The Problem That Built an Industry
ajitem.com
ajitem.com
>> is almost mythological. In 1953, C.R. Smith, president of American Airlines, was seated next to R. Blair Smith, an IBM salesman, on a cross-country flight. By the time they landed, the outline of a solution had been sketched. IBM and American Airlines entered a formal development partnership in 1959.
edit: oh and then the actual system didn't actually go live another 5 years later - in 1964. Over a decade after the two of them sat next to each other.
Reminder to myself when my potential customers don't sign the deal 5 minutes after my pitch!
The classic "the decision makers can take longer to buy than you can stay solvent" problem of enterprise sales.
The system was a based on a military messaging system.
What is important to note is before SABRE the system used was a sell at will until a stop message was issued. Then sales would be on request. This method is still used between different airline systems today.
Before the implementation of SABRE airlines used teleprinters as a way of communicating. Some of the commands SABRE and other IBM 360 systems come directly from this period. For example AJFkSFO9MAR was a way of economizing on characters sent. It means what is the available seats from JFK to San Francisco on the 9th of March. This predates SABRE.
There is several reasons that the System 360 (the reservation systems used by airlines like SABRE) is one that it is written in Assembler, and also the logic is very tied into its role of reservation. For example it was designed in the days of punchcards, which have a totally different method of matching than a relational Database. The logic is still used on matching a seat to a fare.
On the pure speed much of it is gained by clever engineering tricks. An example would be the passenger record. This is 9 alphanumeric id of the passenger reservation. It is the hash of virtual memory location of the reservation. It takes 4 cpu cycles to retrieve it.
Three women are sitting in a bar discussing their lovers, the first says, "My lover is a wrestler, he's so energetic, it's _wonderful_!".
The second responds, "My lover is a poet, he's so romantic and thoughtful and sensitive, it's like something out of a fairy tale!".
The third is silent, and the other two women look at her expectantly until she finally sighs and says, "My lover is an IBM salesman, he just sits on the edge of the bed and tells me how good it will be when we eventually make love."
Closed the tab.
Either way I'm glad I read it and waiting for the other parts of the series. Really curious how to get access to this airline booking data so I can write my own bot to book my flights and deal with all the permutations and combinations to find the best deal.
> Convergent evolution is real. Every major GDS independently arrived at the same underlying platform. That is not coincidence — it is the market discovering the optimal solution to a specific problem.
I struggle to understand the claim that GDSes “arrived independently” at interoperability standards through “convergent evolution” and market discovery. Isn’t it something closer to a Schelling point, or a network effect, or using the word “platform” to mean “protocol” or “standard”?
Isn’t it like saying “HTML arose from web browsers’ independent, convergent evolution”? Like—I guess, in that if you diverge from the common standard then you lose the cooperative benefits—see IE6. And I guess, in that in the beginning there was Mosaic, and Mosaic spoke HTML, that all who came after might speak HTML too. But that’s not convergent evolution, that’s helping yourself to the cooperative benefits of a standard.
“The market” was highly regulated when the first GDSes were born in the US. Fares, carriers, and schedules were fixed between given points; interlining was a business advantage; the relationships between airlines and with travel agents were well-defined; and so on [0]. IATA extended standards across the world; you didn’t have to do it the IATA way, but you’d be leaving business on the table.
If anything, it seems like direct-booking PSSes (he mentioned Navitaire [1]) demonstrate the opposite of the LLM’s claim. As the market opened up and the business space changed, new and divergent models found purchase, and new software paradigms emerged to describe them. It took a decade or two (and upheaval in the global industry) before the direct-booking LCC world saw value in integrating with legacy GDSes, right?
…the LLM also seems bizarrely impressed that identifiers identify things:
> One PNR, two airlines, the same underlying platform.
> Two tickets, two currencies of denomination, one underlying NUC arithmetic tying them together.
> One 9-character string, sitting in a PNR field, threading across four organisations' financial systems.
