In an exclusive Q&A, Robison discusses what prompted him to get involved with a South African startup, the lessons he learnt at Netflix and Yahoo, and the importance of artificial intelligence (AI).
Tell us a bit about yourself - your background and history up until joining Yahoo in 1996?
I graduated from UC San Diego and was working for a healthcare startup part-time during my senior year. The company moved to Silicon Valley in 1991. We were using the early NeXT computers, they ultimately became the foundation for the current Apple computers we all use today.
I worked for a few startups, then came across Yahoo. At the time, nobody really knew they would grow so incredibly fast. They were just another startup in Silicon Valley when I joined.
You studied cognitive science at UC San Diego. Does this field of study intersect with the field of AI in computer science?
Cognitive science was also known as HCI (Human Computer Interaction). The early AI attempts were more closely trying to mimic human thought. The major combined several different disciplines to better understand how people and machines think and interact.
The computing power when I was at UCSD was truly dismal, and AI didn’t start to really flourish for at least another decade (or two!). Today AI is finally reaching maturity, and able to deliver far beyond our wildest dreams at UC San Diego.
Why did you leave Netflix in 2012?
As part of the transition from DVD to streaming, I was involved with the DVD side of things and helped start the process of winding that side of the business down. Ultimately, I made myself obsolete.
The following three principles allowed Yahoo to scale up rapidly and successfully for over a decade and I’ve taken them to every company I’ve joined since:During your time at Yahoo and Netflix respectively, these companies grew in magnitudes, what lessons did you learn from them?
Yahoo and Netflix both succeeded in hiring incredibly smart people and moving very quickly. They maintained the nimbleness of the startup much longer than most companies. Yahoo had a little over 100 people when I joined and there were over 10,000 when I left almost a decade later. That incredible growth puts a huge strain on the underlying technology - if things are not well designed they will easily break with the added load and complexity.
The following three principles allowed Yahoo to scale up rapidly and successfully for over a decade and I’ve taken them to every company I’ve joined since:
1. Hire the best possible people you can;
2. Ensure your development teams remain agile and nimble;
3. Create complex systems that are very loosely coupled with very clear API boundaries.
One big factor in Netflix’s success was the courage to literally dismantle its own DVD business in the pursuit of streaming. Netflix’s CEO, Reed Hastings, was influenced by ‘The Innovator’s Dilemma’, where many companies failed as they avoided competing in the next evolution of their markets.
Netflix is not afraid to compete - even with itself - in the interest of moving forward. That courage and competence have helped propel the organisation to where it is today.
We seem to be in the midst of the fourth industrial revolution. What are your predictions for how the tech industry will change over the coming ten years?
We will continue to see a lot of work handed off to AI and machine learning. Machines are now much better at analysing and solving certain types of problems than humans, and computers can quickly process and analyse the massive amounts of data we are now generating. In fact, machine learning works best with larger data to train on, so it really is a perfect match for the technology.
Expect tech to continue to lead insights, but also to make our world much “smarter” and more personalised. The Nest Thermostat (in learning mode), and Waze (mapping software) are two of my favourite examples. They monitor what is happening and then predict what you would like next.
You won’t have to tell technology what you want ten years from now, it will simply anticipate your needs and provide the solutions. This has incredible implications for areas like healthcare, which has traditionally been very reactive and has adopted computerisation reluctantly.
What valuable product or service would you like to see created, that is not currently in existence?
Well if I knew the answer to that question I’d probably be building it myself! Seriously, I think many of our technological breakthroughs are so focused on short-term monetisation, they miss the opportunity to help our larger society and planet. I’d like to see more of our technology being put to use solving some of our global issues, and helping to put humans and nature into a better state of balance.
What brought you to Xineoh and what do you hope to achieve at the company?
I worked with the CEO, Vian Chinner, at a previous company and was very impressed with him and his team. Having stayed in touch as our careers diverged, I realised when he began building Xineoh that I was interested in where this was going and how flexible his solution was. I am now aware that we are onto something really big here and I am working with the Xineoh team to ensure we are able to package and deliver this exciting technology into different environments quickly and efficiently.
How does Xineoh compare to other Silicon Valley tech startups you’ve been involved with in the past?
It is hard to tell the difference to be honest. Vian is a very smart leader and has a nimble and capable team. He follows the principles we talked about previously, which have helped many companies (not just in Silicon Valley) succeed.
What’s the business case for implementing AI?
Unlike just a few years ago, basic AI today is little more than your ticket to the game. The business case is that you don’t really have a business if you don’t leverage AI and machine learning - it’s that simple. To me, it is clear that any business not leveraging AI already is going to find itself falling behind its competition far more quickly than it might realise.