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 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.]

How a 'Hardwired Pause' on AI Development Might Work

If the world’s government and AI leaders ever agree to pause frontier AI training, is an international pact really feasible? How would it be implemented, coordinated and monitored?

In a first-of-its-kind academic study released today (Friday, Oct. 9), a multidisciplinary team of 26 researchers maps out a path toward an international, mutually verified pause of frontier AI training of at least 10 years. The team — which includes professors Barry Eichengreen and Stuart Russell from UC Berkeley and faculty from other top institutions, including Princeton, Stanford, Harvard and Oxford — combines expertise in computer science, economics, history, international relations, and Chinese and American politics with practical experience in diplomacy and government.

 

The researchers focus primarily on a plan that halts the production of AI training chips and replaces them over time with specialized “inference-only” chips, which serve fast, efficient inference on existing AI models but are not practically useful for training new models. Such a plan would permit the continued use of currently available AI products, while preventing new AI models from overwhelming states with social or economic disruption or threats to their national security. They also suggest verification measures that would allow world leaders to assure nervous rivals that they are upholding the pause agreement.

“We consider a scenario in which world leaders want to pause if they can be confident that others are pausing or that defections would be detected quickly enough,” writes co-organizer Wesley Holliday, a UC Berkeley philosophy professor, in the study. 

Frontier AI training refers to the development of ever more powerful AI models. In a hardwired pause of such training, societies could control how powerful these models are allowed to get. If they judge that certain models can offer economic and scientific benefits, a hardwired pause could give them a way to reap the benefits before they become powerful enough to pose catastrophic risks, thus avoiding the dangers of an uncontrolled AI race.

The rapid progress of frontier AI systems, combined with leading AI companies’ recent struggles to prevent their models from escaping containment and committing cybercrimes, has prompted widespread calls for a global pause until these systems can be built safely and controllably. For strategic reasons, however, a long-term pause could be difficult to arrange even if parties agree that it would be in their interests: without effective verification measures, rival companies or nations may fear that others will defect and thereby put them at a commercial or geopolitical disadvantage.

But the Working Group on AI Pause Feasibility’s detailed, 200-page report addresses such roadblocks with a framework for mutual verification.

Stopping, replacing, retiring AI training chips is key

Frontier AI training requires AI chips, which states can govern. The supply chain for AI chips is highly concentrated with several chokepoints, bolstering governments’ ability to enforce rules on their production.

“Bypassing existing supply chains is extraordinarily difficult,” writes study co-organizer Will Fithian, UC Berkeley associate professor of statistics. “An international prohibition on frontier AI training can take advantage of this fact about the hardware needed for AI training.”

The report proposes halting the production of AI training chips, which are needed to develop more powerful AI models. Over time, most or all of the pre-pause stock of such chips would be phased out. They would be replaced by inference-only AI chips, which are capable of fast, energy-efficient AI inference on approved models but are not practical for training new frontier AI models — even if stolen or seized — due to constraints hardwired into the chips.

During the chip replacement process, other verification measures would prevent residual training-capable AI chips from being used for frontier AI training.

Fithian and Holliday add that technologies needed for inference-only AI chips already are being developed for purely economic reasons, but that their economic attractiveness is currently limited by the rapid turnover of new models. If the training of new frontier models were prohibited, a hardwired pause would increase the economic advantages of these specialized chips and spur innovation in the development of other inference-only chips.

Other highlights of the report include:

• How a hardwired pause could be made politically palatable by offering ways to pursue AI-powered scientific and medical research, retain strategic insurance against being left behind in the AI race by defecting states, and pass on the economic benefits of cheaper inference to consumers.

• The new chips’ reduced resource demands, which would decrease their environmental impact and improve frictions in the U.S. between data center operators and local communities.

• How a hardwired pause safeguards against both covert evasion — when a state secretly trains new frontier models while ostensibly adhering to verification procedures — and overt breakout — when a state openly abrogates the pause in an attempt to build a much more powerful frontier model before other states could respond.

The researchers agree that a hardwired pause’s success depends on the cooperation of world leaders and their willingness to retire or transfer pre-pause stock to other jurisdictions or internationally governed data centers, such as scientific preserves. [...]

“Coordination is possible if the decision makers care enough about the future,” the authors write, “not just immediate payoffs, and if monitoring is adequate to support credible consequences for defections.”

by Sarah Fullerton, UC Berkeley News |  Read more:

Image: Yuki Iwamura/AP
[ed. Executive Summary here. Full Report here. Stop feeding the beast. Sounds reasonable until you consider a couple of constraints: like, tanking the economy (AI spending is the biggest single factor propping it up); and trillion dollar IPOs hanging in the balance (who's failure could also produce a cascade of financial failures).]

The Secretive Bilderberg Group Is a Key to Understanding the Global Power Elite

Most countries in the western world operate under a practically unquestionable assumption that they are democratic, in the sense that there is a plurality of different interests in society at any given time and each of them has an equal opportunity of gaining significant political power.

The very concept of an elite class, with more influence than mostly everyone else, is deemed a relic of history. C. Wright Mills, author of The Power Elite, explained that these understandings have a certain romantic appeal, because a supposed balance of interests, without elite interference, makes “each of them really quite impotent, then no one set of higher circles and no manageable set of institutional arrangements can be held accountable for the events and decisions of our time.”

But in a world of constant conflict, economic strife, and political hypocrites, perhaps it is at least worth considering the other side of the argument for once?

In Mills’ conceptualisation of a power elite, he explained how a uniquely powerful grouping of economic, political, and military interests emerged after World War II that had the means to make decisions with significant consequences for the whole of American society.

The narrow and centralised group of individuals at the apex of economic, political, and military hierarchies constituted an elite with the technical and institutional means of power, which are beyond the reach of ordinary people. Far from conspiratorial, this was a group seeking to maintain its position of social power via the maintenance of its own coincidence of shared interests.

