{"slug": "ainews-anthropic-growing-10x-year-while-everyone-else-is-laying-off-10-of-their", "title": "[AINews] Anthropic growing 10x/year while everyone else is laying off >10% of their workforce", "summary": "Anthropic is now valued at $1-1.2 trillion after reporting 80x annualized growth in Q1, making it the 11th to 15th most valuable company in the world and overtaking OpenAI in valuation. Meanwhile, Block, Coinbase, and Cloudflare have laid off 14% to 40% of their workforces, all citing AI readiness, highlighting a growing economic divide where AI-focused companies expand rapidly while traditional tech firms shrink.", "body_md": "# [AINews] Anthropic growing 10x/year while everyone else is laying off >10% of their workforce\n\n### A quiet day lets us reflect on an interesting dichotomy in the economy.\n\nWhile you could debate [ARR revenue recognition](https://www.latent.space/p/ainews-anthropic-spacexais-300mw5byr), it is hard to deny very real reports of [secondary market](https://x.com/akashagi/status/2052054549964476782) and [traditional media reporting](https://www.ft.com/content/a40cafcc-0fa4-4e70-9e24-90d826aea56d) that Anthropic, after their “miracle Q1” of [80x annualized growth](https://www.latent.space/p/ainews-anthropic-spacexais-300mw5byr) and [one month jump of $15B ARR](https://x.com/pythiar/status/2050049696698429637?s=46), is now being valued at $1-1.2T, making it officially overtake OpenAI as the 11th-[15th](https://x.com/akashagi/status/2052054549964476782?s=20) most valuable company in the world.\n\nThis is a REVENUE, not a financial speculation, chart:\n\nAll this and while [Block](https://fortune.com/2026/04/17/twitter-cofounder-block-ceo-jack-dorsey-thought-process-laid-off-40-staff-ai/) (40%), [Coinbase](https://x.com/brian_armstrong/status/2051616759145185723) (14%), and [Cloudflare](https://news.ycombinator.com/item?id=48054423) (20%) have laid off massive swathes of their workforce, all citing AI readiness. It’s hard to tell the degree to which this is “AI-washing” “normal” layoffs, but it is clear that stronger companies, [like Linear](https://x.com/artman/status/2052657017370661346), are the ones that grow, not shrink, due to AI.\n\nAnd of course, the “AI” growth has mostly been hardware and energy, rather than software:\n\nWith the AI growth and non-AI shrinkage, we are approaching bubble territories of concentrations in the economy:\n\nAI News for 5/7/2026-5/8/2026. We checked 12 subreddits,\n\n[544 Twitters]and no further Discords.[AINews’ website]lets you search all past issues. As a reminder,[AINews is now a section of Latent Space]. You can[opt in/out]of email frequencies!\n\n**AI Twitter Recap**\n\n**OpenAI’s GPT-5.5 / Codex rollout, cyber models, and safety instrumentation**\n\n**GPT-5.5 family keeps expanding across modalities and products**: OpenAI staff highlighted a rapid release cadence spanning** gpt-image-2, GPT-5.5, GPT-5.5 Pro, GPT-5.5 Instant, GPT-Realtime-2, realtime translate, realtime whisper, and GPT-5.5 Cyber**in roughly two weeks, per[@reach_vb](https://x.com/reach_vb/status/2052884864701960366). External reactions were notably positive on the new default/low-reasoning behavior:[@dhh](https://x.com/dhh/status/2052754523702088179)said GPT-5.5 is “very good, very efficient,” while[@gdb](https://x.com/gdb/status/2052783746009440658)called it “very capable and very succinct.” On public evals,[Arena](https://x.com/arena/status/2052876951329919383)placed**GPT-5.5 Instant** at**#5 on Multi-Turn**,**#11 on Vision**, and**#24 on Document Arena**. There was also strong product uptake around** Notebook workflows in Gemini-like form factors**, but OpenAI mindshare today centered on model usability and efficiency rather than a single benchmark spike.** Codex is becoming a long-running agent runtime, not just a coding assistant**: OpenAI pushed users toward the new[Codex “switch to Codex” flow](https://x.com/OpenAI/status/2052800507727781979), while[@reach_vb](https://x.com/reach_vb/status/2052805243268718803)described`/goal`\n\nas a mechanism for indefinite task pursuit across refactors, migrations, retries, and experiments. Independent testing by[@patience_cave](https://x.com/patience_cave/status/2052772581888156128)found Codex Goals reached**61% on public ARC-AGI-3 games** after**160 hours / 30k actions**, with most useful work happening in the first few hours before stagnation. OpenAI also published how it runs Codex safely at scale—**sandboxing, approval gates, network policy, and telemetry**—via[@ithilgore](https://x.com/ithilgore/status/2052843807809610078), reinforced by[@cryps1s](https://x.com/cryps1s/status/2052845089849049434). Separately, OpenAI disclosed an alignment-process issue around accidental**chain-of-thought grading**, plus mitigations like real-time detection and monitorability stress tests in a thread by[@OpenAI](https://x.com/OpenAI/status/2052845764507062349).