Author Archives: Ben Lorica

Synthetic data used to be a fairly narrow idea: pad a small dataset, test a model without touching production data, maybe stress a system for bias. The rise of generative AI and autonomous agents has changed the landscape. Teams use synthetic data to train and evaluate agentic systems, to cover rare failure cases, to meetContinue reading “The Industrialization of Synthetic Data”

As generative AI applications mature, engineering teams are finding that standard API endpoints often fall short on cost and performance. Companies increasingly need to customize and scale their own AI workloads to remain efficient. A recent engineering blog post from Notion illustrates this shift perfectly. To handle billions of vector embeddings, Notion overhauled its infrastructureContinue reading “You Don’t Need a Massive ML Team to Scale AI Affordably”

The existence of an AI bubble is beyond dispute. What remains unclear is when or how it deflates. As investors know all too well, the most costly mistake in business is often being correct prematurely. The infrastructure layer has already booked revenues. This includes sectors like semiconductors, data centers, and power grids. The application sideContinue reading “The AI Bubble Is Real. Enterprise Usage Is Even More Telling.”

Now that AI agents are moving out of the lab and into the real world, we’re realizing that “memory” isn’t one-size-fits-all. Most people think of agent memory like a personal assistant. It remembers your preferences, your travel plans, and the email you composed last week. This is great for a research copilot or a personalContinue reading “What Is a Context File System?”

The Economics of Robotaxis: Are We There Yet? The “Data Center Rebellion” has gone national Ben Lorica and Evangelos Simoudis discuss two critical technology infrastructure topics: the evolving economics of autonomous vehicles and growing local opposition to AI data centers. Simoudis explains how “end-to-end AI” is transforming robotaxi viability, comparing Waymo’s multi-sensor approach to Tesla’sContinue reading “The Economics of the Robotaxi Revolution”

Even the most ardent cheerleaders for artificial intelligence now quietly concede we are navigating a massive AI bubble. The numbers are stark: hyperscalers are deploying roughly $400 billion annually into data centers and specialized chips while AI-related revenue hovers around $20 billion — a 20-to-1 capital-to-revenue ratio that stands out even in infrastructure cycles historicallyContinue reading “Beyond the Chips: The Local Politics of AI Infrastructure”