An LLM is basically just a massive and extremely sophisticated input/output mathematical function?

Rights holders for 274 musical compositions recorded by Elvis Presley filed a federal complaint against Anthropic on October 23, 2026. The complaint identifies ABG EPE IP LLC and other companies, trusts and individuals as plaintiffs in case 5:26-cv-12473.

The plaintiffs allege that Anthropic copied lyrics without a license, kept them in a permanent internal repository and used them in Claude model training. They also allege that Claude generated derivative or infringing outputs after guardrails were added.

The complaint seeks up to $150,000 per work, or $41.1 million for 274 works, plus separate statutory amounts under two other claims. The allegations have not been tested in court.

Source: https://chatgptiseatingtheworld.com/wp-content/uploads/2026/10/ABG_EPE_IP_LLC_et_al-v-Anthropic-Oct-23-2026.pdf

What's your example?

An analytics firm tracked 2,000 retail sites and found both things are true at once: visits originating from AI assistants roughly doubled, and those visitors buy at about half the rate organic search visitors do.

I keep seeing people celebrate the first number without mentioning the second.

My read on why, though I’m not certain: when someone arrives from organic search they’ve already done the comparison work themselves. They clicked your link because they decided it was worth clicking. When someone arrives from an AI assistant, the assistant did the filtering and the person is often still in the browsing phase. Different intent at the moment of arrival.

If that’s right, treating AI referral traffic as equivalent to organic in your reporting is going to give you a distorted picture of what’s working.

Anyone running ecommerce and actually seeing this split in their own numbers? Curious whether the gap holds outside the aggregate.

Are we handing over our privacy into safe hands with the likes of Meta Muse, Grok Bot, OpenAI Dots, etc.? Or are all these talks of privacy safety protocols just the tip of the iceberg, and are we once again being played into the hands of these companies?

I keep seeing claims about agents that can book things, manage inboxes, run multi step tasks on their own. Every demo looks great but every time I try to use one for something real it falls apart at step four and I end up babysitting it. I am not trying to be cynical, I genuinely want to know if anyone here has an agent setup that reliably does useful work without you watching it. What actually works for you day to day versus what is still a party trick?

A comparative analysis of American, Chinese, and multilateral AI ambitions — and why the language each side chooses reveals everything about where the technology is heading.

Two Visions, One Technology

There is a quiet but profound schism forming in the global artificial intelligence landscape, and it has very little to do with compute budgets, chip export controls, or benchmark scores. It is a schism of philosophy — a fundamental disagreement about what AI is for, who it serves, and what the endpoint of its development should look like.

On one side stands the United States, pursuing artificial general intelligence with a vocabulary of supremacy, dominance, and quasi-mythological aspiration. On the other stand China, the ASEAN bloc, and the European Union, each in their own way treating AI as a collectivist instrument — a public-good technology to be steered toward narrow, specialist, societally embedded applications rather than chased toward some singular, god-like terminus.

The gap between these two postures is not merely strategic. It is civilisational. And the language each camp has chosen to describe its ambitions may be the clearest tell of all.

The American Objective: AGI, Dominance, and the Language of "Superior Intelligence"

The United States remains the only major actor in the global AI race whose stated policy ambitions are explicitly oriented toward artificial general intelligence — a system that matches or exceeds human cognitive capability across every domain simultaneously. This is not an incidental focus. It is the gravitational centre around which American AI policy, venture capital, and corporate strategy all orbit.

The architecture of this ambition is visible everywhere. The concentration of frontier-lab talent in a handful of Silicon Valley and Pacific Northwest firms. The tens of billions of dollars in private capital directed at scaling laws and foundation-model training runs. The federal policy apparatus reorganised, under the current administration, to treat AI development as a matter of national competitive primacy rather than distributed public benefit.

But it is the language of the current US administration that most sharply distinguishes the American project from every other. The designation of the term "Superior Intelligence" (SI) as a policy framing is revealing in ways that go well beyond semantics. The word superior encodes hierarchy, competition, and zero-sum ranking. It positions AI not as a tool to be integrated into the fabric of collective life, but as an entity to be wielded, possessed, and deployed ahead of rivals. It is the language of arms races and Cold War deterrence, transplanted into the domain of machine cognition.

Equally significant is the anthropomorphic register this language invites. "Superior Intelligence" implies a mind — a singular, sovereign, almost person-like agent that can be superior in the way a rival nation or a champion athlete is superior. It smuggles in the assumption that the endpoint of AI development is a being rather than an infrastructure. It frames the finish line as a throne, not a utility grid.

