Lovable vs Cursor vs GitHub: how to structure AI‑generated code so you clearly own it

Compare Lovable vs Cursor vs GitHub code ownership, training and exports. See which should be home for your repo and how to keep AI code clearly yours.

Lovable vs Cursor vs GitHub: who owns your AI‑generated codebase?

If you build a production app with Lovable, Cursor and GitHub in the loop, the safest move is to treat GitHub as the canonical home of your codebase. Lovable and Cursor can generate and modify code, but Lovable’s own terms and pricing page explicitly state that you own your apps, code, customer data and AI output. Cursor’s official terms place responsibility for inputs and IP compliance on the user and describe how the service may process content, but the specific “assigns all right, title and interest in suggestions” and “no training unless you opt in” language comes from a secondary legal summary rather than the Cursor ToS itself and should be treated as commentary, not the binding primary text. GitHub’s terms also state that you keep ownership of your content and Copilot output. The practical difference is that GitHub is built to be your long‑term system of record, while Lovable and Cursor are clients on top. This article shows how the terms line up, what happens under failure, and how to structure repos, exports and contracts so you clearly own everything and can hand it off in 3–5 years.

Quick decision: where your code should live

All three platforms have marketing and legal language pointing in the same direction: you keep ownership of your code and AI output.

Lovable: pricing and product terms state that you own your code, apps, customer data and AI output generated in Lovable, and that nothing in the agreement transfers ownership of customer data or AI output to Lovable(Lovable Pricing)(Lovable Product Terms). If you are using Lovable primarily to bootstrap an MVP before moving to a GitHub‑first stack, it pairs cleanly with the repo‑centric approach described in the Lovable + GitHub production workflow.
Cursor: Cursor’s published Terms of Service place responsibility for ensuring inputs do not violate third‑party IP on the user and describe how Cursor may use and process content to provide the service(Cursor ToS). A secondary legal commentary (not an official Cursor document) claims that users retain ownership of their inputs, that Anysphere assigns all right, title and interest in generated suggestions to users, and that content is not used to train models unless users opt in. Because Cursor does not currently spell those points out in the same terms on its own site, that commentary should be treated as interpretation, and specific ownership and training positions should be confirmed with Cursor directly for high‑stakes use(Cursor terms commentary).
GitHub and Copilot: GitHub’s terms of service state that users retain ownership of their content(GitHub ToS), and Copilot’s marketing explicitly says that if a suggestion is capable of being owned, GitHub does not claim ownership(GitHub Copilot). If you are deciding between a Cursor‑heavy editor workflow and a GitHub‑native assistant, compare this with the repo‑centric trade‑offs in GitHub Copilot vs Cursor for 2026.

The ownership question then stops being "who owns the code?" and becomes "where does the source of truth live, and how hard is it to move if a tool disappears?"

For most production teams, the default answer is:

GitHub holds the authoritative repo (or repos).
Lovable and Cursor read from and write to that repo under your control.
Your contracts, training settings and exports are all aligned with that reality.

Decision table: Lovable vs Cursor vs GitHub for code ownership

Platform	Best primary role	Starting price (AI use)	Main strength for ownership	Main limitation / risk
GitHub + Copilot	System of record for code; AI in editor/PRs	Copilot Pro from $10/user/mo; Copilot Business $19/user/mo(Copilot pricing)	Git repo as durable audit trail; terms affirm you retain ownership of content and Copilot suggestions	Under GitHub’s April 27, 2026 Terms of Service, GitHub may access and use publicly accessible content on GitHub for developing or training commercial AI systems, and for AI Features it may use inputs and outputs for model improvement unless you opt out or are covered by certain customer agreements; private code still passes through GitHub’s infrastructure
Lovable	AI app builder and hosting; generator into GitHub	Pro $25/account/mo with 100 credits(Lovable Pricing)	Explicit promise that you own your code, apps, customer data and AI output; first‑class GitHub export and sync	Builder and hosting can become a dependency; most code lives in Lovable until exported
Cursor	AI IDE on top of local/Git repos	Individual Pro $40/user/mo(Cursor pricing)	IDE‑style assistant layered on local and Git repos; official terms emphasise user responsibility for IP compliance; some ownership and training details are described in secondary legal commentary rather than Cursor’s own ToS	If your only copy of work is local and you lose the machine or access, Cursor won’t save you

