Traditional MAC definitions were calibrated for companies with stable revenue streams and predictable financial metrics. AI companies face valuation swings, competitive displacement from foundation model releases, and regulatory intervention that can materially affect their business overnight — none of which cleanly fit existing MAC carve-out language. Buyers and sellers are now negotiating AI-specific MAC provisions because the standard form language creates more uncertainty than clarity.
Standard MAC definitions exclude general market conditions, changes affecting the industry broadly, and changes in the target's stock price. These carve-outs were calibrated for businesses where company-specific events — not industry-level developments — drive dramatic value shifts. In AI, that assumption doesn't hold. A single competitor announcement about model performance can reprice an entire category of AI company. Regulatory action in Europe or a policy shift from a major cloud provider can reshape the business environment overnight. Gurpreet S. Bal has observed that the standard industry-wide carve-out — which buyers agreed to for decades — is now among the most contested provisions in AI acquisition term sheets.
For AI companies where 30% valuation swings can occur based on a single competitive announcement or model release, the traditional MAC standard of a durationally significant impact on long-term earnings power is difficult to apply. Courts have not yet addressed what constitutes material for AI-specific factors, which means the analysis will default to the Akorn standard — requiring buyers to show a substantial, durable change — even when the AI company's competitive position has fundamentally shifted.
The word "material" in MAC definitions has been interpreted by Delaware courts to mean a durationally significant decline affecting the long-term earnings power of the business — not a short-term fluctuation. As of 2026, AI company valuations regularly experience swings that would be catastrophic for a traditional technology business but may be transient for an AI company navigating a rapidly evolving competitive landscape. Gurpreet S. Bal notes that buyers and sellers are now fighting over whether AI-specific benchmark shifts, model deprecation announcements, or changes in the compute cost environment qualify as "material" in the legal sense — a question that no court has answered definitively for AI-specific transactions.
Buyers are pushing for MAC triggers covering loss of a key AI talent cluster, regulatory prohibition of the core AI use case, and foundation model license revocation. Sellers are pushing for carve-outs covering competitive AI announcements, industry-wide regulatory changes, and valuation changes driven by general AI market conditions. These provisions are genuinely novel and there is no established market standard — every deal is negotiated fresh.
In 2026, sophisticated parties are negotiating MAC definitions that explicitly address AI-specific risk factors. Buyers are seeking to include: changes in AI regulatory requirements affecting the target's primary markets, material changes in model performance benchmarks published by third parties, and loss of key AI talent above specified thresholds. Sellers are pushing back with equally specific carve-outs: general AI sector conditions, changes in AI hype cycles that don't affect the underlying technology, and regulatory uncertainty that affects all AI companies equally. Gurpreet S. Bal has navigated these negotiations from both sides of the table: "I've had deals where an AI competitor announcement between signing and closing made the MAC question genuinely complicated." The drafting environment requires practitioners who understand both the technology and the legal doctrine.
The gap between MAC risk at signing and closing in AI deals is managed through interim operating covenants that prohibit major changes to training infrastructure or model deployment, representation updates requiring disclosure of material adverse developments before closing, shortened sign-to-close timelines that reduce the risk window, and reverse termination fees that compensate sellers if buyers walk using MAC as a pretext.
Gurpreet S. Bal recommends that parties in AI acquisitions do three things that are not yet standard practice. First, define the specific AI benchmarks, regulatory frameworks, and competitive landscape factors that will and will not constitute MAC triggers — don't leave it to general language. Second, establish an interim period monitoring framework: what information does the buyer receive between signing and closing, and at what threshold does an update trigger a MAC analysis? Third, build in a negotiated dispute resolution mechanism for MAC questions that arise at closing — a pre-agreed arbitration process is faster and cheaper than litigation over whether a closing can be forced. These are practical solutions to a problem that is becoming more common as AI deal volume increases.
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.