Last year, I talked with the CEO of CB Insights about how Fintech was disrupting the value chain of financial services. 

Since then, it's become even easier to see how disruptors are changing every industry, and becoming leaders in their fields.

 

via McKinsey

 

Companies like Lyft, Tesla, and Netflix changed paradigms and are now leading the pack, but what industries are next?

Looking at a report by McKinsey&Company, we find that the 5 industries with the least digitization account for 34% of GDP and 42% of all employees. 

 

1. Agriculture and Hunting
2. Construction
3. Hospitality
4. Healthcare
5. Government

via Quora

But, being less digitized than competitors, alone, isn't enough to indicate it's ready for disruption: 

 

How Regulated Is The Industry?

Heavily regulated industries mean that any company that can find a way around current regulations and tax structures can become an effective competitor fast … i.e Lyft and Tesla

Is There Too Much Competition Already?

How many competitors are in the space? Is there spare capacity, or does the current model waste resources? Think taxis waiting for customers or empty seats on airplanes. 

Can it Be Automated?

Lot's of industries and tasks are ready and waiting to be automated – and only haven't been due to the cost of switching to new technology. 

 

To put it another way – are you solving a customer headache while lowering costs for you and the customer? 

The only constant is change, and digitization and automation are only increasing the rate of change. The type, quality, and the number of jobs being offered are changing rapidly. 

As digitization increases so does adoption of AI-related technologies. 

McKinsey Institute via Vox

AI is coming, disruption is coming.

It's no longer possible, it's not even just probable, it's inevitable

This interactive graph tells you if your job is automation proof, and what percentage of your job can be automated.

What industry do you think is next?

Machine Learning algorithms are everywhere.

They're helping you pick out your next gift on Amazon, controlling what you find on Google, they're suggesting new music for you on Spotify, and they're doing their best to keep you on their website.

When trying to explain how machines learn we tend to try and describe it in human terms. Unfortunately (or fortunately) machine learning isn't based on how we teach humans.

The video is a bit simple in its explanations, but it describes some important concepts. So, watch how machines learn.

 

CGP Grey via Youtube

The video focuses on Genetic Algorithms, which is one type of machine learning, and neglects some of the other more complicated approaches.

As machine learning gets more complicated and evolved, it gets harder for a human to understand what makes it good … and that's okay. 

It's human nature to feel safer when we understand something. It's human nature to envision machines as making human-like decisions, just faster.

Of course, just because it suits human nature to believe something, that doesn't make it true. 

Part of what makes machine learning exciting is that it can do a lot of things well that humans are really bad at. 

In reality, it doesn't matter why a bot is making a decision, or what inputs the bot is making the decision on. What matters is the performance and level of decision-making in relation to itself and to other options.

One of the lessons I teach when I speak publically is that in trading, caring about the markets is a weakness.

It's a distraction. 

I don't care how markets are doing.

I care how my systems are doing. I care how my portfolio is doing.

There's too much data for me to try and care about anything else. 

It's a brave new world, and not only is big brother watching, but algorithms are too.