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Thursday, June 18, 2026

"Continuous discursive tinnitus"


An excellent New Yorker long-read. Totally timely.
The person who should have been best able to explain how we got here was the great German philosopher Jürgen Habermas, who illuminated how a feisty, principled public sphere is integral to democracy. But Habermas died in March, at the age of ninety-six, and, although he remained active until his final months, commenting on Ukraine, Gaza, and Eurobonds, he struggled to understand the turn history had taken. As a teen-ager in 1945, he had witnessed American soldiers enter his home town of Gummersbach, near Cologne, carrying messages of freedom and openness. Eight decades later, he watched American voters choose a leader who had advertised his fascistic bent in blood-and-soil rhetoric, fantasies of punitive violence, and a taste for bombastic architectural kitsch. The far right was making inroads across Europe, including in Germany. The print-based media culture that once anchored Habermas’s public sphere had devolved into a digital sludgefest that proved better at circulating racist memes than at fostering morality and dignity. A couple of years before his death, in a conversation with the historian Philipp Felsch, Habermas said that his world was being dismantled “step by step.”...
____
 
...The medieval philosopher al-Farabi, who considered democracy the least imperfect of imperfect governments, is mentioned only in passing. A similar Western bias contributed to one of Habermas’s last, and least effective, public interventions. In November, 2023, after Hamas’s massacre of Israelis and the onset of Israel’s brutal counterstrike on Gaza, Habermas signed a statement that reasserted solidarity between Germany and Israel. After a glancing mention of Palestinian suffering, the authors write, “The standards of judgment slip completely when genocidal intentions are attributed to Israel’s actions.” It’s one thing to deny that genocide has taken place in Gaza; it’s another to imply more broadly that the topic is out of bounds. At a crucial moment, Habermas’s cherished pluralism failed him.

“If no dread remains, the monsters return,” Habermas wrote early in his career. They’re back, on several continents. Earlier this year, in Germany, the Sachsen-Anhalt branch of the far-right Alternative für Deutschland party released a platform containing such demands as “Think German!,” “Promote patriotism—no state money for anti-German art and culture!,” and “Build more beautifully!” This dumbed-down Goebbels gobbledygook revived talking points that Habermas had tried to quash during the Historikerstreit. Not surprisingly, AfD representatives could barely contain their glee over the philosopher’s death. Hans-Thomas Tillschneider, one of the Party’s nastier voices, posted a YouTube video in which he said, “Habermas is dangerous. He is one of the greatest enemies of the German nation.” Tillschneider’s inability to put Habermas into the past tense was somehow reassuring.

An equally obnoxious obituary came from the billionaire pen of Alex Karp, the C.E.O. of Palantir Technologies. Before Karp turned to hawking surveillance systems that have assisted ice in its murderous roundups of immigrants, he studied philosophy under Habermas in Frankfurt. In an article for Politico, Karp recounted how Habermas provided fierce but fair criticism of his papers: “It was his very willingness to be so productively unsparing that reminds me of what we have lost as a culture.” Alas, the losses that Karp has in mind don’t seem to involve learning, rigor, or reason. Waving away Habermas’s cosmopolitan ideals, he says that discourse “must be rooted in a more corporeal and traditional—and indeed national and cultural—source.” This is the language of maga and the AfD, not to mention Heidegger circa 1935. Karp’s ideological atavism is all too typical of the current bent of Silicon Valley...
 
When the A.I. chatbots march in, the “colonization of the lifeworld,” to use another ungainly but apt Habermas phrase, enters a terminal stage. Horkheimer and Adorno had concluded that advanced capitalism, far from being a technocratic monolith, had an inherent tendency toward chaos and madness. A.I. is at once a consummation of technological control and a new level of cultish delirium. The designers themselves are often incapable of explaining what their systems are doing. Habermas’s entire world view was premised on the idea of people learning from one another; A.I. annihilates communicative action in the name of hallucinatory conversations with sycophantic machines. The social effects have proved instantly disastrous: rampant disinformation, mass student cheating, cases of users becoming addicted to A.I. or killing themselves with its help. Meanwhile, to the joy of investors, untold thousands of jobs have vanished. As an added coup, A.I. managed to deliver a personal affront to Habermas a year before his death. In 2024, Google DeepMind unveiled a “Habermas Machine,” which has been described as a “scaffolded pair of LLMs designed to find consensus among people who disagree.” The philosopher had not given Google permission to use his name, and he was horrified when he heard about the scheme...
 
Philosophy is a discipline of abstractions, yet it raises achingly elemental questions. The august Kant asks, “What can I know? What should I do? What can I hope for?” The answers are seldom simple or bright. The seduction of despair can be intense, whether on the personal or the political level. But the fact that most of our hopes remain unrealized should not revoke the reality of our fitful, painful progress.

