Travel Tech Podcast

What’s Actually Stopping Air Taxis From Taking Off

Airside Labs — Mon, 13 Apr 2026 07:30:05 -0700

The US is about to publish rules that let drones fly beyond line of sight routinely — here's what that unlocks.

Part 108, the FAA's upcoming rulemaking for beyond visual line of sight (BVLOS) operations, is set to change the economics of commercial drone flight. For the first time, operators will have a clear regulatory path to fly without visual observers — making routine, scalable drone operations commercially viable.

Kraettli L. Epperson, Co-Founder and CEO of Vigilant Aerospace, has spent years building the detect-and-avoid systems that make this possible. His focus isn't the drone itself — it's the invisible layer of data, sensors, and safety logic that allows autonomous aircraft to share airspace without introducing unacceptable collision risk.

This episode unpacks what Part 108 actually enables, why detect-and-avoid is the gating technology, and what still needs to happen before drones — and eventually air taxis — can operate at scale.

What You’ll Learn

Detect-and-avoid is the gating factor for scale: Autonomous flight is limited not by hardware, but by the ability to safely manage shared airspace.
BVLOS is where real commercial value begins: Moving beyond visual line of sight unlocks scalable use cases, but requires regulatory approval and robust safety systems.
Airspace awareness depends on data fusion: Combining multiple data sources—transponders, radar, telemetry—is essential to build a reliable picture of the sky.
Non-cooperative aircraft create real risk: Not every aircraft broadcasts its position, requiring fallback systems like radar and acoustic detection.
Regulation defines what’s commercially viable: FAA frameworks like Part 107 and upcoming Part 108 directly shape what operators can and cannot do.
Routine operations require predictability: Businesses invest when operations become repeatable, not just technically possible.
Autonomy is an infrastructure problem: The future of aviation depends on invisible systems coordinating decisions in real time, not just smarter vehicles.

Time-Stamped Highlights

(03:02) Why Detect-and-Avoid Became the Industry Bottleneck
(07:09) From NASA Research to Commercial Safety Systems
(09:07) Why Collision Avoidance Is Technically Complex
(12:05) Beyond Visual Line of Sight as the Key Unlock
(17:09) The Gradual Shift Toward Autonomous Operations
(18:59) Real Constraints on Range, Altitude, and Scale
(20:21) What Changes When Flying Becomes Routine
(24:05) The Challenge of Non-Cooperative Aircraft
(28:06) Managing Tradeoffs Between Different Airspace Users
(31:08) Where Radar Fits in Drone Safety Systems
(39:34) How Air Taxis Fit Into the Same Safety Framework
(42:45) What a Fully Integrated Airspace Could Look Like by 2035

Guest

Kraettli L. Epperson — Co-Founder and CEO, Vigilant Aerospace
Kraettli L. Epperson is the Co-Founder and CEO of Vigilant Aerospace, a company focused on detect-and-avoid and airspace management systems for drones and advanced air mobility. With a background in software, data systems, and entrepreneurship, he works at the intersection of aviation safety, autonomy, and regulation—helping enable scalable, routine drone operations.
LinkedIn: https://www.linkedin.com/in/klepperson/
Company: https://www.linkedin.com/company/vigilantaero/

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.

The Real Reason One Broken Machine Disrupts an Entire Airport

Airside Labs — Mon, 06 Apr 2026 07:16:13 -0700

Queues move, bags get scanned, and passengers eventually make it through. But beneath that surface is a fragile operational layer held together by fragmented systems, manual workarounds, and frontline teams stitching together processes in real time.

Anne Marie Pellerin has seen both sides of that system—designing queue segmentation at TSA that improved throughput, and later discovering that when security systems fail, the response is often disconnected, slow, and opaque.

This conversation goes beyond passenger experience into something more fundamental: how airports actually recover when critical systems break—and why solving that requires rethinking how data, workflows, and people are connected on the ground.

What You’ll Learn

Segmenting passengers reduces stress and improves throughput: Separating travelers by experience level can increase efficiency by lowering stress-induced errors at checkpoints
Airport operations still rely on fragmented workflows: Many frontline teams use disconnected systems, emails, and even pen-and-paper to manage critical equipment
Downtime creates cascading operational risk: A single equipment failure can lead to long queues, baggage disruptions, or even flight delays
The real problem is coordination, not detection: Technology for identifying threats has advanced rapidly, but operational orchestration has lagged behind
Orchestration layers unlock system-wide visibility: Connecting frontline staff, maintenance teams, and vendors creates shared context and faster resolution
Frontline workers are the missing link in system design: Most tools are not built for the people actually operating equipment day-to-day
AI depends on unified data, not just models: Without a consolidated dataset across systems, predictive analytics and automation remain limited
Automated escalation can replace manual processes: AI-driven workflows can route issues directly to the right technician with full context, even via voice calls
Government and regulated sales cycles require long-term thinking: Success in aviation tech depends on patience, trust, and multi-year relationships
Security operations extend beyond airports: The same operational challenges exist in borders, cruise terminals, data centers, and critical infrastructure

