Robin

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Optimization problems that need to be solved are not exactly rare. Companies optimize productivity, banks optimize portfolios, manufacturers optimize production schedules. Yet identifying valuable optimization opportunities may be much harder than it seems. Providers don't necessarily know where demand exists. Businesses don't always recognize that their challenges can be framed as optimization problems. Researchers don't automatically know where quantum approaches could create meaningful value. Networks don't inherently know which workloads will become recurring. That brings me back to a question I raised previously: "Which platform can identify and connect the most valuable recurring optimization problems?" And @quipnetwork, as a marketplace for both compute and optimization opportunities, may eventually find itself playing a role in that discovery process. Alongside the growth of the network, I suspect discovery and coordination could become just as important as compute availability itself. Nobody predicted all of today's cloud applications in 2006. Nobody predicted ChatGPT's use cases in 2018. And maybe nobody can predict all of Quip's future optimization workloads today. As networks mature, participants experiment, unexpected use cases emerge, and some eventually become recurring. It also makes me wonder whether, as networks mature, the harder challenge gradually shifts away from building compute infrastructure. It may be learning which experiments are merely experiments and which ones are early signals of entirely new categories of demand. That's the question I'll keep thinking about. Created for a project campaign. Not financial advice. Always DYOR.