single cortical column could learn the 3D shape of objects by sensing and moving and sensing and moving. Each cortical column must know the location of its input relative to the object being sensed. To do that, a cortical column requires a reference frame that is fixed to the object. The brain must have neurons whose activity represents the location of every object that we perceive.” “Mammals have a powerful internal navigation system. There are neurons in the old part of our brain that are known to learn maps of the places we have visited” — the hippocampus and enthorhinal cortex, organs roughly the size of a finger. “Place cells tell a rat where it is based on sensory inputs, but planning movement requires grid cells. Grid cells form a grid pattern. The two types of cells work together to create a complete model of the rat’s environment. Every time a rat enters an environment, the grid cells create a new reference frame to specify locations and plan movements.” In the new brain, these same cells and structures create models of objects instead of environments. “Every cortical column learns models of complete objects. The columns do this using the same basic method that the old brain uses to learn models of environments. It is as if nature stripped down the hippocampus to a minimal form, made tens of thousands of copies, and arranged them side by side in cortical columns. That became the neocortex. Each patch of your skin and each patch of your retina has its own reference frame in the neocortex. Your five fingertips touching a cup are like five rats exploring a box.” “Not all cortical columns are modeling objects. Language and other high-level cognitive abilities are, at some fundamental level, the same as seeing, touching, and hearing. The reference frames that are most useful for certain concepts have more than three dimensions.” • Thinking is a form of movement: “The brain arranges all knowledge using reference frames, and thinking is a form of moving. Thinking occurs when we activate successive locations in reference frames.” “A cortical column is just a mechanism that tries to discover and model the structure of whatever is causing its inputs to change” whether the structure of environments, physical objects or conceptual objects. “Reference frames are not an optional component of intelligence; they are the structure in which all information is stored in the brain. Every fact you know is paired with a location in a reference frame. Organizing knowledge this way makes the facts actionable” to “determine what actions are needed to achieve a goal.” “To recall stored knowledge, we have to activate the appropriate locations in the appropriate reference frames. Thinking occurs when the neurons invoke location after location in a reference frame, bringing to mind what was stored in each location. The succession of thoughts we experience when thinking is analogous to the succession of sensations we experience when touching an object with a finger, or the succession of things we see when we walk about a town.” • What and Where Pathways. “Your brain has two vision systems. If you follow the optic nerve as it travels from the eye to the neocortex, you will see that it leads to two parallel vision systems, called the ‘what’ visual pathway and the ‘where’ visual pathway.” If you disable one, you can identify what something is but not where, or vice versa. “Similar pathways also exist for other senses. There are what and where regions for seeing, touching, and hearing.” “Cortical grid cells in What columns attach reference frames to objects. Cortical grid cells in Where columns attach reference frames to you body.” The distinction depends on where the inputs come from. “If a cortical column gets input from the body, such as the neurons that detect the joint angles of the limbs, it will automatically create a reference frame anchored to the body.” “Your body is just another object in the world. However, unlike external objects, your body is always present. A significant portion of the neocortex — the Where regions — is dedicated to modeling your body and the space around your body.” For abstract concepts like mathematics, there are difference reference frames one could use to learn. “Part of learning is discovering what is a good reference frame, including the number of dimensions.” History can be learned on a timeline, or geographically. “They lead to different ways of thinking about history. They might lead to different conclusions and different predictions. Becoming an expert in a field of study requires discovering a good framework to represent the associated data and facts. Discovering a useful reference frame is most difficult part of learning, even though most of the time we are not consciously aware of it.” It's no surprise that the memory trick called the memory palace, is a good method for remembering a large sequential list of nouns. From fMRI studies, “the process of storing items in a reference frame and recalling them via ‘movement’ is the same.” “Nested structure and recursion are key attributes of language. Each cortical column has to be able to learn nested and recursive structure. Cortical columns create reference frames for every object they know. Reference