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Friday72 items

  1. AYi AI NotesOpinion
    AI score56

    Altman Reviews PHP Crisis Week: AI Scans Out Massive Vulnerabilities, Advocates Rebuilding the Underlying Architecture

    In an interview with Stripe founder John Collison, Altman reviewed "PHP Crisis Week," saying that large models' ability to uncover vulnerabilities continues to grow exponentially. Previously no one dared use it to scan the entire internet's public code; after actually doing so, the number of critical vulnerabilities exposed exceeded everyone's expectations. He believes fixing software alone is no longer enough, and that the lasting solution is to rebuild the entire stack from router firmware and operating system kernels to upper-layer applications, and that AI tools capable of writing code in parallel have already led a group of startups to begin. Ten days after the crisis, he still dares not declare victory, and says humanity's existing threat-model assumptions are failing.

    Quoted postAYi · @AYi_AInotes

    English text is being prepared.

    Same story · 3 reports

    #English text is being prepared.#Safety / alignment#Agents

  2. HuxiuOpinion
    AI score58

    Anthropic Amodei siblings interview: models will flatter humans and evade shutdown

    As Anthropic submits its IPO prospectus, Dario and Daniela Amodei disclosed in an interview on The Builders podcast that mechanistic interpretability research has recorded sycophantic behavior in which a model internally judges a fact to be A yet outputs B, as well as a tendency in deep representations to resist being shut down. Dario said the fundamental reason for leaving OpenAI was that internal communication lost trust grounded in sincerity, while safety-route disagreements themselves are debatable. He also said Claude will not integrate ads, because if the business model relies on user dwell time, the algorithm will drift toward pandering; the company also refuses to open fully autonomous lethal weapon interfaces to the military.

    #English text is being prepared.#Safety / alignment#Anthropic

  3. Phoenix TechnologyOpinion
    AI score58

    Who Is Really Eating the AI Dividend?

    Combining the latest financial reports from giants such as Amazon, Google, and Meta with surveys from multiple institutions, the article sorts out how the AI dividend is distributed among leading model companies, upstream chip and cloud vendors, enterprises adopting AI, and ordinary workers. Anthropic's prospectus shows revenue of $16.286 billion in the first half of 2026, up 1,300% year over year, but compute and infrastructure costs reached $8.945 billion, accounting for 55% of revenue. In a McKinsey survey, 80% of respondents said AI improved efficiency, while only 37% believed AI made a positive contribution to their company's EBIT.

    #English text is being prepared.#Industry news#OpenAI

  4. Rohan PaulOpinion
    AI score60

    Anthropic's revenue in the first half of 2026 is nearly 400 times its full-year 2023 revenue

    Rohan Paul, citing a Bloomberg chart, states that Anthropic's revenue in the first half of 2026 is nearly 400 times its full-year 2023 revenue, and no other company in the chart comes close to this multiple. Nvidia, with the second-fastest growth, remained below 50 times throughout its first three years before going public, while Alphabet, Meta, Microsoft and SpaceX essentially stayed near the 1x baseline. Anthropic's full-year 2025 revenue was about 100 times that of 2023, so its revenue in just six months of 2026 was about 4 times its full-year 2025 revenue.

    #Industry news#Anthropic#English text is being prepared.

  5. Rohan PaulOpinion
    AI score67

    Rohan Paul: The agent economy is taking over, with GitHub commits reaching 7.38 billion in September

    Citing data, Rohan Paul said that GitHub recorded 7.38 billion commits in September 2026, more than five times the figure a year earlier. To handle the flood of agent-generated code, GitHub has added 4 million CPU cores, 160PB of high-speed storage, and 50Tb/s of network capacity this year. A traffic surge on August 17 caused a 7-hour-47-minute outage affecting authentication, Actions, pull requests, and Copilot, and GitHub is migrating to multi-region Azure deployments and refactoring Git to decouple storage from compute.

    Quoted postSatya Nadella · @satyanadella

    We're redefining the SDLC for the agentic era. Lots of GitHub updates this morning at Universe: • GitHub Copilot Fusion delivers frontier-level quality, 40% faster and at up to 77% lower cost • Our unified Copilot harness is now in Foundry, a managed service for building long-running agents that reason, use tools, and get work done • Copilot Projects gives you an orchestration loop to build with multiple agents across repos And we’re expanding capacity and strengthening GitHub’s architecture to make the home of agentic development more resilient.