How this comparison treats Lovable, Cursor and GitHub

This comparison is based on public terms of service, pricing pages and product‑specific legal documents from Lovable, Cursor and GitHub, plus a normalised operator reading of what those texts mean for code ownership. Pricing figures and clauses cited here come from the linked official documents and were checked against those pages on the dates noted in the research facts.

The focus is narrow: legal ownership language, model‑training defaults, where the code actually resides, export options and failure behaviour. It does not attempt a full feature comparison between Cursor and Copilot or a full security review of each platform. For a broader look at how these tools fit into a complete development setup, see the best AI coding stack for 2026.

Legal ownership: what each platform actually says

Lovable: explicit “you own your code” language

Lovable’s marketing and legal texts both lean hard into user ownership:

Lovable pricing page section with the bold statement that you own your code, apps, customer data stored in Lovable, and any AI output you generate in Lovable. — Lovable’s public pricing page spells out its ownership promise: you own your code, apps, customer data, and AI output generated in Lovable.

The pricing page states: “You own your code, which means the apps, websites, and other projects you build with Lovable, customer data stored in Lovable, as well as any AI output you generate in Lovable.”(Lovable Pricing)
The product terms specify that nothing in the agreement transfers ownership of customer data or AI output to Lovable(Lovable Product Terms).
The general terms say users own their customer data, including apps they build and AI output generated for them, subject to any third‑party rights in underlying models or training data(Lovable Terms).

Lovable’s enterprise data processing addendum describes Lovable as an AI‑powered builder that “hosts, stores, analyses and generates software development artefacts including source code, configuration files and commit history” to provide and improve the service(Lovable DPA). That means Lovable will process code quite deeply, but still under a processing licence rather than an ownership transfer.

Cursor: official ToS plus secondary commentary

Cursor’s own Terms of Service emphasise user responsibility for input and IP compliance and describe how Cursor may use and process content to operate and improve the service(Cursor ToS).

A separate legal commentary site summarises Cursor’s ownership stance as follows (paraphrased): users retain ownership of their inputs; Anysphere assigns rights in generated suggestions back to users; and content is not used to train AI models unless the user explicitly opts in(Cursor terms commentary). These points do not appear in this exact form in the public Cursor ToS and should be treated as a secondary interpretation, not a substitute for reviewing Cursor’s own legal documents.

GitHub and GitHub Copilot: you own content and suggestions; inputs/outputs may be used for training unless you opt out or are covered by specific agreements

GitHub’s terms of service (effective 27 April 2026) are clear on ownership:

“You retain ownership of your content.” GitHub needs a licence to host, display and process content, but does not take ownership(GitHub ToS).
The same terms include a clause allowing GitHub to access publicly accessible content on GitHub for training commercial AI systems, and users waive conflicting restrictions on their own sites as far as that public GitHub content is concerned(GitHub ToS AI‑training clause).

GitHub’s Copilot page then adds two important ownership and training signals:

“If a suggestion is capable of being owned, our terms are clear: GitHub does not claim ownership.”(GitHub Copilot)
Copilot is powered by generative AI models from GitHub, OpenAI and Microsoft. Under GitHub’s current Terms of Service and Generative AI Services terms, GitHub may use your inputs and Copilot outputs to develop, train and improve AI models unless you opt out in your account settings or your use is governed by a GitHub Customer Agreement or similar volume‑licensing terms that provide different default controls(GitHub Copilot)(GitHub Generative AI Services Terms).

