Meta trains AI on internal engineers’ work as it cuts 8,000 jobs

For the past few years, enterprise AI conversations have been dominated by optimism: bigger models, more pilots, faster automation. The prevailing assumption was simple — pick the right AI platform and progress would follow. Reality has been far less forgiving. Most IT leaders have discovered that production AI is significantly harder than early experimentation suggested. The real work begins not when a model performs well in isolation, but when it must operate inside environments that are secure, observable, and operationally durable. Recent research my company conducted with enterprise cloud architects and IT decision-makers confirms what many engineering teams already know instinctively: experimentation is easy. Operationalizing AI reliably, repeatedly, and at scale is the hard part. Once AI begins influencing real workflows, recommending decisions or triggering actions, the model quickly becomes the least interesting part of the system. The pressure shifts to everything around it.

Coinbase says AI cut account restriction resolution times by 90%, with humans still checking compliance outcomes.

Soluna (NASDAQ: SLNH) Holdings has acquired full ownership of another portion of its flagship Texas campus, continuing a broader effort to transform a bitcoin mining complex into an AI and high-performance computing site backed by owned renewable energy. This article first appeared in The Energy Mag. The original article can be viewed here. The Energy […]

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Google has only one way to measure the phenomenal AI growth it’s seen: in tokens. The company processes 3.2 quadrillion tokens per month, Google CEO Sundar Pichai said during this week’s I/O keynote, adding, “never imagined I’d say quadrillion…, but here we are.” Basically, tokens are a unit of measure used by large language models (LLMs) to process data. Tokens, which have been called the “new oil” fueling the AI revolution, are also a way AI vendors can meter usage and price their services. Enterprises are lusting for tokens, and spending billions of them to grab compute time. As with oil, the demand for tokens is seemingly insatiable — and it is straining an already short GPU supply, which in turn is increasing the cost of running AI tools. What exactly is a token? Similar to the way humans think, LLMs grasp the meaning of a sentence by breaking words down into tokens. Pichai described them as “the fundamental units of data our models process, many representing a problem being solve