Web3 in Travel

Can a laptop do your company's AI work without sending a single byte outside? Luca De Giglio ran real travel-business tasks on a MacBook Pro with 48 GB of RAM, using the open-weight Qwen 3.8 27B model, and compared every answer with Claude Opus 5.5 through the API.On everyday tasks (reading a booking email, replying to a bad review, pricing a tour package) the local model matched Claude, at about a fifth of the speed and a fraction of the cost, and the data never left the laptop. On a 30-page supplier contract it still found the right cancellation charge, but in six minutes instead of sixteen seconds. On 600 bookings checked against a hotel invoice, it hit a wall.Then the questions that matter for a company: why more RAM is not enough, what actually fits on a laptop, why buying a machine is a bet, who maintains it, and where local AI makes sense in travel and hospitality.IN THIS EPISODE• Three light tests and two heavy tests: local vs. the Claude API.• Why a 64 GB server can be useless for AI, and a 48 GB Mac isn't.• The model as a book: capacity and bandwidth.• Ollama, LM Studio, and models tuned for one machine (DwarfStar, Sushi).• The money: 50× cheaper on electricity, and why that's rarely the point.• Maintenance, evals, and why private isn't automatically safe.• Guest data, contracts and margins: the travel use cases.CHAPTERS0:00 Intro0:20 Local AI: is it time to start?1:43 My laptop: 48 GB of RAM3:16 Why a big server is useless for AI4:08 Test 1: a booking email5:43 Test 2: reply to a bad review7:54 Test 3: a tour package margin9:22 Speed: tokens per second10:12 Cost, and where the data went11:25 Heavy test A: a 30-page contract14:05 Heavy test B: 600 bookings vs. an invoice16:43 Electricity: 50× cheaper17:25 The model is a book19:01 What runs on 48 GB, and the tools19:56 Models tuned for one machine22:29 Does local save you money?23:57 Buying a machine is a bet26:09 Finding the ceiling, and evals28:07 Maintenance28:53 Private isn't automatically safe29:32 Local AI in travel and hospitality30:57 A possible setup, and where to startFor collaborations, advisory work or speaking:[email protected]://www.linkedin.com/in/luca-de-giglio/

Most companies are still asking how to use AI. The next question is how to use it privately. When your team plugs Claude or ChatGPT into the company's documents, where does that data go, and what can the AI labs do with it?In this episode of Web3 in Travel, Luca De Giglio explains why your data is worth far more to an AI lab than it ever was to Google, why a $20 subscription buys hundreds of dollars of inference, and what "we don't train on your data" really promises. He then walks through the options for private AI, from the most private to the least: local open-weight models on your own hardware, private APIs for open models, and Trusted Execution Environments (TEEs). For each, what it costs and what it actually protects.He closes with the strategy: route each task to the right model, decide which data must never leave the company, and accept that total privacy does not exist. You manage risk, you don't cancel it.IN THIS EPISODE• Why LLMs extract more value from your data than ads ever did.• The real economics of a $20 AI subscription.• Could an AI lab become a competitor in your industry?• Local models and open weights: DeepSeek, Kimi K3, Qwen, gpt-oss.• Private APIs and anonymized inference: what they cost, how private they are.• TEEs: hardware attestation, and why you still have to verify.• Routing: the right task to the right model.• The first step: decide which data never goes near an LLM.MENTIONED• Vitalik Buterin on a local model deciding what to send to frontier models: https://x.com/VitalikButerin/status/2106537633056969024CHAPTERS0:00 Intro0:20 The next question: how do I use AI privately?0:59 Google sold ads. LLMs take the value1:33 Your best insight, in the next model2:39 Why a $20 subscription is so cheap4:34 Do they train on your data?5:24 When the AI lab enters your industry7:51 Local models and open weights10:07 The cost of running AI yourself11:25 Private APIs: paying full price13:08 How private is anonymized?14:21 TEEs: trusted execution environments16:12 Routing: the right task to the right model17:43 A strategy for your data19:48 The first step: data that never leaves21:21 Nothing is 100% privateFor collaborations, advisory work or speaking:[email protected]://www.linkedin.com/in/luca-de-giglio/

The real leverage from AI starts when you go beyond ChatGPT: a project with agents for each area of your work, in tools like Claude Code or Codex that build their own tools.I share three people I onboarded and what changed for them, why these coding tools are for non-coders too, and why the terminal stops most people. I introduce The White, the software I use to onboard people, and explain who succeeds, who fails, and why you should start small.Want help making the jump? [email protected]://www.linkedin.com/in/luca-de-giglioCHAPTERS0:00 Intro0:20 Why I share new things early1:35 Beyond ChatGPT: a project for everything3:35 How do you make the jump?4:29 Three people I onboarded5:30 Coding tools are not just for coders6:38 A third way to get software7:55 How one CEO automated his company10:45 "What did you do to him?"13:57 The terminal problem and The White16:18 An offer for listeners17:24 Who succeeds19:20 Why AI companies want you out of the chat21:23 Who fails23:03 Start small24:29 Not a CEO? Become a secret superhero

I could not find how to cancel limit orders in my Safe wallet. Claude Code built a JSON transaction file; I imported it, ran a simulation and signed the cancellation myself.I also share my loss-making Token Trade experiment, why I prefer chat to complicated dashboards, and the possibilities for agent payments. The closing discussion explores AI wallet risks, decentralized travel and Trips Community, and a future where we express our intent to an agent. I am not claiming AI wallets are safer or right for every use case.Collaborations: [email protected]:00 Intro0:20 Wallets and the adoption hurdle3:40 Convenience, custody and attention5:26 The Safe limit-order problem7:54 JSON, simulation and signatures11:23 Building Token Trade14:15 Trading losses and on-chain exchanges19:12 Building tools through chat20:42 Why dashboards drift23:13 Stablecoins or credit cards?25:40 AI wallets and security trade-offs26:50 What agents could change in travel28:35 Intent as the interface

The step most people have not taken with AI: from chatting with ChatGPT, a very smart consultant you phone and re-brief every time, to Claude Code working inside your own files, like someone in your office who remembers everything.I explain why that step looks like a mountain and is really a bump, then show my actual setup: Claude Code plus one folder, moved onto a server that works 24/7, one container per project, about forty of them, laid out as a pixelated city. Taxes, property, a China trip planner, this podcast's own pipeline, and a local AI whose job is to keep me focused.Collaborations: [email protected]:00 Intro0:20 What I Actually Do4:00 ChatGPT Is a Consultant on the Phone6:00 The Bump That Looks Like a Mountain9:00 Someone in Your Office Who Builds Tools15:40 Claude Code and One Folder22:20 Moving It to a Server That Works 24/726:00 One Container per Project, and the City32:30 The Business District37:20 Travel, and Why Chat Beats Dashboards42:10 The Coordinator and a Local Consigliere46:40 New Projects in a Minute, and the Stack