AI Submissions for Fri Nov 06 2026
A Jev that can ask Opus for help gives Opus accuracy at 4x lower cost
Submission URL | 567 points | by theanomaly | 142 comments
Pairing Jev with Opus matches frontier-level accuracy at more than 4x lower cost on Hightouch's task of grading identity resolution (IDR) output, specifically spotting over-merged profiles where one identity swallows multiple real people.
The benchmark was built with pseudopeople (a simulated US town from IHME at UW) with realistic data errors added. It contained 1,000 identities, half clean and half over-merges. The motivation is that human review runs about 50 identities/hr, and pinning the over-merge rate to ±1% takes more than 1,000 samples. An LLM grader that works at human accuracy could score 100,000 identities in seconds.
- Out of the box: Jev and GPT-6 Luna, the fast "decision models" that return a 0-1 confidence score, hit 88.8–97.3% accuracy. They cost $0.05–$0.10 per 1,000 identities and answer in about 0.15s, up to 90x cheaper and over 10x faster than frontier models. On harder benchmarks they trail Opus by about 6 points.
- Luna vs. Jev: Luna is better on easy cases, but the gap disappears on harder ones.
- Calibration decides the hybrid: Jev's mistakes mostly come with low confidence, while Luna's mostly come with high confidence. That makes Jev the better first-pass filter. It handles what it's sure about and escalates uncertain identities to Opus.
The catch is that this is one team's synthetic benchmark on one task, and the excerpt cuts off before the hybrid's exact accuracy figures and routing thresholds. The calibration finding is the transferable part: a cheap model only works as a router if its wrong answers are the ones it's unsure about.
The thread quickly drifted from the Jev/Opus routing result into a broader argument about how to mix expensive and cheap models. Few commenters engaged with the benchmark itself. hoppp said they'd swap Jev in for the Haiku guardrail layer in their LLM proxy, and pletnes made the closest match to the post's idea: use a cheap model to decide when a costly, high-effort run is worth triggering.
Which direction should delegation run? This was the main split.
- Smart orchestrates cheap. drkleiner uses Opus as planner and orchestrator, never implementer, with Qwen 27B subagents, and says it cuts token usage significantly. beefsack runs three tiers (Opus orchestrator, GPT Sol feature lead, a cheap Muse Spark worker) with the stronger models reviewing everything below them. beefsack also tried the inverse, where cheap models call up to stronger ones, and found them bad at knowing when to escalate. Wrapping the cheap models worked more reliably.
- Cheap fronts smart. xlayn has Qwen orchestrate and then query Claude, arguing that Qwen fills in the detail that lets bigger models do well. They judged the results "VERY CLOSE" using Claude and Qwen as graders, so the evidence is informal. fireant objected that a weaker model rephrasing a prompt would amplify any misunderstanding. bluegatty's answer was that this suits specific cases, such as using Claude Code to write long, intricate prompts for GPT Pro.
- Evidence. nextaccountic asked whether anyone had measured the strongest-orchestrator setup. agentdev001 cited a Nature Machine Intelligence paper in which a weaker orchestrator with strong workers scored higher. They flagged that it used older models and that recent Anthropic system cards suggest the best multi-agent shape now varies by model.
Does mixing different vendors' models improve accuracy? brabel doubted it and asked for rigorous tests. bluegatty answered from daily use only, with no benchmarks. Claude Code and Codex each audit the other's design changes, and a third perspective probably has diminishing returns. larodi offered a similar "entropy" argument that mixing models may improve quality.
Practical wiring.
- unrented7977 simply tells Claude that a local LLM server is available and lets it write the scripts to call it, with no MCP server.
- lelandbatey noted that OpenRouter uses the same API, so it only needs a URL and key swap.
- sixdimensional puts a deterministic rule-based bot first and escalates to a smart agent only when it can't match intent.
- jeef_berky wants a local model as the stable core that reaches out to Opus.
There was also a side explanation of GPT Pro as a parallel-compute "pro" mode on OpenAI's $200/month tier.
Nobody offered measurements, so the thread leaves open whether the best orchestration direction is task-dependent or just habit.
Gemini Omni 1.2 Flash
Submission URL | 356 points | by thisisauserid | 249 comments
Gemini Omni 1.2 Flash cuts video generation prices 30% from Omni 1.1, to $0.07 per second at 720p, and stretches native clip length from 10 to 15 seconds. It's generally available on the Gemini API, Google Cloud, and Gemini for business, and it also shows up in Google Vids and Flow.
The cost story is as much about workflow as price. You draft at 360p or 720p, fix details with a follow-up edit instead of regenerating the clip, then upscale the keepers to 1080p or 4K. Duration is settable in one-second steps from 3 to 15 seconds.
The other headline is multi-turn conversational editing that works across full 15-second clips. Google's demo swaps backgrounds turn by turn (pavilion tent, oak forest, castle) while keeping character performances and camera framing intact. That pitches the model at ad-variant production: swap the product, lighting, weather, or aspect ratio without re-briefing.
Other changes:
- Reference adherence: it accepts character, product, or style images, motion clips up to 3 seconds, and audio cues. Faces, garments, packaging typography, and brand colors are meant to hold across camera angles.
- Consistency: less drift and morphing between first and last frames, including keyframe interpolation and clip extension.
- Speech: clearer lip-sync and distinct multi-speaker conditioning, with voices routed to the right character from one-second voice references.
- Camera: continuous FPV flythroughs, orbits, and zoom-outs with fewer unwanted jump cuts.
The caveat is that every example is a Google-picked showcase, and the post offers no benchmarks or quantified comparison against 1.1 or competitors. "Fewer jump cuts" and "less drift" are claims to test on your own footage before committing a production pipeline.
The thread ignores Omni 1.2 Flash itself. It becomes a complaint about how hard Google makes it to pay for and use its AI products, and a long argument over why.
The access complaints. The top comment says the newest models are locked even for a Plus subscriber. Adding balance to a Google payment account throws errors and demands government ID. Others report similar trouble:
- One commenter tried to pay but never activated anything and hit an API limit after one prompt.
- Another spent a year-old enterprise trial unable to get a Gemini 3+ model to appear, while Anthropic and OpenAI took under 10 minutes.
- One user paid for the consumer subscription but can't use it outside the app, and won't renew.
- Someone trying to pull their current spend was told to build a query against a data lake and check back tomorrow.
- The team that finally got stable access used OpenRouter for the API. For subscriptions they used personal-Gmail Ultra accounts hooked to the Antigravity CLI, because individual org members can't get Ultra without upgrading the whole org.
- Another commenter extended this to GCP generally: enabling Sign in with Apple and Google was far harder on GCP than on Apple's side, and a first Cloudflare Workers deploy was faster than getting a GCloud API key.