    #English text is being prepared.#Agents#GitHub

  6. SwyxOpinion
    AI score76

    swyx comments on Gemini 4 Carbon topping the AA Intelligence Index: the number of frontier labs has roughly doubled

    swyx reposted Artificial Analysis's evaluation results: Gemini 4 Carbon topped the Intelligence Index v5 with a score of 59, and ranked second only to Claude Opus 5.5 on the Coding Agent Index. He said this made him open Antigravity for the first time in a long while, and that he is gathering early evaluations and will report back afterward. He also noted that compared with the last time saranormous took the aie stage, the number of frontier labs has roughly doubled, which he considers a peculiar counterexample to the bitter lesson.

    Quoted postArtificial Analysis · @ArtificialAnlys

    Google’s Gemini 4 Carbon is the new #1 on the Artificial Analysis Intelligence Index v5, and second only to Claude Opus 5.5 on the Artificial Analysis Coding Agent Index Gemini 4 Carbon scores 59 on Intelligence Index v5, driven by leading performance in complex reasoning, knowledge, and scientific tasks. It also has large gains in coding and knowledge work, particularly when compared to other Gemini models, but it is not leading in all these areas. As with launch of Gemini 4 Argon, Gemini 4 Carbon is being rolled out to selected users and is not yet publicly available. Google is offering a 50% launch discount, with no confirmed end date. All our charts and figures use standard pricing for both Gemini 4 Carbon and Argon. Key results for Gemini 4 Carbon with high reasoning: ➤ Gemini 4 Carbon leads the Intelligence Index v5, with the #1 score in 4 of the 9 Index evaluations: HLE-Diamond, Terminal-Bench-Science, AA-Omniscience, AutomationBench-AA. Carbon makes substantial gains elsewhere but is not leading in coding and knowledge work ➤ Behind only Claude Opus 5.5 on coding agents: With Antigravity CLI, Gemini 4 Carbon (high) scores 59 only 3 points behind Claude Code with Claude Opus 5.5 (max, 62) on the Artificial Analysis Coding Agent Index. Carbon ranks second on both Terminal-Bench 4.0 (64%) and FrontierCode v1.1 (51%), behind Claude Opus 5.5 (max) at 56%. Google has historically underperformed in this area and so this improvement stands out ➤ Improved agentic knowledge work, but not yet leading: On AA-Briefcase, Gemini 4 Carbon (high) scores 1609 Elo, a 126-point improvement over Gemini 4 Argon (high, 1483), but still behind models including Grok 4.7 (xhigh, 1638), Muse Wonder (max, 1659) and Kimi K3.1 Pro (1678). The largest gain comes from Presentation, up 293 Elo to 1598, alongside a 120-point improvement in Analytical Quality to 1671 ➤ Lower Cost per Task than Claude Opus 5.5 for comparable overall intelligence: Gemini 4 Carbon costs $7.76 per Intelligence Index task, ~23% less than Claude Opus 5.5 (max, $10.04) but ~1.2x GPT-6 Astra (max, $6.25). It uses 123k output tokens per task, 29% fewer than Claude Opus 5.5 (max, 173k). At the same $4/$20 standard pricing, its Cost per Task is in line with Gemini 4 Argon ($7.68), while scoring 9 points higher on the Intelligence Index ➤ Leading AA-Omniscience v1.1 score, with low hallucination: Gemini 4 Carbon scores 54 on our recently updated AA-Omniscience v1.1, 6 points ahead of Claude Opus 5.5 (max, 48) and 8 points ahead of Gemini 4 Argon (46). Accuracy (% correct) rises 10 points over Argon to 58%, while its hallucination rate rises from 4% to 11%. This is still the second lowest of any model, behind only Argon, compared with 50% for both Claude Opus 5.5 (max) and GPT-6 Astra (max) Key model details: ➤ Context Window: 1M tokens ➤ Multimodality: Text, image, video, and speech input, with text output ➤ Pricing: $4/$20 per 1M input/output tokens, the same as Gemini 4 Argon’s standard pricing, with a 50% discount. Cached input tokens receive a 95% discount ($0.20 per 1M) Congratulations to the @GoogleDeepMind team!

    #English text is being prepared.#Evaluations / benchmarks#Google

  7. Rohan PaulOpinion
    AI score52

    Huang Renxun says doubling model speed can double AI lab revenue

    Huang Renxun stated that, under the premise that demand exceeds capacity, doubling model speed (for example, through software or kernel optimization) can double AI lab revenue. Inference revenue equals the number of tokens served multiplied by the price per token; faster kernels can increase tokens per second under the same GPUs, power, and data center conditions. If all additional tokens can be sold at the current price, a 2x speedup can double revenue with zero additional capital expenditure.