Time-Stamped Highlights

(00:10) Airport Queues as a Design Problem
(02:09) TSA’s Checkpoint of the Future Program
(03:13) Passenger Segmentation and the Origins of PreCheck
(05:01) U.S. vs. European Airport Security Models
(07:03) The Hidden Complexity of Security Equipment Management
(09:12) How Equipment Failures Disrupt Airport Operations
(10:10) Why Airport Systems Remain Fragmented
(11:04) Building an Orchestration Layer for Security Operations
(13:01) Toward a Unified Operational Control System
(14:17) From Government to Startup: Shifting Perspectives
(18:41) Navigating Long Sales Cycles in Aviation
(22:26) Expanding Beyond Airports Into Other Industries
(24:04) What Actually Happens When Equipment Fails
(26:40) AI in Security Operations and Failure Detection
(29:26) Automated Calls and Real-Time Escalation With AI

Guest

AnneMarie Pellerin — CEO & Co-Founder, Curie Technologies; Managing Partner & Founder, LAM LHA

Anne Marie Pellerin is a former TSA leader who served as Director of Checkpoint of the Future and spent six years as the agency’s representative in Europe. She worked on programs that informed modern checkpoint design and passenger flow, including concepts that influenced TSA PreCheck. She is now co-founder of Curie Technologies, a platform focused on improving operational coordination and uptime for security equipment.
LinkedIn: https://www.linkedin.com/in/anne-marie-pellerin-1007038/
Company: https://www.linkedin.com/company/curie-technologies/

About the Podcast

Host

Brought To You By

Why AI Is Slowing Down Experts Before It Speeds Up Work (Brooker, Painter, Deakin, McKenzie)

Airside Labs — Tue, 31 Mar 2026 07:00:00 -0700

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

Host

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How a Simple Barcode Saved Airlines $1.5 Billion and Replaced Paper Tickets

Airside Labs — Wed, 25 Mar 2026 07:00:00 -0700

That quick moment at the gate when you pull up a boarding pass on your phone and scan a QR code feels routine now. It isn’t.

That interaction represents one of the most successful global standards ever deployed in aviation—a shift from magnetic stripes to barcodes that saved the industry over $1.5 billion annually. But the real story isn’t the technology. It’s how an entire industry coordinated across competitors, regulators, and infrastructure to make it work.

Eric Leopold spent 15 years at IATA working on exactly that kind of industry plumbing. In this episode, Eric Leopold takes us inside the machinery of aviation standards—from boarding passes to APIs to AI—and explains why the next wave of innovation won’t be limited by technology, but by data consistency, trust, identity, and industry alignment.

That becomes even more important once the conversation turns to AI. The interesting question is not whether an LLM can help you shop for flights. It is whether the travel industry can build the identity, data consistency, trust networks, and commercial models needed for AI agents to actually transact on your behalf without breaking the system underneath.

What You’ll Learn

The barcode boarding pass was a standards and adoption challenge, not just a scanning upgrade: Replacing magnetic stripes required industry alignment across airlines, airports, manufacturers, and regulators.
IATA standards only work when multiple airlines share the same problem: A standard starts when airlines identify a common need, build support, test the technical approach, and then push for industry adoption.
The old airline distribution stack was both brilliant and constrained: Long before the web, airlines had global real-time reservation infrastructure, but it was built on private networks and legacy protocols that later needed modernization.
NDC emerged from the need for a common API layer: Airlines had already tested direct API distribution, but agencies would not adapt for one carrier at a time, forcing the industry toward a shared standard.
AI in travel depends on data models more than demos: If the underlying entities, definitions, and relationships are inconsistent, AI systems will produce plausible but wrong answers.
The aviation industry data model matters more now than when it was created: A shared semantic layer becomes much more valuable once AI agents need normalized data they can reason across.
Travel intermediaries may split rather than disappear: AI could create a new model where travelers have trusted buying agents while suppliers are represented by their own selling agents.
Trust, identity, and settlement are still unsolved AI-era problems: For autonomous shopping and booking to work, agents need ways to verify who they represent, enforce agreements, and resolve disputes across the network.