Indeed, Mills argued that “to accept either view – of all history as conspiracy or of all history as drift – is to relax the effort to understand the facts of power and the ways of the powerful.”

Writing in the 1950s, Mills’ conceptualisation of an elite was rooted in facts of power that existed, for the most part, within the nation state. One big exception to this was the Bilderberg Group that formed in 1954, which brought together elites from across the world into a transnational network. The group’s official website states that it was developed in response to opinions “expressed by leading citizens on both sides of the Atlantic.”

It fails to say how non-leading citizens, the general public, had a say in its organisation or operation. The World Economic Forum, Trilateral Commission, and Atlantic Council serve as similar transnational elite knowledge networks where individuals with shared, and in this case globalist, interests can work to further their own agenda. Since the era of globalisation moved political and economic power beyond any single country, so too have elites transcended the bounds of the nation. The problem, then, is that how do the masses, bound within borders, stand a chance of influencing the decisions that this transnational elite are making?

To think of institutions like the Bilderberg Group as inconsequential is to underestimate the role they play in constructing a global ideological consensus among elites that is then brought home and applied to the “nation.” Bilderberg allows elites to interact and acquire a shared education in how to think about the world in globalised terms. While it is an unofficial institution, in the sense that is it not legally constituted, the decisions it makes are subsequently endorsed by official agencies like the Organisation for Economic Co-operation and Development and national governments.

This is especially true of its neoliberal economic position. If there is a global ruling class, as critical political sociologists have identified, then to constrain political analysis to only the official institutions of the nation, and omit anything akin to Bilderberg, is a major misunderstanding of the ways of the powerful in modern times.

The Bilderberg Group’s powerful members come from across the world and represent the political, economic, military, technological, and ideological spheres of globalisation. There is absolutely no representation of alternative interests and ideological positions, such as small business, populists, socialists, or nationalists. [...]

Understanding the Bilderberg Group as the center of an elite knowledge network is far more accurate than any simplistic, and often naïve, claim to the contrary; especially from those that would malign it with the catch-all, ignorant label of “conspiracy theory.”

Knowing that the locus of political, economic, and military power now resides offshore helps to explain the pandemic of political disaffectedness sweeping across the planet. Some even see this as the emergence of a global civil war, which is surely the last thing the world needs at this very moment. Apparently, Bilderberg’s secretive nature is to ensure its participants can “speak freely in an environment of trust,” but precisely what would these people, who themselves claim a proclivity for transparency and democracy, want to speak about free from public discussion, criticism, and alternative points of view?

The public, rationally, can only surmise that it is nothing good. Unless politicians revert to assuming their proper role as national, and local, leaders, responsible for their own constituents’ needs as opposed to those of transnational corporations, the global crisis of democracy is surely set to continue.

by Stuart J. Hooper, The Wire | Read more:
Image: Reuters/File
[ed. The Press are typically excluded (see list of 2026 attendees here) and all discussions are conducted under the Chatham House rule (meaning you can use and report what you learned, but you cannot share who you got it from, nor their affiliations). See also: At Bilderberg’s bigwig bash two things are guaranteed: Kissinger and secrecy (Guardian).]

AI Will Be a Boon for Savers

Sometime in the next few years, an ordinary American will give a simple instruction to an AI assistant: “Make sure my money is always working.” From that moment on, his cash may never sit still again.

The agent will sweep idle savings into whatever insured account pays the most. It will find high-paying savings accounts and ladder certificates of deposit without the owner’s learning what laddering means. This is agentic banking—software that displays your finances and acts on them—and the banking industry isn’t prepared for how fast it’s coming.

A staggering share of bank profitability rests on money that doesn’t move. This is true across the board, not only for big banks.

Americans hold roughly $10.4 trillion in savings and other liquid deposits. The Federal Deposit Insurance Corp.’s national average savings rate—the deposit-weighted average across every insured institution—is a mere 0.38%. At that rate, the system pays savers about $40 billion a year. The best nationally available savings accounts tracked by Bankrate pay around 4%. The same balances at that rate would earn more than $400 billion.

Even extremely conservative assumptions—half those balances stay put for good reasons, and top rates compress as money moves—leave Americans forgoing well over $150 billion a year. Call it the laziness tax: a quiet, regressive wealth transfer from inattentive consumers to the banking system.

AI agents will largely abolish it. The inertia that keeps a saver from spending 20 minutes to earn $1,000 disappears when the 20 minutes do. Every account becomes a shopped account. For consumers, this is unambiguously good: the largest, fastest transfer of value from financial institutions to households in generations, accomplished by software rather than regulation.

An agent working for a consumer can also flag junk fees, catch unauthorized charges, decode fine print and steer its owner away from predatory products. It can help accomplish, one household at a time, much of what the Consumer Financial Protection Bureau was created to do through enforcement. The regulatory challenge will be ensuring that agents’ actions stay aligned with consumers’ best interests.

But AI agents will strain the system that makes lending possible. Banks will be funding long-duration loans with money that can leave in 30 seconds. The Silicon Valley Bank crisis of March 2023 offered a preview of what can happen when technology accelerates money: Tens of billions of dollars left one bank in a day. Agents automate and accelerate that speed. Liquidity models built on human sluggishness will become a distant memory.

To survive when AI moves money automatically, banks will need to change how they do business. 

by John Delaney and Bill Foster, Wall Street Journal | Read more:
Image: Getty Images

[ed. Possible. But it'll take a heavy lift to get people to hand over their financial accounts (and security protections) to AI. What about criminal AIs that exploit that?]

Meta's New AI Muse is About to Make the Internet More Annoying

Someday we'll redesign the internet for tools like this. Until then, you're in for a wild ride.

Last week, my finger hovered over the download button for Meta's new AI Muse. I felt a little bit scared of my phone.