GitHub’s generative AI services terms module reiterates that customers retain ownership of their data and that GitHub takes only the licence needed to process it for Copilot and related generative services(GitHub Generative AI Services Terms).

Licence back and model training: practical differences

Lovable’s processing licence and AI improvements

Lovable’s data processing agreement spells out that it hosts, stores, compiles, scans, indexes, analyses and generates software artefacts to provide and improve the service(Lovable DPA). That implicitly gives Lovable a licence to run code, logs and commit history through its engines, just as GitHub needs a licence to host repos.

Key implications for founders:

Your ownership of code and AI output is not in question.
Lovable will still process your code for features like hosting, deployment and recommendations.
If data‑processing or model‑training limits are crucial (for example, due to regulation or internal policy), an enterprise‑level DPA aligned with those policies is important.

Cursor: training position partly inferred from commentary

A legal commentary site notes that content is not used to train AI models unless you opt in(Cursor terms commentary). A legal commentary site claims that Cursor does not use customer content to train AI models unless the user explicitly opts in (this is not stated in these exact words in Cursor’s own public ToS and should be verified with Cursor for high‑sensitivity use cases).

This makes Cursor potentially attractive as an AI editing layer for sensitive repos, provided GitHub settings and corporate policies are also aligned and any strict no‑training requirements are confirmed directly with Cursor.

GitHub: training on public repos and AI credits model

For GitHub, the important distinctions are between public and private content, and between general hosting and AI Features:

Public repos can be accessed and used by GitHub to develop or train commercial AI systems under the 2026 terms(GitHub ToS AI‑training clause).
For AI Features such as Copilot, GitHub and its affiliates receive a licence to collect and use your inputs and outputs to develop, train and improve AI and ML models unless (a) you opt out in your account settings or (b) your use is governed by a GitHub Customer Agreement or volume licensing agreement that specifies different defaults. Private repos are processed for hosting and Copilot, and—unless an exception applies or you opt out—can be used in aggregated form for model improvement, even though they are not treated as public training data in the same way as public repos(GitHub Copilot)(GitHub Generative AI Services Terms).

GitHub’s move to usage‑based billing for Copilot introduces GitHub AI Credits, where 1 AI credit equals $0.01. Current materials list Copilot Pro at $10/month with 1,500 included AI credits and Copilot Pro+ at $39/month with 7,000 included AI credits, with additional usage drawing down credits and overages billed at per‑model API rates. A higher‑end Copilot Max plan at $100/month includes 20,000 credits, and some plans include an additional non‑guaranteed “flex allotment” of extra credits(Copilot individual plans).

Copilot Business is listed at $19 per user per month for organisations, and Copilot Enterprise at $39 per user per month, under separate enterprise‑oriented terms(Copilot Business & Enterprise billing). This cost model does not change ownership, but it does mean that AI usage in GitHub is tightly tied to both repos and billing settings. For a deeper breakdown of how Cursor, Codex and Claude Code compare on cost when layered into the same workflow, see the Cursor, Codex, Claude Code cost stack.

Where your code actually lives day to day

Lovable: builder and host first, repo second

Lovable is positioned as an AI‑powered application builder and hosting platform. Its DPA describes storage, analysis and generation of code and commit history(Lovable DPA). In practice:

Project structure, generated code, configuration and deployment pipeline initially live inside Lovable’s workspace.
Credits power not just AI generation but also hosting and end‑user AI features(Lovable Pricing).
GitHub integration is a core feature: Lovable’s Builder.io comparison page emphasises “full GitHub integration” and says you can export code at any time and extend it however you want(Builder.io vs Lovable).

Operationally, the danger is not that Lovable claims your IP, but that teams treat Lovable as the single source of truth and postpone repo exports. If Lovable goes down, the builder interface and possibly access to the latest unexported changes are lost.

Cursor: your local/Git repo is the only code home

Cursor is an IDE and AI coding assistant. It connects to local folders and Git repositories; Cursor itself does not become your code host.

