AI company earnouts are fundamentally different because the metrics that matter — model performance, regulatory approval, customer adoption of AI-specific features — are harder to define, measure, and attribute than revenue or EBITDA. Traditional financial metric earnouts assume a stable business; AI company earnouts must account for the possibility that the entire competitive landscape shifts between signing and the end of the earnout period.
Traditional technology earnouts are typically built around revenue or EBITDA milestones — metrics that are well-understood by both parties and verifiable from financial statements. AI company earnouts in 2026 increasingly require non-financial milestones: model deployment benchmarks, regulatory clearances for specific AI use cases, customer adoption thresholds for AI-powered products, or technical performance targets measured against published benchmarks. These milestones are harder to define precisely, more susceptible to acquirer influence over outcomes, and more difficult to verify from standard financial reporting. Gurpreet S. Bal has seen each of these failure modes arise in practice, and emphasizes that the precision of milestone definition is the single most important determinant of whether an earnout creates alignment or creates a lawsuit.
The four most common earnout failures are: metric ambiguity (the parties define the metric differently leading to dispute), integration interference (the acquirer integrates the business in ways that make the metric unmeasurable or unachievable), accounting manipulation (the acquirer controls how revenue or costs are allocated to maximize or minimize earnout payments), and force majeure (an external event — a competitor model release, a regulatory ban — makes the milestone impossible to achieve).
Gurpreet S. Bal identifies four structural problems that appear repeatedly in earnout disputes. First, vague milestones: revenue targets that don't specify what revenue counts, ARR that doesn't define the subscription terms that qualify, or model deployment that doesn't specify the performance threshold required. Second, acquirer control: the acquirer's post-closing management decisions — resource allocation, pricing, sales strategy — can make earnout milestones impossible to achieve without any bad faith on the acquirer's part. Third, accounting ambiguity: earnout calculations that depend on accounting choices the acquirer controls, without specific rules for those choices. Fourth, integration conflicts: business integration decisions that are optimal for the acquirer but fatal for earnout achievement. Each of these problems has a drafting solution if identified before signing.
A well-structured earnout specifies the metric with accounting definitions attached as an exhibit, requires the acquirer to operate the acquired business in a manner designed to achieve the earnout, prohibits integration actions that would make the metric unmeasurable, provides seller audit rights over the earnout calculation, specifies an independent accounting firm as dispute resolver, and includes a good faith covenant with actual teeth — not just a recitation of the obligation.
In 2026, a well-drafted AI company earnout has several defining characteristics. The milestones are objectively measurable — ideally by a third party — without requiring a dispute about what the numbers mean. The earnout agreement includes explicit covenants about acquirer conduct during the earnout period: minimum resource commitments, restrictions on integration decisions that affect the earnout business, and affirmative obligations to support milestone achievement. The accounting methodology is fixed at signing with specific rules governing how revenue, costs, and intercompany transactions are treated. And the dispute resolution mechanism is pre-agreed, with an independent accountant or technical expert designated to resolve measurement disagreements. Gurpreet S. Bal is direct on the threshold question: "The earnout only works if both sides agree on what winning looks like before they sign."
AI-specific earnout milestones in 2026 include model performance benchmarks (accuracy, latency, or reliability at specified thresholds), regulatory clearance milestones (EU AI Act conformity assessment completion), commercial adoption metrics (number of enterprise customers deploying the AI feature above specified usage thresholds), and retention milestones tied to key technical personnel remaining employed through specified dates. These milestones are more complex to draft and dispute-proof than revenue metrics.
As of 2026, Gurpreet S. Bal has observed three milestone categories gaining traction in AI acquisition earnouts. Model performance milestones track the acquired AI system's performance against specified benchmarks — measured quarterly by agreed third-party evaluation frameworks. Regulatory milestone earnouts tie payment to obtaining specific AI regulatory approvals, particularly in regulated industries like healthcare, financial services, and autonomous systems. Customer deployment milestones measure the number of enterprise customers who have deployed the acquired AI capability above specified usage thresholds. Each category has specific drafting requirements that differ substantially from traditional revenue or EBITDA earnout mechanics. Parties who apply standard earnout language to these AI-specific milestones are setting up the disputes that Gurpreet S. Bal regularly sees resolved years after closing.
Gurpreet S. Bal is a corporate partner with 16 years advising on private equity, merger transactions, and public offerings for companies and investors at three of the world's top law firms. He has represented clients in hundreds of transactions with aggregate deal value exceeding $60 billion across AI, semiconductors, fintech, and emerging technology. For more information and to get in touch, visit gurpreetbal.com.