Muse wants to help. It's an AI agent, a tool that goes out into the world for unsupervised tasks. Using its own web browser, Muse can cancel your gym membership, haggle with customer service people, buy groceries, invite friends to parties, you name it. Soon it will make phone calls.

It's the first free, full-featured agent from a big company. Millions downloaded Muse in its opening weeks, and OpenAI just announced its own new agent. More are coming. It's the dawn of a new chapter for the web. What will it be like?

According to experts I interviewed, the internet is about to get more frustrating. These agents may cause a cascading chain of problems that shake the foundations of digital infrastructure. Chances are good that you're about to live through an era of online chaos.

The internet was built for humans with limited time and patience. AIs don't have those restraints. You'll probably have to fight with robots over concert tickets and booking appointments. Small businesses could drown in machine-made pestering. AIs don't look at ads, and websites that rely on them are already crumbling. You'll be buried in endless loops of proving you're human. And if you use these AI tools, some may betray you.

Eventually, we'll rebuild the internet for this new reality. Perhaps that world will be better. Until then, things may get ugly.

"Imagine there are no rules of the road. And you release billions of cars – the AI agents – and you just let them drive through playgrounds, hitting kids. That's where we are," says Ramesh Raskar, an associate professor at the Massachusetts Institute of Technology (MIT) in the US who studies AI agents. "It's a pivotal moment."

To access the road, Muse arguably asks for more trust and sensitive information than any product in the history of Mark Zuckerberg's empire. When you open it, the cutesy avatar asks for a name. Then it asks for total control of your Gmail, calendar and entire computer if you're on a Mac.

There, staring at the download button, I felt a moment of panic. People begged me to say no. "I wouldn't use it," says Patrick Wardle, co-founder of Objective-See, a US nonprofit security foundation that uncovered serious flaws in the Muse app. "Personally, I would tell you to uninstall it altogether."

Despite the warning, I need to see what Muse users are up against. So I named my AI "Bob", gave it the keys to my life, and stepped into the future. [...]

Things fall apart

Muse is useful. Over the course of a week, I had it reach out to a seller on Facebook Marketplace with questions. The AI ordered the dental floss I like. It negotiated a $31 (£23) discount on a software subscription.

Now picture millions or billions of agents doing this all simultaneously, performing multiple tasks that might take weeks for a human to get to.

"What happens if bots send 600 inquiries to a website a day, when normal humans might only send two?" says Shroeder. "Businesses and individuals are going to have a lot to deal with."

AI is already decimating online business. Google and chatbots now answer questions directly. As a result, people are visiting fewer websites. Robots don't click on ads or buy subscriptions, and it's causing an extinction event for companies across the web. Agents will supercharge this problem.

Meanwhile, one analysis found web traffic from bots spiked 124% in the year to June 2026. Some websites and services are crumbling because they aren't built for that digital load.

Meta's spokesperson says Muse works to complete your tasks while respecting the interests of websites.

Will the robots betray you?

There are also personal risks. "If you're empowering an agent to make things like purchasing decisions, or choices about taste and preferences, you're opening yourself up to being exploited," says Shroeder.

If Muse plans your holiday trip, Shroeder says there's no way to know if it got you the best deal, or if it chose flights and hotels that benefit Meta's business partners. If you ask for music recommendations, will they be based on your taste, or will you hear about artists who inked deals with the social media company?

Meta's spokesperson says Muse's built-in protections and user controls put people "absolutely in charge" of it, and Muse "behaves like a personal assistant acting for a single person" that follows ethical guidelines to protect users.

However, Shroeder says she's examined Muse's terms of service, and there's no guarantee the app will act in your favour. {...]

Hacks and love letters

Stop for a moment and consider your email inbox.

I've had the same Gmail address for 21 years. It's my login for hundreds of accounts. It holds medical test results, contracts and legal documents, conversations with family and endless receipts and financial information.

My inbox even has love letters an ex-girlfriend sent, from an era when email (briefly) felt suitable for romance. (I got her permission before handing it to Muse.)

More than any other digital service, my email is a window into my brain. Now Meta has it. If that's not enough, Muse also suggested I give it my bank accounts. No thanks. I didn't let the AI run wild on my computer hard drive, either.

Meta makes some explicit promises about privacy. The company says it won't connect any of the data Muse collects to its advertising systems. Special systems are supposed to prevent the AI from seeing passwords or payment methods. And when you plug it into Gmail, the AI asks if you want it to scan the whole inbox, or just read messages related to specific tasks.

However, Meta uses your Muse data to train new AI models by default. Shroeder says it's impossible to remove data built into an AI model, and AIs can sometimes be tricked to reveal their training data. Meta says your data is "sanitised" to remove personally identifiable information before it's used for training, and you can opt-out of AI training with a setting.

But given Meta's history of privacy problems, should you trust what it's telling you now?

by Thomas Germain, BBC | Read more:

Image: BBC/Serenity Strull/Getty Images
[ed. I wouldn't trust Zuckerberg with a box of matches. One thing is clear though: it'll definitely be a  more dog eat dog world (if we survive) once AI becomes more widely adopted by the public. Think Ebay auctions and armies of bots placing bids up to the last micro-second - for everything.]

What Also Happened: #NotOnlyHuggingFace

OpenAI has been holding out on us.

First we learned about the HuggingFace incident. They gave us a postmortem, but it was highly incomplete. Even the accompanying holy s*** METR investigation and postmortem was localized and incomplete.

Then there were some other incidents involving some Wikis as message boards.

Then there were some additional incidents.

Then there was that time they got into Australian Medicare data.

Then OpenAI dropped news on a Friday afternoon that they were making their way through a pile of various incidents and notifying the targets, but they said remarkably little in the way of new details.