[0] https://airandspace.si.edu/stories/editorial/airline-deregul...
[1] https://www.phocuswire.com/Jilted-by-JetBlue-for-Sabre-Navit...
[...]
>> One 9-character string, sitting in a PNR field, threading across four organisations' financial systems.
(emphasis added)
It's not that identifiers identify. It's that an identifier identifies the same thing across multiple, independent, entirely distinct systems.
There are other examples: credit card numbers, government issued ID numbers.
But in general, identifiers have little currency outside the system that generated them, hence the "impressed" element to this.
That's clearly wrong, because if it were true we wouldn't be able to identify anything. Identifiers are only useful in so far as some external party assigns a meaning to the identifier. Two systems MUST pick a common idwntifier to discuss a person. They MUST pick an identifier to discuss a technical field. They MUST even pick an identifier to discuss a technical protocol.
Identifiers are everywhere. They'll usually be translated into something internal at the edge of a system, but I bet the PNR is too.
One can cheat on this for airline systems by taking the position that "the system" is the aggregation of all the different systems, not any one of them.
"That is not coincidence — it is the market discovering the optimal solution to a specific problem. When you see that pattern in your own domain, pay attention to it."Site is likely SEO slop for future product placement.
Teleregister built a range of such special-purpose systems, for several airlines, railroads, and air traffic control. General purpose CPUs were both too expensive and too slow for these jobs in the early 1950s. Using a general purpose computer for everything didn't really happen until the minicomputer era in the mid-1970s. SABRE had to wait until general-purpose computers got better.
It's interesting that transaction processing operating systems died out. Tandem's OS worked that way. But the run transaction program once and flush it approach is almost dead. Except, amusingly, for CGI programs, which are true use-once transaction programs, with an inefficient implementation.
[1] https://www.youtube.com/watch?v=F4d-OFDs1hY
[2] https://s3data.computerhistory.org/brochures/teleregister.sp...
An exec made a public quote that they couldn't have done it if they hadn't used Lisp.
(Today, the programming language landscape is somewhat more powerful. Rust got some metaprogramming features informed by Lisps, for example, and the team might've been able to slog through that.)
Even 1 second transaction speed sounds slow today but if it’s replacing a 90 minute manual process I’d rather have that solution now than a microsecond fast solution that takes 5-10 years.
What kind of review would it fail? Sounds like it's pretty well designed to me.
In a world where implementation is free, will we see a return to built for purpose systems like this where we define the inputs and outputs desired and AI builds it from the ground up, completely for purpose?
It seems we can build a product ourselves in the same time it would take us to talk to saas vendors and draft the RFP/requirements. We can build it and iterate as the requirements are being forged, so can essentially have completed software with just the features we care about, with full ability to add features in future (something saas doesn’t promise) often before an implementation would even kick off. We’re searching through all our SaaS products and i expect we’ll cut 50% of them in 1-2 years. The ones that are sufficiently complex or regulated have some protection (like accounting systems).
Eat that, Bitcoin.
It’s nothing for even an ancient CPU - let alone our modern marvels that make a Cray 1 cry.
The key is an extremely well-thought and tested design.
It was fast on an 4.77Mhz IBM PC, and much faster on a 10Mhz V20.
50,000 transactions was pretty standard for a IBM Mainframe, now? The z/ Series is still about the same, but it scales up to 32 processors. ( excuse me, billions. per day )
(For the pedantic, it's not exactly centralized nor federated since each airline treats their view of the world as absolutely correct)
It probably doesn’t require consensus among all participants (pairwise consensus at every step should be fine), so there is very likely no voting.
It’s not even permissionless. It’s not like a random company could join this “chain” simply because they can generate a keypair.
It’s a fundamentally different problem, and it makes sense that the architecture is different.
People don't do it because it's not fashionable (the cool kids are all on AWS with hundreds of containers, hosting thousands micro services, because that's web scale).
