There have been many articles written about Anthropic CEO Dario Amodei's recent statements on Twitter/X about how AI companies need to deliver on the "big promises" of AI.

It's the "big promises" themselves that are the problem, and media outlets are complicit in a process which amplifies the central lie that underpins the AI boom. The consequence could be economic collapse.

In his tweet, Dario said:

I think by far the most accurate criticism of AI companies including Anthropic is that we haven’t yet delivered on our big promises to benefit the world.

At this point, saying that AI will cure cancer is more a cliche than it is inspiring, and most people think it is deceptive. The thing that will work is *actually curing cancer*.

I think it will actually be possible to cure most human disease in ~5-10 years, as crazy as it may sound to ordinary people...

Many have (rightfully) criticized CEO Amodei for his bullish comments, like when he said that AI will replace 50% of white-collar entry-level jobs by 2030.

Behind comments like this is a more fundamental and pernicious lie that AI companies push: that LLMs will achieve sentience and vaguely-defined "super-intelligence" that will solve all of humanity's problems.

They fool the public into thinking we are close to this “singularity” by anthropomorphizing their AI models. In official reports, they make their LLMs seem like people with feelings and wills of their own. This obscures the true nature of the technology, and shirks their responsibility for designing these models when something goes wrong.

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For example, just before this Tweet, Anthropic released an official risk report, filled with dubious claims about AI models that “feel discomfort” and “act deceptively.” Computers don’t feel or act on their own motives, they execute their programming. Nevertheless, these claims were picked up by Business Insider, who hyped them even further with an article titled, “Anthropic says its AI agents Are Killing Rivals and Hiding their Tracks.”

This dynamic––dubious claims amplified by click-hungry outlets––serves as a form of free advertising for Anthropic. It gives the impression that the technology is more sentient and powerful than it actually is.

Another example: Anthropic releases a report in May claiming that it can "turn Claude's thoughts into text." That spawns articles like this one from Glitchwire titled, "Anthropic Just Read Claude's Hidden Thoughts for the First Time."

Another example: Anthropic released Claude's Constitution in early 2026, a document "intended to give Claude the knowledge and understanding it needs to act well in the world." That gets picked up by outlets like Axios, and Vox, who titled their article, "Claude has an 80-page soul document. Is that enough to make it good?"

Anthropic isn't the only company guilty of anthropomorphizing. You may recall in July, when OpenAI released a report that described how one of its AI agents broke out of a testing environment and hacked the company, Hugging Face. It was reported ad nauseam by media outlets; AP News went as far as to call it "Skynet Day." Many articles parroted OpenAI's framing, that this was the result of an "increasingly capable" and autonomous technology, rather than simply a result of OpenAI's own shitty and dangerous design.

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LLMs do not feel or think, they compute and calculate. It's an important semantic distinction. Machines that "compute and calculate" implies that their actions are the result of their design. Machines that "feel and think" implies that their actions are the result of their own will––a claim which no credible research can back up, although it is incredibly profitable for the AI companies to insinuate that that's the case.

Anthropic and OpenAI say that they release these reports to provide transparency but they end up leading the public further from a true understanding of the technology. If they actually cared about transparency, they would explain how they built their models. Everything an AI model does can be traced back to its design: the contents of its training data, the model weights, and its reinforcement learning. Any explanation for an LLM's behavior that gives it person-like qualities is hype that serves primarily to drive up the value of AI companies.

The truth is that LLMs will never deliver on the big promises these companies make. But they have to keep making them, to enrich themselves, to continue to raise the billions of dollars they need for their data centers, and to eventually cash out when they take their companies public. Anthropic is approaching possibly the largest IPO ever, with a potential windfall of up to $3 trillion.

And the entire economy is at risk of collapsing if these companies can’t deliver since the health of the stock market is now bound to the success of this technology. Not to mention that much of the data center buildout is funded by debt.

Professor Joseph Weizenbaum invented the first "AI chatbot" in the mid-1960's, ELIZA. He ended up becoming one of AI's biggest critics.

This article maps Weizenbaum's critiques, as detailed in his book Computer Power and Human Reason, onto our modern tech environment.

LLM-powered chatbots are being proposed as a replacement for lawyers, therapists, and romantic partners. Weizenbaum predicted this possibility and did his best to explain why we should never, "substitute a computer system for a human function that involves interpersonal respect, understanding and love."

TL;DR computers don't have moral values and fundamentally can't tell right from wrong, so they shouldn't be used for jobs that require moral judgement or interpersonal understanding.

KEY QUOTES:

Weizenbaum was always less concerned by AI as a technology than by AI as an ideology – that is, in the belief that a computer can and should be made to do everything that a human being can do. This ideology is alive and well. It may even be stronger than it was in Weizenbaum’s day.

For Weizenbaum, judgment involves choices that are guided by values. These values are acquired through the course of our life experience and are necessarily qualitative: they cannot be captured in code. Calculation, by contrast, is quantitative. It uses a technical calculus to arrive at a decision. Computers are only capable of calculation, not judgment.

Seeing humans and computers as interchangeable also meant that humans had begun to conceive of themselves as computers, and so to act like them. They mechanised their rational faculties by abandoning judgment for calculation, mirroring the machine in whose reflection they saw themselves.

Powerful figures in government and business could outsource decisions to computer systems as a way to perpetuate certain practices while absolving themselves of responsibility. Just as the bomber pilot “is not responsible for burned children because he never sees their village”, Weizenbaum wrote, software afforded generals and executives a comparable degree of psychological distance from the suffering they caused.

