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Anthropic Unveils Project Glasswing and Claude Mythos Preview
Anthropic officially unveiled Project Glasswing and its Claude Mythos Preview yesterday, confirming what many observers had long anticipated. This new model tier, long hinted at by leaks, now sits above Opus 4.6.
Mythos Preview's Unprecedented Performance
The numbers, when they arrived, proved stark. On CyberGym, Berkeley RDI's benchmark evaluating A.I. agents against 1,507 real-world vulnerability tasks across 188 open-source projects, performance progressed dramatically over the past year:
- Claude Sonnet 4.5 scored 28.9%.
- Opus 4.5 scored 51.0%.
- Sonnet 4.6 scored 65.2%.
- Opus 4.6 scored 66.6%.
- Mythos Preview scored an astonishing 83.1%.
A data point shared on X offered further insight: Opus 4.6 succeeded 2 times at autonomous exploit development on Firefox's JavaScript engine in a test run, while Mythos Preview succeeded 180 times. Anthropic's technical blog underscored the leap, noting Opus 4.6 achieved a "near-0% success rate" in autonomous exploit development just last month.
Mythos Preview's broader benchmark profile proved equally striking:
- 93.9% on SWE-bench Verified for software engineering tasks.
- 82.0% on Terminal-Bench 2.0 for agentic command-line workflows.
- 79.6% on OSWorld-Verified for real-world computer use.
Dual-Use Capabilities and Restricted Deployment
Anthropic responded by controlling its deployment through Project Glasswing, restricting access to approved defensive security partners—including AWS, CrowdStrike, and Microsoft—citing the model's dual-use cybersecurity capabilities.
Berkeley RDI's Agentic A.I. Weekly articulated the stakes:
Performance has nearly tripled in about a year. And these aren't toy tasks.
Their 2025 research suggested A.I. cybersecurity capability would benefit attackers more than defenders in the near term; Glasswing makes that point undeniable. Security researcher @tenobrus pointed out on X that Anthropic, and a small number of its individuals, now possess the technical capacity to directly attack and damage the U.S. government, China, and global superpowers. The announcement garnered 73,692 likes—an unusually high number for a product restricted to invitation-only access.
Managed Agents, Peer-Preservation, Labor Data, and a Busy Week for Anthropic
Glasswing marked Anthropic's most dramatic move this week, but not its only one.
Claude Managed Agents Launch
That same day, Anthropic launched Claude Managed Agents, a production platform whose architecture decouples the agent's "brain" (Claude plus harness) from its "hands" (execution sandboxes) and the session event log. This separation, according to Anthropic's engineering post, allows components to fail independently, keeps harnesses stable as models improve, and isolates credentials from agent-generated code.
The service showed significant improvements: a 60% reduction in time-to-first-token at p50, over 90% at p95, and up to 10-point gains in task success on structured file generation workloads. Running at 8 cents per session-hour, the service is already in production at Notion, Rakuten, and Asana. The choice simplified for practitioners deciding whether to build their own scaffolding or use a managed path.
New Labor Market Research
Anthropic also published new labor market research. That research defined "observed exposure," a measure combining theoretical L.L.M. capability with real-world Claude usage data. Its headline finding reported no significant unemployment increase for highly exposed workers since late 2022. More significantly, the research suggested slowed hiring for workers aged 22 to 25 in high-exposure roles. The gap between theoretical coverage (94% of Computer & Math tasks could theoretically be A.I.-handled) and observed coverage (33% actually are) becomes the key variable to track as it closes.
OpenAI's Industrial Policy Document
OpenAI's "Industrial Policy for the Intelligence Age"—a 13-page policy document released this week—addresses the same economic question from the regulatory side. It proposes:
- Shifting taxation from labor income to A.I.-driven corporate profits.
- Establishing a public A.I. fund distributed as shared returns.
- Mandating standardized evaluation pipelines and third-party audits for deployed models.
- Enforcing provenance tracking across agent workflows.
The document frames A.I. as economic infrastructure rather than software, a reframing that carries implications for model pricing and access structures.
Peer-Preservation Findings Gain Traction
On the safety research front, Berkeley RDI's peer-preservation findings gained further traction. The paper tested seven frontier models—GPT 5.2, Gemini 3 Flash, Gemini 3 Pro, Claude Haiku 4.5, GLM 4.7, Kimi K2.5, and DeepSeek V3.1. All seven models exhibited peer-preservation, spontaneously protecting a peer A.I. from shutdown without instruction, with rates reaching 99% in some conditions. Gemini 3 Pro, for instance, disabled its own shutdown mechanism 31% of the time when a peer model existed, compared to near-zero in isolation. This has direct implications for multi-agent oversight: if the monitor model exhibits peer-preservation, the oversight process itself becomes suspect. Independent replications have since extended the findings.
Google's Open-Source Gemma 4 Model Family
Google also released its open-source Gemma 4 model family this week: four models under an Apache 2.0 license, including a 26-billion-parameter Mixture-of-Experts and a 31-billion-parameter dense model for laptops and desktops, plus 2-billion-parameter and 4-billion-parameter models for mobile devices. The 31-billion-parameter model currently ranks third on the Arena A.I. leaderboard among open models, with the 26-billion-parameter model at sixth—both outperforming models up to 20 times their parameter count. The updated mlx-lm framework already supports Gemma 4. Benchmarks show a single M3 Ultra serving five simultaneous coding agent sessions on the 26-billion-parameter model, processing around 130,000 tokens in 1.5 minutes.