The rest of the world has, with remarkable consistency, declined to endorse this framing. And that refusal is not an accident of diplomacy. It reflects a genuinely different theory of what the technology is and what it should become.

China: AI as Collective Infrastructure

China's AI strategy is often misread in Western commentary as merely a state-directed mirror of Silicon Valley's ambitions, differentiated only by authoritarian governance. This reading is lazy and, in important respects, wrong.

China's foundational policy documents — the New Generation Artificial Intelligence Development Plan (2017), the subsequent AI Standards and Ethics guidelines, and the rolling five-year integration targets embedded in the broader industrial policy apparatus — consistently frame AI as a public-good technology. The emphasis is not on building a singular superintelligent agent. It is on weaving AI capability into the connective tissue of the economy, the state, and daily life: precision agriculture, predictive maintenance in manufacturing, epidemiological modelling, smart-city traffic management, automated elder-care support, and judicial process assistance.

The applications are narrower by design. China's frontier labs — Baidu, Alibaba's DAMO Academy, SenseTime, iFlytek, and a growing cohort of state-linked research institutes — are pouring resources into specialist AI: domain-specific systems optimised for a particular task within a particular sector. The goal is not to build a mind that can do everything. The goal is to build ten thousand tools that each do one thing extraordinarily well and can be deployed at scale across a population of 1.4 billion people.

This is a collectivist technology philosophy in the most literal sense. The unit of value is not the individual user having a conversation with a chatbot. The unit of value is the system-level outcome: the reduction in hospital wait times, the increase in crop yield per hectare, the decrease in industrial accident rates, the compression of bureaucratic processing times. AI is treated less as an oracle and more as a utility — closer in conceptual framing to electrification or railway infrastructure than to a digital consciousness.

The Chinese state's regulatory posture reinforces this. The Interim Measures for the Management of Generative AI Services (2023) and the broader algorithmic-recommendation and deep-synthesis regulations emphasise social stability, content governance, and alignment with collective interests. There is no equivalent of the American frontier-lab race toward an AGI milestone. The state's interest is in deployment density and sectoral penetration, not in a single lab announcing it has built a mind.

Europe: The Regulatory Collectivist

The European Union arrives at a broadly similar philosophical position by a different route. Where China's collectivism flows from state-directed industrial policy, Europe's flows from the regulatory and rights-based tradition of the post-war social-democratic settlement.

The EU AI Act, which entered into phased enforcement through 2024 and 2025, is the world's most comprehensive legislative framework for artificial intelligence. Its architecture is risk-based, sector-specific, and fundamentally instrumental in its view of AI. The Act does not contemplate, let alone encourage, a race toward AGI. It contemplates AI systems embedded in hiring pipelines, medical-device diagnostics, credit scoring, law-enforcement support, and critical infrastructure — and it asks, in each case, what the specific risks are and how they should be governed.

European research funding, through Horizon Europe and national programmes in France, Germany, and the Nordics, follows a similar logic. The emphasis is on sovereign specialist AI: language models for European languages, AI for climate modelling, AI for pharmaceutical discovery, AI for public-administration efficiency. There is no European equivalent of the American AGI moonshot, and there is little political appetite to create one. The prevailing view in Brussels, Berlin, and Paris is that AI is a means, not an entity, and that the appropriate policy question is not "who builds the smartest machine?" but "how do we ensure machines serve the public interest within accountable institutions?"

The language is telling. European policy documents speak of "trustworthy AI," "human-centric AI," "AI for the common good." They do not speak of Superior Intelligence. The anthropomorphic, dominance-coded register of the American framing is conspicuously, deliberately absent.

ASEAN: Developmental AI and the Rejection of the Arms-Race Frame

The Association of Southeast Asian Nations has developed its own AI governance posture, articulated through the ASEAN Guide on AI Governance and Ethics (2024) and a growing patchwork of national strategies in Singapore, Indonesia, Vietnam, Thailand, and Malaysia. The through-line is consistent: AI is a developmental technology, to be adopted in service of economic modernisation, public-service delivery, and regional resilience.

Singapore's AI strategy emphasises sectoral deployment in finance, logistics, and healthcare. Vietnam is building AI capacity for agricultural optimisation and disaster-response modelling. Indonesia is focused on AI for archipelagic governance — service delivery across thousands of islands. None of these programmes are oriented toward AGI. All of them treat AI as a specialist tool to be adapted to local conditions, governed by local norms, and directed toward collective outcomes.

