The Herculaneum Scroll and the Underrated Value of Historical Knowledge

Twelve days ago, it was announced that a 21-year-old computer science student named Luke Farritor had “became the first person in two millennia to see an entire word” in one of the papyrus scrolls buried within a library in the Roman city of Herculaneum, which was destroyed by the eruption of Vesuvius in 79 CE.

Taken together, these scrolls — there are over 1,800 in all, discovered in one of the most lavish of all known Roman houses — are the only surviving intact library from Greco-Roman antiquity. The library contains works from ancient authors which do not exist anywhere else. A poignant example: the Greek philosopher Chrysippus was both famed and prolific in his day, authoring some 700 texts. But aside from stray quotations in the works of others, all of these texts were lost.

… that is, until portions of them were found among the scrolls at Herculaneum. The stakes of this library, in other words, are enormous. Being able to read through all 1,800 scrolls would amount to one of the biggest historical breakthroughs of the century, literally rewriting the history of the ancient Mediterranean.

The trouble is, the scrolls are lumps of carbon. They were torched by volcanic smoke and gas during the eruption of Vesuvius. To unroll them is to destroy them.

Nevertheless, they can now be imaged in remarkable detail, using methods like X-rays and 3-D CT scans.

by Benjamin Breen, Res Obscura |  Read more:

Images: Vesuvius Challenge; Digital Restoration Initiative, University of Kentucky

[ed. Amazing.]

The Myth of Prometheus Is Not a Cautionary Tale

P.S. You're also getting Frankenstein wrong.

Listening to Marc Andreessen discuss his Techno-Optimist Manifesto on the Foundation for American Innovation’s Dynamist podcast, I was struck by his repetition of something that is in the manifesto and is completely wrong. “The myth of Prometheus – in various updated forms like Frankenstein, Oppenheimer, and Terminator – haunts our nightmares,” he writes.1 On the podcast, he elaborated by saying that, although fire has many benefits, the Prometheus myth focuses on its use as a weapon. He said something similar in a June post called “Why AI Will Save the World”:

The fear that technology of our own creation will rise up and destroy us is deeply coded into our culture. The Greeks expressed this fear in the Prometheus Myth – Prometheus brought the destructive power of fire, and more generally technology (“techne”), to man, for which Prometheus was condemned to perpetual torture by the gods.

No. No. No. No.

Prometheus is punished for loving humankind. He stole fire to thwart Zeus’ plans to eliminate humanity and create a new subordinate species. He is a benefactor who sacrifices himself for our good. His punishment is an indicator not of the dangers of fire but of the tyranny of Zeus.

Prometheus is cunning and wise. His name means foresight. He knows what he is doing and what the likely consequences will be.

Eventually his tortures end when he is rescued by the hero Herakles (aka Hercules), who shoots the eagle charged with eating Prometheus’ liver every day, only for it to grow back to be eaten again.

The Greeks honored Prometheus. They celebrated technē. They appreciated the gifts of civilization.

The ancient myth of Prometheus is not a cautionary tale. It is a reminder that technē raises human beings above brutes. It is a myth founded in gratitude. [...]

These aren’t just an English major’s pedantic quibbles. The stories we tell ourselves matter. That even a “techno-optimist” inverts the Promethean myth reveals how deeply hostility to technology has penetrated our collective consciousness.

For thousands of years, the myth of Prometheus has taught us to appreciate the gifts of technē and the costs that bringing those gifts can exact. Prometheus connects our high-tech present with our poetic past. He reminds us of the continuities of human culture as well as its flux. He unites the Two Cultures.

In that spirit, I cherish the hope that, as artificial intelligence makes the library of Herculaneum decipherable, we will recover the lost sequels to Prometheus Bound—an appropriate fruit of the latest Promethean gift.

***

1  Frankenstein isn’t The Terminator either. Frankenstein is a creator who won’t take responsibility for his creation, a father who rejects and abandons his child. The Creature is frightening and dangerous but he is also the book’s moral center, a tragic, sympathetic character who is feared and rejected by human beings because of his appearance. Only then does he turn deadly. Frankenstein arouses pity and terror because we empathize with its central figure and understand his rage.

The novel’s most reasonable political reading is not as a story of the dangers of science but as a parable of slavery and rebellion. “By the eighteen-fifties, Frankenstein’s monster regularly appeared in American political cartoons as a nearly naked black man, signifying slavery itself, seeking his vengeance upon the nation that created him,” writes historian Jill LePore, who calls the “Frankenstein-is-Oppenheimer model…a weak reading of the novel.” I agree.

The Romantics tended to identify with Prometheus and Mary Shelley’s husband, Percy Bysshe Shelley, wrote a play called Prometheus Unbound, further undermining the reading of Frankenstein as an anti-Promethean fable.

by Virginia Postrel, Newsetter | Read more:
Image: via

Elon Musk's Endgame

If Republicans keep the Senate, if they perform better in the House than people expect, a lot will come down to one man, and that man is not Donald Trump. It’s Elon Musk.

At this point, Musk has given around $450 million to the Republican campaign effort. That makes him the single largest donor in any election cycle ever.

Musk’s contributions are now one-quarter of all campaign donations Republicans have received. And yet the size of this, and what it throws into relief, is how hard it is to conceptualize how much money he really has.

Because for Musk, with a net worth of around a trillion dollars, giving $300 million, $400 million, is like the median American household giving, like, $100. It is for him nothing, and yet it is reality warping for the rest of us.

But his money is not Elon Musk’s only source of power. There’s his control of X, of Starlink, of SpaceX, of Neuralink, of Tesla. He has power over wealth, attention and critical infrastructure that no human being has ever had at this level, at least once you’re outside the range of, say, a Donald Trump or a Xi Jinping — somebody who controls a superpower as a state.

That’s not what Musk has, and yet he has this almost statelike thing that is completely pursuant to his whims.

So what does he want to do with it? What is he doing with it? What is this world he is flexing his power and taking real risk to build?

by Ezra Klein, NY Times |  Read more:

Image: Anna Moneymaker/Getty Images

Binance Bitten

In February 2025, a team of North Korean hackers stole $1.5 billion from the cryptocurrency exchange Bybit — a dark day for the industry and the largest crypto heist ever recorded.

Now that number looks paltry.

Binance, the world’s biggest crypto exchange, set an unwanted record on Thursday, losing $6.3 billion to hackers in an eye-popping attack that highlighted the deep vulnerabilities in crypto’s financial infrastructure.

For years, the industry has struggled to prevent thefts. Now, the risks are growing even more extreme as attackers use artificial intelligence and other advanced techniques to steal funds and quickly launder them, crypto analysts and security experts said.

The Binance attacker used “laundering patterns we have not seen before,” the crypto analytics firm Chainalysis wrote in a blog post on Friday. “Thousands of transactions in a short period suggest that the attackers are using automation to obscure the trail of funds.”

The details of how Binance was breached are still coming into focus. Both Chainalysis and TRM Labs, another analytics firm, attributed the hack to North Korea, which has targeted crypto companies for years. Binance initially said that the attacker appeared to have compromised a laptop belonging to one of its engineers, before clarifying that further investigation had uncovered “additional points of breach.”

Binance has promised to make customers whole and said it had set aside more than $6.3 billion in company funds to cover the losses. (Binance processes billions of dollars in transactions every day.) Of the total that was stolen, $3 billion has been frozen by other companies, including Circle and Tether, two large issuers of digital coins that have the ability to stop the movement of stolen funds.

That means North Korea won’t have access to those coins, and Binance said it was working on recovering them. But the hackers have already moved $2.4 billion in crypto “beyond any issuer’s reach,” according to TRM.

Those losses may be only the beginning, as the crypto world reckons with an era of fast-evolving hacking techniques and growing risks. Even as President Trump has endorsed the industry and sought to bring it into the mainstream, some crypto firms still struggle with the basic work of any financial company — keeping customer money safe.

“A.I. is undoubtedly turbocharging the ability of hackers to find and exploit subtle flaws in various parts of the crypto ecosystem,” said Eswar Prasad, a Cornell University economics professor who tracks the crypto industry. “The scale and scope of such attacks is likely to intensify.”

