AI is moving fast, but the bigger question for companies and governments may be control. Who owns the data, the workflow, the output, and the risk?

In this episode, Amir talks with Shaun Modi, cofounder and CEO of Capitol AI, about sovereign AI, shadow AI, model dependency, government use cases, and why organizations need repeatable, governed, auditable workflows before AI becomes part of core operations.

Shaun also brings a design lens to the conversation, connecting AI adoption to user experience, voice interfaces, and the next wave of AI in the physical world.

Practical Takeaways

• Sovereign AI means having control over your data, outcomes, upside, and risk.

• Shadow AI creates short term productivity, but can also create silos, governance gaps, and data exposure.

• Organizations need repeatable, governed, auditable AI workflows, especially in regulated environments.

• Model independence matters because model costs, performance, and capabilities keep changing.

• Design will come back into focus as AI systems become more powerful and more embedded in work.

Timestamped Highlights

00:40, What Capitol AI does and why decision ready artifacts matter

01:50, Shaun defines sovereign AI in plain language

02:36, The intelligence paradox, more data, less control

04:15, Why shadow AI can become a governance and accuracy problem

10:29, Zero data retention, model independence, and evaluation criteria

17:53, Why AI user experience may be entering a new design cycle

25:56, Where AI may create major impact in the physical world

One Line That Stuck

“Companies and governments have more data than ever, but they are losing control over the outcomes.”

Practical Takeaways For Teams

If AI is moving into real business processes, start by asking what needs to be controlled. Data rights, model choice, accuracy standards, workflow governance, and auditability all matter more once AI is producing work that affects customers, citizens, or critical operations.

Follow The Tech Trek for more conversations on how technical teams are building, operating, and adapting around AI, data, product, and engineering execution.