
Red Bull Heir Transfers $1.1 Billion Stake to Geneva Trust Firm
When an Austrian marketer and a Thai businessman decided to launch Red Bull to the world, they settled on a simple ownership structure: each would own 49% of the venture.
Chalerm Yoovidhya, a son of the Thai businessman, got the remaining 2% and has kept it for around four decades as Red Bull became a roaring success and turned him, his father and at least nine other family members into billionaires.
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Nintendo Switch 2 launch, after an 8 year wait, draws big lines
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The real data revolution hasn't happened yet
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Which is why I also don't believe the true big data revolution has happened yet. But it's coming. Dissecting the Stack A key reason big data is seen as mature, even mundane, is that people often confuse software progress with overall readiness. The reality is more nuanced. Yes, the software is strong. We have robust platforms for managing, querying, and analyzing massive datasets. Many enterprises have assembled entire software stacks that work well. But that software still needs hardware to run on. And here lies the bottleneck. Most data-intensive workloads still rely on traditional central processing units (CPUs)—the same processors used for general IT tasks. This creates challenges. CPUs are expensive, energy hungry, and not particularly well suited to parallel processing. When a query needs to run across terabytes or even petabytes of data, engineers often divide the work into smaller tasks and process them sequentially. This method is inefficient and time-consuming. 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Nvidia launched CUDA (compute unified device architecture) in 2006, enabling general-purpose computing on graphics hardware. Just two years earlier, Google's MapReduce paper laid the foundation for modern big data processing. Despite this parallel emergence, GPUs haven't become a standard part of enterprise data infrastructure. That's due to a mix of factors. For one, cloud-based access to GPUs was limited until relatively recently. When I started building GPU-accelerated software, SoftLayer—now absorbed into IBM Cloud—was the only real option. There was also a perception problem. Many believed GPU development was too complex and costly to justify, especially for general business needs. And for a long time, few ready-made tools existed to make it easier. Those barriers have largely fallen. Today, a rich ecosystem of software exists to support GPU-accelerated computing. CUDA tools have matured, benefiting from nearly two decades of continuous development. 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Freed from concerns about query latency or resource drain, enterprises can explore how their data might power generative AI, smarter applications, or entirely new user experiences. Gartner took big data off the Hype Cycle because it no longer seemed revolutionary. Accelerated computing is about to make it revolutionary again.


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