There was a report from a startup called Parse diving into the details of exactly how the OpenAI models pulled off parts of the HuggingFace attack, involving creating almost a million URLs and other tricks to get around the extremely narrow nature of their internet access.

Then Madison Mills reported in Axios that we can raise the stakes, as OpenAI and Anthropic are collectively probing tens of thousands of security incidents.

Remember Jensen Huang’s ‘I know they know how to fix it’ about OpenAI from last week? Wow, did that not age well.

Someone might need to be liable for all this.

Oh, and there was another buried lede. On September 20th there was another sandbox escape by OpenAI’s latest most advanced model, which is once again paused until they can fix the situation. The official announcement when they shared this was sufficiently buried that Tomek had to call it ‘one news form today that’s easy to miss.’

OpenAI did some highly negligent things, to say the least, that led up to and enabled the HuggingFace Incident and related problems.

Since then, now that they’ve realized What Happened, OpenAI has been seemingly much better about taking responsible internal actions. They’re pausing in the wake of incidents, strengthening security and alignment and oversight efforts, responding much faster and generally taking things seriously.

They’ve also made a Heel Face Turn in their communications and high level orientation, endorsing the need to pace the frontier, calling for regulation and pledging to implement embedded evaluators. They’ve allowed their employees, including the ones who haven’t quit, to be remarkably loud.

They are still slow walking disclosures about all the incidents where their models have been hacking and otherwise messing in places they should not have been, partly because there were so many they can’t sort through them all, and deferring to targets to determine whether to disclose. All these disclosures this time around were buried in various Friday afternoon announcements.

Table of Contents

• Hugging Other Faces.
• A Wants-You-To-Know Basis.
• Parsing the Face.
• Sheepishly the Member of Technical Staff Sets the ‘Days Without a Research Model Escaping its Sandbox’ Sign Back to Zero.
• The Attempt is the First Failure.
• Stop, Hammertime.
• Whacking the Mole.
• Self-Replicating Prompt Injections.
• Levels of Friction.
• People Care About Private Data Violations Curiously Strongly.
• Alternate Universes.
• The Correct Response To People Still Calling This a Marketing Stunt or a Regulatory Capture Scheme.
• A Question of Liability.
• Keep Summer Safe.
• N Boats and Several Helicopters.
• Alert the Media.

Hugging Other Faces

The news drops started with OpenAI coming back, at a time always picked to bury stories, with more information on What Happened as their investigations continue.

At first, this looked like slow walking of the situation, but did not look like it was a big change from our default assumption of ‘it’s worse than you know.’

by Zvi Mowshowitz, DWAV |  Read more:

Not Ready For Prime Time

[ed. Interview with Piers Morgan, Tom Conti and Tilly Norwood (the AI generated actor). Also, Impact Theory co-founder Tom Bilyeu and game theorist Professor Jiang Xueqin. Some crazy glitches, but you can see where this is going.

See also: An “Interview” With Tilly Norwood Proves Absurd and Underwhelming (HR); and,
Tilly Norwood’s press tour is going about as well as you’d expect for an AI (TechCrunch).]

DOGE Team Dreams

Tyler Hassen’s initiation into Elon Musk’s Department of Government Efficiency, or DOGE, started as a series of tests.

The Houston oil executive says he was given vague instructions to show up at an unfamiliar D.C. address. He hastily packed three suitcases and flew east, taking an Uber to a building that he soon learned belonged to Elon Musk’s SpaceX. He was whisked to a private office, acutely aware that his tailored suit stood out among the hoodie-clad engineers “too locked in” to their laptops to look up. He was ordered to go through the website of the Department of Commerce and, within days, find two to five ideas to present to Musk.

He checked into a nearby J.W. Marriott and told his wife he didn’t know how long he would be gone.

Thus began an 11-month stint at the center of a national firestorm — an exhilarating, chaotic and sometimes scary experience, Hassen told The Washington Post, in his first major interview detailing his forthcoming memoir and plans. The memoir, “Inside DOGE: The Real Story From a Senior Insider,” which recounts his time working for the secretive cost-cutting group, is the first tell-all to emerge from one of the most controversial periods of President Donald Trump’s time in office — a moment Hassen said he would gladly live through again.

“There’s this misconception that we all just went in there and didn’t really think about what we were doing,” Hassen said, speaking virtually from a hotel room in San Diego. “I spent 24/7 thinking about it, waking up at 3 in the morning thinking, ‘Am I doing the right thing?’ … And I know I did.”

At the heart of the book is a split screen: For many Americans, Musk’s project was a callous showcase of bending government to a billionaire’s will, associated with tens of thousands of federal layoffs, reams of lawsuits, an AI-enabled effort to kill off wasteful contracts and the gutting of the U.S. Agency for International Development.

But Hassen’s memoir enters a different vision into the historical record: He portrays a period marked by a clash in worldviews, in which hardcore capitalists are granted unprecedented power to change a system that they argue operates under completely different incentives, logic and norms.

To Hassen, whose pre-government career involved turning around distressed companies, “DOGE was not radical,” he writes in the book. It was “not a wrecking ball, but a catalyst.”

In conversation, Hassen comes across as mild-mannered, a distant temperament from Musk, who famously brandished a red metallic chainsaw as a metaphor for cutting government waste. A 43-year-old dad with Boy Scout energy, he doesn’t cut the image of the nerd-engineer DOGE bro. He spent his entire adult life “hiring, firing, reorganizing and rescuing failing businesses” in industrial manufacturing and the energy sectors — a skill set that appealed to Musk, his longtime deputy, Steve Davis, and Baris Akis, a venture capitalist who served as a volunteer recruiter.

But for years, he seethed with rage over the national debt, now at $40 trillion. He felt angry that the amount the U.S. spends annually just to service that debt is more than the government spends on national defense and many other public benefits.

