Disrupting Tech With Applied AI

The whirlwind of this past year surprised me once again.  We began to experience some real potentials of applied AI with synthetics – not just doing more of what humans do faster and better, but doing things never dared before, things that change outcomes and futures.  Our clients pulled, and our business of supplying synthetic environments to tech development projects grew 157X.

Setting the stage with our model – supply, supply, supply

There were a few disruptive things baked into our pioneering business model that enabled our clients to pull and consume our synthetics at will.   We didn’t charge per tick – not per metric, not for scale (any scale), not for time. We wanted to see what would happen if our clients could use our technology how, when and where they saw value.   Our clients include world-class development teams at global tech product companies. And so with a bold idea, synthetic consumption began.

Going back to 2016 – value and opportunity all over the place

In 2016, we started out knowing that there was significant value attached to “more-faster-better” in product development and test. We knew these areas well and knew there was room for improvement.  Our startup in the automation space began in 2014 with the idea to shift everything that was right, entirely left – in IT, development being the far left and the customer being the far right, with lots of steps, cycles, costs and time in between.

The “everything” idea was to recreate everything that happens when the customers arrive. The "shift left" was to make that "everything" happen right in the middle of development.  We could do it with synthetics - intelligent software that uses software. The synthetics could bring systems to life completely, realistically, and at scale, just like their customers would, from every edge, sensor and interface - watching targeted media, driving trucks, closing deals, running with wearables...

We called them Synthetic Customer Environments.

This shift would expose the reality of the customers' arrival (via synthetics) and all of the emergent system behaviour they cause, in the place where it is best observed, understood and solutioned.

Not only could we interrupt some inefficient rework cycles, but we could also add some cool innovations like making reality entirely scriptable, liberating code from hardware and labs, supplying an exponential workforce, reducing test/QA, pre-training support on live systems, adding velocity to development, even eliminating firefighting, brand damage, downtime and field failures.   

2.4 billion synthetic hours were consumed by a handful of companies in our geo. Each synthetic hour is equivalent to an hour of human activity. Not too shabby for 2016.

Then 2017 – shifting to inhumanly possible value

In 2017, some of the synthetic environments we supplied became something different.  We had shifted the synthetic customer entirely left and then things started shifting up, up to the accountable set, the ones everyone counts on to know it all.

It wasn’t the synthetics that changed, but what the environments dared us and our clients to do – ask questions never asked before.  The questions hadn't been asked out loud, because there was no conceivable way of answering them with certainty.

And so, while the dev teams continued to add Synthetic Customer Environments, some of the environments became purposed into full-scale strategic experimental labs for the leadership. Undaunted by sophistication or scale, the synthetics make it possible to bring the most complex systems to life, trial any future scenarios and watch the consequences. 

We called them Synthetic Intelligence Environments.

No more guesswork. Clients could actually experience all of their “what ifs.”   What if our next customer is 10x, a satellite plant changes a configuration, subscribers flock to watch an event, or we push that firmware update? What happens if the degree of rf noise increases in a neighbourhood, we remove micro services, change network typologies, change platform suppliers, re-architect?  How can we optimize? What are our limits?

The scenario floodgates opened and what resulted was unprecedented insight backed by millions of data points.  It provided a strategic opportunity to know it all. The opportunity was well received.  2017 ended 378 billion synthetic hours later.

Now, well into 2018 - learning to be a thoughtful pioneer

In 2018, we are on track to supply 4 trillion synthetic hours of insight. I am looking forward to more adventures in the world of the inhumanly possible.  While the more-better-faster-than-human applications of AI are worthwhile, their value is defined and understandable.

The next leap, the leap we are realizing with synthetic intelligence, is more surprising.  Now, along with our great clients, we get to invent and discover even higher value synthetic applications in ways only limited by our collaborative imaginations. 

Jason Zerbin