Afleveringen
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Every billion pieces of travel content on TikTok and Instagram ends in a dead end. Boop is building the call to action that has been missing the whole time.
Social discovery vs. booking infrastructure: 89% of Gen Z travelers already discover destinations on TikTok and Instagram, but the transaction layer remains a disconnected, multi-tab booking experience that captures intent far too late.On-device AI for privacy: Boop processes photo metadata locally on the user's device, extracting location and trip structure without uploading the camera roll to external servers.The trust graph as a product moat: An AI recommendation grounded in a real friend's verified experience consistently outperforms a general-purpose LLM recommendation because it eliminates the need for the user to evaluate the source.Creator commission mechanics: Boop generates affiliate links automatically across hotel, experience, and activity providers, splitting commissions 50/50 between the platform and the original itinerary creator.Long tail outperforms celebrity: The most-copied trips on Boop are not from large influencers. A Chicago-based flight attendant's eight-day Japan itinerary and an Amsterdam local's guide during the Beyonce tour both outperformed influencer-produced content.The experience economy gap: 80% of experience providers globally are not bookable online. The activity market captures only around 20% of actual experience bookings, and Boop sees that as a primary expansion surface.Frequency signal from data: 50% of trips booked through Boop are four days or fewer, suggesting the platform is activating weekend and micro-trip behavior rather than just annual vacation planning.Network formation without explicit tools: Without building dedicated group-coordination features, Boop already sees users naturally sharing itineraries into WhatsApp groups for pre-trip alignment before anyone books a flight.
Nancy Li Smith is the CEO and founder of Boop, a social travel platform that lets users generate bookable itineraries from their camera roll and share them with friends who can copy, personalize, and book from them directly. This conversation covers how Boop uses on-device AI to extract trip data from photo metadata, how a 50/50 affiliate commission split rewards itinerary creators, and why Nancy believes trust between real people will consistently outperform AI-generated recommendations built on anonymous internet data.
What You'll Learn
(00:00) Introduction and sponsor(00:32) Nancy's background: Meta Ray-Ban glasses, augmented reality, and the path to travel(02:37) The Venice honeymoon moment that sparked Boop(04:51) Live demo: turning a camera roll into a bookable itinerary(07:55) The BootBesties creator network and the 50/50 commission model(10:44) Balancing influencer reach with the long tail of authentic local trips(13:24) Privacy architecture and on-device AI(17:12) How Boop's AI agent uses the social trust graph versus generic LLMs(21:22) Agentic universal cart: booking hotels, experiences, and niche local providers in one transaction(23:46) The shift from search to social in travel distribution(27:00) Brand strategy: Heineken, sports clubs, and fan-generated itinerary libraries(36:01) Growth metrics: 50% week-over-week growth, doubling users every two weeks(48:49) Founding advice and the community-first approach to building a startup
Time-Stamped HighlightsGuest bio
Nancy Li Smith is the CEO and founder of Boop, a social travel platform based in Seattle. She previously led the AI platform behind the Meta Ray-Ban glasses, held CPO roles in enterprise AI, and ran global perception AI and augmented reality partnerships at Microsoft. LinkedIn: linkedin.com/in/nancyliseattle | Company: boopwithme.com
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 bio
Alex Brooker is the founder of Airside Labs, an AI business that applies aviation-grade testing and compliance rigor to enterprise AI systems. Before founding Airside Labs, Alex built and scaled complex software in aviation and safety-critical domains. He also invests in early-stage technology ventures and advocates for disciplined, real-world AI deployment. LinkedIn: linkedin.com/in/alex-brooker-2280002 -
Airlines have been trying to modernize their retailing for over a decade, and most still can't change or refund what they sell.
Ann Cederhall is co-founder of LeapShift and one of the architects of the original NDC Direct Connect implementations at Lufthansa, where she helped build the project that became known as the "16 Euros" surcharge. In this episode, she traces the structural reasons why airline retailing has stalled: from the servicing gaps baked into NDC standards through version 24.1, to the 70% of airlines operating without any order management system, to the $650 million to $3 billion in annual revenue leakage from interline proration disputes. The conversation covers what AI can realistically fix and what it cannot.
NDC servicing gap: Airlines could book via NDC from 2012 but could not change, cancel, or refund those bookings until the 24.1 standard in 2024, a 12-year gap that fundamentally limited what airlines could sell through the channel.Version fragmentation: Approximately 70% of airlines currently on NDC are still running version 17.1, leaving them without the servicing capabilities that would make the standard commercially viable.Order management absence: Nearly 70% of airlines surveyed have no orchestration or order management system, meaning they have no centralized control over what they have sold, to whom, and what can be changed.Ancillary inventory failure: Airlines routinely sell ancillary services (seat upgrades, fast track, bags) with no system to verify that those services actually exist at the time and place of purchase.Upgrade opportunity cost: Willingness to pay increases sharply close to departure and at the airport, but most airlines' systems cannot process an upgrade when the booking is held in a travel seller's PNR rather than the airline's own record.AI's real limits in retailing: Agentic AI can filter and interpret shopping results, but it cannot replace shopping engines that have not been modernized in 30 years and still process 20 million fares to surface a handful of relevant options.Revenue management transformation: AI agents can harvest competitor cancellation rates, demand signals, and real-time market data overnight and present a synthesized briefing. That is a significant shift from traditional RM systems built on historical averages.Revenue leakage scale: Global interline revenue leakage runs between $650 million and $3 billion annually, driven by inaccurate proration calculations, uncollected taxes, missing ancillary settlements, and unsettled ticket coupons. Many of these disputes cost more to resolve than the amounts in question.