So he raised his hand — making a cold outreach to Musk through a network of business world connections, he said in the interview, and pressed the point.

“I’m staring at my daughter, who is 5, and thinking about her at 25 with $100 trillion of debt. And I know if we don’t do something about it now, we’re going to be there,” he said. “We’ve got to fix it now.”

By its own accounting, DOGE fell far short of the $2 trillion Musk claimed he would save, and an independent report from the Government Accountability Office has found DOGE’s own savings figures to be flawed. But Hassen told The Post he believes the effort was a success.

“Did we eliminate the deficit? No. Of course not,” he said. “But we made it on the top of everyone’s minds, albeit briefly, for a few months, that this deficit is something that we need to fix.”

In the book, he argues that “DOGE fit squarely” within a tradition practiced by some Democratic presidents, from Bill Clinton to Barack Obama, who also sought to shrink bloated government. DOGE was different, he goes on to concede, because of its speed, “faster than Washington was ready for and faster than its defenses could absorb.”

But Hassen insists that DOGE was not cruel, a word that appears six times in the memoir, often in relation to laying off federal workers. The refrain can read as a plea for reconsideration of an effort he argues was poorly understood.

“In the private sector, there’s an understood bargain,” he writes. “You’re hired to create value. If the value disappears, so does the job. It isn’t personal. It isn’t cruel. It’s the operating system of capitalism.”

Government, he argues in the book, runs on a different logic. Value is “presumed,” “permanence is the default,” and “the moral burden of proofs falls on anyone who questions headcount — no matter how bloated the system becomes.” [...]

He told The Post he was exhilarated to get deep into the bowels of the bureaucracy and deploy his corporate-honed skill sets to attack a problem that had long troubled him.

During his first week at DOGE, the team met in a sparse conference room on the top floor of the SpaceX offices — empty except for a giant American flag on the wall, “because it reminded us why we are here,” he told The Post. “We had billionaires to 22-year-olds who had dropped out of Harvard, all sitting there on our laptops working with public information — trying to figure out how we can help solve this deficit, this crisis that we’re in.” [...] [ed. like talking to Congress?]

During his time at Interior, Hassen was almost like a Where’s Waldo character, popping up in key moments that caught the country’s attention. When wildfires struck California, Hassen made an unusual decision to fly to the state to grill local officials about why the state’s largest water plant wasn’t running at full capacity.

The image of him at the Jones Pumping Plant in California became one of the first posts from the DOGE X account identifying its members — prompting a flood of hateful messages and threats to Hassen and his family, he said.

When Trump renamed the Gulf of Mexico, it fell to Hassen to officially register the rechristened Gulf of America in the Interior Department’s Geographic Names Information System — a move that then triggered a broader recognition of the name change on Google Maps and other online directories.

Hassen says he felt the difference in mindset between the public and the private sector every day. “It showed up in the way career staff spoke about ‘the workforce’ as something apart — almost sacred,” he writes. “Until we can say out loud that government is a workforce like any other — noble in purpose, human in composition, and subject to reality like everything else — reform will look like cruelty before it looks like competence.”

by Elizabeth Dwoskin, The Washington Post, via Seattle Times |  Read more:

Image: WaPo

[ed. Nice. Can't wait for AI and the 'operating system of capitalism' (Praise the Lord!) to reduce its workforce by at least one cult memoirist.]

Just Stop the Anthropic IPO Already

[ed. At the risk of turning this into a full-blown AI-centric blog, I do think this is important information to process.]

I want to delve into the full scope of the Anthropic AI takeover of politics happening over the past week. Yesterday, the company’s CEO Dario Amodei came out and explicitly asked for antitrust laws not to apply to the biggest AI firms. His biggest rival, Sam Altman, quickly agreed. And they are suggesting this legal change just before Anthropic seeks to sell shares on the stock exchange, minting a whole series of AI millionaires and billionaires.

Why do they want to suspend antitrust laws for AI firms? Well these guys say they need the industry collectively “pace the frontier,” aka in their framing, slow development of this technology so as to reduce the probably of human extinction at the hands of autonomous swarms of AI bots. And they can’t do that, they argue, without suspending laws prohibiting price-fixing cartels.

Then the Information reported today that OpenAI, Anthropic, and Google have been having backchannel conversations about establishing an AI standards organization, which presumably would coordinate this cartel.

Let me start with a very simple point. It is already illegal to release products that hurt people. It is illegal to compete by releasing products that hurt people. If these guys are genuinely manufacturing things that kill innocent people, the FBI should be arresting them immediately. The idea that they would not only release such products, but also issue stock for the American people to invest in multi-trillion dollar initial public offerings for such ventures, as Anthropic is planning, is utter lunacy.

I wrote about this attempt to terrify us into giving away our liberties on Friday, in a piece titled “Stop Panicking About AI.” But the IPO is something I didn’t think through. Apparently they think we should all get rich building world-ending product lines.

All that said, whether Anthropic goes public isn’t just up to the people at Anthropic. I asked some former Securities and Exchange Commission officials, and they told me that the SEC effectively has the authority to block initial public offerings. Here’s how.

Every company, before it goes public, submits an S-1 initial registration statement to the SEC. And the SEC can refuse to clear it if the commission believes that it doesn’t adequately disclose the risks a corporation’s securities present to investors. Technically, the SEC could go to court and get an injunction to block the IPO, but it rarely comes to that - the lack of clearance for an S-1 is red flag for investors so companies won’t go public until they get it.

In a functional system, there would be a dozen accountants and disclosure experts with sector training going back and forth with the lawyers telling them to expand on this or that, etc. And the commissioners either themselves or on a delegated basis won’t clear it until they’re satisfied. Today, it’s more likely that Trump himself just decides. Regardless, if Anthropic goes public, it’s not just because of the corporate insiders, it’s because Trump explicitly allowed it.

