Contrary to the belief that it has no current utility, quantum computing is already being used commercially and generating revenue. Major companies like HSBC and AstraZeneca are leveraging quantum machines via cloud platforms (AWS, Azure) for practical applications like financial modeling and drug discovery, proving its value today.

Companies can build authority and community by transparently sharing the specific third-party AI agents and tools they use for core operations. This "open source" approach to the operational stack serves as a high-value, practical playbook for others in the ecosystem, building trust.

The current trend toward closed, proprietary AI systems is a misguided and ultimately ineffective strategy. Ideas and talent circulate regardless of corporate walls. True, defensible innovation is fostered by openness and the rapid exchange of research, not by secrecy.

Tech companies often use government and military contracts as a proving ground to refine complex technologies. This gives military personnel early access to tools, like Palantir a decade ago, long before they become mainstream in the corporate world.

Convex built 'Chef', a functional AI coding app, not to win end-users, but as a marketing tool. By open-sourcing it and demonstrating the power of their backend, they successfully attracted other AI coding platforms to build on their technology, turning potential competitors into customers.

The choice between open and closed-source AI is not just technical but strategic. For startups, feeding proprietary data to a closed-source provider like OpenAI, which competes across many verticals, creates long-term risk. Open-source models offer "strategic autonomy" and prevent dependency on a potential future rival.

OpenAI has seen no cannibalization from its open source model releases. The use cases, customer profiles, and immense difficulty of operating inference at scale create a natural separation. Open source serves different needs and helps grow the entire AI ecosystem, which benefits the platform leader.

IBM CEO Arvind Krishna argues Watson's core AI tech was sound, but its failure stemmed from a closed, all-in-one product approach. The market, especially developers, preferred modular building blocks to create their own applications, a lesson that informed the WatsonX rebranding with LLMs.

Despite hype around its potential to solve famously complex problems like the "traveling salesman," experts in the field caution that the number of actual, practical problems quantum computing can currently solve is extremely small. The gap between its theoretical power and tangible business application remains vast, making its near-term commercial impact questionable.