What the terms let you do with AI output

This is a reading of published terms, not legal advice. Every clause below was re-read at its live page on 2026-10-16. The full record is in rnd, under experiments/generated-content-terms/ and experiments/stage1-regeneration/.

How it started

The studio has Google AI Ultra, which comes with Flow, Flow Music and Project Genie. Before anything they made could ship, the maintainer asked for a licence check. Reading Google’s terms raised a bigger question, because the studio trains its own small models. Could AI-written text from any provider be used as training data?

The maintainer set the rule for answering it. A use is allowed unless the provider’s contract or licence restricts it, and the search for restrictions is documented: the page, the exact clause, and its date. Uses that providers commonly ban (training on outputs, distillation, competing models) get that explicit check before anything relies on them. A model’s reading of the terms proves nothing, including the studio’s own outside reviewer’s.

The first version of the rule went further: it treated anything not expressly permitted as off limits. That made silence a ban, which it isn’t. Only a contract you agreed to, or a licence, can restrict a use. The rule was reworded the same day, and the verdicts below follow the reworded version.

A second correction followed the next day. The studio had read Google’s “related AI technology” at its widest, so it covered even small pieces of pipeline code. The outside reviewer pointed out that the clause sits under “Don’t abuse our services”, and that its purpose is stopping people distilling Gemini into a rival model. So the clause bars training data and distillation, and code the studio owns is ordinary software whoever wrote it. A two-tier version that made anything touching a model “strict” was tried the same day and withdrawn: it kept producing bars no clause contained. The rule is now simply that a use is barred only by a clause you can name.

The rule earned its keep. The outside model’s first pass got two things wrong:

• it said Claude’s consumer terms bar all commercial use, when that clause covers evaluation access only;
• it said the Llama licence bars training non-Llama models, when Llama 3.3 permits it if the new model’s name begins with “Llama”.

Both were caught by re-reading the source.

The four uses

Use	Google (consumer)	xAI (Grok)	Anthropic (Claude)	Moonshot (Kimi app)
Commercial assets	Allowed. Flow Music grants “Commercial use rights” outright; for the rest, Google disclaims ownership and restricts nothing that applies	Allowed, with attribution (“Written with Grok”)	Allowed: you own the outputs	Barred: non-commercial use only
Training data	Barred	Barred: no distilling outputs	Allowed for non-competing specialized classifiers and tools, never as training targets	Barred for a business: non-commercial use only
Evaluation only	Barred where it serves developing a model; allowed otherwise	Unclear: the AUP’s “develop … machine learning models” clause	Allowed on the same reading	Barred for a business
Code	Allowed when no model trains on it and it builds no rival (the clause is read by its anti-distillation purpose)	Allowed for tooling that develops no model and doesn’t compete, with attribution	Allowed for non-competing specialized tools	Barred for commercial use

The clauses behind the table:

• Google (Terms of Service, effective 2026-07-30) bars “using AI-generated content from our services to develop machine learning models or related AI technology”. It has no competition qualifier, and Antigravity’s terms incorporate it. Google’s Gemini API and Cloud terms are narrower: they bar only models or products that compete.
• xAI (consumer terms, last updated 2026-10-12): “you retain your ownership rights to the User Content”, and using Output requires attribution under the Brand Guidelines. The Acceptable Use Policy (2026-08-14) separately bars “distilling model data or Outputs”.
• Anthropic’s Help Center (article 12326764, 2026-03-16): “You can use Claude’s Outputs to train models that don’t compete with Anthropic’s own models.” It bars general-purpose chatbots, models designed for open-ended text generation, and “Using Outputs as training targets for models”. Anthropic’s support channel, asked about this exact use, said it fits. That reply came from an AI support agent, so the article stays the proof.
• Moonshot’s Kimi app: “you may only use Kimi in a non-commercial, personal research, and study manner.”

Open weights: read what the licence says about outputs

Running an open-weight model yourself removes the service’s terms, but not the model’s licence. Most licences grant rights in the weights and say nothing about what the model writes.

