WasteLinq Co-Founder Sean Easton Named CEO

May 15, 2026
WasteLinq has appointed co-founder Sean Easton as Chief Executive Officer. Easton previously served as Chief Revenue Officer and will now lead the company as it expands its AI capabilities for waste profiling and compliance workflows.

WasteLinq has appointed co-founder Sean Easton as Chief Executive Officer, announced in a press release. Easton most recently served as Chief Revenue Officer, where he helped expand the company’s customer base since its founding in 2017.

In his new role, Easton will lead WasteLinq as it scales its platform and expands AI supported tools for waste profiling, compliance, and operational efficiency. The company has developed technology that reduces manual effort, accelerates approval cycles, and improves accuracy across compliance workflows.

Easton brings more than 30 years of experience in the industrial and hazardous waste industry, with previous leadership positions at E4 Holdings and Effective Environmental. Under his leadership, WasteLinq plans to continue advancing its AI capabilities and improving digital processes such as e-Manifest reporting to regulatory systems.

In 2025, WasteLinq achieved several milestones including SOC 2 Type 2 compliance and recognition as a Top Hazardous Waste Solution provider by Environmental Business Review.

We hope you enjoyed this article

Consider subscribing to one of our newsletters like Enterprise AI Brief, Industrial AI Weekly or Daily AI Brief.

Also, consider following us on social media:

Enterprise AI AI Brief AI Brief (X)
Free newsletter

Enterprise AI Brief

Weekly report on AI business applications, enterprise software releases, automation tools, and industry implementations.

AI Token Webinar

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more

You may also like

Free, six days a week

Daily AI Brief: the AI news that matters, in your inbox.