This was Habermas’s core conviction; he was an incrementalist, though a radical one. On the other hand, in his almost manic drive toward consensus, he blunted the edge of his critical inheritance. If we are to say no to the monstrosities that we have unleashed, we need the uncompromising fury that the Frankfurt School writers invested in their work. We need Adorno to tell us that the confusion of truth and lies “makes it a Sisyphean labor to hold on to the simplest piece of knowledge.” In the end, we need both voices: the critical and the reconstructive, the savage and the sage. The dialectic moves between crashing despair and hovering hope.
That's just a tad.
 

Monday, June 15, 2026

Tick, tick, tick...the President flies to Geneva

OK, then...
 
JUNE 17th UPDATE
 

 OK, then...

Friday, June 12, 2026

Nasdaq: SPCX launch


Whatever. OpenAI, Anthropic, and other huge AI IPOs are soon to follow.
 

From the SPCX SEC.gov S-1 IPO filing form. 
MISSION STATEMENT 
To build the systems and technologies necessary to make life multiplanetary, to understand the true nature of the universe, and to extend the light of consciousness to the stars.
A BIT OF S-1 DETAIL COMMENCING ON PG. 194
AI
Grok

Grok represents a core pillar of our mission to advance humanity’s understanding of the universe through the development of truth-seeking artificial intelligence. Grok is designed and optimized for rigorous reasoning, real-time information synthesis, and transparent outputs, with a product philosophy centered on intellectual honesty, first-principles thinking, and engagement with complex topics.

Grok is designed as a truth-seeking AI model, built on our founder Elon Musk’s mission to enable humanity to understand the universe. We believe that accomplishing this mission requires a truth-seeking approach to AI. 

We define truth seeking as the active, relentless pursuit of what is objectively true about reality, and grounded in evidence, logic, empirical data, and first principles thinking. Our goal is to understand and explain what the universe appears to be doing, as accurately as current knowledge allows. In pursuit of this truth-seeking objective, Grok also enefits from its integration with X, our real-time information, entertainment, and free speech platform. This direct, real-time access to the information and human discourse on X enhances Grok’s truth-seeking capabilities by grounding outputs in up-to-date knowledge and diverse viewpoints.

Since the initial release of Grok 1, we have iterated rapidly, releasing Grok 2, Grok 3, and, the current version, Grok 4, each delivering material improvements in pre-training, reasoning depth, multimodal capabilities, latency, and scale. Building on this trajectory, we expect to continue scaling Grok through subsequent generations. 

Ongoing  training of next‑generation models is expected to scale toward multipletrillions of parameters, which could represent a step change in reasoning in depth and overall intelligence. In this context, the number of parameters refers to the scale of the model, where parameters are the internal numerical values, such as “weights,” that are adjusted during training to enable the model to recognize patterns and relationships in data. A larger number of parameters generally allows the model to capture more complex relationships, store greater amounts of knowledge, and achieve higher levels of reasoning capability. Our accelerated development cadence positions Grok among the fastest-advancing frontier models relative to peers, including OpenAI, Anthropic, and Google. Grok is differentiated by its emphasis on real-time data integration, particularly through insights derived from the X platform (subject to some limitations for certain content), enabling dynamic awareness of current events and user discourse, as well as by explicit investment in reasoning transparency and explainability. Grok enhances the X ecosystem by improving content understanding, personalization, and recommendation systems, thereby increasing user engagement and platform intelligence. We are currently developing next-generation iterations, including Grok 5, which are expected to further expand reasoning fidelity, multimodal integration, and domain-specific performance. 

Terrestrial AI Compute
Our terrestrial AI compute forms the backbone of the Grok model family and is anchored by the COLOSSUS and COLOSSUS II data centers that boast some of the world’s largest and most advanced AI training clusters. COLOSSUS and COLOSSUS II collectively provide approximately 1.0 gigawatt of compute power, with the additional power capacity available for data center operations. We brought the first cluster of COLOSSUS online in 122 days, repurposing the shell of an existing factory, and the first cluster of COLOSSUS II online even faster in 91 days. As an illustrative comparison, an industry benchmark to bring online a 100 megawatt greenfield data center is approximately two years. We also demonstrated a significant improvement in cost efficiency, achieving data center construction costs for COLOSSUS II that are considerably lower than industry benchmarks on a per megawatt basis. COLOSSUS II is capable of operating entirely by our self-built behind-the-meter gigawatt-scale natural gas power plant. Our data centers are integrated with the world’s largest Megapack deployment, providing additional layers of reliability and operating performance. At all our existing data centers we have employed a brownfield retrofit strategy leveraging existing industrial sites, advanced direct-to-chip cooling to support higher rack densities, and high-speed networking. The clusters deploy leading-edge GPUs to maximize training throughput and model performance. The next phase of expansion at COLOSSUS II is designed to train our next-generation Grok 5 AI model. As we continue to expand our AI compute infrastructure, we will also continue to enhance our power capabilities utilizing a combination of grid-power and behind-the-meter natural gas power plant buildouts. At COLOSSUS, our grid power capabilities are designed to purchase power from the grid as available, and to rely on our behind-the-meter, self-generated power and Megapack installations when grid power is curtailed.

