Software as a service

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“All reg tech solutions are alike; each reg tech problem is a problem in its own way.”

—Leo Tolstoy, Anna Karenina

Software as a service /ˈsɒftweər əz ə ˈsɜːvɪs/ (n.)

A fancy way of selling a warranty on a toaster.

Charging a running cost for software which, by reference to its own raison d’être shouldn’t need a lot of maintenance, unless you built it to need maintenance. “SAAS” to its friends; glorified rent-seeking to the poor sods who have it imposed on them, software as a service is the disguised reintermediation of a function by a tool purportedly there to remove it. It is popular as it is the only business model most reg tech entrepreneurs have managed to figure out.

If your software were any good you would design a user-interface easy enough for the meatware to deal with so you didn’t need a service contract. Right?

The reg tech business model conundrum

It is a familiar experience amongst buyers of reg tech that products which look fabulous at the pitch when the general counsel is watching, tend to underwhelm in production when set upon by those who actually need them to work. It is one thing to perform magic on a pre-prepared non-disclosure agreement (“here’s one I made earlier”); it’s quite another to dispatch the knotty, irritating, unpredictable and frequently absurd real-life legal problems that your staff have to solve at the coalface.

Why are our overlords so swooned by the promise of reg tech? Partly, the yen to be a thought-leading agent for step-change in the industry plays to a general counsel’s innate credulity and weakness for flattery. But beneath that there is an operating cause yet more profound: reg tech struggles mightily to contrive a business model that scales. Its big idea is to automate tedious, repetitive and manual tasks, thereby removing a significant cost item from the departmental budget, and accelerating and improving the output quality at the same time. By disintermediating, we taking out expensive, unreliable, high-maintenance meatware and replacing it with a cheap, virtual tool that does the same job for nothing.

If you can get it to work, that’s a big trick. It’s an even bigger if.

If you are buying a product “off the shelf” — assuming it can already do what its vendors claim; by no means a given — observe where the vendor’s energy is going: exclusively, sales. The vendor seeks a costless reprint of something it made earlier, but to charge you an ongoing licence for it, per seat, use, or time period.

When your target audience is narrow — for all the trillions of dollars at stake in the financial services industry, the number of software buyers is limited — you must make decent money out of each licence: Charging a buck a go in the app store is hardly going to catapult you into the megacorn big-legaue.

Any upfront price leaves you without ongoing revenue, unless somehow you can extract rent.

Now this would be fine, of course, if the product did work as billed, intelligently anticipated your particular applications and handled them quickly, quietly and immaculately right out of the box. That would be worth paying for.

Misalignment and configuration problems

But common experience, when you finally get to play with it, is that these applications never quite do what you want them to. Either your intended use isn’t quite the one the vendor had in mind — here the product can’t quite do what you want, and isn’t flexible enough for you to reconfigure it so it can— call this a “misalignment” problem — or it can, but to get the application to be of any use, it will need a good deal of energy, expertise and effort from your people to configure it; energy they will be disinclined to provide — call this a “configuration” problem.

Misalignment and configuration are different problems, but most reg tech offerings suffer from both, because they both stem from the same fact of life: while there is an unquantifiably huge volume of tedium to be automated, no two instances of tedium are quite alike. Tedium is particular, not generic. That is why it is tedious. If the same instance of tedium were common to enough market participants that a glib SaaS solution could fix it, it would have been fixed by now. Fixable, generic tedium is not stable. It gets fixed. Particular tedium is stable. It persists, not because because it is tedious, but because it presents in a unique, abstruse, absurd, obtuse way. These kinds of things do not dissolve in water. They are not susceptible of straightforward, “let’s science the shit out of this” solutions.

Notwithstanding breathless claims to the contrary from people who should really know better — who do, in fact, know better — this has been the story of technological progress in the legal industry in the last thirty years. There has been tons of new legal technology. The BlackBerry. Citrix. Document comparison. Document management. Optical character recognition. Voice recognition. Cloud computing. Remote access. Working from home. Skype. Virtual deal rooms. e-Discovery. Legal process outsourcing. All things that effectively, quickly and cheaply solve generic problems, that are intuitive, that boost productivity from the get-go.

SaaS just never quite manages this. We’ve lost count of the products that do a job, but just not quite the one you’d like them to. That oblige you to abandon Microsoft Word, or export all your data to the cloud, or that can’t handle parallel routing. Data extraction engines that can’t handle regular expressions.

In each case we need this kind of functionality to apply the application to our particular, counter-intuitive and often baffling circumstances. Our institutions shouldn’t be counter-intuitive or baffling of course, and it is hardly the vendor’s fault, but they are. They are complex organic systems with a tendency to complicatedness. Every bureaucratic firm is bureaucratic in its own way.

Thought leaders[1] see the abstract appeal of artificial intelligence while disregarding the challenges of the particular. Neural networks need to be trained. This is a slow, laborious, painstaking job. To extract a re-hypothecation formula from a database of thousands of random prime brokerage agreements, for example, is doable — if you have a specialist who understands not only the legal language and market practice but also how regular expressions work, who is prepared to spend weeks training the neural network to get it started.

Not many people do.

Note, too, some following ironies:

First, most of the effort, expertise, and experience needed to make a neural network work comes not from the tech — which is pretty generic — but from those who train it. This expertise belongs to the client, not the vendor. Given how long the training process will take, this is no small cost — yet, being “sunk”, it will fall off the costing projections when the business case is put together.

Second, this subject matter expert training is exactly the sort of thing that — if a client permits it — the vendor can harness to improve the product for its other clients. To be sure, there is a quid-pro-quo here: the client, too, will benefit from the training the product receives from the hands of the vendor’s other clients, but this only sharpens the irony: the value the vendor itself provides is minimal: merely an application interface on top of open-source neural network technology. What turns a public utility with a glossy front-end into gold-dust is the distributed training the application receives, from the clients. Yet, the vendor gets to bill the clients, and not the other way around.

Now that is truly “indistinguishable from magic”.

Then there’s blockchain, of course

The latest iteration — talked about in tones of reverent optimism here — is “blockchain as a service”. But a service to whom? And did I hear a siren going off?

See also

References

  1. See Daniel Susskind’s A World Without Work for the classic case of this.