Everything Travel Solved

Dr Alexander Wissner Gross and Peter Diamandis have published a blueprint for the next decade called Solve Everything: Achieving Abundance by 2035. It is not a prediction. It is an engineering specification for how artificial superintelligence will systematically collapse entire domains of human endeavour into automated, commoditised utilities as boring and reliable as tap water.

The document introduces a specific vocabulary and a set of mechanisms: the Industrial Intelligence Stack, a Maturation Curve from L0 to L5, an Abundance Flywheel, and fifteen Moonshots designed to force what they call Domain Collapse the moment an entire field of work shifts from being constrained by human expertise to being constrained only by computing power.

Travel and tourism, the world's largest industry, is not mentioned once! 

That silence is deafening. Because when you map this framework onto our industry, you don't see a story of gradual improvement. You see a controlled detonation of the entire distribution, fulfilment, and pricing architecture that tour operators have relied on for three decades.

In this attempt to map it to travel, I have applied their exact methods to our sector: the same structural framework, the same maturation ladder, the same diagnostic questions, and it comes up with uncomfortable conclusions. If the Solve Everything thesis is even half right, the travel industry as currently constructed is sitting directly in the blast radius.

This is my attempt at solving travel. 

The War on Scarcity. Travel's Version

Wissner-Gross and Diamandis frame every civilisational revolution as a war on a single bottleneck. 

1. The Scientific Revolution broke the bottleneck of ignorance. 
2. The Industrial Revolution broke the bottleneck of muscle. 
3. The Digital Revolution broke the bottleneck of distance.

The Intelligence Revolution, they argue, is a war on scarce expert attention.

In travel, I am suggesting the bottleneck has a very specific name: Curation.

For the last fifty years, the fundamental constraint on the travel industry has not been a lack of hotels, flights, or experiences. It has been the scarcity of people who know how to match the right experience to the right traveller at the right moment. We called them travel agents, then we called them OTA algorithms, then we called them "content creators" and "influencers." But the core economic function has always been the same: someone (or something) with knowledge and taste, standing between supply and demand, extracting a margin for the act of intelligent recommendation.

This is the bottleneck AI is breaking.

When Diamandis says cognition becomes a commodity, he means the very thing your travel business charges for. Expertise, local knowledge, the ability to assemble a complex itinerary from hundreds of variables. That tasks is being industrialised. The cost of that cognitive act is collapsing toward the price of the electricity required to run the inference.

The Pattern of Victory Applied to Travel:

The Solve Everything framework identifies four stages every revolution passes through. Let us map them directly:

Stage 1: Legibility. The revolution begins when we can measure the bottleneck. In travel, this has already happened. Google, Booking and TripAdvisor made the previously invisible world of traveller intent, satisfaction, and pricing legible through reviews, click through data, and conversion rates. The bottleneck became visible.

Stage 2: The Harness. Next, we build systems to control the newly visible resource. The OTAs built the first crude harness: algorithms that matched traveller search queries to inventory. But these were blunt instruments, keyword matching and commission driven ranking, not true intelligence.

Stage 3: The Institutions. New institutions emerge that convert this power into trust and capital. We are seeing the very early stages of this now: AI trip planners, conversational booking agents, dynamic packaging engines powered by large language models. These are the new institutions of travel, and they are being built right now, largely without the tour operator community's input.

Stage 4: Abundance. The unit cost of curation collapses. Personalised, expert level travel planning becomes available to everyone, at near zero marginal cost. The question shifts from "Can we match the right experience to the right person?" to "Who controls the system that does the matching, and under what rules?"

Notes for Tour Operators

Stop thinking of your business as selling "tours." You are selling curated outcomes. And the cost of curation is about to collapse by orders of magnitude. The question you must answer today is: when AI can curate as well as your best team member, what does your business actually sell?

The Industrial Intelligence Stack Applied to Travel

The Solve Everything framework defines a nine layer "Industrial Intelligence Stack" that must be complete before any domain can be industrialised. If a layer is missing, the system stalls.

