AI is making knowledge work faster — but it’s also surfacing an uncomfortable tension: when the “doing” becomes cheap, the limiting factor shifts to everything humans do around it. This tension shows up in two places at once: inside engineering teams (identity, craft, and maintainability) and inside go-to-market (trust, distribution, and buying behavior).

In this episode, Ian Painter, Oliver Deakin, and Adrian McKenzie approach this from lived experience rather than speculation. They have built and scaled data-intensive travel technology, operated deep inside enterprise environments, and navigated acquisition into a public-market business. Instead of defaulting to debates about job loss, they focus on a more operational problem: when building no longer creates advantage on its own, what does?

What You’ll Learn

• Why speed shifts the bottleneck rather than removing it: As AI compresses build cycles, advantage moves from execution to decision-making, positioning, and trust.
• How identity shapes resistance to AI tools: Engineers most attached to craft and code quality often struggle more than those focused on outcomes.
• Why “good enough” AI output is still valuable: Treating AI like a junior teammate reframes imperfection as leverage rather than failure.
• Where maintainability breaks in mixed human-AI teams: Code that functions can still create long-term friction when humans need to read, test, and evolve it.
• How startup time-to-market dynamics are collapsing: Mockups, demos, and customer conversations now happen days into company formation.
• Why distribution may matter more than differentiation: When demos converge, embedded relationships and brand trust regain power.
• How build-versus-buy decisions may flip: Internal teams coordinating many agents could replace procurement with custom internal builds.
• Why data becomes the defensible asset again: As software commoditizes, curated, hard-earned datasets grow in relative value.
• What near-term “seniority” may look like: Capability may increasingly be measured by how many agents someone can effectively coordinate.
• How to prepare students for knowledge work amid AI: First-principles thinking, critical evaluation, and tool fluency matter more than any single technology.

Time-Stamped Highlights

• (00:32) AI, Jevons Paradox, and the Framing Question
• (01:37) AI Acceleration in Knowledge Work
• (02:19) Ian Painter’s Founder Perspective
• (03:19) Oliver Deakin on Modern Engineering Practice
• (03:42) Adrian McKenzie on Leadership and Teams
• (05:24) Engineers’ Emotional Responses to AI
• (07:05) Why Imperfect AI Gets Dismissed
• (08:26) Hands-On Experience With AI Coding Tools
• (09:51) Functional Code Versus Maintainable Systems
• (11:26) Startup Dynamics in an AI-Accelerated World
• (13:07) Speed to Market and Competitive Compression
• (15:05) Sales, Marketing, and Distribution Shifts
• (19:42) Humans as the Limiting Factor
• (22:00) Brand Trust and Embedded Distribution
• (35:03) Data as the Enduring Moat
• (42:15) Advice for Future Knowledge Workers

Guests

Ian Painter — Startup Advisor and Mentor. Previously, Vice President, Platform and Data at Cirium; Founder, Snowflake Software
Ian is a seasoned technology leader in aviation data and analytics. He founded Snowflake Software in 2001, building enterprise data exchange and aviation data platforms that were later acquired by Cirium (RELX plc). As VP of Platform and Data, he oversaw data strategy and large-scale platform initiatives at one of the world’s most trusted aviation analytics companies.
LinkedIn: https://www.linkedin.com/in/ianpainter/

Oliver Deakin — Fractional CTO, Advisor and previously Technology Leader at Cirium, Former Snowflake Software CTO, and Senior Engineer at IBM
Oliver has served in senior technical leadership roles, including as CTO at Snowflake Software during its rise in aviation data solutions. He has deep practical experience with software architecture, developer tooling, and emerging technologies applied to complex domains like travel and real-time data systems.
LinkedIn: https://www.linkedin.com/in/olideakin/

Adrian McKenzie — Director of Software Engineering at Cirium
Adrian leads engineering teams responsible for delivering scalable, mission-critical aviation data and analytics solutions. His background includes progressive leadership in software delivery and architecture at both Snowflake Software and Cirium, with decades of experience in team performance, engineering operations, and large-scale systems.
LinkedIn: https://www.linkedin.com/in/adrianmckenzie/

About the Podcast

Travel Tech Podcast features long-form conversations with leaders across travel and technology. The show explores how software, data, operations, and distribution come together in real businesses, with an emphasis on tradeoffs, incentives, and lessons that transfer beyond any single company or role.

Host

Alex Brooker — Founder, Airside Labs
Alex is an engineer, technology leader, and founder with deep expertise in mission-critical systems and AI oversight. He leads Airside Labs, a consultancy that applies aviation-grade testing and compliance rigor to enterprise AI systems, helping organizations uncover bias, privacy risks, and governance gaps in regulated environments. Before founding Airside Labs, Alex built and scaled complex software in aviation and safety-critical domains, blending product innovation with disciplined engineering practices. He also invests in early-stage technology ventures and advocates for thoughtful, real-world AI deployment strategies.
LinkedIn: https://www.linkedin.com/in/alex-brooker-2280002/

Links & References

• Jevons Paradox and efficiency-driven demand
• AI tools mentioned: GitHub Copilot, Claude
• Concepts discussed: software commoditization, distribution moats, curated data assets, agent-based development, human-in-the-loop systems

Brought To You By

Airside Labs — Airside Labs helps organizations deploy AI safely and responsibly by applying aviation-grade testing, assurance, and oversight to complex systems. Learn more at https://airsidelabs.com