Dhiraj Mallick

Quantinuum’s planned IPO is the moment quantum finally hits the public markets in grown‑up form. This isn’t another early‑stage SPAC story; it’s a full‑stack platform spun out of Honeywell, with trapped‑ion hardware that regularly tops industry benchmarks and a real software and applications business on top. By combining industrial‑grade engineering with production‑grade tooling in chemistry, security, and optimization, Quantinuum gives investors exposure not just to “qubits,” but to an integrated quantum stack that already serves enterprise workloads. For technical and business leaders, as well as investors, the key question now is how public markets will price a quantum company that actually looks like an infrastructure and software provider rather than a speculative science project. Whatever the final valuation, this IPO will likely become the benchmark for what “mature” looks like in quantum—deep IP, differentiated hardware, and diversified revenue experiments before ringing the bell. Headline: Honeywell’s Quantinuum Quantum-Computing Unit Plans IPO – https://lnkd.in/euZjkGtG

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The D-Wave / Quantum Circuits deal is another sign that the capital markets in quantum are finally maturing: public companies are no longer relying on single, monolithic architectures or vertically-integrated business models, but instead assembling best‑in‑class capabilities via acquisition to build useful offerings faster than pure R&D could. This mirrors what we’re seeing elsewhere: IonQ buying Oxford Ionics was a bold pivot (and a good one) to captialize and enable the latest in trapped‑ion scalability and semiconductor integration, Google aqui-hiring Atlantic Quantum's superconducting control stack, and now D‑Wave finally getting into gate-based systems and away from its questionable annealing approach. For investors, it is an exciting time and a great opportunity. The old model of “build everything in‑house” is giving way to a new, outward-facing-capital playbook: acquire and integrate specialized teams and IP that accelerates the path to commercial relevance (and revenue). We’re entering an era where the winners in quantum will be those that can seamlessly blend internal execution with external capability. D‑Wave announcement: https://lnkd.in/eQUmPKW3

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Today is a major milestone for Celero as we announce our $140M fundraise, led by CapitalG and Sutter Hill Ventures. From the beginning, we believed coherent DSP would be essential for AI infrastructure to scale across massive accelerator clusters and data centers. This investment is a strong endorsement of that vision and the team building it. Here’s our full announcement: https://lnkd.in/gaXKARyV

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We've been talking about Co-Packaged Optics (CPO) for a long time —now it's here in volume production. The first 51.2T CPO switch, powered by Broadcom’s Tomahawk® 5, is redefining AI-scale networking with 40%+ power savings. The future of efficient, high-speed data centers is happening now! Check out this PR from Micas Networks: https://lnkd.in/gTqcxpU6 #CPO #ConnectedByBroadcom

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Cerebras is now a generational AI company. But between 2018 and 2019, one tiny component nearly killed the company. Back then we were deep into building the first wafer-scale system. Everything depended on a single part most people will never notice. The interposer. It sits between the main board and the dinner-plate-sized silicon wafer. It has to carry enormous power and data loads while having to survive massive thermal cycling. Cold to hot. Off to fully lit. Over and over again. We had just built the largest chip in the world. There was no existing supply chain for it so we had to single source this part. That alone is terrifying. One vendor, no backup. If it failed, the system failed. If the system failed, the company could have failed. For almost three years, this thing was the boogeyman in every board meeting. Would it maintain electrical continuity? Would it warp under load? Would it survive burn-in? Would it work at scale? For long stretches, the honest answer was “we don’t know.” We were burning millions of dollars per month. Time was not our friend. A redesign could have pushed us far enough back that the market might have moved on. At one point, we pulled in a group that I first worked with at IDEO and that used to be called "Failure Analysis". That is not who you call when things are going well! We went back to first principles. Physics. Materials science. Coefficients of thermal expansion. Power dissipation per square millimeter. Analyzing what happens when you push this much power through a system at these temperatures. Again and again. Slowly, painfully, we got there. Finally the interposer worked. Every company pushing the frontier runs into problems like this. There are rarely quick, clever fixes. You just have to do real failure analysis, over and over, until the answer starts to reveal itself. The Cerebras team is itself built from the strongest, toughest material I’ve seen. They earned every inch of the other side of that problem.

