Yesterday, I discussed the three pillars of AI sovereignty. Today, I’m diving into the first pillar—hardware fabrication—and examining where India stands in the global AI chip and GPU landscape.
India’s semiconductor production today is anchored in legacy process nodes—from 65nm down to 28nm—with only incremental progress toward 14nm. For example, the Tata-Powerchip Semiconductor Manufacturing Corporation (PSMC) joint venture in Dholera, Gujarat, is set to produce 28nm chips by 2026. These chips, aimed at automotive, IoT, and power management applications, are produced at giant scale—48 million per day. The wafer factory being set up in parallel, however, has a more modest scale, targeting just 50,000 wafer starts per month (WSPM). When compared to global benchmarks (like TSMC), this capacity is minuscule.
Critically, India’s fabs rely on deep ultraviolet (DUV) lithography systems from ASML and Nikon. Without access to extreme ultraviolet (EUV) lithography—the technology essential for fabricating chips below 10nm—India remains confined to older, less efficient processes. This technological limitation directly affects the power efficiency and performance of chips, posing a significant barrier for advanced GPUs and AI workloads.
With about 20% of the world’s IC design talent, India has launched several initiatives that highlight its design prowess. Consider the Shakti processor—a RISC-V initiative from IIT-Madras that spans a broad range from 180nm (for space applications) to 22nm FinFET designs for more advanced needs. Similarly, InCore Semiconductor is developing high-performance RISC-V cores tailored for AI/ML tasks, while Mindgrove Technologies is focused on secure IoT SoCs. Supported by government schemes like the Design-Linked Incentive (DLI) Program, these projects underscore India’s burgeoning design and IP capabilities.
However, the critical challenge remains: how do we translate this exceptional design talent into high-end manufacturing? Without bridging this gap, innovative ideas risk remaining trapped in simulation labs rather than powering real-world AI applications.
Despite its robust design ecosystem, India’s overall semiconductor output accounts for less than 2% of global electronics production. The planned capacity of the Tata-PSMC fab—targeting 50,000 WSPM—is a mere fraction of what global leaders achieve. For instance, TSMC’s state-of-the-art fabs can reach capacities of up to 1.5 million WSPM for 3nm nodes.
Furthermore, India’s manufacturing ecosystem is burdened by:
• Material Dependencies: Essential inputs such as high-purity argon, photoresists, and silicon wafers are largely imported, leaving the supply chain vulnerable.
• Infrastructure Gaps: Semiconductor fabs require uninterrupted power, ultra-pure water, and sophisticated logistics systems—resources that are inconsistent across many Indian regions.
• TSMC: As the global leader in semiconductor fabrication, TSMC manufactures chips at the cutting-edge 3nm node and is already planning for 2nm by 2025. Their production leverages roughly 20 EUV layers, delivering up to 18% higher performance and 32% lower power consumption compared to 5nm processes.
• ASML: The sole supplier of EUV lithography, ASML’s TWINSCAN NXE:3600D systems—costing around US$200 million each—are indispensable for producing chips below 7nm. Without these systems, advanced node fabrication would simply be unattainable.
• NVIDIA: A fabless design titan, NVIDIA’s GPUs—such as the Hopper (fabricated at 5nm) and the upcoming Blackwell (projected at 4nm)—deliver industry-leading efficiency, with performance metrics around 4.8 TFLOPS per watt for AI workloads. This success stems from a tightly integrated ecosystem that marries cutting-edge design with advanced manufacturing processes provided by partners like TSMC.
• Huawei: Despite facing geopolitical constraints, Huawei’s HiSilicon division once produced competitive chips like the Kirin 9000S on a 7nm node using multipatterning techniques via SMIC. However, these methods involve multiple patterning steps, which increase defect density and production costs—clearly demonstrating the advantages of EUV-enabled processes.
