Yapay Zekânın Türkiye Startup Ekosistemindeki İstihdam ve Fonlama Dinamikleri Üzerindeki Etkisi- Crunchbase Verisiyle Ampirik Bir İnceleme

Öz

Yapay zekâ (YZ) teknolojileri girişimcilik ekosistemini dönüştürmekte; ancak bu dönüşümün gelişmekte olan ekonomilerdeki ampirik yansımaları sınırlı kalmaktadır. Türkiye özelinde girişimlerin YZ kullanımı, istihdam ve fonlama dinamiklerini birlikte ele alan sistematik bir analiz bulunmamakta; ayrıca uluslararası literatürde giderek merkezi bir kavram haline gelen “solopreneur” kavramının Türkçe karşılığı literatürde yer almamaktadır. Bu çalışma, söz konusu boşlukları gidermeyi amaçlayarak Türkiye startup ekosisteminde YZ kullanımının istihdam yapısı ve fonlama üzerindeki etkilerini incelemektedir. Araştırma kapsamında 2015–2025 döneminde kurulan 801 girişime ait Crunchbase verisi analiz edilmiştir. YZ kullanımı anahtar kelime temelli sınıflandırma ile belirlenmiş; analizlerde Ki-Kare, Welch t-testi ve çok değişkenli OLS regresyon yöntemleri kullanılmıştır. Bulgular, YZ kullanan girişimlerin, kullanmayanlara kıyasla ortalama 2,3 kat daha az çalışanla faaliyet gösterdiğini; buna karşın iki grup arasında fonlama düzeyi bakımından istatistiksel olarak anlamlı bir fark bulunmadığını (p=0,065) ortaya koymuştur. 2030 projeksiyon modelleri, YZ kullanan girişimlerin %75,7'sinin mikro girişim yapısına evrileceğini öngörmektedir. Çalışma, “birişimcilik” kavramını literatüre kazandırmaktadır. Anahtar Kelimeler: Birişimcilik, yapay zekâ, startup ekosistemi, istihdam verimliliği, crunchbase.

Anahtar Kelimeler

• Birişimcilik
• Yapay Zeka
• Startup Ekosistemi
• İstihdam Verimliliği
• Crunchbase

The Impact Of Artificial Intelligence On Employment And Funding Dynamics In The Turkish Startup Ecosystem: An Empirical Investigation Using Crunchbase Data

Öz

Artificial intelligence (AI) technologies are transforming the entrepreneurial ecosystem; however, the empirical manifestations of this transformation in developing economies remain limited. In the context of Türkiye, there is no systematic analysis that jointly examines firms’ AI adoption, employment structures, and funding dynamics; moreover, the concept of “solopreneur,” which has become increasingly central in the international literature, lacks a corresponding term in Turkish academia. This study aims to address these gaps by investigating the effects of AI adoption on employment structure and funding outcomes within the Turkish startup ecosystem. The analysis is based on Crunchbase data covering 801 startups founded between 2015 and 2025. AI adoption is identified through a keyword-based classification approach, and the empirical strategy employs Chi-square tests, Welch’s t-test, and multivariate OLS regression models. The findings indicate that AI-adopting startups operate with, on average, 2.3 times fewer employees compared to non-AI firms, while no statistically significant difference is observed in funding levels between the two groups (p = 0.065). Projection models for 2030 suggest that 75.7% of AI startups will evolve into micro enterprise structures. The study contributes to the literature by introducing the term “birişimcilik” as a Turkish equivalent of “solopreneurship.” Keywords: Artificial intelligence, startup Ecosystem, solopreneurship, employment efficiency, crunchbase.

Anahtar Kelimeler

• Artificial intelligence
• startup Ecosystem
• solopreneurship
• employment efficiency
• crunchbase

Kaynakça

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