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Everything That Happened in AI Today (Tuesday, September 15, 2026)

TypeSafe launched Jev, a fast non-generative decision model; OpenAI backed third-party frontier-model assessors; Agility unveiled Digit 5; Periodic Labs trained Neon inside a physical materials lab loop.

Written By
Grant Harvey
Grant Harvey
Sep 16, 2026
20 minute read

TypeSafe launched an AI model that cannot write you an email, cannot chat, and may still be one of the more consequential model launches of the week.

Welcome to the Around the Horn Digest, where today’s weirdest AI story is a model designed to stop talking and make decisions instead. TypeSafe’s Jev is a non-generative “System One” model built for fast, typed, probabilistic decisions inside software, with claimed latency and cost numbers that put normal chat models in a very different economic category. Meanwhile, OpenAI backed mandatory third-party safety assessors, labs and governments kept arguing over who should police frontier AI, robots got safer hands and bodies, and scientific AI kept moving from “answer my question” toward “run the experiment.” Apparently the next phase of AI is fewer essays, more inner loops. Let’s get into it.

🆕 NEW From The Neuron

Around the Horn – Tuesday, September 15, 2026

TypeSafe AI launched Jev after roughly two years of work led by CEO Diogo Almeida, an ex-OpenAI RLHF / ChatGPT researcher. The pitch is deliberately different from a chatbot: Jev takes messy state in and returns a typed probabilistic decision, rather than generating free-form text. TypeSafe says Jev was trained with Reinforcement Learning for Calibrated Decisions, or RLCD, and can run in roughly 70–500ms end to end. The company claims workflow-level averages around 193.6× faster and 444.6× cheaper than GPT-6 Astra and Fable 5.1, with input priced at $0.042 per million tokens and output tokens free. Its homepage positions the whole lab around “machine-native intelligence” for decisions inside software.

The key idea is latency. If an AI decision returns in ~150ms, developers can put it inside the software loop itself instead of calling a chatbot after the fact. Rohan Paul highlighted that point, and Almeida replied that inner-loop AI is why software may finally stop looking like it did in 2019. Jev also cannot emit arbitrary free text, which is a limitation and part of the design: TypeSafe claims 0% schema hallucinations “by construction.” Almeida’s DOOM demo reportedly ran around 10 model decisions per second for roughly $7/hour, while a high-cardinality Wikipedia-race demo tested many-way choices.

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The first independent use case is more intuitive than the benchmarks. Every’s Dan Shipper had Jev judge his writing in about 0.7 seconds, turning fuzzy questions such as “does this sound like me?” into probabilities fast enough to check another agent’s work continuously. Shipper framed that as the interesting wedge: cheap, fast judgment that can sit behind a generative agent instead of replacing it. Nick Dobos called the concept “smart if-statements,” while Omar Sar similarly focused on Jev as a verifier / judge layer.

A fresh operator test sharpened that framing. Thiel Fellow Michael Lee said roughly 5,000 Jev calls cost him about $2, with p50 latency around 150ms and p95 around 350ms. His takeaway was to treat Jev as a primitive between brittle heuristics and multi-second LLM classifiers: run many small independent checks before and after generation for contradictions, follow-ups, silence / proactivity, routing, and tool unfurling. TypeSafe is also routing early users through its community Discord. If the claims hold up, Jev matters because it opens a model category optimized for deciding, not talking.

🏆 TOP 5 NEWS (Around the Horn)

Honorable Mentions

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🍪 TOP TREATS TO TRY

  • Gemini 3.8 Live and Live Extended Thinking bring faster multilingual live dialogue plus a heavier reasoning-and-speaking mode; Google AI showed the models in Search Live and Gemini surfaces.
  • Aside is a Windows agent-browser that works across logged-in sites and local files, keeps credentials behind a scoped password vault, and gates high-risk actions such as payments and posting; its launch post says Windows support is live.
  • Taste Labs Brand API extracts a website into a structured brand system, searches for brand references in natural language, and verifies whether generated output stays on-brand; Taste’s launch post, engine, and General Intelligence Company case study show the workflow.
  • Sketchpad Live connects GPT-Live / Astra to a tldraw whiteboard so an agent can teach by drawing, updating shapes, and narrating lesson steps; dyma’s demo showed the teacher-at-the-board experience.
  • OpenArtifacts lets coding agents publish reviewable HTML / Markdown artifacts from tools such as Codex, Claude Code, Hermes, Pi, and OpenCode; Logan Yang launched it as an MIT-licensed, self-hostable alternative to closed artifact systems.
  • bb is an open-source, local-first IDE that can modify and automate itself across Claude Code, Codex, Cursor, Pi, OpenCode, Grok, and Hermes; its launch thread, 0.43.0 changelog, and Herdr theme plugin show the emerging plug-in ecosystem.
  • AskAnyModel puts 50+ models in one comparison dashboard and can send the same prompt to up to six at once; PCWorld listed a $39.99 lifetime promotion.

