Browse claims.

An em dash, polished tone, or neat formatting can be a clue. None can prove that AI wrote a specific text.

AI updates can break a workflow, but evidence shows uneven trade-offs—not a proven plot to quietly nerf paid users.

No. AI has a real water footprint, but “one prompt equals one bottle” misstates a variable estimate as a universal constant.

Creative tools can flatten output or expand iteration depending on use.

Open weights create real misuse and containment risks, but evidence does not yet establish a general no-release threshold.

Bias is real, but comparisons should include human and institutional baselines.

Privacy risk depends on the product, settings, policy, and contract.

Next-token prediction explains why fluent output is not proof of understanding or reliability, but it does not settle capability by analogy.

Confident falsehoods are real. The trust question depends on the workflow.

Errors matter, but usefulness depends on task, verification, and failure cost.

Tool misuse is real, but cheating and learning support are different claims.

Automation changes tasks first; total replacement is a bigger claim.

Plagiarism, infringement, memorization, and style imitation are not the same thing.

AI training can appropriate creator work, but stealing combines distinct ethical and legal claims.

Water claims need location, cooling method, and data-center baselines.

AI is materially increasing data-center demand; the scale and harm depend on power, location, and workload.