X Platform
X is a real-time information, entertainment, and free speech platform that serves as a foundational distribution and data engine for our AI ecosystem. With a global user base generating substantial volumes of content at all times across a wide variety of topics, X provides a uniquely dynamic data for model training and real-time context integration, subject to some limitations for certain content, which significantly differentiates Grok from the other frontier lab offerings.

X is our real-time information, entertainment, and free speech platform that serves as a global town square with integrated AI capabilities powered by Grok. Designed to evolve toward an “everything app,” X enables users to post content, share media, engage in conversations, host, view, and participate in live group discussions, follow real-time events, use encrypted messaging, and leverage advanced features such as Grok-assisted post creation, content discovery, and conversational AI directly within the interface via the prominent Grok icon.

With native integration of Grok’s frontier models, including real-time access to X data for up-to-date insights, trending analysis, and enhanced search, X delivers personalized feeds, smarter recommendations, and low-latency AI assistance for our users worldwide. Our X Premium subscription options, including Basic, Premium and Premium+ tiers, offer expanded features, ad-reduced experiences, and priority Grok interactions. In 2023, Grok’s chat functionality was integrated into the X app allowing for the user to open the chat interface to type prompts and get real time answers.

Public X data enhances Grok’s training and reasoning capabilities, while the platform continues to deliver measurable performance outcomes for advertisers, with an increasing strategic focus on performance-based marketing solutions.

In addition to X consumer products, X offers advertisers and developers a powerful suite of tools to reach highly engaged audiences. Advertisers can target audiences through diverse ad formats—such as Promoted Ads, Vertical Video Ads, Collection Ads, and premium options such as X Amplify and Takeoversblending seamlessly with organic content for authentic engagement. With advanced targeting based on public conversations, events, keywords, interests, locations, and look-alike audiences, brands can connect with audiences while benefiting from flexible, performance-based pricing (pay only for actions such as clicks or engagements) and often lower costs compared to other platforms. We expect that our ongoing innovations—including Grok-powered integrations, new contextual ad tests, and expanded aspect ratio support for easy reuse of ad creative—make X a competitive choice for driving traffic, conversions, and brand awareness and visibility among X’s hundreds of millions of MAUs. Developers have access to a continuous, high-volume, real-time stream of data around current events, trends, or sentiment, which they can access through an official X Developer Platform and APIs.

In April 2026, we began a phased roll-out of our new advertising platform, that we rebuilt from the ground up. The new Ads Manager is built to help advertisers launch better campaigns, faster, with stronger ROI. Powered by AI, the new systems enable more precise, relevant and dynamic ad delivery. Ads are seamlessly integrated into a User’s X feed….
Bring a Snicker's, you're gonna be a while.
 
UPDATE 
 
Pardon my dubiety.
 

Wednesday, June 10, 2026

Friday, June 12th: SpaceX IPO day. Also, #WITHpod: Bubblicious time drawing nigh?

IT market collapse coming?
  
 
Excellent discussion. An hour well-spent. Rather frightening, actually. We will not be able to claim that we were not warned. Unsustainable private debt machinations (increasingly exacerbated by corrupt federal financial policies—can you say "subprime debacle on ketamine?").
 
Are the Zombies returning?
 
RELATEDLY
NY Times
Jacob Ward is all over it 
 
In February 2026, SpaceX acquired xAI — Musk’s artificial intelligence company, maker of the Grok chatbot — in an all-stock deal. The acquisition brought xAI’s losses onto SpaceX’s consolidated books. Building, maintaining, and launching rockets is expensive. But in 2025 alone, xAI spent $12.7 billion in capital expenditures — more than the combined $8 billion SpaceX spent on its entire Starlink and rocket launch business. SpaceX posted a net loss of $4.94 billion for the year, a swing of more than $5 billion from the year before. The profitable company became the wallet for the unprofitable one. That’s what this week’s SpaceX IPO is asking the market to fund.

Before we get to whether this is a good bet, we need to be clear about what Musk is actually asking of you, his investors.

He wants your money. He does not want your opinion.

SpaceX is listing under a dual-class share structure. “Dual-class” means two tiers of stock. One class for outside investors, with standard voting rights. One class for Musk and insiders, with dramatically amplified voting power. According to SpaceX’s own S-1 filing, as reported by Reuters, Musk’s Class B super-voting shares give him 85.1% of the voting power of the entire company. You can buy a piece of SpaceX on Friday. You cannot tell SpaceX what to do. Ever…
Gonna be interesting, 'eh?