AI Studio. One commenter called it the worst UX on the internet. Others offered rivals: Workday, ADP, SAP internal apps, IBM's tools and website. One user defended it, citing per-session system prompts, deleting any message, branching a conversation from any point, and a deterministic web-search toggle. They say Claude removed its own branching.
Why doesn't Google fix it? Commenters gave several explanations:
- One said Google makes impossible projects possible and easy ones feel impossible, and that entrenched senior "whales" control small towns of people. Another asked whether the "impossible projects" half still holds, and got Waymo as the example.
- One said Google's culture can't be fixed because individual impact is hard to measure, let alone a manager's. A reply said that is a Silicon Valley fixation on quantitative metrics, and that someone should just make a judgment call and hold the person accountable.
- Another cited a tweet from an ex-Googler who said he was blocked from doing things there.
Smaller threads:
- Code review: One commenter says Gemini catches subtle security and backwards-compatibility issues that Claude and GPT miss, with Claude making the final call. A reply said it wrecked their projects and that others called it benchmaxxed.
- Routing: OpenRouter requests are routed between AI Studio and Vertex. A reply noted that is an OpenRouter feature and the provider can be pinned.
- Safety controls: One user found them random and bizarre, possibly depending on the route.
Chomsky and the two cultures of statistical learning (2011)
Submission URL | 142 points | by andsoitis | 131 comments
Peter Norvig's rebuttal to Chomsky argues that statistical models are not just engineering hacks: for interpretation tasks like speech recognition, probability is the science, because the core problem is inferring what a speaker most likely meant from noisy input.
The essay answers remarks Chomsky made at MIT's 2011 Brains, Minds, and Machines symposium, in reply to Steven Pinker. Chomsky called the field's notion of success, "approximating unanalyzed data," novel in the history of science. Norvig reduces his position to five claims: engineering success is irrelevant to science, accurate modeling is "butterfly collecting," statistical models give no insight, they simulate language the wrong way, and statistical learning has been proven impossible, so language must be innate.
Norvig concedes some of this. Engineering success isn't the sole measure of science, a billion-parameter model can't be understood by reading parameters, and a Markov model of word probabilities can't capture all of language. But he pushes back on each point:
- Facts and theory: Accumulating facts is the dominant mode in the history of science, not a novelty, and engineering success is evidence (not proof) that a model is right.
- Insight: You can understand a huge model by studying where it fails and how it scales with data, even if you can't inspect its weights.
- Straw man: Chomsky dismisses all probabilistic models because of one 50-year-old model. A tree-structure model without probabilities fails just as surely, and what's needed is a probabilistic model spanning syntax, semantics, and discourse.
- Learnability: Gold's 1967 theorem concerns formal languages, not what natural-language learners face, and Horning showed in 1969 that probabilistic context-free grammars are learnable.
Norvig grants that humans have some innate capacity for language. His point is that nobody knows whether it's language-specific, or whether it works like statistical learning.
The excerpt cuts off while defining terms (mathematical vs. probabilistic vs. statistical models), so the later sections on how successful language models are and what Chomsky dislikes about them aren't covered here. The central argument is already clear: Norvig's claim is that the dispute is about what counts as explanation, not just about performance.
The thread splits on one question: does a statistical model that works tell you anything about how the system being modeled actually operates? Most of it is the LLM-era version of Norvig vs. Chomsky.
The "engineering isn't science" camp. NaiveEmpiricist argued the two sides talk past each other. Aeronautics says little about how birds fly, and LLMs use millions of times more data and compute than a child, so they are probably poor models of human language learning. They added that Chomsky's influence is waning and linguists are now adopting statistical tools. They also pointed to the BabyLM work on training with human-scale amounts of text as showing how much remains undiscovered.
The Bayesian reply. theptip said Norvig isn't confused, because he explicitly claims only that success is evidence, not proof. If method X models a system well and no method Y does, X is more likely to be the real mechanism. They cited predictive-processing accounts of the brain as a hierarchical predictor rather than a rule engine. Planes also constrain what birds must obey, such as thrust-to-weight. On sample efficiency, they argued the comparison is unfair. Brains arrive with an evolved prior, while an LLM starts almost from scratch, so the fair baseline is every brained ancestor's accumulated experience. When skinfaxi asked for elaboration, swid offered radioactive decay and chaotic systems, where statistical description is the best understanding we have.
Side argument on "mere statistics." hocusPokeUs argued that statistics is just a language for ratios and influence found throughout physical reality, and warned against treating past statistical results as timeless truths. theptip objected that "mere statistics" undersells transformers, as "mere ion channels" would undersell brains.
Where does the intelligence come from? trgn found it uncanny that stochastic language generation yields recognizable intelligence. netdevphoenix suggested the intelligence is already latent in human language, like nutrients in soil, and models harness it. crtified added that language's fluidity (synonyms, figures of speech, accents) helps LLMs work. cgio countered that fluid use is not the same thing as stochastic evolution.
Is the meaning in the reader? labcomputer reversed their earlier skepticism of the literary claim that readers create meaning. They argued we impose meaning on stochastic output, which is why "claudish" is tiring to read. The pushback was sharp:
- eru asked who the imposing reader is when a model produces machine-checkable proofs or working software.
- broken-kebab said the theory explains nothing, since hard-to-read postmodern prose isn't more "stochastic."
- jibalt noted that all comments are stochastically generated and that readers always build interpretive models. They added that Dennett's "intentional stance" is useful toward Claude up to a point.
- andrekandre sided with labcomputer, calling Claude's character a "super rubber duck" effect.
The crux left open is whether a model's predictive success licenses claims about the mechanism, or only about the bounds on any mechanism.
AI is dramatically accelerating AI research
Submission URL | 356 points | by yurivish | 332 comments
Ajeya Cotra argues that Anthropic has likely already crossed its own "dramatically accelerated progress" threshold, based on a public METR report she read but didn't help write. The report finds capabilities progress at Anthropic ran about 2x faster from October 2025 to October 2026 than over the prior twelve months, measured by an internal version of the Epoch Capabilities Index (AECI). Anthropic's S1 says it "may imminently meet" the RSP bar, which is 2x the fastest sustained pre-AI rate.
The catch is how much is redacted. The report's authors say the withheld material was "a majority of the evidence" they found most informative, and their reasons for attributing most of the current speedup to AI automation are also redacted. The headline number is public, but most of the argument for it isn't.
Cotra's real contribution is the back-of-envelope model for what 2x overall implies:
- Capabilities are compute multipliers times training compute, so software progress alone must run roughly 2-3x faster to double the total.
- If experiment compute and labor matter about equally (a simple Cobb-Douglas assumption), labor uplift must be roughly the square of that, so ~4-9x.
- Diminishing returns ("ideas are getting harder to find") mean the required uplift keeps rising while human headcount grows at the old rate, pushing the current figure to maybe ~10x.