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I am excited to share some big news from Cerebras Systems today. Cerebras is announcing a massive expansion of our inference capacity with the addition of 6 new AI inference datacenters across North America and Europe. Thousands of Cerebras CS-3 systems will deliver more than 40 million Llama 70B tokens per second. Many thanks to our strategic partner G42, and our datacenter partners, Enovum Data Centers and Scale Datacenters. https://lnkd.in/geVzEcJP

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We interviewed Andrew Feldman, CEO & Co-founder of Cerebras Systems, on their record-breaking $1.1B raise, the future of AI infrastructure, and why India is becoming central to the global compute map. From wafer-scale chips to sovereign AI strategy, this conversation covers what every enterprise, policymaker, and developer should know. We go live every day on YouTube and LinkedIn with real-time coverage of the latest in AI—interviews, policy, funding, infrastructure, and everything that matters. Follow AIM Network for daily broadcasts and exclusive access to the voices shaping the future of AI. https://lnkd.in/gm_UA7ye

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🚀 Exciting News: I’m thrilled to announce that Deeplite has been acquired by STMicroelectronics! 🚀 Back in 2017, Davis Sawyer and I set out with a vision: to make deep learning viable at the edge where compute, memory, and power limitations make traditional AI models impractical. At Deeplite, we developed a unique approach to AI model optimization and compression, enabling deep neural networks to run efficiently on extremely resource-constrained hardware without compromising accuracy. This acquisition marks a major milestone in that journey. Startups are never easy, but I’ve had the privilege of working alongside some of the most brilliant and dedicated people in the field. A heartfelt thank you to Sudhakar Sah (Sud), our very own co-founder and VP of engineering, for his remarkable contributions, deep expertise and outstanding leadership and to the entire Deeplite team, our partners, and our investors for believing in us from day one. Joining STMicroelectronics is a natural next step. Their leadership in embedded systems, sensors, and microcontrollers—combined with Deeplite’s edge AI software IP—opens up exciting opportunities to deliver intelligent, ultra-efficient solutions across real-world applications, from industrial automation to smart homes, automotive, and beyond. We’re excited for what’s ahead, and ready to scale our vision for efficient edge AI together with ST! #EdgeAI #EmbeddedAI #DeepLearning #ModelOptimization #Deeplite #STMicroelectronics #Acquisition #StartupJourney #AIEngineering

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Photonics continues to play a critical role in the evolution of AI infrastructure. Appreciate this thoughtful article from Patrick Moorhead exploring how optical technologies — including the work happening at Coherent Corp. — are reshaping data centers and the broader tech ecosystem.

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I am excited to share that DARPA has selected Cerebras to advance a next generation compute platform for real time computation, where we integrate a wafer scale engine and a co-packaged optics. We proposed a groundbreaking approach to DARPA, revolutionizing I/O by orders of magnitude and integrating it with our wafer-scale processors. This innovation dramatically accelerates the performance and scale of computing clusters to solve complex AI, simulation, and HPC challenges.

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As AI workloads eat up global computing supply, DRAM prices are surging like never before—creating major challenges for teams trying to plan out their data center spend. One of my portfolio companies, Mext, is addressing the largest cost component in the datacenter: server memory (DRAM). From being on the board of Redis for the last twelve years, I’ve learned a lot about the various aspects of this problem and approaches to solving them. Mext found a breakthrough, using new AI techniques, for dramatically reducing the amount of server-DRAM required to run applications, all while maintaining performance. It intelligently manages the server’s memory, keeping hot pages (i.e., those in use by applications) in fast DRAM and offloading cold pages (i.e., those less used) to a much less expensive memory tier (e.g., NVMe Flash). Key to the approach is ensuring cold memory pages that are about to be accessed by an application are back in DRAM before the application needs them or notices that they were gone. This is done without modifications to the application or the OS – so it can run in the cloud or on-premise. Mext thus allows applications to either run using less DRAM or keep their DRAM footprint but do more with it. They're hosting a webinar along with Fred Weber (former CTO of AMD) on Jan 22nd at 10am pacific time. You can register here: https://lnkd.in/gXe9GCeE