India’s exceptional design talent and innovative projects are undeniable. Yet, its manufacturing ecosystem remains a critical bottleneck. Without access to advanced process nodes and EUV lithography, Indian fabs are locked into legacy technologies, making it extremely challenging to produce the state-of-the-art AI chips and GPUs that will drive the next wave of technological innovation.
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If you have any questions or thoughts, don't hesitate to reach out. You can find me as @viksit on Twitter.
AI is becoming a cornerstone of national power. Nations are locked in an escalating AI arms race, as witnessed even this week at the Paris AI summit. But what does it take for a country to secure its future in AI? Is it solely about foundational models, or is that merely scratching the surface? Here are three essential pillars for anyone shaping AI policy.
Owning the chips that drive AI is a critical capability that every country must develop indigenously. Today, most nations depend on Taiwan’s TSMC for their chips, enabled by the Dutch company ASML. The secret weapon is a $400 million Extreme UltraViolet Lithography machine that enables chip fabrication at a 2nm process. In simple terms, this means chip components are packed so tightly that billions more transistors can fit on a single die, vastly increasing compute power. China’s Huawei, for example, has launched the Mateo60 — a 7nm chip, a couple of generations behind TSMC — while India currently produces chips at 28nm, six generations behind.
Equally important is a robust compute infrastructure. With cloud services dominated by Microsoft, Google, and AWS, countries must hedge against this by establishing national data centers (as seen in India’s AI mission), funding local companies, securing tech transfer deals, or importing GPUs, as Jio is doing in India. Open, decentralized infrastructure is also crucial to prevent monopolies from simply relocating to non-US territories.
AI is built by people — through collecting, cleaning, and labeling data; developing hardware and software; and designing user interfaces. A country’s competitive edge lies in fostering a vibrant ecosystem of researchers, engineers, and visionaries. This requires investing not only in short- and medium-term initiatives like hackathons and competitions but also in long-term funding for research institutions and dynamic public-private collaborations.
Nurturing an AI-native generation will be vital for driving sustainable, homegrown progress.
Data is the raw material of AI, but its true value emerges only when it is controlled locally. Establishing governance and technological frameworks that ethically harness culturally relevant data is key to empowering a nation to tailor AI systems for its unique needs. This autonomy not only shields against external influences that have long plagued AI development but also sparks breakthrough innovations aligned with national priorities.
Lastly, there is electricity. While training AI models is crucial, it is equally important to ensure that there is enough power to run these models at scale during inference. Investing in clean energy and achieving energy independence — rather than relying on foreign sources — will be essential.
The era of endless debate is over — it’s time for decisive action and building a future that relies less on politics, and more about enabling the next generation of builders to do their thing.
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If you have any questions or thoughts, don't hesitate to reach out. You can find me as @viksit on Twitter.
JD Vance’s recent Paris address was less about championing democratic AI, and more a mirror of U.S. companies’ relentless push for fewer regulations. His pledge to “restrict access to all components of the AI stack” is a calculated move to control innovation, lifted straight from Peter Thiel’s Palantir playbook.
The U.S. isn’t just locked in an AI arms race with China. It’s also cornering the Global South. By hoarding chips, code, and energy, American AI companies are turning nations into data colonies, forcing them to trade sovereignty for access to world-changing technology. While China’s Belt and Road Initiative is often decried for binding countries to authoritarian regimes, the U.S. method is just as insidious. Ironically, Indian PM Modi’s call for open-source systems isn’t naive idealism — it’s a rallying cry for nations to break free from a system designed to keep them perpetually indebted to American tech dominance.
Even as Vance touts unbiased AI, his speech, and the agenda of American AI companies tells a different story. Pushing for less regulation isn’t about fostering innovation, it’s about clearing the way for “American” innovation. U.S. AI, trained predominantly on western data, distorts history into a narrow narrative that silences dissent and erases diverse voices. Meanwhile, as the EU tightens data privacy laws, U.S. companies guzzle cheap, dirty energy to power data centers — blatantly sidelining global climate commitments. Not only has the U.S. withdrawn from the climate treaty, but it’s also refusing to sign the new AI treaty that everyone else has embraced.
