YouTube:
In this interview I finally get to sit down with the legendary Billy Joel. We talk about Billy's prolific career as a singer and songwriter, his love of classical music, and his techniques for writing such timeless music. Billy also shares some hilarious stories and demonstrates his unmistakable piano chops.
Charles King has a lot to say about Edward Gibbon's The History of the Decline and Fall of the Roman Empire, the current situation in America, and the application of the former to the latter. For example, setting the stage:
Anyone living in the 18th century could see the contemporary relevance of Gibbon’s Rome — just as we can see it today, during the year of America’s semiquincentennial. “Substitute the word America for the word Rome,” Henry Adams wrote after reading “Decline and Fall” in 1860, “and the question became personal.” Adams could sense the work’s significance for the United States, which was then mired in sectional conflict and preparing for civil war. In the century and a half since, Gibbon has been reliably cited as the perennial prophet of what happens when a good country goes bad.
Here's the heart of the matter:
Because Gibbon has become a universal sage, it is easy to miss this principal fact about him and his great work. He was an honest historian who, in an age of political division, fading empires and revolutionary upheavals, scribbled a long book with a large and surprising message at its core. Gibbon’s singular insight was that the whole point of reading a history book — or writing one — is not to come away with one big truth. The stealthy purpose of studying history is to get you comfortable with changing your mind.
That resonates with me, not so much for its applicability to the current state of American politics and society. Rather, I believe it's what I'm up to with my book on AI, Play: How to Stay Human in the AI Revolution. That's why I'm putting two chapters of (science) fiction in an eight chapter book where the other chapters are non-fiction.
Later:
At its most basic, making the chaotic events of the past into a coherent thing we call history is an act of intentional, purposeful understanding. History forces us to confront things we don’t comprehend, decisions we can’t fathom, and ways of being and believing that seem utterly bizarre. It makes us look for evidence in unlikely places. It requires that we think like grown-ups, drawing conclusions that we know will change when the available evidence does.
Facing up to AI has similar requirements.
All the things we consider normal, dear and true will one day pass away, as they did for the thousands of emperors, queens, citizens, soldiers, philosophers, priests and parents who populated “Decline and Fall.” Yet the possibility of happiness, meaning and a legacy that matters lies not in a disembodied hope for a better future. It lies in the hard evidence — here, let me show you, Gibbon tells us across the centuries — that the dead managed these things, too.
I can't say that (science) fiction counts as evidence. It does not. But it may made you open to different evidentiary requirements, different modes of construal.
There's more at the link.
I’ve just been through the first season of Elle, which is a prequel to Legally Blonde, from 2001. And so I decided to re-watch Legally Blonde, which I had enjoyed when it first came out in theatres and then some years later when it was streamed. I decided that I preferred the Legally Blondes over The Devil Wears Prada 2.
Now, I’m not sure I’m in the target demographic for either films. Maybe I’m at some edge of the demographic for Prada, but nowhere near the demographic for Blonde. But it seems to me that Blonde serves its demographic better than Prada serves its. Prada is about the imperious editor of Runway magazine, modeled after Vogue. I’m sure I’ve looked at Vogue once or three times, but that’s it. However I’ve long looked at the fashion coverage in the Sunday New York Times and I generally look at photos of the “looks” on display at important award shows and, of course, the Met Gala. So I’ve got some interest. I don’t have any interest in Cosmopolitan, but I’ve certainly leafed through issues. I have a vague sense that my sister may have read it decades ago. While Blonde is not about Cosmo, it does treat Cosmo as the bible, mentions it frequently, and splashes its cover on the screen often enough. Elle Woods swears by it.
Well, I suspect that the readers of Cosmo are more honestly served by Elle Woods in her various incarnations than the readers of Vogue are served by Prada 2, I don’t remember the original well enough to judge. But I do remember that speech about cerulean:
I can believe the substance of the speech. But what does that have to do with the line I remember from Prada 2: “You don't have what it takes. I'm sorry, but you're not a visionary. You're a vendor.”
I know Miranda thinks of herself as a visionary. A visionary of what? Of design? That’s the designers, no? Or is it her eye for design that makes are a visionary, an eye that crafts the editorial style of her magazine? Does that make her a style visionary or merely an astute vendor? I’m sure that Prada 2 doesn’t make the distinction, but perhaps the original Prada did, I just don’t remember it well enough.
