Booking a private charter in 2019 required essentially the same process as booking one in 1989: you called a broker, described your trip, received a price with no way to evaluate it, and flew or didn’t. George Peter Murnane, who had spent 30 years managing aircraft fleets at carriers across multiple continents, understood precisely what that price should reflect, and why surfacing it transparently had proven so difficult to do.
The Opacity Problem
Private aviation did not remain opaque by accident. The industry’s structure produced it. There are hundreds of FAA-certificated Part 135 air carrier operators in the United States, each authorized to conduct charter flights under a distinct operating certificate with its own safety audit history, approved aircraft list, and crew qualification records. No central exchange surfaces pricing across that operator universe. No public database aggregates availability in real time. A charter customer in 2019 had no reliable way to know whether the rate they received reflected current market conditions, whether the operator had passed the most recent Wyvern or ARGUS safety evaluation, or whether a comparable aircraft was available at a lower rate from a competing operator two hangar bays away.
The opacity served the industry’s existing participants reasonably well. Fragmented operators with differentiated safety records and pricing structures benefited from information asymmetry. Brokers who managed the knowledge gap charged a premium for managing it. The result was a market that worked for its insiders and presented a genuinely high barrier to everyone else, including the class of travelers who could have afforded occasional charter access on a per-leg basis but had no practical pathway to price and book a flight.
Before the online booking platforms of the late 1990s and early 2000s, airline ticket pricing was comparably fragmented: published fares, travel agents, and no consumer-facing comparison layer. Expedia and Kayak changed access to information, not the underlying economics of commercial flight. A consumer could suddenly compare fares and book a seat without a travel agent intermediating the transaction. George Peter Murnane’s thesis for Jet.AI was that the same structural shift was two decades overdue in private aviation.
What Murnane Brought to the Problem
The argument was not novel when Murnane joined Jet Token as CEO in September 2019. App-based private aviation booking had been attempted before, with limited success. What distinguished his entry was the specific operational knowledge he carried about why those attempts had failed to gain durable traction.
Murnane’s 30 years across aviation C-suites gave him a granular read on the structural costs that determine charter pricing. His career spanned roles as EVP and COO at Atlas Air, as CFO and COO at International Airline Support Group through two restructurings, as CFO of Mesa Air Group during its revenue expansion from $523 million to over $1 billion between 2002 and 2007, and as COO and Acting CFO at VistaJet during the financing of a $1.2 billion Bombardier fleet order. He trained at Merrill Lynch’s transportation banking practice, where he learned to evaluate aviation assets not by reading the headline financials but by tracing economics back to maintenance contract terms, lease structure, and manufacturer relationships. An MBA from The Wharton School completed the financial architecture.
That background meant Murnane understood, at the operator level, what inputs produce a charter price: aircraft acquisition or lease cost, crew type-rating and scheduling overhead, maintenance reserve accruals, insurance, hangar, and the routing efficiency premium or discount that comes with a specific operator’s approval boundaries. A booking app that presented a number without accurately resolving those inputs reproduced the same information asymmetry in a new format.
The prior failed attempts at app-based private aviation largely stumbled at that level. The technology was buildable. The operator integration, safety vetting, and operational depth required to make the price on the screen mean something were not.
What Jet Token Actually Built
The platform Jet Token launched, now operating as CharterGPT under the Jet.AI brand, used natural language processing and fleet logistics optimization to present charter options without requiring a broker as an intermediary. A customer enters the origin, destination, travel date, and passenger count; the platform returns available aircraft, prices, and operator information in a format comparable to that of a commercial airline booking.
The operational infrastructure behind that interface required more than software. Jet.AI built its charter operations through a partnership with Cirrus Aviation Services, one of the higher-rated Part 135 operators in the country, based in Las Vegas with a fleet configured for the Southwest U.S. corridor: routes between Las Vegas, Los Angeles, Phoenix, Aspen, and San Diego. On those routes, charter pricing competes with first-class commercial on a total-trip-time basis: door-to-door elapsed time, not block time, is what a business traveler is actually buying. Light-jet economics work efficiently at those stage lengths. And the passenger profile in that corridor, business travelers and entertainment industry concentrated in Los Angeles and Las Vegas, represented a natural market for occasional charter access at a per-leg price point that avoided the capital commitment of a jet card or fractional share.
