Research institute · Research orgs · Wilmington, DE
AI Objectives Institute (AOI) is a U.S.-based, non-profit research lab focused on “decoding and realigning incentives” across AI, markets, and bureaucratic institutions—aiming to increase the odds that large-scale AI systems and future economic systems pursue genuinely human objectives. AOI frames its work as sociotechnical: governance and alignment are treated not only as technical problems inside model training, but also as problems of how societies design incentives, decision processes, and institutional mechanisms.
AOI’s portfolio emphasizes applied tools for collective sensemaking and deliberation alongside research on alignment-relevant concepts (e.g., markets as governance mechanisms, “gradual disempowerment,” and augmented deliberative systems). Its flagship applied product work is “Talk to the City,” described as an open-source LLM interface for analyzing qualitative public input at scale while preserving argument diversity and linking summaries back to underlying testimonies.
By Philip Tomei and Bouke Klein Teeselink Abstract: Which jobs can AI learn to do? We examine this for every occupation in the US economy. Existing indices measure the overlap between AI capabilities and occupational tasks rather than which tasks AI systems can learn to perform,
AI, Automation, and ExpertiseBy Bouke Klein Teeselink and Daniel Carey Abstract: Occupations with identical AI exposure can experience opposite labor market trajectories depending on which tasks are automated. Building on Autor and Thompson (2025), we formalize this insight in a model that separates two marg
Talk to the City Case Study: Amplifying Youth Voices in AustraliaYWA deployed Talk to the City (T3C) as one of their research platforms, introducing an innovative interface that bridged the gap between large-scale data collection and qualitative insight. The platform's key innovation lay in its interactive visualization interface, which allowe
The Diffusion DilemmaTechnological progress is often equated with invention, yet history shows that invention alone rarely transforms society without effective diffusion. This paper examines the persistent “diffusion dilemma,” the lag between the emergence of general-purpose technologies and their br
Generative AI and Labor Market Outcomes: Evidence from the United KingdomThis paper examines the effects of large language models (LLMs) on UK labor market outcomes, comparing outcomes across firms and occupations from 2021 to 2025 based on their differential exposure to LLM capabilities. Highly exposed firms reduce employment, particularly in junior
Amplifying transformative potential while designing augmented deliberative systemsTo be effective, augmented deliberation must preserve people's ability to genuinely engage with and be transformed by the deliberative process. This article proposes the Goldilocks Framework for Augmented Group Intelligence, and argues that the optimal use of AI in deliberation i
From Voluntary Guidelines to Enforceable StandardsThe EU's AI Act is transitioning from voluntary guidelines to enforceable standards in August 2025, with Codes of Practice serving as interim compliance measures that are technically voluntary but effectively mandatory since they grant regulatory presumption of conformity. These
AI Governance through MarketsThis paper argues that market governance mechanisms should be considered a key approach in the governance of artificial intelligence (AI), alongside traditional regulatory frameworks. We examine four emerging vectors of market governance, demonstrating how these mechanisms can af
Gradual Disempowerment: Systemic Existential Risks from Continuous AI DevelopmentThis paper examines the systemic risks posed by incremental advancements in artificial intelligence, developing the concept of ‘gradual disempowerment’, in contrast to the abrupt takeover scenarios commonly discussed in AI safety.
Wargaming as a Research Method for AI Safety: Finding Productive ApplicationsAs AI capabilities advance, we need robust methods to explore complex scenarios and their implications. This post explores when wargaming is most effective as a research tool for AI safety, and which types of problems are best suited to this methodology.
AI4Democracy: How AI Can Be Used to Inform Policymaking?LLMs offer new capacities of particular relevance to soliciting public input when used to process large volumes of qualitative inputs and produce aggregate descriptions in natural language. In this paper, we discuss the use of an LLM-based collective decision-making tool, Talk to
Machina Economica, Part II: The Commodification of RiskThis is the second entry in a series on AI integration in the economy, exploring its financial, sociopolitical and historical implications. This entry focuses on economic risk and its financialisation.
AOI describes a model and empirical approach separating automation into displacement and expertise-threshold channels, and reports effects including reduced job postings tied to AI exposure and wage impacts associated with expertise-raising automation.
What Jobs Can AI Learn? Measuring Exposure by Reinforcement LearningAOI describes work constructing an “RL Feasibility Index” across US occupations by scoring ONET tasks for training feasibility and comparing it to general AI exposure measures.
Amplifying Youth Voices: Talk to the City Case Study in AustraliaAOI reports Young Women’s Alliance’s deployment of Talk to the City as a research platform integrating large-scale qualitative input with preservation of individual narratives, including an account of integration of long video interviews and reported policy implications.
The Diffusion DilemmaAOI discusses the “diffusion deficit,” using historical examples (e.g., tractor diffusion) to argue that invention alone does not guarantee societal impact, and applies this framing to contemporary AI integration challenges and the role of wrappers translating models into usable systems.
Generative AI and Labor Market Outcomes: Evidence from the United KingdomAOI describes a difference-in-differences analysis of labor market outcomes tied to differential exposure to LLM capabilities across firms and occupations, and reports reductions in employment and hiring for highly exposed firms and notable concentration in high-compensation segments.
AOI proposes a “Goldilocks Framework for Augmented Group Intelligence,” emphasizing the need to balance participants’ agency with their commitment to deliberative outcomes when integrating AI into deliberative processes.