Time-Stamped Highlights

(00:10) Eric Leopold and the Hidden Infrastructure Behind Modern Travel
(02:35) Why 2005 Was a Turning Point for Aviation Technology
(03:13) Designing the Barcode Boarding Pass Standard
(05:56) Why Politics, Not Technology, Slows Aviation Change
(08:13) How IATA Actually Creates Global Standards
(10:30) From Standards to Global Implementation
(13:35) The Shift from Magnetic Stripes to Barcodes
(16:06) How Mobile Phones Accelerated Adoption
(19:57) NDC and the Move to API-Based Distribution
(24:24) Airline Websites vs Online Travel Agents
(28:36) AI Enters Travel Booking
(30:06) Why Data Quality Is the Real AI Bottleneck
(33:27) The Problem of Data Normalization
(36:22) Knowledge Graphs vs LLMs
(41:04) Trust, Identity, and the Future of AI Travel Agents

Guest

Eric Leopold — Founder, Threedot

Eric is the founder of Threedot, a consultancy focused on the travel industry, and a board member and advisor to multiple travel companies. He spent 15 years at IATA, where he worked on some of the most impactful industry standards, including the transition to barcode boarding passes and the development of airline distribution and data models. His work has directly shaped the infrastructure used by billions of passengers worldwide.
LinkedIn: https://www.linkedin.com/in/ericleopold/
Company: https://www.linkedin.com/company/threedot/

About the Podcast

Host

Brought To You By

Why Your Istanbul Airport Sandwich Costs €22: The Economics Behind Drop-Off Fees and Retail

Airside Labs — Mon, 16 Mar 2026 07:00:00 -0700

Airports look like infrastructure businesses. Runways, terminals, aircraft movements. It’s easy to assume they make their money from planes.

But some of the most valuable assets at capacity-constrained airports—slots—generate no direct revenue for the airport at all. Meanwhile, car parks can outperform landing fees, retail margins influence pricing strategy, and regulation quietly determines why your drop-off charge keeps rising.

Professor Achim Czerny has spent decades studying airport economics. In this conversation, he breaks down the real incentive structures shaping airport behavior—from slot allocation and price caps to transfer competition and why a €9 coffee might be entirely rational.

What You’ll Learn

Why airports do not profit from slots: Slots are scarce and valuable, but under global scheduling rules, the economic value primarily accrues to airlines—not airports.
How non-aeronautical revenue drives strategy: Car parking, retail, and drop-off fees can materially outperform traditional landing fees.
Why regulation reshapes pricing incentives: Price caps on aeronautical services push airports to increase non-aeronautical charges instead.
How competition differs by passenger type: Origin-destination passengers create local competition; transfer passengers create global hub competition.
Why some airports may subsidize airlines: Under a “single till” logic, strong retail margins can justify lowering—or even offsetting—aeronautical charges.
Why friction persists despite technology: Priority lanes and congestion can be revenue-generating mechanisms, complicating the push toward full efficiency.
How airports compete for airlines: Route development, incentives, and even marketing tactics are used to attract airline bases.
What the airport of the future might look like: Humanoid robots, biometric boarding, and automation could reshape both labor and passenger experience.

Time-Stamped Highlights

(00:22) Guest Introduction: Professor Achim Czerny
(04:09) Airport Slots and Why Airports Do Not Capture Their Value
(08:28) Aeronautical vs. Non-Aeronautical Revenue Explained
(10:21) Why Car Parking Can Outearn Landing Fees
(13:10) Heathrow Regulation and the Incentive to Raise Drop-Off Charges
(17:08) High Retail Prices at Major Hubs Like Istanbul
(18:50) The 60/40 Revenue Split and How It Has Evolved
(21:14) Catchment Areas and Real Airport Competition
(24:00) Origin-Destination vs. Transfer Passenger Markets
(29:05) Why Transfer Competition Is Globally Intense
(32:04) London Southend’s Route Strategy With Wizz Air
(35:30) Airline Leverage and the Threat to Withdraw Capacity
(38:08) The Future of Airports: Technology and AI
(39:19) Humanoid Robots as a Response to Labor Constraints
(45:06) Priority Channels, Congestion, and Revenue Incentives

Guest

Professor Achim Czerny — Professor, Department of Logistics and Maritime Studies, Hong Kong Polytechnic University

Professor Czerny is a leading scholar in aviation and transportation economics. He serves as Chairman of the German Aviation Research Society, Vice President of the International Transportation Economics Association, and is a member of the executive committees of the European Aviation Conference Institute and the Air Transport Research Society. His work focuses on airport pricing, slot allocation, regulation, and market competition—bringing academic rigor to questions that directly affect passengers, airlines, and policymakers.
LinkedIn: https://www.linkedin.com/in/achim-i-czerny-0b61a1113/

About the Podcast

Host

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Airports Still Run on 1980s Software: Why the Industry Is Moving Beyond AODB-Centric Operations

Airside Labs — Mon, 09 Mar 2026 08:00:00 -0700

Hot on the heels of Heathrow Airport’s decision to use AIRHART as its digital backbone and with the Passenger Terminal Expo in London next week, in this episode, I speak with Martin Bowman, Chief Strategy Officer at Smarter Airports.