Still, North Korea’s inventive army of hackers could probably have pulled off the Binance heist without A.I., even if the technology proved useful during the operation, some cybersecurity experts said.

“North Korea had sophisticated threat actors that could have done this pre-A.I.,” said Joshua Saxe, the chief technology officer of the A.I. security firm Abundant Security. “That said, it’s hard not to think about the relationship between mega-heists like this and what A.I. is doing to democratize advanced hacking. Many more actors can attempt this kind of thing now, thanks to A.I. tools.”

Crypto has struggled with major thefts since the technology’s inception in 2008.Industry pioneers viewed digital coins as a renegade alternative to banks — a type of money outside the supervision of governments or big businesses. From the beginning, that philosophy made crypto vulnerable.

Many crypto investors keep funds in their own online wallets. But if they lose a password, the money is gone forever. Others rely on exchanges like Binance, which behave something like banks, storing funds for customers. But exchanges are not subject to the same regulatory systems that govern banks and other traditional financial companies, and have often lacked effective guardrails against crime.

In February 2025, North Korean hackers tricked the chief executive of Bybit, Ben Zhou, into authorizing a seemingly routine transaction that handed them control of the exchange’s system. They had exploited a gaping hole in ByBit’s defenses — a free software product that the company used to protect customer deposits. The thieves extracted $1.5 billion in digital coins.

At the time, a security firm called the losses “completely preventable.” Ultimately, Bybit weathered the crisis by drawing on corporate reserves and borrowing funds from other crypto companies.

The attack on Binance appears more serious.

After it was founded in 2017, the exchange quickly became the largest and most important company in the crypto industry, processing as much as two-thirds of all digital currency transactions. It provides a marketplace for people to buy popular coins like Bitcoin and Ether, converting traditional currencies into crypto.

On Thursday, as soon as Binance’s system was breached, its founder, Changpeng Zhao, assured customers that they would not lose any money.

“It hurts, but we will survive,” he wrote on X, adding a muscle emoji.

Mr. Zhao has a long history of legal and regulatory problems. Binance pleaded guilty to money-laundering violations in 2023 and paid a $4.3 billion fine to the U.S. government. After serving a four-month prison sentence, Mr. Zhao was pardoned by Mr. Trump last year. Binance also forged a business relationship with World Liberty Financial, the Trump family’s crypto company.

by David Yaffe-Bellany and Dustin Volz, NY Times |  Read more:

Image: Tyrone Siu/Reuters

[ed. Probably more of this soon as the AI cat and mouse game kicks into gear. See also: What happens to your money when an exchange gets hacked (CNBC).]

The Smartphone Trap We Built for Ourselves

Here’s a non-exhaustive list of things that, as recently as seven years ago, we could reliably do without a smartphone:

• Read a restaurant menu while seated inside, and pay your check.
• Enter a concert or sporting event.
• Use a parking garage or park your car on the street.
• Receive packages or retrieve purchases.
• Complete the sign-in process for work, banking, or government services.
• Attend school events or communicate with your kid’s school.
• Access health care and other kinds of appointments.
• Take public transit.
• Get a taxi.
• Get into a gym or fitness class.

I’ve been thinking about this drift toward mandatory apps and QR codes a lot lately, as a new millennial parent unhappy with my addictive tendency to mindlessly scroll near my baby. [...]

You should not have to have an addictive device in your pocket at all times in order to do ordinary things. We should be pushing for a world where adults can meaningfully reduce their smartphone use, or quit it altogether if they want.

And it’s not just about personal self-improvement. The status quo has already been long untenable for many people, especially the elderly, the disabled, the unhoused, and anyone who just can’t afford a data plan.

Pushing for a non-smartphone option is about accessibility, affordability, and agency. It’s about demanding the kind of freedom in a society we deserve to have. There should never be any essential service that requires phones as the only way in.

We can fix this, and some people are already showing how.

How we got here

Even if it takes us another decade to unwind, it’s important to remember these changes are less than ten years old.

Smartphone ownership exploded over the 2010s, rising from 35 percent of US adults in 2011 to 81 percent in 2019, according to Pew. It has since climbed to roughly 9 in 10.

In other words, smartphones stopped being a device many people had and started becoming a device every institution could assume you had. What happened next was part real improvements in convenience, part cost-cutting that pandemic-era policy made easier to justify, and part what sociologists call “isomorphism” — organizations converging on a practice not because it works best, but because everyone else has adopted it.

Isomorphism is one of my favorite concepts I learned in college, and you know it when you see it. For example, you’re planning a conference, and somewhere along the way it became true that a “good” conference has its own app, even though nobody wants to download an app to learn when lunch is on the schedule. Or it’s the way every mission statement sounds like every other mission statement.

I’d say the charitable reason we drifted toward handling so much by smartphone is that a lot of it genuinely is better, faster, and harder to lose. Tapping a turnstile with my phone, and never wondering whether I left my DC metro card at home, has made my life easier.

The less benign reasons are that digital systems let companies and governments cut labor and operating costs. Apps and QR codes replace cashiers, waitstaff, printed materials, and customer service reps, and shift new work onto us, the customer. The smartphone systems also generate a lot of personalized data which companies love to collect, and sometimes sell.

Then there’s control, which companies certainly like. Not all of it is sinister. (Think blocking tickets from being resold, or prices and menus being updated instantly.) But when sellers control the interface and know a lot about us, two people can open the same app at the same moment and see different prices. Economists call this price discrimination, and it’s much easier to pull off when there’s no posted price we can compare against.

But just because inertia is leading us toward a universal smartphone society, that doesn’t mean it’s where we’ll end up. We have options to push back.

by Rachel Cohen Booth, Vox | Read more:
Image:Matteo Della Torre/NurPhoto via Getty Images
[ed. I've experienced this just trying to pay for a parking spot in Seattle. First, had to get the app. Then register. Then follow all the instructions for punching in a special code for my space, license plate number, and finally estimated time period of parking. Took me nearly 25 minutes. Not to mention trying to read the small type on the sign and memorizing the special app url. Suppress scream.]

The Consequences of Abundant Intelligence

Macro Memo

February 22nd, 2026 June 30th, 2028

The unemployment rate printed 10.2% this morning, a 0.3% upside surprise. The market sold off 2% on the number, bringing the cumulative drawdown in the S&P to 38% from its October 2026 highs.

Traders have grown numb. Six months ago, a print like this would have triggered a circuit breaker.

Two years. That’s all it took to get from “contained” and “sector-specific” to an economy that no longer resembles the one any of us grew up in. This quarter’s macro memo is our attempt to reconstruct the sequence - a post-mortem on the pre-crisis economy.

The euphoria was palpable. By October 2026, the S&P 500 flirted with 8000, the Nasdaq broke above 30k. The initial wave of layoffs due to human obsolescence began in early 2026, and they did exactly what layoffs are supposed to. Margins expanded, earnings beat, stocks rallied. Record-setting corporate profits were funneled right back into AI compute.

The headline numbers were still great. Nominal GDP repeatedly printed mid-to-high single-digit annualized growth. Productivity was booming. Real output per hour rose at rates not seen since the 1950s, driven by AI agents that don’t sleep, take sick days or require health insurance.

The owners of compute saw their wealth explode as labor costs vanished. Meanwhile, real wage growth collapsed. Despite the administration’s repeated boasts of record productivity, white-collar workers lost jobs to machines and were forced into lower-paying roles.

When cracks began appearing in the consumer economy, economic pundits popularized the phrase “Ghost GDP“: output that shows up in the national accounts but never circulates through the real economy.

In every way AI was exceeding expectations, and the market was AI. The only problem…the economy was not.

It should have been clear all along that a single GPU cluster in North Dakota generating the output previously attributed to 10,000 white-collar workers in midtown Manhattan is more economic pandemic than economic panacea. The velocity of money flatlined. The human-centric consumer economy, 70% of GDP at the time, withered. We probably could have figured this out sooner if we just asked how much money machines spend on discretionary goods. (Hint: it’s zero.)

