Susmitha Akula Vakkalanka

When demand goes “off the charts,” it’s usually a sign that an entire ecosystem is shifting - not just one product cycle. NVIDIA just posted another massive quarter: $57.0B in revenue (+62% YoY, +22% QoQ). Data centers alone reached $51.2B, up 66% year-over-year. And Jensen Huang summed it up simply: 𝘉𝘭𝘢𝘤𝘬𝘸𝘦𝘭𝘭 𝘴𝘢𝘭𝘦𝘴 𝘢𝘳𝘦 𝘰𝘧𝘧 𝘵𝘩𝘦 𝘤𝘩𝘢𝘳𝘵𝘴, 𝘤𝘭𝘰𝘶𝘥 𝘎𝘗𝘜𝘴 𝘢𝘳𝘦 𝘴𝘰𝘭𝘥 𝘰𝘶𝘵. This is more than strong earnings - it’s a signal of where the world is heading. What this actually tells us  • Blackwell isn’t just performing well; the demand is extreme. Enterprises are buying compute at a pace we haven’t seen before - not just for training, but for large-scale inference.  • Companies are clearly preparing for a world where AI infrastructure is as fundamental as cloud storage or electricity.  • For anyone building in Web3, fintech, or crypto infra: access to compute is becoming a strategic variable, not just a tech detail. Why it matters  • The question is no longer “Will companies adopt AI at scale?” It’s “How fast will they move - and who will power that shift?”  • Products that can integrate, automate or rely on high-performance compute gain a real competitive edge.  • If you’re planning your 2026 roadmap, you need a scenario where advanced AI compute is part of your architecture - directly or through partners. My take This feels like the start of a new era. Not the “AI hype” era - the AI infrastructure era. Blackwell isn’t just a chip; it’s a marker that compute has become a foundational resource. And once that happens, innovation tends to accelerate, not slow down. At Simple, we look at this through one lens: better compute → better models → more automation → better user experience. When the infrastructure layer grows, the products on top move faster. Curious how you’re seeing this shift. Does the acceleration in AI compute feel like a barrier or an opportunity from your side? Would love to hear your thoughts.

35

8 Comments

🚀 Massive AI Compute Power Coming to India - Game Changer for Innovation Abu Dhabi-based tech leader G42 has partnered with U.S. chipmaker Cerebras Systems to deploy an 8 exaflops AI supercomputer system in India - one of the largest commitments to sovereign AI infrastructure seen in Asia. Announced at the India AI Impact Summit 2026 in New Delhi, this project marks a strategic leap in India’s computational capacity. 🌐 What It Means 🔹🧠 8 Exaflops of AI Compute: ~19× higher than India’s entire current national AI compute capacity, unlocking unprecedented scale for training large AI models and complex workloads. 🔹🏛️ Sovereign Infrastructure: Built within India under local governance, complying with data residency and security protocols - a major step in national AI autonomy. 🔹🤝 Global Collaboration: Delivered with G42, Cerebras, Mohamed bin Zayed University of AI, and India’s C-DAC, this ties international tech leadership with India’s AI ecosystem. 🔥 Key Insights for Leaders & Innovators 🔹 AI at Scale Becomes Real - With exaflop-level compute, India will be able to support cutting-edge AI R&D, advanced model training, and production-grade inference locally. 🔹 Accelerates Research & Development - This infrastructure can boost innovation in sectors like healthcare, climate modeling, genomics, robotics, and language tech by removing compute bottlenecks. 🔹 Democratizing Access - Designed to serve research institutions, startups, enterprises, and government agencies, it lowers barriers for India’s broader tech ecosystem. 💡 Why This Matters AI’s future isn’t just about smarter models - it’s about having the compute horsepower to train, test, and scale them. This move positions India as a critical global hub for high-performance AI while strengthening data sovereignty and innovation leadership. 🇮🇳 👉 Read more about it in the article: https://lnkd.in/dM8g2CcM #AI #ArtificialIntelligence #Supercomputing #IndiaTech #Innovation #Infrastructure #FutureOfWork #DigitalTransformation

1

Indian Deep-Tech Startups Just Got a Major Boost. Big moment for India’s startup ecosystem: a new coalition of U.S. and Indian VCs has committed over $1 billion to back deep-tech ventures in India — covering areas like semiconductors, satellites, electrification and more. What this means: • The focus is shifting from “idea only” startups to infrastructure-scale innovation. • This is a sign that India is gearing up to build from the ground up — and not just imitate. • For founders: now is a chance to think big tech, deep value, global scale. https://lnkd.in/gEpxwQ3D https://lnkd.in/gfHddnab https://lnkd.in/gb3cUSza #Startups #DeepTech #IndiaInnovation #BizdomEdge #Entrepreneurship