Licence or card	What it says about outputs	Verdict
OpenMDW-1.1 (Nemotron 3, 3.5 Lightning, 3.5 Super-VL)	“does not impose any restrictions or obligations with respect to any use … of any outputs”	Proven
Muse-Glimmer-30B card	Its intended uses include “Synthetic data generation” for “downstream model development”	Proven
DeepSeek-R1 (MIT)	“including, but not limited to, distillation for training other LLMs”	Proven
Muse Model License (Muse-Spark-816B)	Synthetic data is an intended use, but a model trained on its outputs is a “Derivative” that must carry the licence and display “Built with Muse Spark Open”	Proven, with conditions
Llama 3.3	Permits training on outputs if the new model’s name begins with “Llama”	Proven, with a naming condition
NVIDIA Open Model License	“does not claim ownership” of outputs, and no restriction on them	Allowed
Plain Apache-2.0 or MIT (Qwen, Gemma 4, Granite, Kimi K3)	Nothing on outputs	Allowed: the licence makes no claim and no contract binds a local run. Check any model-specific use policy too (Gemma has one)

On the last two rows, the Linux Foundation’s OpenMDW launch post notes that Apache-2.0 and MIT were written for code and are silent on generated outputs. OpenMDW-1.1 says outright that outputs are unrestricted. Silence and an explicit grant reach the same place: nothing restricts the use.

The route matters too. The studio’s Kimi seat turned out to run in the Zed editor through Ollama Cloud, not Moonshot’s app. Ollama’s terms (May 2026) say “You retain ownership of content you create using our services”, and restrict only building products that compete with Ollama. The Kimi K3 and K2.6 licences are MIT-style. So the app’s non-commercial clause never applied to that code.

What the studio found in its own work

An inventory traced every piece of text in the Stage 1 pipeline to whoever wrote it. Three findings stood out:

• The training rows were written by Kimi (the pilot), Grok (batches 2–8) and Antigravity (the 210 apply items). Grok’s and Google’s terms bar training on them.
• The generator showed the writer finished Kimi and Grok rows as examples, so even a clean model would have been prompted with barred text.
• The “real-source” excerpts weren’t human-written. They came from the studio’s own repos, and those repos were mostly written with Claude.

What changed

• All 810 training rows and 210 apply items are being regenerated by licence-proven open models: Muse-Glimmer and Nemotron on Colab, and a larger Nemotron on a rented GPU if a pilot earns it. The writer and critic always come from different families.
• The method carries over: the lessons, the gates, the defect catalogue and the evaluation sets. Claude wrote those, and Anthropic’s terms allow that use for a specialized verifier. No Claude text becomes a training target.
• The code stays, whoever wrote it. Under the two-tier rule, pipeline code that no model trains on is ordinary software. Three small Antigravity-written pieces had already been re-implemented under the earlier, stricter reading; they stay as they are. Grok’s generator logic stays too, with its text replaced. The tooling Grok and Kimi wrote is credited in CONTRIBUTORS.md files that say “Written with Grok”.
• Provenance is now recorded at the source. Every text-bearing template and pack in the generator gets a record of who wrote it, and the generator’s own check refuses one without it. Seat commits carry a trailer naming the seat.

If you’re doing the same

1. Ask per use. Shipping, training, evaluating and code are separate questions.
2. Read the use limits, not just ownership. Acceptable-use policies are where the training bans live.
3. Don’t mistake silence for a ban, or a ban for silence. Only a contract you agreed to, or a licence, can restrict a use. Find the clauses that do (training, distillation and competing-model bans), and record where you looked.
4. Check which account you used. Consumer, API and enterprise terms differ, and so do apps and hosts serving the same model.
5. Re-read every clause yourself. A model’s summary of the terms, ours included, got things wrong.
6. Record who wrote what when it’s written. An inventory after the fact needed two agents reading PR threads and commit trailers, and still left gaps.
7. Mind the attribution clauses: “Written with Grok”, “Built with Muse Spark Open”, and Llama’s naming rule.
8. Name the clause, or it’s allowed. Never read an undefined phrase at its widest just to be safe, and never add a tier or a filter the terms don’t contain: that ratchet ends up banning a temperature monitor.