The Cirrus partnership gave Jet.AI something no software build could substitute for: an operator whose safety certifications were current, whose operational infrastructure was proven, and whose routing coverage matched the market the platform was designed to serve. Technology delivered the consumer-facing transparency. The partnership delivered the operational credibility behind it.
Where the Uber Analogy Holds, and Where It Doesn’t
Private aviation technology companies have attracted a consistent comparison since the first booking apps appeared: if Uber restructured ground transportation by connecting supply and demand through a consumer interface, why can’t the same model restructure charter aviation? The analogy captures the information-asymmetry argument accurately. Both models use a consumer interface to surface latent supply and present transparent pricing to a customer who previously had to navigate an opaque broker. That much holds.
The cost structure differences break the analogy at the operator level. “Where it breaks down is everything underneath the screen,” Murnane said. “An Uber driver is a marginal-cost operator with a personal car; a Part 135 operator has a $5 to $15 million asset earning against a fixed cost base of crew, maintenance, hangar, and insurance, and the supply curve doesn’t bend the way ride-share’s does.”
That cost structure difference shapes the entire platform design. Uber’s driver supply scales rapidly with demand because individual drivers can enter and exit the network with minimal capital at risk: a driver whose earnings drop in one week can stop driving the next. A Part 135 operator cannot. Aircraft acquisition or lease, crew hiring and type-rating certification, maintenance facility overhead, and insurance are committed months or years before a flight is booked. The operator’s economics require sustained utilization against that fixed cost base. An app that aggregates operators without understanding their utilization profiles, routing constraints, and cost structures will surface inaccurate pricing, which defeats the entire purpose.
The practical consequence is that building a booking platform for private aviation that actually works is structurally harder than building one for ride-share. The supply side is less elastic by orders of magnitude. Safety regulatory requirements are more demanding. And the consequences of matching a customer to the wrong operator are categorically different than a bad Uber ride. The technology layer is the accessible part of the problem.
The Moat Is Not the App
The consumer-facing interface is not where a private aviation technology company builds a durable advantage.
“The interface is the cheapest part of the stack to build,” he said, “and within eighteen months of any successful app, three competitors will have something visually indistinguishable. The expensive parts of the stack are the operator network, the safety vetting capability, the Part 135 regulatory fluency, and the operational depth to handle disruption.”
Each of those expensive stack elements maps to something Murnane’s background provides that a technology-first founder could not replicate on a startup timeline. An operator network built across 30 years of carrier relationships, from Atlas Air to International Airline Support Group to Mesa Air Group to VistaJet to ImperialJet, is not assembled in a development sprint. Safety vetting capability requires regulatory knowledge and audit history familiarity that accumulates through direct operational exposure over years. Part 135 regulatory fluency accumulates through that same direct exposure; no amount of capital spending substitutes for it. And disruption management, re-accommodating passengers when weather grounds aircraft, when mechanical issues arise, when crew scheduling shifts at short notice, requires the kind of operator relationship depth that only comes with time in the industry.
The Cirrus Aviation partnership addressed all four requirements simultaneously. The platform delivered consumer-facing transparency. The 30-year aviation credential delivered the operational credibility that made transparency possible.
As of the first quarter of 2026, Jet.AI was coordinating the transfer of its aviation operations to flyExclusive as part of the companies’ pending merger, with aviation revenues of $1.68 million for Q1 2026 reflecting a deliberate wind-down ahead of the shareholder vote scheduled for June 11. The charter business that transfers to flyExclusive is a functioning operation with an established customer base, not a technology demo waiting for operational substance. The app-first case for private aviation that Murnane built turned out to require exactly the kind of operational depth that only an aviation veteran with his specific background could supply. That combination was the product.