Here is the stack, mapped to travel and tourism:

Layer 1: Purpose and Payoff (The Goal)

The bottom layer. For travel to be "solved," we need clear, quantifiable metrics of success. Not "deliver a nice holiday" but something measurable.

The travel industry's problem: We have historically defined success in the vaguest possible terms. "Customer satisfaction." "Five-star reviews." "Repeat bookings." These are L0-level metrics. Anecdotal, contested, and non comparable. A five-star review on Viator can mean something completely different from a five-star review on Google.

What "solved" looks like: A universal metric like Experience Outcome Score (EOS) a composite measure of pre trip expectation accuracy, real time experience quality (measured through biometric and behavioural signals from wearables and phones), and post trip satisfaction retention measured at 30, 60, and 180 days. When we can measure the actual experienced quality of a trip as precisely as we measure blood pressure, travel becomes legible to AI.

Layer 2: Task Taxonomy (The Map)

We break the complex job of "delivering a travel experience" into tiny, measurable, automatable sub tasks.

As I am in Edinburgh today we will use it as a example. A walking tour of Edinburgh, for example, is not a single "experience." It is a stack of dozens of discrete tasks: route optimisation based on weather and foot traffic, content delivery timed to location and pace, group energy management, accessibility accommodation, real time rerouting for closures or events, post experience knowledge retention. Each of these can be measured. Each of these can be partially or fully automated.

Most tour operators have never decomposed their product into its constituent tasks. They think of a "tour" as a monolithic, artisanal product. This is precisely the mindset that the Solve Everything framework identifies as the obstacle to industrialisation.

Layer 3: Observability (The Eyes)

You cannot optimise what you cannot see. This layer requires sensors, data streams, and feedback loops that give the system a real time picture of what is happening.

Current state in travel: Almost non existent. Most operators have zero real time data during the experience delivery itself. They know what was booked. They know (sometimes) what was reviewed afterwards. The actual experience the moment by moment quality of what the customer felt is a black box.

What's coming: Smartphones already contain accelerometers, GPS, cameras, and microphones. Wearables measure heart rate and stress. The data exists. It is simply not being collected or structured. The first operator or platform to build the observability layer for live travel experiences will have the equivalent of what the Protein Data Bank was for AlphaFold the training dataset that enables domain collapse.

Layer 4: The Targeting System (The Harness)

This is the engine of the stack. It is the collection of rigorous tests that any AI travel system must pass before it is trusted.

The travel industry does not have this. There is no equivalent of CASP (the protein folding competition) for travel. No one is running blinded, adversarial tests where AI trip planners are compared against human experts on real travellers with real budgets. Google and the OTAs have internal metrics, but these optimise for their outcomes (click through, conversion, commission) not for the traveller's actual experience quality.

This is the single biggest gap. Without a targeting system, AI will optimise for whatever the platform owner defines as "success." If that is ad revenue, you get spam. If that is traveller outcome quality, you get abundance.

Layer 5: The Model Layer (The Brain)

This is the AI itself. Large language models, recommendation engines, dynamic pricing algorithms. This layer is the one everyone is talking about. It is also, paradoxically, the least important strategically. Models are commoditising rapidly. The value is in the layers above and below.

Layer 6: Actuation (The Hands)

Decisions are useless unless they affect the world. In travel, this means the AI must be able to book, modify, cancel, reroute, and communicate in real time.

Current state: The booking infrastructure is fragmented, proprietary, and hostile to interoperability. Trying to get an AI agent to book a multi component trip across different reservation systems is like trying to wire a robot to a factory that uses forty different incompatible power sockets.

This is where connectivity providers and reservation systems become critical infrastructure. The companies that build open, AI accessible booking APIs are building the "Action Surfaces" of solved travel.

Layer 7: Verification and Red Teaming (The Immune System)

Continuous, independent attacks on the system to find flaws. In travel: adversarial testing where AI planners are deliberately given edge cases (a wheelchair user in Santorini, a family with severe allergies in rural Japan, a solo female traveller in a complex geopolitical environment) to see where they fail. I know Alex Bainbridge is deep on edge testing with Autorua 

Layer 8: Governance and Incentives (The Rules)

The economic model. This is where the Solve Everything framework's most provocative claim hits travel hardest: The shift from paying for effort to paying for outcomes.