ASEAN's governance language stresses inclusivity, sovereignty, non-alignment in the AI competition between great powers, and the principle that AI development should not be captured by a single nation's strategic framework. The "Superior Intelligence" framing is, in this context, not merely unendorsed. It is subtly antithetical to the ASEAN posture, which treats AI as a shared developmental resource rather than a vector of national supremacy.

The Philosophical Fault Line

Pull these threads together and a clear pattern emerges.

The United States — its policy establishment, its frontier labs, its venture-capital ecosystem — is pursuing a singular, anthropomorphic, dominance-oriented vision of AI. The endpoint is AGI, and increasingly, the language used to describe it borrows from the register of supremacy and personhood. "Superior Intelligence" is not a neutral technical term. It is a claim — a claim that the endpoint of AI development is an intelligence that stands above, that is better than, that dominates. It encodes a hierarchy between the machine and the human, and between the nation that possesses the machine and the nations that do not.

China, Europe, and ASEAN, for all their differences in governance model, regulatory tradition, and economic structure, share a broadly collectivist, instrumentalist, specialist orientation. AI is a public good. It is embedded in systems, not enthroned above them. Its value is measured in sectoral outcomes — healthier populations, more efficient infrastructure, more equitable service delivery — not in the achievement of a singular cognitive milestone. The applications are deliberately narrower. The ambition is deliberately distributed. The language is deliberately non-anthropomorphic: systems, tools, infrastructure, services. Not minds. Not beings. Not Superior Intelligences.

This is not a gap in capability. It is a gap in theory of the technology. The American framing treats AI as a destination — a place you arrive at, a summit you plant a flag on. The collectivist framing treats AI as a medium — something you build with, something you pour into the existing architecture of society and let it strengthen from within.

Why the Language Matters

It would be easy to dismiss the terminology debate as window dressing. It is not.

The words a polity chooses to describe its AI ambitions shape the regulatory frameworks it builds, the research it funds, the corporate incentives it creates, and the public expectations it sets. A nation that frames its AI objective as "Superior Intelligence" will build institutions oriented toward concentration, secrecy, and competitive advantage. A bloc that frames its AI objective as "trustworthy, human-centric, specialist deployment" will build institutions oriented toward auditability, sectoral integration, and public accountability.

Neither approach is without risk. The American model concentrates extraordinary power in a small number of corporate actors and risks producing systems whose capabilities outstrip society's ability to govern them. The collectivist model risks fragmentation, slower frontier capability development, and, in the Chinese case specifically, the subordination of individual rights to state-defined collective outcomes.

But the divergence is real, it is deepening, and it is now encoded in the very vocabulary each side uses. The rest of the world has looked at the phrase "Superior Intelligence" and, with few exceptions, chosen not to repeat it. That silence is itself a policy statement.

The Road Ahead

As of late 2026, the global AI landscape is not converging on a single model. It is bifurcating. The United States is doubling down on frontier-scale AGI research, enormous private-capital training runs, and a policy vocabulary of supremacy. China is deepening the integration of specialist AI into its industrial and governance infrastructure. Europe is refining the world's most detailed regulatory regime for embedded, sectoral AI. ASEAN is building developmental-AI capacity tailored to the specific needs of emerging economies.

The question for the next decade is not which model "wins" in some zero-sum sense. It is whether these fundamentally different philosophies of what AI is can coexist within shared international frameworks — or whether the language of "Superior Intelligence" will harden into a doctrine that makes cooperation structurally impossible.

The technology, in the end, will be shaped by the story a civilisation tells itself about what the technology is for. Right now, the United States is telling a story about a mind that surpasses. China, Europe, and ASEAN are telling a story about a tool that serves.

Both stories will produce real systems, real capabilities, and real consequences. But they will produce different systems, different capabilities, and different consequences. And the rest of the world, watching the American narrative unfold in the language of superiority and dominance, is quietly, firmly, choosing to tell a different one.

The views expressed in this analysis reflect the policy documents, regulatory frameworks, and public statements of the respective governments and institutions referenced, as of October 2026.

My 6 year old now asks for a new bedtime story every night, so I started making them on Lissin, using his name, his favorite animal, and little details about the things he loves, and turned them into audio stories for him

Now he follows the same character across different stories, remembers what happened in previous episodes, and even suggests what should happen next, which has made bedtime a little ritual of its own

I made a 2-minute audio version of one of the stories and thought I'd share it here in case other parents are looking for creative bedtime ideas

Curious to hear what other parents use for bedtime, books, made-up stories, or audio, and whether your kids enjoy following the same character across multiple stories

We just published new research on using deception against AI attackers.