What You'll LearnTime-Stamped Highlights
(00:00) Introduction and Ann's background in travel(01:19) First airline role at Spantax, then Amex, Amadeus, and SAS(04:03) Arriving at Lufthansa in 2014 and the beginning of NDC(05:15) The "16 Euros" surcharge: what it was and why it caused shock(11:02) Why NDC fell short: no servicing, no ecosystem, GDS recapture(19:03) The order management gap: 70% of airlines with no orchestration(24:32) Ancillary failures: selling services that don't exist(30:33) Upgrade economics and the travel wallet effect(36:00) Why auctions and seat upgrades require caution on premium routes(42:04) What AI can and cannot do in airline retailing(49:35) Revenue management: from sky gods and Excel to overnight AI agents(55:12) Where AI genuinely helps: documentation, process archaeology, long-tail demand(01:01:00) The shopping engine problem: 30 years without modernization(01:06:00) Revenue leakage: $3 billion in interline proration disputes explainedGuest bio
Ann Cederhall is co-founder of LeapShift, a consultancy focused on airline retailing, distribution, and commercial strategy. She has held senior roles at Lufthansa, Scandinavian Airlines, Amadeus, and ATPCo, and was involved in the original NDC Direct Connect implementation at Lufthansa in 2015. She is the author of the State of Airline Retailing 2026 report. LinkedIn: https://www.linkedin.com/in/anncederhall/ Company: https://leapshift.com/About the Podcast
The Travel Tech Podcast features long-form conversations with leaders across travel and technology, exploring how software, data, operations, and distribution come together in real businesses â with an emphasis on tradeoffs, incentives, and lessons that transfer beyond any single company or role.Host bio
Alex Brooker is founder of Airside Labs, an AI business that applies aviation-grade testing and compliance rigor to enterprise AI systems. Before founding Airside Labs, he built and scaled complex software in aviation and safety-critical domains. He also invests in early-stage technology ventures. LinkedIn: https://www.linkedin.com/in/alex-brooker-2280002/ -
Zijn er afleveringen die ontbreken?
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Some travel operators ask you to shout your passport number across a crowded desk and think nothing of it. While intentions are good (checking who you are), this episode is about why that is a serious security failure and what it would take to fix it.
Yagub Rahimov is the CEO and founder of Polygraf AI, a company building behavioral security and contextual privacy tools for enterprise environments. In this conversation, he and Alex work through the specific vulnerabilities created when AI agents gain user level access, why human behavior rather than model failure is responsible for the vast majority of data breaches, and what a genuinely privacy respecting travel product would actually look like.
What You'll Learn:
Agent security: AI agents are a new category of user in the digital security pyramid, with the same system access as humans but no training in deception or social engineering.Deep fake risk: Voice cloning is already sophisticated enough to impersonate individuals convincingly to family members and colleagues, without any technical breach of the underlying systems.Mosaic intelligence: Even anonymized data fed repeatedly to an AI can be re-identified over time through behavioral pattern mapping, a concept Rahimov terms "mosaic intelligence."Behavioral control: Addressing human behavior in real time, before a violation occurs, is more effective than after-the-fact audit or punitive controls, demonstrated by a 72% drop in DLP violations for one enterprise client.Data in AI tools: Organizations that deploy internal LLMs without governing what employees input are creating serious exposure, as one $25M chatbot deployment illustrated on its first day.Travel industry failures: Asking passengers to recite passport numbers and dates of birth aloud in crowded gate areas, or type personal data into in-flight entertainment screens, represents a real and unaddressed privacy risk.Tokenization as a fix: Stripping personal data before it reaches an LLM and reuniting it with processed output via tokenization can deliver the same analytical value with substantially less exposure.QA at scale: AI makes universal quality assurance of customer interactions cheap enough that random sampling is no longer the only option, with one call center client processing 500,000 calls daily at 5 to 10 cents per call.Time-Stamped Highlights:
(00:00) Introduction: The airport data disclosure problem(00:00:42) What actually happened with Meta's Instagram AI chatbot(00:06:17) AI agents as a new user type: the security pyramid explained(00:08:57) Deep fakes in practice: voice cloning, elderly parents, and the CEO(00:14:36) North Korean infiltration via data science job interviews(00:20:54) How Polygraf detects synthetic speech in real-time video calls(00:28:42) The meeting note taker with 23 vulnerabilities(00:36:24) How Mr. Paranoid travels: loyalty status, one airline, mid-tier hotels(00:42:58) The oil and gas CEO kidnapping and the email summarizer attack vector(00:49:00) What travel companies get wrong about passenger data collection(00:30:10) Mosaic intelligence and why anonymizing data is not enough(01:07:07) The $25M HR chatbot and the 72% DLP violation reduction(01:14:12) Building the next OTA: tokenization, QA at scale, and simplicity(01:21:01) Red teaming, visibility, and why behavioral control is the next frontierGuest bio: Yagub Rahimov is CEO and founder of Polygraf AI, a company specializing in behavioral security, contextual privacy, and AI risk management for enterprise clients. He works across defense, financial services, and enterprise technology sectors, and is an active contributor to conversations on AI behavioral control at venues including the Gartner Security Summit. LinkedIn: https://www.linkedin.com/in/yrahimov/ | Company: https://polygraf.ai/
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 bio: Alex Brooker is the founder of Airside Labs, an AI business that applies aviation-grade testing and compliance rigor to enterprise AI systems. Before founding Airside Labs, he built and scaled complex software in aviation and safety-critical domains, and he invests in early-stage technology ventures. LinkedIn: https://www.linkedin.com/in/alex-brooker-2280002/
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The travel industry is ten years behind on tech, and AI itinerary builders are making it worse, not better.
Alex Ragin is the founder of Zoftify, a travel focused software agency, and Tourseta, a booking and operations platform built specifically for high volume multi day tour operators. In this conversation, he draws on a decade of building software inside the travel industry to explain why the operational complexity of group travel is so routinely underestimated, why vibe coded solutions collapse against real world edge cases, and where AI is actually delivering value versus where it is still mostly a demo.
Travel tech complexity: The industry is not one market but a collection of micro industries (airlines, hotels, tour operators, cruises), each with distinct workflows that make cross vertical software almost impossible to build well.The AI use case filter: The most reliable test for a legitimate AI application is whether a simpler procedural solution would be faster, cheaper, and more reliable, and in most cases it would be.Itinerary builder limitations: AI itinerary tools still require manual validation at every step because missing supplier data causes errors that directly damage traveler trust and booking relationships.The vibe coding ceiling: Code represents roughly 20% of what makes a complex software product work; the remaining 80% is domain knowledge, process design, and edge case handling that AI cannot yet substitute.Where AI is genuinely productive: Internal development workflows, UI/UX auditing, and unstructured data analysis are the areas where Zoftify has seen consistent, measurable productivity gains from AI tooling.The AI search shift: Tour operators are already seeing meaningful lead quality from ChatGPT and Gemini referrals, often outperforming traditional Google traffic on conversion, and this is where the real near term disruption is happening.Niche focus as a business strategy: Tourseta deliberately avoids FIT and day tour operators to stay laser focused on the bookable multi day, high volume segment, a sub vertical with almost no specialized competition.The group travel operations problem: Managing a 25 or 50 person tour involves payment installment tracking, passport data collection, rooming list management, supplier confirmation, and last minute changes at a scale where a single missed step creates outsized downstream problems.
