Service as Software

"The next $1T company will be a software company masquerading as a services firm." - Julien Bek, Sequoia Capital

Welcome to the era of "Service as Software". 

To understand what this shift means, we need to look at the history, how we got here.

In the 1980s, if your company needed accounting software, you bought a license, installed it on your own servers, and maintained it with your own IT team. The software sat inside your walls. When it broke, that was your problem. When it needed upgrading, that was your budget. This model worked well enough for large enterprises and everyone else was largely left out.

Then, in the late 1990s-2000s, companies figured out they could host software centrally and let customers access it over the internet for a monthly subscription. You stopped owning the software, you rented access to it. The vendor handled the servers, the maintenance, the upgrades. This is what we came to call Software as a Service, and it was genuinely important because it democratized access. 

But notice what both models share. You buy the capability BUT you still supply the labor to use it.

What is changing now, and this is what Sequoia mapped out carefully in a piece published last month, is that AI has crossed a threshold that makes a third model possible. Call it service as software. Instead of selling you the capability to do the work, a vendor sells you the work itself. Your accounting doesn't get better software. Your accounting gets done. You pay for the outcome, not the tool, and underneath that outcome an AI system is handling what used to require a credentialed human.

The reason this is possible now comes down to a distinction worth understanding clearly. 

Most professional work contains two different types of tasks. The first type applies complex rules to inputs. These tasks can be extraordinarily intricate, but they follow rules that can be learned. The second type requires something different: the judgment that comes from years of experience, the wisdom to know which exception matters and which one doesn't.

AI has gotten very good at the first type. The second type remains, for now, distinctly human.

I have been writing for the last three years that wisdom would become the moat. 

Sequoia arrives at the same place, calling it judgment. 

When the intelligence layer becomes cheap and widely available, the ability to direct it well becomes the scarce resource. That is where the asymmetric advantage lives, and it is why the organizations investing in that capacity now, rather than just deploying more capability, are building something that compounds in ways the technology alone cannot replicate.

Impact = (Leverage x Wisdom) / Friction. 

The leverage just got dramatically cheaper. The wisdom side of that equation doesn't update automatically.

Until next time,

Ram

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Ram Srinivasan

MIT Alum | Author, The Conscious Machine | Global Future of Work and AI Adoption Leader published in Business Insider, Fortune, Harvard Business Review, MIT Executive Viewpoints and more.

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Disclaimer:

Ram Srinivasan currently serves as an Innovation Strategist and Transformation Leader, authoring groundbreaking works including "The Conscious Machine" and the upcoming "The Exponential Human."

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Sequoia: https://lnkd.in/g9a4yfTA

The Era of Asymmetric Impact: https://lnkd.in/gnrVwtMq