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Spoiler: semiconductors are about to get a plot twist (and that’s a good thing). Loved sharing the startup perspective at #AIInfraSummit on how AI is bending the rules of chip design with Synopsys Inc and Silicon Catalyst. Some of my takeaways: 1) #AI is reshaping the way we think about #chipdesign; soon, intelligent agents and physics-informed foundation models will be co-designing with us 2) Data is at the core of this transformation and will lead to super-charging #semiconductor partnerships and ecosystems 3) Are semiconductor startups at a disadvantage when it comes to AI? Established companies certainly have valuable data-sets but AI is lowering barriers to entry and democratizing chip design at an astonishing rate. A big THANK YOU to our awesome moderator Sally Ward-Foxton at EE Times | Electronic Engineering Times and the many of you who joined the conversation — from creative questions during the session to great discussions after the panel Learn more about Cognichip and our Artificial Chip Intelligence - ACI(R) technology at https://www.cognichip.ai. Global Semiconductor Alliance Semiconductor Industry Association

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Today's Artificial Analysis data on OpenAI GPT-OSS-120B inference shows Cerebras Systems is fastest whether for one request or 10 requests. The data does not lie: wafer-scale AI compute is unmatched when it comes to large-model inference. No number of GPUs can play here. Source in comments.

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Yesterday, Reuters reported that TSMC has approached Nvidia, AMD, Broadcom, and Qualcomm about taking stakes in a joint venture to operate Intel's foundry division. https://lnkd.in/gDkCAuFU This news struck me deeply, as it mirrors a strategy I proposed in September 2024 - but with a crucial difference. In my article "The Crisis of Silicon Kingdom and New Hope: A Global Semiconductor Industry Paradigm Shift," I advocated for Samsung Electronics and Intel to join forces, spinning off their foundry businesses into a collaborative venture to challenge TSMC's dominance. I argued this would not only create a formidable competitor to TSMC but also mitigate the geopolitical risks associated with Taiwan's semiconductor concentration. https://lnkd.in/gtFQHR9N Six months later, it appears TSMC has recognized the strategic value of such consolidation - but has positioned itself as the orchestrator rather than the target of disruption. According to Reuters, TSMC would run operations of Intel's foundry division while bringing chip designers like Nvidia and AMD as stakeholders, creating precisely the kind of ecosystem integration I envisioned. What's particularly disappointing is Samsung Electronics' apparent decision to overlook this strategic opportunity. While I proposed Samsung as a key player in reshaping the global semiconductor landscape, they have remained on the sidelines. Now, TSMC is executing a similar vision - one that strengthens their position rather than challenges it. This development underscores a crucial lesson in strategic foresight: recognizing industry-transforming opportunities before competitors act on them. While my proposal emphasized balanced competition and supply chain resilience through geographic diversification across South Korea and the United States, TSMC's approach may further consolidate power in the "Silicon Kingdom." For Samsung Electronics, this should serve as a moment of reflection. The opportunity to lead a paradigm shift in the semiconductor industry presented itself, but hesitation has allowed a competitor to potentially seize the initiative instead. As I wrote last September: "History remembers those who make bold decisions in times of crisis." The question now is whether Samsung can still write its chapter in semiconductor history, or if TSMC will continue to author the industry's future. What are your thoughts on this development? Has the window for transformative change in semiconductor manufacturing closed, or is there still room for bold strategic moves?

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GaN is getting a lot of attention right now, and for good reason. We’re at an inflection point in #DataCenter power management. The rise of AI is pushing power demands through the roof, and with big companies like NVIDIA moving toward 800V high-voltage DC architectures, it’s clear the way we manage power at the rack level needs to evolve. Unlike traditional silicon-based power transistors, GaN can handle higher voltages more efficiently, which makes it a strong candidate for the kind of stepdown performance next-gen systems require. Innovation in advanced materials is still needed to unlock GaN’s full potential by improving device performance, enhancing thermal characteristics, and enabling better integration with existing infrastructure. The use of advanced materials is something I’m watching closely through our work at Atomera. I believe that combining GaN with our MST technology could boost reliability and unlock even greater performance, creating a serious wide-bandgap contender for powering the #AI era. We’ll be seeing a lot more GaN-powered progress soon, and I’m excited to see where it takes us.

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