What You'll LearnTime Stamped Highlights
(00:00) Introduction: Group Travel Is Harder Than It Looks(02:07) How Zoftify Started: From Two-Person Consultancy to Travel Agency(04:09) Why the Travel Industry Chose Them (Not the Other Way Around)(06:24) What Makes Travel Tech So Complex: Micro-Industries Within the Industry(10:12) AI Hype in 2022 vs. AI Requests in 2026: What's Actually Changed(14:14) Where AI Earns Its Place: Development, UX Audits, and Data Analysis(19:20) The Chatbot Reality Check: When 70% Resolution Rates Don't Show Up(22:47) Why Itinerary Builders Still Need a Human in the Loop(28:29) You Can't Vibe Code a Tour Operator: The 80% Problem(31:41) Tourseta's Origin: Building the Same Platform Seven Times Before Productizing(36:54) The Multi-Day Tour Operations Stack: Payments, Manifests, Rooming Lists(43:06) Where the Industry Is Headed: AI Search, GDS Adaptation, and Distribution Gaps(48:31) Opportunities Alex Won't Chase: Cruises, Corporate Travel Niches, and More(49:31) How to Reach Alex: LinkedIn, Zoftify, and ToursetaGuest Bio
Alex Ragin is the founder of Zoftify, a travel focused software development agency, and Tourseta, a booking and operations platform for multi day tour operators. He has been building software for the travel industry since 2015, with prior experience in fintech and video streaming for major UK broadcasters. LinkedIn: https://www.linkedin.com/in/alexander-ragin/ | Zoftify: https://zoftify.com/ | Tourseta: https://tourseta.com/
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 Bio
Alex Brooker is the founder of Airside Labs, an AI business that applies aviation-grade testing and compliance rigor to enterprise AI systems. Before founding Airside Labs, he built and scaled complex software in aviation and safety-critical domains. He also invests in early-stage technology ventures and advocates for practical, real-world AI deployment strategies. LinkedIn: https://www.linkedin.com/in/alex-brooker-2280002/ -
The only sustainable innovations that actually scale are the ones customers never have to think about. Josh Dorfman has spent two decades building them.
Josh Dorfman is the co-founder of Planted (plantdmaterials.com), a materials startup building structural panels from fast-growing grass as a direct replacement for wood-derived products in U.S. home construction. He came up through consumer sustainability media (books, Sirius radio, TV) under the Lazy Environmentalist brand before pivoting to B2B climate technology. In this conversation, Josh and Alex explore the mechanics of low-friction sustainability across building materials, aviation, carbon credits, and the unexpected efficiency gains hiding in the GLP-1 drug story.
The Drop-In Rule: Sustainable materials only reach scale when they integrate into existing workflows without asking the customer to change anything.B2B green sales: Even the most environmentally committed executive cannot justify a purchase on environmental grounds alone. The product has to win on performance and price first.The Trove playbook: Climate companies that succeed eventually stop leading with climate, treating sustainability as a downstream brand benefit rather than the sales pitch.Carbon credits: Voluntary offset schemes largely transfer the cost of an airline's impact onto consumers while delivering minimal real-world emissions reduction.GLP-1 and aviation: Jefferies estimates adoption of weight-loss drugs like Ozempic could save U.S. airlines around $580M annually in fuel costs, about 1.5% of fuel spend. Jefferies separately modelled a 2% weight reduction translating to roughly 4% EPS uplift. The point: the most significant efficiency wins are often not engineering solutions. Battery cost curves: Declining battery costs are already reshaping U.S. power grid additions (51% solar, 28% battery storage projected for the next 12 months) and will accelerate electric aviation faster than most forecasts assume.Grass over trees: Planted's core material grows 10x faster than timber and can be harvested annually, enabling carbon sequestration at a scale that tree-planting programs cannot match.Storytelling as company-building: In a venture-backed startup, the founder is simultaneously selling the company and the product. The skill set required is identical.
What You'll LearnTimestamped Highlights
(00:00) Introduction: IATA 2050 targets, SAF adoption, and why materials innovation matters(00:31) Josh's origin story: The Lazy Environmentalist, Vivavi furniture, and going green in Brooklyn(07:01) The pivot: from consumer media to B2B climate materials(12:49) Why sustainability pitches fail, and what actually drives B2B purchasing decisions(18:56) The Trove case study: Fight Club rules for climate companies(25:03) How Planted was born: a SpaceX engineer, six trash bags of hemp, and a phone call(32:52) Testing every biomass: from hemp to Halloween hay(38:41) Bringing it to aviation: SAF, the GLP-1 surprise, and the $580M olive(32:33) Carbon credits: why they're mostly marketing, and what airlines should do instead(38:00) Planted's roadmap: biochar, graphene, and potential aviation materials(43:50) Battery technology and why the cost curve matters more than regulation(44:06) What's coming in 2026 for Planted: furniture launch, new panel systems, homebuilder announcements(49:50) Ground fleet electrification and the Our World reusable cup trialGuest bio Josh Dorfman is the co-founder and CEO of Planted (plantdmaterials.com), a North Carolina-based materials company producing structural building panels from perennial grass as a timber replacement. He previously built the Lazy Environmentalist media brand across books, Sirius Satellite Radio, and television, and hosts the Super Cool podcast (getsuper.cool/podcast), which covers climate technology and founders. LinkedIn: linkedin.com/in/dorfmanjosh/
About the Podcast The Travel Tech Podcast features long-form conversations with leaders across travel and technology, exploring 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 bio Alex Brooker is the founder of Airside Labs, an aviation AI agency applying aviation-grade testing and compliance rigour to AI systems in safety critical and regulated domains. Before founding Airside Labs, he built and scaled complex software across aviation and both business and safety critical domains. LinkedIn: linkedin.com/in/alex-brooker-2280002/
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The travel tech stack has a dirty secret: the more suppliers connect to each other, the higher the chance your inventory ends up competing against itself.
