AI, Software, Coding, Internet Security Thread

[Indos]

 

[Indos]

Is MIT Still Worth It in the Age of AI? I Asked Students​

 

[HERO]

Artificial intelligence is only an aid. Mathematics, physics, and chemistry are the underlying foundation. Without strength in these disciplines, nothing else can even be discussed!

 

[Indos]

 

[Indos]

 

[Indos]

 

[Indos]

Asia’s middle powers shouldn’t waste money on an AI race they won’t win​

William B Smith The Australian National University
Published: 29 September 2026

In Brief​

Asia’s middle powers are investing in domestic foundation models to reduce dependence on US and Chinese providers. But staying near the frontier means financing successive generations of increasingly expensive models, alongside talent, experimentation and failed training runs — and the resulting models depreciate fast. Governments may gain more durable leverage by investing in established strengths such as semiconductors and robotics, which competitors find far harder to replicate.

As US–China competition over artificial intelligence (AI) heats up, Asia’s middle powers are seeking a place in the emerging AI value chain. Several governments have responded by backing domestic foundation models — machine learning models trained on vast datasets — to reduce their dependence on foreign providers. But this approach risks directing scarce resources towards one of the hardest parts of the AI economy in which to catch up.


Foundation models sit near the headwaters of the AI economy. Expectations about their future capabilities are driving the buildout of compute infrastructure worldwide.


Cutting-edge development of these models remains concentrated on either side of the Pacific, with US and Chinese models exchanging the lead on various benchmarks since early 2025. Composite benchmark results suggest that the strongest foundation models developed elsewhere reliably lag behind the frontier. Even Naver’s CLOVA X, one of South Korea’s leading challengers, was named as the most-used generative AI service by only 2 per cent of South Korean users in a 2025 government survey.


The concentration of frontier development has prompted concerns about foreign dependence in several of Asia’s advanced and emerging economies. Such anxieties have been intensified by unpredictable US export decisions under President Donald Trump, whose administration imposed export controls on Anthropic’s leading models in June 2026. Relying on China’s open-weight models offers no guarantee of durable access either, with Beijing reportedly considering similar export controls to combat foreign misuse of its systems.


In August 2025, South Korea selected five companies to compete in developing a homegrown model, supplying 530 billion won (US$374.6 million) to build models rivalling those of the leading US and Chinese firms. India has committed Rs 10,372 crore (US$1.1 billion) to a similar project through its IndiaAI mission, much of it directed towards developing indigenous models trained on Indian languages and datasets. Japan’s GENIAC program likewise supplies compute resources and support to domestic developers.


This approach draws on Asia’s tradition of industrial policy, which is widely credited with driving much of the region’s catch-up growth since the 1970s.


But foundation models are a particularly difficult target for industrial catch-up. The capital required to remain near the frontier is high and rising rapidly. Training Grok 4, a leading model, cost an estimated US$490 million. Epoch AI estimates that training costs for frontier language models have risen roughly 3.5 times annually since 2020.


The next generation of foundation models will cost several times more to train — quickly exceeding the modest subsidies of Asia’s catch-up players. Yet even these estimates understate the cost of reaching the frontier. Firms must also compete for talent, fund extensive experimentation and absorb the costs of failed training runs.


Frontier firms’ advantages compound over time. They can convert superior capabilities into large global customer bases, generating revenue to reinvest in more capable models. Token- and subscription-based business models also make consumers highly responsive to even small differences in model capabilities. So lagging firms have significantly less revenue to invest in improving their models.


This is magnified by the unusually research-intensive nature of foundation-model development. Anthropic spent roughly 1.5 times its revenue in 2025 on research and development, while many of its Chinese rivals spent several times their revenue. For Asia’s middle powers, staying within reach of the frontier would require financing successive generations of increasingly expensive models at a scale far beyond current programs.


The resulting asset also depreciates quickly. In Asia’s early catch-up years, an automobile factory could remain productive for decades and hold market share even when foreign competitors released a better product because product-development cycles were slower and switching was costly. Foundation models are different. A slightly inferior model has weak differentiation unless it offers lower inference costs, better local-language performance, enhanced privacy or superior performance in specialised domains. None of these require governments to train a frontier model from scratch.


Singapore is pursuing this narrower objective. Its SEA-LION v4.5 model, designed for Southeast Asian languages and cultural contexts, was developed by fine-tuning existing open-source models from Alibaba and Google DeepMind. With a budget of only S$70 million (US$55.1 million), the program captures many of the localisation benefits at a far lower cost.


But what fine-tuning can achieve is constrained by the existing model’s capabilities. The most advanced of these, in areas of strategic significance such as cyber operations and scientific research, will remain closely tied to access to increasingly capable foundation models.


