Is coding with AI illegal?

The Short Answer: AI Coding Isn't Illegal, But It Isn't Risk-Free

No, coding with AI is not illegal — but treating that as the end of the conversation is where teams get into trouble. The clearest evidence comes from the highest-profile courtroom test: in the class action over GitHub Copilot, the judge dismissed 20 of 22 claims in a 15-page order, finding the plaintiffs "did not produce a single piece of evidence" of identical code reproduction, according to reports on the federal ruling. The suggestions were ruled "not similar enough" to the plaintiffs' protected code to infer infringement.

That ruling matters, but it isn't a blanket absolution. Only breach of contract and open source license violation claims survived, and the DMCA dismissal is still on appeal before the Ninth Circuit with a decision pending. Meanwhile, over 50 machine learning training cases are pending in U.S. courts with no fair use consensus — and very few of them concern software specifically.

The consensus across legal experts is that AI code generation is lawful but carries real exposure — around licensing, copyright provenance, and compliance — not illegality. That exposure lands on the user, not the AI vendor. FossID's analysis puts it bluntly: identifying open source code "falls on the user receiving the source code from the AI system."

So what does that risk actually look like in practice?

• AI tools can strip license metadata from code, so developers may not know whether a snippet carries a restrictive or permissive license.
• Models can output verbatim open source code — especially copyleft snippets — without flagging the origin, potentially binding your product to source-availability obligations.
• AI assistants can't indicate where a suggestion came from, making independent snippet scanning, as open source authority Heather Meeker puts it, "probably the only way, and the last resort" to catch infringement.
• The exposure compounds at deal time: 99% of audited M&A transactions contained open source code, and 85% included license conflicts, per a 2025 Black Duck study covered by CACM.

None of this is theoretical for a community that has already adopted these tools at scale. Over 76% of developers surveyed by StackOverflow are using or planning to use generative AI coding tools like ChatGPT, GitHub Copilot, and Cursor. When adoption is that widespread and the legal landscape is explicitly unsettled — outcomes so far have been "case-by-case," and the U.S. Copyright Office is still publishing its multi-part report on AI training with software in scope — the question shifts from "is this legal?" to "is this governed?"

That governance gap is exactly why SkillzBench is building license-checking and provenance verification into its skill-testing platform — because published research found roughly 26% of agent skills carry at least one vulnerability, and provenance loss is the core legal problem with AI-generated code.

The short answer, then: AI coding is legal, and the defensible path is managed adoption — paid tools with guardrails, snippet scanning, and code review — not bans, which Meeker notes backfire by pushing developers toward unguarded free versions.

If you're shipping AI-assisted code and want provenance and license risk surfaced before it reaches production, join the SkillzBench waitlist — skills, proven.

Where the Real Legal Risk Lives: Licensing, Not Legality

The copyleft clause you never read could cost you your product. When an AI assistant hands you a snippet of GPL-licensed code without telling you, the obligation to open-source your own code can follow it straight into production.

This is the core legal problem with AI coding, and it has little to do with whether the tools themselves are lawful. Research on AI licensing risks shows that generative tools may strip license metadata from code as they generate it, leaving developers with no way to know whether a snippet carries a permissive or restrictive license. Worse, the compliance burden falls on you — as FossID notes, identifying open source code "falls on the user receiving the source code from the AI system," not the vendor.

The deal-level data makes this exposure concrete. A 2025 Black Duck audit found that 99% of audited M&A transactions contained open source code, and 85% included license conflicts — and AI-generated code compounds that exposure because traditional compliance tools can't reliably detect it. As Steve Argentieri of Goodwin Procter observes, open source in acquisitions presents "more practical deal risk than legal risk," since incorporating copyleft code can trigger an obligation to make your own source code available when you distribute.

The legal backdrop remains unsettled in three ways:

• Over 50 ML-training cases are pending in U.S. courts with no fair use consensus, and very few concern software specifically.
• The GitHub Copilot lawsuit's DMCA dismissal is on appeal before the Ninth Circuit, with oral argument held in February 2026 and a decision pending.
• The U.S. Copyright Office is still issuing its multi-part report on generative AI training, with software explicitly in scope.

Don't count on vendor indemnification to save you. As licensing authority Heather Meeker points out, free tool tiers typically aren't covered at all, and indemnities are often capped at the subscription fees you've paid — a poor hedge against a $1B-scale claim like the one Copilot plaintiffs pursued.

The practical takeaway: provenance is the problem, and verification is the answer. That's why snippet-level scanning and license-checking of AI outputs — before code reaches production — has become the defensible posture, a discipline SkillzBench is building directly into its verification workflow for agent skills, where every skill version gets pinned, license-checked, and security-scanned before adoption.

The Compliance Playbook: Managed Adoption Over Bans

The instinct for many legal teams is simple: ban AI coding tools entirely. Heather Meeker, a leading open source licensing authority, warns that this approach backfires — outright warnings push developers toward unguarded free versions of these tools, leaving the organization with even less visibility and control.

Her recommended path is managed adoption, not prohibition. Choose paid tools with built-in guardrails, deploy snippet-level scanning, and apply your existing open source license policies to AI outputs just as you would to human-written code. Indemnification matters here too: free tool versions typically aren't covered, and even paid indemnities are often capped at subscription fees paid — another reason paid tools with guardrails beat free alternatives.