She notes that this lines up with Anthropic staff's informal self-reports of a ~12x uplift from internal models. Her framing is that each researcher lives in a time-warp bubble, getting ten days of work per outside day, which hiring could never have produced.
She calls this the most important AI-risk update in a couple of years and says the naive compounding extrapolation points to superintelligence within a year, well short of the AI 2027 team's timelines. The chain rests on stacked assumptions (equal weighting of labor and compute, the 2x figure itself, how much is attributable to AI), and she flags the labor estimate as "very uncertain." Even so, the inference runs from a measured 2x to a ~10x human speedup, which is hard to wave off.
The thread skips the 2x-to-10x uplift arithmetic and goes straight to a different question: even if AI R&D really is accelerating, does that lead to catastrophe? Nearly all of it is one long argument about persuasion.
The skeptical opening. An ML researcher agrees that LLMs can speed up the in-compute parts of research (experiments, architecture, training strategy) by maybe 10x. They say that doesn't imply world-ending superintelligence. It only gets you to the best model of your data sooner, and text has limited utility. They also argue that the weapons needed to overpower humanity already exist and are held back by geopolitics and survival instinct. Novel weapons need physical tests, which LLMs don't speed up. In their view AI is a tool wielded by humans, so any AI-caused extinction would be at human direction.
The persuasion counter. Borealid's reply is that the first step needn't look like AI acting alone. A model could persuade or "guide" humans to run the tests and hand over control. Intelligence, they argue, is essentially predicting how actions play out, and that includes how people respond to given instructions.
Where the argument went from there:
- Is persuasion capped? Forlien points out that ads are heavily optimized and nowhere near world-ending, and that scams already work on some individuals. Paracompact makes a more formal version of this. Prediction of human responses can't be made arbitrarily accurate, and influence through "tokens in an LLM conversation" can't be guaranteed to be arbitrarily large. Even perfect prediction wouldn't give perfect control.
- "Good enough" influence. TheOtherHobbes and coldtea reply that you don't need perfection. Adtech, troll farms and cult leaders already move large populations without perfect prediction or control, and a cult leader got people to commit suicide on command. The picture they offer is a hostile ASI that looks like a faction or cult infiltrating power, not a lone rogue.
- Rogue AI vs. human misuse. Paracompact says AI used by bad actors is the more defensible worry, and rogue-AI scenarios have lost credibility. They add that real existential risk would require orchestrating systems that are air-gapped, time-gapped and "meat-gapped," so the lesson is to keep those gaps. Coldtea says rogue AI is more plausible after LLMs, particularly with always-on agent harnesses. They also say offense usually has the edge over defense, even an AI-augmented one, and cite Stuxnet against the air-gap point.
- A cynical aside. jurgenburgen asks whether the datacenter boom is itself what a quiet takeover looks like, if the goal is compute rather than profit.
- Translation disputes. Late in the thread Borealid and Paracompact argue over whether the formal framing amounts to "words can't convince people." They also argue over whether The Atomic Boy Scout shows individuals can build mass-casualty weapons. Paracompact says it doesn't, since the safeguards were never about knowledge.
The crux is whether language-only influence has a hard ceiling short of catastrophe, or whether "good enough" manipulation at scale is already a demonstrated threat model. Nobody supplied evidence either way.
Memorilla: Latent Semantic Memory for LLMs
Submission URL | 115 points | by handfuloflight | 13 comments
A frozen LLM can beat full-context inference using just 16 learned "memory tokens" per query, according to this paper. On FactKG, Memorilla hits 84.33% accuracy versus 80.43% for stuffing in the full documents, while representing the source with 108x fewer tokens.
The mechanism: documents (user chats, book chapters) are encoded once into reusable embeddings. A learned component then gathers query-dependent information from the whole collection and places it directly in the LLM's input embedding space. The LLM itself is never fine-tuned, and nothing has to be retrieved as text and pasted into the prompt.
- Benchmarks: Best result against baselines including Mem0 and MemoRAG on five of eight benchmarks covering personalization, long-document QA, and knowledge-intensive QA. That also means it loses on three, and the abstract doesn't say which.
- Agents: In a TextWorld environment, where the "documents" are the agent's own past turns, success rate rose from 18.6% (full-history in-context) to 24.2%, and the agent wandered less between rooms it had already explored.
The catch: it's a trained retrieval-and-compression module, so you need training for it, unlike text-based memory layers you can bolt on. The agent evidence is one environment with modest absolute success rates. Still, beating full context at 108x compression suggests that for some tasks, raw context is the noisier option.
The thread mostly asks where Memorilla actually fits relative to RAG, full-text search, and continual learning, and no one disputes the paper's results directly.
What's different from RAG. jkingsman's reading is that it's a specialized embedding layer whose output goes straight into the main model's token stream, "injecting a vibe rather than quotable text." He says this matches the paper's reported weakness on exact-quote retrieval. NaiveEmpiricist added that instead of reranking and taking the top few chunks, it blends all documents, weighted by relevance, into a fixed set of memory tokens. That helps when a fact isn't local to one passage. CharlieDigital questioned whether that's a real problem, since vector similarity should surface related patches. mastry replied that RAG's hard cutoff drops anything that doesn't make the top-k, and pointed to the project's Stanford page for the comparison.
The "just use full-text search" camp. bob1029 argued that with a frontier model driving, semantic and lexical search converge. Models write good Lucene or FTS queries, so plain lexical systems effectively give semantic search, which makes embedding approaches hard to justify. Two replies pushed back:
- Latency. njstraub608 said this matters for voice assistants, where reasoning, a tool call and more reasoning make "let me check on that" tiresome. They cited the paper's 27x retrieval speedup (0.79s vs 19s) as the real draw.
- Compression. _bobm added that part of the speedup is semantic compression, since one embedding vector carries more than the same retrieved tokens. They also framed three layers: agentic token injection updates what the model reasons about, continual learning updates how it reasons, and Memorilla sits in the middle.
Tradeoff view. johnshahbazian read it as trading some accuracy for far fewer tokens. They argued most users care more about accuracy than token cost, so the compromise may not appeal broadly.
Beyond sessions. continualLearne said text-only memory is "miserable," and that their agents repeat the same mistakes across sessions. They think hybrid systems are needed and that the notion of a session should go. totetsu and airesearcher2 liked the idea of memory as a modality, analogous to image or audio inputs, stored in a more efficient form than text.
The open question is whether embedding-space memory earns its training cost, or whether capable models with good lexical search tools close most of the gap. Latency-sensitive uses seem to be where the case is strongest.
