A new corrigibility research fund managed through Lightcone Infrastructure will distribute at least $200,000 in grants and prizes in 2026 to support research on making AI systems responsive to human correction and control. The fund's creator argues that alignment research—the work needed to solve core AI safety problems—receives far less funding than other AI safety domains like evaluations and interpretability, making this fund an attempt to address a neglected but critical area.
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A new corrigibility research fund, managed through Lightcone Infrastructure, will award at least $200,000 in grants and prizes during 2026. Roughly half the money will fund traditional grants (with an application deadline of August 23rd), and half will recognize excellent work completed in 2026 via prizes.
Why it matters
The fund's creator argues that alignment research—work aimed at making AI systems corrigible and aligned with human intent—remains severely underfunded relative to other AI safety areas like evals, control, and interpretability. This fund attempts to shift resources toward a research area the creator sees as foundational to solving core AI safety problems.
What to watch
Researchers interested in corrigibility work can apply for grants via email at grants@corrigibilityresearch.org. The first application deadline for grants is August 23rd.
The Corrigibility Research Fund, managed through Lightcone Infrastructure, launches with at least $200,000 available for 2026. The fund operates on a hybrid model: approximately half the budget will support traditional grants awarded through an application process (with an initial deadline of August 23rd), while the remaining half will fund prizes that recognize high-quality corrigibility research completed during 2026. Researchers and teams interested in pursuing corrigibility work can submit grant applications by email to grants@corrigibilityresearch.org. The fund's creator framed the initiative as a response to perceived neglect within AI safety funding. Drawing on experience dating back to 2009 when AI safety was barely established as a field, the creator acknowledged that funding and attention for AI safety have grown considerably in recent years. However, that growth has concentrated in specific areas: evals (testing AI behavior), control (constraining misuse), and interpretability (reverse-engineering how AI systems function). By contrast, alignment research—the conceptual and empirical work aimed at ensuring AI systems are fundamentally corrigible and aligned with human intent—receives comparatively little funding despite being central to solving the underlying safety problems. The fund is intended to address this imbalance and signal that corrigibility research is a priority area worthy of dedicated support.
The corrigibility research fund reflects a strategic assessment within the AI safety field about where funding gaps exist. The creator notes that despite growth in AI safety attention and funding over recent years, the distribution remains heavily skewed toward certain research domains. Evals (evaluation methods for AI systems), control (techniques to prevent misuse), and interpretability (understanding how AI systems work internally) have attracted substantial resources, but alignment research—the theoretical and practical work on making AI systems corrigible and robustly aligned with human values—has lagged behind. This fund is positioned as a corrective measure, channeling resources toward what the creator sees as foundational work that must precede other safety advances.
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