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.

We analyze the structure of the market for foundation models, i.e., large AI models such as those that power ChatGPT and that are adaptable to downstream uses, and we examine the implications for competition policy and regulation. We observe that the most capable models will have a tendency towards natural monopoly and may have potentially vast markets. This calls for a two-pronged regulatory response: (i) Antitrust authorities need to ensure the contestability of the market by tackling strategic behavior, in particular by ensuring that monopolies do not propagate vertically to downstream uses, and (ii) given the diminished potential for market discipline, there is a role for regulators to ensure that the most capable models meet sufficient quality standards (including safety, privacy, non-discrimination, reliability and interoperability standards) to maximally contribute to social welfare. Regulators should also ensure a level regulatory playing field between AI and non-AI applications in all sectors of the economy. For models that are behind the frontier, we expect competition to be quite intense, implying a more limited role for competition policy, although a role for regulation remains.

I love talking about business models because in the end, it's usually the best business model, not the best technology, that wins the day. And digital transformation has the potential to reinvent business models by leveraging superior customer, product and operational insights to disrupt industry value chains and disintermediate customer relationships (see Figure 1). As the title of the book "Moneyball" states ("Moneyball: The Art of Winning an Unfair Game"), some of these reinvented business models will be based on "winning an unfair game". We have historical lessons about how Robber Barons[1] of the late 1800's created and won "an unfair game" that gave them monopoly power over suppliers, customers and competitors. To create this unfair game, Robber Barons leveraged a concept called "vertical integration" to dominate industry value chains and construct indissoluble customer and supplier dependencies. Let's review the lessons of these Robber Barons to understand how digital transformation might enable modern companies to win the digital unfair game.

PolyAI, a London startup founded by experts in the field of "conversational AI" -- including CEO Nikola Mrkšić, who was previously the first engineer at Apple-acquired VocalIQ -- has raised $12 million in Series A funding to deploy its tech in customer support contact centres. The round was led by Point72 Ventures, with participation from Sands Capital Ventures, Amadeus Capital Partners, Passion Capital and Entrepreneur First (EF). PolyAI's founders are graduates of EF, although they didn't meet during the company building program but already knew each other from their time at Cambridge's Dialog Systems Group, part of the Machine Intelligence Lab at the University of Cambridge. "We started PolyAI in 2017, straight after submitting our PhD theses," Mrkšić tells me. "At Cambridge, we developed state-of-the-art conversational technology, and starting a company was the best way to get this tech used in the real world. We brought many of our Cambridge colleagues with us and started building the commercial version of our conversational platform."

In 2018, both social and technological trends will drive the transformation of healthcare. MobiHealthNews spoke to a range of stakeholders in the field to ask what they saw coming down the tracks. And watch this space next week for part two, including predictions about telemedicine, the FDA, and remote patient monitoring. News that CVS-Caremark and Aetna were considering a merger took many by surprise. But experts on health, health tech, and health policy say the move made a lot of sense -- and looking ahead we may well see more like it.

Our society is organized around a number of (interdependent) global systems. Logistic and supply chains, health services, energy networks, financial markets, computer networks, and cities are just a few examples of such global, complex systems. These global systems are socio-technical and involve interactions between complex infrastructures, man-made processes, natural phenomena, multiple stakeholders, and human behavior. For the first time in the history of manking, we have access to data sets of unprecedented scale and accuracy about these infrastructures, processes, natural phenomena, and human behaviors. In addition, progress in high-performancing computing, data mining, machine learning, and decision support opens the possibility of looking at these problems more holistically, capturing many of these aspects simultaneously. This paper addresses emergent architectures enabling controlling, predicting and reaoning on these systems.