The recent emergence of Time Series Foundation Models (TSFMs) offers a powerful new tool for forecasting. Their effectiveness, however, is often constrained by an architectural design that analyzes each time series as an independent entity. This approach overlooks the rich, structured context available in most enterprise data warehouses, where a product’s sales history is interconnectedContinue reading “Time Series Foundation Models: What You Need To Know”
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When Text Helps Time Series (and When It Doesn’t)
Subscribe • Previous Issues Time Series Foundation Models: What You Need To Know The recent emergence of Time Series Foundation Models (TSFMs) offers a powerful new tool for forecasting. Their effectiveness, however, is often constrained by an architectural design that analyzes each time series as an independent entity. This approach overlooks the rich, structured context available inContinue reading “When Text Helps Time Series (and When It Doesn’t)”
Quick Takes: Who Really Loses Their Job When AI Shows Up?
Key sections from the video: Decoding the AI Layoffs: What’s Really Happening? The ROI of AI: Justifying the Trillion-Dollar Bet on AI Infrastructure
What is the PARK Stack?
In the age of generative AI, enterprise teams have three options: extend their legacy data and ML platforms, rely on APIs, or build a custom unified platform for generative AI. For teams choosing to build their own custom AI platform, the PARK Stack (PyTorch, AI Frontier Models, Ray, Kubernetes) is quickly becoming the industry standard.Continue reading “What is the PARK Stack?”
The PARK Stack Is Becoming the Standard for Production AI
In a previous article, I argued that the open-source project Ray has become the compute substrate many modern AI platforms are standardizing on — bridging model development, data pipelines, training, and serving without locking into a single vendor. Ray Summit is my favorite venue for pressure-testing that thesis because it’s where infrastructure and platform teamsContinue reading “The PARK Stack Is Becoming the Standard for Production AI”
Trends shaping the future of AI infrastructure
Subscribe • Previous Issues The PARK Stack Is Becoming the Standard for Production AI In a previous article, I argued that the open-source project Ray has become the compute substrate many modern AI platforms are standardizing on — bridging model development, data pipelines, training, and serving without locking into a single vendor. Ray Summit is my favoriteContinue reading “Trends shaping the future of AI infrastructure”
Boom, Bubble, or Bust? How to Build a Resilient AI Business
Comparisons to the dot-com bust are common but this AI boom rests on short-cycle hardware. Frontier training chases each GPU generation, rendering last year’s chips economically obsolete for training even as they stay serviceable for inference — forcing relentless reinvestment. This dynamic is amplified by a unique, self-referential financial architecture where capital circulates between techContinue reading “Boom, Bubble, or Bust? How to Build a Resilient AI Business”
How to build an AI business that survives the bubble
Subscribe • Previous Issues Boom, Bubble, or Bust? How to Build a Resilient AI Business Comparisons to the dot-com bust are common but this AI boom rests on short-cycle hardware. Frontier training chases each GPU generation, rendering last year’s chips economically obsolete for training even as they stay serviceable for inference — forcing relentless reinvestment. This dynamicContinue reading “How to build an AI business that survives the bubble”
Reimagining the Database for AI Agents
In a recent piece, I explored the growing mismatch between our existing data infrastructure and the demands of emerging AI agents. Since then, I have had the opportunity to speak with some founders and engineering leaders who are tackling this challenge directly. Their work confirms that the rise of agentic AI is not just anContinue reading “Reimagining the Database for AI Agents”
Inside the race to build agent-native databases
Subscribe • Previous Issues Reimagining the Database for AI Agents In a recent piece, I explored the growing mismatch between our existing data infrastructure and the demands of emerging AI agents. Since then, I have had the opportunity to speak with some founders and engineering leaders who are tackling this challenge directly. Their work confirms that theContinue reading “Inside the race to build agent-native databases”