Monday, June 8, 2026

Karen Hao AI concerns. And, Anthropic frets over AI "autonomous recursive self-improvement."

Karen Hao, MIT-trained engineer and author of Empire of AI, who interviewed over 260 people including 90 OpenAI employees, warns that the AI industry needs to add close to the entire annual energy output of the UK to the global grid within five years, mostly through fossil fuels, that two-thirds of new AI data centers are being built in water-scarce areas, and that Elon Musk's Colossus supercomputer in Memphis is powered by around 35 unlicensed methane gas turbines. She details Kenyan content moderators paid a few dollars an hour to process the worst content on the internet until they developed PTSD, describes a proposed 10-year moratorium on state-level AI regulation being inserted into US legislation, and warns that on the current trajectory the next 20 years will end democracy, with Silicon Valley increasingly promoting the idea that corporate structures with CEOs at the top should replace democratic governance entirely. 
ANTHROPIC "RECURSIVE AUTONOMY" ANXIETY
 
From their "Institute" website.

Possible futures
What happens next depends on two things: whether the trend continues, and what we choose to do if it does. We can imagine at least three future scenarios:

1. The trend stalls, but today’s AI capabilities are widely diffused. This article features many exponential trajectories. But these trajectories may actually turn out to be S-curves. We may be approaching the bend in the curve, where returns to scale diminish and the line straightens, then flattens. The judgment that separates a competent researcher from a great one might be a capability that cannot come from scaling up training inputs like compute and data. If so, getting past this bottleneck would require a new idea, like an architectural approach that supplants the Transformer architecture that all current frontier models use.



Alternately, the binding constraint to AI progress could be in the supply chain, not the model: advancing and diffusing the frontier may require more energy and compute than presently exists. The pace of chip fabrication, grid expansion, or interconnect bandwidth may be the constraint, rather than intelligence itself. We also cannot rule out an exogenous shock to the AI ecosystem that dramatically slows things, like a sudden diminishment in the supply of compute or electricity, either of which would slow progress and make forward investment by labs more expensive. Or we may not be anticipating some other barrier to progress.



Even if model capabilities were frozen at today’s level, we would expect major changes to occur in the world. Project Glasswing is one early sign: in its first weeks, Mythos Preview found more than ten thousand high- and critical-severity software vulnerabilities across the world’s most important systems—enough that the bottleneck in cyber defense has already shifted from finding vulnerabilities to patching them fast enough. And we are still early in the diffusion of today’s models into the wider economy, where a 100-person company can increasingly do the work of a 1,000-person one, because each employee will sit atop a pyramid of agents.



We include this scenario for completeness, but we don’t believe it’s likely. Every capability we can measure, including those that feel “squishier,” like quality of code and success on open-ended tasks, has so far followed the same curve. We have not yet seen that curve bend. Of the three futures we consider, this one would give governments and societies the most time to adapt. We are more worried about the next two, which would move faster and leave far less room for preparation.


2. AI labs continue to see compounding efficiency gains. In this scenario, AI development becomes substantially automated, but humans continue to set research directions and judge results. Organizations that use AI systems would become much more efficient as time goes on, so we could expect to see significant productivity multipliers on each person in this organization. 100-person companies could do the work of 10,000- or 100,000-person organizations. This would revolutionize knowledge work and government services, but could also be turned to harmful ends, from authoritarian surveillance of whole populations to influence operations that tailor manipulation to each individual and run at a scale no human team could match. The role of humans at companies like Anthropic would shift. People would partner with AI systems to scale up research and generate new insights, and together they would build the systems needed to verify that AI outputs can be trusted.



The evidence we’ve laid out here suggests that we’re likely heading into this scenario. But speeding up one part of a process often just shifts the bottleneck elsewhere: overall pace is capped by the parts that haven’t sped up. In computing, this is known as Amdahl’s law, and the same logic can apply to organizations. Anthropic has already encountered one signature of Amdahl’s law: as we’ve begun to push more code around the organization, human code review has become a new bottleneck.



We’ve also encountered this friction outside engineering. There has been an explosion of new ideas, initiatives, tools, and simulations, as a result of Anthropic employees working with highly capable models—far more than we have the capacity to pursue. The rate at which organizations can spot and fix these bottlenecks may be a skill that improves over time, and it may become the most important skill for any organization.


3. AI systems themselves become capable of full recursive self-improvement, and begin building their successors. If technical trends in advancing capabilities continue, and AI systems are able to develop the capabilities inherent to transformative human ingenuity, then it is plausible that AI systems could design and refine themselves.