Code is stored on developer machines and in Git remotes (GitHub, GitLab, etc.).
Cursor’s servers see enough context to power the AI features, but they are not your system of record.
If everything is already centred on GitHub, Cursor simply rides on top.

That means ownership and continuity are mostly about how Git is managed, not about Cursor. For a deeper look at running multiple AI coding tools on top of a single GitHub repo without compromising safety, see the AI coding agents CI workflow.

GitHub: durable system of record

GitHub is designed to be the persistent record of a codebase:

Git history, branches and pull requests form a timestamped audit trail.
Issues, discussions and releases add context around decisions.
Exporting or mirroring Git repos is straightforward; the Git format itself is portable.

Given GitHub’s stance that users retain ownership of their content(GitHub ToS), this makes GitHub the natural IP anchor for most teams, even when using Lovable or Cursor heavily.

Export and handoff friction

Lovable → GitHub

Lovable’s own comparison article highlights full GitHub integration and the ability to export code at any time(Builder.io vs Lovable). In the UI (as of the public product), it is possible to:

Connect a Lovable project to a GitHub repo.
Trigger exports or continuous sync of generated code into that repo.
Continue development outside Lovable once the code is in GitHub.

Handoff risk arises if exports are infrequent or tied to a single Lovable account. If a team member leaves or billing lapses, recent changes that never reached GitHub may be missing.

Cursor → GitHub

Cursor works inside whatever Git workflow is already in use. There is no separate export step: when a suggestion or agent change is accepted and committed, it is part of Git history and is pushed to GitHub like any other commit.

This is a clean scenario for auditability: the IDE does not own or store the canonical version of anything; the repo does.

GitHub → anyone

Because Git is decentralised, moving away from GitHub later is straightforward from a technical perspective:

git clone --mirror to another host.
Update remotes and CI/CD configuration.
Preserve Git history as evidence of provenance.

The legal relationship and AI‑training exposure change if you move to another host, but IP and history can move intact because GitHub does not own the content(GitHub ToS).

Failure and lock‑out scenarios

If Lovable is suspended or shuts down

From the perspective of the codebase:

If GitHub is already the canonical repo: exported code and history are retained. Lovable’s builder UX, some deployment automation and any unexported changes are lost.
If Lovable is the only copy: IP is still theoretically the customer’s per Lovable’s terms, but access may be practically lost if the platform is offline and there is no recent export.

Given that Lovable’s pricing and terms stress that customers own their code and can export it any time(Lovable Pricing)(Lovable Product Terms), the main mitigation is procedural: schedule exports and ensure GitHub is up to date.

If Cursor is suspended

If Cursor is suspended or discontinued:

Local repos and GitHub copies remain intact.
Cursor logs or conversation history might not be available for later review.
Teams can switch to another IDE or AI tool (e.g. Copilot) without moving the repo.

Because Cursor does not host code, the risk is mainly around productivity, not access.

If GitHub is suspended or inaccessible

GitHub is the most critical dependency if it is treated as the system of record:

If billing fails or the organisation account is locked, repositories may become read‑only or inaccessible until resolved.
If GitHub itself experiences a major outage, clones on developer machines are the only live copies.

Mitigation is standard Git hygiene: ensure multiple contributors have up‑to‑date local clones, consider periodic push mirrors to another Git provider, and document a recovery process.

Team and contractor scenarios: who owns what

Legally, each platform treats ownership similarly in broad outline: the customer or user supplies inputs, the platform processes and generates outputs, ownership stays with the customer, and the provider gets a processing licence only.

The more subtle risk lies in account ownership and contracts with people, not platforms:

If a contractor builds a Lovable project in a personal account and exports to their own GitHub, the client is relying on their contract and cooperation to hand over those accounts and repos.
If developers connect Cursor or Copilot to personal GitHub accounts, IP may end up scattered across repos the company does not control.

