[AINews] Poolside gets $12B reverse-execuhire to NVIDIA; founders stay for $1B, employees go for $6B, Infraco scaling to 7GW neocloud

NVIDIA has hired 109 employees from AI startup Poolside in a $12 billion deal that the founders describe as 'not an acquisition and not an acquihire,' with founders staying for $1 billion and employees receiving $6 billion. Poolside's infrastructure company, spun out in January 2026, is scaling to a 7GW neocloud. The deal follows NVIDIA's earlier investment of up to $1 billion in Poolside and comes after Poolside failed to raise $2 billion for a 40,000 GB300 cluster.

Less than a month ago we had just featured Poolside’s Model Factory with Eiso Kant https://www.latent.space/p/poolside?utm source=publication-search on the pod following our Paper Club https://www.latent.space/p/community coverage : It appears that Jensen really, really liked Poolside too, as he went from investor https://www.bloomberg.com/news/articles/2025-10-30/nvidia-to-invest-up-to-1-billion-in-ai-startup-poolside to doing licensing their factory and hiring 109 of their employees: Unless things changed drastically, this accounts for the overwhelming majority of the technical Poolside employees https://www.latent.space/i/208082176/hiring-impact-and-closing : Eiso Kant 01:52:31 : We are hiring on every possible role in applied research and engineering in the company, from training all the way to evals to post-training architecture. Like, we are still in a world where, individuals can have massive impact. And I think our pitch to join us —I think we are one of the places where it’s the highest ratio to individual to impact, Right? L ess than 70 people built this model. Less than 115 between engineering and researchers, like, together did this effort, and that’s a very broad definition ‘cause I put myself in the 115 list. As the founders say, this is “not an acquisition and not an acquihire”: We’ve been calling the Windsurf-Google and Character-Google and Scale-Meta and Instacart-OpenAI deals execuhires https://www.latent.space/p/ainews-dreamer-joins-meta-superintelligence?utm source=publication-search because usually the executives go leaving the employees with a rich payout but holding the company remaining, but this is a first time it is happening the other way around. The action amounts to founders pivoting the company extremely hard to SOMETHING, and finding an EXTREMELY comfortable golden parachute for investors and employees to continue on with the original mission or stay aboard for the new pivot: For the last 3 1/2 years we’ve been directionally correct in a race where capital requirements went vertical. At the end of last year, we had a 6 week window in which to raise $2 billion dollars to pay for a 40,000 GB300 cluster coming online in January.We didn’t close it in time, and we lost the cluster. and: We also know that at 10,000-20,000 GB300s we would produce a great model that could rival the current frontier.But the scale of next year’s frontier models requires far more than an order of magnitude larger cluster. And for this the constraint today is not only capital, it isphysical data center space and contracted compute. The compute needed to be at the frontier of the current model recipe is going vertical, and as the world accelerates along the axis of Recursive Self Improvement this will only become more evident. To this end, the PIC infraco, spun out in Jan 2026, is also interesting in its ambitions… We’re confused too, and the founders say they are “not ready to share the updated vision”, but everyone here is coming out with a lot of money so we’re just interested to see what’s next for everyone on the 3 different directions emerging from OG Poolside. The only hints left to us: We wholeheartedly believe that everything economically valuable, scientifically interesting and a lot of what will be personally meaningful is going to be underpinned by Al. The world has not yet reached 0.1% of this transition….