Description

A focused course, tailored for you

The AI Partnership Counsel's Contract Playbook

How an Associate General Counsel for AI partnerships drafts, negotiates, and closes data, model, and content deals that hold up under regulator review.

The deal lands at 4pm. The counterparty wants signature in two weeks. Their training-data reps assume a world without the EU AI Act. Your product team is asking when the data starts flowing. The redline you send back is the only thing standing between a partnership that closes and a partnership that becomes a regulator's case study.

$199 one-time

Tailored to your situation. Access within 24 hours. 30-day money-back.

Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.

Why this course

In-house counsel running AI partnerships occupy a specific contracting band that almost no template covers. You are not doing pure IP licensing, because the asset moves and mutates after the deal closes. You are not doing pure SaaS contracting, because the inputs are training data with provenance liability attached. You are not doing pure data-protection work, because the obligations cut across DPA, AI Act, sectoral content rules, and your counterparty's own platform terms. The deals you draft have to survive a fast counterparty redline, an internal product clock, a policy team that wants regulator-defensible reps, and a future audit that asks how the data was sourced and what the model was allowed to do with it. The standard playbook is to bolt on extra reps and call it done. That produces deals that close but do not hold up. This course gives you the structured drafting approach for the artefacts that actually decide whether a deal survives review: training-data provenance reps, output indemnity carve-outs, GPAI allocation, audit-rights design, breach-notification mechanics, and content-licensing scope. The deliverable is a fully redlined deal file plus the fallback positions you take when the counterparty pushes.

What you walk away with

Draft a training-data representation pack that survives both counterparty redline and post-close regulator review.
Negotiate output indemnity carve-outs that allocate AI Act and copyright risk without killing the deal.
Structure GPAI provider-versus-deployer allocation language that holds up under EU AI Act enforcement scrutiny.
Build an audit-rights clause stack that counterparties will accept and that gives you real visibility into the data pipeline.
Close partnership deals inside the product team's clock without leaving regulator-readiness on the cutting-room floor.

The 12 modules

Module 1. The AI Partnership Term Sheet, Read Three Ways

Walks through a real term sheet from a data partnership deal and reads it three times: through the product team's lens, through the policy team's lens, and through a regulator's lens looking at it eighteen months later. The exercise shows where the same paragraph looks acceptable to one audience and unacceptable to another. The output is a structured term-sheet review checklist you apply before every redline.

Module 2. Training-Data Representation Packs That Hold Up

The reps you draft about how the counterparty's training data was sourced, licensed, scrubbed, and consented are the single highest-stakes clauses in an AI deal. This module walks through the rep architecture that survives EU AI Act review, US copyright litigation, and downstream DPA enforcement. Each rep is paired with a counterparty redline you should accept and one you should reject, with the fallback language in between.

Module 3. Output Indemnity and the Copyright Question

Generative model outputs sit on top of training data the counterparty does not always own cleanly. This module structures the output indemnity clause: scope, carve-outs, caps, and the mechanics of injunctive carve-outs when the indemnified party wants to keep using the model. You leave with a clause stack that allocates copyright exposure without killing commercial appetite on either side.

Module 4. GPAI Provider Versus Deployer Allocation Under the EU AI Act

When your partnership involves a general-purpose AI model, the AI Act allocates obligations between the provider, the downstream deployer, and any party that substantially modifies the model. This module walks through the allocation clause language, the documentation flow that the AI Office expects, and the fallback positions you take when the counterparty resists being classified as a provider. Includes the GPAI code of practice mapping.

Module 5. Content Licensing Scope That Anticipates Fine-Tunes

Publisher and content-creator deals now have to anticipate that the licensed material may be used for fine-tuning, retrieval-augmented generation, embedding storage, and synthetic-data generation. This module structures the scope clause that distinguishes each use, with attribution, revocability, and royalty allocation language that survives the publisher's board review. Includes the negotiation script for the scope-creep question that always comes up at close.

