webAI Names Dr. Paul J. Maykish Chief Intelligence Officer and Launches Intelligence Lab

Jan 20, 2026
webAI has appointed Dr. Paul J. Maykish as Chief Intelligence Officer and launched the webAI Intelligence Lab to develop secure, sovereign AI systems for public and enterprise sectors.

Austin-based webAI Inc. has appointed Dr. Paul J. Maykish as Chief Intelligence Officer and launched the webAI Intelligence Lab, announced in a press release. The new division will focus on building accessible and sovereign AI systems designed for both public sector and enterprise environments.

Dr. Maykish brings over two decades of experience in military operations and technology strategy, including roles as Vice President for Technology Strategy at the Special Competitive Studies Project and Director for Technology Competition at the U.S. National Security Council. He also previously led classified AI research for the National Security Commission on Artificial Intelligence.

The Intelligence Lab will develop interoperable, domain-specific intelligence systems that can operate securely and locally. Initial efforts will target edge intelligence, resilient architectures, and scalable deployment frameworks for mission-critical use cases.

According to the company, the lab’s work will contribute to webAI’s goal of advancing distributed intelligence systems that organizations can fully control—from data and models to runtime performance.

We hope you enjoyed this article

Consider subscribing to one of our newsletters like Defense AI Brief or Daily AI Brief.

Also, consider following us on social media:

Defense AI Brief AI Brief AI Brief (X)
Free newsletter

Defense AI Brief

Your weekly intelligence briefing on the technology shaping modern warfare and national security.

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.