Airport operations are still largely built on systems designed decades ago. Many of the technologies coordinating flights, gates, stands, and turnaround processes trace their lineage back to architectures conceived in the 1980s. They solved a critical problem at the time—distributing flight data across the airport ecosystem—but they were never designed for the integration depth, operational complexity, or rate of change airports face today.

That gap is becoming harder to manage. Modern hubs operate close to capacity, depend on dozens of interconnected stakeholders, and need to respond to disruptions in real time. Yet many still rely on tightly scoped operational systems whose development cycles, data models, and vendor roadmaps reflect a much slower technological era.

Martin Bowman argues the industry is approaching a structural shift. With Heathrow selecting the AIRHART platform to underpin core operations, the conversation moves beyond replacing legacy systems toward something more fundamental: building a configurable operational control layer that allows airports to orchestrate data, rules, integrations, and future automation—including AI—without waiting for vendor roadmaps to catch up.

What You’ll Learn

The limits of the traditional AODB model: Airport Operations Databases were designed to distribute flight data efficiently, but their architecture and vendor delivery model have struggled to evolve alongside modern operational demands.
Platform architecture as an alternative to point solutions: Instead of deploying fixed-function products like AODB, ACDM, and AOP separately, airports can configure reusable components around shared data, rules, and integrations.
A shift in ownership of operational logic: In a platform model, the airport—not the vendor—controls configuration, development pace, and prioritization of new capabilities.
Why Heathrow’s decision matters for the industry: Replacing multiple core operational systems through a platform approach signals growing confidence in a new operating model for airport technology.
Operational credibility built through real deployments: Copenhagen Airport and Munich Airport served as early proving grounds for the platform model before expansion to Heathrow.
The operational realities of running Heathrow: Operating close to full capacity every day means the margin for disruption during technology change is extremely small.
The difference between AI hype and operational AI: Many aviation solutions labeled as AI are advanced analytics or rule-based optimization rather than generative or learning systems.
Operations orchestration as the next phase of airport technology: Future airport platforms will coordinate data, business rules, alerts, integrations, and AI models as part of a unified operational control layer.

Time-Stamped Highlights

(00:10) Heathrow’s New Operations Platform and Why This Decision Matters
(01:28) Martin Bowman’s Career Across Aviation Software, Strategy, and Operations
(07:47) What Changes When You Move Between Vendor, Advisory, and Platform Roles
(10:31) Why Legacy Airport Systems and AODBs Are Starting to Break Down
(22:09) Platform vs. Product: The Real Difference in Airport Operations
(27:18) Why Heathrow Backed the Platform Approach
(31:10) From Copenhagen to Munich to Heathrow: How the Model Gained Credibility
(38:12) What Makes Heathrow So Operationally Complex
(42:45) AI in Aviation: Hype, Mislabeling, and the Real Challenge Ahead
(48:42) Passenger Terminal Expo and Munich’s Push Toward Orchestration

Guest

Martin Bowman — Chief Strategy Officer, Smarter Airports
Martin Bowman is Chief Strategy Officer at Smarter Airports, a joint venture between Copenhagen Airport and Netcompany focused on airport operations technology. He has spent more than 25 years working across aviation and technology, with leadership roles spanning software, airport systems, strategy, and advisory work.
LinkedIn: https://www.linkedin.com/in/martinbowman/

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

Links & References

Netcompany – Airport Solutions: https://netcompany.com/private-sector/airports/
Airport Collaborative Decision Making (A-CDM) – EUROCONTROL: https://www.eurocontrol.int/concept/airport-collaborative-decision-making

The Telecom-to-Aviation Playbook for Scaling Airspace Systems

Airside Labs — Mon, 02 Mar 2026 07:00:00 -0800

Aviation’s next scaling challenge isn’t about aircraft performance or autonomy. It’s about whether the invisible systems behind the scenes can interoperate, certify, and operate reliably in a highly regulated world.