Currently, tour operators are paid for delivering a service a fixed itinerary at a fixed price. The Solve Everything model suggests the future is outcome procurement: paying for a verified quality of experience, measured by the targeting system.

Imagine a world where a traveller does not pay for "a 7 day tour of Japan." They pay for "a verified 85+ Experience Outcome Score for a 7 day Japan trip." The operator is paid only when the outcome is delivered. The operator who consistently hits the target gets more bookings. The one who doesn't, gets automatically downshifted by the platform.

Layer 9: Distribution and Maintenance (The Scale)

Ensuring the system works as a reliable utility. This is the "boring" layer that turns a one off experiment into the quiet hum of a solved world.

Note or Travel Tech Builders

Stop building better chatbots (Layer 5). The defensible value is in Layers 1 through 4 and Layer 6. Build the targeting system, build the observability layer, build the action surfaces. The model layer is a commodity. The infrastructure is the railroad.

The Maturation Curve.  Where Does Travel Sit?

The Solve Everything framework maps every domain onto a five level maturity ladder, from L0 (The Muddle) to L5 (Solved/Commoditised). Each level has specific signatures. Let us diagnose travel.

L0: The Ill Posed Domain ("The Muddle")

At L0, objectives are contested, data is messy, and decisions are driven by gut feeling. There is no agreement on what success even looks like.

Travel's L0 domains: Destination marketing. Tourism policy. Sustainable tourism metrics. These are still deep in The Muddle. Everyone talks about "overtourism" and "sustainable growth," but no one can agree on a measurement framework, let alone a target.

L1: The Measurable Domain

We have agreed on what to measure, even if we do not know how to optimise it yet.

Travel's L1 domains: Customer reviews (we measure them, but they are gamed and unreliable). Booking conversion rates (measured, but optimised for the platform, not the traveller). NPS scores (measured, rarely actioned).

L2: The Repeatable Domain

Best practices have been identified and codified. Checklists exist. The process is manual but consistent.

Travel's L2 domains: Hotel housekeeping. Flight operations. Standard group tour delivery. Airport security processes. These are checklist driven, repeatable, and standardised. This is where most competent tour operators operate today they have SOPs, trained guides, and reliable delivery.

L3: The Automated Domain

AI handles 80% of the volume. Humans handle exceptions.

Travel's L3 domains: Online flight booking. Hotel price optimisation. Basic chatbot customer service. Simple point to point transfers. These are already largely automated. The human only appears when something goes wrong.

L4: The Industrialised Domain

The market has flipped. Buyers purchase verified outcomes from systems, not services from humans.

Travel's L4 domains: Almost nothing. This is the critical gap. The travel industry has very few truly industrialised domains where the buyer is purchasing a verified outcome from an automated system.

L5: The Commoditised Domain ("Solved")

The problem is solved. Multiple providers compete purely on price. The service is as boring as tap water.

Travel's L5 domains: Flight price comparison. Hotel room price comparison. Currency exchange. Basic travel insurance. These are solved. They are utilities. Nobody marvels at Skyscanner.

The Diagnosis:

The bulk of the experiential travel industry the part that tour operators occupy sits at L2, with some elements creeping into L3. The entire sector is at least two full maturity levels behind domains like financial services (largely L3-L4) and logistics (L3-L4).

This is not a comfortable position. The Solve Everything framework argues that when the infrastructure is right, the jump from L2 to L5 can happen in under 36 months. The AlphaFold precedent collapsed structural biology from L2 to L5 in a single publication cycle.

Notes for Operators

Diagnose your own business against the maturation ladder. For every core process (booking, delivery, post trip), ask: "Am I at L2 (repeatable but manual) or L3 (automated with human exceptions)?" If most of your answers are L2, you are operating in the zone of maximum vulnerability to domain collapse.

