[AINews] Anthropic growing 10x/year while everyone else is laying off >10% of their workforce

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.

AINews Anthropic growing 10x/year while everyone else is laying off 10% of their workforce A quiet day lets us reflect on an interesting dichotomy in the economy. While 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. This is a REVENUE, not a financial speculation, chart: All 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. And of course, the “AI” growth has mostly been hardware and energy, rather than software: With the AI growth and non-AI shrinkage, we are approaching bubble territories of concentrations in the economy: AI News for 5/7/2026-5/8/2026. We checked 12 subreddits, 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 AI Twitter Recap OpenAI’s GPT-5.5 / Codex rollout, cyber models, and safety instrumentation 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 as 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. Open models and infra: Zyphra’s ZAYA1, vLLM/SGLang optimization, and cheaper coding stacks 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. Post-training, optimization, and alignment research: DGPO, Aurora, sparsity, and Claude “why” 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. Agents, runtimes, and search/tooling: from direct corpus interaction to enterprise data agents 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. Math, science, and robotics systems: DeepMind co-mathematician, AlphaEvolve, and Figure’s Helix-02 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 . Top tweets by engagement 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 discussion 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. AI Reddit Recap /r/LocalLlama + /r/localLLM Recap 1. Multi-Token Prediction Local Inference Keep reading with a 7-day free trial Subscribe to Latent.Space to keep reading this post and get 7 days of free access to the full post archives.