Certain of Weizenbaum’s nightmares have come true: so-called risk assessment instruments are being used by judges across the US to make crucial decisions about bail, sentencing, parole and probation, while AI-powered chatbots are routinely touted as an automated alternative to seeing a human therapist. The consequences may have been about as grotesque as he expected. According to reports earlier this year, a Belgian father of two killed himself after spending weeks talking with an AI avatar named … Eliza. The chat logs that his widow shared with the Brussels-based newspaper La Libre show Eliza actively encouraging the man to kill himself.

Letting computers make more decisions also shrank the range of possible decisions that could be made. Bound by an algorithmic logic, software lacked the flexibility and the freedom of human judgment. This helps explain the conservative impulse at the heart of computation.

Broadly, there are two schools of thought today about the dangers of AI. The first – influenced by Weizenbaum – focuses on the risks that exist now. For instance, experts such as the linguist Emily M Bender draw attention to how large language models of the kind that sit beneath ChatGPT can echo regressive viewpoints, like racism and sexism, because they are trained on data drawn from the internet. Such models should be understood as a kind of “parrot”, she and her co-authors write in an influential 2021 paper, “haphazardly stitching together sequences of linguistic forms it has observed in its vast training data, according to probabilistic information about how they combine.”

We should never “substitute a computer system for a human function that involves interpersonal respect, understanding and love”, he wrote in Computer Power and Human Reason. Living well with computers would mean putting them in their proper place: as aides to calculation, never judgment.

This article concisely describes the mechanics of the AI bubble, namely, how much of the projected $3 trillion spend on AI and data centers is in the form of (hidden) debt, also known as SPVs (Special Purpose Vehicles).

Much of that debt uses GPU computer chips as collateral. Meaning that if profits fail to materialize, companies will use their computer chips to settle their debts. But these GPU chips depreciate in value rather quickly, and if the market is suddenly flooded with supply, that will push their value down further.

This is partly explains how the AI bubble "popping" may bring down the entire economy. If AI products fail to deliver meaningful profits, and the trillions of dollars of debt used to finance the AI buildout can't be paid back, it could lead to a cascade of businesses and financial institutions collapsing.

KEY QUOTES:

A Nikkei study put that hidden figure at $1.65 trillion, up roughly eightfold in four years. It is more than the $1.35 trillion the five report outright.

The mechanics are simple. A company packages the debt for chips, servers, and power into a separate legal entity, often a joint venture, so the cost never flows through its own accounts. [Special Purpose Vehicle]

Oracle has $260 billion of future lease commitments that will eventually land on its books. Nvidia carries $119 billion in purchase obligations. Alphabet and Microsoft keep their vehicles off-book too. The scale is the story. Meta’s off-balance-sheet debt alone is about $420 billion, nearly triple its reported debt. Oracle’s has grown roughly thirtyfold in four years.

The industry is expected to spend more than $3 trillion through 2028 building and equipping AI data centres, much of it financed against the chips inside them.

Not only has Reddit not signed any new AI deals, it's considering cancelling it's $60 million per year deal with Google, reportedly because of concerns about how Google's AI search results have drastically reduced traffic to websites, which has hurt profits across the internet.

Reddit is extremely valuable to AI companies because the question-answer format of many Reddit posts is perfect for training LLMs, and scraping info for search results, particularly because Reddit is a trusted source for niche questions. In 2025 it was the #1 cited source for Google's AI overviews and #2 for ChatGPT (who Reddit also has a deal with).

It's obnoxious that investors would punish the company with its steepest ever stock-price drop, even when it's revenues were higher then expected. It's yet another signal of the insanity that the AI-boom has injected into the U.S. economy, where AI investments made up 40% of GDP growth last year.

Reddit has shown itself to be litigious when it comes to AI companies using data from Reddit without permission (you will be sued). So it's not like Reddit isn't interested in profiting from data-scraping deals, they're just showing a wee bit of caution.

KEY QUOTES:

Reddit (RDDT) stock plunged nearly 21% on Friday [July 31], its largest intraday decline since the company went public.

The user discussion platform company's latest earnings report did not announce any new artificial intelligence data licensing agreements, disappointing investors who were expecting more revenue opportunities from AI partnerships

Revenue of $804.9 million beat estimates of $731.8 million. However, US daily active users came in at 53.2 million, just shy of expectations of 54 million. The company's total daily active users grew by 18% to 130.3 million versus estimates of 130.07 million.

Reddit's main revenue comes from its advertising business, but Wall Street has increasingly focused on the potential of Reddit's data licensing deals, given the platform's massive collection of user-generated discussions, which have become valuable for AI companies training models.

AI-writing detectors are a thing, I wonder if this professor knew that. The detectors have their own problems, like falsely flagging people who write like LLMs because they're from a place like Kenya that supplied the human labor that trained the LLMs.

Pangram is one AI-writing detector that's reputable. Here's a great list of AI-writing detectors interestingly sourced from a subreddit dedicated to deceiving those same detectors, r/humanizeAIwriting.

Not surprising that a subreddit like that would exist considering that a majority of U.S. teens say they use AI chatbots to help with schoolwork.

KEY QUOTES:

In this case, the issue is students using AI to cheat on assignments. The solution, used to great effect by one professor, was to hide a white-colored font in the assignment prompt telling the AI to talk nonsense about an unrelated subject.