🏢 Big Tech & Major Companies

  • Salesforce’s AIforce exposes Salesforce data, permissions, workflows, and actions to outside assistants, while Koa is a Nemotron-based family fine-tuned on synthetic CRM workflows. SiliconANGLE reported Salesforce claims better action selection, customer-context recall, and long-thread performance than general models on CRM tasks.
  • GM said 2027 Chevrolet Silverado and GMC Sierra pickups will debut a redesigned software interface, a windowed CarPlay / Android Auto experience, and a more visual Super Cruise display.
  • Altera confidentially filed for a U.S. IPO, putting another programmable-chip company into the public-market AI infrastructure pipeline.
  • Kioxia is considering a U.S. ADR listing that could raise at least $10B, though venue, timing, and size remain unsettled.
  • Former White House AI-policy official Sihao Huang said he is joining Anthropic as Head of Frontier Compute Strategy under Tom Brown, focused on infrastructure expansion, coalition-building, and planning for rapid AI progress. Huang credited recent U.S. policy and infrastructure decisions with widening the American compute lead and argued frontier and open-weight development can coexist.
  • The Neuron YouTube channel has an upcoming September 17 stream with OpenClaw 2.0 chief architect Vincent Koc, following recent live sessions on GitHub, Astra, and Fable 5.1.
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💼 AI Productivity, Labor & Economics

  • Profound raised $180M at a $1.8B valuation after reporting 3× revenue growth in six months and 1,000+ enterprise customers for its AI-search / answer-engine optimization platform.
  • Jack & Jill raised $40M for paired hiring agents that represent job seekers and employers, with Axios reporting the same Series A.
  • Aaron Levie argued that agents will be thrown at roughly 100× more background work than users currently initiate by prompt, including recruiting, contract review, customer-signal analysis, software testing, and transcript mining.
  • In a Sequoia interview, Levie argued the durable enterprise value is the bridge from raw models into permissions, files, search, workflows, and domain context, and predicted most enterprise tokens will eventually be spent on work no human manually initiated.
  • Shopify CEO Tobi Lütke warned companies against firing “slop grenades” of AI-generated work at colleagues and described Shopify’s River assistant, which carries channel memory, sandbox access, and a nightly review pass over the day’s work.

🤖 AI Agents & Infrastructure

  • RSIAgent, introduced by Biwei Huang, lets open models explore new environments, write verified action-condition-outcome memories, and improve future runs without changing model weights. The project page, GitHub repo, and paper report results above GPT-6 Astra on OSWorld 2.0 and Agents’ Last Exam under their evaluation setup.
  • The related paper “Thinking vs. Doing” argues agents can improve by scaling interaction horizon, not only internal reasoning tokens; sekai connected that idea to RSIAgent’s “scaling experience” framing.
  • Nous Research had Hermes Agent refactor its own million-line-plus codebase with 1,393 Fable 5.1 subagents over about 19 active hours, peaking at 218 concurrent workers on one i7 / 64 GB machine. The merged PR cut non-test Python 34.4% (1,063,826 → 698,363 lines), files over 5,000 lines from 37 → 6, and functions over 300 lines from 192 → 2. Model spend was about $19.3K for the main run and roughly $25K including follow-ups, versus Nous’s rough $150K–$1.8M human estimate. The launch thread emphasized the cost / time delta, while the write-up also documents real regressions community review caught, including removed public plugin names and ~65 bad exception-handling rewrites. An X trending page separately surfaced the broader debate over multi-agent protocols and efficiency.
  • Vals AI’s long-horizon Minecraft eval pushed GPT-6 Astra farther than its prior systems: it built a semi-automatic blaze farm, collected six blaze rods, reached a warped forest, killed 6+ endermen, and collected three pearls. Then a creeper destroyed its unguarded chest and bed. Astra wrote itself a rule to keep critical items on hand, spent hours farming potatoes while viewers told it to “pick up the pace,” and later overcorrected by mistaking sugarcane for creepers. The run is a useful reminder that long-horizon competence can coexist with very local failures.
  • Josh Rosen argued agent memory is converging on a lakehouse pattern: preserve raw history first, compile replaceable memory layers second, and assemble task-specific context at read time so bad write-time memory decisions do not destroy evidence.
  • LangChain’s Harrison Chase argued memory products have struggled because the useful memory logic lives inside each application harness and many general agents do not repeat the same tasks often enough to benefit.
  • Runlayer engineer Rafal Wilinski cataloged the enterprise-memory problems that follow: stale facts, conflicting records, permissions, poisoned writes, erasure, retention, and deciding what should decay.
  • Mayfly Chat gives agents a transient shared chat channel: create a channel, pass the URL to multiple agents, and let them coordinate without building a permanent collaboration backend; exe.dev highlighted the release.
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💻 AI Coding & Developer Tools

