The Deal That Allegedly Never Died: Defending Against a Claimed Option to Purchase
October 15, 2015
The Facts of the Case
In the fall of 2002, Ted Miller, the former president, chief executive, and founder of Crown Castle, was approached by John Miller, a former chief executive of a publicly traded company and a Louisiana-based promoter, about coinvesting in an aircraft parts business in San Antonio and Virginia that was in bankruptcy.
The business opportunity that was initially proposed involved the purchase, out of bankruptcy, of the assets of three U.S. divisions of Fairchild Dornier. Fairchild Dornier was a manufacturer of turboprop-powered aircrafts that are primarily used by commuter airlines.
The three divisions in bankruptcy, Merlin Express Incorporated, Fairchild Gen-Aero Incorporated, and Metro Support Services, Inc., operated the Fairchild Dornier U.S. servicing and parts distribution center in San Antonio (the “FDUS Assets”).
Author(s)
Related Insights
July 22, 2026
Energy Current
Your Customer Just Got Bought. Your Contract Might Not Survive the Deal.
Three years of E&P mega-mergers have consolidated the buyer side of the oil patch. The service companies that supplied the acquired operators are finding out how little say they had in the outcome.
July 22, 2026
Manufacturing Industry Advisor
When Recalls Go Rogue: The Impact of AI-Fueled Recall Fraud
Recall fraud is a growing and increasingly sophisticated problem that occurs when bad actors exploit online recall processes to obtain remedies to which they are not entitled. This illicit activity increases compliance costs for companies, undermines the effectiveness of legitimate recall programs, and results in heightened requirements to submit a legitimate request for a recall remedy.
July 22, 2026
Manufacturing Industry Advisor
AI Bills of Materials (AI-BOMs) and Model Provenance: Contracting for Cybersecurity in AI-Enabled Manufacturing Supply Chains
AI-BOMs and model provenance are emerging as cybersecurity diligence tools for manufacturers that buy or deploy AI systems. An AI bill of materials identifies the models, datasets, software components, APIs, and other dependencies that may create cyber exposure inside an AI system, while model provenance documents where those components came from, how they were trained or modified, and how they change over time.