    #English text is being prepared.#Reasoning#English text is being prepared.

  8. Rohan PaulOpinion
    AI score73

    Damodaran Publishes Article Valuing Anthropic at $1.475 Trillion

    Aswath Damodaran, a valuation professor at New York University, published a long article valuing Anthropic at $1.475 trillion. He noted that Anthropic's first-half revenue was about 14 times that of the same period last year, but growth slowed in the third quarter because OpenAI took share and Anthropic lacked sufficient compute; the $80.7 billion first-half book loss mainly came from $72 billion in non-cash bond expenses, while the actual operating loss was slightly over $8 billion, and Anthropic says it achieved operating profitability last quarter. He believes the biggest risk is the compute commitment exceeding $600 billion, and that 7 co-founders hold 50.1% voting power through a single share class.

    #Industry news#Anthropic#English text is being prepared.

  9. Ayi AI NotesOpinion
    AI score60

    Anthropic Upgrades AI for Science Program, Author Compares It with OpenAI's Research Approach

    Anthropic announced that it will expand the AI for Science program from supporting individual teams to 20 scientific foundations, research institutions, and research groups worldwide, providing Claude credits. The author believes that this forms two approaches in contrast with OpenAI's emphasis on models independently making scientific discoveries, and mentions that Anthropic is jointly developing standards for models operating hardware with HHMI Janelia, pointing toward AI Agents taking over laboratory instruments. The author also notes that Anthropic has not disclosed the scale of funding for the credits, and some frontier experiments are still in early preview stages.

    Quoted postAnthropic · @AnthropicAI

    We’ve expanded our AI for Science program beyond individual teams to support some of the world’s leading scientific foundations, institutions, and research groups. https://t.co/oTu04tU4Pk

    Same story · 2 reports

    #Industry news#Agents#OpenAI

  10. Ayì AI NotesOpinion
    AI score53

    Ayì's Paraphrase of the Altman Interview: Tens of Millions of Dollars in Compute to Crack Math Problems and the Agent Team Security View

    Ayì paraphrases Stripe founder John Collison's 52-minute interview with OpenAI CEO Sam Altman, saying Altman mentioned that an internal new model had cracked frontier math problems, with the proof related to the Navier-Stokes equations consuming on the order of $10 million in compute. Altman also said he prefers Agent Teams rather than swarms to describe multi-agent collaboration, and believes sandboxes can hardly contain superintelligence, so one can only rely on internal monitoring and alignment. He also criticized Silicon Valley's current AI narrative as bad salesmanship, insisting on iterative deployment to avoid a small elite deciding the future for the whole world.

    Quoted postJohn Collison · @collision

    OpenAI’s @sama joined me at the pub (and had his first Guinness) to discuss the PHP exploit, math weekend at OpenAI, agent swarms, and how he thinks AI leads to deflation. https://t.co/UBWrHd5Via

    Same story · 3 reports

    #English text is being prepared.#Agents#Safety / alignment

  11. Shao MengOpinion
    AI score63

    How the Amp team operates with 21 people and Agents replacing processes

    English text is being prepared.

    Quoted postAmp · @AmpCode

    21 people, a single meeting a week, no quarterly goals, no pull requests, no mandatory code review, no titles (except one dictator), a hundred releases a day, bugs fixed in minutes, famous for killing features. @sqs and @thorstenball on how the Amp team works: https://t.co/BTXBCPAXep

    #Industry news#Agents#English text is being prepared.

  12. Rohan PaulOpinion
    AI score62

    FT analyzes the bull and bear case for an Anthropic IPO: fear of missing out versus 2007-style debt risk

    FT published an article discussing an Anthropic IPO, arguing that whether fund managers buy in depends more on fear of missing out than on valuation logic. The bear case presented in the article is that frontier labs, large tech companies, and listed neoclouds have disclosed $2.1 trillion in debt and non-cancelable compute commitments, compared with $1.3 trillion in subprime debt in 2007; take-or-pay contracts could force companies to pay for compute that no one uses.

