Matthew Sinclair, Engineering Director from the London Center, discusses the question that helped him navigate the complexity of building a new venture and gives a glimpse into recent tech news. This post originally appeared on his Medium.
During most of 2016, I was lucky enough to lead the engineering team that built Coup in Berlin. This was, without a doubt, one of the best ventures I’ve ever been involved with, made all the more satisfying because of the fantastic engineering team that did all the hard work. It was a complicated project with a lot of moving parts across front-end, back-end, security, IoT, as well as an advanced, next-gen electric scooter.
With so many complexities, there were many times early in the project where it wasn’t obvious what the engineering team should focus on. The MVP was more than just getting an app out, because, in order for the service to work at all, a user needed to be able to sign-up and prove that they had a valid license, book a scooter, find it, unlock it, drive away, accept payment, etc, etc. You get the idea. The minimum viable product in this case was quite a lot of work.
Amid this complexity, we came up with a really simple and effective way to keep everyone focused on doing the right thing. In fact, it was so simple, I just assumed it was something everyone did on projects, and I was almost embarrassed to consider it something worth talking about.
But, here it is: We came up the question “Does it move the scooter?” and printed it out in massive letters and stuck it up on the wall. That’s it. But, what this allowed us to do in almost every situation where we wondered what to do next, was just ask ourselves: Does it move the scooter? If it did, then we figured that it was the right thing to do. If it didn’t, then that was a good signal to leave it alone. I have used this basic idea to come up with a similar statement for all the ventures I have been involved with since–and it works a treat.
So, what’s your “Does it move the scooter?” statement?
The rate of change for IoT devices is just incredible. This little device — the N5 from Neutis — is a quad-core “system on module” that would have occupied a large space under a desk ten years ago and cost thousands of dollars. Now it costs $49 and is about 41.0 x 29.5 x 4.3 mm in size. But that’s not even the most interesting thing. What’s cool about this device is the software platform that goes along with it. Because it’s designed to power all manner of IoT applications, there’s an over-the-air (OTA) software update process that comes with it, which is essential when you have (potentially) tens of thousands of these things in the field, and you need to update them for a security patch.
I’m a huge fan of mind maps and make them for just about everything I need to think about or reflect upon with any depth. I find that mind maps are particularly good for structuring uncertainty when you’re starting out with a problem. And I also use them whenever I want to capture everything I need to know about a single topic in one page. However, mind maps are just one way to structure complex thought. Here are five models for making sense of complex systems.
This tweetstorm asks “What questions should we ask of new technology?” The idea of “platform incidence” (cf “tax incidence” in economics) is an interesting one. When we build new tech platforms we really should ask ourselves who ends up “paying” for the services provided, and in what form?
It’s always instructive to go back to the source material. Nick Szabo is one of the original writers on smart contracts (and argued by some to be at least part of, if not the sole progenitor of Bitcoin). Here’s his thoughts on collectibles and the origins of money.
The bit I find most interesting about this whole blockchain topic is decentralization. I still don’t have it clear in my head if its a pipe dream fantasy land, or if some of the promise here really can be delivered, but it is super interesting to contemplate. Some people with a really good track record of predicting the future agree: Why Decentralization Matters.
If you need a refresher on Ethereum tokens, here’s a quick, beginners’ guide refresher. Well worth a read.
Austin wants to use blockchain technology to help the homeless.
One of the challenges with technology that uses deep learning and other modern machine intelligence techniques, is that the path that the system takes to come up with an answer is generally not obvious to human observers. In fact, in most cases, the reasoning for an answer is completely inscrutable. This has problems when used in areas such as medicine and financial services, because it’s very important — and in some cases a statutory requirement — to be able to understand the rationale and reasoning for any decisions made. The Economist thinks that if artificial intelligence is to thrive, it must explain itself. If it cannot, then who is going to trust it?
“In an article published in the Northwestern University Law Review in 2014, Professor Shawn Bayern demonstrated that anyone can confer legal personhood on an autonomous computer algorithm merely by putting it in control of a limited liability company.”If we (as a society) end up doing this kind of thing, the implications for artificial intelligence controlled legal entities are going to present us with some pretty challenging scenarios.
I’ve been in tech for such a long time that it takes quite a bit to get the hairs on the back of my neck to stand up when I hear about new things. However, this edition of the After On podcast with Mary Lou Jepsen is just something else. Imagine combining neural imaging with telepathy, and you’ll have some idea of where this goes. If you only listen to one podcast this week, make it this one.
Only in Hong Kong
Sure, we have lethal snakes and spiders and sharks and jellyfish in Oz, but we don’t have car sized boars that rummage through garbage bins. At least not yet.
To read more from Matthew Sinclair, visit his Medium here.