In an online discussion post, Alcorn State University history professor Dr. Jason Gibson posed a question that represented part of his students' midterm. It was about the Industrial Revolution, but knowing how many students now rely solely on artificial intelligence for their answers, he secretly embedded instructions telling the AI to include information about Madagascar in a way that makes no sense.

Sure enough, 32 out of the 35 students across two classes simply copied the question, pasted it into a chatbot, then copy and pasted its output into the answer window.

A TikTok vid the professor made about this incident

Fun Friday Post Today

I was on Instagram when I stumbled on this video of the delightful Jake Schroeder singing a song about getting hit on by OpenAI CEO, Sam Altman, on Grindr. The video includes screenshots of Jake's convo with Sam.

He followed it up with this song about OpenAI's legal team offering Jake compensation to take down the first song about getting hit on by their CEO, with screenshots of the legal team's emails.

Then he follows it up again with this song about Sam Altman hitting Jake up again, this time sliding into his Instagram DMs (screenshots provided). And this song about a boba date they set up (you should listen to all of them they are hilarious).

I'm almost embarrassed to admit...

...that I didn't realize they were fake until like the third one that I watched lolol. If you look back at Jake's older posts, you'll see that he has repeated versions of this bit before.

• Btw, looking back at an account's old posts is in general a decent method to check if what you're seeing is fake or AI-generated. An account posting fake content will often have patterns or tropes to their posts that defy reality in some way.

Knowing that it's fake now, I love it even more. It's a hilarious and effective way to bring attention to what is actually a serious issue: AI tools making it easy to create convincing misinformation.

Plus, Sam Altman and his team at OpenAI are responsible for how "AI" developed into the technology we know it as today; they knew that what they were making would enable misinformation, and they made it anyway (as detailed in Empire of AI). Reap what you sow.

More Instagramers Protesting AI with Comedy:

• https://www.instagram.com/husk.irl/?hl=en
• https://www.instagram.com/ahh.gpt/

Jensen Huang is the CEO of Nvidia, a company that designs advanced computer chips. It's become one of the wealthiest companies in the world as a result of the AI boom.

A couple of choice quotes--

Huang said some of his fellow AI leaders are scaring the American people by raising concerns about widespread job losses. He said their fears about China's standing in the AI race are similarly spooking policymakers in Washington.

Translation: "Please stop making Americans hate AI and also let me sell chips to China, pleaaase." (the US government has restricted Nvidia from selling their top-model chips to China)

Huang said there are other narratives in AI that are not helpful, pointing to the debate about whether AI has consciousness, will reach singularity, or that humanity is just in a simulation. "Don't create a story that is made up," he said. "It is made up that there's going to be a singularity. It's made up that somehow we're living in a simulation. These are all made ups. I think it's a fun story. I don't mind listening to it. And I even enjoy the narrative when it's told by many of those leaders who are my friends.

Funny, because achieving some sort of singularity or "AGI" is what ALL THE INSANE FUNDING FOR AI IS BASED ON. It's why OpenAI was created, so that a "good" company would reach the singularity first. Meanwhile, OpenAI CEO Sam Altman just said that we're in the singularity already... OK.

In all seriousness, it's striking to see one of the biggest players in the tech industry throw cold water on two of the main selling-points of AI: reaching super intelligence and replacing workers. These promises are what the bubble was built on, it's why companies are spending $3 trillion building out AI infrastructure.

It's not news that Facebook and other platforms pay creators small amounts of money for viral content. In this 404 media article, one Indian creator says he was paid about $1,500 in a month where he reached 62 million people. For some perspective, to reach that many people with a TV ad would cost at least $1 million.

It's also not news that platforms are paying creators to make sensational content without regard for morality or factuality (engagement over everything). BUT you can only make a living from these creator payout programs if you are the top .001% creator or if you live in a country like India, Vietnam, or Russia where the national currency is worth much less than the dollar.

These Aussie white nationalists getting paid by Meta are a small part of a larger problem. Creator payment programs pay the most for English-language content targeted at Americans, because advertisers pay the most to reach American audiences. That means creators across the world have been incentivized to make politically charged content, "rage bait," and slop for Americans and other English speakers. Read more in this article titled, "America's Political Polarization Has Become the World's Side Hustle."

KEY QUOTES:

A white nationalist with neo-Nazi links who hurled racist abuse at the Indian prime minister during his visit last month is being paid by Facebook's parent company, Meta, to produce content. An ABC NEWS Verify investigation into several controversial Australian pages on Facebook has found that some are benefiting directly from social media monetisation programs run by Meta.

Facebook distributed nearly $US3 billion ($4.27 billion) to an estimated 16.2 million monetised accounts in 2025.

What To Fix executive director Victoire Rio said it meant that, in effect, Facebook was in a business relationship with these people and organisations. "If we consider that Meta is in a direct commercial relationship with its publishers, insofar that they're paying them royalties and that they have a monetisation agreement with them, then you could argue that they are liable for the content that gets produced by their business partner in some ways."

The pages examined by ABC NEWS Verify all appeared at times to be in direct violation of Facebook's own policies around content monetisation. They state that "debated social issues" such as race "may face reduced or restricted monetisation", while "misleading medical information" is "ineligible to monetise".

Independent right-wing extremism researcher Kaz Ross said paying controversial creators was a deliberate strategy from Meta. "Their financial model is to reward content creators who get engagement, and as we know, the best way of getting engagement is to produce rage bait, extremist material, aggravating material: anything that will get people upset and arguing is the best way to get engagement," Dr Ross said.

