AI’s checkmate

Nine years ago, between March 9 and 15, 2016, the world witnessed a historic match between accomplished Go player Lee Sedol and AlphaGo, an AI model developed by DeepMind.

In a five-round series, AlphaGo defeated Lee Sedol 4-1, a result that not only challenged long-standing assumptions about human strategic supremacy but also signalled a pivotal advancement in Artificial Intelligence (AI) technology. The famous 4000-year-old game is known for its inherent complexity with an infinite number of moves. Prowess over the game extends beyond computation limits.

AlphaGo’s victory demonstrated that AI could not only match human intelligence in complex tasks but also surpass it in scenarios that require reasoning and creativity. This landmark event catalysed broader debates about the future role of AI in complex decision-making and strategic contexts.

As we mark the ninth anniversary of the match, it is the perfect moment to look at today’s state of AI technology. Over the past decade, AI has redefined our understanding of machine learning and cognitive computation. The global market size of AI is expected to reach $3,680.47 billion by 2034, expanding at a CAGR of 19.20 per cent from 2025 to 2034. Today, both public and private sectors are extensively using AI for numerous applications ranging from supply chain optimisation, agriculture, education, healthcare, and finance to manufacturing. The basic principle underlying AlphaGo -- reinforcement learning, continues to be a driving force behind numerous advancements that we see today.

Much like AlphaGo changed the game of Go in 2016, today’s language learning models -- ChatGPT, Gemini, and DeepSeek etc -- are redefining creative writing, artistic expression, and image formation. They generate human-like content with an efficiency and nuance that pushes the boundaries of traditional creativity. Digital assistants such as Bixby, Alexa and Siri continue to manage daily tasks with considerable ease – with enhanced versions equipped with personalised responses, growing contextual awareness and automation.

Similarly, robotics and autonomous cars have made advancements with minimal human involvement. Companies such as Waymu, Tesla and Baidu are constantly in a race to provide enhanced autonomous features to users. Google, Amazon and Microsoft are also leading innovation and entrepreneurship at breakneck speed. By late 2024, nearly 245 AI unicorns had emerged, demonstrating the potential of the technology

Governments are also making significant investments, with the US and China leading the race at an accelerated pace. In this context, AI is being perceived as a key driver of influence. Notable advancements have been made in the military domain, particularly in data optimisation, Intelligence Surveillance and Reconnaissance (ISR), and precision strikes. Likewise, autonomous drones have demonstrated their utility in recent conflicts such as Nagorno-Karabakh and Russia-Ukraine, manifesting their pronounced impact in future warfare.

Unfortunately, progress on AI regulation remains limited. While frameworks like the European Union’s General Data Protection Regulation (GDPR), EU Artificial Intelligence Act, and Global Partnership on Artificial Intelligence have taken initial steps, the lack of global consensus has stalled the development of cohesive, uniform rules. For instance, GDPR fines are projected to reach nearly 5.88 billion euros by 2025, and the recent refusal by the US and UK to sign the proposed agreement at the Paris AI Action Summit only deepens concerns over the future trajectory of AI governance.

The regulatory challenges are compounded by issues of AI bias, potential job displacement, and the global race for supremacy, all of which dominate corporate and policy discussions. This has become even more pronounced since the associated cost of AI technology is also decreasing at a rapid pace, making it more accessible to a range of entities, from small startups to governments. A recent survey reveals that nearly 41 per cent of companies are likely to downsize their workforce highlighting economic uncertainties posed by rapid technological change.

The black-box nature of AlphaGo was a central aspect of discussion following its victory. This characteristic raises several questions regarding transparency and accountability in AI’s decision-making. Today, we see more advancements towards exploring Explainable AI to develop understandable and transparent models, with potential market size expected to grow to 20.9 per cent by 2028.

The next decade is likely to witness the emergence and proliferation of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). Such advancements would require robust regulatory measures and frameworks of collaboration to overcome potential threats, prevent misuse and ensure safe use. The technological race could see new alliances and shifting geopolitics vis-a-vis AI superpowers. In future, quantum breakthroughs may also compound AI applications. 6G technology could begin to take shape and the Internet of Things (IoT) is sure to expand – introducing new contours to AI integration.

However, like the legendary AlphaGo vs Lee Sedol match, advancements in AI compel us to reflect on the essence of what makes us human. In that game, Sedol’s celebrated move No 78 momentarily outmanoeuvred AlphaGo, serving as a powerful reminder that the human mind, with its capacity for intuition, creativity and emotion, continues to defy even the most sophisticated algorithms.

Although the match concluded within a week, its legacy endures, challenging us to explore how we might responsibly shape the rise of AI through innovative and ethical measures that preserve the irreplaceable qualities of human thought and ingenuity.

The writer is a researcher at the Centre for Aerospace and Security Studies (CASS), Islamabad. She can be reached at: cass.thinkers@gmail.com