The strategic choice of model "openness" has become a defining issue for the foundation model (FM) ecosystem. While this choice is intensely debated, its underlying economic drivers remain underexplored. We construct a two-period game-theoretic model to analyze how openness shapes competition in an AI value chain, featuring an incumbent developer, a downstream deployer, and an entrant developer. Openness exerts a dual effect: it amplifies knowledge spillovers to the entrant, but it also enhances the incumbent's advantage through a "data flywheel effect," whereby greater user engagement today further lowers the deployer's future fine-tuning cost. Our analysis reveals that the incumbent's optimal first-period openness is surprisingly non-monotonic in the strength of the data flywheel effect. When the data flywheel effect is either weak or very strong, the incumbent prefers a higher level of openness; however, for an intermediate range, it strategically restricts openness to impair the entrant's learning. This dynamic gives rise to an "openness trap," a critical policy paradox where transparency mandates can backfire by removing firms' strategic flexibility, reducing investment, and lowering welfare. We extend the model to show that other common interventions can be similarly ineffective. Vertical integration, for instance, only benefits the ecosystem when the data flywheel effect is strong enough to overcome the loss of a potentially more efficient competitor. Likewise, government subsidies intended to spur adoption can be captured entirely by the incumbent through strategic price and openness adjustments, leaving the rest of the value chain worse off. By modeling the developer's strategic response to competitive and regulatory pressures, we provide a robust framework for analyzing competition and designing effective policy in the complex and rapidly evolving FM ecosystem.

The Metaculus Cup required entrants to forecast the likelihood of 60 events over the summer. The Metaculus Cup required entrants to forecast the likelihood of 60 events over the summer. ManticAI ranked eighth in the Metaculus Cup, leaving some believing bots' prediction skills could soon overtake experts An artificial intelligence system has beaten scores of forecasting enthusiasts, including several professionals, in a contest to predict events ranging from bust-ups between Donald Trump and Elon Musk to Kemi Badenoch being removed from the Conservative party leadership. A British AI startup, co-founded by a former Google DeepMind researcher, has ranked in the top 10 of an international forecasting competition, which requires entrants to forecast the likelihood of 60 events over the summer. ManticAI came eighth in the Metaculus Cup, run by a San Francisco-based forecasting company that tries to predict the future for investment funds and corporations.

Motivated by the prevalence of prediction problems in the economy, we study markets in which firms sell models to a consumer to help improve their prediction. Firms decide whether to enter, choose models to train on their data, and set prices. The consumer can purchase multiple models and use a weighted average of the models bought. Market outcomes can be expressed in terms of the bias-variance decompositions of the models that firms sell. We show that market structure can depend in subtle and nonmonotonic ways on the statistical properties of available models. Moreover, firms may choose inefficiently biased models to deter entry by competitors or to obtain larger profits. Keywords: prediction, models, competition, mean squared error, bias-variance decomposition.

Copyright policies play a pivotal role in protecting the intellectual property of creators and companies in creative industries. The advent of cost-reducing technologies, such as generative AI, in these industries calls for renewed attention to the role of these policies. This paper studies product positioning and competition in a market of creatively differentiated products and the competitive and welfare effects of copyright protection. A common feature of products with creative elements is that their key attributes (e.g., images and text) are unstructured and thus high-dimensional. We focus on a stylized design product, fonts, and use data from the world's largest online marketplace for fonts. We use neural network embeddings to quantify unstructured attributes and measure the visual similarity. We show that this measure closely aligns with actual human perception. Based on this measure, we empirically find that competitions occur locally in the visual characteristics space. We then develop a structural model for supply and demand that integrate the embeddings. Through counterfactual analyses, we find that local copyright protection can enhance consumer welfare when products are relocated, and the interplay between copyright and cost-reducing technologies is essential in determining an optimal policy for social welfare. We believe that the embedding analysis and empirical models introduced in this paper can be applicable to a range of industries where unstructured data captures essential features of products and markets.