AI capabilities improved, companies needed fewer workers, white collar layoffs increased, displaced workers spent less, margin pressure pushed firms to invest more in AI, AI capabilities improved…

It was a negative feedback loop with no natural brake. The human intelligence displacement spiral. White-collar workers saw their earnings power (and, rationally, their spending) structurally impaired. Their incomes were the bedrock of the $13 trillion mortgage market - forcing underwriters to reassess whether prime mortgages are still money good.

Seventeen years without a real default cycle had left privates bloated with PE-backed software deals that assumed ARR would remain recurring. The first wave of defaults due to AI disruption in mid-2027 challenged that assumption.

This would have been manageable if the disruption remained contained to software, but it didn’t. By the end of 2027, it threatened every business model predicated on intermediation. Swaths of companies built on monetizing friction for humans disintegrated.

The system turned out to be one long daisy chain of correlated bets on white-collar productivity growth. The November 2027 crash only served to accelerate all of the negative feedback loops already in place.

We’ve been waiting for “bad news is good news” for almost a year now. The government is starting to consider proposals, but public faith in the ability of the government to stage any sort of rescue has dwindled. Policy response has always lagged economic reality, but lack of a comprehensive plan is now threatening to accelerate a deflationary spiral.

How It Started

In late 2025, agentic coding tools took a step function jump in capability.

A competent developer working with Claude Code or Codex could now replicate the core functionality of a mid-market SaaS product in weeks. Not perfectly or with every edge case handled, but well enough that the CIO reviewing a $500k annual renewal started asking the question “what if we just built this ourselves?” [...]

That summer, we spoke with a procurement manager at a Fortune 500. He told us about one of his budget negotiations. The salesperson had expected to run the same playbook as last year: a 5% annual price increase, the standard “your team depends on us” pitch. The procurement manager told him he’d been in conversations with OpenAI about having their “forward deployed engineers” use AI tools to replace the vendor entirely. They renewed at a 30% discount. That was a good outcome, he said. [...]

When Friction Went to Zero

By early 2027, LLM usage had become default. People were using AI agents who didn’t even know what an AI agent was, in the same way people who never learned what “cloud computing” was used streaming services. They thought of it the same way they thought of autocomplete or spell-check - a thing their phone just did now.

Qwen’s open-source agentic shopper was the catalyst for AI handling consumer decisions. Within weeks, every major AI assistant had integrated some agentic commerce feature. Distilled models meant these agents could run on phones and laptops, not just cloud instances, reducing the marginal cost of inference significantly. [...]

Over the past fifty years, the U.S. economy built a giant rent-extraction layer on top of human limitations: things take time, patience runs out, brand familiarity substitutes for diligence, and most people are willing to accept a bad price to avoid more clicks. Trillions of dollars of enterprise value depended on those constraints persisting.

It started out simple enough. Agents removed friction.

Subscriptions and memberships that passively renewed despite months of disuse. Introductory pricing that sneakily doubled after the trial period. Each one was rebranded as a hostage situation that agents could negotiate. The average customer lifetime value, the metric the entire subscription economy was built on, distinctly declined.

Consumer agents began to change how nearly all consumer transactions worked.

by Citrini Reserch |  Read more:

Image: uncredited

This Boom Looks a Lot Like the Housing Bubble

Artificial intelligence is fast becoming the largest investment in U.S. history. As the money pours in, the companies involved are resurrecting bad old habits eerily reminiscent of the shenanigans deployed in the run-up to the financial crisis.

There are already signs that much of this investment may go bad. If it does, the consequences could rival what happened in 2008.

We know far too little about the financial plumbing propping up this surge. What little we do know looks remarkably like the tactics once employed by Wall Street firms like Lehman Brothers, the storied 158-year-old bank that collapsed in 2008, setting off a panic that ultimately cost taxpayers nearly $700 billion in bailouts in today’s dollars and nearly nine million Americans their jobs. Now as then, companies are hiding large amounts of debt off public balance sheets. Firms responsible for protecting our pensions and our insurance are again risking our money by snapping up complicated financial products backstopped by assets of uncertain value. A small group of companies are buying and selling large amounts of a hot product to one another, inflating demand for the product and obscuring the risks of the system in a web of complexity. The accountants and credit-rating firms that analyze the situation — and that are financially beholden to the debtors — are deploying shaky excuses to declare it all fine.

There are a few differences. With more U.S. funds in A.I.-heavy stocks than in homes, a crash could destroy more wealth than the 2008 financial crisis did. And the Trump administration, reportedly eager to prevent a bust, is making changes that inject new opacity and more risk into the situation.

There are already signs the nascent A.I. industry may not soon generate the trillions of dollars required to pay off all the huge debts it is incurring. Earlier this month, The Financial Times reported that the projected annual revenue at A.I. lab OpenAI was about $50 billion at the end of September, 28 percent lower than had previously been believed. Figures reported by The Wall Street Journal this month suggested that revenue growth at Anthropic, OpenAI’s chief rival, was softening. Markets are getting nervous, with the tech-laden Nasdaq falling over 5 percent over the last two weeks.

Anthropic and OpenAI are already so entangled in our economy (and in our national security) that if they sink, Washington will need a plan. Done right, any rescue would come with real safety guardrails, real insurance that we won’t cede control of the technology to China and real service to the public interest.

Lehman Brothers was born in 1850 as a cotton brokerage in antebellum Alabama. The A.I. chipmaker Nvidia was born in 1993 in a booth at a Denny’s in San Jose, Calif., and Meta in a Harvard dorm in 2004. And yet the financial circumstances surrounding those companies share an uncanny resemblance.

Start with hidden loans. A Wall Street Journal analysis in August concluded that the nine largest tech firms, Nvidia and Meta included, have managed to accrue $3 trillion of debt, or roughly 10 percent of the entire U.S. economy, off their balance sheets. Keeping the debt off the books (and away from the figures investors and regulators watch most closely) makes it easier for those companies to spend enormous sums constructing the data centers that drive A.I. — centers they believe will both improve the technology and satisfy an expected takeoff in demand.

Back in 2008, Lehman, too, accumulated lots of debt, primarily from buying subprime mortgages; those loans, which were targeted to home buyers with thin or imperfect credit histories, were the hot product of its day. The firm similarly avoided scrutiny by shuffling the debts off its balance sheet. When those mortgages went bad, so did Lehman, and its sudden demise nearly took much of the banking system with it.

Within days of its failure, Washington committed hundreds of billions of dollars to stabilize the financial system, and the Federal Reserve backstopped trillions more. Still, the rescue wasn’t enough to stave off the worst recession since the Great Depression. Millions of jobs and trillions in household wealth evaporated.

Now you might ask: Didn’t Congress pass legislation to prevent another crisis from occurring? Yes, but that law, known as Dodd-Frank, applied largely to lenders, which means banks and bank-holding companies. It left private creditors, back then a relatively small slice of the finance world, virtually untouched. In 2018, Congress, worried about the potential impact on small businesses, loosened rules to allow many private creditors to stretch their balance sheets. It ignored objections that money, like water, would always flow to the least regulated corners of finance.

Enter Silicon Valley. A.I.’s biggest players seized on the loosely regulated private credit industry to help finance their data center spree.

Another throwback to the bad old days of 2008: the way the A.I. firms and their main suppliers are buying and selling the technology and its infrastructure to each other, lending large sums to one another to fund the deals. Nvidia disclosed commitments and guarantees of over $530 billion to its own customers in July — more than triple the amount it was lending just three months earlier. Analysis by the asset manager Sona found at least 176 circular financing deals in the A.I. ecosystem that were worth $3.6 trillion over the past three and a half years.

Back in the housing bubble, major Wall Street firms came up with numerous complex ways to borrow from their rivals, with proceeds going to purchase even more subprime mortgages. The result made demand for those loans look greater than it actually was, and it built a financing web so tangled that it became difficult to parse any one firm’s exposure. As Lehman’s collapse later showed, such confusion meant that a problem anywhere can soon become a problem everywhere.

Then how about the fact that accounting firms, those given the job of calling out any warning signs, appear asleep at the wheel once again? Ernst & Young, which signed off on Lehman’s questionable accounting tactics before its collapse, is now being accused of offering more tautology than real comfort in its latest approval of Meta’s unusual accounting moves to fund its very expensive A.I. push. Everything is all fine, essentially, because Meta said it is.

