Artificial Intelligence is rapidly evolving from a productivity tool into something much larger: a form of digital infrastructure.

Around the world, governments are exploring how AI can improve public services, support economic development, strengthen institutions, and accelerate progress toward national development goals. Here in Trinidad and Tobago, similar discussions are taking place as Government and UNDP collaborate on initiatives related to Digital Public Infrastructure (DPI), digital identity, interoperability, AI governance, Digital Credentials, Trust and Safety, and the country's emerging Open Source Programme Office (OSPO).

At the same time, a heated debate has emerged among Silicon Valley leaders, governments, universities, startups, and policymakers around the world.

That conversation is about Open Models and Open Source AI.

In July 2026, NVIDIA CEO Jensen Huang made his first-ever post on X (formerly Twitter) to share an open letter supporting open-weight AI models. The letter, co-signed by dozens of technology companies and organizations, argued:

"AI will transform every industry, power every company, and be built by every country. Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty. The world needs both frontier closed models and frontier open models."

The statement immediately sparked debate across the technology sector because it touched a fundamental question:

Should the future of AI be controlled primarily through proprietary services owned by a handful of companies, or should governments, universities, local innovators, and businesses also have the ability to inspect, modify, deploy, and operate advanced AI models themselves?

Supporters of open models see openness as a pathway to innovation, competition, and digital sovereignty. Advocates of proprietary systems argue that closed models provide stronger safety controls and more sustainable business models for the enormous investments required to develop frontier AI.

So who is right?

Before you form an opinion, it helps to understand what these terms actually mean.

An Open Source AI Crash Course

One challenge in discussing AI is that terms such as "open model," "open weight," and "open source" are frequently used interchangeably, even though they describe very different things.

Think of AI like a car.

A company could let you drive the car for a fee (proprietary). It could let you look under the hood and inspect the engine, without being able to alter any component (open-weight). Or it could give you the complete engineering plans and permission to modify the design to suit your needs (open-source).

Each represents a very different level of openness.

Most people are familiar with proprietary AI models, such as ChatGPT, Claude, Gemini, and Microsoft Copilot. These systems are owned and controlled by the companies that developed them. 

Using a proprietary model is a bit like using Netflix.

You can enjoy the service, but you do not own the platform, control its pricing, or decide how it operates.

You access the technology through an application or API (to be explained shortly), and the provider determines what happens behind the scenes.

Users benefit from powerful capabilities, but have limited visibility into how the models were built and little control over how they evolve.

At the other end of the spectrum is Open-Source AI. The Open Source Initiative (OSI), an organization that has long defined what constitutes "open source" software, recently published an Open-Source AI Definition  that emphasizes users' ability to study, modify, use, and share AI systems. To achieve this level of openness, users typically need access not only to the model itself but also to information about how it was created, trained, and maintained.

Between these two extremes sits a category that has become increasingly important: Open Weight Models.

These models publicly release the "weights" that contain the model's learned knowledge. In practice, this means that organizations can often download the model, run it themselves, customize it, or adapt it for specific use cases. Models such as Llama, DeepSeek, Qwen, Mistral, Gemma, and Kimi fall into this broader category.

A useful analogy is baking a cake.

A proprietary model lets you buy the finished cake.

An open-weight model lets you buy the cake and inspect many of the ingredients.

A truly open-source AI system provides the recipe, instructions, and permission to bake your own version.

It’s best to visualize the “openness” of AI models on a spectrum, with one end being fully closed systems and the other end being fully open. All AI models fall somewhere along this spectrum, according to the amount of transparency and downloadability.

Understanding Tokens: The Hidden Economics of AI

Before we go further, it is worth understanding one more important concept: tokens.

AI models think in tokens.

AI systems do not process information the way humans read words and sentences. Instead, they break text into smaller units called tokens.

Every question you ask an AI model consumes tokens. Every response generated by the model consumes tokens.

Most commercial AI providers charge based on this usage.

A useful analogy is a taxi meter.

You do not pay for owning the taxi. You pay for how far you travel.

Similarly, many AI services charge according to how much of the model you use.

This detail may seem technical, but it has enormous implications for governments, businesses, and public institutions seeking to deploy AI at scale.