That framework aligns with a four-step process reported by CACM for managing AI code risk:

• Review all AI-generated code before it reaches production
• Track the origins of code entering your codebase
• Scan with detection tools designed to catch copied snippets
• Consider limiting AI to prototyping if your risk tolerance is low

That last step reflects a legitimate school of thought. Oleh Komenchuk of Uptech recommends GenAI for prototyping rather than production code, arguing that "AI speeds up our thinking, not our shipping." But for teams adopting AI in production, FossID offers a three-scenario framework for understanding license obligations: code that is 100% AI-generated and original carries no open source license obligations; code mixing AI output with open source snippets triggers compliance requirements like attribution, license compatibility, and source availability; and code that is 100% copied open source carries full copyleft obligations.

The catch, as FossID notes, is that identifying which scenario you're in "falls on the user receiving the source code from the AI system" — not the vendor. AI tools can strip license metadata and rarely flag when a suggestion matches existing open source code, which is why snippet scanning is "probably the only way, and the last resort" to catch infringement.

This is the same provenance gap we're building SkillzBench to close for agent skills — pinning versions to commit SHAs, license-checking, and security-scanning before anything enters your workflow, since published research found roughly 26% of agent skills carry at least one vulnerability.

The stakes are real: a 2025 Black Duck study found that 99% of audited M&A transactions contained open source code, and 85% included license conflicts. AI-generated code compounds that exposure. Managed adoption with guardrails turns compliance from a deal-blocking liability into a defensible, auditable practice.

What This Means for Agent Skills: Provenance Is the New Compliance

Every AI-generated line of code your agent runs carries a hidden question mark: where did it come from, and under what terms? That question — provenance — is where the legal risk of AI coding actually lives, and it's about to collide head-on with the fast-growing ecosystem of agent skills.

The research is blunt about why. GenAI tools may strip licensing from code snippets as they generate them, leaving developers unable to tell whether code carries a restrictive or permissive license. As licensing authority Heather Meeker notes, AI assistants cannot indicate where a suggestion originated, making independent snippet scanning "probably the only way, and the last resort" to catch infringement. And the burden of identifying open source code, per compliance analysis, falls on the user receiving the code from the AI system — not the vendor.

Now translate that to agent skills. A SKILL.md package pulled from a random GitHub repo is code-by-instruction your agent will follow verbatim, with no review gate and no license metadata you can trust. Published research found that roughly 26% of skills carry at least one vulnerability — and that's before you ask what the instructions themselves might instruct your agent to do.

The stakes are not theoretical. A 2025 Black Duck audit found 99% of audited M&A transactions contained open source code, with 85% including license conflicts. AI-generated code compounds that exposure, and traditional compliance tools are not equipped to reliably detect AI-generated snippets at all.

This is the gap SkillzBench is being built to close. Its verification pipeline is designed to do what AI output can't do for itself:

• Pin every skill version to a commit SHA, so what you adopt is exactly what was reviewed — no silent upstream drift
• License-check each skill so copyleft or restrictive terms surface before adoption, not during due diligence
• Security-scan for the vulnerabilities that affect roughly a quarter of published skills
• Quality-judge and benchmark every skill in a sandbox, publishing the measured Lift with and without the skill

The point is sequencing. Today, legal and security review happens after an incident — after the skill is installed, after the agent acts, after the audit finds the conflict. SkillzBench's model moves that review upstream, so provenance, licensing, and security are established before a skill ever reaches your agents. Ongoing tracking and re-scanning of upstream changes keeps the record current as repos evolve.

Provenance is the new compliance. In an agent-skills ecosystem where anyone can publish anything, the differentiator isn't access to skills — it's knowing, provably, what you just let into your codebase.

Your Action Plan: Staying Legal and Audit-Ready

Knowing that AI coding is legal but risky is only half the battle — the other half is building practices that keep your codebase defensible when lawyers, acquirers, or regulators come knocking.

Start with managed adoption, not bans. Open source licensing expert Heather Meeker's advice is blunt: outright warnings push developers toward unguarded free tools, so the defensible path is to choose paid tools, enable guardrails, use snippet scanning, and apply your existing license policies to AI outputs. Paid tiers also matter for indemnification — free versions typically aren't covered, and indemnities are often capped at fees paid.

Next, scan every AI-generated snippet before it ships. AI assistants strip license metadata and can't tell you where a suggestion originated, which makes independent snippet-scanning tools "probably the only way, and the last resort" to catch infringement. When a scan flags a conflict, resolve it deliberately: remove, remediate, rewrite, or relicense the code.

Then make your compliance records audit-ready. This is where practical risk lives: a 2025 Black Duck study found 99% of audited M&A transactions contained open source code, and 85% included license conflicts. With SBOM enforcement ramping up for large manufacturers and their suppliers, undocumented AI-generated code becomes deal friction — and clean records become a competitive advantage.

Finally, verify third-party components before deployment — including the agent skills your coding assistants load. Published research found roughly 26% of skills carry at least one vulnerability, and provenance is usually missing entirely. Before adopting any skill or snippet, check:

• License terms — is the component permissive, copyleft, or unknown?
• Provenance — can the version be pinned to a specific commit or source?
• Security posture — has it been scanned for known vulnerabilities?
• Maintenance signals — is the upstream actively updated or abandoned?

That verification gap is exactly why we're building SkillzBench: an independent testing lab that pins every agent skill to a commit SHA, runs license checks and security scans, and benchmarks each skill in a sandbox so you can see its measured Lift before it touches your codebase. If you're assembling agent loadouts for Claude Code, Cursor, or Codex, verification shouldn't be an afterthought.

The legal landscape will keep shifting — over 50 ML-training cases remain pending in U.S. courts, and the Copilot appeal is still undecided. But the playbook above doesn't depend on how those cases land. Managed tools, snippet scanning, audit-ready records, and verified components will keep you compliant under virtually any outcome. Skills, proven. Join the SkillzBench waitlist to get verified, benchmarked skills with provenance and license checks built in.