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AI Image Generators and Copyright

AI Image Generators and Copyright

Image Generators

A practical overview of the copyright questions around AI-generated images, including ownership, tool terms of use, and training data disputes.

The legal picture around AI-generated images is genuinely unsettled in places, but a few practical points are clear enough to act on right now.

Copyright offices in multiple jurisdictions have taken the position that purely AI-generated content, with no meaningful human creative input, may not qualify for copyright protection at all. An image where a person made substantial creative choices using AI as a tool has generally been treated more favorably.

Each image generator has its own terms governing what commercial rights you actually get on output, and these vary between providers. Read the specific terms before using generated images commercially.

Whether models trained on copyrighted images without a license infringe the original artists’ rights is being actively litigated against multiple AI companies, a separate question from whether your specific output is legally yours to use.

This is general information, not legal advice. Avoid generating anything closely resembling a specific existing character or work, keep records of your own creative input, and read your tool’s commercial terms directly. See the U.S. Copyright Office’s own AI guidance for current policy.

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Building Your Own MCP Server: A Guide

Building Your Own MCP Server: A Guide

MCP & Protocols

A practical starter guide to building your own MCP server, covering tool design, description writing, and permission scoping.

If you’ve connected AI assistants to existing MCP servers and want to expose your own tool, the actual process is more approachable than it sounds.

An MCP server exposes a defined set of tools, resources, and prompt templates in a standard format any MCP-compatible client can discover and use. You’re building something any compliant client can plug into, not a custom integration for one product.

Start with one well-defined tool: a specific internal database query, one well-understood workflow, rather than exposing your whole system at once. Official SDKs exist for the most common languages, handling most of the boilerplate for you.

Design tool descriptions carefully. A model decides whether to call your tool based largely on its description, so a vague one leads to it being used incorrectly or not at all. Write descriptions the way you’d document a new team member’s role.

Given how many MCP vulnerabilities trace back to overly broad access, scope permissions to exactly what’s needed from the start, not broad access restricted later. See the full specification at modelcontextprotocol.io.