AI Contracts Need New Thinking Beyond Copy-and-Paste SaaS Terms
Remember when SaaS was the undisputed king of tech? AI has shaken the throne—lawyers, are you ready to evolve?
Raise your hand if you’ve been personally victimized by opposing counsel clinging to contract language from a decade ago?
It’s okay if you have, and it’s okay if you’re the one who is clinging.
But times are changing.
The SaaS era is losing its shine, with commoditized software and shifting business models redefining technology’s role in value creation.
As AI reshapes industries, lawyers must rethink how we approach AI-related contracts.
AI isn’t just “SaaS 2.0”—and holding onto to that mindset risks stifling innovation and creating gaps in risk management. (I see you, “this is how we’ve always done it” redline warriors).
Key difference: AI’s value lies in unique datasets, continuous learning, and bespoke integrations—not just static licenses or subscription fees.
Klarna’s move from SaaS reliance to in-house AI is a preview of what 2025 could bring: companies building proprietary AI ecosystems to reduce dependencies and unlock new efficiencies. More control, less vendor bloat.
This shift may accelerate the rise of AI-native agents and services—an “AI OS,” as Aki Ranin aptly puts it (read more here).
To keep pace, AI contracts must move beyond static templates and one-size-fits-all clauses lingering around from a decade ago.
They need to address dynamic risks like regulatory shifts and ethical concerns while embracing AI’s evolving technological potential. For instance:
Use Case-Specific Terms: Don’t try to force all AI vendors onto the same template. What you need for a healthcare chatbot is going to be different from what you need for a meeting summary tool. You might need zero data retention for one (where the vendor sets this configuration with its third-party LLM provider), but you might need abuse monitoring controls on another.
Understand the Players: Remember to see the forest through the trees and begin with a foundational question: are you contracting with an LLM developer like OpenAI or a deployer integrating a third-party model into their services? Each scenario demands tailored clauses.
Be Fluid: Build playbooks, not rigid templates. Use mind maps to identify risks like data ownership, retention, and liability caps. And ensure contracts allow for flexible review periods to adapt to ongoing regulatory and technology changes.
These principles emphasize one key truth: AI contracts require a new mindset. By tailoring terms to specific use cases, understanding the distinct roles of AI providers, and staying adaptable, you can future-proof your agreements. This isn’t about abandoning structure—it’s about building flexibility into your approach.
TL;DR: The era of “cut-and-paste” clauses is over. In AI’s dynamic landscape, flexibility, creativity, and collaboration will define success.
What do you think? Let’s discuss.
📚 Additional Reading
The End Of The SaaS Era: Rethinking Software’s Role In Business via Forbes
Key Considerations in AI-Related Contracts via Husch Blackwell
The impact of artificial intelligence on technology transactions lawyers — Adapt or perish! via Reuters
Key considerations in negotiating generative AI agreements via Hogan Lovells
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