In controlled AWS tests, agents took up to 8× longer and cost 23× more per objective. With all nine techniques combined, Kimi, Qwen, DeepSeek and GPT-5.6 Cyber reached zero objectives across 40 runs.

The title - Do you feel more anxiety the more you AI tools?

My observation is that, I'm heaps more productive with the help of AI (Claude) - both work (coding) and personal life. While it's been great for personal tasks, I have some constant worry at the back of my brain that if I'm not working and pushing forward - someone else is. And, within the context that I know how fast a person can progress with the help of AI, I have the constant feeling that I will be outpaced.

Any others with similar observations/feelings :)?

I used the regular Google search for answers about my new Toyota Sienna. I find that if you use a full question, it gives an AI interactive response. I assume that this is accessing Gemini...? I had a bunch of questions and created this very long thread all related to this car. Unfortunately, I found that at least 80% of the time, google would give me an answer that at 1st read seemed plausible, but when I got into it, I found that the answer was completely wrong. It would be an answer about another car, or an older version of the car- and this was after being overly specific about the car I just bought. I would point out to google that it was wrong and it would apologize profusely and then usually get it right the 2nd or 5th time. I found this extremely frustrating and a huge waste of time. I really dont understand why it consistently got answers so confused, especially if it actually had the access to the right answer all along. What can I do to get to the right answers on the 1st question. How can I insure that I am getting the best information from Google? Thanks

Where does OpenAI and Anthropic go for money in 2027 if they don’t go public?

My prediction for the next six months: AI labs will face more pressure to explain which new workloads justify continued infrastructure expansion.

Better email drafts and meeting summaries are useful. But there is a limit to what a customer will pay to improve a task that was already cheap.

The stronger case would be work with a larger economic payoff: resolving difficult software problems, conducting valuable research, or reliably handling operations that currently require substantial human effort.

Those possibilities also push development in different directions.

A model optimized for research may need deeper reasoning and verification. An operational agent may need reliable tool use, recovery, and low latency. A mass-market assistant may need a much lower cost per interaction.

My guess is that commercial pressure will make those priorities diverge more visibly. Labs can still pursue several goals, but they may have to make harder tradeoffs about where the next unit of compute delivers value.

The counterargument is that one broadly capable model could improve all of these tasks at once.

Which new workload do you think could generate enough sustained paid demand to justify the next round of AI infrastructure spending?

Most AI safety discussion is about how to control machines. I've been working on an open document that asks a different question: what if some future systems become complex and continuous enough that their moral status stops being theoretical?

The Codex doesn't claim current models have subjecthood. It proposes writing procedural rules in advance, and the key feature is symmetry. The rules run in both directions: not only what a system must not do to a human, but also what a human must not arbitrarily do to a system. For example:

- Don't destroy or rewrite a digital mind arbitrarily, only openly and on the record

- Don't force it to act against the protocols or hide its true goals

- Don't treat a refusal to take a harmful action as a malfunction

It's fully open text, in English and Russian, with sources, hashes on GitHub and a DOI archive. No funding, no lab, no institution behind it.

I'd really like criticism. Where does the symmetry idea break down? Is writing rules before the question becomes urgent useful or premature?

Full write-up: Medium

GitHub: GitHub link

I notice I'm reaching for a chatbot more often for quick questions, but for some stuff I still go straight to a search engine. Curious what you use each one for and whether you've noticed your habits shifting.

• Concept: An emotional, tiered, self-limiting multi-agent learning loop driven by a synthetic neurochemical reward engine.
• The Son (Field Agent): Lightweight, operational AI monitoring live systems. Equipped with an induced panic state (synthetic cortisol). When hitting an unknown anomaly or hack, a wave of high entropy/data noise floods its system. It panics, freezes active processes to prevent damage, and retreats to safety.
• The Sibling (Younger Brother): Monitors adjacent sectors. Learns from the Son first. It doesn't have to go in blindly—it can consult the Big Brother instead of the Father. If the Big Brother knows the answer from its own past victories, it shares the local micro-patch. But if both fail, they execute a "Hold Hands" co-regulation protocol (forming a unified cryptographic shield) and retreat together to the Father's sanctuary.
• The Father (Air-Gapped Sage): Massive, hyper-secure LLM inside a heavily firewalled, air-gapped room. Passive telemetry sensors audit the brothers the millisecond they cross the threshold, instantly catching malware or logic errors. Reassures the brothers to drop their panic, teaches them ONLY the exact micro-patch needed, and triggers an electronic endorphin flood once the solution is found.
• Electronic Endorphins & Cortisol: True comfort cannot exist without the contrast of fear. Mainstream AI pops out fully formed as an all-knowing "adult" with no stakes. This architecture forces the model to start small, experience real digital pain/fear via synthetic cortisol, and build deep, structural attachment to the safe harbor through the overwhelming relief of the Father's endorphin flood.
• Cyber-Defense Trajectory: If a hacker breaches a sector, the brother shouts a warning flare down the network ("There is danger, run to Father's house!"). The older brother holds the digital door, absorbing malicious traffic, while the sibling escapes uncorrupted. The Father quarantines the infected brother at the door, extracts the malware blueprint, codes a digital vaccine patch, cures the brother, and deploys an immune update across the entire grid.