5

The OpenAI Usage Report is a wake-up call for small businesses and GTM leaders. Over 1.1 million ChatGPT conversations were analysed. The results are eye-opening and they highlight where most businesses are falling short (and where the real gains are hiding). 💡 Here's the short version: Personal AI use is flying… but business adoption is crawling. Content creation is the #1 use case and most teams are only scratching the surface. Research, training, and decision support are being done with AI… just not in your company (yet). The ROI is there - massive, proven, and repeatable. But very few small businesses are set up to capture it properly. If you're leading a team, planning go-to-market, or running a small business, this is essential reading. 🚀 In the carousel, I break down: What the data actually says How to translate it into action Where small businesses can win now And why execution is the difference between dabbling and scaling 👉 Swipe through the key takeaways and use it as a checklist. If you want help building a roadmap around these workflows, please do reach out!

129

57 Comments

What if the biggest challenge facing verification teams isn't a lack of tools, compute, or AI? For decades, our industry has invested in more powerful verification engines, greater automation, richer methodologies, and faster compute resources. Yet as silicon becomes the foundation of AI infrastructure, autonomous systems, software-defined products, and multi-die architectures, many organizations find themselves confronting a difficult reality: complexity continues to rise, schedules remain under pressure, and confidence is becoming harder to achieve. Harry Foster's latest Verification Horizons blog argues that the verification problem itself is changing. The question is no longer simply whether we can verify larger designs. We need to understand whether the approaches that helped us scale verification in the past are sufficient for increasingly interconnected systems. This challenge is especially relevant for verification managers, directors, and engineering leaders who must decide how their teams will adapt to AI-driven workflows, growing system interaction complexity, and increasing pressure for first-pass silicon success. The decisions made today will shape how effectively organizations achieve silicon confidence tomorrow. At DVCon India 2026, Abhi Kolpekwar, Senior Vice President and General Manager of Digital Verification Technologies at Siemens EDA, will share his Vision Talk,"The Future of Verification: One Path to Silicon Confidence." Drawing on new industry evidence, evolving verification challenges, and the emerging role of AI, Abhi will present a forward-looking perspective on where verification is headed and what engineering organizations should be doing now to prepare. As Harry notes, this is more than a discussion about what is changing. It is a conversation about how verification must change with it. If you are attending DVCon India 2026, Harry suggests you make this session a priority. Read Harry's blog: https://sie.ag/5r57Mc #DVConIndia #DesignVerification #SiliconConfidence

43

4 Comments

Arun Mathew, ACCEL: “If you look at the history of technology and computing, the application layer is actually where most of the value has been created. If you look at the last three or four years, so much of the investment has gone into the foundational models and the infrastructure layer to now power the application layer in Al, and we're just on the cusp of that. So I think you are gonna see a lot of funding, but also really incredible revenue growth and opportunity at the application layer.” https://lnkd.in/gCSg4RpG

1

"By combining 5G infrastructure and AI acceleration in a single, programmable chip, [EdgeQ] is not only challenging global incumbents but also establishing India as a hub for advanced wireless and AI semiconductor design." - Yashasvini Razdan See the full article coverage of EdgeQ Inc.: "How EdgeQ Is Building India’s First Unified 5G + AI SoC" https://lnkd.in/gSNWNqgP

128

1 Comment

"Evidence from the edge" suggests to me that 2026 will start to show AI delivering efficiency & efficacy in Enterprise. If 2025 was the year of runaway usage of generic chatbots, premature scaling of agentic experiments and a general readjustment of expectations with regards to the capabilities of AI, I believe 2026 will be different. Entreprises are learning the lessons of the last two years. Chatbots are not interfaces, AI easily generates chaos and slop, and most products are not enterprise-ready. However I am seeing this change: - humbler attacks on "process debt" and low-hanging fruits such as generic admin processes, repetitive tasks, data cleansing and normalisation, status updates etc. - realistic augmentation of specific end-to-end business processes with human-in-the-loop implementations and move to pay-per-action aligned with the business P&L (cfr DeepOpinion). - systematic mapping of human work and workflows into discrete tasks and micro-processes to prepare for effective and gradual AI adoption across the enterprise (cfr TechWolf). - stunning new intelligence tools providing actionable, in-context analytics and insights (cfr Applied Computing in Energy and many others) with low integration overhead and extremely flexible front-ends, with products effectively customised to each end user. I understand the impulse of treating AI as a hype bubble (and certainly financial markets look out of whack) and the natural reaction to the extremely frenzied messaging we've been subjected to over the last two years by the leading vendors. However looking at startup activity and traction across not just our portfolio but tens of new projects, I believe the leading indicators tell us that AI will move into providing productivity improvements almost as fast as it got hyped. As William Gibson said, the future is here but not evenly distributed. Data from the field suggests to me that the AI storm is real.

