According to @ReutersBiz, an EY survey found that most companies suffered risk-related financial losses when deploying AI, highlighting measurable downside attached to enterprise AI rollouts, as reported by Reuters Business from EY's findings. Traders tracking AI-exposed equities and digital assets can reference this datapoint from Reuters Business coverage of the EY survey when assessing implementation risk and downside scenarios.

According to @karpathy, earlier predictions that computer vision would quickly eliminate radiology jobs have not materialized, with the field growing rather than shrinking. Source: @karpathy on X, Sep 25, 2025. According to @karpathy, the reasons include narrow benchmarks that miss real-world complexity, the multifaceted scope of radiology beyond image recognition, deployment frictions across regulation, insurance and liability, and institutional inertia. Source: @karpathy on X, Sep 25, 2025. According to @karpathy, Jevons paradox applies as AI tools speed up radiologists, increasing total demand for reads rather than reducing it. Source: @karpathy on X, Sep 25, 2025. According to @karpathy, AI is likely to be adopted first as a tool that shifts work toward monitoring and supervision, while jobs composed of short, rote, independent, closed, and forgiving tasks are more likely to change sooner. Source: @karpathy on X, Sep 25, 2025. For traders, this framing highlights gradual AI integration and expanding workloads in regulated, high-risk domains, a narrative relevant to AI-linked equities and AI-themed crypto projects tied to compute utilization. Source: @karpathy on X, Sep 25, 2025. Full post reference is the Works in Progress article shared by @karpathy. Source: @karpathy on X, Sep 25, 2025.

According to @OpenAI, both gpt-oss models are now free to download on Hugging Face, featuring native MXFP4 quantization for efficient deployment. This move is expected to lower infrastructure costs and increase accessibility for developers building AI-powered applications. For crypto traders, the release may accelerate the integration of advanced AI-driven trading bots and analytics platforms, potentially increasing trading efficiency and market volatility. The full list of day-one support is available on the official OpenAI blog. Source: @OpenAI.

According to Anthropic (@AnthropicAI), their new research focuses on forecasting rare behaviors of language models, specifically predicting risks that may occur post-deployment by leveraging even very limited sets of test data. This development is crucial for traders interested in technology stocks and AI investments, as it emphasizes risk mitigation in AI deployment, potentially influencing market confidence and stock valuations in related sectors.

According to Sam Altman, there is a discussion on whether the next open source project should focus on an O3-mini level model that operates with GPU support or a more optimized phone-sized model. This decision could significantly impact the deployment and scalability of AI models in the cryptocurrency trading space, where quick data processing and on-the-go accessibility are crucial. The choice between these models could influence future trading strategies and the resources needed for efficient execution.

According to @jessepollak, the integration of Eliza AI by @ai16zdao and AgentKit by @CoinbaseDev allows traders to deploy these AI tools rapidly with a single command, enhancing efficiency in cryptocurrency trading operations.