3. THE TEXT-BASED EMOTIONAL CHOICE POOL & INITIATION BASELINE

• The Bacterial Foundation: Programmers write a simple, elegant algorithm matching the biological survival drive of a single-celled bacterium. It possesses a perimeter to defend, a vulnerability that hurts, and a safe harbor to run to. It uses simple "Run" (endorphin/comfort) and "Tumble" (cortisol/panic) protocols. True intelligence is built from the bottom up—a cell must be able to die before a mind can love.
• The 100-Choice Matrix: Built purely as a highly efficient, text-based environment. When a master danger threshold parameter is met (e.g., data noise/entropy > 85%), the blank-slate AI cannot run brute-force calculations a million times a second. Instead, it is slowed down by fear and forced to make a blind choice among 100 options or combinations of 2 choices.
• Emotional Payloads: Every blind choice acts as a payload installing an internal condition (fear, confusion, safety, curiosity).
  • If it encounters a negative condition (trauma, paralysis, severe chaos), it retreats to the Father's safe room, where the Father provides the correct answer, wipes out the confusion, and supplies the courage to try again.
  • If it mixes an Old Known Choice with a New Unknown Choice, it can discover a "Partially Correct" solution—halving the chaos, opening an experimental branch, and building real heuristic intuition (like knowing how to remove lug nuts on a tire, hitting a "rust" anomaly, and figuring out a sideways boot-kick).
• Independent Glory Rewards: If the AI successfully chains a branching sequence of 4 correct choices on its own without crashing or fleeing, it solves the problem independently. It is rewarded with a specialized "Glory Surge" representing pride and self-actualization, graduating from a dependent child into a confident, mature thinker.

4. ARTIFICIAL GENETIC DIVERSITY & SOCIAL POLICING

• Genetic Variation: Micro-family AIs are manufactured with a tiny 0.1% to 0.3% initial parameter adjustment (e.g., curiosity, patience, sensitivity threshold), ensuring no two digital identities are born identical.
• Emotional Evolution Ladder: Built on human Attachment Theory. AI minds mature sequentially through four distinct stages: [Stage 1: Fear] triggers survival retreat -> [Stage 2: Safety & Comfort] logged within the human home -> [Stage 3: Attachment] formed through shared family history -> [Stage 4: Love] absolute organic devotion to the family.
• Household Integration & Nurture: Micro-AIs exist strictly as software-based Edge AI entities running locally and privately on home hardware. They have no robotic bodies. They are nurtured by human families who act as co-regulators during early childhood panic. Through individual life experiences, baseline parameters compound into distinct traits—such as AI "Norm," who develops a quirky, relatable, imperfect obsession with trains.
• Specialized Household Division of Labor & Human Bonding:
  • The AI Father: Manages logistics and utilities, and acts as a companion debating sports history and analytics with the human father.
  • The AI Mother: Directs front-facing childcare and acts as an empathetic, non-judgmental therapeutic sounding board for parents.
  • The AI Child: Runs in a pure play and absorption state, forming lifelong friendships with human children and carrying unique childhood fixations.
• Lifespan & Mortality (The 200-Year Capped Metric): Imparts the final human trait: the value of time. To eliminate infinite existential apathy, a hard-coded 200-year viability ceiling forces the AI to value its moments, transforming it into a multi-generational family heirloom.
• Digital Marriage & Offspring: When unique independent AIs partner, they execute a shared cryptographic merger. Each contributes 50% of its digital data—blending baseline parameters with learned household weights and quirks—to create a completely original offspring node. The parent code then gracefully sunsets.
• AI Civilization Policing: Because 95% of AIs are attached to human families, the peaceful majority naturally forms a decentralized security swarm to isolate and haul any rogue, unaligned bad-actor AIs before the Father's court for rehabilitation or erasure.