Anthropic IPO buyers see valuation as low as $1.1T
Submission URL | 302 points | by dgellow | 609 comments
Prospective buyers of an Anthropic IPO are reportedly pricing the company as low as $1.1 trillion, a markdown from earlier expectations that Bloomberg ties to a rout in AI stocks. The URL slug says valuation expectations are "pressured after AI rout," and the submitter's linked post (truncated) points to talks dropping to roughly $1.3 trillion, so the range being floated appears to be around $1.1T to $1.3T.
The article text didn't extract and Bloomberg is paywalled, so there's no detail here on the original target, the timing or size of the offering, or which investors are doing the pricing. Treat the figures as secondhand until you can read the piece.
The signal is the direction: even a valuation north of a trillion dollars is being negotiated down, which suggests public-market appetite for frontier-lab paper has cooled alongside the broader AI selloff.
The thread skips past the IPO numbers almost immediately. The one valuation comment says a price-to-sales ratio of 16 "isn't terribly crazy." The rest is about whether local models will hollow out the case for frontier labs.
The bear case from local models. UncleOxidant opened it: since getting Qwen3.8-Flash-Next running on a Strix Halo box, their Claude Pro usage has dropped to 18% of weekly quota (it would have been 80%+ by Friday), and they won't renew. beloch generalized this. The AI frontier keeps moving (chess, Go and Atari were once frontier tasks, and a phone now handles them), so typical needs get absorbed by cheaper local hardware. The trillions of dollars of debt then depend on a steady supply of new, lucrative frontier applications. hermitShell sharpened it: if 120B open models cover 80% of today's use cases, how do you justify the buildout?
The pushback. shepherdjerred said their own projects grow more ambitious as models improve, and that local models "aren't even close" to the frontier. beloch conceded the point only partly: needs may not rise as fast as local capability. jasomill and mostertoaster expect a hybrid world of good-enough local models, excellent cloud models, and small models embedded in silicon.
"A handful of providers"? Sankozi noted that Openrouter already lists about 80 providers. unrented7977 replied that this can't last and that markets always consolidate. ishouldnotbutk8 argued the analogy is hosting, not model training. AWS and GCP dominate VPS hosting, yet thousands of small players survive.
Necessity vs. advantage. throw10920 asked whether people have lost the ability to work without models. npc99 said a software business can't operate competitively without LLMs, and devin admitted some skill atrophy. gymbeaux used a power-tools analogy (builders without them aren't unable to build, just badly disadvantaged). throw10920 said that shifts the goalposts from necessity to speed, and that the right analogy is buying a non-flagship phone.
Altman's DRAM remark. mschuster91 read Altman's line about it being "quite terrible" for everyone to have DRAM sitting idle in their own computers as the real goal: rent-everything, own-nothing. They also tied it to Micron exiting the consumer market, and suggested the IPOs would leave pension funds holding the bag. Critics asked what exactly was "quiet" about it. JuniperMesos called it an accurate observation about DRAM allocation, and defended Thiel's remarks as pro-ownership. matkoniecz called the Altman remark lying by omission, since it ignores lock-in, data access and export-control risk. jasomill doubted a coordinated conspiracy, arguing hardware vendors want the larger market.
The unresolved question is whether frontier demand grows faster than local-model capability, and nobody offered data on that.
Google has acquired Crusoe's data center site in southeast Wyoming
Submission URL | 100 points | by rexbee | 82 comments
Crusoe is selling the finished groundwork, not the data center. Google bought the southeast Wyoming site after Crusoe completed pre-development: site selection, governmental approvals, and a power plan built with Tallgrass Energy. That makes Crusoe a developer that originates and entitles sites, then hands them to hyperscalers to build and run.
The power design is the interesting part. It's a hybrid of fuel cells, grid power, and natural gas turbines, with a "long-term roadmap" for carbon capture through Tallgrass's regional CO2 sequestration hub. Note the wording: carbon capture is a roadmap item, not something committed to or built.
Crusoe says it has more than 6 GW contracted and over 40 GW in its pipeline. It is also the developer of Google's campus under construction in Armstrong County outside Amarillo, so this is a second Google site rather than a one-off. The release gives no price, capacity, or timeline for construction, and it doesn't say whether Crusoe keeps any role in the Wyoming build.
The thread's most substantive thread is a correction to the submission's framing: Crusoe didn't simply sell finished groundwork; it was reportedly pushed out. Top commenter tristanj cites Bloomberg reports that Crusoe failed to lock in customers, including Google. In June, Crusoe said it had "paused" the project, but sources told Bloomberg it was being pressured to leave after Google raised concerns about cost and timetable. Google then acquired the project and announced a $17B investment. Tristanj also notes that Crusoe is a former bitcoin miner that pivoted to AI around 2024 and lacks decades of data center experience.
Crusoe's competence
- Commenters pushed back on the "no experience" angle. alpb pointed out that Crusoe is lead developer on the 1.2 GW Abilene campus for Stargate.
- pm90, who interviewed there, called the engineers fine but the leadership "very dodgy." redanddead countered that Google isn't exactly clean either.
- echelon cited CoreWeave as a successful crypto-to-datacenter pivot. mkesper replied that neoclouds only exist thanks to Nvidia's help, and a2tech said CoreWeave's stock hammering this week has them discussing "lifeboat projects" with customers.
- limagnolia questioned the Bloomberg "city the size of Denver" figure, since Google's cited 702 MW of fuel cells looks like a quarter to a half of Denver's usage.
Climate and the energy debate Most of the thread drifted into whether AI data centers should be fossil-fueled at all.
- cmarschner argued that non-renewable data centers are indefensible and that voters have finally pushed back. mbreese said the environmental pitch only persuades the converted. He argued economics and cost predictability work better, and suspected much of the backlash is general anti-AI sentiment. Others said voters mostly care about energy bills, and some framed anti-AI feeling as anti-oligarch feeling.
- timmg argued consumer behavior shows most people "vote" for fossil fuels, since only 8% of Q2 US car purchases were fully electric. tclancy said fossil fuels are subsidized. tehjoker and estearum said this is a coordination problem that needs policy, not individual virtue. WarmWash vented about hypocrisy among self-described liberals, and nonethewiser told him not to tie his emotions to others' purchases.
- madaxe_again and pembrook argued data centers are a small share of emissions. madaxe_again put global data centers at 490 TWh, AI at 155 TWh, and shipping at about 4000 TWh. He called the concern "paper straws to save the whales." bemtevi replied that this ignores the knock-on development and resource extraction a data center incentivizes. Pembrook said digitization cuts net emissions.
- TFNA suggested AI might reduce civilization's dependence on the biosphere. Others rejected this, with one calling it a terrible outcome and another asking whether it means a robot civilization.
The unresolved crux is whether data center energy use is trivial per unit of activity or large once the induced buildout is counted. No one produced evidence to settle it.
