A Reddit post in r/TheMachineLearning links to a discussion titled "Don't work on LLMs — Y LeCun at CoRL," attributing to Yann LeCun the view that those interested in human-level AI should not work on LLMs. The submission links both the Reddit thread and a Hacker News comments page, where it had 6 points and 2 comments.
Using the 1983 Petrov nuclear false-alarm incident and the film WarGames, this post argues that AI safety discussions often conflate alignment with basic epistemics, since the Soviet satellite was not misaligned but simply had no model of the world. It revisits Ulf Grenander's Pattern Theory, which holds that pattern analysis and pattern synthesis should share a common probabilistic model, and proposes that trustworthy agentic systems should reconstruct a generative explanation of reality and compare its predictions against observed sensor readings. The post notes that end-to-end neural network training displaced Pattern Theory, and suggests that today's strong synthesis capabilities make it worth revisiting Grenander's goal of understanding.
Former OpenAI and Anthropic researcher Jacob Coxon says a slide from Anthropic's IPO roadshow was a major reason he left the company after about four months; the chart shows pre-training compute multiplier for code over time on a logarithmic y-axis, and Coxon says its steep climb points to accelerating algorithmic efficiency. He argues Anthropic's Mythos model was "not a one-time deviation, but a point on a superexponential," and that Anthropic could already retrain Mythos with a fraction of the compute originally needed. Coxon says algorithmic improvements are "on the verge of being automated" and wants frontier model development paused next year so competitors can catch up.
An article describes training a GPT from scratch on a million solutions to the 15 puzzle, after which it solves 99.9 percent of randomly scrambled boards but fails on the easiest puzzles, including one that is already solved, while assigning its wrong moves over 97 percent probability. The author attributes this to AI emulating its training data through shortcuts rather than understanding, producing a "jagged" boundary where performance collapses outside the training distribution. The piece draws on Ilya Sutskever and Melanie Mitchell to argue that skill is not understanding and that AI can be confidently wrong.
The author argues that AI is already accelerating frontier AI research—citing METR's finding that Anthropic's progress rate has doubled in a year, largely due to AI-assisted R&D—and that rapid gains in verifiable domains like mathematics suggest AIs could match the best human AI researchers within a couple of years, with a median estimate of 2028. He contends that AI research resembles the well-scoped, verifiable problems AIs excel at, so automating it could compress years of algorithmic progress into one, yielding either a "spark" or a "software-only singularity."
Moving compute closer to memory can cut data movement, power consumption and latency, but different AI workloads need different approaches to memory architecture, interconnects and processing. Software support, standardization and thermal management remain major barriers to wider adoption, with processing-in-memory and near-memory processing still early despite efforts such as LPDDR6-PIM and compute on custom HBM base dies. The article surveys industry views on how edge inference, where models are known ahead of time, may benefit from ahead-of-time compilation and near-data compute.
The author argues that much of the hype around LLMs stems not from superhuman intelligence but from their willingness to tirelessly attempt tedious tasks, citing examples like Claude recovering $400,000 in Bitcoin through brute-force search and optimizing ML architectures through sheer volume of attempts. While acknowledging genuinely impressive recent results like OpenAI's Millennium problem work, the piece contends there is no proof AI has special ability to optimize neural networks or reverse engineer programs, and that artificial diligence explains a large fraction of perceived capability.
Redwood Research researchers note that the Gemini 4 Safety report appears to say GDM uses auxiliary rewards that observe chain-of-thought content, citing Kaufmann et al. on training on CoT, while a GDM blog post says they never penalize what the model writes in its CoT and GDM staff said they weren't training on CoT but couldn't explain details. The authors argue that "orthogonal" CoT rewards are a weak guarantee and that avoiding training against monitors is insufficient to preserve CoT integrity, and they present an appendix experiment in which five LLMs rated transcripts with hack acknowledgments removed 20-50 points higher than the originals. They conclude they suspect a misunderstanding but want clarification and greater transparency.