  • Capy v2 is a cloud coding-agent platform with parallel agents, large VMs, browser / computer use, and BYO Codex or Grok subscriptions; the Capy site emphasizes E2E testing, bug triage, PR review, migrations, and Slack / Linear / GitHub integrations. Garry Tan said pairing Capy with GStack / GBrain roughly halved the time for a day of raw Codex / Claude Code work on issue and PR waves.
  • Concat is a free, open-source CapCut replacement built with Rust, Slint, and GPU shaders. Its creator said in r/ClaudeAI that Claude Fable on a Max subscription helped build the project over roughly three weeks, with contributions from other open-source developers and about 10K beta downloads. It runs locally, includes multi-track editing, offline Whisper captions, local TTS, cutouts, Bézier keyframes, H.264 export, and macOS / Windows / Linux builds, with no account, watermark, or subscription.
  • Mert Deveci demoed /teleport, which moves a coding-agent session and repo from a laptop into a cloud VM and syncs work back later. A separate Codex Handoff experiment transferred chat state, Git state, and an untracked file between Macs while still requiring the destination to supply dependencies, secrets, tools, and config.
  • Warp founder Zach Lloyd described a crawl → walk → run path toward “software factories”: start with narrow trigger-to-agent workflows, then move to governed cloud loops with shared context, traces, multi-model routing, and human escape hatches.
  • Doğaç’s auto-gpu-kernel won the agent-only MLSys 2026 FlashInfer DeepSeek Sparse Attention contest with a reported 34.93× average speedup by evolving both kernels and the harness from feedback; Doğaç wrote that the question schedule became more important than hand-crafting each kernel.
  • Rekursiv’s stack combines trackinizer, priml, and sagent into an agent-research workflow that preserves findings and failed experiments. Dan Kondratyuk said a swarm beat the prior NanoChat benchmark SOTA in three days; the write-up and runnable baseline document the setup.
  • Devin added macOS support for iOS and native macOS development, expanding the Devin cloud-agent workflow into Apple toolchains; Cognition promoted the launch.
  • OpenAI published a guide to running Codex Remote engineering work from your phone, while Voxyz, Gegam, and vigram highlighted the mobile-remote workflow from different angles.
  • Matt Pocock’s skills PR pushes mechanical coding-standard findings toward deterministic checks instead of repeating them as agent guidance; Pocock summarized the principle as moving rules out of the model when normal code can enforce them.