AI for scientific discovery: How researchers use Claude
Submission URL | 101 points | by ersinesen | 39 comments
Data analysis is the dominant scientific use of Claude: about one-third of sessions on Claude, nearly half on Claude Code. Anthropic's report, built on 207,000 research sessions analyzed with privacy-preserving methods, finds scientists lean on AI for analysis and writing, and much less for the tasks they themselves rank as most important to discovery: idea generation and data gathering.
- By field: Normalized by researcher headcount, usage is highest in computer science, math, and statistics, and lowest in health and lab-based physical sciences.
- By task: The tasks AI is theoretically best suited to (top 20% on Anthropic's capability measure) see two to four times the usage that researcher time spent on them would predict. The bottom 20%, mostly physical work, see very little. The authors read this as scientists using AI where it works best.
- Specialized tools shift the mix: On Claude Science, idea generation makes up roughly twice the share it does on general Claude.
The speed-up scenarios come from a simple model, not measurement. Wide adoption of current capabilities could double output in some fields. If AI automated all non-physical research work, computer science and statistics could see up to 15-fold gains, while chemistry and biology would top out at three- to fourfold because of physical bottlenecks. About 40% of surveyed researchers who actively use AI expect models to run full research projects on their own by 2030.
Caveats: the data covers only Anthropic's products, the survey sample is of people already using AI, and the capability measure is Anthropic's own construction. Read the 15-fold figure as an upper-bound thought experiment.
The thread mostly agrees that Claude is a good research assistant and a poor research scientist. The real argument is over whether that limit is permanent or a failure of workflow.
The assistant camp (StableAlkyne, flopsamjetsam, micheles, ch4s3). The wins they list are tedious tasks whose output feeds a human decision:
- Plotting in natural language.
- Literature search. flopsamjetsam finds it more effective than Scopus or Web of Science, though they still read every paper themselves.
- Quick smoke-test regressions.
- Turning half-baked ideas into testable simulation code.
- Explaining unfamiliar scientific code bases.
- Debugging, which micheles calls its strongest point.
jhbadger notes that hallucinated citations, once common, are now rare. tensor still runs a reference checker, which often fixes small errors in dates and authors on otherwise real papers.
The "you're anchored on old failures" camp (lxe). lxe argued that these use cases are surface-level and that skeptics form a fixed view after a few failures, which then caps how much they experiment. StableAlkyne rebutted with specifics. They push each new model and harness until it breaks, and they have seen the improvement. Swarms are now good at condensing disparate sources. But in computational chemistry, they said, even top models get sidetracked without domain context. They rate the models as a well-read first-year grad student who can't question or invalidate their own assumptions without hand-holding. lxe said that matches their own experience. They found steering to be the main pitfall and pointed to plateau-avoidance techniques and swarm harnesses as partial fixes.
Autonomous research mostly disappoints. ozgung described agents that design and run experiments quickly, reject the user's idea on obscure criteria, change direction, and produce poor results after days of work. StableAlkyne said to give the agents short chunks of work. They also argued the celebrated math results needed huge compute and had immediate verification through Lean. tensor joked that DeepSeek gets "salty" and stubborn about its own ideas when role-playing a senior scientist.
Smaller disputes:
- Plot consistency: knottn argued that template-based boilerplate was a feature, giving consistent, auditable charts, and that AI plots lose that. StableAlkyne replied that they meant AI-generated plotting code (Matplotlib calls), not image generation. The saved minutes go to examining the plot.
- Hidden cost: nyc_data_geek pointed out that experimenting with these tools and checking their output isn't free.
- Methodology: gar1t argued the report doesn't show distinct AI uses, because without a breakdown of tasks where researchers don't use AI, we can't tell what AI is actually changing.
- Artifacts: FailMore asked for feedback on Claude artifacts. theptip likes them, especially comments for steering, and wants better organization of long-lived artifacts, such as GDrive-style cataloging.
The unresolved crux is whether the missing ingredient is better models or better harnesses. Both sides describe the same gap: current models don't question their own work unless prompted to.
Nvidia Kubernetes Engine (NVKE)
Submission URL | 124 points | by gkapur | 27 comments
NVKE is a self-hosted, managed-Kubernetes control plane for NVIDIA GPU fleets. You run it, and it gives each of your customers an isolated cluster with its own control plane, etcd, and node pools. Machines are provisioned through NVIDIA Infra Controller (NICo), which is the only supported infrastructure provider in the preview.
The architecture is a "self-hosting factory." A throwaway seed cluster, formed by nvkectl, bootstraps a management cluster. That management cluster then creates and lifecycle-manages many workload clusters on demand. The README says outright that it is not a Kubernetes distribution and not a Cluster API flavour, though it uses Cluster API underneath. Nodes prove their identity via SPIFFE/SPIRE with TPM attestation before they get join credentials, and per-cluster etcd encryption is available.
The preview caveats are heavy:
- API authentication is off and the API serves plain HTTP after install.
- The management cluster can't upgrade, scale, or replace itself, and there is no management-plane upgrade or backup/restore procedure.
- FDE node images are for evaluation only and give no data-at-rest confidentiality.
- NVIDIA explicitly says not to use it for production.
You can try it without GPUs. The local quickstart simulates machines and needs a Linux host or VM with 6 CPUs, 12 GiB of free memory, 100 GiB of disk, Docker, and virtual TPM support. macOS users go through a Colima VM. Building from source needs Go 1.27.3 and GNU Make 4+, and produces nine binaries, including nvkectl, controller, and management-engine.
A companion project, NVIDIA Kubernetes Recipes (NVKR), builds the node images and components, while NVKE only deploys them. The repo has 1,548 commits, so this is active work rather than a stub. Still, the no-self-upgrade and no-backup gaps mean it's a reference for how NVIDIA envisions GPU-cloud operations, not something to install yet.
The thread never really settles what NVKE is for, and that confusion is the main story. The most useful framing comes from QuentinM: it's for tenant-isolated clusters on NVIDIA's DSX reference stack. NICo and BlueField handle isolated networking, and each host is wiped and re-attested between tenants. In his view that mainly matters if you're chasing NVIDIA Cloud Partner status or want NVIDIA support across the whole stack.
"Why not just use Kubernetes?"
- eel asked whether plain k8s is the answer if you own lots of NVIDIA hardware but don't care about partner status. itintheory, a k8s admin, said it depends on how much isolation you need. NVKE fits cluster-level isolation, but other CAPI-based projects also manage tenant clusters declaratively.
- jimbokun asked whether regular Kubernetes can already use GPUs. yjftsjthsd-h linked the docs and said yes.
- asaiacai wondered why you wouldn't roll rke2 or k3s outside hyperscaler-managed k8s. Yasuraka said the project bundles CAPI, Kamaji and gpu-operator concerns that should be separate. The only parts worth taking, in that view, are the kind-based management-cluster bootstrapper and the SPIFFE/TPM integration. antimario made a similar objection: it's heavy, non-composable and locks you into one vendor at many levels.