Decentralized, open-source, and national AI is more crucial now than ever. Nations must ramp up investments in hardware and chips at every level to break Silicon Valley’s stranglehold.
The battle for technological sovereignty is here, with tensions mounting worldwide. Wars will be fought over Taiwan because of TSMC, and the future of AI will be the ultimate battleground for global dominance.
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If you have any questions or thoughts, don't hesitate to reach out. You can find me as @viksit on Twitter.
Over the years, I’ve had the good fortune to build two startups supported by amazing people. In my evolution as a founder, I realized that building a product isn’t just about the “what”: it’s about the “why.”
The “why” is driven by a story that explains why your product exists and how it solves a customer’s problems — a story that must be both rational and emotional, distilling complex ideas into simple, relatable concepts that drive every decision you make.
To shape this story, you talk to customers and refine your vision until it resonates. If the story doesn’t work, your product won’t either. For Myra — the language model chatbot company I started — the story evolved with customer feedback, shifting from a consumer chatbot like ChatGPT to an enterprise workflow agent driven by real needs.
As Tony Fadell, creator of the iPod and iPhone, says:
“And when I say ‘story,’ I don’t just mean words. Your product’s story is its design, its features, images and videos, quotes from customers, tips from reviewers, conversations with support agents. It’s the sum of what people see and feel about this thing that you’ve created.”
The process of telling a product story over and over and refining it is as much an art as it is a science. A good story is empathetic, blending facts and feelings to connect with people’s worries, fears, and aspirations.
This truth applies to our personal stories, too. Our experiences, ideas, and aspirations define who we are — yet we often assume others already know what we’re about without ever sharing or refining our narratives. In the age of AI, these stories will be even more critical, serving as the bridge between human insight and a fast evolving world.
So, it’s essential that we tell our stories and polish them with feedback from our communities. By writing consistently, we refine the most valuable asset we have — ourselves — and all that defines us: our ideas, our thoughts, our dreams, and our aspirations.
Let your story speak for you.
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If you have any questions or thoughts, don't hesitate to reach out. You can find me as @viksit on Twitter.
In the most literal sense, an “end user” is someone who receives a final product. Someone who has no hand in its design or development. But as large language models (LLMs) continue to evolve, that dynamic is about to vanish. Regardless of technical expertise, we’ll be able to talk our way into building personalized software and orchestrating online services. What does it mean to live in a world where every conversation turns into code?
Imagine describing your daily needs to an AI assistant and watching a custom solution materialize instantly. Gone are the days of app installs — every casual conversation becomes a blueprint for a digital tool. The barrier between developer and user collapses, and each interaction transforms into an act of creation. Your everyday chat isn’t mere small talk; it’s a command that shapes your personal digital environment.
When every conversation is a command, our digital world records our spoken intents. Offhand remarks could trigger transformations we never planned, blurring the line between creative spontaneity and lasting consequence. While this freedom enables rapid innovation, it also risks locking us into unintended constraints. Our ability to customize on the fly may inadvertently embed patterns and biases in real time, making us both the architects and captives of our own digital designs.
Imagine that the tools you help create begin to evolve on their own. Every conversation spawns its own snippet of code, and localized digital ecosystems emerge independently. For instance, a community-generated scheduling tool might, through autonomous refinements, evolve into a robust resource management system tailored to local needs. These micro-systems could mutate, adapt, and even compete — much like organisms in nature. This emergent digital Darwinism raises serious questions about oversight and control as our creations grow beyond our original intent.
The demise of the traditional end user is more than a tech upgrade. It’s a rethinking of human agency. In this new reality, you’re not just using software; you’re sparking a self-evolving digital ecosystem every time you speak.
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If you have any questions or thoughts, don't hesitate to reach out. You can find me as @viksit on Twitter.