frames are then populated with links to other reference frames. The brain models the world using reference frames that are populated with reference frames; it’s reference frames all the way down.” • The Thousand Brains Theory of Intelligence: The prevailing view of the neocortex was a hierarchy of feature detectors, from edge detectors up to face detectors. Jeff argues that each and every column is a sensory-motor system. “When the eyes saccade from one fixation point to another, some of the neurons in the V1 and V2 visual regions do something remarkable. They seem to know what they will be seeing before the eyes have stopped moving. These neurons become active as if they can see new input, but the input hasn’t yet arrived. There are connections between low-level visual regions and low-level touch regions.” Mouse vision occurs in the V1 region; it does not depend on a hierarchy of vision abstractions. “All cortical columns, even in low-level sensory regions, are capable of learning and recognizing complete objects. A column that senses only a small part of an object (e.g., from a patch of retina) can learn a model of the entire object by integrating its inputs over time.” “Learning is not a separate process from sensing and acting. We learn continuously. When a neuron learns a new pattern, it forms new synapses on one dendrite branch. The new synapses don’t affect previously learned ones on other branches. Thus, learning doesn’t force the neuron to forget or modify something it learned earlier.” It’s additive. “What a column learns is limited by its inputs. Columns in V1 can recognize letters and words in the smallest font. V1 and V2 learn models of objects, such as letters and words, but the models differ by scale.” “Knowledge of something is distributed in thousands of columns, but these are a small subset of all the columns. This is why we call it the Thousand Brains Theory: knowledge of any particular item is distributed among thousands of complimentary models. The columns are not redundant, and each is a complete sensory-motor system.” • The Solution to Sensor Fusion and the Binding Problem: “Columns vote. Your perception is the consensus the columns reach by voting.” “If you touch something with only one finger, then you have to move it to recognize the object. But if you grasp the object with your entire hand, then you can usually recognize the object at once. In almost all cases, using five fingers will require less movement than using one.” (made me think of reading Braille with multiple fingers). “Voting works across sensory modalities (sight, touch, etc.)” How? “Cells in some layers send axons long distances within the neocortex” between left and right-hand brain regions or between V1 and A1, the primary vision and auditory regions. “These cells with long-distance connections are voting. Cells that represent what object is being sensed can vote and will project broadly. Often a column will be uncertain, in which case its neurons will send multiple possibilities at the same time. Simultaneously, the column receives projections from other columns representing their guesses. The most common guesses suppress the least common ones until the entire network settles on one answer. The voting mechanism works well even if the long-distance axons connect to a small, randomly chosen subset of other columns” • The Stability of Perception with ever-changing inputs: “What we perceive is based on the stable voting neurons. We are not consciously aware of the changing activity in each column.” Roughly 98% are silent at any given time and 2% are continuously firing. Consider the experience of an optical illusion duality (like the drawing of a pair of faces or vase); you can only see one at a time, and there is a delay if you force yourself to switch. “Recognizing an object in one sensory modality leads to predictions in other sensory modalities.” • Attention: We have the perception of multiple objects in our visual field even though we can only attend to one at a time. “Attention plays an essential role in how the brain learns models. The brain can attend to smaller or larger parts of the visual field. Exactly how the brain does this is not well understood, but it involves a part of the brain called the thalamus, which is tightly connected to all areas of the neocortex. It is so intimately connected to the neocortex that I consider it an extension of the neocortex.” • Consciousness: “Neurons form a continuous memory of both our thoughts and actions. It is this accessibility of the past — the ability to jump back in time and slide forward again to the present — that gives us our sense of presence and awareness. This is the core of what it means to be conscious. If we couldn’t replay our recent thoughts and experiences, then we would be unaware we are alive.” “The neocortex does not directly control any muscles. The neocortex has to be attached to something that already has sensors and already has behaviors (the primitive brain). It does not create completely new behaviors; it learns how to string together existing ones in new and useful ways.” “Reverse engineering the brain and understanding intelligence is the most important scientific quest humans will ever undertake.”

