
AI evaluation nonprofit METR published a report estimating that Anthropic’s AI development progress has doubled over the past year, largely due to its own AI tools accelerating research. Based on 11 weeks of internal data access and staff interviews, METR found no clear bottlenecks slowing this acceleration, with AI contributing approximately 80% of the speedup. The company redacted much of the strongest evidence to prevent competitors from gaining a roadmap, while also revealing internal models that outperform its current public flagship, Claude Opus 5.5. This development raises questions about the alignment between Anthropic's public safety commitments and its actual internal velocity.
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© The AI Daily BriefMeter reports that research driven by artificial intelligence is accelerating, highlighting a trend in scientific and technical discovery.
© MIT News AICheMeleon is a foundation model for chemistry that learns general molecular patterns from a million PubChem entries rather than specific property labels. This pre-training allows it to outperform classical expert-driven models and other AI baselines when fine-tuned on just a few data points, solving the critical bottleneck of sparse experimental data in drug discovery. Its impact is already visible: nine of the top twenty entries in a major OpenADMET contest used it, and one pharmaceutical company has made it their default starting point for modeling. This shifts molecular screening from trial-and-error to pattern-based prediction, significantly reducing the need for expensive synthesis.
© WIRED AIThe long-held assumption that closed frontier models hold a definitive edge in cyber capabilities has collapsed. Vals research shows DeepSeek matching OpenAI’s Daybreak on complex network attacks, completing 73% of tasks versus 81%. This parity arrives just months after Anthropic predicted such a shift, rendering gated access strategies largely moot for slowing capability proliferation. The cost disparity is stark: $2 to $3 for open models versus up to $46 for proprietary APIs, creating a powerful economic incentive for widespread adoption by both defenders and attackers. We are now in a landscape where advanced offensive AI is cheap, accessible, and effectively democratized.