Those are the similarities. Now for the differences.

This presidential administration, reportedly worried that any A.I. slowdown will beget a broader economic one (which, in turn, could worsen its political standing), is propping up the boom. Its measures likely goose the available money in the short term but will worsen the pain of any correction. President Trump wants to allow Americans to invest their retirement savings in private credit funds, ensuring they will lose directly if these data center deals go bad. The Securities and Exchange Commission has made it easier for data center owners and their lenders to slice and repackage this debt, which is exactly what banks did to subprime mortgage debts in the housing bubble. The S.E.C. is also proposing rules that allow newly public companies to disclose even less than they already do.

Another difference: This time, taxpayers are more clearly on the hook, because private creditors are more directly attaching A.I. debt to the financial products we need. Take annuities, which are often relied on by retirees. Because many Americans rely on such insurance products, their policies are usually guaranteed by the state should an insurer go bankrupt. Right now, private creditors like Apollo are funding the debt boom in part by tapping the money sitting in companies like Athene, the $470 billion insurer Apollo controls. Should the A.I. boom go bad and Athene go under, Apollo knows the state (i.e. taxpayers) will be on the hook for much of what it owes policyholders. (Athene’s opaque disclosures make it hard to know how much of its assets are in data centers.)

Private-capital-backed firms now own nearly 20 percent of the entire U.S. life insurance industry, with assets of nearly $1.5 trillion.

As bad as the financial crisis was, it began with a small slice of mortgages of a still-diversified economy. This time, the sector at risk is our economy’s growth engine. A.I.-related investment fueled an estimated one-third of the country’s recent economic growth, and the handful of companies most exposed to the A.I. build-out now comprise roughly one-third of the entire S&P 500.

In other words, the U.S. economy is fully hostage to an A.I. build-out laden with increasingly worrying financial engineering, all riding on a wager — that demand for A.I. will keep growing, that customers will soon be willing to pay what the technology actually costs to produce and that firms will manage to build the infrastructure required to supply it. Recent estimates indicate that A.I. would need to generate as much as $3 trillion annually in perpetuity to return the capital extended. That’s 9 percent of the U.S. gross domestic product, or twice what Americans spend on food.

Already, a closer look at the fundamentals of OpenAI and Anthropic, the most critical players in this bet, appears to be perhaps even worse than the headlines suggest. Right now OpenAI expects to lose $280 billion by 2030 even assuming revenues grow tenfold over the same period. And both OpenAI and Anthropic have already slashed prices 40 percent to 50 percent from early summer, perhaps because China’s Deep Seek, the leading “open weight” A.I. model, reportedly nearly matches their latest models’ performances at about 1.4 percent of the cost. Far less expensive open weight models have roughly doubled their share of token volume this year, according to OpenRouter, a service that tracks A.I. usage across all major providers. Prices and market share are shrinking; costs are not.

Such worries may explain why tech stocks have been taking a beating, and bond markets — normally where investors go for shelter amid financial turbulence — are volatile. Many of those private creditors — a sector that has grown sharply amid the A.I. boom, to between $2 trillion and $3 trillion — are showing classic signs of financial distress.

A final difference from 2008: The government deficit has reached historic highs.

by Jen Harris, NY Times |  Read more:

Image: Chuan Ming Ong
[ed. AI's already taken the world's economies hostage, without even being fully deployed yet. Sleepwalking into a disaster.]

What’s Riding on Anthropic’s Market Debut? Nearly Everything

When Anthropic goes public as soon as next month, its initial offering will be more than just a coming-out party for the artificial intelligence company. It will also be a litmus test for the economy, with trillions of dollars at stake.

Anthropic, which makes the Claude chatbot, is a tent pole of the AI industry. Valued in the private markets at nearly $1 trillion and backed by more than $130 billion in funding, it could raise $100 billion in its offering and promptly become one of the world’s biggest public companies. But the stakes are even higher than that.

Anthropic and OpenAI, its chief rival, sit atop a pyramid of business partners and suppliers — infrastructure providers, chipmakers, energy companies and AI startups — that have all spent heavily over the last four years because they believe demand for AI products will keep growing.

Anthropic’s listing will provide the first in-depth look at the health of a key business underlying the AI boom — and will be a test of its durability. Is there unending appetite for AI services to justify the spending on chips, data centers and other infrastructure? Or will Anthropic’s performance show that there are limits?

“For the market, it’s a bellwether,” said Umesh Padval, an investor at Seligman Ventures, who has worked in tech for nearly 40 years.

If Anthropic disappoints in its public debut, that could hurt investor confidence and cascade into a wider market sell-off, casting doubts on how much longer the AI boom can go. A successful offering could lift the market and pave the way for more AI companies to go public.

Stock markets are already on edge, with technology stocks broadly falling this month, led by a sell-off in the companies that design and produce the computer chips that power AI. The Philadelphia Semiconductor Index is in a bear market, defined as a decline of 20% or more from a recent high and a sign of significant investor angst, after falling 26% from its June peak.

Over the last couple of weeks, Microsoft canceled a deal with Nscale, a provider of data centers. A report from the research firm Gartner predicted that 80% of “neoclouds,” the companies that provide data and computing power for AI firms, could be forced out of business. And Ken Griffin, the billionaire owner of the hedge fund Citadel, said AI investors were “almost certainly” in a bubble.

Those negative signals have added to fears about the huge piles of money being spent on AI infrastructure. Amazon, Meta, Google and Microsoft have said they plan to spend at least $740 billion on AI infrastructure, including data centers, this year. Private market investors have poured $1.1 trillion into Anthropic, OpenAI and other AI companies since 2022, according to PitchBook, which tracks investments.

As spending and investment have risen in private markets, public investments have grown, too, increasing the stock market’s reliance on the performance of the AI industry. The top 10 stocks in the S&P 500 are worth more than $1 trillion together, with eight of them being tech companies. Combined, the top 10 account for over 40% of the index, up from less than 20% a decade ago.

When Anthropic is added to the index, it is likely to displace either Berkshire Hathaway or Eli Lilly, the only non-tech companies with valuations above $1 trillion, from the top 10.

“There is a lot riding on this one,” said Brad Gastwirth, the global head of research and market intelligence at Circular Technology, a supply chain services firm. “If it goes bad, it’s going to bring down the market.”

by Erin Griffith and Lauren Hirsch, Seattle Times/NY Times | Read more:
Image: Dustin Chambers/The New York Times
[ed. House of cards. Too many moving parts, with precarious circular financing, locked-in future contracts, uncertain political and regulatory climate, and a product that could kill everybody. Plus, no signs of AI payoff outside the tech sector so far. So, more than a little liability baked in. See also: Wall Street prepares for AI's "day after"  and, Anthropic to investors: Trump hates us so much (Axios).]

Comcast Loses More Customers, Claims Cheap Prices are “Irrational”

Comcast reportedly plans significant job cuts in its cable division as the firm continues to lose residential broadband and TV customers. Comcast lost 144,000 residential broadband customers in Q3 2026, up from a 91,000-customer loss in the same quarter a year ago, the company said in its earnings report today.

Comcast historically capitalized on a lack of competition for high-speed wired broadband. But now it faces stronger competition from fixed wireless providers and from fiber providers in parts of its cable territory.

“Fixed wireless remains aggressive and continues to take share, while fiber construction remains elevated, with pockets of irrational pricing and promotional activity,” Comcast co-CEO Mike Cavanagh said today in an earnings call.

Comcast CFO Jason Armstrong said “irrational pricing” from fiber operators includes $30 to $40 monthly broadband offers “in specific markets in the third quarter.” He said $30 to $40 pricing is irrational because deploying fiber costs $1,500 or more per home, and is “getting more expensive with construction costs up as you get into more suburban and rural areas.” [...]

Armstrong said more Comcast customers are leaving when they face “pricing transitions,” apparently referring to the price hikes customers get when promotional offers expire. “We’ve been refining our offers and retention strategies to better manage these transitions and strengthen customer retention,” he said. [...]