Module 6. Audit Rights, Inspection, and the Real Diligence Stack

Audit rights in AI partnership deals are theatrical unless they map to a real inspection workflow. This module designs the audit-rights stack the counterparty will sign and that gives you actual visibility: data lineage logs, model-card delivery cadence, training-run documentation, and the inspection trigger language. Includes the falling-back script for when the counterparty resists inspection rights and you need to land an alternative.

Module 7. Breach Notification, DPA Plus AI Act Plus Sectoral

The breach-notification clause now has to coordinate at least three regimes: data-protection authority timelines, AI Act incident-reporting under article 73 for high-risk systems, and any sectoral notification you have inherited. This module structures the notification mechanics so the clauses do not contradict each other and so the counterparty cannot use one regime to slow down compliance with another. Output is a coordinated notification clause stack.

Module 8. Data-Sharing and DPA Architecture for Joint Training

Joint-training arrangements between two parties trigger a data-processing relationship that the standard SCCs do not cleanly address. This module walks through the controller-controller versus joint-controller analysis, the international transfer mechanics under the EU-US Data Privacy Framework, and the standard contractual clauses you actually need to attach. Includes a worked DPA schedule for a joint-training deal.

Module 9. Internal Approvals: Drafting the Memo Your Deputy GC Will Sign

Closing a deal inside the product team's clock requires that your deputy GC and your policy team sign off without a renegotiation. This module structures the internal memo that flags the three or four clauses where you took commercial trade-offs, explains the regulator-readiness analysis, and recommends a position. The memo template is the one you adapt for every deal you bring forward.

Module 10. Negotiation Mechanics: When To Hold, When To Trade

Walks through the actual negotiation flow on a real AI partnership deal: which clauses you hold, which you trade, which you reopen at the last call, and which you let the counterparty win to preserve a more important clause. Includes the script you use when the counterparty's commercial lead tries to escalate around their own legal team. The output is a structured trade-off map you bring to each deal.

Module 11. Post-Close: The Deal File Your Successor Can Read

An AI partnership deal becomes a living document the moment the counterparty fine-tunes the model or expands the dataset. This module structures the post-close deal file: amendment cadence, exception log, the regulator-readiness memo that lives alongside the executed agreement, and the handoff brief you write so your successor inherits a deal they can defend in audit. Includes the one-page deal-summary template product and policy teams actually read.

Module 12. Building Your Own Clause Library and Fallback Positions

The final module is the operating layer. Walks through how you build, version, and maintain a clause library for AI partnership deals: the master rep pack, the indemnity ladder, the audit-rights stack, the breach-notification matrix, and the fallback positions you take when each clause comes under pressure. You leave with the structure for a personal clause library you can use across every future deal you draft.

How this addresses your situation

Specific modules that map to what you said you are dealing with.

Module 2 (training-data reps) and module 5 (content-licensing scope) when a publisher or data vendor partnership is the active deal.

Module 3 (output indemnity) and module 4 (GPAI allocation) when the partnership involves model access, fine-tuning, or downstream deployment.

Module 6 (audit rights) and module 8 (DPA architecture) when joint training or joint dataset construction is on the table.

Module 9 (internal memo) and module 10 (negotiation mechanics) when the product team's clock is the binding constraint on close.

What you get with this course

Twelve written modules with worked examples drawn from real AI partnership deal artefacts.
A fully redlined data partnership agreement with margin notes on every clause that has been tested in negotiation.
Downloadable clause-library templates: training-data rep pack, output indemnity ladder, GPAI allocation block, audit-rights stack, breach-notification matrix, content-licensing scope module.
Internal-memo template for the deputy GC sign-off conversation.
Negotiation-flow worksheet for the trade-off map you bring to each deal.
The hand-built implementation playbook tailored to the specific deal types in your pipeline.