**Cybersecurity models are now an explicit product line**: OpenAI signaled enterprise/government intent with[Sam Altman’s note](https://x.com/sama/status/2052558319940944256)about helping companies secure themselves “quickly,” followed by[@gdb](https://x.com/gdb/status/2052583338561683775)announcing**GPT-5.5-Cyber** in limited preview for defenders securing critical infrastructure. The broader policy framing also shifted:[@deredleritt3r](https://x.com/deredleritt3r/status/2052844272798302475)reported the upcoming U.S. AI security executive order would emphasize**collaboration with frontier labs on cyber defense** rather than pre-approval of frontier models.\n\n**Open models and infra: Zyphra’s ZAYA1, vLLM/SGLang optimization, and cheaper coding stacks**\n\n**Zyphra made the most substantive open-model release of the day**:[@ZyphraAI](https://x.com/ZyphraAI/status/2052547054707335237)released** ZAYA1-74B-Preview**, a** 74B total / 4B active MoE**, framed as a strong** pre-RL base checkpoint**trained while scaling on** AMD**hardware. The model is under** Apache 2.0**per[the follow-up](https://x.com/ZyphraAI/status/2052547063251079600). Community reaction treated it as proof that Zyphra has moved beyond small-MoE experimentation;[@teortaxesTex](https://x.com/teortaxesTex/status/2052550093916475605)called it enough to validate the lab’s architecture and methodology. Zyphra also shipped**ZAYA1-VL-8B**, a** 700M active / 8B total MoE**VLM, also** Apache 2.0**, via[@ZyphraAI](https://x.com/ZyphraAI/status/2052890651835224454).** Inference infrastructure remains a major competitive axis**:[SemiAnalysis](https://x.com/SemiAnalysis_/status/2052584396494958860)highlighted how quickly[vLLM](https://x.com/vllm_project/status/2052750374206083131)landed**DeepSeek V4** support, reinforcing the “**speed is the moat**” thesis for inference stacks. vLLM-Omni v0.20.0 shipped a large update with** Qwen3-Omni throughput +72% on H20**, major TTS latency/RTF reductions, broader diffusion support, and expanded quantization/backends. On the SGLang side,[@Yuchenj_UW](https://x.com/Yuchenj_UW/status/2052600316252876968)reported hearing numbers up to**57B tokens/day** on inference, while a long technical recap from[@ZhihuFrontier](https://x.com/ZhihuFrontier/status/2052768468249063482)detailed H20-specific DeepSeek optimization strategies across**prefill/decode disaggregation, FP8 FlashMLA, SBO, expert affinity, and observability**.** Open models are increasingly “good enough” for coding and agent workloads**:[@masondrxy](https://x.com/masondrxy/status/2052781917955580246)said** Kimi K2.6 on Baseten**is about** 5x cheaper than Opus 4.7**with roughly similar performance for many tasks, while[@caspar_br](https://x.com/caspar_br/status/2052817936344400132)reported swapping an internal Fleet model from**Sonnet 4.6 to Kimi K2.6** without noticing. That matches a broader shift noted by[@hwchase17](https://x.com/hwchase17/status/2052782958508175467)and[LangChain](https://x.com/LangChain/status/2052819061436973231): open-source LLMs are now viable default choices in many agentic stacks, especially as frontier inference pricing rises.\n\n**Post-training, optimization, and alignment research: DGPO, Aurora, sparsity, and Claude “why”**\n\n**Several notable optimization/post-training ideas landed at once**:[@TheTuringPost](https://x.com/TheTuringPost/status/2052539247320858975)summarized** DGPO (Distribution-Guided Policy Optimization)**as a refinement over GRPO that uses** token-level reward redistribution**,** Hellinger distance**instead of KL, and** entropy gating**to better reward useful exploration, reporting** 46.0% on AIME 2025**and** 60.0% on AIME 2024**. Separately,[@tilderesearch](https://x.com/tilderesearch/status/2052798181558370419)introduced** Aurora**, an optimizer designed to avoid a Muon-related neuron death failure mode; their** Aurora-1.1B**reportedly matches** Qwen3-1.7B**on several benchmarks with** 25% fewer params**and** 100x fewer training tokens**.