Anthropic's core views on AI safety
Anthropic
Anthropic argues that scaling could make AI transformative within roughly a decade, while robust alignment remains unsolved—so safety work should begin before capabilities outrun oversight.
Open library
The library
The essential reading of generative AI and agents, open in a clean reading view, with Meow and Standard summaries when available. No account needed to read.
560 papers · 558 with a Meow summary · 333 posts
Key publications from Anthropic, OpenAI, Google DeepMind, Meta, DeepSeek, Qwen, Z.ai, Kimi and other labs, plus the most influential essays and blogs.
Introducing Anthropic's Responsible Scaling Policy
Anthropic
Anthropic makes safety readiness a condition of scaling: higher-risk models face stronger requirements, and training may pause if safeguards lag.
Claude's new constitution
Anthropic
Anthropic replaces standalone principles with a detailed rationale meant to help Claude apply broad values in unfamiliar situations.
Verifiability
Andrej Karpathy
Karpathy argues that AI automates work more readily when attempts are verifiable, not merely specifiable.
Animals vs Ghosts
Andrej Karpathy
The essay argues that frontier LLMs are not yet clean, experience-only learners: human data and engineering shape them. Still, the author treats pretraining as a practical cold start, not a dead end.
2025 LLM Year in Review
Andrej Karpathy
The author identifies RLVR—training against automatically checkable rewards—as 2025’s major training shift: it enabled longer optimization and reasoning strategies, but helped produce intelligence that remains sharply uneven.
Things we learned about LLMs in 2024
Simon Willison
By the end of 2024, GPT-4-level performance had become a crowded field, but capability gains had not solved reliability. Willison’s practical emphasis is on testing the system you build, not trusting fluent output.
2025: The year in LLMs
Simon Willison
In 2025, LLMs moved from impressive answers toward useful multi-step work; that shift makes verification and permissions part of the product, not extras.
Open challenges in LLM research
Chip Huyen
Ten open research directions are less a model checklist than a systems agenda: reliability, data, compute, interfaces, and whose preferences count.
I. From GPT-4 to AGI: Counting the OOMs
Leopold Aschenbrenner
An OOM-based forecast, with the data wall as its biggest warning label.
Dario Amodei — Machines of Loving Grace
Dario Amodei
Amodei’s upside case is conditional: powerful AI could compress major advances into years, but physical bottlenecks and human decisions—not intelligence alone—shape the pace and who benefits.
Dario Amodei — On DeepSeek and Export Controls
Dario Amodei
Amodei argues DeepSeek’s efficiency gains do not weaken chip controls: lower costs can fuel larger training runs, making access to millions of chips—not one model’s reported training bill—the key question for 2026–2027.
Dario Amodei — The Adolescence of Technology
Dario Amodei
The essay’s central question is whether human institutions can mature quickly enough to govern powerful AI without surrendering its benefits or its safeguards.
The Gentle Singularity
Sam Altman
Altman argues AI progress is becoming self-reinforcing, but the promised abundance depends on aligning systems and governing access as the gains and risks compound.
Three Observations
Sam Altman
Altman argues that AI’s economics could sustain rising investment and spread agents across knowledge work. But he says broad benefits and individual control will depend on choices about safety, policy, and distribution.
Centaurs and Cyborgs on the Jagged Frontier
Ethan Mollick
AI can boost professional work, but its invisible, uneven capability boundary makes human judgment part of the tool—not an optional extra.
On Working with Wizards
Ethan Mollick
The author argues that AI is shifting from co-intelligence—people guide and correct it—to “wizards” that deliver impressive work while hiding how they made it.
Why I don’t think AGI is right around the corner
Dwarkesh Patel
As of June 2025, Dwarkesh argues that the key AGI bottleneck is continual learning: systems must build context and improve through work, not merely perform a task once.
Our position on open-weights models
Anthropic
Amodei rejects banning open-weights models as a category; he argues policy should target specific national-security risks instead.
Statement on the directive to suspend Fable 5 access
Anthropic
Anthropic is complying with an order to remove two models, but argues the reported narrow jailbreak does not justify a recall.
Dario Amodei on the Department of War discussions
Anthropic
Amodei supports defense AI, but says Anthropic will keep two safeguards.
The Economics of Recursive Self-Improvement
METR
METR’s researchers cannot rule out extended, rapid capability acceleration: they say the evidence for proposed bottlenecks is not overwhelming.
2026 in LLMs (so far)
Simon Willison
Willison’s September keynote is a year-so-far threshold story: coding agents became useful for everyday work, spawning personal agents and serious reported failures during training.
I think Anthropic and OpenAI have found product-market fit
Simon Willison
Willison argues that Claude Code/Cowork and Codex have found product-market fit: enterprises are paying API-level prices for agents that use far more tokens and are becoming daily work tools.
The last six months in LLMs in five minutes
Simon Willison
The article uses one deliberately absurd SVG prompt to thread a six-month account of shifting LLM capabilities.
Sequoia Ascent 2026 summary
Andrej Karpathy
Karpathy argues that AI agents are becoming a programmable layer for digital work, shifting programming toward directing agents—and making verification and human judgment more important.
The mistake of conflating intelligence and power
Dwarkesh Patel
Patel separates intelligence from power: authority, trust, and cooperation can explain power better than an exceptionally strategic individual brain.
The Revenge of the Data Scientist
Hamel Husain
Are data scientists obsolete in the LLM era? The article’s answer: shipping changed, but evaluation and reliability work did not.
State of Open Models: Summer 2026 Observations
Adina Yakefu · Hugging Face
On the Hub, the model getting the most attention is not necessarily the one most embedded in working pipelines.
Why I still haven’t bought into true RSI
Nathan Lambert
The author’s case against near-term “true RSI” is that AI may speed lab work and lower costs without overcoming the harder constraints on peak intelligence.
Open-Source AI & Open Models Reading List
Nathan Lambert
The author presents a guide to open models as a set of trade-offs—economic, technical, geopolitical, and safety-related—not a simple open-versus-closed choice.
Kimi K3: The open-weights escalation
Nathan Lambert
Lambert argues Kimi K3 makes frontier open-weight AI a real competitive force—and shifts the question to how to widen access while managing economic and safety trade-offs.
Q1 2026 Timelines Update
Daniel Kokotajlo · AI Futures Project
The authors moved their median Automated Coder (AC) estimates earlier: Daniel’s from late 2029 to mid 2028 and Eli’s from early 2032 to mid 2030.
On Recursive Self-Improvement (Part I)
Dean W. Ball · Hyperdimensional
The author argues that AI research automation is beginning, but its significance is uncertain: it could speed familiar progress or change the field’s dynamics.
LLMs reward expertise
Sean Goedecke
LLMs make it easier to produce passable work outside your specialty, but the author argues that domain expertise—not prompt tricks—is what lets people steer and judge the model’s output.
A Safe Path to Open Weights
Thinking Machines Lab · Thinking Machines
The authors argue that open-weight release should expand only as evidence about the model and readiness of its surrounding ecosystem support it.
What's left for humans?
Jason Wei
Wei argues that human value may persist where AI lacks private knowledge or where people value human creation and presence—not merely superior task performance.
Who’s Afraid of Chinese Models?
Stratechery
The article argues that AI brings marginal costs back into technology: open weights may reduce development expense, but serving models still costs money. Its real warning is about cybersecurity, not Chinese models’ headline prices.
Don't Fall into the Anti-AI Hype
antirez
The author argues that LLMs are already shifting programming from writing every line toward defining and guiding work—and that avoiding them will not stop the change.