Latest news with #CliveHumby
Hindustan Times
2 days ago
- Business
- Hindustan Times
Data sovereignty is a strategic imperative
In 2006, Clive Humby, a mathematician and data scientist, delivered a talk titled 'Data is the new oil'. He emphasised that similar to oil, data is valuable, but only when refined and processed. When first coined, the metaphor highlighted the economic potential of data as a resource. While this remains relevant, the value of data has today expanded to become a strategic asset of national power and influence. Control over the collection, storage, processing, and flow of data is now fundamental to a nation's strategic sovereignty. India generates about 20% of global data but holds only a 3% share of global data centre capacity. It therefore remains deeply reliant on foreign-controlled digital infrastructure. (Shutterstock) Algorithms, fuelled by vast datasets, shape nearly every aspect of our daily lives. They influence what we buy, how we commute, what food we order, what information we see on social media, and how we access services. This promotes convenience and efficiency, but there is also a darker side. The algorithms operate with little transparency and can become instruments of strategic surveillance and behavioural modification. For example, the algorithmic curation of information on social media platforms can influence public opinion and political outcomes. Cambridge Analytica claimed to have influenced the 2016 US election by harvesting data from millions of Facebook users to build detailed psychographic profiles. Using this information, they deployed microtargeted political ads tailored to voters' personalities, fears, and beliefs, often exploiting polarising issues like immigration, Islamophobia, and racial biases. Artificial Intelligence (AI) has significantly amplified the risks associated with data. Unlike earlier uses of data that supported routine processes, AI uses data to forecast outcomes and influence real-time decision-making. This gives unprecedented power to those who control the data and the models trained on it. AI systems could also quickly spread bias and disinformation in ways that are difficult to detect or regulate. There is also the risk of data colonisation. This term refers to scenarios where countries provide citizen behaviour, biometric records, health data and cultural patterns to foreign companies. These companies train AI systems on this data but do not share the value that is generated, mirroring colonial-era raw material extraction. Apart from creating technological dependency, there is also a fear that national decision-making can be subtly shaped by algorithms optimised for foreign interests. Sovereign control over data has thus become crucial for national security. Countries like the US and China have embedded data sovereignty into their strategic doctrines. The US asserts data sovereignty through a mix of legislation and technological dominance. Laws such as the CLOUD Act and FISA 702 allow US authorities to access data held by American companies, even when stored overseas. Through dominance in cloud services (Amazon, Microsoft, Google), foundational AI models, and data infrastructure, the US retains strategic control over much of the world's data flows. Export controls on semiconductors and AI chips further reflect America's intent to maintain its global tech leadership. China pursues a more State-centric and restrictive model of digital sovereignty. Through laws like the cybersecurity law (2017), data security law (2021), and personal information protection law (2021), China mandates that critical data generated within its borders be stored locally and imposes strict controls on data exports. It has also cultivated national technology champions such as Baidu, Alibaba, Tencent, and Xiaomi, while maintaining tight state oversight of digital platforms. China's Great Firewall embodies the country's concept of cyber sovereignty, which holds that each nation has the absolute right to regulate and control its own digital space. India generates about 20% of global data but holds only a 3% share of global data centre capacity. It therefore remains deeply reliant on foreign-controlled digital infrastructure. From Aadhaar and CoWIN to DigiYatra and UPI, India has made impressive strides in building public digital platforms. Yet, most Indian entities and startups still store and process data through foreign cloud providers. Even if servers are located within India, jurisdictional control often lies with the host nations of these corporations. This legal loophole has significant national security implications. The dangers are not limited to infrastructure disruption alone. Foreign access to sensitive data can enable sophisticated forms of cognitive warfare, where targeted misinformation, psychological operations, and algorithmic manipulation could be used to fracture social cohesion, manipulate public opinion, and erode trust in institutions. In a country as socially diverse as India, influence campaigns can be deeply destabilising. The Digital Personal Data Protection (DPDP) Act was enacted in 2023, but its substantive provisions have not yet come into force. The act allows transfer of personal data to any country or territory outside India, except where the government restricts such transfers explicitly by official notification. While sectors like banking, telecom, and insurance already mandate data localisation, others, such as healthcare, education, and e-commerce, lag in compliance and clarity. India must, therefore, move beyond viewing data as a mere economic asset and treat it as a pillar of national resilience and strategic autonomy. Policies must ensure not just localisation, but jurisdictional clarity and