The piece explores how the current AI boom is reshaping the relationship between software companies and capital. It references Richard Sutton's "bitter lesson," which argues that brute-force compute is more effective than clever algorithms when it comes to AI. As a result, today’s AI systems can be developed by small teams who leverage vast amounts of data and GPU resources, leading to rapid growth in user adoption and revenue. Traditional software development, in contrast, has often faced limits due to the complexities of scaling and coordinating talent, making it hard to deploy capital effectively. AI projects are changing the investment landscape. Instead of large teams working on complex software solutions, smaller teams are now able to achieve significant results with substantial funding. This shift has sparked new ambitions within the tech industry, as companies tackle problems previously deemed too complex. The article highlights two main considerations for future AI projects: the availability of data and GPUs, and the economic viability of the solutions being developed. Despite the rise of AI, traditional software isn’t going away. AI is often inefficient for certain problems, and there are still vast markets where conventional software excels. The need for software engineers will persist, though their roles may evolve with advancements in AI development tools. The article suggests that the economic implications of AI extend beyond the quest for Artificial General Intelligence (AGI), as the current AI landscape reduces complex technical challenges to economic considerations.