… We believe human level capabilities of intelligence will be fully commoditized by open source models, while super intelligence will likelynot be.The world has two types of economically valuable problems, those that are intelligence bound, and those that are experiment bound. The first are problems which we can solve by scaling up intelligence e.g. building software, doing accounting, solving a math theorem. The second are ones that require real world experimentation to progress, andno amount of increased intelligence without experimental results will make progress. We could put 100,000 of the world’s brightest minds together to solve cancer but without a real world experimental feedback loop, they likely never will.Today’s model revenue is from coding and soon from all of knowledge work. In the future, companies who can go beyond human level capabilities will tap into revenue coming from scientific discoverieswhere there is a true data moat derived from real world experimentation. In our humble opinion, Al’s ultimate value will not derive from the first kind, that will become a low margin commodity, but it will from the second.Al will become the world’s most valuable scientific discovery engine. Fascinating. Sounds like we could not have timed our AI for Science podcast https://www.latent.space/p/science?utm source=publication-search better. AI News for 8/19/2026-8/20/2026. 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 and Anthropic Expand the Agent Product Surface OpenAI pushed several desktop and builder features in one wave : @ChatGPT https://x.com/ChatGPT/status/2090499359641329950 launched an Apple Messages plugin for ChatGPT Work/Codex on Mac, enabling message search, catch-up, drafting, and sending from the desktop app. @OpenAIDevs https://x.com/OpenAIDevs/status/2090515079058108745 also added collaborative editing for ChatGPT Sites , with teammates sharing a project while Codex manages git/CI; shared read-only conversation links https://x.com/ChatGPT/status/2090517084262551917 and PR-context sharing https://x.com/OpenAIDevs/status/2090555241343418814 further push ChatGPT/Codex toward being a coordination surface, not just a chat UI. On the API side, transparent backgrounds in GPT-Image-2 https://x.com/OpenAIDevs/status/2090536933571330440 are now in preview for reusable design assets. OpenAI’s desktop memory/workflow features continue rolling out geographically : @OpenAIDevs https://x.com/OpenAIDevs/status/2090487766442512398 said Computer History and cross-app memory are now available in the EEA, UK, and Switzerland for Pro/Business/Enterprise Mac users, with Record & Replay https://x.com/OpenAIDevs/status/2090487779587477626 also live there. Together, these features point to a product strategy of capturing user workflows on-device and turning repeated actions into reusable skills. Anthropic made its agent platform more composable and production-ready : @ClaudeDevs https://x.com/ClaudeDevs/status/2090540270219567575 announced general availability for computer use, browser tool, Skills API, and Files API on the Claude Platform. The Skills API https://x.com/ClaudeDevs/status/2090540273939996958 adds versioned reusable procedures; the Files API https://x.com/ClaudeDevs/status/2090540275357606263 now supports expiration control, 5x higher rate limits to 500 RPM , and 1 TB/org . Anthropic also published an AG-UI adapter for Claude Managed Agents https://x.com/ClaudeDevs/status/2090511582531072265 , mapping chat threads to managed sessions and streaming text, tool calls, and thinking into custom UIs. Model Economics, Usage Limits, and the Enterprise Shift Toward Open Models AT&T became the clearest public case study yet for hybrid routing : the most consequential enterprise datapoint in the set came via @Hesamation https://x.com/Hesamation/status/2090518831349268851 , summarizing AT&T’s internal AI deployment: 40% of employee AI usage already routes to open models , with a target of 60–70% ; coding costs are down 56% for only a 2% quality drop , at 45B tokens/day . That supports the increasingly common view that frontier closed models remain reserved for the hardest tasks, while “good-enough” open models eat the broad middle of enterprise demand. @amir https://x.com/amir/status/2090515013635305683 explicitly framed this as a warning sign for OpenAI/Anthropic’s enterprise moat, while @ollama https://x.com/ollama/status/2090601698402447748 welcomed AT&T to open models. Pricing pressure is intensifying across closed-model distribution : @eglyman https://x.com/eglyman/status/2090521785909309572 announced GPT-5.6 Sol at 50% off through Router, and both @github https://x.com/github/status/2090577927905874389 and @code https://x.com/code/status/2090583188326187464 amplified the temporary discount for GitHub Copilot / VS Code users. At the same time, user sentiment suggests supply constraints are surfacing as usage caps rather than degraded quality: @bridgemindai https://x.com/bridgemindai/status/2090386359743893620 complained that a $200/mo OpenAI Pro plan could be exhausted in a single heavy Codex day, and @theo https://x.com/theo/status/2090621019476427174 noted it was possible to continue consuming substantial tokens after hitting the stated cap. The broader signal: labs are still