Olivier Boinet is the founder of room-matching.com and Omnitravel.ai, two tools built to solve the data normalization and room-mapping problems at the root of travel distribution chaos. In this conversation, Alex and Olivier work through why hotel data loses quality and identity as it moves through the distribution chain, how the current API landscape creates circular inventory loops, and what hoteliers need to do right now to ensure AI search agents can find and trust their properties.
Room mapping: Identical hotel rooms listed under different names and codes across suppliers create significant matching errors that still require manual comparison in most agencies.Data normalization: Pushing inventory through intermediary systems strips away a hotel's personality, including the specific content, offers, and experiences that differentiate the property.Distribution loops: In B2B travel, strategic partnerships between suppliers are so interlocking that a hotel's own inventory can circulate back to it through a chain of partners, marked up along the way.AI discoverability: LLMs evaluate hotels first as websites. If a property's content isn't structured for machine legibility, it won't surface in AI-powered search results or recommendations.Dynamic content personalization: Corpus-based retrieval architectures allow a single property's content to respond differently depending on whether the searcher is a Gen Z solo traveler, a British couple, or a corporate booker.Vibe booking: High-quality, experience-focused content drives significantly higher conversion, whether the audience is a human or an LLM scanning for properties to recommend.Direct booking imperative: As LLMs increasingly route booking intent straight to properties, hotels without structured, AI-ready web pages will lose direct channel share to those that have invested in content quality.The confirmation paradox: The industry-wide check-recheck-check loop across API chains consumes enormous resources and still produces availability errors, a structural inefficiency that AI pressure is beginning to expose.
What You'll LearnTime-Stamped Highlights
(00:00) Introduction and context: the fake hotel booking episode that sparked this conversation(00:01:17) Olivier's origin story: from software developer to travel agency floor shock(00:02:00) 20 agents, 10 portals each: the room comparison problem in practice(00:03:05) Building room-matching.com: applying NLP and heuristics to dynamic room deduplication(00:05:00) The normalization trap: why pushing data through intermediaries erases hotel identity(00:06:14) Omnitravel's approach: using the live website as the source of truth for AI-ready data(00:09:22) The circular inventory problem: how B2B partnerships create self-distribution loops(00:11:23) What LLMs are actually doing when they evaluate hotel websites(00:13:10) Dynamic personalization via corpus-based retrieval: serving different content to different traveler profiles(00:10:40) Vibe booking: why content quality is now a distribution strategy(00:09:01) The check-recheck-check loop and its cost to the industry(00:15:14) Open-source tools that can power personalized AI content distribution todayGuest bio
Olivier Boinet is the founder of room-matching.com, a dynamic room-mapping platform used across the travel industry, and Omnitravel.ai, a data normalization and AI-readiness tool for hotels and tour operators. With 30 years of software development experience spanning antivirus heuristics, NLP, and travel technology, he brings an unusually technical lens to the distribution and content quality problems facing the hospitality sector. Connect with him at linkedin.com/in/olivier-boinet-3b328023, room-matching.com, and omnitravel.ai.
About the Podcast
The Travel Tech Podcast features long-form conversations with leaders across travel and technology, exploring 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 bio
Alex Brooker is the founder of Airside Labs, an AI business that applies aviation-grade testing and compliance rigor to enterprise AI systems. Before founding Airside Labs, he built and scaled complex software in aviation and safety-critical domains. He also invests in early-stage technology ventures and advocates for thoughtful, real-world AI deployment strategies. LinkedIn: linkedin.com/in/alex-brooker-2280002 -
What if the AI moment in travel is less about building a better OTA, and more about making the OTA unnecessary?
Christopher Olivares is the solo founder of Elyo (elyo.io), a conversational AI travel assistant that helps travelers find the cheapest flights across flexible destinations and dates, with no commissions, intermediaries, or date pickers. In this episode, Christopher traces his path from OECD policy analyst and expat traveler to vibe-coded solopreneur, and explains how generative AI unlocked both the product idea and the ability to build it without a technical background. The conversation covers the incentive problems embedded in OTAs, the economics of airline distribution, the future of travel discovery, and why AI may finally enable a return to genuinely traveler-first service.
Traveler intent vs. traveler input: Elyo is built around decomposing what a traveler wants (cheapest meeting point, most flexible weekend, best value destination) rather than the rigid inputs legacy search UIs require.The OTA commission problem: Using a "free" platform isn't free. Commissions get reflected in prices, and the traveler absorbs costs they never see.Freemium as a trust mechanism: Elyo's subscription model exists specifically so the platform doesn't have to earn commissions, which keeps the traveler's interest as the unconditional North Star.AI as a leveler for solo founders: Christopher built Elyo without any prior coding experience, using LLMs both to imagine the product and to build it, illustrating a real shift in who can launch a technical startup.GDS access is getting harder for startups: At least one major GDS has closed its developer portal, raising barriers for early-stage builders trying to validate ideas before committing to full commercialization.The seller-of-record problem: Many white-label distribution APIs make startups the seller of record for tickets, a liability that most early-stage founders (Elyo included) want no part of.AI as a return to the travel agent era: By removing the human cost of advisory, AI can deliver the personalization of pre-internet travel agents alongside the price transparency the metasearch era created, without the commission layer.Corporate travel is an underserved use case: The remote-team "where should we rendezvous?" problem is a direct extension of Elyo's core optimization, and today's corporate booking platforms remain shockingly poor on UX.