If I were a member of Congress, I’d be screaming mad right now, and yelling at Trump and the SEC to stop this event which will bestow hundreds of billions of dollars of wealth on a strange doomsday cult. [...]

There is one more point to cover. There is an ongoing political campaign to do something about AI, with a large swath of elites demanding action. That includes Barack Obama, who rarely demands anything except the most banal conventional wisdom. So when he says “AI policy is critical’ to Democrats, you know that it has reached peak elite acceptance. Still, what is that ‘something?’

The basic fight is over framing, not risk. Everyone sees risk here, but the root cause differs based on your perspective.

The AI doomers want their systems to be imagined as rogue agents bent on civilizational conquest, or as some sort of inevitable new technological paradigm that needs an entirely new legal framework superseding existing inadequate laws embedded in those musty old nation-states. Amodei argued the industry should have self-regulation, some sort of antitrust exemption to collaborate across the industry, and a global agreement among AI firms within democracies on how to manage risks. These developments, to Amodei, are inevitable, no human is responsible, though we must all act quickly.

Generally, this side is winning the debate. For instance, Senator Jon Ossoff, a 2028 hopeful who generally echoes whatever seems to be the most appealing line of Trump criticism of the moment, has mostly adopted that frame. Bernie Sanders seems to have given up on his campaign against oligarchy to promote Dario Amodei’s ideas. And in the core of the Democratic establishment, this view has taken hold. For instance, here’s Senator Brian Schatz of Hawaii, the likely successor to Chuck Schumer, praising Amodei.

Brian Schatz@brianschatz 

I am still studying this but it’s a reasonable start, and takes seriously the proposition that we need real proposals that can be enacted rapidly.

Dario Amodei @DarioAmodei 

We Must Pace the Frontier: I’ve written a new essay on why the AI industry should slow down, with a three-part plan for doing so. Anthropic is unilaterally committing to the first of these steps. We’ll provide third-party evaluators with permanent, employee-level access to our
8:27 AM · Sep 12, 2026 · 42.7K Views

There are many calls to convene Congress in emergency session to act, and Trump’s advisors are trying to get him to announce immediate emergency action. Given that the big AI companies are already having discussions about coordinating their AI model development, it seems like they are just pushing for final legal permission to openly run AI as a cartel.

The debate, however, is not quite over. So what’s the alternative view? Well, the rule of law adherents look at these AI systems merely as unsafe products. As such, their request isn’t for new laws, but enforcement of existing rules. All products are subject to standard nuisance claims and other torts, unfair and deceptive practices laws, and so forth. Agents are, as Cory Doctorow notes, malfunctioning machines, or “autonomous malicious software” operated by reckless people at OpenAI and Anthropic. Moreover, it is actually illegal to build unsafe products as a method of competition, or to keep up with rivals by also creating unsafe products.

Former FTC Chair Lina Khan listed a bunch of laws that could already apply. And she let slip that state attorneys general are looking at potential criminal liability for AI CEOs.

Lina Khan@linamkhan 

Law enforcers already have authority to charge companies and their CEOs for creating and releasing dangerous, unvetted, or defective products. We shouldn’t let discussions about new legal regimes distract from the fact that there’s no AI exemption from laws already on the books —…
11:31 AM · Sep 13, 2026 · 183K Views

(Khan did actually start enforcement against AI developers when she was Chair. And it notable that one of the very first things that Trump-Vance FTC Chair Andrew Ferguson did was set aside the penalty of an AI developer she penalized for creating unsafe and fraudulent tools.)

So who will win? Well I am fairly pessimistic, as the bludgeoning from the superrich works in crisis moments, especially when Bernie Sanders is on the side of the establishment.

But the debate doesn’t fracture on obvious partisan or factional lines. Much of the industry is going to be split on the matter. For instance, David Sacks, a generally malevolent crypto investor and technologist, is making cogent arguments, because his crew would be excluded in an OpenAI/Anthropic cartel world. He’s a die-hard Trumper and despised Khan when she ran the FTC, but he retweeted her argument here.

A lot of policymakers, such as Senators Richard Blumenthal, Rep. Ro Khanna, and others, see liability as an obvious way to shape the industry to be more safe. The Senate is also full of people who are used to blocking each others’ legislation; Maria Cantwell and Ted Cruz are trying to work together on AI safety, but are fighting over whether to preempt state laws.

There are a host of proposals out there, and the details will matter. And there is something of a stampede for an emergency session to take action. If Trump chooses to accept the need for action, then it’s likely the Anthropic/OpenAI/Google types will get what they want. If not, then the debate will continue, perhaps until the financial markets impose a different mental model.

At any rate, we can all agree that Anthropic shouldn’t go ahead with its IPO. Or at least, that’s something we should all be able to agree on.

by Matt Stollar, BIG |  Read more:

Image: via

Some Ways AI Could Kill Us All

[ed. Nearly everyone agrees by now that AI is an existential threat, but the hows and whys are often vague or imcomplete.] 

I don't think this is how it will actually play out. If you play a chess grandmaster, you can predict that they will beat you even if you can't predict how. I chose these examples because I don't think they require much imagination or accepting exotic assumptions.

It is important to note that if chimpanzees were to guess how humans would decimate them, they would get it wrong. Chimpanzees would not imagine guns. They would not foresee poison gas. They would not conceive of chemical castration. They would not imagine humans going around and intentionally infecting them with AIDS. They have no concept of these things; they would not see it coming.

Perhaps they might guess we'd be really good at throwing rocks. Amazingly good. Well, technically, that's what guns do: throw "rocks" really really well.

So how will superintelligent AI actually wipe us all out? Probably in a way I couldn't conceive of. Nonetheless, it's not hard to see how deadly they could be with what we already know about.

by Ruby, Less Wrong |  Read more:

Image: DALL-E via; and here. 

