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Indonesia's AI journey: Innovation or overreach?
AI development isn't just about writing code; it demands powerful computing infrastructure, massive datasets and continuous investment. Can Indonesia, with its underdeveloped cloud and hardware ecosystem, sustain such costs?
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Visitors are silhouetted on July 6, 2023, behind a sign for artificial intelligence at the World Artificial Intelligence Conference (WAIC) in Shanghai, China. (Reuters/Aly Song)
D
eepSeek, a large language model from China, has been making waves with its efficiency and lower training costs compared to its predecessor, GPT-4o. Not wanting to be left behind, Indonesia announced plans to develop a similar AI tool, within an astonishing two weeks.
At the Indonesia Economic Summit, Luhut Binsar Pandjaitan declared that Indonesia had the talent to build its own DeepSeek, stating, “If we don’t try, we’ll never know.” But is talent and optimism enough to build world-class AI?
Building an AI model like DeepSeek or GPT-4o is not just a question of talent; it demands massive research infrastructure, computational power and billions in investment. OpenAI, the company behind GPT-4o, had over 300 top-tier researchers, many trained in the best AI labs in the US and China. Indonesia, despite its bright minds, does not yet operate at this scale.
Indonesia contributes only 2.27 percent of global scientific research, compared to China’s 36.05 percent and the US’s 12 percent. AI research is the foundation of cutting-edge models, yet Indonesia’s output in this field remains limited. Can we expect to lead in AI if we haven't even invested in fundamental research?
AI development isn't just about writing code; it demands powerful computing infrastructure, massive datasets and continuous investment. Can Indonesia, with its underdeveloped cloud and hardware ecosystem, sustain such costs?
Indonesian AI faces a unique challenge: data scarcity. Unlike English, which has vast, high-quality datasets, Indonesian suffers from limited formal text corpora.
One feasible approach to achieve a system akin to DeepSeek within such a short timeframe is by leveraging and modifying existing open-source AI code. This strategy can significantly reduce development time and costs.