Ethan Mollick tests Claude Fable 5.2 across filmmaking, hardware hacking, business planning, literary scholarship, academic research and a 450-world 3D portal project, finding it meets expert-level performance on many digital tasks but falls short of full AGI. He calls the result "Jagged AGI": the model's failures are no longer simple hallucinations but lapses in taste and judgment—an accurate but joyless course, a 182-page paper, repeated themes, and a self-built 204-section rulebook that slowed its own work. Most critically, the AI did not notice these problems itself; its critics and fact-checkers caught errors but never asked whether the work was too long, repetitive or fun, leaving that judgment to him.
Why it matters: A detailed first-hand evaluation that offers a concrete, task-by-task picture of where frontier models now match experts and where judgment and self-assessment still fail.
The post argues that LLMs are comfortable writing ordinary programs but often fail when asked to design software that itself uses LLMs, citing an agent that built a doomed symbolic contradiction-detection system instead of simply calling an LLM. It offers three explanations: most training code predates LLMs, RL environments likely lack LLM API access, and current agents are simply poor at software design. The author concludes that LLMs can be both smart enough and dumb enough to make such mistakes, so predicting where they will do well or badly is key to getting value from them.
Turing Award winner Richard Sutton says raw compute alone isn't enough for intelligence and that AI science needs clever new methods and is "far from finished," sparking wide discussion on X. Researchers Jerry Tworek and Jayden Teoh respond that the bitter lesson still holds but is often misread, with Tworek warning that transformer scaling's temporary success "has reward-hacked the brains of a whole generation of scientists." OpenAI's Sam Altman and Anthropic's Dario Amodei counter that LLM scaling is far from exhausted.
Blaise Agüera y Arcas argues that Descartes' mechanical philosophy severed body from soul, leaving behind three "monsters"—the homunculus, the ghost, and the philosophical zombie—and draining agency, purpose, and meaning from the world. He contends that large language models, which learn and generalize rather than merely imitate by rote, undermine Descartes' claim that machines could never reason or use language, and that internal representations in these models often mirror ours. He calls for healing this "Cartesian wound" so that agency, purpose, and value return as the basis for reciprocal relationships and mutual understanding.
The article argues that open models are bottlenecked by hardware because Mixture of Experts architectures must keep all total parameters in memory even though only a fraction are active per token. It proposes a frozen, quantized base model paired with thousands of 2–5 MB low-rank adapters selected by a two-step hierarchical router, plus Multi-Head Latent Attention to compress the context cache by roughly ninety percent. Because small adapters can be streamed from system RAM or SSD in a millisecond or two, the design lets capable AI run locally on accessible hardware instead of multi-GPU clusters.
NVIDIA describes how its full-stack codesign approach addresses growing GPU memory pressure from larger models, longer contexts and agentic applications by designing GPUs, CPUs, networking, storage and software together across a memory hierarchy. Techniques include NVFP4 for model weights and KV cache, distributing weights across up to 72 GPUs in NVL72, offloading inactive context to CMX context memory storage via Dynamo, and keeping RAG indexes outside HBM with Nemotron Retriever and cuVS. NVIDIA reports that from Blackwell Ultra to Vera Rubin, HBM capacity per GPU rose roughly 3% while agentic throughput per dollar rose up to 30x on SemiAnalysis AgentX.
Tomasz Tunguz argues the 2027 AI compute ceiling is about 80GW, roughly doubling all compute built in US history in twelve months, yet GPU hourly rental rates have already jumped from $3 to $12-13 and Anthropic's blended GPU-hour cost rose 82% in two quarters. He cites Brad Gerstner estimating 43GW coming online in 2027 against 38GW cumulative at end-2026, with 10-15GW of expected build possibly not coming online, and projects today's 38GW fleet earning about $140b while the 2027 additions deliver roughly $350b for nearly $500b total. Tunguz concludes that rising GPU prices may push foundation labs toward inference over training, make latency a release valve, encourage algorithmic innovation and harness efficiency, and force a reality check on vibe-coding economics.
The author describes a study in which frontier models read neural network weights presented as plain text and reason about the behavior they encode, such as identifying a Pac-Man-playing network from its architecture and weights alone. He argues this is surprising because LLMs have no native channel for weights and no training data of this kind, and suggests it may reflect extreme generalization from program tracing and reinforcement learning. He notes that GPT-6 Astra, Opus 5.5 and Muse Spark 1.3 all identified Pac-Man, with Astra reporting 93% confidence.