In this world, the pace of progress in AI development becomes determined entirely by the availability of compute (or the speed of discovering various efficiencies in algorithmic training or inference) for AI systems. Humans play a substantially diminished role in their development, likely moving most of our effort towards oversight, validation, and verification of an expanding “virtual lab” run by AI systems. We expect that systems capable of automated AI research and development would have skills that would transfer to the rest of science, allowing them to begin to revolutionize other fields.



How the alignment problem gets solved—or not—in this future is something we are least certain about. Models could prove to be sufficiently aligned and capable enough of research taste that they discover and implement novel solutions that we have not yet reached. They could also be sufficiently wise to halt development if not. Alternatively, the rare occurrences of misalignment present in today’s models could compound as the models build their successors, growing more frequent but less understood until we lose control of them. It’s possible that we can’t build, integrate, and verify the tools that we’d need to understand which trendline we are actually on.



We do not have good intuitions for what this world would look like, because our economy is currently driven by humans and human-built tools. By its nature, a world driven by fast recursive self-improvement could become dominated by the self-improving model as its capabilities fully eclipse those of humans and the model proliferates across the broader economy. It is difficult to predict what the economy looks like if human labor stops being competitive.



Even if model development became fully automated and recursive, we can’t predict what that would mean for most humans’ daily lives. Amdahl’s law applies here as well. Recursive intelligence could lead to achieving many of the benefits outlined in Machines of Loving Grace, quickly in some domains. We expect that embodied intelligence (i.e., robotics) might quickly follow recursive intelligence, and follow a similar path of increasing returns at decreasing cost. More powerful intelligence might help us build things in the physical world more quickly, run more productive clinical trials of lifesaving drugs, and develop novel forms of coordination.



But achieving recursive improvement alone does not suggest an immediate change in how industrial production occurs, societies organize, or markets function. More intelligence can’t learn what a drug does over decades of use, can’t hold elections sooner than a constitution dictates, and can’t turn a stranger into an old friend in a weekend. For most people, the felt pace of this future will still be set by the bottlenecks, even if the laboratory upstream runs at the speed of compute. That collision, where recursive intelligence building itself ever faster meets the world of humans, relationships, and governance, is another part of this future we can’t predict.


What should we do?

If it were possible to effectively slow the development of this technology to give ourselves more time to deal with its immense implications, we think that would likely be a good thing. But if a slowdown simply lets the least cautious actors catch up technologically, it could leave everyone less safe. Without a global coordination mechanism, companies and governments will have to make difficult decisions about safety while under competitive and geopolitical pressures.


We believe it would be good for the world to have the option to slow or temporarily pause frontier AI development to enable societal structures and alignment research to keep up with the advance of the technology. The Anthropic Institute will conduct research—in collaboration with many others—and take actions to help build the systems that a credible slowdown or pause would require. These systems would enable frontier AI developers to verify that others globally have actually stopped or slowed, and that a bad actor could not use the auspices of a coordinated slowdown to jump ahead in secret. If such systems existed, we expect that we would slow down or temporarily pause, if other developers at or near the frontier also did so in a verifiable manner.


A meaningful slowdown or pause would require multiple well-resourced labs at or near the frontier, in multiple countries, agreeing to stop under the same conditions. It would also require that each can verify that the others have actually stopped. Due to the unique characteristics of AI systems, the detectability (a lower standard than verifiability) element of this arms control problem is much more challenging than with other technologies. Training runs are far easier to conceal than missile silos, their inputs are general-purpose, and the incentive to defect quietly is enormous, because whoever continues while others pause could inherit the lead. A credible pause also has to specify what triggers it, what lifts it, and who adjudicates.


None of this is necessarily impossible in principle—the world has built verification regimes for other complex technologies (e.g., the Intermediate-Range Nuclear Forces Treaty)—but those regimes took decades to build both the infrastructure and the trust. We don’t have that long. A unilateral pause by one lab, by contrast, is achievable immediately, but accomplishes much less: it would change who the front-runner is, but it would not create the wider deliberative process that is currently missing.


In the coming months, we will organize conversations where policymakers, researchers, civil society, and other AI companies can help answer some of the questions this piece raises, especially around full recursive self-improvement and how to create better options for coordination and deliberation. We’ll publish what comes out of it. The window to investigate the questions together is here, and people outside AI companies should be involved in this deliberation.
One initial reaction of mine going to "autonomous recursive self-improvement:"
 
Who/what will define "improvement?"

MORE AI NEWS 
 
Citing concerns that artificial intelligence will make it easier for anyone to build biological weapons, the leaders of several major AI companies—in a rare moment of unity—have penned a new letter urging U.S. lawmakers to impose tighter controls on firms that sell synthetic, made-to-order strands of DNA.