searching for the right product boundary between high-end model access and economically sustainable agentic usage. Open-weight adoption and distribution continue to broaden : @ollama https://x.com/ollama/status/2090505028998140182 said Kimi K3 is now rolled out to over half its subscription base with US/EU hosting and zero data retention . On the open ecosystem side, @Google https://x.com/Google/status/2090497445826322464 and @osanseviero https://x.com/osanseviero/status/2090490264112738579 highlighted Gemma surpassing 1B downloads , while @ philschmid https://x.com/ philschmid/status/2090485095396180034 launched an Awesome Gemma repo aggregating variants, deployment guides, and fine-tuning recipes. Multimodal and Agent Benchmarks: Muse Spark, GLM-5.3, Gemini 3.7 Flash Meta’s Muse Spark 1.2 had a strong benchmark day across multimodal/agentic evals : @AIatMeta https://x.com/AIatMeta/status/2090485743034716420 presented demos spanning visual coding, robotics planning, and audio-visual understanding , and previewed WildArtifactBench https://x.com/AIatMeta/status/2090505413817246050 , an internal eval using win rates and Elo from human/agentic judges for practical multimodal tasks. Third-party measurements were favorable: @arena https://x.com/arena/status/2090484142408618033 reported +2.1% net improvement in Agent Arena, up from 0.9% in v1.1, with particularly strong Bash Recovery +11.4% ; @DesignArena https://x.com/DesignArena/status/2090498670685020639 placed Muse Spark 1.2 1 for Video-to-Website , 2 for Image-to-HTML , and 3 for Image-to-Frontend , while noting it sits on the price-preference Pareto frontier . Zhipu’s GLM-5.3 keeps showing up in agentic/code evals : @AutoClawAIer https://x.com/AutoClawAIer/status/2090446256342708724 announced GLM-5.3 integration into AutoClaw , Z.ai’s work agent. More importantly, @arena https://x.com/arena/status/2090581559262798055 said GLM-5.3 Max shifts the Code Arena: WebDev Pareto frontier , projecting to 2 among open models and 8 overall at 1597 pts and $3.65/M . Separately, @ZixuanLi https://x.com/ZixuanLi /status/2090564295696306436 resurfaced SAO Single-Rollout Asynchronous Optimization as a key GLM-5.2/5.3 RL advance for stable asynchronous agentic RL. Gemini 3.7 Flash keeps accumulating “cheap and strong” evidence : @arcprize https://x.com/arcprize/status/2090500144550539327 reported ARC-AGI-2: 84.6% at $0.25/task and ARC-AGI-1: 95.5% at $0.12/task , making Gemini 3.7 Flash stand out on cost-adjusted reasoning performance. @JonathanJarvis https://x.com/JonathanJarvis/status/2090479013344993579 separately called it excellent for agentic vision tasks . Infra, Hardware, and Systems Work: Rubin, Cerebras, Linux Agents, Caching OpenAI’s next pretraining stack is moving onto Rubin : @udayruddarraju https://x.com/udayruddarraju/status/2090343188393246973 posted that OpenAI’s first NVIDIA Vera Rubin racks are now installed and running the training stack, explicitly tied to next-generation frontier pre-training . @gdb https://x.com/gdb/status/2090515992506147198 called it a major milestone in the OpenAI-NVIDIA partnership. Cerebras’ CS-4 drew attention for inference scaling without a node shrink : @kimmonismus https://x.com/kimmonismus/status/2090468333476860347 summarized the launch as essentially doubling performance on the same 5nm wafer , 4T transistors , and 900k AI cores , via redesigned power delivery and cooling. Reported specs include 250 PFLOPs per WSE-3 Turbo , 43.2 PB/s memory bandwidth , and a 3-wafer CS-4 rack at 750 PFLOPs . The notable claim for practitioners: 4,400+ tok/s per user on GPT-OSS-120B , up to 30x faster than GPU-based systems. Agent runtime ergonomics are becoming a systems bottleneck : @theo https://x.com/theo/status/2090528543746965991 argued that Linux materially outperforms macOS for agent workloads , especially on filesystem-heavy operations. @Qdrant engine https://x.com/qdrant engine/status/2090461354557673915 shared a practical semantic-caching writeup showing 57.1% hit rate , 55.7% fewer tokens , and ~15 ms hit latency. @MParakhin https://x.com/MParakhin/status/2090494322101957006 pushed gisting as an underused production technique, citing ~40% lower end-to-end latency and ~15% higher throughput with better results, and linked a Shopify engineering writeup https://x.com/MParakhin/status/2090495141371093407 . Agents, Memory, and Harness-Centric Learning Chroma launched a research preview of self-improving memory : @jeffreyhuber https://x.com/jeffreyhuber/status/2090466566743974191 announced Foundation , Chroma’s approach to agent memory, built from prior agent sessions. This landed