The article discusses the challenges and stagnation in healthcare AI, highlighting that the industry is significantly behind other sectors despite advancements in technology. It also emphasizes the need for transparency and innovation in healthcare, mentioning ongoing investigations into unethical practices by certain organizations.

• Healthcare's core incentive problem: treating illness is more profitable than preventing it, which actively discourages AI innovation aimed at improving outcomes
• Many hyped claims of AI outperforming human doctors in diagnostics don't hold up under scrutiny
• The author's investigations into Commure and Mayo Clinic point to unethical practices warranting transparency and accountability
• A complex, fragmented system, entrenched incumbents, and compliance-focused regulation are structurally blocking healthcare AI progress

The article discusses the competitive landscape of artificial general intelligence (AGI) development, likening it to an all-pay auction where participants must invest heavily regardless of the outcome. It argues that this model can lead to inefficiencies and raises concerns about resource allocation in the race towards AGI. The implications of such a competitive framework on innovation and ethical considerations are also explored.

• The AGI race functions as an all-pay auction where every competitor pays their bid (massive capex) regardless of whether they win, driving "value dissipation" toward the total prize value
• Microsoft (>$30B/quarter) and Alphabet (~$85B by 2025) exemplify capex levels that only make sense if losing the race means losing everything already invested
• Because AGI has no agreed definition or finish line, bidders tend to overbid, risking a bubble where combined spending outstrips any realistic returns
• Ordinary investors and pension holders bear outsized risk since most bidders will likely lose while only one winner captures the prize