The Hype of Closed AI: A Temporary Reign

While closed-source AI giants like OpenAI and Anthropic have dominated the limelight, the future belongs to open source. The allure of proprietary systems is undeniable, but the discerning eye should gaze towards open source for enduring benefits.

The Perils of Outsourcing Intelligence

For some, tapping into closed AI APIs might be the way forward. However, for companies deeply rooted in AI, this is akin to playing with fire. The strategy? Initiate with closed AI, but swiftly transition to nurturing in-house models.

The Myth of Reasoning Supremacy

Contrary to popular belief, tasks of immense value such as summarization and Q&A don’t hinge on reasoning. Open-source models are already champions in this arena. The real game-changers? Expansive contexts and the ability to generate content with precision.

The Open Source Mantra: Control is King

Open-source AI is synonymous with unparalleled customization. Whether it’s tweaking latency, optimizing throughput, structuring outputs, or refining with unique data, open source is the key. In contrast, closed models are akin to unpredictable chameleons, constantly shifting without notice.

Decoding the Black Box: Trust Through Transparency

The enigmatic nature of closed models is their Achilles’ heel. Open source models, on the other hand, invite global scrutiny, fostering a culture of peer review and rigorous auditing. To trust is to understand, and understanding stems from diving deep into the AI’s inner workings.

The Illusion of Ease: A Passing Phase

Yes, closed APIs currently win the ease-of-use race. But as open source evolves, it promises not just ease but also unmatched customization. The message is clear: sidestep the transient allure of closed systems and lay the foundation with open source.

References:

• Bommasani, R., et al. (2021). On the Opportunities and Risks of Foundation Models. arXiv:2108.07258.
• Bosworth, M. (2010). Why you shouldn’t build your own user authentication system. Coding Horror Blog.
• McMahan, H. B., et al. (2018). A general approach to adding differential privacy to iterative training procedures. arXiv:1812.06210.
• Raposo, D., et al. (2023). Loras improve image generation quality and coherence. arXiv:2302.08359.
• Sinitsin, I., et al. (2023). Axiom: Fast, flexible and efficient training of large & giant language models. arXiv:2302.00911.