KÜRESEL YAPAY ZEKÂ KÜMELERİ: YETENEK, BECERİLER VE GİRİŞİMCİLİKTEKİ YAPISAL EĞİLİMLER

Abstract

Yapay zekâ (YZ), ülkelerin dijital dönüşüm kapasitesini, inovasyon potansiyelini ve işgücü piyasalarını yeniden şekillendiren stratejik bir alandır. Ancak ülkelerin YZ alanındaki yetenek temelli kapasite farklarının hangi göstergelerle ne düzeyde ayrıştığı, nicel olarak karşılaştırmaya ve sınıflandırmaya yönelik ampirik çalışmalar sınırlıdır. Bu çalışma, bu literatür boşluğunu doldurmayı amaçlayarak, YZ’ye ilişkin beş stratejik gösterge üzerinden ülkeler arasında anlamlı kümeler olup olmadığını analiz etmektedir: İşe alım oranı, beceri yayılımı, yetenek yoğunluğu, net yetenek göçü ve fonlanan YZ girişim sayısı. 2024 dönemine ait 47 ülkenin verilerini kullanarak K-ortalamalar algoritmasıyla kümeleme analizi gerçekleştirilmiş, küme sayısı dirsek yöntemiyle dört olarak belirlenmiştir. Sonuçlar, ülkeler arasında YZ kapasitesi bakımından yapısal, istatistiksel olarak anlamlı ve çok boyutlu ayrışmalar olduğunu göstermektedir. Çalışmada gerçekleştirilen K-ortalamalar kümeleme analizi sonucunda ülkeler, yapay zekâya ilişkin beş temel göstergeye göre dört anlamlı kümeye ayrılmıştır. Bulgular, ülkeler arasındaki farkların yalnızca tekil göstergelere değil, göstergeler arasındaki dengeye ve bütüncül yapıya bağlı olarak şekillendiğini ortaya koymaktadır. Çalışma ayrıca politika önerilerinde bulunmaktadır. Bu çalışma, YZ alanında ülkelerin çok boyutlu konumlarını karşılaştırmalı olarak analiz eden nadir ampirik çalışmalardan biridir ve politika yapıcılar için önceliklendirme ve stratejik planlama açısından önemli bir referans noktası sunmaktadır.

Keywords

• Yapay Zekâ
• K-ortalama Kümeleme
• Ülkeler Arası Karşılaştırma

GLOBAL AI CLUSTERS: STRUCTURAL PATTERNS IN SKILLS, TALENT, AND ENTREPRENEURSHIP

Abstract

Artificial intelligence (AI) acts as a strategic catalyst for digital transformation, fostering innovation and redefining labor market dynamics. However, empirical studies that quantitatively compare and classify the extent and nature of cross-country differences in AI talent capacities remain limited. This study aims to address the existing gap in the literature by analyzing the presence of significant clusters among countries based on five strategic AI-related indicators: Hiring, Skill Penetration, Talent Concentration, Talent Migration, and Newly Funded AI Companies. Using 2024 data from 47 countries, a K-means clustering analysis was performed, with the optimal number of clusters identified as four through the elbow method. The results reveal significant structural and multidimensional divergences in AI capacity across countries. Based on the K-means clustering analysis, nations were categorized into four distinct clusters according to the five core AI indicators. The findings indicate that cross-country differences are influenced not only by individual indicators but also by the balance and overall structure among these indicators. The study also offers policy recommendations to support the AI-related development of countries. This research represents one of the few empirical studies that comparatively analyze the multidimensional positioning of countries in AI, providing policymakers with a vital reference point for prioritization and strategic planning.

Keywords

• Artificial Intelligence
• K-means Clustering
• Cross-country Comparison

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