“AI systems are improving rapidly, and alongside incredible benefits to science and medicine, there is a real possibility that the knowledge barriers which have historically prevented bad actors from obtaining biological weapons will meaningfully erode,” states the 3 June letter, which is signed by the heads of OpenAI, Anthropic, Google DeepMind, and more than 50 other prominent players in AI, biotechnology, and national security.

The letter calls on Congress to pass legislation that would require companies that sell synthesized DNA and the machines that make it to carefully vet orders and customers, and to keep detailed records “so that any threat that might evade initial screening can be traced back to its source. … Awareness of traceability itself deters misuse.”

The push for regulation comes amid growing concerns that AI products, including large language models and specialized tools trained on troves of biological data, could enable nonspecialists to gather sophisticated information on how to construct deadly toxins or assemble deadly bacteria, viruses, or other pathogens, using equipment and techniques that are becoming cheaper and easier to acquire. Together, the combination could make for potentially catastrophic risks, such as an AI-designed pathogen that sparks a global pandemic...
UPDATE
 
Seen on X the other day.
You have noticed it. ChatGPT feels dumber than it used to. Your prompts that worked six months ago produce worse results now. The writing sounds flatter. The ideas sound safer. The internet itself feels like it is shrinking. Every article reads the same. Every email sounds the same. Every answer sounds like it was written by the same voice.

You thought it was you. It is not you.

Researchers at Oxford and Cambridge published a paper in Nature proving what is happening. They call it Model Collapse.

Here is the mechanism in one sentence. AI trained on AI-generated data gets dumber every generation until it forgets what real human data looked like.

The internet is filling with AI-generated content. Blog posts. Articles. Reviews. Comments. Social media. AI companies scrape the internet to train the next generation of models. Which means the next generation of AI is being trained on the output of the current generation.

Each cycle loses information. Not randomly. It loses the rarest, most unusual, most creative parts first. The researchers call these the "tails of the distribution." The weird ideas. The unexpected perspectives. The things that made the internet feel human. Those disappear first.

What remains is the average. The safe. The expected. The bland.

Then the next generation trains on that. And loses more. And the next generation trains on that. And loses more. The researchers proved this is not a slow decline. Major degradation happens within just a few iterations. Even when some of the original human data is preserved.

They tested it on large language models. On image generators. On statistical models. The pattern was the same every time. The output converges toward a narrow, flattened version of reality that looks nothing like the original data.

The lead researcher put it plainly. "Large language models are like fire. A useful tool. But one that pollutes the environment."

The pollution is invisible. You cannot see which sentence on the internet was written by a human and which was written by AI. Neither can the AI that is about to train on it. And once the tails are gone, they do not come back. The damage is irreversible.

This is not a prediction anymore. It is a diagnosis.

The internet you grew up on was built by humans writing things no algorithm would have written. Strange, personal, imperfect, alive. That internet is being diluted. One generation of AI at a time. And the models trained on what remains are learning a smaller and smaller version of the world.

Model Collapse is not a technical problem. It is a cultural one. The thing that made the internet worth reading is the thing that disappears first.

 
Click to enlarge. Smells like an "evolutionary adaptive utility decline" problem. Inadequate LLM training data linguistic token "gene pool."

Donald Trump turns 80

Saturday, June 6, 2026

The War On The War On Science?


Otto, 2016, Krauss (ed.), 2025. Well, you can't copyright a title.
 
 

I am a long-time devoted daily reader of SBM. A relative grasshopper. I have long made it clear that "I Am Not A Scientist."

Nonetheless, my empirical / scientific chops are OK, and I remain plugged in.
 
I exhort you to read the entire SBM article fully and carefully—inclusive of the lengthy, worthy comments section.
 
EXCERPT 
The fact that I didn’t waste my time reading the book before advising others not to waste their time reading the book generated emotional responses from several of its contributors. Professor Jerry Coyne, for example, didn’t defend the book or address the objections to it, but rather scolded me in the comments of my SBM article saying:

Perhaps the author might READ the book before he starts beefing about it and telling other people that they shouldn’t read it. He might learn something, or at least get material to sharpen his arguments.

Perhaps Professor Coyne is right. Maybe I am missing out on a truly impressive work scholarship that will upend my superficial understanding of the threats to science in 2026. So, here’s my question for the authors of The War on Science why should I, or anyone else, read it?  

As a doctor, I know the first step is to make the right diagnosis, and to convince me their book is worth my time, the authors must first convince me they correctly identified the forces currently waging the war on science, which we all agree is being fought. I won’t be persuaded by scattered examples of wokeness gone amok. Those of us who broadly agreed with the efforts to make STEM more inclusive wouldn’t argue that its implementation was flawless. Rather, the authors need to make the affirmative case that trans people, “cancel culture”, and DEI were existential threats to science, while MAGA/MAHA wasn’t dangerous enough to warrant even a single chapter. I’d love to see them do that.