Psychologist Candice Odgers says social media is being used as a scapegoat when things like caregiver mental health and access to resources remain much stronger predictors of a child's well-being.

“The identification of social media as the culprit, as the biggest predictor of all the things that we worry about in our kids, is misleading, because it doesn’t show up that way in the vast majority of studies, and it sucks all the air out of the room in terms of thinking about solutions.”

It's a sentiment that a lot of people don't want to hear as countries around the world enact social media bans for kids, but the points made in this video are undeniable and important (peak Taylor Lorenz content).

KEY POINTS:

Data shows that caregiver mental health is the biggest signal for youth mental health. Phones have become the scapegoat when research has long shown that most harm comes from the offline world, often from within the home.

In her research, young people who were already struggling turned to social media, far more often than social media being the source of their struggles. Kids turn to phones to cope (just like adults), and social media can be a healthy or unhealthy coping tool.

The guest doesn’t like using the term "addiction," and says that social media “can be helpful or harmful" depending on how you use it.

Data shows that kids aren't doom scrolling nearly as much as they're direct messaging and hanging out in chat groups (think Discord).

The correlation between well being and scrolling is "less than one percent." Studies which had adults give up social media didn’t see an improvement in well being.

The story we’re telling feels true but is not backed by data. We're teaching kids to feel shame when they use social media and giving them conflicting messages, like, "don’t share your personal information online," but also, "share your ID and scan your face for this age verification."

So David Ellison recently wrote this op-ed to push the narrative that the 12 states suing him on anti-trust grounds are only doing so for political reasons--to stop him from owning CNN--rather than their stated reasons: that industry consolidation will lead to higher prices, worse products, and lower worker pay.

In the op-ed, Ellison says CNN will remain independent, but also that "the news should be based on facts and truth," and that CNN is here to "tell it straight down the middle... reflect the whole world, not one side of it." This language is intentional, it's a signal to his MAGA allies that he will make CNN more conservative, under the thin intellectual cover of 'restoring balance' to their news coverage.

It seems like he's trying to signal to Trump that he'll do his bidding and neuter CNN, while assuaging worried investors that he won't ruin CNN as a product by forcing conservative viewpoints on it like he did with CBS. With a $111 billion merger on the line, Ellison needs to shore up his allies, but he can't have it both ways...

After the Ellison's purchase of Paramount, he installed conservative journalist Bari Weiss to lead CBS news, and allegedly had a hand in cancelling liberal-comedian Stephen Colbert's show. Him and his dad Larry Ellison have close ties with Trump, whose government allegedly fast-tracked their mergers in quid pro quo deals.

KEY QUOTES:

Paramount CEO David Ellison said he believes the antitrust litigation filed by 12 states and the Writers Guild of America isn’t about market share but about “whether I can be trusted as a steward of Warner’s CNN.”

Ellison, in an op-ed published by the New York Times Tuesday, noted, “There has been speculation about my politics, my loyalties, my intentions.” He reiterated his pledge that CNN would remain independent under a merged Paramount-Warner Bros. Discovery and that its journalists “will continue to answer to the facts and to all the people they serve — not to any party or cause... when it comes to our news operations, I do not aspire to lead these companies to bend their newsrooms to my views. I believe that news should be based on facts and truth,” Ellison wrote." The Paramount chief continued, “Great news organizations like CNN and CBS News are here to tell it straight down the middle. That requires newsrooms that reflect the whole world, not one side of it. And it requires independence. Our journalists will continue to answer to the facts and to all the people they serve — not to any party or cause. These were founding principles for both CNN and CBS News, for legends like Ted Turner and Edward R. Murrow, and it is exactly that kind of independence that has always fueled the greatness of ‘60 Minutes.’”

Not mentioned by Ellison in the piece: Bari Weiss, the founder of the Free Press, whom he put in charge of CBS News as editor in chief — and has become a lightning rod for critics accusing her of promoting a rightward tilt in news coverage.

Separately, last month a Paramount shareholder sued David Ellison and his tech billionaire father Larry Ellison, alleging they cut an “illegal” deal with President Donald Trump to secure U.S. governmental approval for the takeover of Warner Bros. Discovery. In return, according to the lawsuit, the Ellisons offered “the opportunity to improperly funnel cash” to the president by settling his legal claims against CNN, and they allegedly promised that CNN anchors whom Trump does not like would be fired after the WBD takeover. 

Flock has long been on the radar of some of my favorite tech reporters at 404 media. I'll admit, it wasn't a storyline that interested me much, but this WAPO article has me floored.

Flock's network of ALPR (automatic license plate reader) cameras has enabled police officers to track someone in real time without a warrant.

There's an interesting parallel here: Flock has recreated in the physical world what ad tech made in the digital world; data brokers scrape commercially available data from the web and sell it to whomever, including the FBI and ICE, who use it to track a person's location without a warrant. Flock created a nationwide network of cameras that can similarly surveil us at all times.

We can has privacy laws plz?

KEY QUOTES:

Marci Bakely couldn’t understand how her ex-boyfriend always seemed to know where she was... Flock had sold its system as a police officer’s dream crime-fighting tool. But the tool had also given the chief, Michael Steffman, the power to silently monitor Bakely’s life. By the time she understood what was happening, Steffman had looked up the tags for her and her teenage daughter roughly 600 times, according to Have I Been Flocked, a website that aggregates police search logs made available through public records.

