Podcast Summary: Liftoff with Keith Newman

Episode Title: Trustworthy AI: Why Explainability Will Define the Next Decade of Enterprise Technology
Guest: Pat Condo, Founder & CEO of Seeker Technologies
Date: March 3, 2026
Host: Keith Newman

Episode Overview

Keith Newman sits down with serial entrepreneur Pat Condo, founder and CEO of Seeker Technologies, to explore why explainable, trustworthy AI is quickly becoming a baseline requirement for mission-critical enterprise and government technology. Drawing on decades of leadership—from space navigation systems to cutting-edge AI—Pat shares his “founder philosophy,” real-world lessons for building competitive advantage, and detailed insights into how the next decade will be shaped by explainability, trust, and specialized AI. The conversation blends hard-won wisdom, pragmatic business advice, and a touch of industry humor.

Key Themes & Discussion Points

1. A New Tech Boom in Virginia (00:36 – 02:35)

• Pat’s Background: From building navigation and space systems to founding six prior companies before Seeker.
• Virginia’s Emergence: Data center development now rivals Silicon Valley, with a major confluence of both government and commercial tech innovation.
• Quote:
  “I think there’s been…more capacity built here [in Virginia] than any part of the world in a concentrated fashion. And it’s created, you know, an enormous economy here of not just engineers, but remarkably, construction, electricians, plumbing, shipping, you know, everything.” — Pat Condo (00:47)

2. Founder Philosophy: Curiosity & Leading by Example (02:35 – 05:46)

• Serial Entrepreneurship: Pat’s drive comes from curiosity and continual learning, paired with a belief in leading from the front.
• Unexpected Lessons: The transition from backroom engineering to front-line sales and executive leadership.
• Quote:
  “The definition of salesman is…quite broad. It’s not only selling to investors…but your own employees. If you could combine that kind of capability with your…curiosity and knowledge to move forward, that could become a very powerful way to build and lead a company forward.” — Pat Condo (04:14)

3. Trustworthy AI: ‘Explainability’ as the Next Frontier (05:46 – 14:19)

• The Rise of Explainability: In complex, mission-critical environments (e.g., banking, defense, nuclear energy), trust in AI is paramount and explainability is non-negotiable.
• Data Quality: Garbage in, garbage out—high-quality domain-specific data is essential.
• Enterprise Needs vs. Consumer Tolerance: Enterprises and governments have zero tolerance for AI errors or hallucinations that consumers might overlook.
• Quote:
  “What we call ‘explainability’—that means that you should be able to understand how you got the result in AI. Where today it’s a bit of a mystery, but what corporations want... is that they want to be able to say, here’s the end result, and trace it back like a corporate auditor.” — Pat Condo (08:10)
• Mission Critical Examples:
  “If it’s managing nuclear plants…flying autonomous jets using AI, well that’s a catastrophe [if it fails].” — Pat Condo (10:34)

4. Seeker’s Strategy: Specialization, Domain Expertise, and Trust (11:09 – 16:11)

• Verticalization Over Time: As in the early Internet, initial market dominance gives way to domain-specific expertise.
• Content Evaluation by Design: Seeker builds specialized agents on a robust, domain-focused platform.
• Mix of Commercial and Government Clients: Seeker’s client base is moving from 60% commercial/40% government to the inverse, as government AI adoption accelerates.
• Explainability as ‘Absolute Must’: Especially in defense tech and financial systems.

5. AI Economics and Infrastructure: Inference Costs & Pricing Models (17:21 – 21:41)

• Training vs. Inference: Training is resource-intensive; inference (application) is a cost battleground. New entrants and specialized hardware are driving competitive pricing.
• Skepticism Toward Tokenization:
  “Over time you’re going to go away from some of those [token] models and more toward… a user or a volume of data. There’s going to be some combination there. But… it’s just too expensive.” — Pat Condo (19:28)
• Enterprise Lessons: Smart buyers now look closely at ROI, driving more practical and pragmatic sales cycles.

