The artificial intelligence strategies of companies such as OpenAI, Meta Platforms, Nvidia Corporation, and Google are based on developing closed-source AI tools and models with the intent of making and marketing practical and profitable AI applications. Profit is at the core of the advantages of developing AI technologies via the closed-source route. However, despite its benefits to for-profit organizations, its disadvantages or issues and challenges represent a gap that is being addressed by the increasing popularity of open-source AI projects. This article explores and discusses the notable advantages and disadvantages of closed-source AI.
Explaining the Advantages and Disadvantages of Closed-Source AI: Applications, Opportunities, Issues, and Challenges
A closed-source AI tech is a system, tool, algorithm, model, or product that is developed and owned by a single entity. The source code is proprietary and an intellectual property of the developer. Hence, unlike open-source AI technologies, this means that the public does not have access to the source code and it cannot be inspected, modified, or redistributed.
Specific AI products based on the closed-source route are either sold to end-use consumers under a one-time-payment or subscription-based scheme or licensed to third parties who want to build their respective AI applications. OpenAI is one of the biggest developers of various AI algorithms and AI models for end-use and business-to-business consumers.
Other companies develop and deploy closed-source AI tools and models to improve their value chains. Google uses its own large language models for Google Search, the Bard chatbot, and Google Assistant products. Meta Platforms and Amazon have also developed machine learning tools for recommendation features and web services optimization.
Pros of Closed-Source AI Projects: Advantages and Applications
1. Better Quality Assurance and Long-Term Support
One of the main advantages of closed-source AI is that models or tools and products developed following this route have better overall quality than some of their open-source counterparts. There is a strong motivation and established incentive for profit-oriented developers to ensure that their AI products are of the highest quality and standards.
It is also worth underscoring the fact that these models or tools and products are sold and provided with attached after-sales support. This is important for end-users or business clients who depend on using a particular AI product but do not have the expertise to troubleshoot in case of issues or make relevant product updates or upgrades.
2. Competitive Advantage and Intellectual Property
The proprietary AI algorithms and AI models of companies can also be their source of competitive advantage. OpenAI banks on its numerous transformer-based models such as its GPT-4 large language model to power its end-use products and business-to-business services while Nvidia has built foundation models as part of its enterprise solutions.
Furthermore, because it is a source of competitive advantage for an organization, preventing public access to the source code provides a substantial level of intellectual property protection. This prevents competitors from copying AI technologies or specific algorithms and models or using a similar implementation of closed-source AI tech.
3. Promotes Data Privacy and Provides Overall Security
Another advantage of closed-source AI or developing AI tech via the closed-source route is that the developers have better control over the privacy of their data and even the privacy of the users of their AI-based products. There is also a strong motivation and incentive for these developers to ensure their data and the data of their customers are protected.
It is important to note that customers or clients operating in regulated industries or markets or those handling sensitive information need to use products or solutions and services from providers that have robust security measures. Take note that privacy issues and security concerns are two of the most critical challenges of open-source artificial intelligence.
4. Better Streamline Integration and Higher Scalability
Most closed-sourced AI products are developed and deployed to seamlessly integrate with existing systems, infrastructure, or tech stack. This simplifies the process of adaption and implementation from the end of customers or clients while also eliminating or reducing the burden on internal information technology resources and capabilities.
These same products are also scalable because they are built to handle large volumes of data and processing tasks. Most developers also provide simplified pricing schemes or purchase options to abridge the entire adaption and implementation process and the needed scaling requirements. This makes them suitable for enterprise-level applications
Cons of Closed-Source AI Projects: Disadvantages and Challenges
1. Issues with Lack of Control and Limited Flexibility
An open-source AI model has a higher degree of customization because its source code is open to the public and there is a community of volunteers that can provide inputs. This is not the case in a closed-source AI model and even other similar AI technologies. This can make it difficult for customers to customize an AI product to suit their needs.
The aforementioned also translates to limited flexibility that also limits the possibilities that a particular AI product can do. A customer is left with no choice but to either work on what a particular product can provide or find another product that meets its requirements. The lack of control is a notable disadvantage of a closed-source AI.
2. High Dependence on Developer or Vendor Support
It is important to highlight the fact the source codes of AI models or tools and products developed under the closed-source route are the properties of their developers. This also means that a particular AI application itself is both theoretically and technically at the helm of its developer. This creates dependence on the part of the users.
The users or customers and clients of an AI product are specifically dependent on its developer or vendor for support and updates. It is true that the long-term support for the product is better and more reliable than an open-source counterpart but this can be a problem if the developer goes out of the business or stops providing routine updates to the product.
3. Vendor Lock-In Due to Closed Product Ecosystem
Similar to the aforementioned dependence on developers or vendors for support, another disadvantage of closed-source AI models or tools and products is that a user can become more dependent. This happens if this user becomes too reliant on a single developer or vendor for all of its AI needs or for implementing and expanding its artificial intelligence strategy.
A closed product ecosystem has become a revenue model for most tech companies. It is possible that AI companies can follow the same model to maximize the earnings potential of a particular customer or client. This developer or vendor lock-in is a problem because it limits options while increasing the switching costs to alternatives and substitutes.
4. Possible High Costs from Scaling AI Solutions
Companies such as OpenAI have a slew of AI products that are free. Accessing better features comes with a cost. This is the case with the paid ChatGPT Plus version. Clients who want to access the AI models behind the AI applications of OpenAI have to use its API key. Accessing and availing of this API key comes with a meter-based pricing scheme.
The large-scale deployment of a proprietary AI model can be expensive. The cost accumulates as the scale levels up or increases. This comes from the fact that the model runs on the technological infrastructure of its developer. Running the model is a computationally intensive task that involves power-hungry and cooling-dependent data centers.
Key Takeaways and Conclusions from the Pros and Cons of Closed-Source AI: Important Points to Consider and Reconsider
Choosing a closed-source AI product or a specific AI model can be a better option than a counterpart built via an open source route. This comes the higher quality assurance, better support, and higher reliability. However, because of the associated cost implications and other limitations, this option might not be attractive to budget-limited users or those who want to develop and deploy their own AI solution with greater freedom. It is important to weigh the pros and cons of both closed-source and open-source.