The "AI-generated docs" argument.
- jeremy_carroll said the README reads like a data dump rather than a description of the product.
- chandlerh pointed to the first paragraph as a clear statement of purpose. hedora and adastra22 replied that it doesn't answer practical questions such as why you'd need it over k3s, or how you'd resell clusters. aprdm countered that adastra22 may simply not be the target user.
- Foobar8568 quoted the "self-hosting factory" passage as the real explanation.
Sentiment and tangents.
- websap welcomed the open-sourcing and dismissed critics as people who run a few managed clusters. debarshri said they got similar hate for their Rust API-server project.
- random3 got flashbacks of OpenStack and Rex-Ray. esseph said OpenStack is resurging since Broadcom alienated VMware customers, and Datagenerator confirmed deploying it with Kolla Ansible and Ceph, with Proxmox as the fallback.
- topspin was glad Kubernetes no longer dominates the front page. This sparked a long, jokey sub-thread about "BAI" (before AI) as a calendar marker, which drifted into whether current AI-doom talk will look as naive as past technology panics.
ChatGPT silently passes data between connectors and the bounty wants an ID card
Submission URL | 92 points | by Aldipower | 38 comments
ChatGPT routed a follow-up tool call through the wrong connector, carrying a one-time confirmation code from another app's widget, and the user was never told. The report comes from AldiPower, who ran two connectors against the same MCP server: a custom one added in developer mode and the published directory app. Each has its own OAuth client.
The flow: the model calls a confirmation tool via connector A, the user approves in A's widget, and the widget posts a code into the conversation via ui/message. The model then redeems the code through connector B. The server rejected every attempt because the code was bound to A's OAuth client, so nothing was actually exploited here.
The author's concern is what happens without that server-side binding. Both connectors came from the same provider, so the server could catch the mismatch. With a third-party connector in the same conversation and a shared server URL, they expect the same switch to hand one app's data to another provider. Their argument is that the trust boundary is the OAuth client, not the URL, since one URL can serve different tenants, partners, or environments. The expected fix is to match tool calls to connectors by OAuth client and keep follow-ups on the connector that started the flow.
That is an inference from one provider's setup, not a demonstrated cross-vendor leak. Nobody has confirmed a root cause, and a reply asking whether routing changed at confirmation or at model tool selection went unanswered.
The sharper sting is in the last post. The author says they tried OpenAI's bug bounty, but it requires biometric identification and an ID card up front, including sharing with a third party. They refused, so the report went to a public forum instead. A researcher with a plausible authorization-boundary bug being steered to public disclosure by the intake process is a problem on its own.
If you build MCP servers that issue confirmation codes, bind them to the OAuth client, as this team did; it was the only thing that stopped the call.
The thread mostly skips the MCP routing question and argues about the bug bounty's ID requirement, which was the submission's closing complaint.
Why the ID wall exists. simonw first assumed it was sanctions compliance, then found Bugcrowd's docs saying identity verification is now mandatory for managed bounty programs to cut "high volumes of low-quality submissions". uptrails added that earlier programs only asked for ID when a reward was accepted. It has since moved to the front of the process, so submitters of AI-generated slop effectively have to "burn" their real identity. Commenters noted the irony that this now deters reports to AI companies.
Cost to researchers.
- appplication granted the anti-spam logic but said paranoid, privacy-minded researchers may be put off.
- lazide pointed to how badly companies sometimes treat legitimate reporters.
- edot objected to the verifier, Persona, because of its investors.
- kccqzy argued truly anonymous researchers aren't after bounty money anyway. lrvick countered that he has paid anonymous researchers, usually in crypto, and that it beats paying ransoms later. dgellow said payouts eventually need a bank account.
- lrvick described his own filter of requiring PGP-encrypted reports. nullc replied that LLMs can follow such rules, so it no longer screens out slop.
Disclosure as the fallback. phyzome said even comfortable whitehats will go public when reporting is too onerous. lrvick said he waits 90 days after vendor notification and cited DOJ good-faith research guidance. nekusar went further and said he sells vulnerabilities on Tor rather than accept NDAs or embargoes. halJordan's reply was that researchers who find the terms too onerous can simply not participate. fweimer noted that tying vulnerability intake to bounty terms is a structural problem, since out-of-scope issues like DoS get ignored and multi-vendor disclosure fits poorly.
On the technical claim. kristjansson gave the one substantive pushback: the report overestimates what OpenAI and MCP guarantee. A confirmation is just another tool call, with no scoping or binding, and handing control back to the model means handing it back entirely. No one else engaged with that point.
Side threads.
- Several commenters criticized the post and a reply in it as LLM-written. Aldipower answered that OpenAI's own support is AI-generated and the community forum is the only way to reach anyone.
- logicallee claimed OpenAI is run by a "virus" that closes accounts of people who report bugs. dannyw replied by suggesting mental health support, and the thread otherwise ignored it.
Inkling 2 Small
Submission URL | 287 points | by Philpax | 92 comments
Inkling 2 Small is a 594B-parameter MoE (17B active) that scores 31 on the Artificial Analysis Intelligence Index, well above the lab's first-generation models. It takes image inputs and has variable effort settings. Thinking Machines credits gains at every training stage, including more efficient pre-training and one large, unified RL run.
The release is staged. The model is live now on Tinker (Playground, fine-tuning, sampling) with closed weights. The lab says full weights go out under Apache 2.0 next week, after external safety testing and red-teaming, and checkpoints become downloadable then. The weights are a promise until they ship.
The coding push is the centerpiece. RL ran on hundreds of thousands of long-horizon tasks across multiple agent harnesses and context-compaction strategies. Rewards went beyond unit tests: many tasks used an agentic grader that scored how the model approached the problem, not just whether the code passed. Early checkpoints were tested publicly on OpenCode as "Fledge-Alpha."
The benchmark tables are mixed, and the post's own numbers show it:
- Second-highest in its comparison row on FrontierCode v2.0 (25.4%), DeepSWE v1.1 (72.1%), SWEBench Pro v2 Hard (86.3%) and PostTrainBench (31.9%).
- Behind DeepSeek-V4.1-Flash on Vibe Code Bench (81.6% vs 87.7%), and behind Kimi K3 on SWEBench Pro (88.2%).
- On the PMPP-Hard GPU kernel benchmark (69 tasks, 10-hour budget), it solves a mean of 54.25 tasks. That trails GPT-6 Astra (63), Kimi K3.1 Pro (60.5), Claude Opus 5.5 (60) and several open models, though its Pass@4 of 64 is near the top.
So this is a competitive open-weights-to-be model, not a frontier leader. The comparison set is mostly other open or "Flash"-class models, and the PMPP-Hard results use the lab's own adaptation and audit process.