amid a broader shift from “single-shot agent” thinking toward persistent harnesses with accumulated state, skills, and memories. The most interesting agent research in the set was about harness evolution, not model weights : @omarsar0 https://x.com/omarsar0/status/2090533587066249514 highlighted a paper on harness continual learning , where prompts, memories, skills, and routing rules evolve independently of the model. The key failure mode is harness-level forgetting : improving one component can silently break previously reliable behavior. The proposed solution, guarded harness evolution , separates proposing updates from committing them, with reported 10% gains across textual, multimodal, and open-world tasks. Related negative results matter too : @dair ai https://x.com/dair ai/status/2090559561128407336 flagged a study showing that memory-based self-improving agents look worse once you control for task order effects and evaluation variance . @omarsar0 https://x.com/omarsar0/status/2090466402809561334 also summarized a paper arguing post-training agents tend to lock into an initial strategy early and spend the remaining budget on local refinement rather than revisiting the strategic choice itself. Top Tweets by engagement ChatGPT desktop + Messages : @ChatGPT’s Apple Messages plugin launch https://x.com/ChatGPT/status/2090499359641329950 was the single biggest product tweet in the set and reflects the shift toward desktop-native, action-taking assistants. AT&T’s open-model routing economics : @Hesamation’s summary https://x.com/Hesamation/status/2090518831349268851 is arguably the most strategically important enterprise datapoint: 40% open now, 60–70% later, 56% coding cost reduction . OpenAI’s Rubin racks : @udayruddarraju https://x.com/udayruddarraju/status/2090343188393246973 provided a rare concrete infrastructure signal about frontier pretraining scale-up. Claude Platform GA for computer use / Skills / Files : @ClaudeDevs https://x.com/ClaudeDevs/status/2090540270219567575 marked a significant maturity step for Anthropic’s agent platform. Gemini 3.7 Flash on ARC-AGI : @arcprize https://x.com/arcprize/status/2090500144550539327 reinforced Google’s positioning around strong low-cost reasoning. AI Reddit Recap /r/LocalLlama + /r/localLLM Recap 1. Qwen3.8-27B Quantization and Coding Benchmarks Activity: 2059 : Introducing Qwen3.8-27B Dynamic v3 Unsloth GGUFs https://www.reddit.com/r/LocalLLaMA/comments/1vsr67c/introducing qwen3827b dynamic v3 unsloth ggufs/ The image https://i.redd.it/it09zxtsxckh1.jpeg is a technical announcement graphic for Unsloth Dynamic v3.0 GGUF quantizations of Qwen3.8-27B, claiming 10% better top-1 accuracy at the same model size versus other quant providers. It highlights post-training quantization only—no QAT/QAD and no training on the imatrix calibration dataset—plus memory targets from 1-bit quants runnable on ~8GB RAM up to BF16, with evaluation framed around Divergence-300 @32, KLD, and top-1% accuracy comparisons. The linked release points to the Unsloth blog and Hugging Face GGUF repo: Commenters were broadly positive but asked for more comparative data, especially adding the prior https://unsloth.ai/docs/basics/dynamic-3.0-ggufs https://unsloth.ai/docs/basics/dynamic-3.0-ggufs and https://huggingface.co/unsloth/Qwen3.8-27B-GGUF https://huggingface.co/unsloth/Qwen3.8-27B-GGUF . Qwen 3.8 27B UD 2.0 quants to the chart so users can judge whether upgrading is worthwhile. One user also noted practical hardware interest: whether IQ4XS can now run on 16GB VRAM without MTP.Users requested comparative quantization metrics against the prior Qwen 3.8 27B UD 2.0 GGUFs, specifically asking for KLD and/or top-1 error lines on the graph so existing local files can be directly compared to the new Dynamic v3 quants.A technical point was raised that the new IQ4XS quant may fit within 16 GB VRAM without MTP , which would be significant for single-GPU local inference if quality degradation remains low. Another user noted the apparent ~15 GB size for Q4 K M , asking whether it preserves quality well enough to be practically useful.One commenter asked for more granular evaluation now that oobabooga is involved, specifically per-category KLD and KV-cache quantization KLD metrics similar to those shown by localbench.substack.com https://localbench.substack.com/ , to better understand where quantization loss appears across tasks and cache settings. Qwen3.8-27B took a serious hit to https://www.reddit.com/r/LocalLLaMA/comments/1vt7l3e/qwen3827b took a serious hit to knowledge vs 36/ knowledge https://www.reddit.com/r/LocalLLaMA/comments/1vt7l3e/qwen3827b