What You'll LearnTime-Stamped Highlights
(00:00) Introduction and episode overview(00:01:14) Christopher's background: diplomacy, teaching English in Japan and Spain, and the OECD(00:04:26) The data standards challenge: why counting schools is harder than it sounds(00:07:05) The original idea: meeting friends in a cheap third city(00:10:34) Why generative AI unlocked both the product concept and the build(00:14:19) Elyo: what it is, how it works, and why the date picker had to die(00:24:12) The traveler-first business model: freemium, no commissions, direct airline links(00:32:07) Navigating airline distribution: GDSs, NDC, white-label APIs, and the seller-of-record problem(00:37:45) Incentive structures in travel: why "free" platforms aren't free(00:40:01) The AI moment in travel distribution: OTA integrations into chat services(00:44:45) Corporate travel and the remote-team rendezvous use case(00:45:51) The return of the travel agent: personalization plus democratization(00:48:12) Early adopters, honest pricing, and what's coming next for Elyo(00:47:37) Where to find Elyo and the origin of the name
Guest Bio
Christopher Olivares is the solo founder of Elyo, a conversational AI travel assistant. Before launching Elyo, he spent four and a half years at the OECD in Paris working on internal ethics, education policy, and international statistical indicators, and is currently completing an executive master's in statistics and artificial intelligence at Université Paris Dauphine. LinkedIn: https://www.linkedin.com/in/christopher-olivares-40b8b283/
Elyo: https://elyo.io/?ref=travel-tech-podcast&utm_source=podcast&utm_medium=podcast
About the Podcast
The Travel Tech Podcast features long-form conversations with leaders across travel and technology, exploring 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 Bio
Alex Brooker is the founder of Airside Labs, an AI business that applies aviation-grade testing and compliance rigor to building enterprise AI systems. Before founding Airside Labs, he built and scaled complex software in aviation and safety-critical domains. 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/ -
Airline distribution is sitting on decades of tech debt and AI might be the only thing that can fix it.
Jim Hetzel is a travel and airline technology veteran who now leads retailing strategy at TWAI. In this conversation, he traces the full arc of airline distribution from fragmented pre-GDS ticketing to the NDC standards work and makes the case that AI is positioned to become the new orchestration layer the industry desperately needs. The discussion also explores the trust problem that no one in the AI agent era has solved yet: who plays the role of IATA when billions of bots are buying plane tickets?
GDS origin: The Global Distribution System was built to solve a fragmentation problem giving travel sellers a single electronic marketplace instead of supplier-by-supplier chaos.NDC's limits: NDC is a messaging standard, not a retailing platform; airlines that want to become true retailers still need massive investments in CRM, personalization, and revenue management.Standards incompatibility: NDC versions are not backwards compatible with each other, which means early adopters face millions of dollars in re-implementation costs every major release cycle.AI as normalizer: AI can sit on top of both legacy GDS infrastructure and modern NDC standards simultaneously, acting as an intelligent interpreter rather than waiting for the industry to agree on a single format.Bot demand risk: AI shopping agents never stop checking, unlike human travelers, which means airline systems could face look-to-book ratios of one million to one, infrastructure costs that dwarf current GDS fees.Trust gap: IATA's historic role was to certify agents and airlines as legitimate counterparties; no equivalent trust and authentication layer exists yet for machine-to-machine AI agent transactions.Fare calculation art: Even today, skilled international pricing specialists can find fare combinations that GDS pricing engines miss and that variability, tolerance for imprecision, is baked into the industry by design.Intermediaries survive: AI doesn't kill intermediaries wholesale; it kills the weak ones. The players who solve orchestration, trust, and content normalization at scale will define the next generation of distribution.
What You'll Learn
(00:00) Introduction and episode framing(00:21) Distribution before computers: fragmentation and the pre-GDS world(03:04) How the GDS created an electronic marketplace buffer(04:11) NDC: messaging standard vs. retailing platform(09:59) Why backwards incompatibility made NDC costly for early adopters(12:17) "A dumpster fire": the current state of airline distribution standards(15:25) Travel agencies caught supporting both GDS and NDC simultaneously(17:22) AI as the normalization layer across incompatible standards(22:21) Bot demand: look-to-book ratios and machine-generated traffic at scale(23:55) IATA's dual trust role and why AI agents have no equivalent(35:05) GDS pricing discrepancies: three systems, same itinerary, different fares(38:39) The "art" of international fare calculation and AI's opportunity there(40:25) Who builds the next trust and orchestration layer?(45:53) TWAI: modern retailing across GDS, NDC, and non-air content today
Time-Stamped Highlights
Guest Bio
Jim Hetzel is a travel and airline technology executive with a career spanning corporate travel agencies to enterprise distribution platforms. He currently works at TWAI, a travel retailing technology company that enables airlines and travel sellers to offer multi-source content: GDS, NDC, hotels, car rental, activities through a unified platform. LinkedIn: https://www.linkedin.com/in/jhetzel/ | Company: https://twai.com
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 Bio
Alex Brooker is the founder of Airside Labs, an AI business that applies aviation-grade testing and compliance rigor to enterprise AI systems. Before founding Airside Labs, Alex built and scaled complex software in aviation and safety-critical domains. LinkedIn: https://www.linkedin.com/in/alex-brooker-2280002/ | Company: https://airsidelabs.comtkVDKZ6VNIo9a6ml0xrc
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Hotels are sitting on millions in uncollected revenue and corrupted content and most of them don't even know it.
Fred Bean is the founder of HotelPORT, a hospitality content governance and distribution technology company he launched in 2019 after three decades working across hotel reservations, GDS connectivity, and online distribution. His career spans roles at Hyatt, Sabre, and TravelWeb, where he helped build foundational infrastructure for hotel bookings online.
This conversation covers the persistent structural problems in hotel distribution from inaccurate third-party content and uncollected OTA payments to the misapplication of AI and how governed data is the prerequisite for every meaningful technology deployment in hospitality.
