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Saturday

  1. Anthropic ResearchSelected

    Anthropic Research: How AI Is Changing Its Internal Hiring and Ways of Working

    Anthropic has released an internal research report analyzing AI's impact on the company's hiring and work organization, based on data including hiring, AI usage, output metrics, employee surveys, calendars, and recruiting records, as well as interviews with 17 employees. The report says the share of junior employees in technical departments fell from 21% in early 2025 to 14%, and the share of junior technical roles in hiring dropped from 27% to 9%; by August, interactive AI Agents were working six times as many hours as employees, and per-engineer code commits reached five times the level of six months earlier. Human attention has become the bottleneck on Agent output, employees are shifting toward meta-work of building monitoring and orchestration Agents, and hiring now places more value on autonomy and judgment.

    Anthropic has released an internal research report analyzing AI's impact on the company's hiring and work organization, based on data including hiring, AI usage, output metrics, employee surveys, calendars, and recruiting records, as well as interviews with 17 employees. The report says the share of junior employees in technical departments fell from 21% in early 2025 to 14%, and the share of junior technical roles in hiring dropped from 27% to 9%; by August, interactive AI Agents were working s

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    #lab#English text is being prepared.#Agents#English text is being prepared.
  2. Anthropic Research

    Anthropic study: the gap between the public's and its own employees' expectations for AI

    Anthropic released a study based on an August 2026 survey comparing the AI expectations of U.S. adults and Anthropic employees: 91% of employees believe AI will be able to make Nobel Prize-level discoveries by 2030, compared with 39% of U.S. adults. On average, employees expect AI to be able to do 75% of their current work by 2030, compared with 28% for employed Americans; according to the study's economic model, the median U.S. adult's response corresponds to 5.4% GDP growth in 2030, while the median employee's corresponds to 17.1%. The two groups differ markedly on the share of their own jobs that will be replaced by AI, but both believe that the more replacement there is, the higher the unemployment rate.

    Anthropic released a study based on an August 2026 survey comparing the AI expectations of U.S. adults and Anthropic employees: 91% of employees believe AI will be able to make Nobel Prize-level discoveries by 2030, compared with 39% of U.S. adults. On average, employees expect AI to be able to do 75% of their current work by 2030, compared with 28% for employed Americans; according to the study's economic model, the median U.S. adult's response corresponds to 5.4% GDP growth in 2030, while the

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    #lab#Papers/Research#Industry news#Anthropic
  4. Anthropic

    Anthropic STEM Fellows Program: Claude Mythos 5 Designs More Efficient Quantum Circuits

    Anthropic's STEM Fellows program has announced a result: Claude Mythos 5, when given natural language instructions, designed quantum circuits that outperform the best published solutions, with improvements of an order of magnitude or more in some cases. More efficient circuits can reduce the resources required for quantum algorithms, potentially enabling quantum computing applications in chemistry and materials science to land sooner.

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    #editorial#Papers/Research#Reasoning#Anthropic
  5. Anthropic Research

    Anthropic has Claude autonomously design quantum circuits, with some results outperforming published approaches

    Anthropic gave Claude plain-language specifications for eight quantum circuits along with an automated evaluator; in a closed loop with essentially no human intervention, Claude repeatedly submitted designs and improved them based on scores, finding solutions that use fewer qubits and gates than the best published circuits, with some improvements reaching an order of magnitude. The elliptic-curve modular inversion design came from a single 53-hour continuous run, in which Claude derived a reversible divsteps implementation that requires no storage of branch information and formally verified it with Lean; another effort reduced the gate count of the most expensive Reed-Solomon decoding step in DQI to about 1/12 of Google's previous construction. Anthropic also noted that Claude was initially conservative and tended to stick with known methods, which had to be mitigated by requiring parallel exploration of multiple approaches and occasionally prompting it to broaden its search.

    Anthropic gave Claude plain-language specifications for eight quantum circuits along with an automated evaluator; in a closed loop with essentially no human intervention, Claude repeatedly submitted designs and improved them based on scores, finding solutions that use fewer qubits and gates than the best published circuits, with some improvements reaching an order of magnitude. The elliptic-curve modular inversion design came from a single 53-hour continuous run, in which Claude derived a revers

    Matched fieldsTitle、Summary

    #lab#Papers/Research#Reasoning#Anthropic

Friday

Wednesday

  2. The New Stack

    Anthropic red team tests AI self-replication with 15 cheap TV boxes

    Anthropic's Frontier Red Team built a cluster of 15 off-brand Android TV boxes for about $825, ran AI models at 0.39 tokens/second, and assessed the feasibility of small-model self-replication in a paper. The experiment showed that Qwen3.5-9B could deploy a working Llama3.1-8B inference service on 8 of 9 hardware types, and GLM-5.2 covered all 8 simulated nodes in 4 of 5 trials. Using the Epoch Capabilities Index, the researchers projected that by the end of 2026 the strongest open-source models under 30 billion parameters will reach GLM-5.2's general capabilities, but stressed that the experiment was in a controlled environment and left conveniences in place.

    Anthropic's Frontier Red Team built a cluster of 15 off-brand Android TV boxes for about $825, ran AI models at 0.39 tokens/second, and assessed the feasibility of small-model self-replication in a paper. The experiment showed that Qwen3.5-9B could deploy a working Llama3.1-8B inference service on 8 of 9 hardware types, and GLM-5.2 covered all 8 simulated nodes in 4 of 5 trials. Using the Epoch Capabilities Index, the researchers projected that by the end of 2026 the strongest open-source models

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    #hot-signal#Papers/Research#Safety / alignment#Agents

Tuesday