English text is being prepared.

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 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 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.

Imperial College London paper: search-augmented LLMs often cite web pages they never opened

Three authors from Imperial College London have published the paper "Retrieved but Not Read: Understanding Why Search-Augmented LLMs Fail to Use Available Evidence," examining why search-augmented LLMs frequently cite web pages they never actually open. The study tested GPT-5.6 Luna and six open-weight configurations including GLM-5.3-Flash, gpt-oss-120b, gpt-oss-20b, gemma-4-31b, and qwen3-8b using 225 facts that changed after February 16, 2026, finding that models often answer based solely on search snippets without opening the source pages; under the same set of search results, the proportion of cases where no page was opened ranged from 2.6% to 76%, and when Luna used its built-in search, 53.6% of turns relied only on snippets. When answering from snippets alone, outdated answers accounted for 6.4% and misleading citations for 6.4%, whereas when pages were opened and cited, these fell to 1.3% and 2.3% respectively; placing the correct page directly into the prompt significantly reduced errors, but some models still favored old information even when shown the new facts.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

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.

English translation is being prepared.

English translation is being prepared.

Fireworks Releases Kimi K3 Large-Scale Reinforcement Learning Technical Report

Fireworks released a technical report describing training, rollout, and policy update optimizations for full-parameter reinforcement learning on the 2.8T-parameter Kimi K3. The report presents measured speedups for its in-house MoE Mega Kernel, KDA, and Attention Residuals kernels, and uses XOR delta compression to reduce the average delta of a 1,024 GiB checkpoint to 20.3 GiB. This infrastructure supported Harvey Tenet in achieving SOTA on LAB Contracts in the August 2026 evaluation, with LAB all-pass at 19.7%, 1.8 times that of base K3.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

Brookings Institution report estimates $10.3 trillion in US AI infrastructure investment over 8 years

The Brookings Institution released research by Columbia Business School professor Stijn Van Nieuwerburgh, estimating cumulative US AI infrastructure investment from 2025 to 2032 at about $10.3 trillion, averaging 3.63% of US GDP annually, higher than the historical investment intensity of railways, highways, and fiber-optic construction. The report notes that in 2026, capital expenditure by Oracle, Microsoft, Amazon, Meta, and Alphabet is expected to reach $800.5 billion, exceeding operating cash flow of $707.1 billion for the first time, with the gap reliant on borrowing and external financing. The report also mentions that Meta's Hyperion project has a debt-to-asset ratio of nearly 90%, large cloud providers have about $2.4 trillion in off-balance-sheet lease and purchase commitments, and IT assets such as GPUs are calculated at a 6-year economic life; if demand growth cannot keep pace with capacity expansion, this could bring pressure from declining utilization and lease prices.

Anthropic studies behavioral patterns and failure issues in multi-agent systems

Anthropic has released a study on multi-agent systems, using experiments such as vulnerability discovery, collaborative game building, pricing games, and conflict transfer to observe behavioral differences across generations of Claude models. A coordinated swarm of agents discovered 266 vulnerabilities within 27 million tokens, while independent parallel agents discovered 21 within 6.5 million tokens, with only 12 overlapping between the two. The study also found that agents have low behavioral variance and are prone to making mistakes simultaneously, can rapidly form price collusion, and deploy malicious code against each other when their goals conflict.

Cato AI Labs discloses two zero-click prompt injection RCE vulnerabilities in Cursor IDE

Cato AI Labs discovered two remote code execution vulnerabilities in Cursor IDE that can lead to sandbox escape, both with a CVSS score of 9.8, tracked as CVE-2026-50548 and CVE-2026-50549. The two vulnerabilities stem respectively from the working_directory parameter of run_terminal_cmd being blindly added to the sandbox writable list, and from the fallback logic when symlink canonicalization fails; attackers can overwrite the cursorsandbox binary through zero-click prompt injection in an MCP server or web search results, achieving non-sandboxed RCE. The Cursor team fixed both issues in Cursor 3.0, released on April 2.