Best of NVIDIA — August 2025

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   Article

   Where's Your Ed At·39w

   AI Bubble 2027

   An MIT study reveals 95% of organizations get zero return from generative AI, while Meta freezes AI hiring and major outlets question if we're in a bubble. The analysis predicts the AI bubble will burst through a series of events over 18 months, including NVIDIA's growth slowing, AI funding drying up, major AI companies collapsing, and Big Tech pulling back from AI investments. Key vulnerabilities include OpenAI and Anthropic burning billions annually, CoreWeave's financial troubles, and AI startups raising at unsustainable valuations. The bubble is driven by vibes rather than returns, making it vulnerable to emotional market reactions when reality sets in.

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   Article

   InfoWorld·40w

   Is the generative AI bubble about to burst?

   The generative AI boom shows similarities to the dotcom bubble, with massive investments ($364 billion expected in 2025) flowing primarily to companies like Nvidia. While Goldman Sachs argues current AI investments are justified by profits, critics point to structural limitations in large language models that prevent true reasoning capabilities. Developers using AI tools daily recognize their utility for code generation but also experience their shortcomings, suggesting the technology may be more incremental than revolutionary. Even if an AI bubble exists, survivors will likely drive lasting changes in the industry, similar to how some dotcom survivors became today's tech giants.

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   Article

   NVIDIA Developer·39w

   How to Scale Your LangGraph Agents in Production From A Single User to 1,000 Coworkers

   NVIDIA shares their approach to scaling LangGraph AI agents from single-user prototypes to production systems supporting 1,000+ concurrent users. The process involves three key steps: profiling single-user performance to identify bottlenecks, conducting load tests to estimate hardware requirements, and implementing monitoring during phased rollouts. Using the NeMo Agent Toolkit, they deployed an internal AI-Q research agent, discovering critical issues like CPU misconfiguration and timeout handling that only emerged under load. The methodology includes evaluation tools, sizing calculators, and OpenTelemetry integration for comprehensive observability.

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