public trust. Cloud systems hosting critical infrastructure must be subject to Indian law, not foreign statutes. AI models trained on Indian data must be accessible to Indian institutions. Without a robust domestic AI infrastructure, India risks becoming a digital colony, exporting raw data while increasing its dependency on foreign solutions for cutting-edge solutions. Finally, cognitive warfare must enter the mainstream of national security thinking. The manipulation of narratives, emotions, and opinions through algorithmically targeted content is no longer in the realm of a dystopian future, but rather a present menace. India must craft an information warfare doctrine to deal with this very real threat. In today's world, data is the new battlefield, and its control will shape national futures. For India, asserting data sovereignty is not just a technological challenge but a strategic imperative. Lieutenant General (retired) Deependra Singh Hooda is the co-founder of the Council for Strategic and Defence Research and a senior fellow at the Delhi Policy Group. The views expressed are personal
Forbes
04-08-2025
- Business
- Forbes
Micron Emerges As A Critical Supplier For Advanced AI
When it comes to AI, all eyes are on the GPUs, as evidenced by Nvidia's mind-blowing $4 trillion market value. However, all of the AI training and inference processing requires more than just the GPU. It requires networking between the GPUs, infrastructure to power and cool the GPUs, and most of all, it requires storage and memory to manage all the raw data and models. As mathematician Clive Humby indicated, 'data is the new oil.' It drives the world economy and is the heart of AI. Without the data, there is no AI. As a result, the storage and memory subsystem, sometimes referred to as the AI data layer or the AI data pipeline, is one of the most critical elements of the system. Another American company, Micron, is rapidly emerging as a key supplier for this crucial AI data layer. Disclosure: My company, Tirias Research, has consulted for AMD, Nvidia, Micron and other companies mentioned in this article. The AI data layer The memory and storage hierarchy of a server is very complex and has evolved over many decades. The constant need to increase memory performance and density to keep pace with the performance of the processing elements and the demands of increasingly complex workloads, such as AI, has driven innovation at every layer of the hierarchy. Innovations ranging from on-chip SRAM to closely coupled high-bandwidth memory (HBM) to system main memory to pooled memory resources to SSD storage. For AI workloads, memory and storage have become critical, non-commodity elements in processing AI workloads. There are only three major vendors that supply both major components: Micron, Samsung, and SK Hynix, with only one US company among them Micron. Micron's data center acceleration While also enjoying a substantial presence in consumer and embedded/IoT applications, Micron's success in the data center is closely tied to the growth of AI, particularly in high-performance HBM memory and SSD storage. Micron was initially focused on an alternative memory called Hybrid Memory Cube (HMC) and pivoted to HBM around seven years ago. The company's initial challenges with HBM2 and HBM2E left it trailing its competitors. However, Micron capitalized on the breakout growth of Nvidia's Hopper generation of AI GPU accelerators with the HBM3 and HBM3E generations to take over the number two spot in less than a year and appears to be ahead of the curve for the next generation of AI GPU accelerators from both AMD and Nvidia. Micron's HBM3E is designed into the AMD newest Instinct MI350 platform, and the company reportedly is shipping HBM4 to key customers for future AI platforms. Leveraging the company's proprietary 1-beta process node for the HBM3E and HBM4 generations, combined with advanced interposer and die stacking, Micron's HBM products offer the industry's highest bandwidth at up to 30% better performance efficiency than products from Samsung and SK Hynix. Similar to how Micron collaborates with other key customers in mobile and computing, the company has worked closely with AI accelerator customers, including AMD and Nvidia, to ensure optimal performance, quality, and manufacturability, thereby earning a spot as one of the leading memory suppliers for the next generation of high-performance AI platforms. In addition to investing in new memory and storage architectures, Micron has a $200 billion manufacturing expansion plan that includes the expansion of facilities in Idaho, Virginia, and Japan, as well as a new complex of fabs in New York. The expansion will not only meet the needs of its customers in AI but also support the push for onshore manufacturing in the US. Final thoughts While memory and storage are often classified as two different segments, they form a single subsystem or data layer. This data layer is essential for meeting the performance and scalability requirements of AI workloads. The AI demands are so high that the data layer must be designed in conjunction with the processing layer to ensure optimal performance. So, when it comes to data center AI, memory and storage are not commodities that can be easily substituted by a lower-cost alternative. The data layer is a unique piece of the entire AI platform and AI data center. Micron provides a complete AI data layer solution through the combination of its high-performance HBM, DRAM, and SSDs. Additionally, Micron has demonstrated that it possesses the technology and resources to become a leader in this segment in a very short period, making it valuable to the entire electronics ecosystem and to the onshoring aspirations of the US government.