Amit Ganjoo has lived this problem twice. Before founding ANRA Technologies, he worked in telecoms during the era when fragmented standards made global connectivity impossible. Scale only arrived once interoperability, shared frameworks, and regulatory alignment replaced proprietary black boxes.

In this episode, Amit explains how those same lessons now apply to drones, UTM, and advanced air mobility. He walks through why complex systems fail at the seams, how certification reshapes organizations, and what it really takes to move from experimentation to operational airspace infrastructure.

What You’ll Learn

Complex systems tend to fail at interfaces, not core logic: Edge cases and handoffs define reliability in real-world aviation systems.
Telecom standardization offers a blueprint for airspace scale: Interoperability unlocked global mobility in telecom and remains aviation’s missing ingredient.
Black-box architectures create long-term risk in regulated markets: Proprietary systems increase migration costs and slow ecosystem-wide progress.
Operational scale requires regulatory trust, not just technology: Iterative collaboration enables regulators and operators to move faster together.
BVLOS operations represent the first true commercial unlock: Infrastructure inspection, security, and logistics drive repeatable revenue.
Certification changes how companies build and operate: EASA approval forced process rigor across safety, security, and software assurance.
Reducing regulatory ambiguity accelerates deployment: Shared interpretation matters as much as written rules.
AI’s near-term value is decision support, not autonomy: Advisory systems help humans act faster without compromising safety.

Time-Stamped Highlights

(02:13) Maker Mindset and First-Principles Engineering
(04:09) How Complex Systems Fail at the Seams
(06:04) Telecom Standards as a Blueprint for Aviation
(09:11) Interoperability Versus Black-Box Airspace Systems
(13:22) Fragmentation Risk in Global UTM and U-Space
(15:27) Commercial Drivers Behind Scalable UAS Operations
(17:07) Why BVLOS Is the Real Unlock for Scale
(18:08) Certification as a Strategic Commitment
(21:10) Regulatory Iteration Over Prescriptive Rulemaking
(24:00) Reducing Ambiguity Through Real-World Operations
(27:00) Trust-Building With Regulators and Standards Bodies
(30:06) AI as Decision Support in Safety-Critical Systems
(33:40) Human Accountability in Automated Aviation Systems
(37:17) From Experimentation to Operational Airspace
(39:10) Infrastructure as the Foundation for Advanced Air Mobility

Guest

Amit Ganjoo — Founder & CEO, ANRA Technologies
Amit is the founder and CEO of ANRA Technologies and a long-standing leader in drone traffic management, UTM, and U-Space systems. With a background spanning telecoms, defense, and aviation, he has played a central role in shaping interoperable airspace standards and regulatory frameworks globally.
LinkedIn: https://www.linkedin.com/in/amitganjoo/
Company: https://www.anratechnologies.com/

About the Podcast

Host

Links & References

3GPP Telecom Standards Organization: https://www.3gpp.org
Airports Council International, Airspace Modernization: https://aci.aero
ICAO Unmanned Aircraft Systems (UAS): https://www.icao.int/safety/UA
FAA UTM Concept of Operations (ConOps): https://www.faa.gov/uas/research_development/traffic_management

Is AI in a Bubble? What Happens When Hype Meets Regulation

Airside Labs — Mon, 23 Feb 2026 07:00:00 -0800

AI “bubble” talk usually collapses into a lazy argument: either everything is hype, or everything is inevitable. Rather than picking a side, this discussion breaks the topic into clearer components: public market valuations, hyperscaler infrastructure spending, and a fast-growing layer of venture-backed startups selling “AI strategy” before they have durable product advantage.

Alex, Ian, Oli, and Adrian have spent years building and operating real platforms in aviation data—systems where reliability, cost structures, and incentives matter more than narratives. They bring that operator lens to the AI moment: what genuinely looks bubble-like, what looks structurally sound, and which signals actually matter if you’re trying to anticipate where corrections will land.

In this episode, we pressure-test whether today’s AI wave is closer to dot-com speculation or an infrastructure buildout with real demand underneath it. We explore why the bottleneck has shifted to GPUs, power, and data centers, why “sawtooth” corrections are more likely than a single collapse, and how regulation, evaluation standards, and platform incentives—including the rise of AI-generated “slop”—will determine what survives.

