{
  "id": 7753043,
  "title": "AI ambition needs smarter computing, not just more power",
  "url": "https://urgent.news/2026/09/16/ai-ambition-needs-smarter-computing-not-just-more-power",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-16T08:55:53.000Z",
  "source": {
    "name": "New Straits Times",
    "slug": "new-straits-times",
    "url": "https://www.nst.com.my/opinion/letters/2026/09/1534104/ai-ambition-needs-smarter-computing-not-just-more-power"
  },
  "original_language": "en",
  "account": "The excitement around artificial intelligence (AI) can lead people to believe that merely increasing computing power will result in superior innovation. However, this is not always the case. The optimal solution depends on various factors such as the nature of the problem, the quality of data, the desired level of accuracy, and the potential consequences of errors. It is crucial to recognize when larger AI systems are not necessary.\n\nGood data, basic analytics, and appropriate visualization may provide the required insights. Responsible AI involves understanding when not to employ the most powerful AI systems available. Students should grasp that quality software is not just about functionality, but also efficiency. When multiple algorithms solve the same problem, which one requires fewer computational steps? When should data be processed locally instead of being repeatedly transferred to distant servers? When is a smaller AI model sufficient? How can a database be designed to minimize unnecessary processing? Future technology professionals must consider computational efficiency alongside functionality.\n\nInfrastructure alone does not guarantee Malaysia's advancement into a digital economy. People do. The nation requires professionals in cloud computing, networking, cybersecurity, software engineering, data engineering, AI, and high-performance computing, along with mathematicians, statisticians, and data analysts. The AI economy is mistakenly perceived as being solely for programmers. In truth, mathematics and statistics are essential for optimization, machine learning, forecasting, and evidence-based decision-making. This is why universities and computing and mathematical sciences faculties should collaborate closely in developing Malaysia's data center ecosystem.\n\nStudents need to understand not only how to utilize AI applications but also how data flows through systems, how models consume computing resources, how networks transmit information, and how infrastructure must be secured. The ultimate goal should be to convert infrastructure investments into national technological capability. Malaysia's AI ambitions are worthwhile. The country has the chance to build digital infrastructure, attract investment, and develop expertise that could support economic growth for decades. The focus should not solely be on having more servers, larger models, or increased computing capacity. Instead, the aim should be to generate more value from every unit of computing power and use it wisely.",
  "summary": "LETTER: The current excitement around artificial intelligence can create the impression that more computing power automatically produces better innovation. That is not necessarily true.",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}