Where Does Turkey's AI Ecosystem Stand?

From startups to university research, from Turkish-language models to the brain drain: the strengths and weaknesses of the local ecosystem.

The Turkish Language Model Question

Large models speak Turkish better and better, but because of the language's agglutinative structure, the same sentence is split into more tokens than it would be in English. That drives up both cost and response time. Models trained with a Turkish-specific vocabulary aim to close that gap, and the number of local efforts released as open source has grown noticeably in recent years.

Where Startups Are Concentrated

Local startups cluster mostly at the application layer: demand forecasting for retail, call center automation, image-assisted diagnosis in healthcare, image processing for the defense industry. Rather than building foundation models, mastering the data of a specific sector is the most sensible way to compete with limited capital.

The University–Industry Link

On the research side there are groups producing strong publications, but the move from academia to product is still the weak link. Joint laboratory models and industry-backed thesis programs are trying to close that gap.

The Brain Drain

This is the most frequently discussed problem. Experienced researchers heading abroad leaves teams with a gap at the senior level. As remote work has spread, the picture has become two-directional: the same people can stay in the country and still work for global companies, which makes salary competition harder for local firms.

Conclusion

Turkey's advantage isn't entering the race to build large models but being able to move fast on sector-specific solutions. In areas with access to data and a well-defined problem, local teams are quite competitive.