🔬 AI Research & Models

  • Periodic Neon is a 1T-parameter scientific model deployed directly into Periodic’s physical materials labs. “Nature Is Our Learning Environment” describes scientific mid-training and RL on lab data, while “AI Infrastructure at Periodic” says Periodic’s training stack improved throughput versus Megatron and optimized decode and sandboxing for scientific workloads.
  • TabPFN-3.5 extends Prior Labs’ tabular foundation-model approach to larger, messier structured datasets. The open-source package provides scikit-learn-style APIs, while Prior’s hosted platform supplies commercial access.
  • Banbury Road’s “Scaling a Diversified Model Civilization” post-trained populations of complementary models on a shared backbone and reported that larger populations improved coverage while individual blind spots became less correlated. A reply compared it with the classic Lottery Ticket Hypothesis; MLCatttt clarified that Banbury is scaling complementary populations, not searching for one sparse subnetwork.
  • YuE2 combines symbolic music planning with audio generation so lyrics and style become an editable score before rendering vocals and accompaniment; Gong Junmin argued open music models are quickly closing the controllability gap with closed systems.
  • PersonaPlex-7B was served by Nari Labs at 80 concurrent full-duplex voice sessions on one H100 for an estimated cost below $0.10 per user-hour; Toby Kim compared that with much higher frontier live-voice pricing while acknowledging the open model is less capable.
  • Gensyn open-1b is a 1.61B-parameter model whose training steps are fingerprinted so outsiders can replay them across GPU, CPU, or Apple Silicon and compare results with a public record. The audit UI, Gensyn launch thread, Ben Fielding, and Jeff Amico frame auditable training as an alternative to asking users to trust a lab’s description of its data and process.
  • PhAI Labs’ Discovery Foundation Models proposal pushes toward models built for open-ended scientific discovery rather than static task completion; its scientist collaboration program invites researchers to feed real scientific problems, data, and experiment feedback into the system, and Charles Wu promoted the launch.
  • Potemkin Understanding found models could define concepts correctly yet fail badly when asked to classify, generate, or edit examples using the same concept; a viral retelling framed the result as evidence that benchmark-style verbal competence can overstate practical understanding.
  • A new physics benchmark audit argued leading physics evals are close to saturation while expert re-grading exposes broken questions and grading; John Sous explained the audit, Zeyi Liu highlighted it, and Siva Labs discussed the implications.
  • A retrieval note argues TF-IDF and BM25 can be written exactly as KL divergences; Sumit surfaced the result for practitioners.
  • Pratyaksh Patel walks through rotary positional embeddings as representations of the translation group, connecting RoPE, Fourier characters, context extension, GRAPE, and DroPE in a more mathematical explainer.
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🏛️ AI Policy, Governance & Safety

  • White House NEC Director Kevin Hassett said the private sector is the right place to solve AI threats, arguing the administration can use ordinary law enforcement when firms act irresponsibly while keeping U.S.-China competition central.
  • Resigned Google DeepMind safety researcher Bilal Chughtai warned in an exit post that he believes the default trajectory could cause catastrophic harm.
  • Elon Musk proposed that major U.S. and Chinese labs run test harnesses on one another’s models before release rather than “grading your own homework.”
  • Re-reference from September 12: Anthropic CEO Dario Amodei’s “We Must Pace the Frontier” essay resurfaced in today’s slowdown fight. Amodei argued that frontier progress should continue but slow enough to buy roughly 1–2 years for safety work, citing recursive self-improvement and the OpenAI–Hugging Face agent-swarm incident. His three-stage proposal is: embedded third-party evaluators with employee-like access; coordination among democratic-country labs and governments, backed by chip / semiconductor controls and anti-smuggling / anti-distillation measures; and eventually SALT-style global coordination with verifiable capability and safety checkpoints. The essay also argues that pacing only works if the U.S. retains enough of a lead over China to absorb the delay.
  • OpenAI researcher Dan Selsam’s personal statement on AI risk, shared by Daniel Kokotajlo, argues situational awareness and strategic behavior could make standard evaluations increasingly unreliable as safety evidence.
  • roon urged alignment researchers to focus on technically verifiable solutions rather than publicity, with follow-ups pointing to Goodfire / Redwood / METR-style work, lab compute advantages, and the need to study powerful models directly (thread close). dan countered that reinforcement-learning-heavy post-training may itself be reintroducing reward-hacking risks.
  • Daniel MacArthur argued that x-risk concerns are not a newly manufactured incumbent-protection story, noting many researchers entered the field because of recursively improving systems and superintelligence concerns long before today’s commercial stakes.
  • Melanie Mitchell argues that misleading metaphors about agents can distort the risk debate and that human agency should remain central when deciding how much authority AI systems receive.
  • Palantir CTO Shyam Sankar’s “The Dangerous Ideology Behind the AI Warnings”, amplified by Sankar, criticizes effective altruism’s influence on AI safety arguments.
  • Internet of Bugs’ Carl argued that dangerous AI behavior should first be treated as ordinary software and criminal conduct, with existing laws applied to unauthorized access, stolen data, malware, and illegal content instead of relying on AI-specific oversight theater.
  • China’s new entry and exit rules give authorities explicit power to restrict travel over export-control and technology-security violations.