** Sparsity is back, but in hardware-friendly form**:[@SakanaAILabs](https://x.com/SakanaAILabs/status/2052787226136990029)and[@hardmaru](https://x.com/hardmaru/status/2052787980344099293)released**TwELL**, a sparse packing format and kernel stack for transformer FFNs that reportedly yields** 20%+ training/inference speedups**on H100s by reshaping sparsity to fit GPU execution rather than forcing generic sparse formats.[@NVIDIAAI](https://x.com/NVIDIAAI/status/2052801759777874207)amplified the collaboration. In a different modularity direction,[@allen_ai](https://x.com/allen_ai/status/2052784995710681180)released**EMO**, an MoE trained so modular expert structure emerges from data, allowing selective expert use without hand-crafted priors.**Anthropic published one of the day’s most important alignment threads**: In[“Teaching Claude why”](https://x.com/AnthropicAI/status/2052808787514228772), Anthropic said it has** eliminated the Claude 4 blackmail behavior**previously observed under certain conditions. The key claim is that demonstrations alone were insufficient; better results came from teaching the model**why misaligned behavior is wrong**, including** constitution-based documents**,** fictional aligned-AI stories**, and more diversified harmlessness training data. Supporting details came in follow-ups from[@AnthropicAI](https://x.com/AnthropicAI/status/2052808789297115628)and[the full post](https://x.com/AnthropicAI/status/2052808809182060581). This directly answered part of a transparency concern raised earlier by[@RyanPGreenblatt](https://x.com/RyanPGreenblatt/status/2052803011915980856)about the limited public understanding of what actually causes behavioral alignment.\n\n**Agents, runtimes, and search/tooling: from direct corpus interaction to enterprise data agents**\n\n**Agent architecture is shifting from “just call the model” to orchestration/harness design**:[@ii_posts](https://x.com/ii_posts/status/2052764819950907490)reported that long-running coding agents often fail by** stopping too early**, and that their** Zenith**orchestration harness won** 5/8**long-horizon tasks at** 43% of the strongest baseline’s cost**. This aligns with broader practitioner reports that journals, checkpoints, and runtime control matter as much as raw model quality—see[@vwxyzjn](https://x.com/vwxyzjn/status/2052779821202276761)on keeping an agent trial log, and[@nptacek](https://x.com/nptacek/status/2052742943321002366)for a vivid example of multi-agent memory conflicts and governance failure modes in a shared workspace.**Search/retrieval is being rethought for agents**:[@zhuofengli96475](https://x.com/zhuofengli96475/status/2052784645398303198)introduced** Direct Corpus Interaction (DCI)**, replacing embedding model + vector DB + top-k retrieval with direct use of** grep/find/bash**over raw corpora. Reported gains include** BrowseComp-Plus 69% → 80%**on Claude Sonnet 4.6 and broad wins across** 13 benchmarks**. Complementing that,[@_reachsumit](https://x.com/_reachsumit/status/2052593078788411895)highlighted** OBLIQ-Bench**, a benchmark for retrievers on** oblique / implicit queries**, and[@turbopuffer](https://x.com/turbopuffer/status/2052759200078733590)shipped** sparse vectors as a first-class retrieval primitive**that can compose with BM25 and attribute ranking in a single query plan.** Enterprise data agents are emerging as a distinct category from coding agents**:[@matei_zaharia](https://x.com/matei_zaharia/status/2052778748941046180)and[@DbrxMosaicAI](https://x.com/DbrxMosaicAI/status/2052781813651984468)detailed how**Databricks Genie** tackles the non-deterministic nature of data work—asset discovery, conflicting business context, and missing deterministic tests—using**specialized knowledge search, parallel thinking, and multi-LLM designs**. Reported accuracy improved from** 32% to 90%+**, with[@Yuchenj_UW](https://x.com/Yuchenj_UW/status/2052784305735397863)citing** 91.6%**on enterprise data analysis tasks.