The International AI Summit is set to convene in India this February. As the former back office of the global software industry, India currently finds itself in a position fraught with contradiction and tension. This is not merely the growing pains of a single nation's transformation, but a microcosm of the challenges facing all middle powers.

Over the past three decades, India leveraged its massive engineering dividend to forge deep ties with Silicon Valley. However, the wave of generative AI is rapidly redefining the value of software outsourcing.

In July 2025, Indian software giant Tata Consultancy Services (TCS) initiated the largest workforce restructuring in its history, laying off over 12,000 employees (approximately 2% of its workforce). This was not merely a signal of recession, but more like an alarm bell for industrial upgrading. The traditional outsourcing model is crumbling, and enterprises must free up resources to pivot toward high-value AI solutions. This wave of layoffs demonstrates India's struggle to complete the "elephant's pivot" before the old model collapses entirely.

In the mobile internet era, the Indian government built its acclaimed digital public infrastructure, "India Stack". Through the Unified Payments Interface (UPI) and Aadhaar identity authentication, India successfully compelled tech giants like Google and Meta to comply with local rules as a condition for market entry.

However, the AI era has introduced new rule-breakers. As users become accustomed to obtaining answers and services directly through AI, the traffic gateways and protocol defenses previously built upon app interfaces face the risk of being bypassed. This is not a failure of the India Stack, but a shift in the dimensions of the battlefield—the fortifications remain, but the enemy is airdropping directly from the "cloud."

Last year, in the absence of top-tier indigenous foundation models, India's startup ecosystem exhibited a pragmatic yet dangerous trend: the widespread adoption of models like DeepSeek. This reflects a common dilemma for middle powers—without domestic computing power support, this remains one of the few strategies to achieve rapid Time-to-market.

But this choice is not without cost. Cybersecurity giant CrowdStrike conducted an in-depth analysis of DeepSeek-R1, noting that the "implicit steering" embedded within the model could constitute a security vulnerability. The report indicated that when test scenarios involved specific politically sensitive groups (such as Uyghurs), the software architectures generated by the model exhibited abnormally high vulnerability rates.

This emergent model bias directly impacts code security and stability. When downstream software developers use these tainted engines, the bias spreads throughout the entire application ecosystem.

Taking India as a lesson, since no middle power can train a top-tier model in isolation, countries should all the more pool computing power and high-quality corpora to establish public models akin to public libraries. Starting with general-purpose models, nations can systematically correct cognitive biases and perform deep alignment with local cultures, thereby possessing their own digital brains without relying on specific capital interests.

Leveraging its chip advantage, Taiwan is well-positioned to actively collaborate globally in promoting "Cooperative AI Sovereignty." Only by mastering our own digital brains can we secure an autonomous and safe space for survival beneath the shadow of AI monopoly.