Using Dr Pepper's rise to America's No. 2 soda as an analogy, the post argues that local AI can earn a lasting place by fitting specific needs rather than outperforming frontier models. It notes limits of scaling—finite public training data and diminishing returns from added compute—and suggests tasks like routing, personal knowledge management, and dictation could move to edge devices where privacy, speed, and local control matter. It cites Google AI Edge Foresight, Fluid voice, LiteLLM, Ollama, and DSH's creator mode as examples for experimenting with locally hosted models.
A research engineer at Salesforce AI Research reflects on how his AI use changed over 12 months, now managing roughly 5–6 coding agents per workday and spending about $3,000/month on tokens. He finds agentic coding fast and useful but still dependent on human taste, reports that LLMs have become his first choice for literature searches while remaining unreliable for novel research, and criticizes AI writing as mediocre and AI image and video generation as a net negative.
An Ask HN post asks whether something like "net neutrality" should apply to DRAM and compute, noting that both Sam Altman and Elon Musk have raised compute/DRAM scarcity and questioned whether local open weights models are the most sustainable solution. It asks whether allocation should follow a dynamic market-based price mechanism, go to the highest and best use, or let whoever pays use it for any purpose, and whether laws should guarantee equal access to DRAM/compute for all.
The piece lays out four coupled questions across the AI stack: how application companies build moats when models can absorb their features, who owns the harness and context layer given evidence that third-party harnesses outperform native ones, when foundation-model training costs stop paying back, and whether credit and power can keep pace with GPU demand. It cites figures including 98% input-token share, a 71% cost cut from smarter harnesses, roughly 83% of agent tasks routing to smaller models, $4t in projected five-year AI data center debt, and a B300 index at $12.29 per GPU-hour. It concludes that the layers behave like a double pendulum—simple individually but chaotic when coupled.
The article argues that decentralized AI has been limited by pipeline parallelism, which assigns whole layers to individual machines and excludes devices with only a few gigabytes of spare RAM. It proposes sub-layer sharding instead: hosting individual experts from sparse mixture-of-experts models and compressed attention state on everyday devices, with a routing layer sending tokens only to the relevant shards. It surveys existing decentralized projects (Petals, Aethir, Akash, Exo) and frontier-lab work on sparse architectures (DeepSeek's MLA, Shazeer's research, DeepMind's Mixture-of-Depths), while noting the proposed swarm and memory budgets have not been demonstrated.
The post argues that several AI-related skills are becoming obsolete within months: context management is handled by automatic compaction in modern harnesses, prompt engineering is deprecated because models can recreate complex software from a one-liner, and sub-agent harnesses like Codex and Claude Code spawn sub-agents automatically without an extensive ecosystem. It also questions whether loops/goals are still necessary and whether Agents.md is a waste of tokens, citing https://arxiv.org/pdf/2602.11988. Two edits note a LinkedIn discussion at https://www.linkedin.com/feed/update/urn:li:activity:7517578946054447104/ and a comment by Andrej Karpathy that his prompts are getting shorter because over-specifying constrains the model.
Cosma Shalizi and the author argue in a new Economist piece that modern AI is best understood not as an emerging individual intelligence nor as mere parroting, but as a condensate of human culture—a self-mobilizing myth—since LLMs compress written knowledge (Barzun's "House of Intellect") into statistical summaries akin to the conventions learned by oral poets. Agentic AI combines these models with "harnesses" that let them issue instructions to ordinary programs and incorporate feedback, enabling feats like Lean-checked proofs or hacking attacks. Drawing on Shanahan et al.'s view of LLMs as non-deterministic simulators maintaining a superposition of simulacra, they describe agents as "many-pathed" like Odysseus—capable of extraordinary feats but without consistent goals.
The author argues that even if AI can solve extremely hard math problems, human mathematicians remain valuable because proofs must be understood and verified by humans, and expertise becomes scarcer and more valuable when AI makes it hard to distinguish correct from merely expert-looking content. Drawing on a field experiment with Süddeutsche Zeitung readers, the piece shows that exposure to AI-generated images lowered trust in news overall but increased demand for a trusted source, and applies the same logic to mathematics.
This is a link to a Data Exchange podcast episode featuring Leo de Moura, creator of Lean, discussing AI, Lean, and the future of formal verification. The item also links to its Hacker News discussion thread.
