Should every company build its own AI? The case for open-source AI processes

Should every company build its own AI? The case for open-source AI processes

In nearly all the AI training sessions we deliver in Turkey, the process culminates in the same question: Can we build our own AI?

The answer to this question is both simple and deeply complex. On one hand, from a code complexity perspective, "building AI" may be easier than writing a desktop or web application; however, there is another factor that is neither easy nor inexpensive, pushing costs thousands of times above the average of standard application development. That factor is R&D cost.

Contrary to common belief, the fundamental functions that bring AI to life, such as attention, tokenizer, and transformer blocks, are not simply the culmination of historical best practices bounded by current physical laws. They represent a specific set of architectural choices.

While each of these topics deserves individual attention, the most critical point to grasp for the purpose of this article is the existence of different tokenizer, attention mechanisms, and transformer blocks, along with the reality that new variations will continue to emerge. In fact, it is not an error for an individual to write their own tokenizer for a specific task they wish to automate.

However, when the goal in a corporate setting is to scale a product, one must not forget that the volume of R&D required reaches the scale of millions of dollars. This applies equally to Attention, transformer blocks, and other AI components.

Even if all these processes are executed successfully, an even larger cost item is the collection and curation of training data, and the gathering of computational power sufficient to ensure the training process completes in a reasonable timeframe. Together, these two items require an investment of billions of dollars. If the end result is not intended to be transformed into a commercial product, this investment often becomes a sunk cost.

At this point, we must discuss the existence of open-source models. Open-source models are those shared as open weight by independent developers, GPU manufacturers, or leading industry firms, allowing users to deploy them on appropriate GPU hardware.

If these models are not released as open weight, they can still be customized through a process called fine-tuning, which enhances the model's proficiency in specific areas. Nevertheless, given the rapid pace of capability improvement in non-open-source frontier models as of October 2026, fine-tuning open-source models may not deliver the desired level of performance, particularly for tasks requiring long-context synthetic analysis.

Of course, the outlook for the future is not so bleak. As technologies advance, R&D costs will show a downward trend. This will reduce the requirements for local execution of adequate models in the future, while their ease of development will allow them to compete with paid models.

Here, it is crucial to remember that the sole contribution of open-source models is not just pricing, but also preventing data privacy breaches. This factor alone presents a significant added value.