According to @godofprompt, innovative AI safety approaches often fail to get published because there are no established benchmarks to evaluate their effectiveness. For example, when researchers propose new ways to measure real-world AI harm, peer reviewers typically demand results on standard tests like TruthfulQA, even if those benchmarks are not relevant to the new approach. As a result, research that does not align with existing quantitative comparisons is frequently rejected, leading to slow progress and a field stuck in a local optimum (source: @godofprompt, Jan 14, 2026). This highlights a critical business opportunity for developing new, widely accepted AI safety benchmarks, which could unlock innovation and drive industry adoption.

According to God of Prompt, the AI industry requires institutional reform at three levels to address real safety concerns and prevent the gaming of benchmarks: publishing should accept novel metrics without benchmark comparison, funding agencies should reserve 30% of resources for research creating new evaluation methods, and peer reviewers must be trained to assess work without relying on standard baselines (source: God of Prompt, Jan 14, 2026). This approach could drive practical improvements in AI safety evaluation, open new business opportunities in developing innovative metrics tools, and encourage a broader range of AI risk assessment solutions.

According to God of Prompt on Twitter, current grant funding structures from organizations like NSF and DARPA mandate measurable progress on established safety metrics, driving researchers to prioritize benchmark scores over novel safety innovations (source: @godofprompt, Jan 14, 2026). This creates a cycle where new, potentially more effective AI safety metrics that are not easily quantifiable become unfundable, resulting in widespread optimization for existing benchmarks rather than substantive advancements. For AI industry stakeholders, this trend influences the allocation of resources and could limit true innovation in AI safety, emphasizing the need for funding models that reward qualitative as well as quantitative improvements.

According to Anthropic (@AnthropicAI), the Anthropic AI Fellowship will provide fellows with a competitive weekly stipend of $2,100, approximately $15,000 per month dedicated to compute and research expenses, personalized 1:1 mentorship with an Anthropic researcher, and access to shared workspaces in the Bay Area or London. This program highlights Anthropic's commitment to advancing artificial intelligence research by directly supporting talent with financial resources, infrastructure, and expert guidance. The initiative is positioned to attract top AI researchers and accelerate progress on cutting-edge machine learning and safety solutions, fostering new business opportunities and innovation in the AI industry (Source: AnthropicAI on Twitter, July 29, 2025).