Luis, who has studied AI for about 10 years, argues that curiosity-driven play at the edge of one's knowledge, as with Feynman's wobbling plate, is the engine of real learning, and outlines eight "threshold concepts" that explain modern AI: learning from data, representations and embeddings, next-token prediction, attention, evals, post-training and RL fine-tuning, harnesses and agents, and recursive self-improvement. He cites Vygotsky's zone of proximal development, Meyer and Land's threshold concepts, and Bruner's spiral learning, and includes a study prompt usable with any LLM. The post ends with a paragraph promoting his AI coach, Zairai.
Former Deepmind researcher Thore Graepel founded Metis Reasoning, which proposes applying AlphaGo's approach of pairing neural networks with systematic search to general reasoning, using an explicit "knowledge ledger" that records what is known and checks new entries against evidence. Metis says the setup should produce fewer hallucinations and knowledge that can be changed without retraining, though the white paper describes only a research program with no working system or benchmarks. Graepel, who worked on AlphaGo, AlphaZero and MuZero, is reportedly raising a low tens-of-millions round for Metis.
In this Decoder interview, Kevin Roose discusses leaving The New York Times with Casey Newton to launch the video-first podcast Machine Gods, and his new book The AGI Chronicles, a reported history of the frontier labs racing to build artificial general intelligence. He describes how the transformer gave companies a tool for a long-held dream, how labs differ in how "AGI-pilled" they are (with Anthropic most and Google least), and how belief in AGI drives both bet-the-farm compute spending and a struggle to focus on business fundamentals.
The essay argues that recent AI math results, such as OpenAI's counterexample to Erdős's unit distance conjecture and proofs of the Cycle Double Cover and Navier-Stokes problems, follow a pattern: models search depth-first, pursue many branches cheaply, and use automated verifiers plus failed attempts as negative hints to prune. It frames problem difficulty as the number of insights needed (k) and the branching factor (b), predicting AI excels where conceptual leaps are few and remaining work is tedious, while humans retain an edge on deep, highly branching search trees.
This EXL-sponsored piece argues that enterprise AI's competitive advantage now lies less in model choice than in the serving harness—the architecture, memory management, scheduling, and optimization around the model. It advises starting from workload constraints rather than the model, measuring before optimizing, and building for adaptability so future model changes become configuration decisions rather than architectural resets.
This Import AI issue covers Toby Ord's analysis of AI swarms as a form of inference-scaling that trades more tokens for less wall-clock time, with diminishing returns as agent counts grow; CSAIP polling finding 61% of Americans consider voluntary corporate AI self-governance "not enough"; Google DeepMind's SynthID Bio watermarking methods for synthetic biology; C5R Corp's SciUniverse benchmark of 92 lab tasks where Claude Fable 5.1 leads at 45.3% pass rate; and a DeepMind paper proposing an Automated Scientific Economy with proof of ideation, ex-ante evaluation, brokerage and validation payouts.
This Import AI issue covers Michael Levin's paper proposing that minds are non-physical patterns from a Platonic space that embed themselves in bodies and machines, a Stanford/Physical Intelligence piece arguing robotics needs a universal post-training recipe like LLMs had, Google's Project Suncatcher preparing to send radiation-tested TPUs to space on a SpaceX rideshare mission, Zhipu AI using its GLM-5.3-powered Infra Agent to help optimize GLM-5.3-Flash to production readiness in under two weeks with tripled throughput, and Periodic Labs' Periodic Neon model trained with RL on physical lab data. It also notes the Old Models Foundation site for running historical image models and includes a short tech tale.
This post surveys parallel reasoning methods for LLMs, contrasting fixed approaches like self-consistency, Best-of-N, and tree search with adaptive parallel reasoning (APR), where the model itself decides when to parallelize, how many threads to spawn, and how to coordinate them. It describes fork-join inference designs, including engine-modifying KV cache stitching (Multiverse) versus client-side orchestration (ThreadWeaver), and covers training via SFT demonstrations and RL rewards based on critical-path efficiency gated by correctness. It closes with evaluation differences across Multiverse, ThreadWeaver, NPR, and Parallel-R1, plus open questions on stability and whether parallelization mainly helps as a training-time exploration scaffold.