To keep ownership clean:

Use organisation‑owned GitHub, Lovable and, where feasible, Cursor accounts.
Ensure contracts state that all code, configuration and AI‑generated output created in the course of work are works made for hire (where applicable) and assigned to the company, regardless of which AI tool was used.
Mandate that all production code flows into organisation‑owned GitHub repos, not personal forks. For teams also experimenting with autonomous agents on top of these repos, pair this with a proof‑gated CI pipeline like the one in AI coding agents in GitHub Actions.

Long‑term maintainability without the original tool

Maintaining a Lovable‑generated app outside Lovable

Lovable promotes the idea that, because of full GitHub integration, users can export code and continue development elsewhere(Builder.io vs Lovable). In practice, maintainability will depend on:

How idiomatic the generated framework code is.
Whether infrastructure and hosting details are tied to Lovable Cloud features.
How cleanly the exported repo is structured (mono‑repo, app vs infra, environment configuration).

Once the code is in GitHub, it can be run and hosted elsewhere, but some adaptation work is likely. The earlier Lovable is treated as a generator and not the only runtime, the smoother that transition becomes.

Maintaining Cursor‑assisted code without Cursor

Because Cursor simply edits files, maintainability without Cursor is straightforward. Any other IDE or AI assistant can work on the same repo. The only pieces potentially lost are Cursor‑specific configuration or histories, which are usually not required for ongoing operation.

Maintaining GitHub‑hosted code without Copilot

Copilot does not change the structure of a repo; it just suggests code. If Copilot disappeared, Git history would remain and work could continue manually or with another tool.

An end‑to‑end ownership‑safe workflow

An ownership‑conservative setup for a founder using Lovable, GitHub and Cursor (or Copilot) together is:

GitHub as the single source of truth.
Lovable as a generator and early‑stage host.
Cursor or Copilot as AI layers on top of the repo.

Below is a concrete workflow that implements this.

Step 1: set up organisation‑owned accounts and repos

Create or use a GitHub organisation owned by the company.
Inside that organisation, create a main app repo (product-app) and, if needed, supporting repos (infra, design-system).
Ensure that founders, staff and contractors work only inside organisation repos for production work.
Decide on mono‑repo vs multi‑repo:
- Mono‑repo is simpler for early SaaS: one place for app, infra and infrastructure‑as‑code.
- Multi‑repo may help when there are shared libraries across products.

Step 2: configure Lovable to treat GitHub as primary

Sign up for Lovable under a company‑owned account or email alias.
Connect Lovable to the organisation‑owned GitHub, not to personal accounts.
Create a new Lovable project and, when prompted, choose the organisation repo as the sync target (e.g. product-app).
Use Lovable to generate the initial MVP. As iterations proceed, ensure that Lovable commits and pushes to the GitHub repo on each material change, or configure continuous sync if available.
Adopt a simple internal rule of thumb: no feature is considered real until it is visible in GitHub.

Cost context for this phase:

Lovable Pro at $25 per account per month with 100 credits gives a solo founder a full year of builder usage for $300 in direct subscription cost (25 × 12), excluding GitHub’s own pricing(Lovable Pricing). For a deeper breakdown of how credits interact with real workloads, see the Lovable pricing 2026 analysis.

Step 3: structure the GitHub repo for future maintainers

Once Lovable has created the initial codebase and synced it to GitHub, it is helpful to invest in restructuring quickly so the repo stands on its own without Lovable:

Ensure a README.md explains how to run the app locally without Lovable.
Separate environment configuration and secrets from Lovable‑specific deployment hooks.
Create folders for infra/ (Terraform, Pulumi or similar) if there is an intention to migrate away from Lovable Cloud later.
Tag early Lovable‑generated releases (for example, v0.1-lovable) to provide a clear cut‑off for later audits.

Step 4: bring Cursor or Copilot into the GitHub‑centred workflow

AI tools can then be treated as clients on top of the GitHub repo.

