AI
articles
AI Didn’t Break Your Data Platform; It Exposed Your Data Debt
Enterprise AI doesn’t create fragility; it reveals undocumented assumptions, missing ownership, and invisible pipeline debt. Fix the foundations and AI gets cheaper, faster, and more trusted.
GlobalFoundries to Acquire Synopsys ARC Processor IP Business
The deal moves Synopsys’ ARC processor IP and ASIP Designer/Programmer tools to GF’s MIPS business, while Synopsys keeps interface and foundation IP and leans further into AI-era engineering.
AI Didn’t Break Your Data Platform; It Exposed Your Data Debt
Enterprise AI doesn’t create fragility; it reveals undocumented assumptions, missing ownership, and invisible pipeline debt. Fix the foundations and AI gets cheaper, faster, and more trusted.
GlobalFoundries to Acquire Synopsys ARC Processor IP Business
The deal moves Synopsys’ ARC processor IP and ASIP Designer/Programmer tools to GF’s MIPS business, while Synopsys keeps interface and foundation IP and leans further into AI-era engineering.
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Podcasts
How Hedgehog Brings Hyperscaler Networking to Enterprise AI
Hedgehog CEO Marc Austin joins Data Insights to break down open-source, automated networking for AI clusters—cutting cost, avoiding lock-in, and keeping GPUs fed from training to inference.
Nebius at SC25: Building the Neocloud for Enterprise AI
From SC25 in St. Louis, Nebius shares how its neocloud, Token Factory PaaS, and supercomputer-class infrastructure are reshaping AI workloads, enterprise adoption, and efficiency at hyperscale.
Inside Runpod’s GPU Cloud for Long-Running AI Agents
Runpod head of engineering Brennen Smith joins a Data Insights episode to unpack GPU-dense clouds, hidden storage bottlenecks, and a “universal orchestrator” for long-running AI agents at scale.
Walmart and Cornelis Expose the Costs of Network Inefficiency
Billions of customer interactions during peak seasons expose critical network bottlenecks, which is why critical infrastructure decisions must happen before you write a single line of code.
Giga Computing on Rack-Scale AI: From Training to Inference
Recorded at #OCPSummit25, Allyson Klein and Jeniece Wnorowski sit down with Giga Computing’s Chen Lee to unpack GIGAPOD and GPM, DLC/immersion cooling, regional assembly, and the pivot to inference.
Data Insights at OCP: DataraAI on Edge Robotics
Durgesh Srivastava unpacks a data-loop approach that powers reliable edge inference, captures anomalies, and encodes technician know-how so robots weld, inspect, and recover like seasoned operators.
Advanced Packaging Innovations in the Semiconductor Industry with Chee Ping Lee of Lam Research
In this podcast, learn about the challenges of silicon advancement, the importance of advanced packaging, and how Lam is driving breakthroughs to support the future of AI and chiplet ecosystems.
Uncovering the Future of AI Agents with Sema4
TechArena host Allyson Klein chats with Sema4.ai co-founder Antti Karjalainen about his vision for AI agents and how he sees these powerful tools surpassing even what current AI models deliver today.
Taboola Talks Re-Architecting the Marketing Landscape with AI - Data Insights Sponsored by Solidigm
TechArena host Allyson Klein and Solidigm’s Jeniece Wnorowski chat with Taboola Vice President of Information Technology and Cyber, Ariel Pisetzky, about how his company is reshaping the marketing landscape with AI infused customer engagement tools.
Exploring Requirements for AI with EY
TechArena host Allyson Klein chats with EY’s Global Innovation AI Officer, Rodrigo Madanes, about what he’s seeing from clients in their advancement with AI and what this means for the industry requirements for innovation.
Compute Requirements in the AI Era with Intel’s Lisa Spelman
TechArena host Allyson Klein chats with Intel’s Lisa Spelman about how compute requirements are changing for the AI era, where we are with broad enterprise adoption of AI, and how software, tools and standards are required to help implement solutions at scale.
The Demand for Memory Innovation to Fuel ML with Netflix
TechArena host Allyson Klein interviews Netflix’s Tejas Chopra about how Netflix’s recommendation engines require memory innovation across performance and efficiency in advance of his keynote at MemCon 2024 later this month.