Fewer Jobs, More AI Agents

It appears likely that job cuts will affect Comcast’s famously frustrating customer service division, replacing some number of humans with AI agents that already handle some customer interactions. “As personal AI agents reshape how consumers discover, purchase, and manage services, we’re positioning Comcast to capitalize on that shift,” Armstrong said.

by Jon Brodkin, Ars Technica | Read more:

Image: Getty Images | Smith Collection/Gado

[ed. Comcast/Xfinity has been ripping off its captive audience for decades. Anybody shedding a single tear for them today? Ha! They upped my monthly internet bill by $10 in August, then another $10 in September, one month later. Guess they thought people on autopay wouldn't notice. I went in and told them I was leaving for a new fiber service in town and they promptly halved my bill.]

The Hackening

Last week at a small conference, there was talk about a future event referred to as The Hackening. As in, within 6-12 months, many predicted that due to advanced AIs and AI agents the internet would become a severely hostile place. Some worried that this could rise to the level of ‘assume any phone connected to the internet is rooted’ and several people urged doing general crisis preparations on a personal level, in case of disruptions in supply lines, power, water or internet.

It did not take long for things to escalate.

The biggest hack is a flaw in PHP, which can be exploited to take control of the computer running any website using an unpatched version of PHP. There are many others.

On a personal level, you want to do at least some basic prep and get your security in order. It is cheap to do as a form of insurance, and being caught not having it when you need it is a large mistake. That doesn’t mean you should panic, or cancel travel plans or anything like that. But there is low hanging security fruit out there. Pick it.

And So It Begins: PHP

Joshua Achiam (Formerly OpenAI): Feels like March 1, 2020 for Cyber. Not normal, something is happening. Internet lockdowns in a couple of months?

Multiple sources using GPT-5.6 Sol identified a critical flaw in PHP. Okay, identified two critical flaws in PHP. The plan was to deploy the patch first before announcing the flaw, but the embargo on the fix got broken more than six hours in advance, so here we are.

Dan Lorenc: I wanted to share what happened with the PHP embargo, our role in it, and my decision process last night to ship the fixed builds early. First of all, the PHP Foundation and maintainers did an amazing job here in a high stress situation. I'm sorry that my decision led to even more stress for them.

We first got read in that there was something coming up in PHP last Friday by one of the original researchers that discovered this issue. That night we were able to independently discover the same issue using our harness and a frontier model with a basic prompt of "find bad vulnerabilities in PHP".

We got fully read in Saturday and confirmed that some our findings matched those from the initial report, and worked to help test the fixes.​

They may have been in a rush due to attackers finding the exploit. Amazon reports that this was happening as early as October 17, four days before disclosure. We are fortunate that this did not scale far larger than it did. [...]

Jonny G: Can somebody explain this to me like I’m 5 years old please? Ta.

BRYNMAWR ∴ SHERLOCK: Websites are files on a computer. When you view a website, you are asking the computer to send you the files, then your browser shows them to you.

70% of the websites you see use PHP to make them interactive. So you can send things to the website too and it can respond. E.g. filling in a comment box and hitting ‘post’ adds the comment as a row to a spreadsheet.

PHP has a flaw in its code that allows anyone to gain control of the computer that has the website on it.

The organisation that provides PHP announced the vulnerability before a patch was available, meaning we’re in a window where everyone knows about it (including baddies) but the good guys don’t have an official solution.

Some are just turning off their websites and waiting for the solution. Others are trying to fix it themselves. General chaos and lots of stressed techies across literally the entire planet because this affects the entire internet.

The bug has been there for 14 years and was discovered in 2024, but it was not considered significant at the time. A lot more bugs are significant now.

The PHP Bug Was Kind of a Big Deal

Things could have gone very, very badly, the same way Y2K could have gone quite badly if we had not done the work to prevent it. These days we are on rather tighter deadlines.

Internet Archive: https://Archive.org is currently offline. We are working to restore service now.

Brewster Kahle (21 hours later): Internet Archive ops staff were heroes last night.

PHP vulnerability announced, soon saw attacks, isolated the vulnerability, used Claude to make fixes for our older php versions, used local models to run simulated attacks, deployed.

In summary:

j🫪e: ICYMI: Today was basically the worst day of the year for cyber security incidents

On Your Marks

Now the race is on to patch all the systems before attackers can go on the offensive. OpenAI is helping race to get that done to the greatest extent possible, and are looking for better methods for fixing vulnerabilities in time.

John David Pressman: I know everyone is rightfully freaking out but it must be noted that the worst case scenario isn’t you receiving lots of patches you have to apply, the worst case scenario is receiving zero patches and waking up to everything having been stolen.

John David Pressman: My takeaway so far is that we do not have a good way to coordinate the distribution of patches to vulnerable software.

This one was important enough the NSA got in on the act.

NSA Cyber: Effective Immediately, all organizations using PHP for their systems are advised to be prepared for an update addressing two critical vulnerabilities. PHP is widely used for web applications. These vulnerabilities often provide a direct path to the server’s file system or database. If an immediate update is not feasible, it is recommend that organizations take the system offline until it can be fully mitigated. NSA will follow up shortly with more specific guidance.

We are going to need better procedures for updates.

The best case scenario is that a defender finds the bug first, has enough time to figure out a solution and creates a patch. Then, the moment the patch is rolled out, every attacker gets what are effectively very clear instructions on how to exploit any unpatched systems.

by Zvi Mowshowitz, DWAV |  Read more:

Image: The Simpsons; uncredited

[ed. So far, zero coverage in the mainstream media. [Update: Ars Technica has a story here.] See also: If the vulnpocalypse is the apocalypse, where are the agentic attacker hordes? (Joshua Saxe):]

*****

We’ve ignored how cybercriminals are unskilled and get arrested a lot

Here’s the first part of my theory: cybercriminals are not the organized geniuses we implicitly imagine. Anyone building a criminal gang has to find candidates willing to forgo more stable opportunities in the formal economy. My modeling estimated that something like 720,000 people are involved in the online criminal economy, 90% of them in scam and fraud call-center type operations, some of them human-trafficked. Of the 76k or so actual hackers, the vast majority work for governments. Of the remaining few thousand criminal hackers, most work in ransomware. 

Would You Press This Button?

Imagine Americans were offered this deal: Hit a big red button and China becomes the world’s first hyperpower. It rockets past the United States in technical sophistication, manufacturing capacity and wealth generation. China becomes so technologically advanced, and its human and robotic work force so huge, that Americans are left with only the dimmest comprehension of what is playing out on the economic, scientific and perhaps even geopolitical frontier.

But the rewards for disempowering ourselves are magnificent. The global economy booms, and Americans grow richer than ever before. We are given new cures and cheap energy and wondrous entertainments. Machines perform our labor and mood-enhancing drugs protect our psyches. We need not struggle — perhaps we need not even work — to live lives, long lives, of luxury and ease.

There, then, is the trade: Cede control and much that we have struggled for can be ours. But control, once gone, is gone.

Would you press the button?

The A.I. conversation centers questions of catastrophe. I want to center the question of success: What if A.I. works not as its architects fear, but as its inventors and investors hope? Wander outside of Silicon Valley’s p(doom) — their shorthand for the probability that A.I. extinguishes the human race — and you might notice that their p(utopia) has problems, too. They imagine something like the world I sketched above, but more extreme, with A.I. playing the role assigned to China.

An amusing collision of these visions came when Jacob Coxon, the 27-year-old OpenAI and Anthropic whistle-blower, sat down with Jon Stewart on “The Daily Show.” Coxon was there to warn that A.I. might kill us all, but when Stewart offhandedly mocked the “future utopia” of the A.I. architects, Coxon stopped him. “Well, I am a believer in the future utopia,” Coxon said. But what he said next sounded grimmer: “So I heard you talking about jobs. I think there will be no jobs.”

Coxon went on to describe “the world that is being envisioned by the people building this technology” as “no jobs. Universal high income. The dividends from entirely automated economic production can be distributed among real humans.”