Countries seeking insurance against restrictions on frontier systems may be better placed to direct scarce public funds towards parts of the AI value chain where they possess durable comparative advantages. These include South Korea’s memory-chip economy, Taiwan’s semiconductor industry and Japan’s strength in robotics.


These positions are already protected by a deep moat — large upfront investment, significant technical expertise and supplier ecosystems that have taken decades to develop — making them costly to replicate at scale. Public investment can deepen this moat, raising the costs for competitors seeking to displace them while giving middle powers greater leverage to secure reliable access to top foundation models.


Asia’s middle powers are right to worry about dependence on foreign AI systems. But attempting to recreate foundation models at home is a costly endeavour that will likely fail to secure access to the most strategically significant foundation models. For most middle powers, doubling down on existing advantages offers a more reliable route to security.


William B Smith is Project Assistant at the East Asian Bureau of Economic Research, Crawford School of Public Policy, The Australian National University.

https://eastasiaforum.org/2026/09/29/asias-middle-powers-shouldnt-waste-money-on-an-ai-race-they-wont-win/

 

[Indos]

 

[baqai]

with all due respect, vibe coding is GREAT but one needs to have SOME sort of background to actually ask it to do what they want, just generic prompt is not going to give you a production ready secure MFA app, my 2 cents

 

[Zhuge Liang]

with all due respect, vibe coding is GREAT but one needs to have SOME sort of background to actually ask it to do what they want, just generic prompt is not going to give you a production ready secure MFA app, my 2 cents

For now. Remember that AI went from barely coding anything to what it does today in how long? 4 years?

The pace of change is insane. ChatGPT was released 4 years ago, less time than passed between Apple II and Commodore 64.

 

[Indos]

Former Employee Deletes Servers Worth Rp11 Billion After Being Fired​

CNBC Indonesia Editorial Team
10 October 2026



Jakarta, CNBC Indonesia –

A former employee from India reportedly sabotaged his former employer’s computer systems after being dismissed from his job. The man, identified as Kandula Nagaraju, 39, accessed the company’s internal systems and deleted hundreds of virtual servers, causing substantial financial losses.

The incident occurred after Kandula had left NCS, a Singapore-based technology company. Using his previous access credentials, he entered the company’s testing system and executed commands that deleted a total of 180 virtual servers.

The company suffered estimated losses of approximately S$918,000 (Rp11.1 billion). The case resulted in criminal proceedings, with Kandula sentenced to two years and eight months in prison.

During the trial, it emerged that Kandula had been unhappy about the termination of his employment contract. He expressed disappointment because he believed he had performed well during his time at the company.

According to CNA, Kandula’s contract with NCS was terminated in October 2022 due to poor performance. His last working day was November 16, 2022.

Court documents revealed that Kandula felt confused and upset when he was dismissed, as he believed he had performed well and made valuable contributions to NCS during his employment. After leaving NCS, he was unable to find another job in Singapore and had to return to India.

Chronology of Events

Between November 2021 and October 2022, Kandula was part of a 20-member team responsible for managing NCS’s quality assurance (QA) computer system. NCS provides information, communications, and technology services.

The system managed by Kandula’s former team was used to test new software and programs before their release.

In a statement to CNA on Wednesday, NCS described the infrastructure as a standalone testing system comprising approximately 180 virtual servers. The company said no sensitive information was stored on the system.

After his contract was terminated and he returned to India, Kandula used his laptop to gain unauthorized access to the system using administrator login credentials. He accessed the system six times between January 6 and January 17, 2023.

In February 2023, Kandula returned to Singapore after securing a new job. He rented a room from a former NCS colleague and used the colleague’s Wi-Fi network to access NCS’s system once on February 23, 2023.

During these unauthorized access attempts in January and February, Kandula wrote several computer scripts to test whether they could be used to delete servers from the system.

In March 2023, he accessed NCS’s QA system 13 times. On March 18 and 19, he executed a script programmed to delete all 180 virtual servers. The script was designed to delete the servers one by one.

The following day, the NCS team discovered that the system was inaccessible. They attempted to troubleshoot the problem but were unsuccessful. Further investigation revealed that the servers had been deleted.

On April 11, 2023, a police report was filed, and several IP addresses identified during the company’s internal investigation were handed over to the authorities.

Police seized Kandula’s laptop and discovered the script used to delete the servers.

The investigation also revealed that Kandula had searched Google for scripts capable of deleting virtual servers and used the information to write his own script.

As a result of his actions, NCS suffered financial losses totaling S$917,832.

(fsd/fsd)

Tak Terima Dipecat, Eks Pegawai Hapus Server Senilai Rp11 Miliar

Mantan karyawan NCS, Kandula Nagaraju, dihukum penjara setelah merusak 180 server virtual perusahaan. Kerugian ditaksir mencapai S$918.000.

www.cnbcindonesia.com