5 things that interest me in 2016

As I’ve been doing some reading and thinking over the past week, a few things have caught my eye. Here are a few things that I’m watching and looking forward to seeing develop in the new year.

1. Abstraction of work as a service

This thing we call the “Sharing Economy” is interesting, but mostly because it takes the idea of “something as a service” to the next level, and I think we’re going to see that trajectory increase. I believe there is going to be a whole trend of work being bought as a service that people never would have imagined before.

The trend of buying “as a service” something you’ve previously assumed that you had to do yourself (yourself being either you personally or your organization) is only going to continue. Especially when the work involves expensive capital assets.

Workflows are being disrupted at every level of every company. From the new class of enterprise tools like Slack and Github to traditional industries like energy, transportation, and medicine, the definition of jobs are changing. Where things we have been assumed for generations could not be outsourced or automated, they are. This pattern has repeated itself over and over, and I expect will continue to repeat.

The cyclical trend on this is pretty interesting as well. It seems to fluctuate seasonally?

So much of what I used to do for myself is now bought as a service: blog hosting, email hosting, not to mention dry cleaning, house cleaning, and ordering paper towels.

2. Operational Social Networks

Platforms like Slack help people collaborate around shared work, which is awesome. But I think there’s a more structured use here for social interactions, which is touchpoints between teams and coordinating distributed workflows.

I’m really interested in how the “Work as a Service” construct popularized by Uber can be used internally at companies to create a disciplined workflow. This potentially solves two big problems: how do you make functions of an organization interchangeable so that you can upgrade them at will (including buying them as a service), and how do you cleanly separate work so that multiple teams can work on it at once (in order to scale)?

This really comes down to interfaces between operational functions, and this is a really hard problem to solve because it involves humans. We’ve seen it tried using scrum, waterfall, and a number of different ways, but it comes down to making sure that the functions in an organization are as decoupled as possible but yet can work together.

I think we’ll start to see organizational operations influenced by software architecture. If you think of organizations as systems then it’s not a big leap to start applying systems-thinking to them and begin to optimize them using tools. I think that some kind of social network has the ability to do this well, but probably in a more structured way than Slack (and Hipchat etc).

One potential way that interfaces between teams can be defined and enforced is using bots.

3. Bots

I think there’s a good chance that Bots are the glue that will tie together operational social networks, and Slack has done a great job of bringing that construct some mainstream acknowledgement.

Sometimes the way you think about building software really affects what you end up with. Bots are a great example of that.

But right now bots are pretty reactive. When we start feeding them predicted data from algorithms, they’ll become really interesting and capable of kicking off workflows on their own.

4. The Open Source Algorithm Community

Those bots will need algorithmic data, and where will they get it from? For that you need both input data and the algorithms themselves. Companies now recognize that their data is an extremely valuable asset, but there’s an ongoing debate as to whether the data or the algorithm is more important. Some, like Peter Sondergaard, think that the algorithm is the most important thing. Others like Cade Metz make a strong argument that data is more valuable.

Which is right? It seems like proprietary software is only defensible for a limited period of time, and we’re watching the proliferation of open source algorithm implementations happen almost in real-time. The latest example is Google open sourcing Tensorflow, which I’m looking forward to playing with.

TensorFlow is a new open source machine learning tool with some really advanced computation locality capabilities which will have interesting applications in IoT.

In any case, the array of open source tools is only going to increase and get better, and it will be interesting to see what the next generation looks like.

5. Blockchain Applications

The blockchain concept is really fascinating because it’s one of those things that’s so super-abstract that it’s hard to project use cases onto it. But I’m really interested in the concept of trust as a service, particularly when it can be integrated into software. And now we’re starting to see securities in the blockchain, which means we can actually integrate them into software.

Projects like Ethereum take this even further and embed logic and contracts in the blockchain itself, which is a really interesting feature. A few use cases around data ownership come to mind, but I’m sure there will be some really interesting projects developed on these in the next year.


Happy 2016 everyone. May it be a healthy and prosperous one for you.