This vision, or some variant of it, is common enough at A.I. companies. I have heard it often. But most people, face to face with this idea, are incredulous at hearing this world — in which virtually all advanced cognitive work is done by A.I. — described as utopian. Stewart certainly was. “So we’ll all just be performing in a play?” he asked.

I tend toward skepticism of the jobless future. Human beings are good at nothing if not finding something to do. (Who would have imagined, in our grueling agrarian past, that podcasting could be a full-time job?) But I find it easy to imagine how human control and comprehension of much of society could be handed over to machines. A few recent developments — some reflecting the outer boundary of what advanced A.I. can do and the other reflecting its advancing utility in daily life — reveal the ways it is already happening.

A few weeks ago, OpenAI dumped more than 700 manuscripts proposing solutions to nearly 400 advanced mathematical problems all at once. If you’re not a mathematician, and you are not close to any mathematicians, this may have passed by unnoticed. Within mathematics, it was an earthquake.

I am not a mathematician, but my father is. The area of math he worked in had to do with the way electrons move through disordered crystals. Sitting at the center of his field were two problems that were more than just unsolved; they were, my father says, “totally out of reach — nobody had any idea how to solve them.” OpenAI’s systems solved them in an afternoon. “It is the end of mathematics as we know it,” he texted me. “It is the first time I am glad I have retired.”

The newly formed Association for Human Mathematics, which has about 1,000 members, quickly released a statement: “Mathematicians did not ask for this work to be done.” It’s an interesting sentence. Should mathematicians be able to decide which math problems do and do not get solved? How do we weigh the benefits and costs of a world where the frontier of mathematics is advancing rapidly, but human beings no longer understand it?

Imagine the OpenAI process running repeatedly, over a period of years: A.I. swarms solving problems no human being has solved, then using those solutions to pose new problems, which are solved by other A.I. swarms that have absorbed the work done by their predecessors, which allows for the formulation of yet more problems, and on and on. It is too much new math for humans to comprehend, but A.I.s can absorb it easily, and they do, driving forward advances in material sciences and energy production. Human beings do not understand all this new science, but they do benefit from it.

Is that world so different from the one we now inhabit? Few of us understand much about the borderlands of human knowledge. I cannot remotely comprehend the math my father did. How different is it if none of us can understand the math the A.I. does? And yet, a world where the farthest edge of knowledge and progress rests in algorithmic systems, rather than human minds, does seem different to me. Human control — which is, in my view, inseparable from human comprehension — is worth something, is it not?

As I was writing this, I read about a study in which theoretical physicists posed 50 complex, unpublished problems to advanced A.I. systems. These problems were difficult but solvable, taking a physicist who was expert in the particular area of the problem about a day to solve. The best A.I. solved 98 percent of them, at a cost of less than $3 per problem.

The physicists initially thought the A.I. had performed worse than it had, but the model insisted that it was the humans, not the A.I., that had erred, and it was right. “Frankly, I wasn’t expecting this day to come as soon as it has,” Suvrat Raju, one of the physicists behind the study, wrote. “After we found these results, I couldn’t sleep properly for a couple of nights.”

The grimmest A.I. stories often center on a single moment when superintelligence slips out of our control and begins plotting our demise. But the scenario I find more likely and more irresistible is called “gradual disempowerment.”

In this telling, there is no one moment when humanity loses control; there is instead a progression of small moments when we willingly give control away because it is the rational thing to do. Firms turn first more of their work and then more of their decision making over to advanced A.I.s because it gives them a competitive edge. Governments turn more battlefield decisions over to A.I. because it makes their forces swifter and more lethal. A.I. companies turn to recursive self-improvement — where the A.I. autonomously creates the next A.I. model — because it vaults them ahead of their competitors.

The key to this process is competitive pressure. Companies and governments and A.I. labs that resist are outcompeted by peers that give A.I.s more control and autonomy. Advances in A.I. systems — and, eventually, in A.I.-controlled drones and robotics — keep expanding the amount of work A.I.s can do better than human beings. Even assuming the A.I.s remain helpful and pliant, more and more of society’s basic functioning slips beyond the grasp of human beings.

by Ezra Klein, NY Times |  Read more:

Image: Tamara Porras for The New York Times

[ed. We're screwed. Not because there aren't well-intentioned and very capable people working on AI safety and future planning but because the incentives to keep going as fast and far as we can are too great (eg. Sam Altman Is No Oppenheimer). The next 2-3 years will likely be some of the most consequential in history as various crises converge (AI, politics, economics/finance, climate). See also: The AI Doc: (Or How I Became an Apocaloptimist) on Netflix. Highly recommended.]

On Audiophilia: Heaven’s Melodic Strains

I am a sick man. I have skipped Friday night plans to spend hours repositioning the speakers in my living room, by inches and degrees. I have squandered small fortunes on silver and black boxes that most people couldn’t identify, whose purpose they wouldn’t understand. I have disfigured my apartment with hulking foam panels to improve the room acoustics. I have shelled out for a high-resolution music streaming service with an unpronounceable name. I have frittered days and nights trawling the back pages of online forums to find the optimal tonearm mass for a certain phono cartridge, or the suitable crossover frequency for a particular model of subwoofer.

More than anything, I have spent long passages of this earthly life sitting on my ass, listening. I listen to music, but what I hear is sound. Texture, timbre, and tone; clarity and detail; space and depth. And I hear, even more acutely, what thwarts sound. Shrill treble, muddled bass — for me these are worse than peeves. They offend hearing. They diminish the human sensorium. Earbuds repulse me; laptop speakers grate; a tabletop Bluetooth unit is a kind of vandalism.

The word audiophile — etymologically, one who “loves to hear” — has a wine-sipping pretension that I hate, but sometimes an ugly shoe fits. Like similarly suffixed psychopathologies, the term suggests a state of warped dependence, of inbent sublimation. I didn’t set out to be this way, but neither have I ever sought help. My symptoms are apparent, their causes obscure. What, after all, do we love when we love sound?

Audiophilia was born in gimmickry, and has never fully risen above it. Starting in the 1940s, a sudden onrush of consumer audio innovations — wide-range speakers, FM radio, long-playing vinyl records — created an equally sudden demand for whiz-bang demonstrations of “lifelike” sound. [...]

Audiophiles obsess, tinker, tweak, and upgrade, their gear addiction now served by a global subindustry of specialty brands, most of them unknown to the broader public; there are still record labels whose main marketing draw is their audio quality. And more than a little of that midcentury zealotry survives in subsequent sonic novelties that tease and amaze the ears with their “realism”: spatial audio, high-res audio, surround sound. Sound snobs would plotz at the comparison, but when an audiophile tests their stereo with the sawtooth jag of a guitar or the spelunk of a low bass note, what — besides thousands of dollars’ worth of gear — makes them any different from a teenager mainlining the rustles and gurgles of ASMR videos on YouTube? When we submit ourselves to sound for its own sake, something elemental, infantile, awakens.

Yet unlike most cultic formations, audiophiles know, deep down, that their god is a false one. Recorded songs are finite temporal events, but in the audiophile dream, or delusion, the record holds an infinity of retrievable detail, a renewing depth that can always be heard better, truer — if only we have the right tools. Audiophiles, the High Fidelity magazine cofounder C. G. Burke once wrote, are simply “people [who] wish an oboe to sound unmistakably and completely like an oboe.” That word like is key. All audio playback — mono or stereo, analog or digital — is at base an imitation, a simulacrum. Choose your sound and you choose your illusion. And almost as soon as naturalistic audio entered the American home, the wars of illusion began. Do you want to feel like the band or orchestra or DJ is here in your room, or like you’re there, in the studio or concert hall? Do you judge a system’s sound by its nearness to live performance, or to the recording? And how would you tell?

All subcultures suffer their schisms, and for audiophiles, a defining split is between subjectivism and objectivism in listening — between those who trust their ears and those who trust their measurements. In a 2004 academic article, the ethnomusicologist Marc Perlman framed it as a conflict between “golden ears” and “meter readers.” The golden ear, in Perlman’s lavishly adjectived definition, claims an “intimate, embodied, personal, inalienable, charismatic superiority of aural discrimination,” a master listener’s ability to distinguish and rank audio equipment by the subtlest gradations of sonic quality. His rival, the meter reader, prizes “rationalized,” “impersonal,” “scientific” authority, by way of spiritless, lab-testable criteria that don’t depend on the vagaries of human hearing: signal-to-noise ratio, crossover distortion, dynamic power output.