What You’ll Learn

Bubble mechanics versus hype cycles: Why “we’re early on the hype curve” can still coexist with overvaluation and fragile venture behavior.
CapEx as a leading indicator of real demand: How the data-center and power buildout reframes AI from software adoption to industrial-scale infrastructure.
The profitability opacity problem: Why product adoption doesn’t automatically translate into clear margins once compute costs and inference economics are accounted for.
Startup fragility under rapid model iteration: How release velocity compresses time-to-market advantages, making “layer-on-top” products easier to commoditize.
Key-person risk in elite research teams: Why talent mobility and compensation packages can function like “mini exits” before products exist.
Accounting choices that shape perception: How longer amortization periods can improve reported income—and why the justification hinges on utilization and asset life.
AI misuse as a platform risk: How “AI slop,” bot saturation, and engagement incentives can degrade user experience and threaten existing revenue streams.
Regulation lessons from aviation: Why private, domain-specific evaluations matter more than public benchmarks when models can train to the test.

Time-Stamped Highlights

(01:33) AI Bubble Framed: Hype Curve vs. Financial Bubble
(02:12) Systemic Shock Scenario: Productivity, Labor, and Market Corrections
(03:03) Overvaluation Cycles and Comparisons to Prior Financial Bubbles
(03:46) “Neo Labs” and Billion-Dollar Seed Rounds with No Product
(07:33) Big Hyperscalers vs. Fragile Layered Startups
(10:30) $600B CapEx: Data Centers, Power, and Physical Infrastructure
(11:29) Efficiency Breakthrough Risk: What If Compute Becomes 10x–100x Cheaper?
(12:37) Cyclic Investment Loops and Market Stability Concerns
(15:25) After the First Wave: What Are Generation Two and Three Use Cases?
(16:55) Coding Tools and Measurable Gains in Knowledge Work
(22:32) Backlash Vectors: Education, Labor Displacement, and Social Pushback
(31:34) AI Slop, Bot Saturation, and Platform Quality Degradation
(38:07) Engagement Incentives and the Monetization of Low-Quality Content
(44:46) Regulation, Benchmarks, and Why Domain-Specific Testing Matters
(48:37) Trust Threshold: When Do We Accept AI in Safety-Critical Systems?

Guests

About the Podcast

Host

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. ...

“Dude, where’s my car?”: The Hidden Cost of Broken Indoor Navigation

Airside Labs — Mon, 16 Feb 2026 07:00:00 -0800

Indoor wayfinding fails in the exact moments it matters most: when someone is stressed, unfamiliar with the space, short on time, or navigating in a second language. Airports and hospitals amplify that pressure, and traditional indoor navigation systems often add friction—apps, logins, hardware dependencies, and imprecise positioning—right when users have the least cognitive bandwidth.

Dustin Gimbel is the co-founder of RouteMe, a video-based indoor navigation platform designed to remove that friction entirely. Instead of relying on GPS-like abstractions indoors, RouteMe uses recorded video routes that people can preview before arrival or follow on-site, without downloading an app or creating an account. The system prioritizes clarity, familiarity, and speed over technical novelty.

In this episode, Dustin breaks down how RouteMe reframed navigation as a pre-arrival problem rather than an in-the-moment fix. He explains why video scaled where augmented reality failed, how airlines and airports are using navigation to reduce both passenger anxiety and operating costs, and where AI meaningfully improves deployment efficiency without becoming the product story.

What You’ll Learn

Indoor navigation success depends more on cognitive clarity than positional accuracy: Sub-meter precision matters less than reducing decision-making under stress.
Pre-arrival route visibility reshapes traveler behavior: Seeing the path in advance lowers anxiety, confusion, and reliance on on-site assistance.
Blue-dot navigation models struggle at enterprise scale: Hardware requirements, beacon maintenance, and calibration costs limit deployment velocity.
Video-based routing simplifies rollout and ongoing updates: Locations can be launched and maintained without physical infrastructure or complex recalibration.
Augmented reality introduces usability constraints in travel environments: Device handling, physical fatigue, and environmental variability reduce real-world adoption.
Accessibility-first design unlocks measurable airline cost savings: Language support and confidence-building reduced unnecessary use of paid mobility services.
AI’s value sits in operational efficiency, not user-facing novelty: Automated route stitching, arrow placement, and translation enable rapid scaling.
Systems built for edge cases outperform for average users: Designing for anxiety, language barriers, and unfamiliarity improves outcomes across the full passenger base.