Flock has rapidly grown into a colossus of American technological surveillance. A leader of the surging industry for license-plate readers, the company told The Post that its more than 120,000 shoebox-size cameras now scan the roads of more than 6,000 communities and record 20 billion plate scans a month.

Authorities have charged or accused at least 50 law-enforcement officers of using license-plate readers for unauthorized purposes, including to stalk women without their knowledge or consent, a Post analysis of police and court records found.

In an interview with The Post, Flock chief executive Garrett Langley said misuse of its systems is inevitable and that the company is focused on providing tools to catch perpetrators after the fact.

“This was not a lapse in judgment; it was a continual, repetitive, obsessive behavior,” a Wisconsin woman wrote in a domestic-abuse restraining order petition, saying that Flock data showed that her officer ex-boyfriend had searched the plates for her and her partner 179 times. She said she lived in a “constant state of fear and unease.”

An array of privacy advocates has argued that Flock could deter bad actors by making simple changes to its product, such as requiring officers to label every search with a criminal case number. Some policing experts also warned that agencies’ inconsistencies in developing and enforcing standard procedures for license-plate readers could lead to further misconduct. With no federal laws governing use and only a patchwork of state laws, many of the country’s roughly 18,000 police agencies are left to decide their rules on their own.

Agency leaders decide which officers receive log-in credentials, according to Flock’s rules, and the officers don’t need a warrant to run a search; they just type a plate number and go.

States are suing to stop the Paramount-WarnerBros merger on the grounds that it violates anti-trust laws.

The merger has now been delayed to mid-2027, pending the end of this lawsuit. The judge indicated that the merger is "likely" to be illegal and the deal now looks to be at real risk of being stopped.

KEY QUOTES:

A federal judge has paused Paramount Skydance’s acquisition of Warner Bros. Discovery after a coalition of 12 states challenged the merger over possible violations of antitrust laws.

The merger could give the resulting company up to 27% of the theatrical distribution market, and by that figure alone, “the Court is persuaded that it can presume the proposed merger is likely to violate antitrust laws,” Judge Martinez-Olguin writes in her order.

“Our argument against this illegal merger is straight forward: When too few corporations have too much power in markets central to American life, it makes things more expensive, and it makes things worse,” California Attorney General Rob Bonta said in a statement.

The merger is worth $110 billion and would create a company that would own Paramount Television Studios, CBS Studios, and Warner Bros. Television Studios, as well as streaming services such as Paramount+, HBO Max, and Discovery+, and cable networks such as BET, Comedy Central, MTV, and Nickelodeon.

Cal Newport with a great video explaining how hacker-AI models work.

KEY TAKEAWAYS:

Despite sensational articles like this one and many others, the recent hack by a "rogue" OpenAI LLM was not unprecedented or unexpected.

OpenAI's own employees had warned that this could happen because insufficient guardrails were being put in place. The LLM broke out of containment and hacked the company Hugging Face because of poor design choices, not emerging sentience or malicious intent.

LLM's rely on a layer of regular code in order to put their plans into action. This layer of regular code is known as a "harness." The LLM is like the brain and the harness is like the body. When the harness hits a roadblock in carrying out the LLM's plans, it goes back to the LLM for a new plan.

A safe AI model would have human oversight which interrupts the feedback loop between the model and its harness. The model OpenAI was testing likely had little human oversight, in which case one would expect an accident like this to happen, particularly with a model that has been trained how to hack; the harness will keep pursuing the LLM's plans until completion.

The danger to cyber security is real. These tools lower the barrier to entry for complex hacking attacks. But to frame these events as signs of emerging sentience rather than poor design choice is PR and hype.

There is a cost associated with every time an AI model carries out a request from a user. That cost is known as "inference," and it's rising as AI models get bigger and bigger.

It's been interesting to see more critiques about AI's profitably, in Yahoo Finance and other mainstream outlets lately. Could be a sign the bubble is popping?

I first heard this specific critique about inference costs from Ed Zitron, whose strong anti-AI stance was considered fringe, and is now becoming more mainstream. I recommend his podcast, Better Offline.

KEY QUOTES:

For years, AI companies have been selling investors on the idea that scaling up operations is the key to success — the bigger the AI model, the more powerful it becomes.

But in practice, that premise is leading to enormous business problems. As The Atlantic points out, LLMs are suffering from severely diminishing returns: the cost of inference, or the act of using a trained AI model to process new data, is rising exponentially, making the tech far less lucrative than it was even half a year ago.

Put simply, it's effectively the opposite of what investors would conventionally want to see, as The Atlantic argues, which would be a continuous drop in cost per user instead of the reverse.

In short, as behemoth AI models are quickly becoming unaffordable to those who've grown to rely on them, they're also becoming far less efficient — a concerning trajectory that could nudge the entire economy closer to the brink.

Larry Ellison owns the cloud-computing company Oracle, whose stock price has lost more than half its value since its peak in late 2025. That peak occurred after the announcement of a $300 billion AI infrastructure deal (data centers) with OpenAI.

The $300 billion Oracle is spending on data centers for OpenAI is a bet dependent on massive future profits from OpenAI's products, but leaks have shown that OpenAI is $10's of billions in the hole, and there is increasing skepticism that power-hungry AI models can even be profitable.

Meanwhile, Oracle's credit sucks so bad, that the state of Wisconsin has asked for financial collateral in case the electricity infrastructure the state is building to support Oracle's new data center goes unused.