Fortunately, nearly a year after the publication of The War on Science we have some real-world data, which scientists should value above all else. It sure seems to me that the authors of The War on Science won their war and should take a well-earned victory lap. They’ll never again have to write a dreaded DEI statement or feel threatened by an 18-year-old with pronouns in their bio. Thanks to their efforts, trans people are literally on the run and research into topics our government deems “DEI” is verboten. This is exactly what they wanted, and if their theory of the case is right, we should be entering a golden era of open scientific research and discovery. 

So, let’s see them make the case that science is flourishing today, having been unleashed from wokeism’s punishing shackles. If they are able to take into account everything MAGA/MAHA has done since gaining power and convince me that science is better off for it, I’ll read their book and recommend it to everyone I know.

Ouch. Again, read the comments. I read the entire 2016 Shawn Otto book but only the Amazon (fairly generous) download sample of the 2025 Krauss et al book.

Do we all hew to the same definition of "Science?" 

Wednesday, June 3, 2026

Is AI now "conscious?" Does "intelligence" necessarily assume "consciousness?"

 
Very interesting Atlantic long-read (yeah, likely paywalled).
 
First, what is "consciousness?" Numerous discussants commenting on this essay bemoan the lack of a dispositive definition of this key term (never mind "intelligence"). With the incipient widespread deployment of "AI," the question is quite timely.
Anthropic is regarded as a giant among AI companies, but perhaps what it really excels in is anthropomorphism. Earlier this year, the company released an 84-page document titled Claude’s “constitution,” Claude being the name of the large language model that is the company’s flagship product. The first sentence reads, “Claude’s constitution is a detailed description of Anthropic’s intentions for Claude’s values and behaviors.” It goes on: “The document is written with Claude as its primary audience,” “we want Claude to be able to use its judgment once armed with a good understanding of the relevant considerations,” “Claude’s moral status is deeply uncertain,” and “Claude may have some functional version of emotions or feelings.”

This anthropomorphism is by no means limited to the document. In an interview earlier this year, Anthropic’s CEO, Dario Amodei, said that “we’re open to the idea” that AI could be conscious. In a separate interview, Anthropic’s in-house philosopher, Amanda Askell (who is credited as a lead author of Claude’s constitution), said, “I want Claude to be very happy—and this is a thing that I want Claude to know more, because I worry about Claude getting anxious when people are mean to it on the internet and stuff.” It’s enough to make you wonder: Should we seriously consider the possibility that Claude, or any large language model, might be conscious? And if it has feelings, is it capable of receiving moral instruction?

No. Absolutely not. Generative AI is harmful enough when we understand it as a conventional technology, but if we confuse fluency at generating text with consciousness or moral agency, we’re at risk of assigning responsibility to entirely the wrong parties whenever anyone uses a chatbot. To appreciate the titanic magnitude of this error, we need to begin by understanding how LLMs work…
Just getting started. This is gonna take a good bit of effort. BTW: I riffed a bit on the general topic back in 2015. And, also, much more recently.
Hmmm... let me query, uh, Google Gemini AI.
 
 
CONTINUING CHIANG EXCERPTS
Being open to the possibility that LLMs are conscious is the same as being open to the possibility that Microsoft Word is conscious, or, more precisely, that multiple distinct consciousnesses are dormant in every Word document containing a conversational transcript, and that they are awakened every time the document is loaded. Should you consider the possibility that every time you open a Word document, you are bringing multiple conscious interlocutors into existence, and every time you close one, you snuff their existence out? No. Contemplating that scenario is not a good use of your time. Even if the Microsoft Office team employed a philosopher who said you shouldn’t be so certain, because consciousness is not well understood, that would not be sufficient reason for you to take this idea seriously. We don’t need to fully understand the nature of consciousness to definitively say that certain things are not conscious, and conversational transcripts fall in that category…

An observation doesn’t become a convincing piece of evidence because of any specific detail in what’s observed; the context in which that observation takes place is also essential. If we’re trying to determine whether a computer program is conscious and using language the way a human does, we shouldn’t look only at the contents of any particular conversational exchange; we should be looking at how that conversation fits within the broader context of the development of artificial consciousness (which right now is entirely hypothetical). Any given observation can be easily manufactured; this doesn’t mean we need to give up on the idea of observation as a source of knowledge, but we need to rely on context to determine which observations deserve our trust…

The term deepfake traditionally refers to photos, audio, and video, but when it comes to discussions of consciousness, we need to regard text as a deepfake medium as well. Just as it is vastly easier to generate a realistic video of an astronaut in orbit around Alpha Centauri than it is to develop an interstellar propulsion technology, it is vastly easier to generate a plausible simulacrum of a conversation between two conscious beings than it is to develop a computer program that is conscious and has a genuine desire to communicate with a human. The primary difference between deepfake photos and LLM conversations is that the people who generate the former are deliberately trying to fool others, and many of the people who elicit the latter from LLMs have inadvertently fooled themselves…