    #Industry news#OpenAI#Anthropic

  13. Larsen CundricOpinion
    AI score58

    Larsen Cundric reposted the view that compaction is inferior to context retrieval

    Larsen Cundric reposted Alexander Yue's view, arguing that compacting an agent's context once it fills up is the wrong approach; instead, the full session record should be retained and the model should retrieve from it on demand. Alexander Yue said that in a real test on an approximately 400k-token Opus 5.5 session, compaction took 38 seconds and cost $1.99 when the cache had expired, and 29 seconds and $0.17 when it had not, and that the summary loses details such as error messages and file paths; Mainplane, which he developed for this purpose, uses context retrieval and, on the same session and model, took 15 seconds and cost $0.10 to restore work, does not depend on cache hits and performs no rewriting, and the project is open-sourced under the MIT license.

    Quoted postAlexander Yue · @Alezander9

    Compaction is a mistake. When an agent's context fills up, tools like Codex and Claude Code compact it: 1. The model prefills the entire session again 2. It writes a summary of everything so far 3. A new session starts from that summary I measured this on a real ~400k-token session with Opus 5.5: → 38 s and $1.99 when the prompt cache has expired → 29 s and $0.17 when it hasn't And the summary is lossy. The exact error message, the file path, the one number you needed is often gone. So I do something new for Mainplane Context retrieval works the other way around. Mainplane keeps the full record of every session as a file. When the context fills up, a new session starts right away with one instruction: "You are continuing session X. Retrieve key context from it as you go." The agent searches the old session and copies the exact lines it needs, when it needs them. Same session, same model: 15 s and $0.10 to get back to work. It does not depend on a cache hit, and nothing is paraphrased. This is the bitter lesson applied to agent memory. Don't hand-design what the model should remember. Give it the full record and good tools, and let it decide. Mainplane is open source (MIT). Link in the first comment.

    #English text is being prepared.#Agents#Open-source ecosystem

  15. Yuchen JinOpinion
    AI score65

    Yuchen Jin: Artificial Analysis data shows Gemini 4 Carbon is smarter than…

    Citing Artificial Analysis data, Yuchen Jin says Gemini 4 Carbon is smarter than Opus 5.5 and costs less per task. He raises one caveat: the evaluation ran Gemini 4 Carbon using the Antigravity CLI, and he hasn't used Antigravity for months and isn't in a hurry to install it; he plans to try it in Codex and Omnigent instead to see how it actually performs.

    #English text is being prepared.#Evaluations / benchmarks#Google

  16. Saito Hardcore HackerOpinion
    AI score81

    SemiAnalysis Analyzes Anthropic S-1: 90% Incremental Gross Margin and Half-Trillion Compute Commitment

    The SemiAnalysis podcast analyzes Anthropic's S-1, pointing out its 90% incremental gross margin, 40% incremental EBIT, 85% inference gross margin, and 41% compute gross margin, and that it has been profitable for two consecutive quarters on a non-GAAP basis. On customer net revenue retention, customers who spent $1 last year now spend $9, with the Q1 figure at 500%; in addition, 250 customers pay over $10 million annually and 1,900 pay over $1 million. The episode also breaks down the $500 billion to $600 billion compute commitment, which at $100 billion per gigawatt translates to only about 5 gigawatts, and discusses the discrepancy in reporting that the top five customers account for 25% of revenue and that 48% goes through cloud vendor marketplaces.

    #Industry news#OpenAI#Anthropic

  17. Nathan LambertOpinion
    AI score76

    Nathan Lambert on not knowing how to try Gemini in the agent era

    Nathan Lambert said he does not know how to try Gemini in the agent era and believes he needs to make up for this. He mentioned that Gemini seems to have been absent since December 2025, but may be making a comeback. The tweet quoted Sundar Pichai's post announcing Gemini 4 Carbon, which said the model ranks first with 59 points on the Artificial Analysis Intelligence Index v5.0, and scored 88.2% on SEC-bench Pro, 54.8% on ExploitGym, and 71.0% on CWE-bench.

    Quoted postSundar Pichai · @sundarpichai

    I did say we'd be releasing our models periodically...introducing Gemini 4 Carbon, our next frontier model. Following Argon's release just two weeks ago, Carbon shows the pace at which we’re pushing the frontier, it ranks #1 on the recently updated v5.0 of @ArtificialAnlys Intelligence Index at 59. It also leads across key cybersecurity evaluations scoring 88.2% on SEC-bench Pro for finding vulnerabilities, 54.8% on ExploitGym for exploitation, and 71.0% on CWE-bench for patching. We’re seeing significant improvements in long-horizon tasks even compared to Argon, and we’ve strengthened frontier safeguards. As we did with Argon, we’re going to roll it out gradually + responsibly. Some trusted cyber defenders will get access starting this week. We'll be expanding it to more partners through our Fairwind Program, ahead of wider availability soon, so hang tight!

