In a discussion led by Steve from Kindred, the ongoing global debate between open source and closed source AI models is explored, highlighting their implications for national interests in sectors like healthcare and defense. The conversation emphasizes the need for regulatory measures to ensure safety while fostering innovation, as well as the challenges faced by startups in competing with established tech giants.
In a recent discussion, Steve from Kindred highlighted the ongoing debate in the AI industry regarding open source versus closed source models. This theme is not only prevalent in the U.S. but also resonates globally, particularly in regions like Asia, the Middle East, and the EU. The conversation emphasizes the importance of understanding how these models are being utilized across different sectors critical to national interests, such as healthcare and defense. The distinction between countries like China and other East Asian nations, which are more open to U.S. models, is also noted.
The conversation shifts to the role of advisors in shaping AI policy, particularly mentioning David Sachs, who advocates for open source models. Steve suggests that while promoting open source can foster competition and innovation, there is a need for regulatory measures to ensure safety and alignment with national interests. This balance is crucial for industries that rely heavily on AI technologies, and Sachs’ team is seen as capable of navigating these complexities.
Steve addresses concerns surrounding the accessibility of models like Deep Seek, an open-source AI model from China. He points out that while there are geopolitical fears associated with such models, the open-source nature allows for modifications to enhance safety. Companies like Perplexity and North Link are mentioned as examples of organizations that are providing safer versions of these models for developers, showcasing the potential benefits of open-source technology despite its challenges.
The discussion also touches on the difficulties faced by companies like Humane AI, which aimed to create consumer-facing applications. Steve attributes their struggles to the inherent challenges of hardware development, which is capital-intensive and reliant on concurrent software advancements. Competing with established giants like Apple and Samsung requires significant investment and a long-term vision, making it a daunting task for startups.
Ultimately, Steve expresses a desire to see more entrepreneurs take on ambitious projects in the AI space, emphasizing the importance of moonshot bets in venture capital. He believes that fostering innovation and supporting founders willing to tackle these challenges is essential for the future of AI development. The conversation encapsulates the complexities of navigating AI policy, the potential of open-source models, and the need for sustained investment in groundbreaking technologies.