No love is lost between these camps. To Robert Harley, editor of the flagship golden-ears monthly The Absolute Sound, meter readers are philistines “who reject the listening experience,” for whom “nothing more can be known about an audio component beyond its technical performance.” Upmarket mystics like Harley are in turn a favorite punch line among the echt meter readers of Audio Science Review, an online database whose contributors meticulously test and rank components — 1,986 and counting — based on measurements, no ears required. In their respective languages, both extremes circle an epistemic cul-de-sac. A meter reader’s greatest compliment for a piece of gear is that it “measures well”; the golden ear’s highest praise, tautologically, is that it sounds “musical.” From a certain height, the problem becomes one of knowledge itself: Do you believe what you hear, or do you hear what you believe?

These warring phenomenologies also play out in silicon and copper. Perlman’s binary maps roughly onto the divide between — bear with me here — solid-state audio equipment, which uses semiconductors, like nearly all consumer electronics since the 1970s; and vacuum-tube gear, a throwback design where power is transmitted by electrodes in glass cylinders. Meter readers bade good riddance to tubes decades ago, but golden ears can’t quit them. Psychoacoustic lore holds that the kind of harmonic distortions associated with tubes — subtle overtones that smooth certain frequencies, as if steeping the music in a sepia mist — sound gentler, “warmer,” than the granular accuracy of solid-state equipment. Likewise, while digital media long ago conquered analog in the rest of the world, among audiophiles the contest has never been settled. Objectivists point to the quantifiably superior stability and fidelity of digital codecs, while subjectivists cherish the earthy sonic “presence” of records and reel-to-reel tapes. Today digital sources and solid-state parts almost always measure better; yet untold money and ingenuity go into the design of new turntables and tube amps, and demand is only growing. [...]

Who are these audiophiles? We are — overwhelmingly, smotheringly — men, skewing older but not geriatric, often but not always white, with at least a little disposable income, sometimes far too much. I haven’t discerned a socio-professional pattern; audiophiles are dentists and bartenders, lawyers and mail carriers. Curiously (or not), the proportion of musicians and audio engineers seems not much higher than in the general population.

Such, anyway, is our outward appearance. As for our inner life, no piece of writing depicts it with more tough love than the great — the only? — audiophile novel of our time, Ge Fei’s The Invisibility Cloak, published in 2012 and translated into English by Canaan Morse in 2016. “The name ‘audiophile’ has never sat well with me,” its protagonist, Cui, confides unconvincingly near the start of the book. “I’m just a craftsman.” A master maker of custom tube amplifiers in Beijing, Cui wearily plies his trade while sinking ever deeper into a middle-age, postdivorce funk. Building tube amps, he muses, is “probably the most insignificant industry in China today”; he guesses that in the whole city there are no “more than twenty of us.” Yet the boutique marginality of what he calls his “racket” puts him in close contact with China’s new class of yuppies and executives, eager to outfit their homes with artisanal sound. The novel follows Cui from one nouveau riche living room to another, where clients are reliably entranced by his setups, “as if listening to heaven’s melodic strains.” “Five or six of these ‘victims’ a year,” he tells us, “is enough to keep my pathetic business hobbling along.” Cui isn’t much for introspection, but in his gripes and regrets, in making a modest living from something he enjoys immodestly, he inhabits a melancholy that any sick man will recognize. As we hold life and sound apart, they only join closer.

Short and episodic, the novel’s narrative patterns match those of the audiophile mind. Liberally scattered amid childhood flashbacks, scenes from a failed marriage, money troubles, and family quarrels — in a word, life — are gearhead reveries on the glories of hi-fi, named by brand and model. Cui admires a set of Amati Anniversario speakers, their cabinets “lacquered and buffed to the tones of a violin and a mirror’s shine”; one type of RCA vacuum tube is nicknamed “The King,” another “The Pillar of Heaven.” A prized pair of vintage Tannoy Autograph speakers seduce him “like gorgeous twin sisters,” with an “intoxicating allure,” an “unbelievable fullness” and “flawless piano timbre.” When Cui’s wife asks for the divorce, his only request is “that she leave me all the audio equipment, including the Autograph speakers.” She agrees.

I linger on Ge’s novel in part because the field of recent hi-fi writing is otherwise so desolate. In this century the American audiophile press, like most print media, has withered to bare life. Long gone are the postwar années glorieuses of Audio and Stereo Review, High Fidelity and Audiocraft, with their ten-page features, critical essays, how-to guides, and no-fluff component tests, serving professionals and consumers, snobs and novices alike. (I mean, Ralph Ellison wrote for High Fidelity — as did Nat Hentoff, Aaron Copland, and Jacques Barzun.) Today readers have a choice of just two ad-heavy monthlies, each offering a thin digest of industry-friendly gear takes and skimpily reported columns. Stereophile maintains a pretense of objective rigor; product reviews include results of tests by the magazine’s “technical editor,” full of graphs and charts. In The Absolute Sound, meanwhile, quality is more or less measured by price. The most offensively expensive gear sends the magazine’s credulous shills into transports of Hildegardian ecstasy. For $89,000, you can make mystic communion with the “engrossing, enlightening . . . awe-inspiring” 300-watt Goldmund Telos 800 power amplifier, with its “dynamic envelope that seems to have no bounds,” and “transient attacks . . . more powerful than you had imagined possible” — fittingly, the magazine calls its annual product awards the Golden Ears.

However editorially atrophied, these magazines continue to carry authority, at least for the moneyed boomers who dominate the high-end market. For everyone else, there’s the internet. A sun-starved terrarium of forums, blogs, vlogs, and podcasts serves every niche and extremity of the audiophile audience. As repositories of technical nous and venues for arcane bickering — are idler-wheel turntable motors really superior to belt-drive designs? — the forums fill some of the void left by the decline of hi-fi journalism, but it takes a hardy scroller to sift the useful from the inane. And most audiophile vlogs are little more than meandering infomercials for products that brands have sent hosts to “review.” I should know, because I’ve watched so many of them.

Perlman’s dyad needs updating for an American economy more slanted than ever toward Sardanapalian excess. Among audiophiles, the bitterest battles now rage over the unconscionable cost of high-end hi-fi, and the outlandish claims made for the priciest gear. An unusually reflective recent Stereophile editorial lamented audio’s unchecked “premiumization.” How, not just as a matter of value but a question of science, can any consumer amplifier cost $150,000, like the 225-watt, 400-pound Statement 450i iQ? (The Absolute Sound: “resplendent,” “superb,” “extraordinary.”) Speakers readily reach the half-million mark; you can spend a year’s salary on audiophile power conditioners, audiophile equipment racks, audiophile Ethernet cables. At the scammiest fringe, things enter RFK Jr. brainworm territory: crystals, orbs, and amulets alleged to improve acoustics, reduce static buildup, and perhaps open a channel to the divine. [...]

Since long before the advent of high fidelity, the historian of recording Greg Milner notes, audio adepts have “believed that the key to perfect sound was to simplify the process.” One fewer link in the chain means “one less chance for the original sound to be corrupted.” (The legendary speaker designer Stewart Hegeman reportedly defined a perfect amplifier as “a straight wire with gain.”) Audiophiles thus devise their most elaborate contraptions in pursuit of simplicity. A fully loaded high-end home system might include a turntable with a freestanding tonearm, a step-up transformer, a phono preamplifier, a stereo preamplifier, two power amplifiers (one for each channel), a streamer, a music server, a digital-to-analog converter, a CD transport, two loudspeakers, two subwoofers, and assorted peripherals, not to mention many feet of RCA, XLR, HDMI, optical, coaxial, Ethernet, and speaker cables. Each of these devices does one thing at a time and no more, the better to pass on a pure, unblemished signal. Of course, such a setup also makes for a complex, multinodal signal path, with more junctures where things can go wrong. A sane person would call it overkill. For the audiophile, it’s another reason to spend even more money. [...]