What You'll Learn
Variable Net Rates: The mechanism that allowed hotels to revenue-manage against net rates after 9/11 â locking in OTA margins while letting room rates float with demand â was pioneered at TravelWeb in 2002 and became an industry standard adopted by every major distribution player.Content Governance: Inaccurate hotel content on third-party channels is not an edge case â it is the norm, affecting even hotels with active distribution connectivity, and the downstream impact on bookings and guest experience is systematically underestimated.Revenue Leakage: An audit across 2,000 hotels found $7 million in uncollected OTA virtual credit card payments, with over $500,000 already expired⊠a direct result of resource constraints at property level, not negligence.AI Prerequisite: AI deployed on top of ungoverned data will hallucinate and erode guest trust; the correct sequence is governance first, activation second: verify the source of truth before connecting any AI-facing interface.Distribution Expertise Decline: Institutional knowledge of how hotel distribution systems interconnect is eroding as experienced practitioners retire without adequate replacements, creating an industry-wide vulnerability that neither software nor AI can currently compensate for.Channel Misalignment: Digital marketing and distribution teams within hotels frequently operate without visibility into each other's decisions: resulting in spend on paid search during periods of zero availability, a problem that requires internal alignment before technology can solve it.Generational Engagement Shift: Voice, text, and chat AI are not competing formats: they serve different traveler cohorts simultaneously, and hospitality operators need human off-ramps in AI voice flows and multi-channel support to avoid alienating any segment.OTA Consolidation Risk: The consolidation of major OTAs into a few parent companies has created an illusion of channel choice for consumers, reducing competitive pressure on incumbents and opening genuine opportunity for startups that solve problems the big platforms have deprioritized.Time-Stamped Highlights
(00:00) Introduction â Why a Call Center in Omaha Started a 30-Year Career(01:10) From Reservation Agent to Distribution Architect at Hyatt and Sabre(02:58) Building the First Internet-Bookable Hotel Reservations in the Late 1990s(08:10) Inventing Variable Net Rates: How Hotels Took Back Margin from OTAs Post-9/11(11:25) Data at Scale: Why More Channels Has Made Content Accuracy Worse, Not Better(15:00) The Long Tail Problem: How Smaller Hotels Get Overwhelmed and Where They Fall Short(20:35) AI Skepticism Grounded in Experience: Dot-Com Parallels and the Pets.com Generation(30:11) Governance Before Activation: The Two-Step Framework for Responsible AI Deployment in Hotels(36:12) PropertyView and the $7 Million Discovery: Auditing Revenue Leakage Across 2,000 Hotels(42:20) Engage: Voice, Text, and Chat AI Powered by Verified Hospitality Data(45:30) Generational Divergence in Guest Communication: Designing for All Three Cohorts(50:00) OTA Consolidation, Fake Hotel Websites, and the Fraud Problem AI Is Making Worse(55:00) Where Startups Can Still Win: Packaging, Event Travel, and Value-Based Selling(58:30) The BIG Foundation: Teaching Food-Insecure Youth to Cook as a Pathway into HospitalityGuest Bio
Fred Bean is the Founder and CEO of HotelPORT, a hospitality content governance platform he launched in 2019 after 30 years working in hotel reservations, GDS connectivity, and distribution technology at companies including Hyatt, Sabre, and TravelWeb, where he co-developed the variable net rate model adopted across the industry. He also founded the BIG Foundation, a Miami-based initiative addressing food insecurity among hospitality-industry families by giving students culinary skills and a pathway into the workforce.LinkedIn: https://www.linkedin.com/in/fredbean/ | Company: hotelport.com | Foundation: https://bigfoundation.net/
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 â 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/
đ Explore 6,500+ Aviation AI Use Cases. We've catalogued over 6,500 real AI applications across airlines, airports, ATM, MRO, and more into an interactive browser. Filter by sector and see where AI is actually being deployed across aviation: airsidelabs.com/aviation-use-cases
Brought To You By
Airside Labs â 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.
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AI is rapidly changing the economics of software: code is cheaper to generate than ever, but significantly harder to reason about, validate, and secure. As systems become more automated, the real constraint is no longer building functionality, itâs maintaining confidence in what those systems will actually do once deployed.
To unpack this shift, Alex Brooker is joined by Jen Reid-Schram, an AI practitioner and former VP of Technology with deep roots in QA, engineering leadership, and executive transformation. Jen brings a systems-level view of how quality thinking evolved inside engineering teamsâand why it may need to re-emerge in a new form as AI reshapes how software is produced.
Sheâs joined by Oli Deakin, former CTO of Snowflake Software and ex-technology leader at Cirium, who brings hands-on experience building and operating complex technical systems in aviation and enterprise environments. Together, they explore how AI is redefining QA, amplifying security risk, and forcing a rethink of what âgood softwareâ even means in an era of superhuman code generation.What Youâll Learn
QA is fundamentally about translating intent into system behaviorâShift-leftâ eliminated QA as a team, but not as a needAI reduces the cost of writing code, not verifying itSpec-driven development is becoming a primary control mechanismEngineering is shifting from writing code to defining behaviorQA thinking is rooted in empathy and adversarial reasoningAI amplifies both productivity and systemic risk simultaneouslyZero-day vulnerabilities highlight unknown risks in software systemsCVE management remains a high-stakes tradeoffAI adoption is reshaping incentives between productivity and burnoutSecurity and QA are converging again under AI-driven development
(00:11) AI focus and recent industry developments(01:38) The evolving role of QA engineers(02:19) Jenâs start in QA and early tech career(03:01) Defining the QA âquality mindsetâ(03:23) Shift-left development model explained(04:17) Erosion of standalone QA teams(05:57) Core traits of effective QA thinking(08:27) AI and the return of test-driven development(10:30) Spec-driven development in AI workflows(11:50) AI as a leveling force across roles(14:28) Mythos, superintelligence, and AI risk discussion(18:11) Zero-day vulnerabilities explained in context
Time-Stamped Highlights
Guests
Jen Reid-Schram â AI Practitioner, Former VP of Technology, Founder of Level Up ExperienceJen is a technology leader with deep experience in QA, engineering leadership, and executive transformation. She now focuses on helping organizations adopt AI through hands-on training, bridging the gap between technical capability and operational understanding.
LinkedIn: https://www.linkedin.com/in/jen-reid-schram
Company: https://www.levelup-experience.comOliver Deakin â 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/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 â 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/đ Explore 6,500+ Aviation AI Use Cases. We've catalogued over 6,500 real AI applications across airlines, airports, ATM, MRO, and more into an interactive browser. Filter by sector and see where AI is actually being deployed across aviation: airsidelabs.com/aviation-use-cases
Brought To You By
Airside Labs â 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.