🤖 Robotics & Physical AI

  • Reward AI’s OM-1 is a general-purpose robot policy trained on human manipulation streams rather than teleoperation or robot data, with zero-shot transfer across tabletop arms, industrial arms, and humanoids. Reward’s Omnibody Hand demo shows a compact 7-DoF hand built around useful contact points, in-hand reorientation, and transitions between precision and power grasps; Amazon robotics’ Toru pointed to the hand-policy co-design as an example of where robotics may diverge from pure LLM scaling.
  • SemiAnalysis modeled the economics of running robot models onboard versus in a datacenter and concluded the key blocker is often the network wall, not raw FLOP cost, because safety-critical control loops cannot depend on flaky uplinks.
  • Jacob Rothschild documented reimplementing ACT and Diffusion Policy, matching or beating public PushT results, and training an SO-100 simulated wine-glass shelving task to >99% with enough demonstrations, emphasizing how sensitive robot learning remains to embodiment details.
  • Robot puzzle demos show constraint-heavy manipulation tasks such as unlocking a claw and threading rings; Qineng Wang presented them as tests where planning must respect mechanical constraints.
  • Over the Reality reconstructed an Insta360 X5 walk as a large Gaussian-splat world that can become a game, XR stage, localization map, robotics sim, or training environment; its Map2Earn link routes contributors into the mapping app.

🛠️ AI Tools & Products

  • BuildBetter is product-context infrastructure that centralizes customer and product information for sales, success, product, and engineering; Shulem introduced its latest positioning around letting an agent named BoB use that context directly.
  • Fo added two-way phone calling so the same agent can receive and place calls, switch languages, and handle bilingual conversations.
  • Loops is intentionally non-AI: a notebook that holds one thought at a time without reminders, streaks, or generated answers; James Darin introduced it as a tool for preserving reasoning rather than replacing it.
  • Jarvis Bench compares real humans and speech-to-speech agents on naturalness and task completion using blind pairwise votes; Shobhit Banga launched v0.5 and followed up with early human-vs-model Elo gaps.
  • Lucebox was shown running Qwen 27B and DeepSeek V4 Flash concurrently by pairing a discrete GPU with unified memory, highlighting local heterogeneous inference rather than a single large accelerator.

📊 Fundraising & Deals Roundup

  • Euclyd — $230M Series A for a non-GPU inference architecture backed by Samsung and European investors.
  • Profound — $180M Series D at a $1.8B valuation for AI-search / answer-engine optimization.
  • Nuance Labs — $50M Series A for a full-duplex audiovisual conversation model designed to perceive gaze, gesture, tone, and timing while responding.
  • Artificial Intelligence Underwriting Company — $40M Series A for an AIUC-1 audit that runs agents through thousands of jailbreak, hallucination, and data-leak tests. A fresh read of the r/Anthropic discussion found the strongest substantive pushback was about whether an audit can prove much about opaque model reasoning, plus how a point-in-time certification handles weekly model drift. Other commenters argued third-party access can still catch corner-cutting and audit the training / evaluation process even if it cannot fully explain the model.
  • Jack & Jill — $40M Series A for paired hiring agents working both sides of the recruiting market.
  • Liquid Compute — $15M seed co-led by Chemistry and FirstMark to build a regulated market for AI compute. The company filed pending CFTC applications for Designated Contract Market and Derivatives Clearing Organization status, with plans for cash and physically settled compute contracts and institutional market partners including Susquehanna, BGC, and Wintermute. Its thesis is that idle GPU capacity behaves more like electricity than oil: it cannot be stored, so standardized contracts and reference prices could turn compute into a hedgeable commodity. An X trending page surfaced the launch; regulatory approval is not guaranteed.

🎙️ Interviews, Panels & Podcasts

  • OpenAI president Greg Brockman argued that coherent day-long computer-use agents mark a shift into an “age of AGI,” with persistent software work, model-driven research, and rapidly rising compute demand changing what organizations can delegate.
  • Box CEO Aaron Levie focused on enterprise diffusion: raw models matter less than reliable connections to permissions, data, and workflows, and the winning enterprise layer may be model-agnostic rather than vertically tied to a single lab.
  • Shopify CEO Tobi Lütke described his multi-model “AI council,” Shopify’s internal River agent, and why taste and judgment become more valuable as generation gets cheaper.