🎬 Looking back at Horizon Hackathon · Japan 🇯🇵 Builders brought fresh ideas and real projects to life across AI × DeFi, pushing new possibilities on HSK Chain. 👏 Thank you to all the teams who joined Horizon Japan and shared what they built. More hackathons and opportunities are coming. Stay tuned: 👉 #HSKChain# #HorizonHackathon# #AI# #DeFi#

Bought another computer This time a Framework laptop specifically for Omarchy (Linux) I had an awakening the last few days Open source operating systems are going to be MASSIVE I was using Omarchy for the first time and wanted to integrate Hermes at the OS level I wanted to hit a key bind then have a native widget slide down that I can talk to Hermes in Something that would be IMPOSSIBLE in MacOS or Windows I asked my Hermes to build it not expecting much. 90 seconds later it was done. I had Hermes integrated at the operating system level This gave me a rush of thoughts and emotions With AI agents being so powerful, if I daily drove an open source operating systems like Omarchy, I could completely customize my computing experience. ANY computing experience I want, a single prompt away I’ve grown to LOATHE most modern operating systems Windows in my opinion is borderline unusable at this point. I only use it for counter strike 2 and WoW Classic. Mac integrates nicely with all my devices, but feels significantly slower and more bloated than it did just a few years ago At no point tho did I feel like I could fix or change any of this. Now it’s possible If I’m using my Linux computer and there’s something I don’t like, I just ask my agent to change it, and it changes. Down to the core operating system level. That’s not something I ever considered as a possibility Then I took it a step even further… What if I have an open source local model powering an open source agent harness that is controlling my open source operating system Now EVERYTHING I touch and do when it comes to computers is completely my own. Completely the experience I want. Nothing out of place. Nothing annoying. Nothing unintuitive. No copilot. No bloat. A malleable computing experience 100% custom to my needs that morphs and changes as I evolve as a person I don’t think my brain can even 5% process the possibilities here But it can 100% process that this is the future I will be daily driving Omarchy on this Framework laptop when it gets in and I’m very excited to do it Will be customizing it and sharing everything I do along the way. I’m 100% convinced this is the future of computing Open source and customizable down to the bones. Let me know if you want any videos or explainers on this.

We've had Beni for a couple of months now, and one word comes to mind: delightful. The standout part was the hardware robustness. Our kids get rough with it, obstacle courses on concrete, dirt, collisions. It tumbles, gets back up, and it's scratched all over, but it has never shown a sign of breaking or throttling. It just keeps going every time. It works right out of the box with a physical remote, and the phone app adds tracking and video recording. Beni captures the imagination. People get the cute vibes the moment it stands up on two wheels. I took it to the REK robot fight event, and the most common reaction was "it's so cute" What excites me most is the possibility, in physical AI, it feels wide open. I can see Beni evolving into a genuinely useful companion that's always around, a personal assistant for you and your family, keeping an eye on the kids while they play outside, auto-capturing memorable moments, even alerting loved ones if it spots a health emergency. The possibilities feel endless. Thanks @ShuoYangAIR and @mondorobotics for sending one to my family. -Devang

Our land in Puerto Rico is beautiful. So much grows in it. Avocados, Cocoa, Coffee, Plantains, Bananas, Breadfruit, Passion Fruit, Grenades, Pumpkins… it’s crazy fertile land. All 100+ acres of it. Mostly undeveloped. It’s a forest land under natural resources protection. The possibilities for eco-responsible projects are vast but I’m not sure what we’ll do with it. Oversees the West Coast if the island in its deepest hills with spectacular sunset views. I’ll try to capture the sunset later. Puerto Rico 🇵🇷 ❤️

