Open vs Closed AI: The Key Decision for Future-Proofing Your Company

Shyam Balagurumurthy Viswanathan, Sr Integrity Science Engineer - AI research

In today's rapidly changing technological environment, companies across the world are working to integrate or trying to integrate artificial intelligence (AI) into their day-to-day work and brainstorming different ways to do so. There is no doubt that AI has been transformative. It is used for customer service, operations, programming, engineering, and a multitude of other tasks to improve efficiency. One key question that companies or leaders in these organizations have in mind is whether to choose open AI models or closed, proprietary systems.

Why Decide Now?

Companies do not want to be left behind in the competitive landscape, so they need to act now. The decision between open and closed AI models is critical as it impacts the implementation, scalability, and strategic direction of a company’s AI initiatives. Foundation models need to be supported in the long run to ensure they remain relevant and effective. Additionally, future versions of these models must be developed with bigger and better capabilities to address increasingly complex challenges. This ongoing development and support are essential for maintaining a competitive edge and leveraging AI to its fullest potential.

Understanding Different Open and Closed Models

When considering AI models, it's important to understand the distinctions and examples of both open and closed systems. Models like LLAMA and Mistral are typically open-source and available for public use and modification. These models offer flexibility and can be customized to meet specific business needs. Open models allow companies to fine-tune and adapt the AI to their proprietary data, providing tailored solutions that can be highly effective for unique applications.

On the other hand, closed AI models are proprietary systems controlled by the companies that develop them. Examples include OpenAI's ChatGPT and Google's Gemini. These models are designed to be robust, out-of-the-box solutions that come with extensive support and regular updates. While they may not offer the same level of customization as open models, they provide reliability and state-of-the-art capabilities that can be easily integrated into existing business processes.

Choosing between open and closed models depends on a business's specific requirements, such as the need for customization, the available technical expertise, and the strategic goals related to AI deployment.

The Case for Closed Models

Closed AI models, such as OpenAI's ChatGPT and Google's Gemini, offer several advantages that make them attractive to many businesses. These proprietary systems are typically developed and maintained by leading tech companies, ensuring they are at the forefront of technological advancements. Here are some key benefits:

1. Reliability and Performance: Closed models are extensively tested and optimized for performance. They often incorporate the latest advancements in AI research, providing robust capabilities out-of-the-box. For example, ChatGPT is known for its advanced natural language processing abilities, making it suitable for applications like customer support and content generation.
2. Comprehensive Support and Updates: Companies that develop closed models provide ongoing support and regular updates. This ensures that the models remain up-to-date with the latest features and security enhancements. This continuous improvement is critical for maintaining high performance and addressing emerging challenges in AI deployment.
3. Ease of Integration: Closed models are designed for seamless integration into existing business systems. They come with extensive documentation, user-friendly interfaces, and often, dedicated support teams to assist with deployment and troubleshooting. This can significantly reduce the time and technical expertise required to implement AI solutions. Documentation also often includes guidelines for security and compliance, helping businesses adhere to regulatory standards.
4. Security and Compliance: Proprietary models often include built-in security features and compliance with industry standards. This is particularly important for businesses operating in regulated industries such as finance and healthcare, where data security and privacy are paramount.
5. Advanced Capabilities: Closed models frequently offer advanced capabilities, such as integrating with your email (like Google Workspace) or different apps (like exploring GPTs). These features enhance productivity and provide comprehensive solutions that are tailored to specific business needs.
6. Costs and Vendor Lock-In: Costs tend to be higher for closed models, and companies may find themselves locked in with the vendor. This dependency can lead to higher expenses over time and reduced flexibility to switch to alternative solutions if needed.
7. Integrity and Ethical Considerations: When using closed models, companies must rely on the vendor's strategy for integrity and ethical considerations. This means businesses lose the liberty to customize the ethical standards and transparency of the models, which can be a significant concern for companies with specific ethical guidelines or those requiring greater control over their AI systems.

The Case for Open Models

Open AI models, such as LLAMA and Mistral, provide a different set of advantages that can be equally compelling for businesses. Here are some reasons why companies might choose open models:

1. Flexibility and Customization: Open models are typically open-source, meaning businesses can modify and fine-tune the underlying parameters to meet specific needs. This flexibility is ideal for companies with unique operational requirements or those looking to innovate in niche markets. The ability to fine-tune the models allows for a high degree of customization, making them highly adaptable to various use cases.
2. Cost-Effectiveness: Open models can be more cost-effective, particularly for smaller businesses or startups. This is especially true for smaller models when the task is customized and specific. Since they are open-source, there are no licensing fees, and companies can leverage community support and resources to implement and maintain these models.
3. Community and Collaboration: Open-source AI models benefit from a large community of developers and researchers who contribute to their improvement. This collaborative environment fosters rapid innovation and the sharing of best practices. Companies using open models can tap into this collective knowledge and benefit from continuous enhancements driven by the community.
4. Transparency: Open models offer greater transparency and reproducibility to some extent, allowing businesses to better understand and trust the AI's processes. This can be important for ensuring ethical AI practices, as companies can audit the model’s decision-making processes and address any biases or ethical concerns.
5. Scalability and Portability: Open AI models can be deployed on various hardware configurations, from powerful servers to small devices like Raspberry Pis. This scalability makes them suitable for a wide range of applications, from large-scale data processing to edge computing in IoT devices.
6. Avoiding Vendor Lock-In: By using open-source models, companies can avoid dependency on a single vendor. This reduces the risk associated with vendor lock-in, such as price increases or discontinued support, and provides greater control over the AI implementation.
7. Risk of Losing Support: One potential risk with open models is the possibility of losing support in the future. The development of open-source models relies on community contributions, which can wane over time. Therefore, companies need to choose their models carefully, considering the longevity and active development of the project.
8. Licensing Terms: Historically, a minor percentage of open-source projects have changed their licensing terms, which can cause confusion and potential legal issues. Companies must consider this possibility and ensure they stay updated on any changes to licensing terms to avoid disruptions.

Conclusion

In conclusion, the decision between open and closed AI models is a pivotal one that can shape the future trajectory of a company's technological and strategic development. Closed models like ChatGPT and Gemini provide robust, well-supported solutions that integrate seamlessly into existing systems, ensuring reliability and performance. They come with higher costs and potential vendor lock-in, but they offer comprehensive support and advanced capabilities.

Open models like LLAMA and Mistral, meanwhile, offer flexibility, cost-effectiveness, and the ability to customize and fine-tune models to specific needs. They benefit from community-driven innovation and greater transparency but come with risks related to long-term support and potential changes in licensing terms.

Ultimately, the choice should be guided by a thorough understanding of the company's specific use cases, technical capabilities, and long-term strategic goals. By carefully evaluating these factors, companies can make informed decisions that leverage AI to drive efficiency, innovation, and competitive advantage in the digital age.