AiThority Interview with Dinakar Munagala, CEO and Co-Founder of Blaize
Dinakar Munagala, CEO and Co-Founder of Blaize, highlights a few fundamentals around AI-powered edge computing, how governments globally are using edge AI, and key takeaways for global bodies and governments to work better with innovative systems.
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Hi Dinakar, take us through your years in tech in brief…
I’ve always been driven by curiosity and a passion for solving real-world problems. My journey started as an engineer focused on making systems faster and more efficient. Over time, I became drawn to the idea that intelligence — real, useful intelligence — needed to move closer to where data is created.
I spent 12 years at Intel, where I helped design their next-generation multi-core graphics chips and worked on the first product line that combined CPU and GPU technologies into a single platform.
In 2010, a few of us left Intel with a big idea: reimagine how AI could work in the real world, outside the walls of data centers. That idea became Blaize. We went public earlier this year through a SPAC merger. Today, our mission is simple: help machines think faster and smarter, using less energy, right where life happens.
As AI moves beyond cloud computing into real-world applications, from defense to smart cities to healthcare, Blaize is building the tools to power that future.
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Can you share fundamentals on AI-powered Edge Computing for the uninitiated?
In simple terms, edge AI means bringing intelligence closer to where things are actually happening — out in the real world, not trapped inside faraway cloud servers.
Traditionally, AI has relied on cloud data centers to process information. In the old model, devices like cameras or sensors would send their data to big data centers in the cloud for processing. But that takes time and uses a lot of bandwidth. It’s not ideal if you need immediate decisions, like a self-driving car recognizing a stop sign or a hospital device monitoring a patient.
Individuals need AI where they operate, not where cloud providers dictate. Just as Intel and Apple helped shift computing from mainframes to personal devices, AI is now moving from the cloud to the edge, where data is created, and decisions need to happen in real-time. Now, the “thinking” happens right at the device itself — whether it’s a traffic camera, a medical monitor, or a factory robot. The result is faster decision-making, lower energy usage, reduced costs, and the ability to scale smarter systems across industries. This shift is powering the next generation of AI in the real world.
How are AI-powered Edge Computing Systems redefining processes for telecom and broadcast businesses today?
AI-powered edge computing platforms, like those developed by Blaize, are transforming telecom and broadcast infrastructures by moving intelligence directly to the edge, where data is created and decisions need to be made instantly. Users can deploy low-power, high-performance AI systems that support applications like video analytics, rapid emergency response systems, intelligent traffic flow, and localized content customization – all in real-time. AI edge computing systems not only enhance user experience but also improve operational efficiency and reduce costs.
By minimizing latency, increasing reliability, and enabling smarter, localized decision-making, edge AI technology empowers providers to deliver high-quality video and data services right where users need them. This marks a fundamental shift toward scalable, physical-world AI that brings intelligence closer to the edge.
Can you highlight more on how global governments are using these systems to impact various facets of upkeep and governance?
Governments worldwide are beginning to harness the power of edge AI, and the possibilities are transformational.
U.S. agencies, such as the Department of Defense’s Joint Artificial Intelligence Center (JAIC), are actively exploring AI technologies to test and improve mission-critical systems for national security. Edge AI is playing a growing role in enabling those efforts, bringing intelligence to the frontlines where decisions must be made in real time.
In mission-critical environments like smart cities, latency, bandwidth constraints, and reliance on centralized cloud systems simply won’t cut it. Edge computing delivers high-performance AI where it’s needed most, with ultra-low power consumption and latency-optimized architecture. A great example of this in action is the Chungbuk Institute of Science and Technology (CBIST) in South Korea, which is leveraging Blaize’s AI-enabled edge computing solutions to power its Digital Innovation Hub. This initiative is helping scale AI, edge computing, IoT, and data analytics across Chungcheongbuk-do, accelerating the region’s smart city transformation.
From public safety to smarter traffic systems to faster emergency responses, edge AI makes cities safer, faster, and more efficient by bringing real-time decision-making exactly where it’s needed.
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What should global bodies and governments keep in mind as they open up to working with these innovative systems for the first time?
Global governments, as with any other industry applying physical AI, should prioritize security, scalability, and real-time performance as they adopt innovative AI systems. Edge AI must be energy-efficient, low-latency, and adaptable to mission-critical environments – whether it’s for smart cities, defense, or public safety. Success starts with deploying solutions that are not only powerful but also trusted and built for the complexities of the physical world.
Some thoughts on the future of AI-powered Edge Computing before we wrap up?
The future is already here. Thanks to advances from companies like DeepSeek and Meta, AI models are becoming smaller, faster, and smarter — perfectly suited for the edge. It’s no longer a question of whether it is technologically possible; the world is officially moving to edge AI. The time is now, as the demand and necessity for real-time feedback and analysis require it. Doctors need access to data to make life-saving decisions, and cities rely on real-time camera feedback for safety monitoring. Edge AI allows for quick access to data, improving processes and making it all possible.
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Dinakar brings more than 22 years of experience successfully leading global teams building graphics chips and has been recognized for technical excellence, new initiatives, and a results-focused approach. Starting with a bootstrapped development effort to create a more energy efficient processor architecture for the needs of a new generation of computing, Dinakar has led Blaize in delivering a transformational compute solution that unites silicon and software to optimize AI at scale from the edge to the core. Under his leadership, the company has brought together marquee investors to back the vision of the founding team, grown to ship products to multiple customers across automotive and other sectors. Dinakar holds a Bachelor of Engineering degree, Electrical and Computer Engineering, from Osmania University, India, and a Master of Science degree, Electrical and Computer Engineering, from Purdue University.
Blaize provides a full-stack programmable processor architecture suite and low-code/no-code software platform that enables AI processing solutions for high-performance computing at the network’s edge and in the data center. Blaize solutions deliver real-time insights and decision-making capabilities at low power consumption, high efficiency, minimal size and low cost.
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