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For decades, building a travel business meant stitching together fragmented supply: GDS systems, hotel APIs, pricing layers, and fulfillment infrastructure. It was complex, expensive, and slow to scale.
Now, thatâs changing fast.
With the rise of AI agents and MCP-powered infrastructure, what once took years of engineering can now be deployed in minutesâfundamentally shifting who can participate in the travel ecosystem, and how distribution works.
Mike Putman, CEO and Founder of Custom Travel Solutions, has been at the center of this evolutionâfrom launching one of the earliest online travel agencies in the mid-90s to building the infrastructure powering modern AI-driven booking systems.
This episode explores how travel distribution is being rebuilt, why agentsânot brandsâmay control the customer relationship, and what it means when any company can become a travel seller overnight.
What Youâll Learn
Building a travel booking engine has gone from multi-year projects to minutes with AI agentsThe real complexity in travel isnât searchâitâs data normalization, deduplication, and pricing logicAgentic AI shifts power away from brands toward personalized user-controlled experiencesLoyalty programs may weaken as agents optimize for outcomes, not brand preferenceThe âlast mileâ problemsâlike booking failures at hotelsâstill cost the industry ~2% of transactionsAI can now solve operational gaps (like reconfirmation) that were previously too expensive to fix with humansTravel distribution is becoming infrastructure-first, where aggregation layers power entire ecosystemsIn the future, agents may transact directly with other agents, reshaping how commerce works
(01:03) Early Days of Online Travel (01:55) Evolution of Travel Technology (03:11) Launch of 11th Hour Vacations (04:10) Gaining First Customers (06:03) Pre-Google Search Engines (07:02) Partnership with Lastminute.com (09:18) Amadeus Acquisition and OneTravel (10:19) How Custom Travel Solutions Works (14:34) Lessons from Previous Ventures (16:20) Scaling Challenges (18:23) AI in Travel Industry (32:39) Future of Agentic AI in Travel
Time-Stamped Highlights
GuestMike Putman â CEO & Founder, Custom Travel Solutions
Mike Putman is a travel industry veteran with over four decades of experience across distribution and technology. He founded one of the earliest online travel agencies in the 1990s and has since worked with major global travel brands. Today, he leads Custom Travel Solutions, building infrastructure that powers modern travel booking, including AI-driven aggregation, agentic APIs, and back-office automation tools.LinkedIn: https://www.linkedin.com/in/mikeputman/
Company: https://customtravelsolutions.com
Routestack: Routestack.aiAbout 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 â 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/đ Explore 6,500+ Aviation AI Use Cases. We've catalogued over 6,500 real AI applications across airlines, airports, ATM, MRO, and more into an interactive browser. Filter by sector and see where AI is actually being deployed across aviation: airsidelabs.com/aviation-use-cases
Brought To You By
Airside Labs â 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.
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The US is about to publish rules that let drones fly beyond line of sight routinely â 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 â 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 â 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 â and eventually air taxis â can operate at scale.
What Youâll 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 sourcesâtransponders, radar, telemetryâis 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 whatâs 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.
(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
Time-Stamped Highlights
GuestKraettli L. Epperson â 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 regulationâhelping 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 â 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/đ Explore 6,500+ Aviation AI Use Cases. We've catalogued over 6,500 real AI applications across airlines, airports, ATM, MRO, and more into an interactive browser. Filter by sector and see where AI is actually being deployed across aviation: airsidelabs.com/aviation-use-cases
Brought To You By
Airside Labs â 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.
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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 systemâdesigning 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 breakâand why solving that requires rethinking how data, workflows, and people are connected on the ground.
What Youâll Learn
Segmenting passengers reduces stress and improves throughput: Separating travelers by experience level can increase efficiency by lowering stress-induced errors at checkpointsAirport operations still rely on fragmented workflows: Many frontline teams use disconnected systems, emails, and even pen-and-paper to manage critical equipmentDowntime creates cascading operational risk: A single equipment failure can lead to long queues, baggage disruptions, or even flight delaysThe real problem is coordination, not detection: Technology for identifying threats has advanced rapidly, but operational orchestration has lagged behindOrchestration layers unlock system-wide visibility: Connecting frontline staff, maintenance teams, and vendors creates shared context and faster resolutionFrontline workers are the missing link in system design: Most tools are not built for the people actually operating equipment day-to-dayAI depends on unified data, not just models: Without a consolidated dataset across systems, predictive analytics and automation remain limitedAutomated escalation can replace manual processes: AI-driven workflows can route issues directly to the right technician with full context, even via voice callsGovernment and regulated sales cycles require long-term thinking: Success in aviation tech depends on patience, trust, and multi-year relationshipsSecurity operations extend beyond airports: The same operational challenges exist in borders, cruise terminals, data centers, and critical infrastructureTime-Stamped Highlights
(00:10) Airport Queues as a Design Problem(02:09) TSAâs 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
GuestAnneMarie Pellerin â 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 agencyâs 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
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 â 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/đ Explore 6,500+ Aviation AI Use Cases. We've catalogued over 6,500 real AI applications across airlines, airports, ATM, MRO, and more into an interactive browser. Filter by sector and see where AI is actually being deployed across aviation: airsidelabs.com/aviation-use-cases
Brought To You By
Airside Labs â 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.
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AI adoption inside teams is not following the narrative most people expect. In some cases, the most experienced engineersâthe ones expected to benefit the mostâare actually getting slower.
That friction reveals something deeper. The challenge is not just about tools or capability. Itâs about trust, accountability, and how work itself is structured. In high-stakes environments, where someone must sign off and take responsibility, AI doesnât simply slot inâit 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 Youâll 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 accountabilityânot 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.
(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
Time-Stamped HighlightsGuests
Ian Painter â 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 worldâs most trusted aviation analytics companies.
LinkedIn: https://www.linkedin.com/in/ianpainter/Oliver Deakin â 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 â 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 PodcastThe 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 â 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/đ Explore 6,500+ Aviation AI Use Cases. We've catalogued over 6,500 real AI applications across airlines, airports, ATM, MRO, and more into an interactive browser. Filter by sector and see where AI is actually being deployed across aviation: airsidelabs.com/aviation-use-cases
Brought To You By
Airside Labs â 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.
