Policy
Regulation, safety, industry, and economics
Fast Lane to the Grid: FERC Orders Six Operators to Make Room for AI Data Centers
FERC gave six grid operators 30-60 days to rewrite the rules slowing AI data centers onto the power grid. The catch: it can't conjure new ge
SpaceX Becomes a Cloud: The $6.3 Billion Reflection AI Compute Deal
SpaceX will rent Nvidia GB300 capacity at its Colossus 2 site to open-model lab Reflection AI for up to $6.3B—turning a rocket company into
Washington Pulls a Frontier Model: Inside the Fable 5 Export-Control Standoff
A US export-control directive forced Anthropic to suspend Fable 5 and Mythos 5 worldwide—the first such move aimed at a single AI model.
Privacy and LLMs: what leaves your machine
When you type into an LLM, where does that text actually go — and what happens to it after? A plain-language guide to the data trail.
Transparency and disclosure: telling people it's AI
When should you tell people that AI was involved? A plain-language guide to disclosure norms — why they matter and how to decide what is honest.
Concentration of AI power: who controls the models
Powerful AI is expensive to build, which pushes control toward a few players. A plain-language guide to why concentration happens and what counterweights it.
Data licensing: the real constraint behind AI products
The hardest part of many AI products is not the model — it is whether you are allowed to use the data at all. A plain-language tour of the constraint that quietly decides what gets built.
Watermarking and detecting AI content
Can you mark or detect AI-generated content reliably? A clear look at how watermarking and detection work, and why neither is a magic solution.
Open-weight licenses decoded: MIT, Apache, and the gray zones
"Open" model weights come with very different strings attached. A plain-language guide to reading the license before you build.
AI and your data: what training on your inputs means
When a service says it may train on your inputs, what does that actually mean for your text, files, and ideas? A plain-language guide to the trade.
AI and jobs: what we can and can't say
The honest answer about AI and employment is more careful than the headlines. A plain-language guide to what the evidence supports and what it does not.
Who owns AI output? Copyright basics for creators
When a model writes your draft or paints your image, who owns the result? A plain-language map of the questions that decide it.
The environmental cost of AI, honestly
AI uses real energy and water, but the story is more specific than the headlines. A grounded look at where the cost lives and what it depends on.
Bias in AI, explained without the hype
Bias in AI is neither a myth nor a moral failing of machines. It is a predictable result of how these systems learn. Here is the calm version.
Liability when AI gets it wrong
When an AI system causes harm, who is responsible? A plain-language map of how accountability is reasoned about when there is no single obvious culprit.
The economics of inference: why "cheap AI" still adds up
A single AI call looks almost free. So why do AI bills balloon? A plain-language tour of the economics that turn pennies into real money.
Vendor lock-in with AI providers
Building on a single AI provider is convenient until you want to leave. A plain-language guide to where lock-in hides and how to keep your options open.
Safety vs capability: the core tension
Making an AI system more capable and making it safer often pull in different directions. A plain-language look at the tension that shapes the whole field.
Regulation of AI: the broad shape
AI regulation looks like chaos up close, but it has a recognizable shape. A durable map of the approaches, tensions, and ideas that keep recurring.