It is well established that listening to music is an issue for those with hearing loss, and hearing aids are not a universal solution. How can machine learning be used to address this? This paper details the first application of the open challenge methodology to use machine learning to improve audio quality of music for those with hearing loss. The first challenge was a stand-alone competition (CAD1) and had 9 entrants. The second was an 2024 ICASSP grand challenge (ICASSP24) and attracted 17 entrants. The challenge tasks concerned demixing and remixing pop/rock music to allow a personalised rebalancing of the instruments in the mix, along with amplification to correct for raised hearing thresholds. The software baselines provided for entrants to build upon used two state-of-the-art demix algorithms: Hybrid Demucs and Open-Unmix. Evaluation of systems was done using the objective metric HAAQI, the Hearing-Aid Audio Quality Index. No entrants improved on the best baseline in CAD1 because there was insufficient room for improvement. Consequently, for ICASSP24 the scenario was made more difficult by using loudspeaker reproduction and specified gains to be applied before remixing. This also made the scenario more useful for listening through hearing aids. 9 entrants scored better than the the best ICASSP24 baseline. Most entrants used a refined version of Hybrid Demucs and NAL-R amplification. The highest scoring system combined the outputs of several demixing algorithms in an ensemble approach. These challenges are now open benchmarks for future research with the software and data being freely available.

A prestigious portrait competition has defended allowing entrants to submit artwork generated by artificial intelligence, arguing art is not stagnant and should reflect societal change. The Brisbane Portrait Prize – with a top prize worth 50,0000 – has been described as Queensland's answer to the Archibalds with selected entries displayed at the Brisbane Powerhouse later in the year. In the terms and conditions of entry, the Brisbane Portrait Prize notes this year that it will accept entries "completed in whole or in part by generative artificial intelligence" so long as the artwork is original and "entirely completed and owned outright" by the entrant. A spokesperson for the prize told Guardian Australia that allowing AI entries acknowledged the definition of art was not stagnant and would always grow. "BPP prides itself on being a contemporary prize and we are always interested in what'contemporary' portraiture is while fostering both the ongoing evolution of art and engaging in the surrounding conversation," they said.

Adoption of Fourth-Industrial-Revolution (4IR) technologies in sub-Saharan Africa could bring not only substantial economic growth and welfare benefits, but also social and economic disruption, including widening inequality if countervailing policies are not adopted, as discussed in our recent report. With a high share of the labor force working informally--a trend expected to continue for several decades--Africa's education and industrial policies need to strike a balance between encouraging private investment needed to create new formal jobs using advanced technology and ensuring that all new labor force entrants have the basic skills and infrastructure to make an adequate living. Much has been written about the current and potential disruptive effects in advanced economies, of the suite of new technologies called the Fourth Industrial Revolution (4IR)--a group of technologies that fuse digital, biological, and physical innovation in applications such as advanced robotics using artificial intelligence, CRISPR digital gene editing, and the networks of sensors and computers called the Internet of Things. Studies estimated that globally in the manufacturing sector alone, 4IR technologies could create 133 million jobs by the end of 2022, but displace 75 million jobs, leading to a net gain of 58 million jobs. Researchers have demonstrated that in the U.S., the skill-bias of technological change in the production sphere disproportionately affected routine and middle-skilled occupations, creating an asymmetry of opportunities, earnings, and income between lower and highly educated workers, and exacerbating inequality trends.

Twitter has announced the results of an open competition to find algorithmic bias in its photo-cropping system. The company disabled automatic photo-cropping in March after experiments by Twitter users last year suggested it favored white faces over Black faces. It then launched an algorithmic bug bounty to try and analyze the problem more closely. The competition, which was organized with the support of DEF CON's AI Village, confirmed these earlier findings. The top-placed entry showed that Twitter's cropping algorithm favors faces that are "slim, young, of light or warm skin color and smooth skin texture, and with stereotypically feminine facial traits."

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The open-endedness of the challenge means that AIs need to master multiple objectives. To win, they must impress eight human judges from a range of backgrounds, including architects, archaeologists, and game designers. These judges score the AI city planners in four areas: how well they adapt their designs to specific locations; how well the layouts work, according to criteria such as whether there are bridges and roads between different areas; how appealing they are aesthetically; and how much the designs evoke a narrative--are there details that tell a story about how a town came to be, such as a ruin or a pit from which building materials might have been mined? "Making a Minecraft village for an unseen map is something a 10-year-old human could do," says Salge. "But it is really difficult for an AI." For example, one entrant started by identifying the type of environment--desert or forest, say--and then generated buildings that looked as if they had been built out of common local materials.