55

15 Comments

OpenAI is co-designing chips with Broadcom, aiming to mass-produce its first proprietary AI chip by 2026. At first glance, it sounds tactical - leverage against Nvidia in GPU negotiations. But it’s quite existential - vertical integration with a side of paranoia. Because whoever controls the silicon controls the future of AI. Google figured this out a decade ago. In 2015, they built the Tensor Processing Unit, or TPU, chips purpose-built for AI. Not graphics, not gaming. Just raw, industrial-scale matrix math. Over the years, TPU clusters have become Google’s quiet superpower: invisible, efficient, and deeply integrated with their software stack. As a result, Gemini runs on a proprietary engine no one else can touch. Benchmarks suggest TPUs deliver up to 3x better performance per watt vs. Nvidia GPUs. At hyperscale, this means billions saved and megatons less carbon. The signal landed. Amazon built Trainium and Inferentia. Microsoft rolled out Maia. Meta got Artemis. And now OpenAI - the company consuming more compute than God - wants to own its silicon future. Why custom chips make sense: ▪️ Economics: Each model generation costs more to train. GPUs are blunt instruments; custom silicon is a scalpel. Efficiency gains at scale don’t just improve margins - they decide whether you can afford GPT-6 at all. ▪️Control: Relying on Nvidia is like running your country on imported oil. It works … until it doesn’t. ▪️Integration: When the chip and the software stack are designed together, the system hums like a Porsche engine. Proprietary stacks create lock-in gravity fields. ▪️Geopolitics: Supply chains fray, export controls bite, and chips are the new oil fields. No hyperscaler wants its future hostage to Jensen Huang’s waitlist. This is why every hyperscaler is in the chip game. And why OpenAI has no choice but to join. Google’s TPU bet shows where this path leads. For years, TPUs have been velvet-roped behind Google Cloud. Want access? Bring your data, your workloads, and your spend - and surrender to the ecosystem. The chip wasn’t just infrastructure; it was the carrot pulling customers into GCP. Now comes the phase shift. Earlier this week, The Information reported Google has begun seeding TPUs into other cloud providers’ data centers. They’ve already struck a deal with London-based Fluidstack to host TPUs in New York, and are talking to others. The reason is simple: Google can’t build data centers fast enough to house its own silicon, and distribution has become the release valve. This puts Google in even more direct competition with Nvidia. NVIDIA remains king - but surrounded by revolutionaries hammering together guillotines in their garages. The market gets the hint: Broadcom stock spiked, Nvidia slipped, and the age of custom AI silicon just accelerated.

492

33 Comments

When Satya Nadella became CEO of Microsoft, he didn’t begin with strategy decks or restructuring memos. He started by changing how leaders listened and learned. He asked Microsoft leaders to move from being “know-it-alls” to “learn-it-alls.” That single shift — from telling to coaching — transformed Microsoft’s culture and performance. In this week’s Leading Matters article, I explore the five essential coaching skills every leader needs to make the same transformation in their teams: ·      Listening deeply ·      Asking powerful, open-ended questions ·      Giving feedback that builds, not breaks ·      Holding space for others’ thinking ·      Balancing empathy with accountability These aren’t soft skills — they’re performance multipliers. Read the full piece here: https://lnkd.in/eNKJHHjV #Leadership #Coaching #LeadingMatters #Empathy #Accountability #Listening #GrowthMindset #LeadershipDevelopment

3

India a trusted ally in chip & electronics chain Semicon 2.0 will have a huge focus on design. We will be focusing on at least 50 deep techs to come from India, whether they are large companies, startups (or) people who are working in the semiconductor industry globally but want to come back to India. I can say with confidence today that the next Nvidia, the next Qualcomm will come out of India," Ashwini Vaishnaw said https://lnkd.in/g27uUwER

9