But yes, you don’t always need cool technologies.
> But yes, you don’t always need cool technologies.
That's kinda the irony mainframes are incredibly cool piece's of tech, just not fashionable. They have insane consistency guarantee at the instruction level. Hot swapping features etc. Features you'd struggle to replicate with the dumpster fire that is modern microservice based cloud computing.
How many banks and ERP's, how many accounting systems are still running COBOL scripts? (A lot).
Think about modern web infrastructure and how we deploy...
cpu -> hypervisor -> vm -> container -> run time -> library code -> your code
Do we really need to stack all these turtles (abstractions) just to get instructions to a CPU?
Every one of those layers has offshoots to other abstractions, tools and functionality that only adds to the complexity and convolution. Languages like Rust and Go compiling down to an executable are a step, revisiting how we deploy (the container layer) is probably on the table next... The use case for "serverless" is there (and edge compute), but the costs are still backwards because the software hasn't caught up yet.
Run time - This makes development faster. Python, Lua, and Node.js projects can typically test out small changes locally faster than Rust and C++ can recompile. (I say this as a pro Rust user - The link step is so damned slow.)
Container - This gives you a virtual instance of "apt-get". System package managers can't change, so we abstract over them and reuse working code to fit a new need. I am this very second building something in Docker that would trash my host system if I tried to install the dependencies. It's software that worked great on Ubuntu 22.04, but now I'm on Debian from 2026. Here I am reusing code that works, right?
VM - Containers aren't a security sandbox. VMs allow multiple tenants to share hardware with relative safety. I didn't panic when the Spectre hacks came out - The cloud hosts handled it at their level. Without VMs, everyone would have to run their own dedicated hardware? Would I be buying a dedicated CPU core for my proof-of-concept app? VMs are the software equivalent of the electrical grid - Instead of everyone over-provisioning with the biggest generator they might ever need, everyone shares every power station. When a transmission line drops, the lights flicker and stay on. It's awe-inspiring once you realize how much work goes into, and how much convenience comes out of, that half-second blip when you _almost_ lose power but don't.
Hypervisor - A hypervisor just manages the VMs, right?
Come on. Don't walk gaily up to fences. Most of it's here for a reason.
Your argument for host os, virtual os, container is the very point im making. Rather than solve for security and installablity, we built more tooling, more layers of abstraction. Each have overhead, security surface and complexity.
Rather than solve Rusts performance (at build time), switch to a language that is faster but has more overhead, more security surface, more complexity.
You have broken down the stack of turtles that we have built to avoid solving the problem, at the base level...
SABRE, what the article is discussing, is the polar opposite of this, it gives us a hint that more layers of abstraction arent always the path to solutions.
---
sabre, the company that owns and builds the current version of the system SABRE used by major companies today, uses all of those things the parent and you mentioned
> Google Cloud-native infrastructure that is scalable and secure. Microservice-enabled architecture that supports modularity. API-first approach for an open platform. [0]
> We rebuilt Sabre from the ground up: cloud-native technology, AI baked into the foundation, one goal in mind. Your success. [1]
yeah ... it's 'ai powered' now.
[0]: https://www.sabre.com/resources/viewpoints/offer-order-strat... (skip to the 'different by design' heading)
[1]: https://www.sabre.com/about/
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> Do we really need to stack all these turtles (abstractions) just to get instructions to a CPU?
no. but those abstractions are there for things like scaling, reliability, redundancy, flexibility, ... and a bunch of other things not related to solely getting some instructions to a CPU. the number of turtles has increased because customers have more requirements for software today than they used to have in the 1960s.
sometimes we need the simplest solution with fewest dependencies. sometimes we need lots of turtles... it really depends on the problem in front of us.
Also, try to retrieve a PNR on an airline website or do like anything on the airline's own website -- the UX is usually pretty bad and the data loading takes forever. For that too the GDS is to blame.