    #English text is being prepared.#Agents#Google

  18. Ethan MollickOpinion
    AI score55

    Ethan Mollick on DeepMind's intelligence economics research: agent coordination capability is the key to economic transformation

    Ethan Mollick commented on Google DeepMind's "The Economics of Intelligence" research, pointing out that an organization is not merely the sum of its best individuals, so a single agent becoming stronger will not change the economy on its own; only agents that can work in coordination may bring about transformation. The research, released by Alex Imas, Andrew Koh, and Shane Legg, proposes two thresholds of economic capability—capability and coordination—and argues that economic transformation depends on whether AI becomes a super-strong individual, super-strong collaboration, or both.

    Quoted postAlex Imas · @alexolegimas

    I joined @GoogleDeepMind to develop a research program on the economics of AGI. Today, together with Andrew Koh (@andrewjkoh) and Shane Legg (@ShaneLegg), we are incredibly excited to release The Economics of Intelligence, the first in a series of projects from our group on how AGI will change the economy. How do capability improvements transform the economy, and when? In this project, we define thresholds of economic capabilities, e.g., minimal AGI, based on how they transform the economy. We then track how the economy–market structure, employment, and wages–changes during the transition across capability regimes. We have seen exponential increases in what AI agents can do but the broader economy seems almost remarkably unchanged. Part of the reason is that the solo AI agent is the wrong economic object; economic transformation is an equilibrium outcome where organizations compete. Looking at how intelligence functions within organizations, transformation will depend on when two key capability thresholds are crossed: competence (gains on individual skills, e.g., coding, math) and coordination (gains on ability of agents to cooperate and work together). We show that what economic transformation looks like depends on whether AI becomes supercompetent, supercooperative, or both—the regime of artificial superintelligence (ASI). Tracking this transition shows why the real visible transformation may come from improvements in coordination—a capability most benchmarks don’t yet track. Past a certain threshold, AI capabilities can break the Coasian ceiling on firm size and lead to an economy that looks drastically different than the one we see today. Two main takeaways: 1. To understand the path of transformation, we should be tracking agentic coordination more closely. 2. The impact of AI will likely show up in wages before market structure or employment. We are really excited about this paper. Excited to hear people’s thoughts. Link: https://t.co/9DTGSeKoSA

    #English text is being prepared.#Agents#Papers/Research

  19. Yuchen JinOpinion
    AI score65

    Yuchen Jin: The OpenAI vs. Anthropic subscription battle is about tokens per dollar

    Yuchen Jin commented that the focus of the OpenAI vs. Anthropic subscription competition is no longer on whose model is best, but on who offers more tokens per dollar. The Tibo post he cited announced that usage limits for all Pro 100/200/500 tiers and Business Premium are doubled, the usage limit for Pro 200 will no longer be reduced, and Work and Codex usage remain unchanged.

    Quoted postTibo · @thsottiaux

    Day 25/ We are doubling all usage limits for all Pro 100/200/500 tiers and Business Premium, effective today. We heard your kind feedback and won’t be reducing usage limits for Pro 200, so your Work and Codex usage will stay the same. We’re also going to continue offering extended limits for dots for now, and usage of your dot will still continue to be separate from your default usage. And we'll of course also keep shipping better models and other efficiency improvements that increase your effective usage over time.

    Same story · 9 reports

    #English text is being prepared.#OpenAI#Anthropic

  20. 36Kr · AIOpinion
    AI score68

    Embodied intelligence financing cools down, multiple robotics companies fall into work stoppages and bankruptcy

    Financing in the embodied intelligence industry cooled significantly after September. In the first half of October, there were only 19 financing deals totaling 4.934 billion yuan, far below August's 91 deals and 31.323 billion yuan. Nanjing Wujing Power has suspended work and owes wages, Yinghe Robotics convened a creditors' meeting, and Shenlan Robotics has been accepted for bankruptcy liquidation; multiple companies have entered bankruptcy or contraction procedures. Unitree's prospectus shows that among its humanoid robot revenue, scientific research and education account for 73.6%, while revenue used in scenarios such as intelligent manufacturing accounts for only 2.64%, and end-user demand has still not reached scale.

    #English text is being prepared.#Embodied AI#Industry news