High-stakes construction deals now move at digital speed, yet a single vague indemnity clause or stray endorsement can shift millions in risk and derail a project before a shovel hits the ground. The comparison is stark: AI promises instant output at scale, while the legal system demands reliability, precision, and accountability. That tension defines how today’s tools are used—or restrained—across contract formation and litigation support.
This piece compares AI’s velocity and reach against the reliability required in construction law. It examines where each side excels, where each fails, and how seasoned attorneys channel speed without surrendering legal substance. The question is not whether AI helps, but whether it helps enough to trust it with outcomes that carry real exposure.
Background and Context for Construction Legal Work
In 2022, an experiment produced an AI-drafted mixed-use, design-bid-build contract that looked polished yet missed material terms. The text read cleanly, but the risk allocation was wrong, critical definitions were soft, and insurance and termination mechanics lacked teeth. That result framed the present challenge: more power, same stakes.
On one pole stands AI’s speed and scale; on the other sits the legal system’s demand for accuracy and responsibility. Construction contracts are unforgiving because risk allocation, indemnity carve-outs, insurance endorsements, scopes, change mechanisms, and termination triggers hinge on exact language. Large language models like ChatGPT now assist with research direction, triage, clause locating, and draft refinement, but expert views diverge on where to draw limits.
Megan Shapiro voices deep skepticism about AI-drafted contracts, Michael Vardaro stresses operational gains over push-button readiness, and Trent Cotney supports supervised adoption as a cross-check and brainstorming aid. The core question is whether current advances closed the gap between fast output and trustworthy legal substance in contracting and litigation support.
Comparative Performance Across Key Dimensions
The comparison centers on fitness for use versus the pace of generation. AI produces fluent clauses, plausible structures, and quick templates. Reliability, though, depends on tailoring: project specifics, enforceability, and risk terms that match a client’s appetite. In practice, velocity without judgment creates attractive but brittle drafts.
Moreover, the 2022 test still mirrors current practice: faster drafting does not equal readiness for signature. Shapiro warns against AI-drafted contracts altogether, Vardaro rejects any notion of “push-button” finality, and Cotney confines AI to refinement and cross-checking. Each points to the same divide—speed helps, but expert review makes it safe.
Drafting Velocity vs. Contract Fitness for Use
AI can stack a table of contents in seconds and surface candidate clauses on indemnity, changes, and insurance with uncanny fluency. However, a contract that is ready for signature must reflect project delivery method, jurisdictional quirks, and bespoke risk shifts. That step demands legal judgment that a generative model does not supply.
Attorneys echo this separation of roles. Shapiro calls AI-drafted contracts unsafe; Vardaro says ready-to-go terms are never push-button; Cotney employs AI to refine and compare, not to finalize. The result is clear: speed is valuable, but without human calibration, it becomes a liability rather than an advantage.
Accuracy, Hallucinations, and Citation Integrity
Generative tools write confidently, but confidence is not accuracy. Fabricated cases, misapplied citations, and tidy but wrong assertions still appear. Courts now encounter “AI slop” in filings, and sanctions have followed. A growing database tracks hundreds of hallucination incidents, signaling an institutional memory.
That reality shifts burden to professionals who must source-check every authority and fact. The verification tax is unavoidable; unsupervised reliance is unsafe. Fluency should be treated as a lead, not a conclusion, especially when a court or counterparty will scrutinize every footnote.
Domain Nuance, Risk Allocation, and Litigation Support
Construction law rewards nuance: indemnity carve-outs tied to sole negligence, precise AIAs with negotiated riders, additional insured endorsements that match actual risk, and change-order pathways that prevent constructive acceleration. AI can locate related language quickly but often fails to prioritize competing risks or reconcile conflicting documents.
Where AI shines is volume work—sifting emails, drawing sets, and specs to surface issues and extract clauses. Vardaro highlights weeks turning into days. Yet judgment on what matters most, and how to balance leverage with enforceability, remains human work. The benefit is speed; the cost of skipping calibration is exposure.
Challenges, Limitations, and Ethical Considerations
Persistent hallucinations and shifting model quality erode trust in high-stakes work. A tool that changes tone or accuracy between versions cannot anchor a litigated position or a nine-figure EPC contract. Lawyers face sanctions, reputational harm, and client distrust if filings include invented authorities or subtle misstatements.
Client-side misuse compounds the risk. As confidence in AI rises, some firms self-draft to avoid fees, embedding hidden liabilities that surface during disputes. Governance has become the central problem: not access, but disciplined workflows, documentation, and audit trails. Data security concerns and confidentiality guardrails add another layer, and meaningful gains require training, strong prompts, and time.
Synthesis and Recommendations
The bottom line is comparative: AI excels at speed, structure, and search; legal reliability comes from human judgment, accountability, and tailoring. Practical use sits in the middle—an accelerator for supervised tasks, not a drafter of final construction contracts. That distinction protects clients without wasting the advantage of automation.
For drafting, AI should structure documents, surface gaps, compare clauses, and suggest checks. Final language must be attorney-crafted to align risk, insurance, changes, and termination with the project and jurisdiction. For litigation support, AI should organize voluminous records and pinpoint relevant sections, with authorities and facts validated before any filing. Teams benefit from expert prompting, version control, and mandatory verification checklists. Clients need clear boundaries that discourage self-help drafting and explain the danger of unverified outputs. In practice, advantage favored those who integrated AI responsibly, preserved reliability, and met rising court scrutiny with strong governance and expert oversight.
