The rise of AI in legal tech is being hailed as a turning point for contract negotiations. We’re told that AI will make contracting more efficient, reduce friction, and speed up the review process. The assumption is that if we can process contracts faster, we will negotiate faster.
But speed has never been the fundamental problem. Contracts don’t take too long because we lack processing power. They take too long because we lack consensus.
We assume that negotiation is a necessary function of contract complexity – that the longer it takes, the more thorough the process. But that isn’t true. Most negotiations don’t take time because of real differences in risk tolerance or strategic needs. They take time because we don’t have a shared understanding of what’s actually fair, balanced, and market-aligned.
Every company has its own playbook. Every lawyer has their own experience. Every organization has inherited ways of thinking from past deals, from former mentors, from institutional precedent. None of these are rooted in a collective market truth. They are simply the best proxies we have for objectivity.
And this is why AI-powered contract review, as it exists today, hasn’t solved the problem.
AI can process contracts faster than any human, but it can’t tell us whether the things we’re flagging actually need to be negotiated. It can tell us what’s different, but not whether that difference is meaningful.
If an AI tool flags a clause as “non-standard,” the natural next question is: non-standard compared to what?
- Compared to your internal playbook?
- Compared to past deals?
- Compared to the broader market?
And even if it does compare to the broader market, what dataset is it pulling from? Is it reflective of today’s realities, or simply an aggregation of historical biases?
Right now, contract review – whether done manually or with AI – is fundamentally a comparative exercise without a stable reference point. The problem isn’t that contracts take too long to negotiate. The problem is that we’re negotiating in the dark.
The Tech-First Fallacy: Why AI Alone Won’t Fix Contracts
Legal tech has been driven by a simple narrative: contracts are inefficient, so automation will solve the problem. We’ve seen contract lifecycle management (CLM) tools, automated drafting, AI-powered contract review – all promising to reduce friction.
But what these solutions fail to address is that negotiation inefficiency isn’t a speed problem – it’s an alignment problem.
Tech that processes contracts faster still leaves us arguing over what should and shouldn’t be negotiated in the first place. It makes review more efficient, but it does not make the underlying contract more standardized, more predictable, or more aligned with actual market norms.
This is why contract negotiations remain frustrating. We aren’t arguing about what’s actually risky – we’re arguing about what feels risky, based on individual histories rather than collective knowledge.
To truly fix contracts, we need more than automation – we need a shared foundation.
The Future: From Forms to Data, From Playbooks to Standards
The first step toward this was the creation of oneNDA, oneDPA and oneSaaS – not just as templates, but as an attempt to establish consensus. By bringing companies together to define terms once, we eliminated the need for thousands of companies to negotiate those same terms separately, over and over again.
But standard templates are just the beginning.
A true standard isn’t just a form – it’s a dataset.
It’s not just about agreeing on wording. It’s about understanding what is actually market—not based on instinct, but on empirical evidence. Not based on past preferences, but on present realities.
Imagine a world where contract negotiations are grounded in a shared, data-driven understanding rather than individual experience. Where, instead of relying on instinct or internal precedent, we could objectively determine whether a particular risk allocation is genuinely out of step with industry norms. Where a clause flagged as “non-standard” is assessed not just against a single organization’s historical preferences, but against a broader market consensus – revealing whether it is truly off-market or simply different from what one party is accustomed to. Where terms that have historically been points of contention are no longer negotiated out of habit, but because there is real, current evidence that they remain unsettled in the market.
This shift – from assumption to data, from isolated playbooks to collective intelligence – is what will truly change the way we contract.
Beyond Automation: The Path to True Intelligence
The next evolution of contract technology isn’t just AI-powered review. It’s AI-powered standardization – a system that doesn’t just process contracts, but understands them in context. A system that doesn’t just compare language, but aligns contracting parties on what is fair.
Imagine a world where AI doesn’t just flag risks – it helps eliminate unnecessary negotiation by showing what is actually accepted in the market.
Imagine contract review that isn’t just fast, but intelligent – grounded in real market alignment, not just inferences drawn from limited datasets.
This is the shift we need. Not just better tools, but a better foundation. Not just automation, but consensus.
That’s what we’re building toward. Stay tuned, because we’re about to change the world 👀