took a serious hit to knowledge vs 36/ Activity: 758 : vs 3.6 https://www.reddit.com/r/LocalLLaMA/comments/1vt7l3e/qwen3827b took a serious hit to knowledge vs 36/ Users report Qwen3.8-27B regresses vs Qwen3.6-27B on offline/weight-only factual recall, aligning with lower scores on Artificial Analysis’s Commenters generally frame this as an intentional or acceptable specialization tradeoff: Qwen 3.x may be shifting toward coding/agentic use where external retrieval is expected, while models like Omniscience knowledge benchmark https://artificialanalysis.ai/evaluations/omniscience?models=qwen3-6-27b%2Cqwen3-8-27b omniscience-accuracy-tabs . The observed tradeoff is that Qwen3.8 appears stronger for tool calling, coding, and agentic workflows, but weaker when web/search tools are disabled for obscure trivia, historical/location identification, or airgapped knowledge retrieval. Gemma may be preferable for broad “mini Google” factual recall. One commenter explicitly preferred not allocating parameters to niche trivia if it improves coding performance.Several commenters converged on the view that Qwen 3.8-27B appears optimized away from memorized factual recall and toward coding/agentic workflows . One user reported that with web search/fetch disabled, Qwen 3.8 regressed on niche knowledge tasks such as identifying stamps, historical locations, and old photos, while tool calling and coding were “impressive” when retrieval tools were available.The discussion framed the regression as a deliberate parameter-capacity tradeoff for a 27B model: reduce obscure memorized knowledge while preserving reasoning, coding, and tool-use competence. Commenters suggested using other models such as Gemma for trivia or broad factual recall, while positioning Qwen 3.x as better suited to agentic tasks that retrieve information externally before acting on it.One technically interesting speculation was around future modular model knowledge/skill extensions , described as “neural plugins” similar to LoRAs . The proposed architecture would keep the base model lean while adding native domain or language competence—e.g. Japanese support or financial-services knowledge—through optional plugins rather than baking all knowledge into the base model. Activity: 422 : I ran Qwen3.8-27B against Opus, Sonnet, GPT and others. Results inside. https://www.reddit.com/r/LocalLLM/comments/1vst6ua/i ran qwen3827b against opus sonnet gpt and/ The image is a benchmark dashboard for the author’s home-built coding eval comparing Qwen3.8-27B, DS4 0731, GPT-5.6-sol, Opus 5, Sonnet 5, and Haiku 4.5 across algorithm tests, repo bugfix/feature tasks, wall-clock completion time, and blind-judged code quality image https://i.redd.it/m2o5ur8z8dkh1.png . The main technical takeaway is that GPT-5.6-sol leads overall with perfect repo-task performance and near-perfect algorithms, while local models are surprisingly competitive: Qwen3.8-27B xhigh scores strongly on hard algorithms and “surgical fixes” but is much slower, and DS4 0731 achieves 8/8 on both repo tiers despite being a 2-bit local quantization. The author notes a practical tradeoff: higher “thinking” improves some hard reasoning/code-quality cases but can overthink, increase latency, and even reduce repo-task accuracy compared with medium thinking. Commenters questioned benchmark saturation and task difficulty, arguing that if nearly all models score near the top then the eval may not distinguish frontier/local capability well. Others asked for more detail on the definitions of “algorithm” and “repo work” tasks, expected outputs, and hidden test design to make the results more reproducible and interpretable.Several commenters argued the benchmark appears saturated , with “all models at the top” , making it hard to distinguish Qwen3.8-27B from Opus, Sonnet, GPT, and others. One analogy framed it as testing stronger models on tasks too easy to separate capability, implying the suite needs harder or more discriminative evaluations.A commenter requested more precise methodology for the “algorithm” and “repo work” tasks, specifically asking for expanded task descriptions and expected results. This points to reproducibility concerns: without clear prompts, grading criteria, and target outputs, cross-model comparisons are difficult to interpret.One technically relevant question asked what “DNF” means for Qwen3.8 medium , in the context of a comparison between Qwen3.8 xhigh and medium settings. This suggests the benchmark table included incomplete or failed runs, but the failure semantics were not defined clearly enough for readers to assess the result.