AI adoption inside teams is not following the narrative most people expect. In some cases, the most experienced engineers—the ones expected to benefit the most—are actually getting slower.

That friction reveals something deeper. The challenge is not just about tools or capability. It’s about trust, accountability, and how work itself is structured. In high-stakes environments, where someone must sign off and take responsibility, AI doesn’t simply slot in—it fundamentally reshapes how teams operate.

This conversation with Alex, Ian, Oli, and Adrian explores what happens when AI moves from experimentation into real production environments, and why the bottlenecks are as much human and organizational as they are technical.

What You’ll Learn

• AI can reduce productivity before improving it: Senior engineers may initially slow down due to context switching and deeply ingrained workflows.
• Trust is not abstract, it is operational: In regulated or high-risk systems, adoption depends on proof, repeatability, and accountability—not just perceived capability.
• Accountability remains human even in AI-driven systems: Someone must still sign off on outputs, especially in safety-critical environments.
• Team roles are shifting from building to assuring systems: The future focus moves from writing code to validating system behavior and outcomes.
• Junior career paths are being disrupted: Traditional entry-level tasks are increasingly automated, forcing a rethink of how engineers are trained.
• AI adoption varies dramatically by domain: Safety-critical industries like aviation will adopt far more slowly than consumer or enterprise software.
• Larger code generation introduces new risks: AI can produce more code faster, but also increases bug rates and cognitive load for reviewers.
• The real constraint is system-level understanding: Teams must still comprehend architecture and system behavior, even if AI generates the code.
• Productivity gains follow a J-curve: Teams must go slower first to learn how to work effectively with AI tools. 
• AI is already contributing to real production work: A measurable share of global code commits is now AI-assisted, with rapid growth expected.

Time-Stamped Highlights

• (00:48) Anthropic Future of Work Data and Real Usage Gap 
• (01:10) Theoretical AI Capability vs Actual Adoption 
• (02:28) Why AI Agents Cluster in Certain Domains 
• (03:31) Early Signals of AI Impact on Teams 
• (05:19) Trust and Accountability as the Real Constraint 
• (07:04) Why High-Trust Environments Adopt AI Slower 
• (10:06) Proof vs Trust in AI System Validation 
• (12:06) Shift from Coding to System Assurance 
• (15:03) Disruption of Junior Developer Career Paths 
• (17:03) Rethinking Learning and Skill Development 
• (18:05) Why Senior Engineers Can Get Slower with AI 
• (20:21) Rise of AI-Generated Code in GitHub 
• (21:45) Larger Code Output and Increased Bug Rates 
• (23:04) The J-Curve of AI Productivity 
• (24:46) Human Oversight and AI in Production Systems

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

The 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, an AI business that applies aviation-grade testing and compliance rigor to enterprise AI systems, helping organizations build and test AI agents 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/

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Brought To You By

Airside Labs — Airside Labs supports aviation and travel operators with tools to test, deploy, and scale modern data and AI systems in safety-critical environments. Learn more at https://airsidelabs.com.