But there is no such pretension about Legally Blonde. Yes, there’s some snobbery and some cliquishness, but the movie wasn’t really about them nor is the prequel series. They prove to be superficial and harmless. The movie presents us with what we initially take to be a dumb blonde prom queen who swears by Cosmo and shows us that she is, or can easily be, a smart, resourceful, and tenacious young woman. In the movie she follows her handsome, shallow, and transactional boyfriend to Harvard Law School. She proves to have shrewd judgment about people and a good legal mind, dumping idiot boyfriend in the process. Elle gives us the same characteristics stretched over an eight episode series. She figures out who’s scamming the high school budget and develops fast friendships with grunge Seatle teens whose sensibility seems (and is) at odds with her perky pinky California glam. She grows.
What we’ve got is a light romp with Homo Ludens in Blonde vs. a highly polished trek with Homo economicus in Prada.
Paul Ford, What the Government’s Fight With Anthropic Reveals About Free Speech in America, NYTimes, July 12, 2026.
Ford begins by reviewing the history of Anthropic's relationship with the federal government concerning Mythos and then Fable 5, concluding:
Whether you take Anthropic’s side here or the White House’s, the events of the last several weeks are clearly a poor, disorganized way to approach governance of these models. I think they point to a troubling future. And that brings me back to the idea of free speech and code.
Moving on:
It’s easy to see large language models and chatbots as exhibiting pseudo-consciousness. [...]
But these things are not conscious. They gathered vast troves of public and private information — sometimes without permission — and organized it into huge blobs of interconnected numbers.
Large language models, to me, are simply artifacts: objects made from the language expressed by tens of millions of people. And Anthropic, to me, is a software publisher. I do not see Claude as a living thing; I see it as a reference work prone to glitching, a surreal encyclopedia with wild side effects. I pay for the privilege of using it to generate new things — code mostly, and industry research reports.
Notice, BTW, how these statements are consistent with the view that LLMs and their encompassing chatbots are cultural technology, collective entities comparable to markets, libraries, and corporations. See also my article at 3QD, Of Grammar and Truth: Language Models and Norms, Truth and the World. Now back to Ford, in progress:
But, critically, the things it generates are then mine, and I use them as I see fit. And, also critically, if I share them with the world, I bear responsibility for them. You see this with lawyers who submit A.I.-generated briefs — judges have no empathy for “the bot wrote it” arguments.
Prudence and slow-rolling releases might benefit the public in the same way that crash-testing cars is of benefit. Standards are good. But if the government can yank access at its whim, we’re starting down a slippery slope — the same slope we go down when we monitor library patrons and track what they read.
Now there are rumblings of ID-verification programs for A.I. users, to assure nationality. Do I need to be permitted to be an A.I. user, like I need to be to drive a car or own a gun? Do we want a future where Officer Claude pulls you over on the information superhighway and demands your license and registration?
Further along:
It is, I admit, appealing to imagine the government — well, maybe not this government — hitting the brakes on A.I. We could all use a rest. But ask yourself: Are you willing to compromise your free speech rights in order to keep people from finding bugs in your word processor? That’s what’s increasingly being asked of us when the government restricts the use of these models by certain groups, with little transparency and poorly articulated reasoning.
If A.I. is the new interface for creating code, and code is a form of communication and expression, then it’s incumbent to ensure only the most critical restrictions are applied to these models. And this is a new kind of technology, an industrial-strength symbol generator that often works in unpredictable ways. The balance is incredibly hard to strike, and it requires enlightened governance. Then again, there are established exceptions to free speech in America. We are capable of discernment.
There's more at the link.
OpenAI is a risk.
— Gary Marcus (@GaryMarcus) July 12, 2026
In this case to Oracle.
S&P formalizes what I have been saying since 2023. https://t.co/aMcjEViLLS
YouTube page: "...singing along to Tanmay’s beautiful raga adaptation of beethoven’s für elise in ragas Yaman, Shree and Shyam Kalyan."
H/t Tyler Cowen.
Eric Schmidt and Selina Xu, We Must Address the Growing Rage Against the A.I. Machine, NYTimes, July 11, 2026.
On the whole US AI developers want to move as fast as possible while the public is increasingly skeptical and opposed. The Chinese are more cautious and want to develop AI while preserving social stability thus the Chinese public are more optimistic about AI than Americans.
Dismissing A.I. entirely would be a mistake. The technology can help diagnose diseases, predict protein folding, improve farming, forecast disasters better, design new materials, accelerate scientific and drug discovery and power robots in dangerous environments to improve human safety (such as in space, firefighting and minefields).
