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

> Source: <https://www.latent.space/p/ainews-anthropic-growing-10xyear>
> Published: 2026-05-09 01:08:28+00:00

# [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**

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