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Professionals 62 results
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Practice Area
Product Liability & Mass Torts
Major multinational companies trust Winston to defend their products and reputations in high-profile, high-stakes product liability and mass tort claims. Our Product Liability & Mass Torts Practice is one of the industry’s most seasoned, with a deep bench of experienced lawyers and a unique combination of extensive trial experience and expansive technical knowledge.
Practice Area
Addressing a crisis in today’s climate requires both swift action and careful consideration of all potential risks. Winston & Strawn is a trusted business adviser to clients facing complex, fast-moving, high-profile crises.
Practice Area
Environmental Litigation & Enforcement
With today’s rapidly evolving rules of engagement, paired with the often contentious nature of environmental disputes, clients rely on Winston for our practical experience and substantive knowledge of the broad-ranging issues involved in environmental litigation, enforcement, and investigations.
Experience 87 results
Experience
|February 4, 2026
A Winston & Strawn deal team led by Partners Justin Levy in New York and Paul Amiss in London advised Aeron Defense, a newly formed defense platform established by Ventus Industrial Partners in partnership with GenNx360 Capital Partners and Admiralty Partners, on its acquisition of defense contracting specialists General Tool Company and Magna Machine Company.
Experience
|February 2, 2026
A Winston & Strawn deal team led by Partners Justin Levy in New York and Paul Amiss in London advised Aeron Defense, a newly formed defense platform established by Ventus Industrial Partners in partnership with GenNx360 Capital Partners and Admiralty Partners, on its acquisition of defense contracting specialists General Tool Company and Magna Machine Company.
Experience
|January 29, 2026
A team led by Justin Levy, Austin Leach, and Ben Popeck served as sell-side advisors to GenNx360 Capital Partners in the sale of its portfolio company, Precision Aviation Group, to VSE Corporation for a total upfront consideration of approximately $2.025 billion in cash and equity. PAG expects to generate approximately $615 million of adjusted revenue for the fiscal year ended December 31, 2025. Together, VSE and PAG will create a more diversified, globally scaled aviation aftermarket platform with broader technical capabilities and an expanded portfolio of proprietary repair and solutions content designed to strengthen customer support, extend asset life, and reduce total cost of ownership.
Insights & News 1,009 results
Webinar
|February 24, 2026
Venezuela in Transition: The Evolving Investment Landscape
Recent political developments in Venezuela are significantly impacting the country’s outlook, presenting novel challenges and opportunities for investment in the region.
In the Media
|February 20, 2026
|2 Min Read
Patrick Valenti Joins Winston & Strawn in Chicago
Winston & Strawn recently announced that Patrick Valenti has joined the firm’s Chicago office as a partner in the Transactions Department and a member of the Private Investment Funds Practice. Patrick advises private equity sponsors on the formation, fundraising, and operation of private investment funds, with a focus on sponsors raising middle-market private equity funds from $250 million to $3 billion.
Client Alert
|February 19, 2026
|4 Min Read
Upcoming Registration and Reporting Deadlines for VC Funds with CA Nexus
Under California’s Fair Investment Practices by Venture Capital Companies Law (the FIPVCC), venture capital funds with a nexus to California (Covered Entities) will be required to register with the California Department of Financial Protection & Innovation (the DFPI) starting in March 2026 and report demographic information about their portfolio companies to the DFPI starting in April 2026. The purpose of the FIPVCC is to increase transparency regarding the demographics of the founding teams of the portfolio companies in which Covered Entities invest.
Other Results 11 results
Site Content
In the product liability context, general causation is the legal and scientific determination of whether the product in question has the inherent capability to cause the alleged injury under certain circumstances. Establishment of general causation is a critical aspect of a plaintiff’s case, and typically requires scientific evidence, such as epidemiology or toxicology, and expert testimony to establish a causal connection between the product’s design, manufacturing, or warnings and the injury or injuries at issue. As contrasted to specific causation, the focus in a general causation inquiry is on whether the product or exposure, as a general matter, can cause the type of injury claimed by the plaintiff. A general causation inquiry does not assess any facts unique to a particular plaintiff.
Site Content
Specific causation is the legal and scientific determination of whether the product in question was the direct cause of a plaintiff's specific injury. While general causation inquires whether a broader connection exists between a product and a type of harm, a specific causation assessment focuses on the unique circumstances surrounding a particular plaintiff's case. It typically requires expert testimony demonstrating that the alleged defect or issue in the product was responsible for the specific injuries suffered by the plaintiff. Alternative explanations for the plaintiff’s specific injuries must also be ruled out—typically via a differential etiology—for specific causation to be established.
Site Content
Generative AI tools can create new content, such as text, computer code, images, audio, sound, and video, in response to a user’s prompt, often in the form of a short written description of the desired output. Generative AI tools are based on machine learning, trained using enormous amounts of data.[1] Generative AI tools are built on a system of inputs and outputs. First, the tool goes through a machine learning period whereby it is trained to generate predictive models and creative outputs through a large data set, often varied and diverse but tailored to the goal of the tool (i.e., customer service, generating scientific or marketing models, etc.). For in-house tools, this can be done with the company’s own data; for larger tools such as ChatGPT, this is done with the creator’s data set.[2] Once the tool has been trained, the individual user “inputs” a short prompt for the tool to synthesize and produce an “output.” Inputs are often retained on the servers controlled by the company that supports the tool, for monitoring of the tool’s performance and, in some cases, continued learning. The “outputs” are created by combining the machine learning during the training period with the inputs to produce an output.[3]