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.

 

Read THIS book.

 

Also see "Something New, Something Old."

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?

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

The War On The War On Science?

  

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

 

From SBM: ScienceBasedMedicine:

 

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?" 

The "Mistakes Were Made" thing.

Kara Swisher interviews Theo Baker.

   

Mom and Dad and Theo.

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."

ERIC HOEL

"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...

POTUS Late Night Chirping

The Therapy Bot Will See You Now

Slingshotai.com

 

Really? Concerns abound. On deck.

 

Watched a Laurie Segall podcast on the topic. 

 

Then saw this book reviewed in Science Magazine.

 

As conversational artificial intelligence (AI) systems increasingly occupy social roles once reserved for humans, questions about the nature and limits of machine-mediated relationships have moved from speculative philosophy into everyday life. In Artificially Yours, philosopher Valerie Tiberius offers a timely contribution to this debate by turning to one of the most foundational yet comparatively understudied relational categories: friendship.

Tiberius does not simply ask whether chatbots can be friends but instead dissects key constitutive elements of friendship—such as mutuality, enjoyment, helpfulness, and shared understanding—using them as a lens to consider how, and to what extent, these might be instantiated in interactions with artificial agents. (Spoiler: Some chapters suggest that current AIs can meet certain criteria of friendship but not others.) Rather than offering definitive answers, the book maps a spectrum of possibilities, highlighting where current systems fall short while remaining open to future developments.

Tiberius treats individuals who report meaningful connections with chatbots neither as naïve nor as pathological but rather as participants in a social phenomenon that merits careful ethical consideration. At the same time, she does not shy away from critical questions, including ones related to the motivations of the companies behind these relationships. Although issues such as power asymmetries, privacy, and data security are acknowledged, they are not explored in extensive detail… 

Deep into it at the moment. Stay tuned... 

Is this a great country, or WTAF?

THANK YOU FOR YOUR ATTENTION TO THIS MATTER

President Trump, May 29th, 2026

Shockingly, a Judge appointed by Barack Hussein Obama, Christopher Cooper, ruled that The Kennedy Center, which was going to close in early July for largescale renovations and construction due to years of neglect, decay, and poor maintenance, and which was to be transformed by the Trump Administration into the Finest Facility of its kind, anywhere in the World, is not allowed to close for these renovations, which would not be possible to properly do without such a closure. Additionally, Judge Cooper ruled that the 36 Member Board of Trustees, which unanimously voted to add the name “TRUMP” onto the former Kennedy Center, making it The Trump Kennedy Center, did not have the right to do such an addition, and the name, “TRUMP,” must be removed.

The Kennedy Center has lost, over the years, prior to our getting involved a short while ago, Hundreds of Millions of Dollars — In some cases, including ridiculous construction jobs that were done, over 100 Million Dollars a year. I took great pride in taking over a losing Institution, and looked forward to making it into a Great and Prestigious WINNER for Washington, D.C., and indeed, the United States of America. Unfortunately, Judge Cooper and the Radical Left would rather see it DIE than have President Trump transform it into something that everyone could be proud of, much as I have done, in many cases, throughout my life, and recently, with all of the construction, renovations, and “fix ups” that we have completed with the Department of Interior on Waterfalls, Fountains, Monuments, and other things of Beauty that we have brought back to life in a now SAFE AND SECURE, after Record Setting Crime, Washington, D.C., which is thriving like, perhaps, never before!

Therefore, based on the fact that the Radical Left Democrats care more about opposing your favorite President, ME, than saving a dying Performing Arts Center, almost all of which lose large amounts of money throughout the Country, we are going to be working with Congress to transfer this failing Institution back to them so they can make a determination as to what to do with it. Judge Cooper was given a presentation by leading Building and Construction Experts as to how structurally dangerous the Building is, with rotting beams, parking areas that are subject to collapse, and various other Life and Safety problems, in addition to the fact that it also needs a MAJOR renovation, from an aesthetic standpoint, but he was not “swayed,” and said he wants the Building to, incredibly, remain open and, therefore, dangerous. Judge Cooper should be ashamed of himself! I cannot be involved with a situation where danger to the Public is allowed to flourish in plain and open sight. Unless I am free to do what I do better than anyone else, bring this Institution back, physically, financially, and artistically, I have no interest in continuing what could only be a hopeless journey into “NEVER NEVER LAND.” There has never been a President of the United States who has been treated so unfairly by the Courts as I but, that’s OK, I will continue to do, what is considered to be, a great job for the wonderful people of our Country. I have instructed the Department of Commerce to make all necessary arrangements with Congress to allow a full and complete transfer of this Institution, giving them the responsibility for its Operation, Maintenance, and Management. Thank you for your attention to this matter! President DONALD J. TRUMP

don ipsa loquitur

YIPS UPDATE

  

How to Change Your Mind

Been thinking a lot today in the wake of finishing Theo Baker's exhilarating book. Ran into this Michael Pollan series on NetFlix tonight. apropos, see "Untying the knot of self."

 

As I've alluded to before, I arrived in the SF Bay Area at the age of 21, settling in in North Beach (Broadway & Columbus). Took my first acid trip in early 1968 in Chinatown at night ("Window Pane"). It was like being in the "Roger Rabbit" movie.

While the music played, you worked by candlelight 
Those San Francisco nights 
You were the best in town 
Just by chance you crossed the diamond with the pearl 
You turned it on the world 
That's when you turned the world around
Did you realize 
That you were a champion in their eyes?

On the hill the stuff was laced with kerosene 
But yours was kitchen-clean 
Everyone stopped to stare at your technicolor motor home 
Every A-Frame had your number on the wall 
You must have had it all 
You'd go to L.A. on a dare and you'd go it alone
Could you see the day?
Could you feel your whole world fall apart and fade away?

Get along, get along, Kid Charlemagne
Get along, Kid Charlemagne

Now your patrons have all left you in the red 
Your low-rent friends are dead 
This life can be very strange 
All those Day-Glo freaks who used to paint the face 
They've joined the human race 
Some things will never change
You are obsolete 
Look at all the white men on the street

Get along, get along, Kid Charlemagne
Get along, Kid Charlemagne

Clean this mess up else we'll all end up in jail 
Those test tubes and the scale 
Just get it all out of here 
Is there gas in the car?
Yes, there's gas in the car 
I think the people down the hall know who you are
'Cause the man is wise 
You are still an outlaw in their eyes

Get along (get along), get along, Kid Charlemagne (get along)
Get along, Kid Charlemagne... 

"Is there gas in the car?" 

   

More shortly. Just finished Josh Tyrangiel's new book AI for Good. Lots to mull over.