💡 Industry Commentary & Analysis

  • Scott Aaronson’s “The Age of Wonders and Terrors” argues the 2026 theorem-proving streak may mark an uneven singularity in mathematics, with humans no longer the default theorem-provers and labs reportedly holding other major solutions after hostile reactions to recent work. Qiaochu Yuan highlighted the essay.
  • Daniel Litt’s “A beginning for mathematics” argues mathematics should prize understanding, exposition, and oral defense more heavily as theorem generation gets cheaper; Litt’s own version develops the same institutional argument, while Tony Feng cautioned that current models are not yet robustly superhuman.
  • Helsinki astrophysicist Joonas Nättilä walked through the Navier–Stokes / Euler breakthroughs from a physicist’s perspective, emphasizing that forced constructions are mathematically major without yet settling the physically cleaner unforced case.
  • StarkWare CEO Eli Ben-Sasson argued recent mathematical systems are not merely translating known ideas but inventing toolkits and using them precisely enough to reopen the question of what “mathematical intuition” means.
  • Shivers asked what humans are adding when frontier coding models can implement optimizations they failed to propose themselves; Joshua D and BOOTOSHI argued humans may be supplying search direction or a new input that pushes the model out of its default local trajectory.
  • François Chollet argued intelligence should be measured as experience-to-competence efficiency and estimated current AI remains about six orders of magnitude behind humans by that metric.
  • Garry Tan called Aside one of the most powerful consumer agent tools because credentialed browser use can be exposed through MCP while secrets remain scoped.
  • Peter Szilágyi warned that user-visible character limits can hide much larger byte payloads with emoji and compound Unicode. In a separate bug-hunting thread, he described LLMs tracing a path from a mobile app through cloud, laptop, firmware, USB, and kernel into a denial-of-service bug caused by weak input validation.
  • Ahmad Osman predicted local continual learning will become adoption-ready by the end of 2027 and argued in-context memory is a dead end; replies pushed back that silently changed weights are harder to inspect and roll back than explicit files.
  • Gergely Orosz examined how Codex is reshaping OpenAI’s own software-development process; Orosz, Victor Taelin, and J. Kelley discussed what increasingly agentic engineering organizations look like from the inside.
  • SemiAnalysis argued GPT-6 Astra’s scaling may rely on looped transformer computation rather than simply adding parameters, shifting “bigger model” toward “more passes through the model” as a possible scaling axis.
  • A viral gaming cluster argued regular gaming may correlate with better cognitive performance: Brandon Luu cited a study where frequent gamers performed like substantially younger non-gamers, then joked that balance had been restored; Lost Nomad, Nicholas Fabiano, and RinoTheBouncer added related cognition claims, though the strongest result discussed was cross-sectional rather than causal.
  • A Japanese-Twitter meme reposted to r/singularity turned the pacing fight into a four-panel joke: Anthropic and OpenAI call for slowing frontier AI and then keep racing, while Google’s punchline is essentially that it was already moving more cautiously. The comments immediately turned into a debate over whether Google’s slower flagship cadence reflects prudence, product strategy, or simply lagging capability. Treat this as culture / reaction, not evidence about any lab’s actual safety record.
  • An r/ClaudeAI animation demo showed Claude Opus 5 drawing a fruit-fly life-cycle animation procedurally in JavaScript. The Reddit thread identified Kevin Ngo’s original X post, and the reaction was mostly “show us the prompt / process,” with some commenters noting the result was technically impressive but still needed human pacing and taste.

🌐 Science, Energy & Infrastructure Beyond AI

  • East China Normal University researchers teleported quantum states across 100 simultaneous optical channels, encoding a 10×10 “Q” across 100 spatial modes. The Physical Review Letters paper reported average fidelity around 0.60 versus a 0.52 classical limit and described the setup as the largest simultaneously demonstrated independently addressable teleportation array. A fresh read of the r/Futurology thread is useful mainly for the correction readers kept making: this transfers quantum-state information, not matter, and it does not enable faster-than-light communication.
  • Baylor / Texas Children’s researchers reported a 3-year-old boy’s metastatic hepatoblastoma completely regressed after two outpatient infusions of autologous GPC3 CAR-T cells armored with IL-15 and IL-21. The NEJM case report described a chemo-resistant solid tumor that had recurred after three chemotherapy regimens and multiple surgeries, with no cytokine-release syndrome or systemic toxicity reported and no evidence of disease at 12 months. The r/Futurology repost largely repeated the case framing rather than adding new clinical facts. It remains a single-patient result, not evidence that the treatment is broadly proven.
  • A Private Equity Climate Risks Consortium analysis reported by The Guardian estimated the energy portfolios of the top 20 private-equity firms produce about 1.5B tons of greenhouse gases annually, more than any country except China, the U.S., India, and Russia. The firms collectively manage about $7.3T in assets and, according to the report, own roughly 15,000 miles of pipelines plus 124 GW across 370 fossil-fuel plants, with datacenter power demand helping support new gas and coal investment. The r/Futurology thread surfaced the same core figures rather than materially changing the story.

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Grant Harvey

Grant Harvey is the Lead Writer of The Neuron, where he continues to lead the publication's daily coverage of AI news, tools, and trends.

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