Time-Stamped Highlights

(00:21) RouteMe Overview and Core Use Cases
(02:18) RouteMe’s Origin in Accessibility and Low Vision
(05:08) Why Indoor Navigation Is Technically Hard
(07:10) Low-Friction Design Without Apps or Logins
(09:03) Miami International Pilot to Multi-Year Contract
(10:29) Airline Expansion and Avianca Partnership
(12:07) Pre-Arrival Navigation as Anxiety Reduction
(14:13) Healthcare Use Cases and MyChart Integration
(18:02) AI for Video Routing, Stitching, and Scale
(20:31) Sixt Car Rental Use Case
(28:05) Reducing Misuse of Mobility Services
(34:08) Motion Tracking and Off-Path Correction
(37:03) Pivot From AR to Video-Based Navigation
(39:10) Integration Into Airline and Healthcare Systems
(51:09) Simplicity as a Competitive Advantage

Guest

Dustin Gimbel — Co-Founder, RouteMe
Dustin is the co-founder of RouteMe, a company building video-based indoor navigation for airports, hospitals, and other high-stress environments. His work focuses on accessibility, pre-arrival guidance, and reducing friction in complex indoor spaces.
LinkedIn: https://www.linkedin.com/in/dustin-gimbel-23384661/
Company: https://www.routeme.ai

About the Podcast

Host

Links & References

Airports Council International (ACI World), Airports and Accessible Travel Guidance: https://aci.aero/airport-advocacy/airport-and-passenger-facilitation/accessibility/
U.S. Department of Transportation, Traveling With a Disability: https://www.transportation.gov/individuals/aviation-consumer-protection/traveling-disability
IATA, Air Travel Accessibility Program: https://www.iata.org/en/programs/passenger/accessibility/

Brought To You By

This Ex-Pilot Is Building AI for the Cockpit

Airside Labs — Mon, 09 Feb 2026 07:00:00 -0800

Aviation safety depends on having the right information at the right moment. The problem is that the information is fragmented, voluminous, and hard to retrieve when no flight is entirely the same. In non-standard situations, crews aren’t short on rules—they’re short on time to find and verify the one that matters.

Leo Kotil built Overwatch AI after spending a decade in the cockpit and airline operations. His bet is simple: GenAI is most valuable in aviation when it turns manuals, NOTAMs, and operational data into fast, source-backed answers that crews can verify—and still use when connectivity drops.

This conversation examines where AI belongs in flight operations. Not in decision authority, but in compressing the time from a non-standard situation to the verified reference that governs it without breaking the safety model that makes aviation work.

What You’ll Learn

Why aviation’s real bottleneck is retrieval, not knowledge: The documents exist; the problem is locating the right section fast enough when the situation isn’t standard.
How “digital” still leaves crews doing manual search work: iPads and PDFs replaced paper, but many workflows still rely on folder navigation and keyword search.
What makes an AI assistant usable in regulated ops: Answers must surface the exact source passages so pilots and frontline teams can confirm and trust the output.
Why context is the product, not a nice add-on: Pulling in live and structured data (weather, aeronautical publications, flight context) removes extra steps and reduces mistakes.
How multilingual reality changes system design: Crews ask in their native language while documents stay in English, often mixing aviation terms—retrieval has to handle that reliably.
How startups ship into aviation within a regulated environment: Deploy as a supplemental layer on top of existing certified tools, then prove value before becoming “core.”
Why offline capability is mandatory: Aviation software needs a usable fallback when connectivity is unavailable, not just a degraded mode in theory.
The tradeoffs of using proprietary LLM APIs in airlines: Provider dependency, infrastructure variability, and sensitive data processing create risks beyond normal cloud hosting.

Time-Stamped Highlights

(00:20) From Airline Pilot to Aviation Founder
(01:08) Career Path Across Airline and Business Aviation
(03:01) The Operational Reality of a Pilot’s Day
(05:54) Pre-Flight Procedures and Checklist Pressure
(09:10) EFBs, Manuals, and Information Overload
(12:12) Founding Overwatch AI and Meeting a Co-Founder
(13:56) Early Traction and the Techstars Accelerator
(15:41) Networks, Credibility, and Selling Into Airlines
(20:04) Designing an AI Assistant for Frontline Ops
(21:29) Native Language, Voice Input, and Real Usage
(23:01) Regulatory Constraints and Compliance Strategy
(27:01) Product Roadmap and Near-Term Focus
(29:09) Contextual Data as the Core Differentiator
(37:39) Offline AI, Edge Constraints, and Aviation Grade
(40:52) Model Lock-In, APIs, and Enterprise Risk Tradeoffs

Guest

Leo Kotil — Founder and CEO, Overwatch AI
Leo is the co-founder and CEO of Overwatch AI, where he is building AI systems to support pilots, cabin crew, operations control centers (OCC), and ground staff in managing flight disruptions and non-standard operations. A former airline pilot, he brings firsthand experience of aviation frontline workflows and operational decision-making into the design of practical, regulation-aware AI tools for airlines.
LinkedIn: https://www.linkedin.com/in/leo-kotil/
Company: https://overwatch-ai.com/