What you will have in hand by Day 1, Week 1, Month 1

Within 24 hours of purchase your learning environment account is provisioned and the implementation playbook is delivered alongside it.

All twelve modules are unlocked at provisioning. Work through them in any order matched to your active deal pipeline.

The clause-library templates and the worked deal file are downloadable from module one onward.

The hand-built implementation playbook is tailored to the specific partnership deal types you flag during onboarding.

Before and after

Before

You inherit term sheets, redline by instinct, and rely on outside counsel to flag the clauses that may not hold up under regulator review. Every deal is bespoke. Every approval cycle is its own negotiation. The clause library lives in your head.

After

You draft from a structured clause library with tested fallback positions. The training-data reps, output indemnity, GPAI allocation, audit rights, and breach-notification mechanics are pre-architected. The deputy GC memo writes itself from a template. Your deals close inside the product team's clock and survive regulator review eighteen months out.

What happens if you do not address this

AI partnership deals are now the artefact a regulator opens first when an enforcement action lands. A training-data rep that was acceptable last quarter may not survive the next AI Office investigation or the next copyright suit. Continuing to draft from a generic template produces deals that close cleanly and fall apart on review. The cost of getting it wrong is not a renegotiation, it is an enforcement reference.

Who it is for

You are an Associate General Counsel sitting inside an AI partnerships function. You draft, negotiate, and close data licences, model-access agreements, content partnerships, and joint-development deals where AI inputs or outputs are central. You report into a deputy GC or chief partnerships counsel. Your counterparties range from publishers and data vendors to other AI labs and infrastructure providers. You are accountable for deals that close inside the quarter and for the regulator-readiness of every clause you sign off on. You read the EU AI Act, the AI Office GPAI code of practice, FTC consent orders, and DPA enforcement decisions as part of your job.

Who this is NOT for. Not for litigators, not for pure transactional M&A counsel, not for compliance officers who do not draft contracts, and not for outside counsel who only review final drafts. The course assumes you are the person redlining the term sheet, drafting the schedule, and sitting on the negotiation call.

How it arrives

Text-based course in the Art of Service learning environment, plus downloadable templates and worked examples for every module, plus the hand-built implementation playbook delivered alongside course access.

Time investment. Plan on four to six hours total reading across the twelve modules, plus the time you spend adapting the clause-library templates to your own deal pipeline. The course is structured so each module maps to a discrete clause stack you can pull into your next active redline.

Why $199 is the right number

Outside counsel will redline any individual deal for you at partner rates, but they will not build you a clause library or a fallback structure across deals. Industry conferences cover the AI Act at a policy level, not at the clause-drafting level. Generic IP licensing courses do not address training-data reps, output indemnity, or GPAI allocation. This course is built specifically for in-house counsel who draft AI partnership deals and need a clause-level operating layer.

FAQ

Is this jurisdiction-specific?

The course covers EU AI Act and GPAI code-of-practice mapping in depth, US copyright and FTC posture, and the international-transfer mechanics under the EU-US Data Privacy Framework. The clause templates are drafted to be portable across counterparties in those regimes, with notes on where sectoral or country-specific overlays apply.

Do I need a contracts background to follow this?

Yes. The course assumes you are the person drafting and negotiating. It does not teach contract drafting from scratch. It teaches the specific clause stack that AI partnership deals require on top of standard transactional skills.

What does the implementation playbook cover?

The implementation playbook is hand-built after purchase, tailored to the partnership deal types you flag during onboarding. If your active pipeline is publisher deals and model-access agreements, the playbook focuses on training-data reps, content-licensing scope, and output indemnity. If your pipeline is joint-training and infrastructure deals, the playbook focuses on DPA architecture, GPAI allocation, and audit rights.

Is there a refund window?

Yes, thirty days from purchase, no questions.

30-day money-back guarantee. If after a week of working through the materials this is not what you needed, reply to the receipt email and a full refund is processed. No questions, no forms.

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.