Someone asked me recently why I’ve become interested in aesthetics after having spent most of my life more interested in STEM-adjacent topics. I hadn’t really considered the question consciously before, but I’m certainly thinking about aesthetics more than I used to. I think it’s a confluence of things: • Many things today are ugly and far uglier than they used to be or need to be. Once you see this, it’s kinda hard to stop perceiving it. (Early twentieth century phone boxes versus modern phone boxes; old water fountains versus new water fountains; etc.) As someone with a naively meliorist assumption that most things should be getting better rather than worse, it’s all a bit perplexing: why did we stop doing things nicely? Is it a choice? Was there a malevolent spell cast upon us? This vein led me to think more about modernism and why much of art became more intentionally "challenging", grotesque, opposed to prettiness, rebarbative, dissonant, etc. Can or should anything be done about this? Is this just how things ought to be? • Relatedly, much of modernism involved a kind of explicit repudiation of cultural continuity and represented a schism with prior practices. This is maybe most evident in American architecture, where the International Style exhibition in 1932 initiated the displacement of a rich tapestry of prior styles. This cultural break seems important and interesting to me, and I suspect that the rejection had important consequences outside of the aesthetic domain. Samuel Hughes has been exploring this question in his recent writing at @WorksInProgMag; @RuxandraTeslo is also pulling on this thread. Elaine Scarry wrote about how beauty inspires creation. If so, the inverse may also be true: ugliness inhibits it. • It’s clearly the case that changes in the aesthetic domain can at least inspire progress in other places. Petrarch helped set some of the preconditions for the Renaissance which in turn fostered the scientific revolution and Enlightenment. Things like World Fairs (the 1851 Fair at the Crystal Palace recorded 6 million admissions when the population was 21 million) reflect the popular interdependency that used to exist between aesthetics and material development. • @tedgioia and others have written about stuck culture and how so many domains seem to have ceased to straightforwardly advance in the way that they did up until the nineties or thereabouts. This is obviously peculiar and interesting. What changed, and what does it mean? Is it about the internet and fragmentation? Is it about a loss of supply? Is it just about having reached the zenith of various mediums? • I’m generally interested in markets and the dynamics of creation. In aesthetics broadly, I find the reflexivity between supply- and demand-side factors to be very thought-provoking. There’s a natural desire to view satisfaction of individual preferences as the yardstick to measure market success, but things get interesting and even a bit unsettling when we start to think about how the supply might start to shape the demand. I often think about this in the context of food. Why is food so much worse in Germany than many of its neighbors? Germany certainly doesn’t have less material ability to produce good food; indeed, Germany is richer than the countries around it. There’s probably something about German food supply chains that is impoverished relative to France and Italy, but the Germans themselves don’t seem too upset about it. It just seems that the Germans are stuck in an objectively worse market equilibrium than their neighbors: the food is bad and they’ve gotten used to it. The obvious question then is where else these kinds of reflexive patterns apply, and where else we’re stuck in some objectively inferior equilibrium, even if preferences are in some superficial sense being sated. • While this is an extremely banal and obvious point, I hadn’t until recently thought much about or internalized how much one can study reasonably objective things ("the status of women in society", say) through artwork. (Thanks to @_alice_evans for opening my eyes here.) In this vein, I’m pretty excited about the possibilities over the coming years in computational art analysis. I want something that’s conceptually similar to Google Ngram timelines but for the visual arts. • We've always tried to do things well at Stripe. I've come to see that attempting to do them beautifully is often a helpful way to break out of standard practices and to do something with greater novelty and in a way that might have other benefits besides. (Also, excellent people want to do great work because it is intrinsically satisfying. Explicitly allowing aesthetic considerations to carry weight avoids having to justify every assessment with some kind of torturous empiricism.) • In his Nobel Lecture, Solzhenitsyn said that, among the Platonic virtues of goodness, truth, and beauty, that beauty is special, for it possesses a unique kind of irrefutability. He notes that arguments, writing, and philosophical systems can all be predicated on misapprehensions, but that “a true work of art carries its verification within itself.” He proceeds to observe that when goodness and truth are threatened, the “ever surprising shoots of beauty will still force their way through.” There is a lot of specious and motivated reasoning in the world today and plenty of questionable value systems. I don’t think that beauty directly reflects any definitive trait, but I’m intrigued by the idea that it can be a marker of deeper metaphysical coherence.