The Lock In. Travel's 18 Month Window

Wissner-Gross and Diamandis argue that we are living through a "Regulatory Foundry Window" a brief period where the fundamental architecture of the next economy is being set. The decisions made in the next 18 months will create path dependencies that last decades.

In travel, the equivalent window is being defined by four forces:

1. Who Builds the Targeting System Wins

The first credible "Experience Quality Benchmark" that gains adoption will define how AI evaluates and routes travellers to experiences. If Google builds it, it will optimise for advertising revenue. If Booking builds it, it will optimise for accommodation bookings. If Viator or GetYourGuide build it, it will optimise for their marketplace commissions.

If the operator community builds it if Tourpreneur, WTTC, or an independent body creates an open, adversarial, blinded Experience Quality Targeting System then the benchmark optimises for what actually matters: the traveller's verified outcome.

Whoever defines the metric defines the economy. This is the single highest leverage action the industry can take.

2. The AI Agent Booking Infrastructure Is Being Built Now

The "Action Surfaces" for AI travel agents  the APIs and protocols that allow an AI to search, book, modify, and cancel across operators are being designed right now. If these are built by the platforms (Google, Apple, Meta), operators become invisible backend suppliers, like contract manufacturers in electronics. If operators build open, interoperable booking protocols, they retain their identity and relationship with the customer.

The Model Context Protocol (MCP), the OpenTravelAlliance specifications, and emerging AI agent standards are the battleground. Most operators are not paying attention.

3. Data Economies Are Being Set

The Solve Everything framework describes how data locked in institutional silos gets converted into "reusable capital" through Data Trusts. In travel, the equivalent is this: every operator has invaluable data about what actually happens on the ground which routes work, which restaurants fail, which guides cause repeat bookings, which experiences genuinely transform people.

This data is currently scattered across thousands of small businesses with no standardisation, no structure, and no collective bargaining power. The platforms are hoovering it up through reviews and behavioural signals and using it to train their own models.

The operator community has approximately 18 months to organise its data into a collective asset. After that, the platforms will have enough synthetic and scraped data to render operator knowledge redundant for training purposes.

4. Cultural Expectations Are Hardening

If the first mass market AI travel agent experience is a hallucinating chatbot that recommends a restaurant that closed two years ago, the cultural stigma will set. If it is a genuinely useful tool that saves a traveller four hours of planning and surfaces an experience they would never have found then trust compounds.

The operators who participate in building and training the first generation of genuinely good AI travel agents are shaping the culture. The ones who sit on the sidelines are ceding the narrative.

Notes for the Tourpreneur Community

The industry needs its own Targeting Authority an independent body that defines, hosts, and governs experience quality metrics. It needs a Data Trust a collective, privacy preserving data structure that gives operators leverage in the AI economy. And it needs open Action Surfaces booking APIs that AI agents can use without going through a platform gatekeeper. These are the three rails. Start laying them.

The Three Futures of Travel

The Solve Everything framework presents three scenarios: The Bright Path, The Muddle Path, and The Dark Path. Here they are for travel.

Scenario 1: The Bright Path. Solved Travel

AI travel agents, trained on a high quality, operator contributed data trust, deliver hyper personalised itineraries that consistently outperform human planners on verified experience quality scores. Operators shift from selling fixed products to delivering outcomes. Guaranteed quality of experience measured by an independent targeting system.

The cost of trip planning collapses to near zero. But the value of delivery, the human guide, the local relationship, the on the ground magic increases because it becomes the verified differentiator that the AI optimises for. Operators who deliver measurably better outcomes get algorithmically prioritised. The industry experiences a massive expansion in total addressable market because travel planning, previously a friction laden, time intensive process, becomes effortless.

The tourist of 2030 does not browse Booking. They tell their AI agent their budget, constraints, and desired emotional state. The agent drawing on the operator data trust, real time ground truth data, and the experience quality targeting system assembles and books a trip that has a 94% predicted Experience Outcome Score. The operator is paid on delivery of that outcome.