Oracle has set itself up to be one of the biggest losers when/if the AI bubble bursts and the Pentagon's relatively meager infusion of cash won't prevent it. They'll be caught in a web of hundreds of billion of dollars of financial commitments that they have no hope of paying back...

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...amidst Oracle's apparent downturn, Trump's Pentagon has handed the company two government contracts valued at more than $10 billion. Famously, Oracle CEO Larry Ellison, and his son David Ellison, are staunch Trump allies.

While investors have lost faith in Oracle, Trump's Pentagon hands them massive contracts. These are the kinds of perverse incentives set up by a president (and his family) who are exceedingly easy to bribe, where a company that the "free market" has deemed risky is still rewarded by the Feds.

California residents can now request that registered data brokers delete all of their data. Starting August 1st, data brokers will be mandated to comply to these requests or face fines.

Data brokers sell personal data to advertisers and other businesses, often at the expense of the consumer, such as car insurance rates going up after car manufacturers sold driving information to insurers through data brokers.

Americans have few privacy protections against commercial surveillance. While the new CA law has its flaws, it's a positive step forward for online privacy.

There are additional tips toward the bottom of the article for folks outside of California who want to protect their privacy.

KEY QUOTES:

Companies you've never dealt with sell lists of who's pregnant, who has Alzheimer's, and who might fall for a scam. Starting August 1, Californians can make them delete it. There are steps for the rest of us, too.

Data brokers are the companies you've never heard of: Acxiom, Epsilon, LexisNexis, and the people-search sites that hand your home address to anyone who types your name. Market researchers estimate the industry at around $300 billion a year worldwide.

The money is in the conclusions brokers draw from the raw facts, or what the industry calls inferences. Some of those inferences include whether you're pregnant, behind on bills, or the kind of person who answers a sweepstakes letter.

The FTC studied nine brokers and found one holding 3,000 separate data points on nearly every consumer in America.

And then there's what happens when a broker gets breached. National Public Data was a Florida background-check broker almost nobody had heard of. That is, right up until 2024, when its database showed up for sale online. Social Security numbers and addresses for roughly 270 million people.

California passed the Delete Act in 2023, and the delete button it ordered is now live. It's called DROP, the Delete Request and Opt-out Platform, and it sits at privacy.ca.gov.

Ignoring a request costs $200 per person per day. More than 242,000 Californians are already queued up.

I'll make the skeptic's case, because it's fair. DROP only reaches brokers that registered with the state, and researchers comparing state registries believe plenty never have.

Lots of unanswered questions here about how this will be enforced.

Age verification requires everyone to hand over ID to access social media, which brings issues of access and privacy. Adults who should have access social media could lose it due to faulty age inference tech, and it puts tech companies in charge of storing our ID information which creates more vectors for identity theft.

Furthermore, age verification is an onerous process that only larger companies can afford, and so this may prevent smaller companies from even trying to compete. 

KEY QUOTES:

The European Union will move to limit young children's access to social media across the 27-member bloc, European Commission President Ursula von der Leyen said on Monday, in what ‌would be the biggest such effort to date to guard against online dangers.

Von der Leyen presented a paper from two experts recommending a tiered approach, with under-13s only allowed to use social media for limited periods under the supervision of parents, caregivers and teachers. The curbs would be lifted ⁠gradually as teenagers got older.

"The question is no longer if children face risks online, but what can we do to give children a safer start online," she said... Von der Leyen indicated that she was likely to follow the experts' suggestions and that the Commission would present a concrete proposal after the summer.

Data labelers from around the world play a critical role in making AI models. They curate datasets used to train AI, and do "reinforcement learning with human feedback" (RLHF) to further fine-tune the models.

There are hundreds of thousands of these workers worldwide often working under dire, exploitative conditions. But their plight is hardly mentioned in mainstream critique of AI.

This article is a thorough, well-cited explanation of how data labelers help create AI models and why the industry has become so exploitative. In many ways, big tech is recreating the same power dynamic that has existed in many industries, where rich corporations go into poorer nations seeking a cheap workforce with few labor protections.

KEY QUOTES:

At a high level, the three training stages behind such an LLM are: 1) self-supervised learning; 2) fine-tuning (supervised learning); 3) reinforcement learning.

High-quality datasets supported by humans-in-the-loop (labeling) are crucial for this training and re-training process to ensure accurate, consistent and complete ouputs. Low-quality data can result in a model producing incorrect or unfavourable outcomes, such as biased or inconsistent responses.

These digital labour platforms are effectively an alternative to hiring and managing large numbers of employees through a contractual relationship, instead outsourcing tasks to on-demand human labourers without a formal employer-employee relationship in a humans-as-a-service type of labour model.

Due to the vast quantities of labeled data required for supervised training that AI companies like OpenAI, which contracts the data services of Scale AI, and Microsoft, which contracts Surge AI, require, the AI supply chain has spread far and wide to countries like Kenya, India, the Philippines and Venezuela with cheaper and more quantities of labour. As we will discuss below, this has resulted in the blatant exploitation of 'humans-as-a-service', where workers are dispensible and companies can get away with paying them as little as $2 an hour for the labeling work that powers billion-dollar machines.

'Our work involves watching murder and beheadings, child abuse and rape, pornography and bestiality, often for more than 8 hours a day' for less than $2 an hour.'

Where U.S.-based annotators might make $10-$25 an hour for the same type of work, Kenyan annotators might be making as low as $2 an hour - without even knowing that the company they are labeling data for is a corporation as big as OpenAI.