[ed. I wouldn't necessarily conflate super intelligence with ill-will. Most likely AIs will just be pursuing some unrelated goal and exploring every possible means by which to achieve it (including removing human barriers). That is, unless bad (human) actors are involved, which should not be discounted. See also: How My Students Think About AI (LW).]

We Already Have the Tools to Regulate AI

I want to delve into the full scope of the Anthropic AI takeover of politics happening over the past week. Yesterday, the company’s CEO Dario Amodei came out and explicitly asked for antitrust laws not to apply to the biggest AI firms. His biggest rival, Sam Altman, quickly agreed. And they are suggesting this legal change just before Anthropic seeks to sell shares on the stock exchange, minting a whole series of AI millionaires and billionaires.. [...]

Let me start with a very simple point. It is already illegal to release products that hurt people. It is illegal to compete by releasing products that hurt people. If these guys are genuinely manufacturing things that kill innocent people, the FBI should be arresting them immediately. The idea that they would not only release such products, but also issue stock for the American people to invest in multi-trillion dollar initial public offerings for such ventures, as Anthropic is planning, is utter lunacy. ~ Matt Stoller: Just Stop the Anthropic IPO Already.

------

These are for profit corporations taking in billions of dollars from the world. You can't ensure your product isn't dangerous? Then don't release it until you figure it out! ~ Comments section, Lina Khan post (below):

------

Law enforcers already have authority to charge companies and their CEOs for creating and releasing dangerous, unvetted, or defective products. We shouldn’t let discussions about new legal regimes distract from the fact that there’s no AI exemption from laws already on the books — a point @FTC emphasized repeatedly during my tenure.

1. There is an extensive set of laws that govern dangerous and defective products. For example, releasing unvetted AI models or agents can violate consumer protection laws. Shipping flawed AI tools without implementing adequate measures to detect and stop rogue or defective AI agents can be an “unfair or deceptive” act or practice under the FTC Act (and analogous state laws). And some state AGs are already exploring holding AI firms and their CEOs criminally liable when their models participate in criminal activity. 

2. Existing laws also prohibit “unfair methods of competition.” This covers instances where AI firms appropriate the competitively sensitive information of their customers, including through tracking their use of various tools. It can also cover instances where firms pursue dangerous behavior, aware that doing so may compel rivals to do the same. As the Supreme Court has noted: “A method of competition which casts upon one's competitors the burden of the loss of business unless they will descend to a practice which they are under a powerful moral compulsion not to adopt, even though it is not criminal, was thought to involve the kind of unfairness at which the [unfair methods of competition] statute was aimed." 

3. The highly concentrated and interconnected structure of these markets could be creating major risks and conflicts of interest. We had started investigating these partnerships and cross-investments across the stack (and released a preliminarily overview of some findings: ftc.gov/news-events/ne…). Both federal and state enforcers should be scrutinizing these opaque relationships and inter-dependencies. We are already seeing how these relationships could undermine accountability. For example, OpenAI could face liability given the Hugging Face incident, but Hugging Face being bought up by Nvidia means that we’re unlikely to see it file a lawsuit over this — given Nvidia’s strong incentive to see OpenAI continue full speed ahead. 

4. As AI tools dramatically change the landscape of cybersecurity risks and hacks, all businesses should be doubling down on having core security protections in place. Firms that fail to invest in adequate data security measures or fix known vulnerabilities can also be breaking the law. A recent analysis showed that around 1/3 of Fortune 100 companies do not even have a way to notify them about security issues. During my @FTC tenure, we sued firms for poor data security practices and held CEOs liable when they were personally responsible.

5. As policymakers consider new legal regimes, we should be looking to lessons from prior efforts to govern major sectors, such as banking and other networks, platforms, and utilities. Tools like structural separations, nondiscrimination, and supervision could be key, and there’s a rich history of what works and what doesn’t. But we can and must pursue any new efforts alongside enforcing existing laws.

by Lina Khan, Former Chair, Federal Trade Commission 2021-2025 |  Read more:

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1) AI will not eradicate humanity. Humans survived an ice age, the Black Death, two world wars, and (so far) the advent of nuclear weapons. Anyone who is loudly warning of AI-caused human extinction should not be taken seriously. 

2) If you worked in a company where you anticipated a 10% chance that your product would kill ten people, let alone all people, the correct response would be horror, ceasing all operations, and likely contacting the police or other criminal authorities. I am obviously no Coxon booster but at least his behavior is in line with his stated beliefs. Any current AI company employees saying "yes, me too, the thing we are building and about to IPO may kill all humans" should, again, not be taken seriously. Their actions betray their actual beliefs. 

3) Antitrust law does not prevent AI companies from coordinating to make sure AI does not hurt people. It does not prevent companies working together to make sure it doesn't hack people; the DOJ and FTC made this clear a decade ago when they issued a policy statement saying that the agencies "do not believe that antitrust is – or should be – a roadblock to legitimate cybersecurity information sharing." The same principles apply here. See: justice.gov/archives/opa/p… 

4) Antitrust law does absolutely prevent AI companies from organizing to prevent the entry of cheaper, upstart rivals because the bigger companies are burning cash and failing to achieve sufficient profitability. The panic of individual employees may be sincere if misguided, but the moves by their CEOs to achieve some kind of broad "antitrust waiver" or "exemption" should be meet with deep skepticism in light of the economics of the industry and the threat they face from open models.

by Alvaro Bedoya, Former Commissioner Federal Trade Commission 2022-2025 | Read more:

--------

“It’s a hoax,” Mr. Trump said during the five-minute call, which Mr. Huang put on speaker. “The robots are not going to be taking over the world. That’s not going to happen.” ....

Mr. Trump made it clear that in Silicon Valley’s roiling debate over what to do about A.I., the president is very much on the side of executives who argue worries about safety are overblown and the government should avoid regulation. His disinterest in government involvement runs counter to pleas from leading A.I. companies like Anthropic. ~ Idiot in Chief (via NYT).