Using Cursor

Developers clone the organisation repo locally: git clone git@github.com:org/product-app.git.
Open the folder in Cursor, and sign into Cursor with a company‑owned seat.
Check Cursor settings for any controls related to data use and training, and, if strict no‑training is a requirement, verify the current position directly with Cursor in addition to relying on secondary commentary(Cursor ToS)(Cursor terms commentary).
Use Cursor to implement features, but keep the Git flow conventional: feature branch → pull request → review → merge.

Cost context:

Cursor Individual Pro at $40 per user per month costs $480 per year for one heavy user (40 × 12), with overage billed at model API rates(Cursor Models & Pricing).

Using GitHub Copilot

Enable GitHub Copilot Business or Enterprise on organisation repos as required.
Developers use VS Code, JetBrains or the GitHub web IDE with Copilot enabled.
Ensure admins understand Copilot’s terms around using prompts and suggestions to improve models, including the ability to opt out or rely on enterprise controls where applicable(GitHub Copilot)(GitHub Generative AI Services Terms).
Require that all AI‑generated suggestions are committed via normal GitHub PRs for auditability.

Cost context for a small team:

Five developers on Copilot Business at $19/user/month is $1,140 per year (19 × 5 × 12) for team‑wide AI assistance tightly coupled to GitHub(Copilot Business billing).

Step 5: contract language and account control

People and contracts should be aligned with what the tools’ terms say:

Work‑made‑for‑hire / assignment clause: contracts with employees and contractors should state that all code, configuration, documentation and AI‑generated output created in the course of work are assigned to the company.
Account usage: require the use of company accounts for Lovable, GitHub and (where possible) Cursor. Prohibit storing production code solely in personal accounts.
Export requirement: specify in contractor SOWs that any Lovable projects or other AI builder outputs must be exported into the company GitHub repos before milestones are considered complete.

Step 6: configure AI‑training and privacy settings

Given the documented stances:

GitHub: understand that public repos can be used by GitHub for training commercial AI systems per the 2026 ToS(GitHub ToS AI‑training clause). For proprietary product code where this is a concern, keep repos private.
Copilot: factor in that prompts and suggestions may be used to improve models unless you opt out or are covered by specific enterprise arrangements(GitHub Copilot)(GitHub Generative AI Services Terms). For highly sensitive projects, consider limiting Copilot exposure or using stricter enterprise controls.
Lovable: rely on the product terms for ownership but use an enterprise‑grade DPA if explicit commitments on how data is used to improve the service are required(Lovable DPA).
Cursor: according to one secondary legal commentary, Cursor does not use user content to train AI models unless the user explicitly opts in; Cursor’s own public terms do not currently spell this out in the same language, so this should be verified directly with Cursor for any strict no‑training requirement(Cursor terms commentary).

Step 7: resilience if one tool fails

With this architecture, behaviour under failure becomes predictable:

If Lovable fails: the GitHub repo remains available; teams can redeploy elsewhere, using the exported code and infra scripts. Lovable’s builder is lost, but not the IP that has been exported.
If Cursor or Copilot fails: the repo remains untouched; organisations can switch to another AI assistant or traditional development.
If GitHub is inaccessible: local clones remain as backups; mirroring to another Git host later is possible. Ownership still sits with the customer per GitHub’s terms; the risk is operational, not legal.

Cost model: ownership‑safe stack over a year

Using the pricing figures above, it is possible to roughly estimate the subscription cost of different ownership‑safe patterns (excluding hosting, CI/CD and API overages):

Lovable‑first with GitHub as mirror: Lovable Pro at $25/month is $300/year (25 × 12). GitHub can often fit on an existing paid or free plan.
Cursor‑first on GitHub: Cursor Individual Pro at $40/month is $480/year (40 × 12) per heavy user.
Team on Copilot Business: 5 developers at $19/user/month is $1,140/year (19 × 5 × 12).
Hybrid Lovable then Cursor: 3 months of Lovable Pro ($75 total) plus 9 months of Cursor Pro ($360) totals $435/year in AI tools, with GitHub as the anchor throughout.

