AI Training Data and M&A Risk: What Founders and Acquirers Need to Know

As artificial intelligence continues to reshape the software landscape, a new class of due diligence questions is emerging—particularly around how AI models are trained. For founders and CEOs of AI-driven companies, one question looms large during M&A discussions: Could training your AI model on copyrighted or web-scraped content create legal or valuation risks during or after an acquisition?

The short answer is yes. And the implications can be material—both in terms of deal structure and post-close liability. In this article, we’ll explore how training data provenance affects M&A due diligence, what buyers are looking for, and how sellers can proactively mitigate risk.

Why Training Data Matters in M&A

In traditional software M&A, diligence focuses on code ownership, customer contracts, and financial performance. But in AI transactions, the model itself—and the data used to train it—becomes a core asset. If that data includes copyrighted material or was scraped from the web without proper authorization, it can raise red flags for acquirers, especially those with public market exposure or institutional LPs.

Buyers are increasingly asking:

• Was the training data obtained legally and ethically?
• Does the company have documentation of data sources and licenses?
• Could the model’s outputs infringe on third-party IP rights?
• Are there any pending or foreseeable legal challenges related to data use?

These questions aren’t theoretical. In recent years, lawsuits have been filed against AI companies for allegedly using copyrighted images, text, and code in training datasets. While the legal landscape is still evolving, the risk is real—and buyers are taking notice.

How This Affects Deal Structuring and Valuation

From an M&A perspective, questionable training data can impact a deal in several ways:

1. Reps and Warranties

Buyers will likely require specific representations and warranties around data ownership and usage rights. If the seller can’t make those reps confidently, it may lead to carve-outs, indemnities, or even escrow holdbacks. For more on this, see our article on Mergers and Acquisitions: Reps and Warranties Negotiations.

2. Valuation Haircuts

Uncertainty around data provenance can lead to discounted valuations. Buyers may apply a risk-adjusted multiple or shift more of the purchase price into contingent earn-outs.

3. Post-Close Liability

If a lawsuit arises after the deal closes, the acquirer could be on the hook—unless protections were built into the agreement. This is especially concerning for strategic buyers with brand exposure or public shareholders.

Case Study: A Hypothetical AI SaaS Exit

Consider a fictional AI SaaS company, “LexIQ,” which built a natural language model trained on millions of web pages, including news articles, blogs, and academic papers. The company scraped this data without explicit permission, assuming it fell under “fair use.”

During diligence, a strategic buyer’s legal team flags the issue. They determine that some of the training data likely includes copyrighted material from major publishers. As a result:

• The buyer reduces the offer by 20% to account for potential legal exposure.
• They require a $2M indemnity cap and a 12-month escrow.
• The deal shifts from a stock purchase to an asset purchase to isolate liability.

LexIQ’s founders, who were expecting a clean exit, now face a more complex and less favorable transaction. This scenario is increasingly common in AI M&A.

What Sellers Can Do to Prepare

Founders and CEOs of AI companies should take proactive steps to de-risk their training data before entering the market:

1. Audit Your Data Sources

Document where your training data came from, how it was collected, and under what terms. If you used third-party datasets, ensure you have the appropriate licenses.

2. Segregate or Retrain Risky Models

If parts of your model were trained on questionable data, consider retraining using licensed or synthetic datasets. This can be a significant investment, but it may preserve deal value.

3. Work with Legal Counsel

Engage IP counsel familiar with AI to assess your exposure and help craft defensible positions. This is especially important if you’re preparing for a sale or capital raise.

4. Prepare for Buyer Diligence

As we noted in Due Diligence Checklist for Software (SaaS) Companies, buyers will scrutinize your IP, data, and compliance practices. Having a clean, well-documented data pipeline can accelerate the process and build buyer confidence.

How iMerge Helps Navigate AI-Specific Risks

At iMerge, we’ve advised on numerous software and AI transactions where data provenance played a pivotal role. Our team helps founders anticipate diligence questions, structure deals to mitigate risk, and position their companies for maximum value. Whether you’re preparing for a strategic exit or evaluating unsolicited offers, we bring deep experience in software M&A and a nuanced understanding of emerging AI issues.

We also help clients assess whether an asset versus stock sale structure is more appropriate given potential liabilities—an increasingly relevant consideration in AI deals.

Conclusion

As AI becomes more central to software M&A, the legal and ethical sourcing of training data is no longer a back-office concern—it’s a boardroom issue. Founders who address it early can preserve deal value, reduce friction, and build trust with acquirers. Those who ignore it may find themselves negotiating from a position of weakness.

Use this insight in your next board discussion or strategic planning session. When you’re ready, iMerge is available for private, advisor-level conversations.