Scenario 2: The Muddle Path. Platform Capture

The OTAs and tech giants build AI travel agents optimised for their own metrics (commission, ad revenue, time on platform). Operators become anonymous backend suppliers, interchangeable and replaceable. The AI recommends the cheapest option that meets a minimum quality threshold, because the platform's incentive is conversion, not transformation.

Travel becomes efficient but soulless. The industry grows modestly, but all the margin flows to the platform layer. The small, independent operator is squeezed out or reduced to a gig economy contractor fulfilling platform dispatched orders. I suspect this is not what operators want however

This is the most likely outcome if operators do nothing!

Scenario 3: The Dark Path.The Freeze

A high-profile AI travel failure a misrouted group, a safety incident, a data breach triggers regulatory panic. Heavy handed legislation freezes AI deployment in travel. Innovation stalls. Meanwhile, underground AI travel agents operate on social media and messaging platforms, unregulated and frequently dangerous. Operators are stuck in The Muddle unable to use AI effectively, unable to compete with those who flout the rules.

The Abundance Flywheel for Travel

The Solve Everything framework's central mechanism is the Abundance Flywheel a five step cycle that industrialises progress:

Commitment → Focus → Collapse → Surplus → Reinvestment

Here is how it works in travel:

Step 1 Commitment: An independent body defines the Experience Quality metric and attaches a financial incentive. Operators who score above the threshold get priority placement in AI agent recommendations. Those who don't, get downshifted.

Step 2 Focus: R&D and capital focus on the target. Travel tech companies build tools to help operators hit the quality metric. Operators invest in observability (real time experience tracking), training, and experience design. The entire ecosystem aligns around a single, verifiable standard.

Step 3 Domain Collapse: The hardest problem consistently delivering a verified high quality, personalised travel experience at scale gets solved. The tools and methods created to hit the target become available to every operator. What previously required a genius destination expert becomes accessible to a competent operator using the right AI augmented stack.

Step 4 Surplus Capture: The cost of trip planning collapses. The total addressable market for experiential travel explodes because the friction of planning has been eliminated. Operators who hit the quality target capture a surge in demand.

Step 5 Reinvestment: The surplus funds more data collection, better targeting systems, and broader action surfaces. The flywheel accelerates.

Notes for Travel Investors

Do not invest in AI travel models. They are commoditising. Invest in the primitives: the targeting systems, the data trusts, the open booking APIs, and the operator enablement platforms. These are the railways. The trains are interchangeable.

The Moonshot for Travel

The Solve Everything framework proposes fifteen Moonshots across healthcare, energy, education, and physics. Travel needs its own.

Moonshot: The Verified Experience Economy

The Mission: Build the industrial infrastructure for a global, AI native experience economy where the quality of every travel experience is measurable, verifiable, and continuously improving.

The Target: Reduce the global Experience Quality Variance (the gap between the best and worst rated versions of comparable experiences) by 80% within five years, while increasing the total number of unique experiences available to travellers by 10x.

The Components:

1. The Experience Targeting System: An open, adversarial, blinded quality benchmark for travel experiences. AI systems and human planners compete on verified, prospective experience outcomes. Travellers who have never visited a destination are matched to experiences, and their verified satisfaction is measured against predictions.

2. The Operator Data Trust: A collective, privacy preserving data infrastructure where operators contribute ground truth data (what actually happens, not what TripAdvisor says happens) in exchange for AI training credit and algorithmic visibility. Governed by the operator community, not a platform.

3. Open Booking Action Surfaces: A universal, AI accessible booking protocol that allows any AI agent to search, compare, reserve, modify, and cancel across any participating operator. No platform lock in. No commission gatekeeping.

4. Real Time Experience Observability: A sensor and feedback infrastructure that gives operators and the AI system real time visibility into experience quality during delivery. This includes everything from GPS tracked pace and route adherence to ambient noise levels and group sentiment signals.

5. Outcome Procurement Contracts: A new commercial framework where travel is purchased on verified outcomes, not fixed inputs. The traveller buys a guaranteed quality level. The operator is paid when it is delivered.

The Spillover: Building this infrastructure for travel creates a platform that can be applied to any experience economy: live events, hospitality, education tourism, wellness retreats, corporate team building. The travel Moonshot becomes the template for the broader experience economy.