6. Building Trust, Gaining Credibility in a Skeptical Market (21:41 – 24:26)

• Proof Over Promise: Seeker’s best customers are those already burned by larger AI platforms.
• Faster, More Defined Pilots:
  “In 30 days…already produced a model, can tell them whether they’re going to get an ROI or not, can tell them how much it’s going to cost and can tell them the next 10 apps… Much, much more smoothly.” — Pat Condo (22:51)
• Smarter Customers: Industry vernacular has simplified; adoption is accelerating as knowledge deepens.

7. Operating and Leading Companies in the AI Era (24:26 – 25:39)

• Unprecedented Pace:
  “It’s an order of magnitude faster pace. Number two, it’s a lot more focused on… the right talent pool.” — Pat Condo (24:45)
• Capital, Talent, and Market Pressure: Balancing money-raising, building the optimal team, and riding the AI boom versus the risk of a bubble.

8. Raising Ambition (and Capital) (25:40 – 26:54)

• Recent Fundraising: Seeker has raised nearly $280 million, with over 100 AI engineers building specialized products.
• Quote:
  “In relative terms, 280 million is an enormous amount… but next to someone who raised a trillion, it doesn’t look like anything.” — Pat Condo (25:43)
• Space as the Next Frontier: “Earth is not big enough anymore… I’ve got to go to the moon and Mars.” (26:24)

9. Industry Contrarianism: Policy and Monopolies (26:54 – 29:29)

• Policy Pet Peeve:
  “Why would you go to the monopolies and ask them how to create a fair and level playing field?... Expand your point of view…pick people who are in different kind of aspects of AI…” — Pat Condo (27:29)

10. Lightning Round: Underappreciated AI Investment, Metrics, and Team Philosophy (29:29 – 34:54)

• Most Under-Invested Area:
  “Manufacturing… The more energy you put into AI to build digital factories… it’ll bring so many more benefits…” — Pat Condo (29:47)
• Favorite Metric: Not always raw ‘brilliance’ but ‘teamwork’ and the ability of hires to mesh as an effective, collaborative unit.
• Quote:
  “I don’t have to hire the person who is the absolute best… I look at someone who can work in a team and… effectively work with many other people. I get more productivity out of that…” — Pat Condo (31:47)

11. Defining ‘Winning’ for Seeker and the Future of AI (34:54 – 36:21)

• Measuring Success: Investor and employee rewards, enduring company impact, and “right data” as the linchpin of trustworthy AI.
• Quote/Metaphor:
  “If you have the wrong data, you’re going to go to the wrong planet.” — Pat Condo (36:21)

Notable Quotes & Timestamps

• On Virginia’s transformation:
  “Every Silicon Valley company… has now established more than a major outpost here.” (01:44)
• On explainability:
  “Explainability is not a nice-to-have. In critical information infrastructures… it becomes an absolute must.” (15:04)
• On customer skepticism:
  “Our best customer is someone who’s already tried them [the giants] and it hasn’t worked.” (22:10)
• On team-building:
  “A small company cannot sustain that kind of disaffectiveness where everyone’s a superstar… I look at who we can bring in that can really create that leadership.” (33:18)
• On the importance of purpose:
  “Are they doing something they really love and making a difference? That also is very important.” (34:54)

Closing Thoughts

Pat Condo's vision for Seeker Technologies and the AI industry is rooted in trust, explainability, and practical specialization, with a strong dose of humility and realism about what it takes to build resilient companies in a fast-moving market. The episode delivers practical playbook advice for founders, a prescient look at enterprise trends, and cautionary notes for policymakers and investors.

Timestamps for Key Segments

• 00:47 — Virginia’s data center boom
• 03:00 — Pat’s founder philosophy & serial entrepreneurship
• 05:46 — Trust and explainability in AI
• 08:10 — “Explainability” contrasted with corporate auditing
• 10:34 — AI errors in critical systems
• 14:19 — Explainability’s importance in different industries
• 17:54 — AI economics: inference costs & infrastructure
• 19:28 — Skepticism about tokenization models
• 22:10 — Building trust with skeptical customers
• 25:43 — Fundraising: $280M vs. “a trillion”
• 27:29 — Contrarian view on policy and monopolies
• 29:47 — Most under-invested AI use case: manufacturing
• 31:47 — Pat’s favorite hiring metric: teamwork
• 36:21 — “If you have the wrong data, you’re going to go to the wrong planet.”

[End of Summary]