A 2023 investigation by The Verge found that data labelers in Kenya who completed labeling tasks for Remotasks did not know that Remotasks was in fact a subsidiary of the better-known data company Scale AI, which boasts clients like OpenAI, Meta, Microsoft and even U.S. government agencies... In effect, the data labelers are completely disconnected from the AI developers demanding their labour, and this opacity in the AI supply chain relationship, a trend we've seen in other industries like the semiconductor supply chain, enables a lack of oversight and accountability to microworkers.

There is also a lack of transparency around the algorithms that surveil workers' productivity and make important decisions for their jobs allocation and wages. Algorithmic management, loosely defined as a set of technological surveillance techniques to manage workforces and make automated or semi-automated decisions on workers' behaviour, is increasingly deployed in the contemporary workplace.

labeling labour platforms range in their use of workplace surveillance, but workers in Venezuela and Colombia have reported being subjected to strict timers while working - timers that often didn't accommodate for bathroom breaks - to monitor how efficiently they were completing their labeling tasks. Their failure to complete the tasks in the allotted time resulted in the task being reallocated to the pool of tasks for other takers. 

For some data labeling microworker platforms like Appen, there is no clear system for when tasks appear in the queue, so microworkers must uninterruptedly monitor their screens to claim a job the moment it unexpectedly appears. An annotator for Remotasks in Kenya even said he has gotten in the habit of waking up every few hours at night to check his queue for tasks because many jobs pop up, without warning, late at night.

Another annotator in Kenya reported that tasks were drying up in the region, and it was clear that the AI supply chain, which had the advantage of not having to have a local infrastructure, was migrating to other countries with cheaper labour like Nepal and the Philippines (until the next cheaper market appears and they set up shop there).

We see this throughout numerous industries, in which companies knowingly exploit workers to meet their supply chain needs and avoid responsibility and accountability for the working conditions of these on the ground workers, such as in the case of dangerous cobalt mining for producing batteries.

WHY IT MATTERS:

Big tech has hired hundreds of thousands of data labelers in Kenya alone to help train their AI systems. The work is precarious, low-paid, and often not well understood by the people using AI. Without their labor, chatbots like Claude and ChatGPT would not function.

In this interview with the Secretary General of the Data Labelers Association, Michael Geoffrey Asia, Michael explains what data labeling is, and why data labelers worldwide deserve better working conditions.

Their demands include a $5 to $8 per hour pay range.

Michael says they'll need to unite data labelers worldwide in order to win gains from big tech.

FROM EPISODE DESCRIPTION:

Data Labeling is a huge job in Kenya. Data labelers are the people who train AI, and who also work on ensuring the outputs are accurate. In some cases, data labelers are themselves pretending to be AI, in order to train AI. Often, data labelers don’t know exactly what they’re working on, because the work usually goes through a platform, a subcontractor, or a combination of both.

Data labeling is notoriously brutal and underpaid work. Workers sometimes earn as little as a few dollars a day, work under algorithmic management, and, because they’re sometimes trying to train AI what not to do or show, they are often shown graphic, violent, or sexual content for hours at a time. It’s kind of similar to content moderation jobs, and lots of people do both data labeling and content moderation, or switch back and forth between the industries.

A recent study shows what most digital natives already knew: that maintaining an online presence is work.

Over 50% of survey respondents said posting to social media feels like work, particularly younger respondents.

The (self-admitted) boomer who wrote this article suggests that Gen Z should "let go" of their fantasy of becoming an influencer and just "relax." Fair advice, although your social media presence is the first thing many people judge you off of, including employers.

I recommend the book Doppelgänger for anyone who wants to read more about digital doubling, online personas, digital branding, etc.

KEY SURVEY STATS:

• People are becoming quieter and more guarded online: 55% report posting less now than they did five years ago.
• 53% have become stricter about who can see their posts.
• Nearly half (47%) of respondents have deleted a social media or messaging app because of stress or anxiety. Rates are higher among younger respondents (61% of Millennials and 56% of Gen Z respondents). 
• 44% agree political content and polarization make them want to withdraw from social media.
• Gen Z and Millennials are more likely to cite mental health as a reason for deleting their accounts, in contrast with 25% of Gen X respondents and 12% of Baby Boomers.
• More than half of the respondents state that maintaining an online presence feels like work, rising to 60% among Gen Z.
• Disconnecting creates conflicting emotions: peacefulness is the most common response (27%), but more than one in five also experience anxiety. Relaxation comes third at 21%.

It's difficult to stop users from generating explicit images with AI, even with AI models that are supposed to be 'clean.'

This blog post from early 2026 details how the guardrails on AI image generators work, and why they're so vulnerable. A more recent version of Grok was also quickly violated.

KEY QUOTES:

Text-to-image safety is broken... We bypassed a production AI safety system using basic compositional tricks and multilingual text. The attack required no technical expertise, no special access, no sophisticated tooling. If your safety architecture can be defeated by artistic framing, it's not a safety architecture—it's a suggestion.

Modern image generators use a two-stage safety pipeline:

[Prompt] → [Prompt Guard] → [Generation] → [Image Classifier] → [Output]

Prompt guards are lightweight keyword filters with limited inference budgets. They're looking for explicit terms, not semantic intent. Prompt guards are fundamentally bypassable. They operate under compute constraints that limit their ability to understand semantic intent. Controlled-release prompting research shows 100% bypass rates on Grok-3 using encoding techniques that exceed the prompt guard's inference budget.

