About
Vivek Kumkar is a Senior AI Engineering Leader at Amazon Web Services (AWS), where he directs organizational vision, technical architecture, and multi-team execution across Amazon Bedrock Data Automation, Amazon Rekognition, and Amazon Textract. Leading multi-disciplinary engineering, applied science, and product organizations, Vivek oversees the core AI and multimodal infrastructure powering mission-critical, high-availability workloads for Fortune 500 enterprises across media, financial services, healthcare, and the public sector. With 18+ years of cloud and AI leadership across AWS, SAP, and Intel, he has built and scaled global portfolios spanning both artificial intelligence and core cloud infrastructure. Throughout his tenure, Vivek has conceptualized, designed, built, and led multiple teams to launch and scale Tier-1 services including Amazon Bedrock Data Automation (BDA), Rekognition, and Textract, as well as foundational cloud infrastructure such as EBS Snapshots (Archive Tiering and Copy) and EBS Volumes. Beyond his technical leadership, Vivek holds U.S. Patent 11,262,918 B1 in distributed storage systems and actively contributes to the broader AI ecosystem. An IEEE Senior Member, AWS Bar Raiser, and member of the Senior Executive AI Think Tank, he serves as a peer reviewer for premier conferences including COLM (DAIH Workshop), ISPOR, and MedBioAI, while judging global hackathons such as MIT Sloan Hack-Nation, UnitedHacks and MLH Build with AI.
Vivek Kumkar
Published content
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The AI hardware race is about to get a lot more interesting. For years, the industry has focused on building faster, more powerful chips to keep up with increasingly demanding AI models. But what if the next breakthrough isn't a faster processor at all? What if it comes from rethinking how computers handle memory, move data or even where AI processing happens? From chips that use light to move information to processors that bring computing closer to memory, a new wave of architectures is challenging the way we've traditionally thought about AI infrastructure.Members of the Senior Executive AI Think Tank, a curated group of experts specializing in machine learning, generative AI and enterprise AI applications, weigh in on the emerging hardware architectures they believe could have the greatest impact on AI computing and what will ultimately separate transformative technologies from those that struggle to gain commercial traction.
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AI has learned to write, reason, code and create at remarkable scale. But as artificial intelligence generates more of the content used to train future systems, the industry faces an uncomfortable possibility: What happens when AI increasingly learns from itself?A 2024 study published in Nature found that repeatedly training models on AI-generated outputs can cause them to lose information from the original data distribution, with less common patterns among the first to disappear. Researchers call this phenomenon “model collapse”—a warning that more data does not automatically translate into better AI.Synthetic data offers a compelling answer to the industry's growing demand for training material. It can simulate rare events, generate examples for specialized tasks and help developers test systems in situations that would be expensive, impractical or risky to reproduce in the real world. But synthetic data can also create a closed feedback loop, amplifying existing errors and narrowing the range of patterns a model recognizes.Members of the Senior Executive AI Think Tank, a curated group of experts specializing in machine learning, generative AI and enterprise AI applications, examine what it will take to keep AI learning rather than looping. Below, they offer a roadmap for executives navigating the next phase of AI adoption: how to distinguish useful synthetic data from convincing but uninformative output, how to protect the evidence that makes AI smarter and how to determine whether a new model is genuinely more capable than the one before it.
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Writing code has always taken time. Now, with AI, developers can produce in minutes what might once have taken hours. For companies under pressure to move faster and do more with less, that sounds like a clear win. But getting code written is only one part of building software. Someone still has to review it, test it, make sure it’s secure and keep it running once it reaches customers.So if teams are producing more code than ever, how do they know they’re actually getting more productive? Faster development doesn’t mean much if work piles up waiting for approval, bugs make their way into production or the cost of maintaining new software keeps climbing. And the more code a company produces, the more important it becomes to know what’s worth building in the first place.Members of the Senior Executive AI Think Tank, a curated group of experts in machine learning, generative AI and enterprise AI applications, weigh in on what AI-driven productivity really looks like, where the biggest bottlenecks are emerging and how leaders can tell whether faster coding is translating into better business results.
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For an AI company with limited computing resources, competing with well-funded technology giants can seem like an uphill battle. The largest AI developers can invest billions of dollars in advanced chips, massive training runs and increasingly sophisticated foundation models. Smaller companies rarely have the capital to match that spending—and trying to do so can be a costly strategic mistake.The opportunity lies in recognizing that customers do not necessarily need the biggest model. They need solutions that solve specific problems reliably, affordably and in ways that fit their businesses.Members of the Senior Executive AI Think Tank, a curated group of experts specializing in machine learning, generative AI and enterprise AI applications, share their perspectives on how AI companies with limited compute resources can compete in an increasingly crowded market. Drawing on their experience across AI development, enterprise technology and business strategy, they explore the challenges facing resource-constrained companies, the strategic decisions that can shape their competitive position and the opportunities emerging as AI technology evolves.
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For a long time, the central challenge in robotics was teaching machines exactly what to do. Now, some of the most interesting advances are coming from robots that can learn what to do for themselves.That shift is changing expectations for how quickly robots could become genuinely useful. New systems can learn from demonstrations, adapt to unfamiliar situations and transfer what they’ve learned across tasks and machines—bringing robotics closer to the flexibility that made generative AI so disruptive.Members of the Senior Executive AI Think Tank—a curated group of experts specializing in machine learning, generative AI and enterprise AI applications—have been watching these developments from across technology, healthcare, infrastructure, transformation and enterprise AI. We asked them what development in AI robotics has been most interesting to them this year, what it changed their minds about and what it suggests about how quickly robots will become capable of doing more in the real world.
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AI progress is often reduced to a leaderboard: a model scores higher on reasoning, coding or multimodal benchmarks, and the industry moves on to the next release. But benchmarks are snapshots of what a model can demonstrate under controlled conditions. They do not always capture what happens when increasingly capable models are connected to an organization’s data, software, physical environments and decision-making processes.The 2026 AI Index from Stanford HAI reports that frontier AI capabilities are advancing faster than many established benchmarks can measure, with some evaluations reaching saturation within months.Members of the Senior Executive AI Think Tank see a similar pattern from the front lines of enterprise AI, machine learning, robotics, cybersecurity, cloud technology and digital product development. They point to a broader question for business leaders: What are today’s most capable AI models beginning to do that could matter far more than benchmark gains suggest? Here, they explore the emerging capabilities they believe deserve closer attention—and what those developments could mean for the way organizations build products, make decisions and operate in the years ahead.