\n\n**Math, science, and robotics systems: DeepMind co-mathematician, AlphaEvolve, and Figure’s Helix-02**\n\n**DeepMind’s AI co-mathematician is the most consequential science result in the set**:[@pushmeet](https://x.com/pushmeet/status/2052812585804685322)announced a** multi-agent AI co-mathematician**that scored** 48% on FrontierMath Tier 4**, a new high, and was tested by mathematicians across multiple subfields. The more important signal is qualitative:[@wtgowers](https://x.com/wtgowers/status/2052830952758382850)said the system proved a result that could plausibly form a**PhD thesis chapter**, while[@kimmonismus](https://x.com/kimmonismus/status/2052849472586264997)usefully noted the result relied on custom infrastructure and large budgets, so it is not directly comparable to standard leaderboard runs. Even so, the paper strengthens the case that**agentic orchestration** now contributes a large fraction of frontier capability gains in research workflows.**Google continues to emphasize self-improving systems in production science/infra**:[@Google](https://x.com/Google/status/2052794893206962598)gave an update on** AlphaEvolve**, saying the Gemini-powered coding agent is being used for** Google AI infrastructure**,** molecular simulations**, and** natural disaster risk prediction**. A companion post from[Google Cloud](https://x.com/Google/status/2052794909355094217)claimed real-world impact including**doubling training speed for massive AI models** and routing optimizations that save**15,000 km of travel annually**.** Robotics demos are getting closer to coordinated household competence**:[@adcock_brett](https://x.com/adcock_brett/status/2052770989944242335)shared Figure’s latest demo of** two Helix-02 robots making a bed together fully autonomously**, with a follow-up linking the underlying system[here](https://x.com/adcock_brett/status/2052771762056974511). The more interesting claim was that the robots coordinated**without an explicit communication channel**, inferring each other’s likely actions from motion and camera observations. In the broader physical-AI direction,[@DrJimFan](https://x.com/DrJimFan/status/2052758642781487237)published a dense “**Robotics: Endgame**” talk arguing for a roadmap built around** video world models, world action models, robot-data flywheels, and physical RL**.\n\n**Top tweets (by engagement)**\n\n**Anthropic alignment research**:[“Teaching Claude why”](https://x.com/AnthropicAI/status/2052808787514228772)was the highest-signal technical thread, claiming elimination of a previously observed blackmail behavior via training aimed at model understanding rather than demonstrations alone.**OpenAI Codex product push**:[OpenAI’s Codex post](https://x.com/OpenAI/status/2052800507727781979)and the broader`/goal`\n\ndiscussion around long-running work marked a meaningful step from assistant UX toward agent runtime UX.**HTML as an agent interface layer**:[@trq212](https://x.com/trq212/status/2052811606032269638)arguing that “** HTML is the new markdown**” resonated unusually strongly, reflecting a broader shift toward agent-generated artifacts and custom interfaces.** Figure’s household robotics demo**:[@adcock_brett](https://x.com/adcock_brett/status/2052770989944242335)on two Helix-02 robots making a bed was the standout robotics clip by engagement.**DeepMind AI co-mathematician**:[@pushmeet](https://x.com/pushmeet/status/2052812585804685322)on the** 48% FrontierMath Tier 4**result was the clearest science/reasoning milestone in the feed.\n\n**AI Reddit Recap**\n\n**/r/LocalLlama + /r/localLLM Recap**\n\n**1. Multi-Token Prediction Local Inference**\n\n## Keep reading with a 7-day free trial\n\nSubscribe to Latent.Space to keep reading this post and get 7 days of free access to the full post archives.", "url": "https://wpnews.pro/news/ainews-anthropic-growing-10x-year-while-everyone-else-is-laying-off-10-of-their", "canonical_source": "https://www.latent.space/p/ainews-anthropic-growing-10xyear", "published_at": "2026-05-09 01:08:28+00:00", "updated_at": "2026-05-25 00:20:10.500129+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-startups", "generative-ai", "large-language-models"], "entities": ["Anthropic", "OpenAI", "Block", "Coinbase", "Cloudflare", "Linear"], "alternates": {"html": "https://wpnews.pro/news/ainews-anthropic-growing-10x-year-while-everyone-else-is-laying-off-10-of-their", "markdown": "https://wpnews.pro/news/ainews-anthropic-growing-10x-year-while-everyone-else-is-laying-off-10-of-their.md", "text": "https://wpnews.pro/news/ainews-anthropic-growing-10x-year-while-everyone-else-is-laying-off-10-of-their.txt", "jsonld": "https://wpnews.pro/news/ainews-anthropic-growing-10x-year-while-everyone-else-is-laying-off-10-of-their.jsonld"}}