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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 isnât.
That interaction represents one of the most successful global standards ever deployed in aviationâa shift from magnetic stripes to barcodes that saved the industry over $1.5 billion annually. But the real story isnât the technology. Itâs 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 standardsâfrom boarding passes to APIs to AIâand explains why the next wave of innovation wonât 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.
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.
What Youâll LearnTime-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
GuestEric Leopold â 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
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 â 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/
đ Explore 6,500+ Aviation AI Use Cases. We've catalogued over 6,500 real AI applications across airlines, airports, ATM, MRO, and more into an interactive browser. Filter by sector and see where AI is actually being deployed across aviation: airsidelabs.com/aviation-use-casesBrought To You By
Airside Labs â 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.
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Airports look like infrastructure businesses. Runways, terminals, aircraft movements. Itâs easy to assume they make their money from planes.
But some of the most valuable assets at capacity-constrained airportsâslotsâgenerate 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 behaviorâfrom slot allocation and price caps to transfer competition and why a âŹ9 coffee might be entirely rational.
What Youâll Learn
Why airports do not profit from slots: Slots are scarce and valuable, but under global scheduling rules, the economic value primarily accrues to airlinesânot 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 âsingle tillâ logic, strong retail margins can justify loweringâor even offsettingâaeronautical 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.
(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 Southendâs 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
Time-Stamped Highlights
GuestProfessor Achim Czerny â 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 competitionâbringing academic rigor to questions that directly affect passengers, airlines, and policymakers.
LinkedIn: https://www.linkedin.com/in/achim-i-czerny-0b61a1113/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 â 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/
đ Explore 6,500+ Aviation AI Use Cases. We've catalogued over 6,500 real AI applications across airlines, airports, ATM, MRO, and more into an interactive browser. Filter by sector and see where AI is actually being deployed across aviation: airsidelabs.com/aviation-use-casesBrought To You By
Airside Labs â 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.
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Hot on the heels of Heathrow Airportâs 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 timeâdistributing flight data across the airport ecosystemâbut 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 automationâincluding AIâwithout waiting for vendor roadmaps to catch up.
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 airportânot the vendorâcontrols configuration, development pace, and prioritization of new capabilities.Why Heathrowâs 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.
What Youâll Learn
(00:10) Heathrowâs New Operations Platform and Why This Decision Matters(01:28) Martin Bowmanâs 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 Munichâs Push Toward Orchestration
Time-Stamped Highlights
GuestMartin Bowman â 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.
HostAlex Brooker â 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/
Netcompany â Airport Solutions: https://netcompany.com/private-sector/airports/Airport Collaborative Decision Making (A-CDM) â EUROCONTROL: https://www.eurocontrol.int/concept/airport-collaborative-decision-making
Links & References
đ Explore 6,500+ Aviation AI Use Cases. We've catalogued over 6,500 real AI applications across airlines, airports, ATM, MRO, and more into an interactive browser. Filter by sector and see where AI is actually being deployed across aviation: airsidelabs.com/aviation-use-cases
Brought To You ByAirside Labs â 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.
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Aviationâs next scaling challenge isnât about aircraft performance or autonomy. Itâs 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.
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 aviationâs 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.AIâs near-term value is decision support, not autonomy: Advisory systems help humans act faster without compromising safety.
What Youâll Learn
(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
Time-Stamped Highlights
GuestAmit Ganjoo â 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
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.
HostAlex Brooker â 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/
3GPP Telecom Standards Organization: https://www.3gpp.orgAirports Council International, Airspace Modernization: https://aci.aeroICAO Unmanned Aircraft Systems (UAS): https://www.icao.int/safety/UAFAA UTM Concept of Operations (ConOps): https://www.faa.gov/uas/research_development/traffic_management
Links & References
đ Explore 6,500+ Aviation AI Use Cases. We've catalogued over 6,500 real AI applications across airlines, airports, ATM, MRO, and more into an interactive browser. Filter by sector and see where AI is actually being deployed across aviation: airsidelabs.com/aviation-use-cases
Brought To You ByAirside Labs â 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.
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AI âbubbleâ 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 âAI strategyâ before they have durable product advantage.
Alex, Ian, Oli, and Adrian have spent years building and operating real platforms in aviation dataâsystems 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 youâre trying to anticipate where corrections will land.
In this episode, we pressure-test whether todayâs 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 âsawtoothâ corrections are more likely than a single collapse, and how regulation, evaluation standards, and platform incentivesâincluding the rise of AI-generated âslopââwill determine what survives.
Bubble mechanics versus hype cycles: Why âweâre early on the hype curveâ 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 doesnât 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 âlayer-on-topâ products easier to commoditize.Key-person risk in elite research teams: Why talent mobility and compensation packages can function like âmini exitsâ before products exist.Accounting choices that shape perception: How longer amortization periods can improve reported incomeâand why the justification hinges on utilization and asset life.AI misuse as a platform risk: How âAI slop,â 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.
What Youâll Learn
(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) âNeo Labsâ 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 10xâ100x 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?
Time-Stamped Highlights
GuestsIan Painter â 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 worldâs most trusted aviation analytics companies.
LinkedIn: https://www.linkedin.com/in/ianpainter/Oliver Deakin â 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 â 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 PodcastThe 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 â 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/
đ Explore 6,500+ Aviation AI Use Cases. We've catalogued over 6,500 real AI applications across airlines, airports, ATM, MRO, and more into an interactive browser. Filter by sector and see where AI is actually being deployed across aviation: airsidelabs.com/aviation-use-casesBrought To You By
Airside Labs â Airside Labs supports aviation and travel operators with tools to test, deploy, and scale modern data and AI systems in safety-critical environments. ...
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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 frictionâapps, logins, hardware dependencies, and imprecise positioningâright 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 Youâll 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.AIâs 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.
(00:21) RouteMe Overview and Core Use Cases(02:18) RouteMeâs 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
Time-Stamped Highlights
GuestDustin Gimbel â 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 PodcastTravel 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.
HostAlex Brooker â 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/
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-disabilityIATA, Air Travel Accessibility Program: https://www.iata.org/en/programs/passenger/accessibility/
Links & References
Brought To You ByAirside Labs â 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.
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