Across all these scenarios, ownership patterns do not change: the tools’ terms continue to state that customers own their content and AI output; the main variable is where code actually resides and how easy it is to move.

When the decision flips away from GitHub‑first

GitHub as system of record plus Lovable/Cursor as clients is a sensible default. There are, however, legitimate scenarios where the emphasis shifts.

Non‑technical or design/ops‑heavy team shipping prototypes

If an early team is non‑technical and needs to ship prototypes without learning Git, it can be pragmatic to run Lovable as the primary workspace for a few months and treat GitHub as a periodic backup. The ownership risk is mitigated if exports are scheduled at each milestone and Lovable is connected to a company GitHub organisation from day one. A deeper look at cost trade‑offs is available in analyses of the Lovable credits model and real‑world monthly cost.

Existing GitHub‑heavy teams already using Copilot

If engineers already live in GitHub and rely on IDE‑centric workflows, there is little reason to move primary code ownership to Lovable. Cursor or Copilot can sit on top of the existing repos; the system of record remains GitHub.

Strict no‑training requirements

If code must not be used to train third‑party models, the decision becomes about configuration:

Keep sensitive repos private on GitHub and review Copilot settings and enterprise controls carefully.
Use Cursor with any relevant training opt‑out controls, and confirm current practice directly with Cursor.
Use Lovable only under a DPA that is acceptable, or keep such code entirely outside Lovable.

Impending acquisition or external audit

For due diligence, auditors mainly want a clear, timestamped history and proof that the company owns what it says it owns. A GitHub‑first mono‑repo or well‑structured multi‑repo, with Lovable exports and Cursor‑assisted commits all visible there, is a clean story to present.

Lovable‑centric product with no appetite for infra

If Lovable’s managed hosting and builder UX are part of the product strategy, that implies accepting more platform dependence as a trade‑off for speed. In that case, run Lovable as primary, but:

Keep GitHub as a hot standby and export on a schedule.
Document the procedure for running the app without Lovable, even if there is no immediate intention to use it.

What would change this recommendation

The central recommendation in this article is to anchor the codebase’s ownership and audit trail around GitHub, with Lovable and Cursor acting as replaceable layers on top. That recommendation would need revisiting if any of the following happened:

GitHub Terms of Service page showing the Ownership and License Grants section where it states that users retain ownership of their content. — GitHub’s Terms of Service explicitly state that you retain ownership of your content, with GitHub receiving only the licenses needed to host and process it.

GitHub Copilot marketing page section containing text that if a suggestion is capable of being owned, GitHub does not claim ownership, alongside other description of Copilot. — On the Copilot product page, GitHub reiterates that if a suggestion is capable of being owned, it does not claim ownership, aligning the marketing with the legal position.

Material terms changes: if Lovable, Cursor or GitHub altered their terms to claim joint ownership of AI output, to assert stronger rights over customer code, or to force aggressive model‑training rights without opt‑out.
Deep platform‑native infra: if Lovable or a similar builder evolved into an environment where large parts of app logic only exist as platform‑specific configuration that cannot be exported into conventional repos.
Regulatory shifts: if regulatory regimes began treating AI‑generated code’s copyright in a way that required a specific type of repository or attestation that GitHub could not provide.
Enterprise‑only hosting: if a company reached a scale where all code had to reside in self‑hosted or dedicated environments, making a cloud service like GitHub itself non‑viable as a long‑term system of record.

Until such changes occur, the combination of public terms, Git’s portability and GitHub’s role as the de facto source of truth for many software projects makes a GitHub‑first, Lovable/Cursor‑as‑clients pattern a robust way to ensure that AI‑assisted codebases remain clearly owned and can be handed off cleanly in three to five years.

Cursor documentation page listing subscription plans, per-seat prices, and model usage rates, including notes about API usage and overage costs. — Cursor’s models and pricing page shows its per-seat subscription costs and how model usage is billed, reinforcing that Cursor is a client over your existing repos and APIs rather than a code host.