Cooley’s GO Public offering uses ChatGPT Work to surface issues earlier, focus expertise, and help clients reach market faster.
Cooley is an international law firm with a reputation for helping companies navigate capital markets and initial public offerings (IPOs). In 2025, the firm advised on 180 deals globally, totaling more than $51.5 billion in deal volume. Cooley has a decade-long track record at the top of the US issuer-side IPO market and has advised on more venture-backed IPOs than any other firm over the past 20-plus years.
Capital markets involve huge amounts of information that need to be synthesized and analyzed. “For our lawyers, it’s always very busy,” explains David Wang, Chief Innovation Officer at Cooley. “When there’s an IPO, there are thousands of things that need to be done constantly.”
Cooley developed GO Public, a proprietary AI product offering built on ChatGPT Work. Its agentic harness analyzes information so lawyers can review and validate the work. The workflow comes together into a tailored starting point for an IPO.
ChatGPT Work is changing legal work by bringing intelligence to every step of the process faster, more effectively, and more democratically.
“An IPO is one of the most consequential moments in a company’s life, and it can consume enormous management attention,” says Dave Peinsipp, partner and co-chair of Cooley’s global capital markets group. “GO Public helps us move through the intensive preparation faster so lawyers and management teams can spend more time on the decisions that shape the transaction — where judgment, market experience and strategic thinking matter most.”
Before GO Public, teams often began with a precedent from a comparable company and adapted it to fit the client’s circumstances. GO Public allows Cooley to begin with the client itself, bringing together the information provided, relevant public sources, and carefully curated precedents to create a more tailored starting point for lawyer review.
“Everybody understands that as the leading capital markets firm, we know what to do in an IPO,” Wang says. Cooley’s legal engineers, innovation counsel, and practitioners worked together to translate the firm’s capital markets experience into a proprietary agentic system. “With GO Public and the agentic harness that we built using OpenAI’s technology, we were able to bring this forward into the AI era,” Wang says.
The harness provides a controlled workflow for agents that lays out which steps agents can perform automatically, where lawyers must review or validate the work, and how it all comes together. Information curated from previous analyses provides what Wang calls “baked-in know-how.” Cooley partners closely with OpenAI to combine subject-matter expertise and AI engineering knowledge to ensure the right processes produce the right outcomes.
With GO Public, Cooley can redirect more lawyer and management time toward the substantive work that drives an IPO. “The amazing thing about integrating ChatGPT Work into GO Public is that it brings intelligence to this very large array of information that had to be manually sorted before,” Wang says. “Once you do that first cut, you’re able to really concentrate the human effort and expertise on the highest value surface areas.”
“That’s what we mean by speed to quality,” Peinsipp says. “The point isn’t simply to do the same work faster. It’s to get to a strong starting point sooner, so our lawyers can spend more time applying judgment, challenging the disclosure and thinking strategically about the issues that matter most to the company.”
For management teams, the benefit extends beyond the legal work itself. IPO preparation competes for the attention of executives who are also running fast-moving businesses.
ChatGPT Work allows us to move more quickly through intensive preparation and give valuable time back to management. We want their attention focused where only they can add value — on the business, the story and the decisions that will ultimately shape the offering.
“Traditionally, the legal industry has been relatively change-averse,” Wang says. But AI is giving Cooley an opportunity to rethink how capital markets work gets done while preserving the professional judgment and accountability clients depend on.
“As attorneys, we have professional duties and obligations to make sure that the best, most legally defensible outcome occurs for our clients, and that just takes a lot of work,” Wang explains. GO Public, powered by OpenAI, is designed to help lawyers consider more information while directing their expertise toward the decisions that matter most.
Peinsipp sees GO Public as the beginning of a broader shift in how capital markets work is delivered.
“GO Public is our vision for the future of capital markets practice,” Peinsipp says. “Our collaboration with OpenAI has allowed us to rethink how this work gets done. We see enormous potential not only for IPOs but for capital markets transactions more broadly.”
Facts Only
* Cooley is an international law firm.
* Cooley advised on 180 deals in 2025.
* Total deal volume for those 2025 deals exceeded $51.5 billion.
* Cooley developed GO Public, a proprietary AI product.
* GO Public is built on ChatGPT Work.
* GO Public utilizes an agentic harness to analyze information.
* David Wang is the Chief Innovation Officer at Cooley.
* Dave Peinsipp is a partner and co-chair of Cooley’s global capital markets group.
* The system incorporates public sources, curated precedents, and client-provided information.
* The workflow includes automated steps and manual lawyer validation.
Executive Summary
Cooley has integrated OpenAI's ChatGPT Work into a proprietary system called GO Public to streamline the initial public offering (IPO) process. By utilizing an "agentic harness," the firm automates the synthesis of vast amounts of data—including public records and historical precedents—to create tailored starting points for legal review. This transition from using generic precedents to client-specific AI analysis is intended to reduce the manual labor involved in early-stage preparation.
The primary objective is "speed to quality," allowing lawyers and company management to redirect their attention from administrative sorting to high-value strategic decisions and professional judgment. While the legal industry is historically resistant to change, Cooley is positioning this AI integration as a way to enhance the professional accountability and defensibility of legal outcomes. The firm views this as a scalable model that could eventually extend beyond IPOs to broader capital markets transactions.
Full Take
The strongest version of this narrative is that AI is evolving from a simple chatbot into a sophisticated "agentic" layer capable of handling complex, high-stakes professional workflows, thereby liberating human experts to focus on judgment rather than data processing.
However, this narrative is delivered as a vendor-partner success story. It relies heavily on the "Authority Game," where the efficacy of the tool is validated exclusively by the firm that built it and the provider that powers it. There is no independent verification of "speed to quality"—only the assertion that it exists. By framing the shift as a move from "generic precedents" to "tailored starting points," the narrative creates a compelling value proposition while remaining vague on the actual error rates or the nature of the "validation" lawyers must perform.
The underlying paradigm is the "industrialization of expertise." The assumption is that legal "know-how" can be "baked-in" to a system, effectively turning experience into a software feature. While this benefits the firm through efficiency and the client through speed, the second-order consequence may be a degradation of the apprentice model in law. If the "first cut" of an IPO is handled by an agentic harness, junior lawyers lose the formative experience of manually synthesizing data—the very process that typically builds the "judgment" the partners now claim to prioritize.
Patterns detected: ARC-0043 Authority Game
Counterstrike Scan: A coordinated influence campaign would use high-status professional firms to "normalize" AI adoption in conservative sectors to drive enterprise software sales. The content aligns structurally with this pattern, acting as a case study to signal reliability to other risk-averse institutional clients.
Bridge Questions:
1. If the "grunt work" of synthesis is automated, how do junior practitioners develop the intuition required for high-level strategic judgment?
2. What are the specific failure modes of an "agentic harness" in a regulatory environment where a single hallucination could lead to significant legal liability?
3. Does this shift fundamentally change the billable hour model, and if so, how does that affect the incentive for firms to implement these tools?
Sentinel — Human
The text reads like a carefully crafted case study highlighting a real corporate innovation process, framed by executive vision rather than purely abstract speculation.