About the Podcast

Host

Links & References

EASA (European Union Aviation Safety Agency): https://www.easa.europa.eu/
Electronic Flight Bag (EFB): https://www.easa.europa.eu/en/domains/operations/electronic-flight-bag-efb
NOTAMs (Notices to Air Missions): https://www.icao.int/airnavigation/information-management/notams/Pages/default.aspx

Brought To You By

Beyond Line of Sight: The Infrastructure Drones Need to Fly

Airside Labs — Mon, 02 Feb 2026 07:00:00 -0800

Most drone use cases fail for a surprisingly mundane reason: they can’t safely or legally scale past a few hundred meters. The aircraft are capable of flying kilometers, but operations collapse once you factor in regulatory limits, detection physics, and fragile surveillance infrastructure.

James Dunthorne has encountered this constraint from every angle. From PhD research on collision avoidance to early agricultural drone deployments and high-precision surveying over railways and landmark sites, he’s seen exactly where theory breaks when exposed to real airspace.

This conversation digs into what actually blocks BVLOS operations: mixed transponder environments, latency requirements measured in seconds, why centralized flight-tracking systems struggle under regulatory scrutiny, and how edge-based sensor networks change what’s possible for drones, aviation, and AI-driven systems in the physical world.

What You’ll Learn

Why BVLOS is an infrastructure problem, not a drone problem: Aircraft capability is rarely the constraint; reliable surveillance and separation in mixed low-altitude airspace is.
Why visual line of sight is a weak safety mechanism: Human vision and standard cameras fail at meaningful ranges given aircraft closing speeds.
How electronic conspicuity fragments the airspace picture: Multiple non-interoperable transponders and non-transponding aircraft create unavoidable gaps.
Why multilateration enables detection without GPS: Legacy Mode S aircraft can be located using precise timing across multiple ground sensors.
Why centralized flight-tracking systems fail safety-critical tests: Single points of failure, variable latency, and opaque architectures undermine regulatory confidence.
How edge networks localize failure and reduce latency: Direct sensor-to-consumer connections keep surveillance resilient and deterministic.
Why incentives matter when scaling physical networks: Revenue sharing turns infrastructure deployment into a distributed, maintainable system.
How this architecture extends beyond aviation: A real-time physical-world data layer becomes foundational for AI, robotics, and autonomous systems.

Time-Stamped Highlights

(01:10) Aerospace Engineering and Autonomous Systems Origins
(03:10) Collision Avoidance and Regulatory Safety Limits
(05:10) Agricultural Drones and NDVI in Practice
(07:10) The 500-Meter Constraint and Operational Inefficiency
(09:15) From Drones to GIS and Surveying
(12:10) High-Accuracy Rail Inspections From the Air
(14:30) Discovering the BVLOS Bottleneck
(18:10) Why Closing Airspace Doesn’t Scale
(22:00) Radar, Cameras, and Detection Physics
(26:15) Transponder Fragmentation in Low Airspace
(30:15) Multilateration and Time Synchronization
(34:20) Why Surveillance Must Be Ground-Based
(37:40) Latency, Reliability, and Centralized SaaS Limits
(41:30) Edge Networks and Failure Localization
(46:10) Cyber Risk in Safety-Critical Systems
(51:05) Scaling Sensors With Economic Incentives
(54:30) From Local Coverage to Global Networks
(58:10) AI, Software Moats, and Physical Data
(01:03:40) Travel, Trust, and the Return of Face-to-Face

Guest

James Dunthorne — CEO & Co-Founder, Neuron
James is the CEO and co-founder of Neuron, where he leads the development of edge-based sensor networks and surveillance infrastructure that enable beyond-visual-line-of-sight drone operations in mixed airspace. He brings over 15 years of experience across aerospace engineering, autonomous systems, drone operations, and high-accuracy surveying, with a background spanning academic research, regulated aviation environments, and real-world deployments.
LinkedIn: https://www.linkedin.com/in/jamesdunthorne/

About the Podcast

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

Neuron: https://neuron.world
Neuron on X: https://x.com/neuron_world
Jet Vision (flight surveillance partner referenced)
NDVI (Normalized Difference Vegetation Index)
ADS-B, Mode S, and multilateration (aviation surveillance concepts)
4D Sky: https://4dsky.com
Land’s End Airport (operational deployment referenced)
NATO (project referenced)
Gatwick Airport drone incident (airspace security reference)

Brought To You By

Jevons Paradox for Knowledge Work

Airside Labs — Fri, 23 Jan 2026 08:30:00 -0800

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

About the Podcast

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

Trailer

Airside Labs — Thu, 18 Dec 2025 01:47:18 -0800