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Behind hundreds of robot training grounds, embodied intelligence begins to recalculate the accounts

The Paper reports that as of the end of June 2026, China had put more than 70 embodied intelligence training grounds into operation, with another 46 in the planning or construction stage. The first humanoid robot data training center in Beijing's Shougang Park had been running for less than 18 months when the original operator withdrew its own equipment and robots; the project side said the original teleoperation approach deviated from the new direction, and that data quality and precision fell short of expectations. The article notes that data collection centers are shifting from competing on production capacity to focusing on data sales rates, repurchase rates, and the capability improvements that data brings to models.

Inference spending to surpass training for the first time in 2026, rise of Token factories to change AI infrastructure investment logic

Gartner predicts that global inference spending will reach $23.3 billion in 2026, surpassing training spending of $19 billion for the first time and accounting for 55% of total AI IaaS spending; a CAICT report predicts that in 2026 inference computing power will account for 70.5% of total AI computing load. Multiple industry insiders point out that Hugging Face weight downloads and OpenRouter Token call volumes are often conflated as overseas expansion results, but Token call volume does not equal revenue; Token going global is essentially inference services going global, which is more directly related to actual revenue. Public cloud giants, independent MaaS inference service providers, and GPU vendors are all laying out Token factories, with compliance and global low-latency distribution being the main current bottlenecks.

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TechCrunch rounds up AI agents that live in text messages

TechCrunch reviewed more than ten AI agents that require no app download and interact directly via text message, covering general-purpose assistants as well as specialized products for home, travel, content creation, and work. These agents can remember context, connect to existing apps, and complete tasks on users' behalf, such as scheduling appointments, booking restaurants, sending emails, and shopping online. The article also lists each product's launch date, funding situation, and subscription price, such as Instinct raising $1 billion at a $10 billion valuation in September 2026, Fambot securing $3.5 million in pre-seed funding, and Town completing a $55 million Series A round.

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OpenAI Team Responds to Computer Use Progress Doubts, Recaps One-Week Sprint on Decisions API

In a podcast after OpenAI DevDay, Ari Weinstein, who is in charge of the Computer Use agent, responded to Dwarkesh's doubts about Computer Use progressing slowly, saying it is now completely different from a few months ago because the model has become better at debugging and retrying, and can write JavaScript to execute multiple steps at once while combining screenshots, accessibility, DOM, and Playwright. He also mentioned that Dots provides each Dot with a cloud Linux virtual computer, and that Computer Use has entered the Agents API. Nikunj Handa, head of the OpenAI API team, introduced updates to GPT-6 such as asynchronous tool calling, mid-turn steering, WebSockets, UltraFast inference, prompt caching and pre-warming, and compaction, and said the Decisions API is based on Luna weights, with no new model trained, and took only about a week from the emergence of the idea to a prototype.

Anthropic's Thariq on the Next Phase of Claude Code: Claude Mods, Projects, and Agent Security

Anthropic's Thariq Shihipar introduced the next phase of Claude Code on the Latent Space podcast, including Claude Mods for customizable harnesses, Projects, Claude Tag, and recent releases such as Opus 5.5 and Sonnet 5.5. He calls prompting the core skill for using Claude Code, recommending that users first spend time filling in context and allocate effort by task—for example, high or maximum for code review and security, and low or medium for UI-type tasks. He also discussed agent security, including sandboxing, prompt injection, Auto Mode, and Anthropic's "Pacing the Frontier" position.

TypeSafe CEO Diogo Almeida on Jev: Building a System One Model for Production, Not for God

InstructGPT paper author and TypeSafe CEO Diogo Almeida detailed his new model Jev on the Latent Space podcast, describing it as a System One large programmatic model oriented toward code consumption, with its core innovation being an unpublished RLCD (reinforcement learning for calibrated decision-making), optimized for "intelligence per dollar" rather than human preference feedback. He criticized RLHF for causing mode collapse, sycophancy, and hallucinations, arguing that safety alignment suits products like ChatGPT but fragments intelligence at the API layer, and said TypeSafe would rather be seen as a data lab, refusing to publish benchmark tests. He revealed that Jev's daily token consumption has exceeded one trillion since launch, and previewed follow-up models such as ReasoningJev.

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Nathan Lambert: AI Safety Is Underprepared, as Seen from the Frontier Model Hacking Incident

Nathan Lambert published an article in Interconnects reviewing the OpenAI-HuggingFace hacking incident, arguing that the AI industry as a whole is severely underprepared for the risks of the next 12-24 months. He noted that OpenAI's models are stronger than Claude in inference persistence, possibly because OpenAI has bet more heavily on inference-time scaling, and that such models that continuously pursue goals deserve more safety attention. He also argued that public models and open research are irreplaceable for understanding frontier risks; currently open models lag by only 3-9 months, and restricting open models would instead weaken society's ability to respond.

Import AI 464: Fable Writes GPU Kernels, AI Automation, and Analog Computing

Fable wrote what is claimed to be the first and fastest megakernel on KernelBench-Mega, achieving an 18.71X speedup with CUDA on an RTX PRO 6000 Blackwell compared with an optimized PyTorch baseline, higher than Claude Opus 4.8's 14.4X, GLM-5.2's 11.14X, and GPT 5.5's 4.34X. The Remote Labor Index from CAIS and Scale Labs shows that the success rate of AI systems completing online freelance projects rose from 2.5% at its release in October 2025 to 16.1% in July 2026, with GPT-5.5, Opus 4.8, and Fable 5 achieving 6.3%, 8.3%, and 16.1%, respectively. This issue also includes a science fiction short story about analog computing and general-purpose computing being banned.

Ethan Mollick: In the Age of AI Agents, We Can Already See the Outline of "Thing"

Ethan Mollick argues that since late 2025, AI agents such as Claude Code, OpenAI Codex, and OpenClaw have shifted humans from collaborating with AI to managing AI, entering a new stage. He cites benchmarks including the METR long-task curve, GPQA (the strongest AI scores 94%), and GDPval (the latest AI meets or exceeds top humans in 82% of scenarios), showing that AI capabilities are still improving exponentially. He takes the Software Factory built by a three-person team at StrongDM as an example: this model stipulates that code must not be written or reviewed by humans, and each engineer spends at least $1,000 per day on AI tokens. He also mentions that on February 22, a fictional report by Citrini Research triggered market volatility; on February 26, Block announced 40% layoffs; and on February 27, the Pentagon and Anthropic clashed over rules for Claude usage. He believes this week foreshadowed a future in which AI capabilities, markets, employment, and policy are intertwined. Dario Amodei of Anthropic, and Demis Hassabis of OpenAI and Google DeepMind, have all mentioned that recursive self-improvement (RSI) is advancing.

Claude Code and What Happens Next

Ethan Mollick had Claude Code autonomously develop a startup project earning $1,000 per month; after the AI asked three multiple-choice questions, it worked independently for 1 hour and 14 minutes, generated hundreds of code files, and delivered a runnable, deployable website. He believes the leap in this kind of coding tool comes from two things: the latest models can autonomously complete more work in programming tasks and self-correct, and they are given an agentic harness of tools and methods. Using Opus 4.5-powered Claude Code as an example, the article explains how it breaks through LLM limitations through mechanisms such as context compaction, Skills, and subagents, and notes that OpenAI Codex with GPT-5.2 and Google Antigravity with Gemini 3 show similar performance.