8 articles in this category
For a decade, "scaling AI" mostly meant "buy more GPUs." You could squint at a scaling law, convince yourself that another order of magnitude of compute would buy you another chunk of capability, and assume the cloud would take care of the details. That era is ending.
Most AI pitches for the power grid start the same way. Colorful map. Lots of renewables. A curve labeled "demand" and another one labeled "supply." Then a line: "We'll use AI to balance all of this in real time." If you actually run a grid, you know that's not how this works.
Most interfaces to AI systems still pretend the model is sure. You get a single answer. No visible uncertainty. No hint about how it might fail. Maybe a tiny "results may be inaccurate" disclaimer somewhere legal asked to add.
Product decks are full of "agents" now. Animated cursors flying across screens. Flows that supposedly "run themselves." A button that says "Let the AI handle it." In the demo, the agent glides through forms, opens the right tools, clicks the right buttons, and hands you a tidy result.
Most teams talk about code assistants as if they were smarter autocomplete. They help you write tests faster. They fill in boilerplate. They remember API calls so you do not have to. All true. But that is not the interesting part.
Most teams say the same thing when they roll out AI tools. "We'll let the models handle the busywork so people can focus on higher-value tasks." Sounds clean. Then you watch what actually happens.
In 2026, "AI literacy" is no longer a differentiator. It is the entry ticket. Most professionals now know how to prompt a model, generate a draft, or get a first pass at analysis. That is not a strategy. That is hygiene.
Every AI vendor has a slide that looks the same. "37 percent productivity gain." "2.3 times faster completion." The numbers are clean. The world is not. Inside real teams, what you see is messier.