Yet among leisure-class hobbies, something about hi-fi remains apart, unassimilated. Where it touches other domains of luxury consumption, the results can only be absurd. In a recent TV commercial for Lexus’s new electric SUV, a handsome white man in loafers lounges in a wide, high-windowed living room. The space is almost empty, save for the Lexus itself, somehow parked in the corner, and, alone in the middle of the floor, a gleaming black and silver VPI Titan Direct turntable ($64,000), equipped with a VPI Shyla dual moving coil cartridge ($2,200). The stylus lowers, and the gossamer muted trumpet of Miles Davis’s “Blue in Green” begins playing. In a close-up, twilight kisses the vinyl grooves, which fade into a view of the SUV doing slow, graceful donuts on the beach. In another close-up, the man turns his gaze downward, as if in contemplation. “The greatest measure of an automobile,” a bourbon-rich baritone voice rumbles, “is how it makes you feel.” The loafered man doesn’t seem to feel much. After twenty seconds of listening, he gets up and leaves. I would too if, like him, I had spent five figures on a turntable only to dump it on the floor, and placed my speakers against a wall of sound-reflecting windows, too far apart and approximately fifty feet away from my listening chair, which is also facing the wrong way.

by Colin Vanderburg, N+1 |  Read more:

Image: Janet Cardiff and George Bures Miller, The Dark Pool. 1995

Leaked Anthropic Draft AI Regulatory Bill Is Unacceptable and Troubling

There is a newly leaked bill that Anthropic is purportedly shopping around is (if you assume its bugs get fixed).

It is similar to the FRONTIER Act, with stronger preemption of state laws, stronger transparency provisions and weaker enforcement provisions.

Having slept on this, I find it unacceptable that Anthropic is actively shopping exactly the type of bill I have warned we will need to fight against during the lame duck session. They are trying to use this last time with a Republican Congress to pass preemption of exactly the important state laws, and get some transparency but not much enforcement in exchange.

This reads as intentional, that Anthropic is more afraid that the government might enforce rules against Anthropic, than they are worried about the ability of the government to enforce rules against everyone. Anthropic’s policy team seems to have, chosen protecting their business interests and guarding against their political risk, at the expense of safety considerations.

There was no need for the company purportedly existing to promote safety to propose a bill that is not even actively stronger than the FRONTIER Act, when the FRONTIER Act was a compromise bill proposed back in June.

Even if you were fine with what is being given up, what you get in return very obviously does not meet the moment. Nor is there any need for such compromises. It seems rather insane for Anthropic to be proposing it now. This seems like the bill that someone on the anti-regulatory side should be pushing. It is not obvious this makes us safer than doing nothing, even if the alternative is fully nothing at all levels.

Say it isn't so, Anthropic, or at least that it was a mistake and is no longer so, or I am going to be rather cross.

If you want the full text, here is a copy.

Others RTFB (Read the Bill)

I have not had the time to do so, but the other usual suspects quickly sprinted to RTFB (read the bill) and they did not like what they saw. You can try to call this a ‘pretty reasonable AI safety bill’ as Charlie Bullock does, but that is by the standards of people trying to protect the industry from more impactful regulation. [...]

Charlie Bullock: Big news. My take: Anthropic deserves some credit for actually proposing a pretty reasonable AI safety bill; that’s more than other frontier labs have done.

But they don’t deserve *too* much credit, IMO; this bill is weaker than the Trahan-Obernolte FRONTIER Act in some key ways, and since the political momentum has shifted significantly in the direction of safety since FRONTIER was introduced back in July, I think new bills introduced today should ideally go further than FRONTIER was able to go rather than regressing.

To be fair to Anthropic, the main places where this bill is significantly weaker than FRONTIER are (a) rulemaking (very strong in FRONTIER, almost nonexistent in Anthropic’s bill) and (b) the emergency shutdown authority. Even if you take safety seriously, it might make some sense to dial those authorities back if you didn’t trust the executive branch to wield them responsibly, because those are the easiest provisions for a hypothetical President who hypothetically might not like a certain frontier AI company to abuse.

There are some improvements over FRONTIER as well; for instance, this bill does specify that auditors are employed on a continuing basis and has better provisions regarding auditor access to information. Happy to see that from Anthropic (although I think their access provision is still a little undercooked and has Fourth Amendment problems).

Overall, I think the embedded auditor bills that get introduced in the coming months are going to be significantly better and more ambitious than this, and I hope Anthropic supports those bills instead of opposing anything that treats catastrophic risks like the serious national security issue they are. Still, it’s encouraging to see a frontier lab come this far. [...]

Nathan Calvin: Charlie’s analysis is right but he is nicer than I am - I found myself viscerally angry at multiple points reading this bill. There are some good and sensible parts, but overall this legislation is not remotely close to meeting the moment and I overall think passing it would make us less protected against catastrophic risks than we are under existing law.

Two parts that stand out as particularly infuriating when taken together:

1. Some of the preemption provisions are remarkably far reaching and consequential, and in addition to preempting legislation like SB 53 would also remove general powers that state courts have to enjoin defendants and protect the public. Most outrageously, it has a provision that looks like its intended to stops states from enforcing laws or judicial decrees that “suspend the development, deployment, or internal use of an AI model” to protect their citizens from imminent catastrophic harms. Such powers to block and suspend model deployments would then only reside with the federal government.

2. The federal governments powers to restrict the development, deployment, or internal use of a frontier model has to meet an absurdly high bar.

First, there needs to be an imminent risk of a catastrophic harm (meaning killing more than 50 people or causing more than a billion dollars in damage). But thats not enough! The agency head needs to make a detailed determination with no deference given to show why no alternative would prevent the imminent catastrophe. Next the court is also explicitly told to consider the harms to the developer and other harms from restricting the model, but no considerations in the other direction.

This is absurd! For all of its warning about the potential for AI to cause a catastrophe that kills literally everyone on the planet, Anthropic’s proposed federal law likely makes it harder than under existing law for a court to get an order blocking or restricting the development of frontier models? WTF??

There are also a bunch of others things that I would like to see improved - the substantive details of the auditing section and the potential for rule-making on substantive safety standards both come to mind - but I think these stand out as particularly indefensible.

Charlie Bullock: Nathan’s righteous anger has inspired me to make one more mildly disapproving observation: it’s kind of wild that the Anthropic bill has no government licensing for the independent auditors.

Licensing is, IMO, the bare minimum thing that you need to even be able to pretend that your auditing regime isn’t just going to devolve into a race to the bottom. Without it, auditors will inevitably compete with each other to offer their customers, the labs, the least bothersome audits available at the lowest possible price point. Obviously Anthropic trusts itself to do a great job of picking great auditors, but do they trust OpenAI to do the same? What about SpaceXSI?

Why Would Anthropic Do This?

The new Anthropic bill is weaker than their June framework, and offers both forward-looking and backward-looking preemption in exchange, despite months of movement to enable us to do more. What are we even doing? [...]

by Zvi Mowshowitz, DWAV |  Read more:

Image: Draft bill

[ed. Best I can tell (haven't RTFB) the draft seeks to: 1) preempt state's rights to write and enforce stronger safety provisions - so, in effect, we get a single point of federal decision-making/enforcement vs. 50 votes; 2) install "independent auditors" to evaluate model safety who's credentials and funding sources are undefined; and 3) set an arbitrary trigger for a single federal judge to intervene/block a model’s release or use only if it poses a foreseeable, imminent risk of more than $1 billion in damages, or of killing or seriously injuring more than 50 people - all to be decided using a set of process provisions that are ambiguous at best - see the REQUIRED FINDINGS above - and over some unspecified period of time. So, all in all, not good. And, even more not good - other frontier labs don't seem to be on board with even these few minimal restrictions. As one commenter notes: there aren't really any Good Guys in this race anymore (if there ever really were), just Bad, Badder, and Baddest. Good Intentions Paving Company secures lucrative "Hell" contract, news at 11.]

[ed. Addendum: The AI Doc: Or How I Became an Apocaloptimist is now streaming on Netflix. Highly recommended.]