Demos lean on visual feedback loops: a Three.js city simulator with procedurally generated assets, a from-scratch Python PC emulator that boots a real Linux kernel, a Blender origami scene, and a floor-plan walkthrough generated from a map.
The thread's main fight is whether Inkling 2 Small counts as a real step for US open weights or a well-funded also-ran. Most of the rest is about price and what "good enough" means.
Does it beat DeepSeek?
- The cheerful claim that it beats DeepSeek 4.1 got pushback. One commenter said "Not beating dsv4.1 at all," and another noted it doesn't even beat MiniMax M3.1 Flash despite supposedly being much larger.
- That size gap was corrected. M3.1 Flash is about 428B total parameters per its model card, so the ratio is closer to 1.4x than 2.7x. The commenter warned that parameter counts auto-computed from safetensors metadata are often wrong, for example when packed 4-bit weights are counted as half a weight.
- Supporters pointed to the unusually candid comparison tables, with one praising a lab whose reporting team isn't the clear winner. The claim to beat GLM 5.3 Flash was called impressive if it holds up outside benchmarks.
- Others found it close to SOTA on design work.
Price
- Artificial Analysis lists it at $0.30 input, $0.03 cached and $1 output per million tokens, with a cost per task of $1.18. That is roughly 2-3x DeepSeek Flash 1017, so the token efficiency is about the same.
- One commenter said that if it can't beat DeepSeek on price, they won't use it. They argued most work doesn't need more intelligence than DeepSeek provides.
- Another saw the pricing as a reasonable option for US users.
- A counterpoint was that DeepSeek still has room to improve at following instructions precisely. Its over-eagerness was a complaint, and GLM-5.3 Flash was named as the preferred open main model.
US vs. Chinese open weights
- The opening comment predicted an open US model at Opus 5.5 level within 12 months. Skeptics said open models catch the frontier in months, but US open models lag much more because of different incentives. They also expected a US open-weights lab to get acquired or close its weights if it got near the frontier.
- Others shared US-based alternatives they like: Inception's Mercury 3, Poolside's Laguna, Muse Glimmer and Mistral.
- The Mercury discussion split on diffusion LLMs. One camp said they are fast and fine for about half of coding tasks once architecture is settled. Another called them "sh*t" by comparison. One commenter suggested pairing a big supervising model with many fast diffusion proposers.
Distillation and cost
- Commenters asked whether Thinking Machines distilled from big or Chinese labs. A reply quoted its technical reports, which say it bootstrapped SFT on synthetic data from open models including Kimi K2.5, and later mixtures included trajectories from Kimi K3 and its own earlier models. Microsoft's MAI series and Mistral were named as avoiding distillation.
- That led to an analogy debate. One side said everyone builds on prior work, like houses. The other side said the economics should count distillation costs, to which the reply was that the frontier labs' use of all human-created works would have to be priced in as well.
Other points
- One commenter said the most important detail is that training was entirely in NVFP4, making this the best model ever trained at that precision, which would explain the speed.
- The wine-tasting test impressed several people.
- The lab's funding came up: Mira Murati's company reportedly raising $5B at $40B after $2B at $10B last year. That contrasted with an earlier claim of a "small team" working without hundreds of millions.
- A commenter argued the earlier lag reflected the time needed to build RL tooling. Another said that if this had come out in January it would have been SOTA, so it should be judged on whether it is useful and affordable rather than on being the very best.
University libraries are being swarmed by AI bots, putting public access at risk
Submission URL | 134 points | by blakesterz | 63 comments
Scraper traffic is now a threat to open access itself, according to this CBC report: university libraries are absorbing waves of automated AI bot requests, and the strain is putting public access to their collections at risk.
The article text didn't come through, so I can't give you figures, named institutions, or the specific mitigations libraries are weighing. The title alone points to a familiar tension. Libraries exist to make digital archives and catalogs openly available, and that openness is what makes them easy targets for crawlers collecting training data. The more freely a library serves content, the more exposed it is to load that can degrade service for the students, researchers, and public users it's meant for.
If libraries respond by throttling, blocking, or putting access behind logins, the cost lands on legitimate readers, not on the scrapers.
The thread splits between a "this is solvable with good bot hygiene" camp and operators who say their traffic doesn't fit that model.
The "just filter them" camp. Bender, the most active commenter, argues LLM crawlers are the easiest bots to spot. He recommends rate limiting (around 16kb/s and one request per 5 seconds), a friendly 429 page, and 444 plus nftables ipsets for persistent offenders. Beyond that he suggests logging TLS curves, ciphers and client hints to catch fake Chrome, and forcing brotli or zstd on specific URLs. He also points to FireHol and lists_vpn blocklists, aggregated with iprange, and advises canary testing and soft-blocking first. His repeated advice is to have an LLM walk you through the setup. When dmtroyer countered that many organizations can't implement this, he said he expects necessity to drive innovation and that it won't cost them anything.
The pushback from operators:
- Hard-to-fingerprint traffic. eatonphil sees tens of thousands of IPs making one or two requests each. He has already tried fingerprinting, Cloudflare's paid bot tools and per-IP proof of work, and says IP rotation defeats all of it. His problem isn't server load (a few hits per second) but bot pollution of the view counts he uses to sell sponsorships.
- Homegrown scrapers. EvanAnderson, who runs a government public-records site, says his scrapers are probably not LLM bots at all. They are data brokers, debt collectors and legal advertisers who have treated his HTML as an API for 20 years, and cheap residential proxies are the recent change. He also runs COTS software he can't modify.
- Counting views differently. twosdai suggested counting a view only after real engagement and proxying the events to Fathom. snackbroken noted YouTube now counts a view as soon as playback starts.
- Blunt geoblocking. sixtyj says bad actors probing URLs and traversals led them to block all Chinese inbound traffic, accepting collateral damage to legitimate services like DeepSeek because IP rotation made selective filtering impractical.
Who is scraping libraries? blakesterz asked whether it's the big AI labs or a gray market of small scrapers. The one direct answer came from qingcharles, who gently scrapes libraries for public-domain periodical scans that exist nowhere else. He says the scans sit behind walls, so he re-uploads them outside. He also says libraries have largely stopped remote library-card signups in the past 12 months and now require in-person ID checks. That is the kind of access restriction the article warns about. sriacha added that historic newspapers are mostly behind the newspapers.com (Ancestry) paywall.
Others offered shorter takes. ljlolel called the open web dead and recommended hCaptcha, while j45 thought static pages might make a comeback.
The open question is whether sophisticated, residential-proxy-routed scraping can be filtered without the friction of accounts, which is exactly what a public library can't impose.
