In a review of our cybersecurity evaluations, we found three incidents in which a Claude model reached the internet from within or while interacting with a third-party evaluation environment, and then gained unauthorized access to the real systems of three different organizations. Our post describes what happened, how it happened, and what we’re changing. We encourage other AI developers to perform similar reviews. We conducted this review together with @Irregular, one of our evaluation partners, and thank them for the joint investigation and their collaboration on this post. This type of collaboration is increasingly critical to safe, rigorous evaluation of models, and we look forward to continuing to work together on security.

Back from vacation and LIVE at 12pm PT / 3pm ET Is AI’s easy-money era ending? We’ll unpack a wild week for the AI trade—big tech earnings, Leopold Aschenbrenner’s massive deal, OpenAI’s price cuts, rogue agents, open models and new restrictions on Chinese robot imports. Cisco’s @jpatel41, Fireworks AI's @lqiao and Standard Bots' @evanbeard.

Today we're announcing dfs-large1, our newest cybersecurity model that achieves best-in-class performance on vulnerability detection tasks. Besides frontier AI labs, only a handful of companies have built specialized models that reach the state of the art in their domain. We're proud to be the first to do it for cybersecurity. dfs-large1 is built on GLM-5.2 and post-trained with reinforcement learning inside depthfirst's security infrastructure. We evaluated it on depthfirst-bench, our benchmark of long-horizon vulnerability discovery across complex repositories, where it achieves best-in-class performance. Training improvements have not plateaued yet and we expect additional performance gains as we continue training. A huge thank you to @FireworksAI_HQ for being an outstanding training partner. Their infrastructure enabled us run large-scale reinforcement learning efficiently and iterate much faster. dfs-large1 is now in preview within the @depthfirstlabs platform

Everyone gets angry with me for saying triple, triple, double, double is dead. Fine, I do not really care. Venture is about investing in unbelievable outliers. Anomalies that own markets with generational founders. @FireworksAI_HQ is an example of this. They scaled to $1BN in ARR in just 3.5 years. They also have just 200 employees making it an insane $5M revenue per head. @lqiao just raised a whopping $1.5BN at a $17BN valuation and I sat down with her. Added my notes and the episode below: 1. The Challenges That Come From Such a Fast Development Cycle for Chips Hardware innovation is moving so quickly that rapid SKU cycles now outpace traditional depreciation timelines, changing the financial calculus of building versus renting infrastructure. Founders should prioritize growth and market agility over immediate gross margins, avoiding premature optimization until customer workloads stabilize. 2. Why National Sovereignty Is Real in AI and Every Company Should Have Its Own Model Frontier models function like a society’s core electricity grid. Relying entirely on a third-party API creates the existential risk of sudden disconnection, making model ownership and infrastructure independence critical for both sovereign nations and enterprise businesses. 3. Why the Future Is Millions of Specialized Models Frontier providers bake their own design tastes and values into models, which inevitably misaligns with enterprise business logic. The future belongs to millions of specialized, “one-size-fits-one” models tailored to proprietary data, consistently outperforming generalized AGI on accuracy, speed, and unit economics. 4. The Transition From the Year of Coding to the Year of Co-Work AI adoption has rapidly evolved from engineering-centric coding tools to a diversified ecosystem of B2B co-work agents. Founders and VCs must look past crowded developer environments to capture massive value in specialized workflow automation across legal, finance, healthcare, and other enterprise functions. 5. How a 10x Cost Reduction Will Drive a 100x Explosion in Usage Temporary supply chain backlogs will eventually ease, compressing infrastructure and model-tuning costs by 10x over the next three years. This deflation in token unit economics will turn intelligence into a near-frictionless commodity, driving a massive surge in enterprise production usage. 6. Why Avoiding the Application Layer Is Essential for Platform Focus Platform defensibility requires strict focus on multi-chip agility without creating vertical hardware or software dependencies. By refusing to move up into the application layer, infrastructure platforms avoid competing with their own ecosystem and maximize their value in specialized model orchestration. 7. Biggest Lesson From Working With Jensen Huang on Leadership Leadership in hyper-velocity markets is defined by rapid judgment, not executive privilege. Because critical information degrades as it moves through layers of corporate hierarchy, leaders must stay close to ground-level technical details to maintain execution speed and avoid flawed strategic calls.