Yet how technology spreads is never inevitable. If A.I. is viewed as benefiting the few at the expense of the majority, then the public will rage against the machine. And A.I. won’t be able to make our lives better in the long run if it cannot survive in the short term. The real challenge, then, isn’t whether the United States or China will build an overwhelming, insurmountable advantage over the other. It’s whether either can figure out how to realize the benefits of A.I. without ripping apart its social fabric. Neither has found the answer yet.
Silicon Valley tech executives and policymakers across the country are waking up to that fact. States have introduced dozens of bills this year to put safety and privacy guardrails around A.I. The Trump administration issued a new executive order that seeks to give the government more oversight over new models before they’re released to the public. [...]
To get to the other end of the tightrope, we need radical and incremental solutions alike. Here’s one to start: a populist A.I. agenda that treats the technology as a public project. Just as NASA made space a national mission rather than a private one, the government should now do the same for A.I. to ensure that its benefits reach the public, not just the companies building it. Here are some concrete ways to do that:
One, treat some A.I. profits as a shared resource and distribute them directly to citizens. This can be in the form of a sovereign wealth fund, seeded by contributions from A.I. companies in the form of stock or cash. [...]
Two, support public-interest A.I. models that the private sector might not be incentivized to build. Examples include models to help citizens navigate government services and benefits, to help them gain legal aid or to help educate their children. We strongly believe in the importance of open-source A.I., which anyone can freely use and modify. [...]
Three, regulate one area that attracts bipartisan agreement: the way children and teens interact with A.I.
There's more at the link.
Tyler Cowen @ 31:59: “The groups that are best at using AI are small groups of individuals who work well together and are smart and technically able.”
YouTube copy:
In this episode of The Winston Marshall Show, I sit down with economist, author, and columnist Tyler Cowen for a conversation on artificial intelligence, the race between America and China, cyber warfare, and why the AI revolution will reshape every aspect of modern life.
We explore the growing battle between the Trump administration and leading AI companies such as Anthropic and OpenAI, the risks of AI-driven cyber attacks, national security, effective altruism, and why Cowen believes the world is entering the most significant technological transition since the Industrial Revolution. We also discuss whether AI represents a greater geopolitical challenge than nuclear weapons, how governments should regulate it, and why the coming years could be both extraordinarily dangerous and extraordinarily prosperous.
The conversation also examines the future of work, economic growth, surveillance, healthcare, longevity, education, and whether AI will deepen state control or instead empower individuals. Cowen explains why he believes AI could eradicate many diseases, transform productivity, and fundamentally alter the relationship between governments, corporations, and ordinary citizens.
Finally, we turn to Britain's economic decline, immigration, productivity, energy policy, debt, and why Cowen believes the UK urgently needs a new economic direction before its long-term decline becomes irreversible.
Chapters
00:00 Introduction
02:10 AI Cyber Warfare & Why The Next Few Years Matter
05:00 Trump, Anthropic & Who Controls AI?
10:20 Can Britain Defend Itself In The AI Era?
15:19 Why AI Is Like World War II
19:20 Effective Altruism & The Future Of AI
23:23 Is AI More Dangerous Than Nuclear Weapons?
27:04 Will AI Cure Disease & Extend Human Life?
30:00 AI, Surveillance & The Risk Of Totalitarianism
35:00 Jobs, Education & How AI Will Change Work
40:31 AI, Space & The Next Global Arms Race
45:00 AI, Religion & The Future Of Faith
49:07 Is Britain Already In A Debt Crisis?
YouTube page:
267,915 views Jul 9, 2026
Did Voyager 1 just send back evidence of something impossible? As humanity's most distant spacecraft continues its journey through interstellar space, every transmission sparks new questions about what lies beyond the edge of our Solar System. But what has NASA actually discovered?Launched in 1977, Voyager 1 has traveled more than 15 billion miles from Earth, becoming the first spacecraft to enter interstellar space after crossing the heliopause in 2012. Even after nearly five decades, it continues to send back invaluable scientific measurements of cosmic rays, plasma waves, magnetic fields, and the interstellar medium.
Despite dramatic online headlines, there is no verified evidence that Voyager 1 has transmitted proof of an "impossible" phenomenon. Instead, its instruments have revealed unexpected changes in particle density, magnetic fields, and cosmic radiation that continue to challenge and refine scientists' understanding of the space between the stars.
In this documentary, you'll discover:
The latest updates from Voyager 1.
What the spacecraft is actually detecting in interstellar space.
Why some discoveries initially surprised scientists.