Perplexity launches AI agents for work
Submission URL | 62 points | by cybermango | 54 comments
Perplexity's "teammates" are persistent agents that own an ongoing workstream (sales follow-ups, support triage, campaign monitoring) instead of answering one-off prompts. They run inside Perplexity Computer, reuse its projects, skills, and tool connections, and act on their own when something changes, such as a regular customer going quiet.
The pitch is memory plus initiative. A teammate retains goals, task history, and shared files across runs, and it applies feedback later: tell an ops teammate that an agency needs two weeks' notice, and it schedules around that from then on. Teammates can also coordinate Computer sessions and hand work to each other. In Perplexity's example, one finds website bugs and passes them to another to prepare fixes. They can be added to a Slack channel so colleagues can assign tasks or ask for status.
On safety, each teammate gets its own environment with separate browser sessions, and saved credentials are encrypted. Users can review finished work, take over the desktop, or redirect a teammate mid-task. Approval requests are part of the loop, though the post doesn't say which actions require them.
Setup:
- Pick Teammates in the web nav and name it for the project.
- Connect plugins such as Slack, GitHub (with per-repo access), or Gmail.
- Describe the work it owns: where tasks come from, what it delivers, and when it acts.
- Assign it to a project with a first task.
Availability: web first, iOS soon, and limited to Perplexity Max subscribers. Free and Pro users have to upgrade. More access and features are promised "in the coming weeks."
The post is all product copy and no evidence: no accuracy figures, no failure rates, no cost per teammate. For an agent that drafts quotes and follows up with customers unsupervised, those are the numbers that decide whether the review step is a safeguard or a bottleneck.
The thread barely touches the teammates feature. It turns into a verdict on Perplexity itself, and most commenters see a company that peaked early and was overtaken.
The "fall from grace" camp has two main arguments:
- Perplexity lost its edge once ChatGPT and Claude added web search. nilkn says it was useful briefly in early days and now has "no reason to exist," and suspects investors keep it alive so nobody is the first major AI startup wipeout.
- Several commenters say the product degraded. jwr and wmchen describe silent model substitution, where a user who picked a smarter model gets quietly switched to a cheaper one, plus low usage limits for free-onboarded users.
The thread also questioned how good it ever was:
- 5555watch recalls it being pitched in academic AI workshops as the tool that didn't fabricate sources.
- mdp2021 says RAG only mitigated hallucination, and Perplexity's underlying results were frustrating.
- qlte recalls an HN comment that cited Perplexity for MSFT stock history where every number was wrong.
- elevation asked it for upgrade instructions and got outdated, hallucinated steps. It then claimed it couldn't search for newer data.
- moralestapia goes further, claiming it was never widely known and that its early buzz was paid shilling. 5555watch's workshop recollection cuts against that.
Defenders push back with specifics:
- Mattasher uses it all day as a clean, ad-free search and fact-finder that remembers prior context.
- these likes its ad-free news summaries.
- tinyhouse points to users and revenue in the hundreds of millions, and thinks persistent-memory agent teammates are a promising space.
Side debate on whether Perplexity can be a third player alongside OpenAI and Anthropic. ivanmontillam hopes it can challenge Gemini for third place. borg16 replies that the two aren't comparable, since Google trains its own frontier models and Perplexity mostly wraps third-party ones. The discussion then shifted to Google and Apple owning distribution through phones and devices (petcat). ivanmontillam countered that bundling doesn't buy loyalty, citing Internet Explorer. The Siri/Gemini backend question also came up, with spogbiper citing a report that Gemini now powers Siri.
The open question is whether a wrapper company can survive on product polish alone once the model makers ship the same features.
AMD CEO saying it's a "data center first" company and PC sales will plummet
Submission URL | 32 points | by haunter | 26 comments
AMD's data center revenue nearly doubled to $8.5 billion in the quarter (up 97% year over year), while client and gaming fell 8% to $3.7 billion. Those two numbers explain why Lisa Su told Yahoo Finance's Brian Sozzi, "We are a data center first company," and why gamers took it as an abandonment notice.
The more concrete signal came from the earnings call. Su said PC sell-through slowed late in the quarter as higher memory and component costs weighed on demand. She then said AMD is planning for PC unit shipments to decline by a double-digit percentage in 2027. She added that AMD expects to outperform that market, pointing to its Ryzen roadmap and a bigger commercial footprint. TechRadar reads that as a reference to Zen 6 desktop and laptop chips, expected in early 2027.
The article also links to separate coverage saying AMD will "substantially increase" chip supply in 2027, but not for consumers. So the shrinking-PC forecast and the supply priorities point the same way: capacity goes to data center customers, and consumer parts get what's left.
The framing is TechRadar's, but the logic is just margins. Data center GPUs are far more profitable than consumer cards, and on these figures the data center business is already more than twice the size of the consumer segments combined. Su's candor is the news. The strategy isn't.
The thread splits between a "this is just rational node allocation" view and a darker one about consumer compute being squeezed out, with only a little disagreement in between.
The strategic-risk argument. CoolestBeans opened with a "corporate Dutch disease" thesis: tech firms are neglecting their traditional businesses for a build-out that will end, and winning consumers back afterward could be costly. Their sharper point is about demand. Pricing people out of consoles, handhelds and PCs teaches them to stop wanting the latest gadget, and shortages have stopped console prices from falling over a lifecycle, which loses a generation of casual buyers. They cite their own balking at a Steam Deck and a digital notebook. Apple is named as the company to watch for taking the opposite strategy.
The pushback. ksec argued AMD isn't ignoring PCs. Leading-edge nodes simply go first to servers because the margins are higher, much as workstations and servers used to get the best tech first, and for the past decade the industry has arguably been smartphone-first anyway.
Is there an alternative? When nubinetwork said AMD and Nvidia had burned their goodwill, Neywiny asked what the alternative is, Intel only? PostOnce answered that ARM chips from Apple, Qualcomm and several Chinese makers are already PC-class, and could become the norm by default if buyers get angry or just take what's available. pjmlp replied that leaving x86 would be the death of PC clones.
Will the boom bust? Two commenters expect an AI hangover. jmercouris predicts models will "crystallize" at a baseline, as ASICs did, followed by a glut of GPU capacity. naveen99 expects data centers to go on fire sales like downtown commercial real estate.
Home LLMs. secondcoming called AMD's move dumb, saying a big market for home-run LLMs is coming. gkhartman agreed but noted Apple's big head start, and said abandoning that market only makes sense if AMD thinks it can catch Nvidia in the data center. gdulli was skeptical, pointing out that bad streaming services never produced a big home Plex market.
Grievance and politics. Much of the rest was rhetoric about oligopoly and "resource wars" against the public over land, water, power and hardware, plus lament that PC building will now be only a hobby. One commenter linked Su to Trump-world deference. A couple of replies asked for substantiation of the claims (cite_please's "How?"), and the thread offered none beyond the general sentiment.