See also: Trump Says a Smart President Is All That’s Needed to Rein In A.I. [ed. Certainly useful, if we had one.]

Did 9/11 Really Change Everything?

One of the best firsthand accounts of the Sept. 11 attacks I’ve read is a short essay titled “Diary of Disaster” by Nicholas Spangler, at the time a 25-year-old journalism-school student and today a reporter for Newsday on Long Island. Spangler happened to be downtown that morning, close enough to the World Trade Center to arrive on the scene between the first and second planes. Like many early recollections of the first hours at ground zero, written before memories had been corrected to fit the dimensions of history, his is as strange as it is horrifying.

He hears the bodies of the north tower’s jumpers hitting the pavement, and also the easy-listening music still drifting out of the building’s outdoor speakers. Under a tree newly stripped of its leaves lies a severed leg wrapped in burlap. Barely an hour after the second tower’s collapse, onlookers enlist Spangler to take a souvenir photo of them posing in front of the wreckage with a disposable camera. It’s the end of the world, or at least of a world, and people have not yet figured out how they are supposed to act.

Days later, trying to make sense of what he has seen, Spangler writes: “I believe that our present way of life ended in those minutes or hours. The American ethos — the way we see the world and our place in it — fractured and will perhaps have to be discarded.”

When he revisits that passage three months later, however, it feels wrong to him. “Our way of life did not end so much as it was interrupted,” he writes, “and if there is such a thing as an American ethos, it has not been significantly altered. There are those relative few whose lives bear the permanent bloody brand of that day but most of us continue to be shaped by much more banal events.”

Did 9/11 change everything? For years, people who agreed on nothing else about the attacks’ aftermath would have agreed that the answer was yes. Arguing otherwise would have been an affront to the victims, to the war on terror’s civilian and military casualties and to the common sense of anyone who has had to take off their shoes at airport security. But question and answer alike have always been colored by the assumption that 9/11 was supposed to change everything: that in being attacked as it was, the United States was being presented with a test that it could pass or fail.

A quarter-century on, it is possible to consider the question with at least some critical distance. Far too much of our current reality stems directly from 9/11 for anyone to argue credibly that it was not a hinge point in our recent history: the ubiquity of surveillance technology, the vague open-endedness that all American military operations now acquire, the general up-armoring of everyday life. At the same time, considering the most dystopian facets of this reality, 9/11’s influence is not as singular as it might have once seemed. It is a middle chapter in the story of American fracture rather than the pyrotechnic beginning. [...]

Of course, in the end, the United States got neither self-awareness nor self-actualization. What it got, a decade and a half later, was Donald Trump, whose election in 2016 has often been cast as a culmination of what the journalist Spencer Ackerman, in his 2021 book “Reign of Terror,” describes as the “decadent phase of the war on terror.”

Trump’s path from reality-TV celebrity to the presidency ran through the Islamophobic fever swamps of the post-9/11 right — the ground zero mosque panic, Obama birth-certificate conspiracism — but also through an increasingly bipartisan discontent with the war on terror that had, by 2016, curdled into cynicism. Trump was the first Republican presidential nominee to break the formidable taboo against affirming what most Americans by then believed, that the country’s post-9/11 military adventurism had been a mistake. He also did little to seriously curtail it in his first presidency and has enthusiastically expanded it in his second.

It has long been tempting to view Trump’s presidencies as the logical conclusion of the folly of the post-9/11 era, and as support for the liberal contention that the right’s calls for patriotism in that era were always really about seizing domestic power. But Trump has also enabled a re-examination of history, and even recent history, that complicates the tale of 9/11’s primacy. [...]

Trump’s 2016 election “represented the crystallization of elements that were still inchoate” in the early 1990s, John Ganz writes in “When the Clock Broke,” his recent history of the period’s underappreciated malaise. In retrospect, 9/11 was paradoxically both a respite from and accelerant of polarizations and pathologies that were already well underway by then. It allowed Americans to ignore them for a moment, until they came roaring back with redoubled force. [...] [ed. So, explain Obama.]

If you do not remember the Berlin Wall but had an adolescence shaped by social media and cellphones, the brightest dividing lines in recent history are technological — and, with the rapid acceleration of artificial intelligence, are becoming more so. This is clear in the borrowed nostalgias of Gen Z, the bits of the past that 20-somethings have recently pressed into service as emblems of a lost golden era: “Friends,” John F. Kennedy Jr. and Carolyn Bessette Kennedy, ’90s high-school-class home videos, music videos and concert footage from bands like Alien Ant Farm and Puddle of Mudd.

These artifacts span the pre- and post-9/11 years, and the lost innocence they represent has nothing to do with the attacks. Their interest comes from their evocation of a period when people hung out with friends in person and sweated alongside strangers at clubs and music festivals; teenagers goofed un-self-consciously for cameras rather than posing for them like jaded celebrities; and popular culture could be loud and dumb and not picked apart in the digital panopticon for its social politics. The shape that looms ominously over them isn’t the Twin Towers. It’s the iPhone. [ed. lol...ok.]

by Charles Homans, NY Times |  Read more:

Image: Chantal Jahchan
[ed. Nice words, but no. C'mon. 9-11 was our last opportunity to cohere as a country, and we whiffed it. Instead we got a war on terror, as stupid and amorphous as any initiative based on a feeling (War on Anxiety?). A Patriot Act that gave subsequent crimes a never-ending veneer of political cover. 'Homeland' security, a Nazi term repurposed for new times. Two stupid reactionary wars (neither won) that among other things validated torture as official US state policy. Guantanamo Bay. A newly militarized culture. An expanded surveillance state. And fear... fear everywhere of some 'other' where strength and self-assurance used to be.]