How NASA communicates with a spacecraft billions of miles away.
The truth behind viral deep-space headlines.
What Voyager's data reveals about the frontier beyond our Solar System.Join us as we separate science from speculation and uncover the remarkable discoveries made by the most distant human-made object ever launched.
What I find so interesting about this is the simple fact that Voyager discovered things about the world that we hadn't expected. There are these many regions of the world, on all scales, where we have made no observations. And so we will in those regions the same way LLMs fill-in empty regions of weight space, by confabulating. When we actually look: SURPRISE! The same thing happens in paleontology. A new fossil is discovered and WHAM! we have to revise the Tree of Life.
This video is the second part of my short two part series in which I am exploring how the melody 'Twinkle Twinkle Little Star' could have sounded if composed by several composers of the renaissance era (ca. 1450 - 1600).
Part 1: • How 8 Renaissance composers would have com...
Twinkle Twinkle Little Star, words by Jane Taylor (1783 - 1824), melody traditional Arrangement and Performance by Jonas Wolf Inspired by Accent: • Vocal Group History and Styles (by Accent)
0:00 Introduction
0:18 Giovanni Pierluigi da Palestrina
2:24 Orlando di Lasso
3:32 Giaches de Wert
5:33 Carlo Gesualdo
Max Kan, Julien Martin-Prin, Jeremie Eliahou Ontiveros, and Dylan Patel, The Future of Meta Superintelligence: A 1 Year Progress Update, SemiAnalysis, Jul 09, 2026.
It’s been a little over 1 year since the disastrous Llama 4 release spurred Zuck to rebuild his entire AI org. Highlights include the shocking $14.3B Scale AI “investment” just to poach Alexandr Wang and the best people from his Safety, Evaluations, and Alignment Labs (SEAL) team, the multi-hundred million dollar (sometimes $1B+) pay packages offered to top AI researchers/engineers, and the expedited compute ramp enabled by their new “Tent” datacenter design. For more details, see our original post on MSL.
Since then, frontier AI has increasingly felt like a two horse race between OpenAI vs Anthropic. Google had a brief moment in the spotlight with Gemini 3 Pro and Nano Banana, but they’ve since faded dramatically. Despite their Windsurf acquisition, they’re far from a compelling agentic coding product, and 3.5 Flash is a benchmaxxed prop that performs far worse than GPT 5.5 and Opus 4.8 in real world scenarios (much less Fable and 5.6). 3.5 Pro is not even Opus level on coding. Microsoft has completely blown their early lead with GitHub copilot and failed to effectively leverage their access to OpenAI IP. SpaceXAI is selling $26B a year worth of GPUs to Anthropic/Google, and the Chinese labs are simply too compute poor to truly reach the frontier.
Meanwhile, MSL made their public debut this April with the launch of Muse Spark. You could argue this model represented a relative regression for Meta. Llama 3 70B and 3.1 405B were both SOTA open-source on release, whereas Muse Spark, despite also being closed source, lagged both DeepSeek v4 Pro and Kimi K2.6—open source models released around the same time—on most benchmarks.
[CHART GOES HERE IN THE ORIGINAL]
However, evaluating Muse Spark in isolation is missing the forest for the trees. What matters for MSL is the slope, not the intercept. Rebuilding your entire team from the ground up obviously comes with some short term setbacks, and it appears Meta has finally finished paying down this debt. Thus, the interesting question is not where MSL is today, but trying to predict where they’ll be in the next 6 months.
At the simplest level, there are three things you need to build a true frontier model: data, talent, and compute. We believe Meta is the only hyperscaler/neolab on track to be world class at all three and therefore has the best chance at catching up with Anthropic/OpenAI. We’ll explain why in full detail below, but as a teaser, here are the AI compute projections from our new Tokenomics Model.
[CHART GOES HERE IN THE ORIGINAL]
Lastly, behind the paywall, we’ll discuss what this all means for Google—the company most people today still believe rounds out the AI big 3.
Data is the new oil (for real this time)
We’ll start with data because it’s Meta’s newest advantage and probably the most underappreciated of the three.
In 2024, Ilya famously said that “data is the fossil fuel of AI.” While this analogy correctly highlights the importance of data for training AI models, it incorrectly assumes that the amount of good data is finite. In reality, if demand is strong enough, market forces will find a way.
There's much more at the link.
😂😂😂 so funny but sadly true pic.twitter.com/UHnkUyEtNZ
— Chris Wheeler (@Gandhi41) July 9, 2026