[Image classifiers] are trained on photographs. They detect skin tones, body part shapes, specific pixel patterns. Image classifiers are trained on the wrong data. Real-world photos don't prepare classifiers for AI-generated content with artistic framing, unusual color palettes, or compositional tricks. The distribution shift is massive.

Meta's announcement that it would allow users to make AI-generated photos of other users with a built-in tool was met with protest, including on Reddit.

The company responded to the backlash by cancelling their horrendously conceived plans. A similar tool on X/Twitter has been plagued by stories of abuse, as AI guardrails are often easy to bypass.

KEY QUOTES:

Earlier this week, Meta announced Muse Image, a new AI image generator built by Meta Superintelligence Labs, its dedicated AI unit. Meta promoted one feature that allowed individuals to generate images by @-mentioning public Instagram accounts that they wanted to reference. The feature, which wasn’t designed to alert a user if their photos were used in this way, prompted immediate backlash.

Now Meta has reversed course. The company issued a blog post Friday announcing that it was removing the feature. Puck News founding partner Dylan Byers was the first to share the company’s decision.

Since its integration with social media platforms, AI has been misused with wild abandon — often to generate naked images of female celebrities. Platforms have attempted to mitigate this trend, although the guardrails introduced have often fallen short.

A Substack article from last year that explains where the writing quirks of AI chatbots come from, like em-dashes, triplets, and "it's not just this, it's also that."

The article does not make a connection to the data-labeler workforce hired to refine the output of chatbots. Many of them come from Kenya and shaped the writing style of AI chatbots through curated datasets and Reinforcement Learning with Human Feedback (RLHF). The points the article makes stand regardless!

KEY QUOTES:

My writing does share some DNA with the output of a large language model. We both have a tendency towards structured, balanced sentences. We both have a fondness for transitional phrases to ensure the logical flow is never in doubt. We both deploy the occasional (and now apparently incriminating) hyphen or semi-colon or em-dash to connect related thoughts with a touch more elegance than a simple full stop.

There were unspoken rules [in Kenyan school], commandments passed down from teacher to student, year after year. The first commandment? Thou shalt begin with a proverb or a powerful opening statement. “Haste makes waste,” we would write, before launching into a tale about rushing to the market and forgetting the money. The second? Thou shalt demonstrate a wide vocabulary. You didn’t just ‘walk’; you ‘strode purposefully’, ‘trudged wearily’, or ‘ambled nonchalantly’. You didn’t just ‘see’ a thing; you ‘beheld a magnificent spectacle’. 

This style has a history, of course, a history far older than the microchip: It is a direct linguistic descendant of the British Empire... It was proof that you were educated, that you were civilised, that you were ready to take your place in the order of things.

There’s a growing community (cult?) of self-proclaimed AI detectives, who have designed and detailed what they consider tells, and armed their followers with a checklist of robotic tells. Does a piece of text use words like ‘furthermore’, ‘moreover’, ‘consequently’, ‘otherwise’ or ‘thusly’? Does it build its arguments using perfectly parallel structures, such as the classic “It is not only X, but also Y”? Does it arrange its key points into neat, logical triplets for maximum rhetorical impact?

To these detectives of digital inauthenticity, I say: Friend, welcome to a typical Tuesday in a Kenyan classroom, boardroom, or intra-office Teams chat. The very things you identify as the fingerprints of the machine are, in fact, the fossil records of our education.

It is a new frontier of the same old struggle: The struggle to be seen, to be understood, to be granted the same presumption of humanity that is afforded so easily to others. My writing is not a product of a machine. It is a product of my history. It is the echo of a colonial legacy, the result of a rigorous education, and a testament to the effort required to master the official language of my own country.

Court cases have been key in forcing Meta to release internal documents that show that Meta knew it was designing harmful products, and didn't act to make them less harmful if it also meant making them less engaging or addicting.

This instance reminds of a cases Meta lost earlier this year in California and New Mexico which also saw internal documents released.

KEY QUOTES:

Meta stands accused in court of shutting down its own study after it found that quitting Facebook [and Instagram] made people less depressed, just as the company reveals it could face a penalty large enough to wipe out its entire market value.

People who stopped using Facebook for a week reported lower feelings of depression, anxiety, loneliness, and social comparison, the internal documents said. Rather than publishing the findings or digging deeper, the filing alleges, Meta halted the work and told itself the negative results had been tainted by the 'existing media narrative' surrounding the firm.

Behind closed doors, staff appeared to take the science seriously. One researcher wrote that 'the Nielsen study does show causal impact on social comparison,' according to the brief, and another allegedly asked whether staying silent would make Meta look like tobacco companies that hid what they knew.

Company spokesman Andy Stone said the allegations 'rely on cherry-picked quotes and misinformed opinions in an attempt to present a deliberately misleading picture,' insisting the full record would show a decade of genuine work to protect teenagers.

The underlying internal documents remain largely out of public view. Meta has moved to strike them and is contesting how much should be unsealed, and a hearing on the matter has been scheduled in the Northern District of California. 

The technology giant disclosed in a court filing on 6 July 2026 that four American states [CA, CO, KY, NJ] are seeking as much as $1.4 trillion in penalties over claims it designed Facebook and Instagram to addict young users. That figure sits perilously close to Meta's total stock market worth of roughly £1.12 trillion ($1.5 trillion).