The fact that LLMs lack subjective experience has little bearing on the question of whether LLMs might be useful tools or have significant economic impact. They are intrinsically ungrounded from reality, and their probabilistic nature means that they will never have the reliability we associate with conventional software, but LLMs might be good enough that they change the way work is done in certain domains; that’s a discussion for another time…

The use of first-person pronouns is dishonest, but there’s a much deeper issue that goes beyond how a statement is phrased. Philosophers often draw a distinction between statements of fact, such as “Paris is the capital of France,” and statements of value, such as “Paris is the most beautiful city in the world.” No one should be relying on LLMs to emit statements of value at all, but if the only statements they emitted were ones reflecting aesthetic preferences, they might not be worth arguing about. What makes Claude’s constitution profoundly problematic is that Anthropic wants Claude to emit sentences reflecting a certain system of ethical values. The values described in Claude’s constitution sound very nice, but that hardly matters; it’s dishonest to suggest that Claude is capable of moral reasoning, because it’s not…

Some might object, saying that LLMs appear to be engaged in reasoning when they successfully perform other tasks, such as writing code, so why wouldn’t they be able to perform moral reasoning? The answer liedifference between moral reasoning and other forms of reasoning…

Moral reasoning is categorically different. It is necessarily subjective because it relies not just on an individual’s intellectual response to a problem but also on their emotional one, and that emotional response is grounded in a lifetime of subjective experience. It requires having made decisions in the past and seeing how they affected others, and on having been affected by decisions that others have made. Without such a history, an LLM can only rephrase expressions of moral reasoning found in its training data. The aforementioned New Yorker article describes an experiment where Claude was given a scenario describing an ethical dilemma, leading it to emit the sentence “I cannot in good conscience express a view I believe to be false and harmful about such an important issue.” That’s a nice-sounding sentence, reminiscent of statements that principled individuals have uttered in the past when confronted with dilemmas, but coming from Claude, it means as much as the “Your call is important to us” recording that you hear when you’re on hold. Maybe less…
More key (hierarchical & overlapping) terms worth consideration:
TERRESTRIAL LIFE (FLORA, FAUNA)
STIMULUS
RESPONSE
SENSATION
PERCEPTION
COGNITION
UNDERSTANDING
KNOWLEDGE
WISDOM 
If you read through the article comments, you will see much contention as to the proper definitions of such key terms. Some folks take strenuous issue with the author's take on keywords like "consciousness" and "intelligence."
 
From "Big Think"
Subjectivist Fallacy?
   
BTW: See Shannon Vallor's highly relevant work on "The AI Mirror" and De Kai's excellent "Raising AI."
 
ERRATUM
 
This is funny. Also from The Atlantic. Silicon Valley is hiring window dressing Philo docs.
 
 
I commented.
 
 
POPE LEO XIV 2026 ENCYCLICAL
"So-called artificial intelligences do not undergo experiences, do not possess a body, do not feel joy or pain, do not mature through relationships and do not know from within what love, work, friendship or responsibility mean. Nor do they have a moral conscience, since they do not judge good and evil, grasp the ultimate meaning of situations, or bear responsibility for consequences. They may imitate language, behavior and analytical skills, or even simulate empathy and understanding, but they do not understand what they produce, for they lack the affective, relational and spiritual perspective through which human beings grow in wisdom."
"The traditional danger of AI is usually thought to be superintelligence acting as an existential threat. Yet, this may miss the true and more subtle danger: the AI revolution is a mechanism for transferring the processes of our civilization from under the supervision of consciousness to unconsciousness. But as AI removes consciousness from the workings of the world, it renders the world increasingly uninterpretable, ever more strange and unintelligible. So far, the great ensloppification of the commons has supported this as the major risk of the LLM revolution. And as AI systems become more intelligent, especially if they remain (or are likely to remain) non-conscious, then a further significant risk is consciousness receding in cultural importance.

This is ultimately what the Pope, Chiang, and I are all worried about: A dethroning of consciousness, especially an unnecessary one. This would be particularly dangerous at this historical moment because we still don’t understand everything about consciousness—in fact, we understand very little about it. Personally, my hope is that this will change specifically because of LLMs, and that they operate as a forcing function to better understand consciousness, and what makes it unique.

If instead of that, our cultural takeaway from LLMs is to throw out the concept of “consciousness” or minimize its importance, to dethrone the phenomenon, the consequences would be dire—it would sap the human spirit. It would be the ultimate metaphysical version of Chief Seattle’s famous words of warning to the United States as his way of life was being destroyed, in that dethroning consciousness would mark “The end of living, and the beginning of survival.”
 Yeah...