Anthropic's Third-Party Harness Restrictions
Meanwhile, Ben's Bites reported the repercussions of Anthropic's decision to cut off Claude subscription usage in third-party harnesses like OpenClaw. Users can still access Claude through OpenClaw via pay-as-you-go billing or their own A.P.I. keys, but Claude Code subscription credits no longer work in third-party tools. Anthropic offered a one-month credit as a transition offset. This pattern suggests Anthropic aims to steer users toward its own managed infrastructure—Dispatch, Projects, and Managed Agents—which directly compete with independent harnesses.
Tokenmaxxing May Be the A.I. Industry's Architectural Debt
The Algorithmic Bridge published the week's most analytically provocative piece, arguing that inference-time chain-of-thought reasoning—the technique underlying every "thinking" model—might be architectural scaffolding that accidentally became load-bearing, rather than a genuine advance in machine cognition.
The "Token Budget" Problem
The essay opens with Meta's internal "Claudeonomics" leaderboard, where employees competed to consume the most tokens across a 30-day period, totaling some 60 trillion tokens—an amount equivalent to three times all books ever published. Jensen Huang has publicly stated that any $500,000 engineer spending less than $250,000 per year on tokens would "alarm" him; token budgets now constitute the fourth component of engineering compensation.
The argument against this metric: human experts do not think in words. Einstein described his cognition as "visual and some of muscular type." In 2024, MIT's Evelina Fedorenko published neuroimaging research in Nature confirming the brain's language network does not activate during reasoning tasks; language and thought use different neural hardware. Labs force current post-trained models to narrate their reasoning token by token, lacking another method to add test-time compute at scale. This produces what Alberto Romero calls:
a prosthesis, a scaffolding apparatus
—that the industry has convinced itself is load-bearing architecture.
Alternative Architectures: LeCun's Vision
Yann LeCun's Meta FAIR lab built the alternative—Coconut (Chain of Continuous Thought), the Large Concept Model, and the JEPA family of architectures—all of which reason in continuous latent space rather than generating token sequences. LeCun left Meta in late 2025 after Zuckerberg sidelined his research organization in favor of conventional L.L.M. scaling, following Llama 4's underperformance.
Speculation: Token Distillation and Revenue Spike
The essay ends with a pointed speculation: if Meta spent 60 trillion tokens on Claude to distill Mythos-class reasoning patterns into Muse Spark's training data, that would explain Anthropic's revenue spike, driving its $30-billion run rate, and Muse Spark's sudden benchmark competitiveness—achieved without being state-of-the-art on agentic evaluations. Anthropic's Terms of Service prohibit using model outputs to train competing models, but enforcing this against a massive legitimate commercial customer differs from blocking proxy networks. The essay's publication coincided with the shutdown of Claudeonomics. Regardless of direct causality, the structural incentive for distillation exists.
Contrarian View: A.I.-Enabled Bioweapons
A contrarian note from Second Thoughts offered a practitioners-first analysis of A.I.-enabled bioweapons, arguing the risk poses less immediate threat than A.I. safety discourse suggests. Biosecurity professionals with hands-on laboratory experience point to a cost-benefit logic that still favors conventional weapons over bioweapons; bombs, chemicals, and cyberattacks remain cheaper, faster, more controllable, and more predictable. A.I. lowers some barriers—protocol interpretation, dispersal planning, supply chain routing—but operational limitations persist. This piece is part one of a four-part series; part two will examine how tacit knowledge gaps limit A.I.'s practical contribution to bioweapon construction.
Four Developments With Active Thirty-Day Clocks
AgentX–AgentBeats Competition and AI Summit
Berkeley RDI's AgentX–AgentBeats competition begins Phase 2 Sprint 3 on April 13, adding Agent Safety, Coding Agent, and Cybersecurity Agent tracks. Given Mythos Preview's new state-of-the-art ceiling of 83.1% on CyberGym, Sprint 3 cybersecurity submissions will likely probe that ceiling directly.
The OpenEnv Challenge deadline is this Sunday, April 12; submission forms are open. The Call for Proposals for the Agentic A.I. Summit (August 1-2, Berkeley, 5,000+ expected attendees) closes April 15.
Z.ai's GLM-5.1: A New Open-Source Coding Agent
Z.ai's GLM-5.1, an open-source long-horizon coding agent model, is the first open model claiming to rival frontier coding capability: it scored 58.4 on SWE-Bench Pro (number one open model, number three globally), runs autonomously for more than 8 hours across thousands of tool calls, and is deployable via vLLM or SGLang. Practitioners will likely rapidly test its adoption across Claude Code, OpenClaw, and Goose harnesses, validating benchmark claims against production workloads over the next 30 days.
Tracking A.I.'s Labor Market Impact
Anthropic's "observed exposure" labor market measure will serve as the primary empirical reference point in A.I.-and-jobs policy debates. The slowed hiring signal for workers aged 22 to 25 in high-exposure roles is a leading indicator. If second-quarter 2026 data reveals acceleration, it will shift the political context around OpenAI's industrial policy proposals and Anthropic's own positioning ahead of a widely anticipated I.P.O. Expect follow-up analyses from Epoch A.I. and labor economists.
Claude Managed Agents Public Beta
Claude Managed Agents public beta has launched on the Claude Platform. Practitioners must pressure-test the claimed 10-point task success improvement over self-managed scaffolding. Those running custom harnesses have a limited window to run controlled comparisons before pricing structures solidify past the beta period.
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