The Muddle vs The Machine. What Operators Must Do

The Solve Everything framework identifies "The Muddle" as the primary obstacle to abundance the entrenched layer of bureaucracy, input based pricing, and scarcity era thinking that resists change.

In travel, The Muddle has specific faces:

1. Commission based distribution that pays platforms for referrals rather than outcomes. 

2. Per person, per day pricing that commoditises experiences into units of time rather than units of quality. 

3. PDF itineraries that are static documents rather than live, adaptive data streams. 

4. Annual brochure/website cycles that treat product development as a discrete event rather than a continuous pipeline. 

5.Gut feeling product design that relies on the operator's personal taste rather than verified demand signals.

Every one of these is a Muddle practice. Every one of them will be routed around by AI systems that optimise for outcomes.

The Operational Playbook

Applying the Solve Everything framework's "Build the Rails" chapter directly:

1. Publish Your Targeting System. Define 3 to 5 measurable metrics that capture the value your business creates. Not "great reviews" but specific, quantifiable outcomes: verified learning gain (for educational tours), measurable wellbeing improvement (for wellness retreats), demonstrated cultural competence increase (for cultural immersion programmes). If you cannot express your value as a number, you are not yet legible to the AI economy.

2. Build the Test Harness First. Before you deploy any AI tool in your business, define how you will measure its success. What does "good" look like in numbers? What are the failure cases? What are the adversarial edge cases that should force the system to escalate to a human? Automate the evaluation before you automate the work.

3. Treat Your Data as Capital. Your booking records, guide feedback, customer behaviour data, and operational logs are not administrative waste. They are the raw material of the AI economy. Structure them. Protect them. And negotiate collectively for their value.

4. Build Your Action Surfaces. Make your inventory bookable by AI agents. This means clean, real time availability data, machine readable product descriptions, and API accessible booking endpoints. If an AI agent cannot find you, you do not exist.

5. Shift to Outcome Pricing. Begin experimenting with pricing models that reflect the quality of the experience, not just its duration. A four hour tour that delivers a transformative experience is worth more than an eight hour tour that delivers boredom. Price accordingly, and build the measurement infrastructure to prove it.

Note for Every Operator Reading This

The single most important thing you can do this month is decompose your flagship product into its constituent tasks, measure each one, and identify which can be augmented or automated. This is not about replacing your guides. It is about making the invisible visible. It is about moving from L2 to L3 on the maturity ladder before the platforms do it for you and take the margin.

The Quiet Hum of Solved Travel

The Solve Everything framework ends with a vision of "The Quiet Hum" a world where the screaming exponential of technological change settles into the reliable silence of systems that simply work.

For travel, The Quiet Hum sounds like this:

A traveller in Nottingham has a long weekend free. She mentions this to her AI agent while cooking dinner on a Thursday evening. The agent, drawing on her preferences (it knows she loves coastal walks, local food, and quiet mornings), her biometric baseline (it knows she has been stressed and recommends restorative rather than adventurous), and the real time ground truth data from the operator data trust (it knows the weather in Northumberland is perfect and a particular coastal walk operator consistently scores 92+ on the Experience Quality metric), assembles a complete trip.

It books the train. It reserves the walk. It finds a small inn with an open fire and a kitchen that sources from the farms she will pass on her walk. It sends a complete, adaptive itinerary to her phone. The walk operator's guide has already received her profile her pace preference, her dietary requirements, and the fact that she is most engaged when she understands the geological history of the landscape.

She does not think about any of this. She just goes. And it is wonderful.

The operator is paid because the Experience Quality score measured by her real-time feedback, her biometrics, and her 30 day reflection survey hit the guaranteed threshold. The AI agent learns from the outcome and gets fractionally better at matching future travellers to this operator.

Nobody marvels at the system. It just works. Like electricity. Like tap water.

That is the Quiet Hum of Solved Travel.

The question is whether the tour operator community builds the rails for this future, or whether the platforms build them and extract the margin.

Pete