We have been looking at Digital Twins from many perspectives so far: Technical, Theoretical, Conceptual and Practical. But how do all these discussed technologies weigh up from a Venture Capitalist perspective?
We look at Digital Twins from an investment perspective: Is it worth the hype it generates? What potential do technology tools unlock for organisations? How do these great ideas actually manifest practically within industries and how can it help with the turmoil that COVID-19 has created for us on an individual and industrial level?
I’m originally from the Chicago area. I started my career doing forecasting for Supply Chain. I finished university in 2009, the economy was in freefall and I took the first job I could get. My background isn’t in technology, I was a history major. It was after my first foray into supply chains and I soon realised that I could apply storytelling to any kind of process.
However, like many 20-year-olds, I wanted a career that sounded more exciting. I landed an opportunity to work in Washington DC for former Pentagon officials at a policy think tank, and from there ended up in defence policy and then moved to M&A (Merges and Acquisitions) for defence and government service contractors. Most of those were technology providers and, ironically for me, logistics providers.
Then my lovely wife took a PhD position at the University of Leeds, which meant popping over to this side of the Atlantic, where I’ve now been for the last five years. I initially continued in corporate finance with KPMG, but I wanted to work with more early stage businesses which took me down the route of a secondment to then working for Mercia and the Northern Powerhouse Fund where I am today.
Is it just a bunch of Buzzwords?
All of those areas are really exciting as they show a huge amount of potential but I get the sense that they are, to a degree, mirroring other buzzwords that have come and gone over the years. I used to see this a lot in Washington DC where everyone could be talking about “Business Transformation” for a while but then they would move on to another phrase. These buzzwords often failed to translate into material change, or at least not in the way that it was discussed. Whilst this landscape is exciting, a lot of the people driving the conversation tend to be thought leaders but rather than the people that end up using the product.
This stems from the gap between technologists, thought leaders, and practitioners. Technologists want to prove what is actually possible, the thought leaders want to talk about the vision and potential, and the practitioners are caught in the middle, trying to figure out how these new technologies fit into their jobs practically.
However, I do think things are moving in the right direction and we are moving away from fluffy buzzwords to actually seeing some implementation.
Possibility vs. Practicality
A big part of moving in the right direction is very much in line with what Slingshot is doing: Bringing together hard to merge elements like cloud computing and simulation where there are a lot of disparate tools that have amazing capabilities but need hooking up to a bigger engine that can unify and simplify, at least from the user perspective.
Currently, these disparate tools mean that simulation is confined to people with a high level of know-how in the area that can only use the tool to its full potential if they have sophisticated qualifications, resulting in a load of really great tools that most workers don’t know how to use or don’t have the time to learn.
A common example I see is enterprises that have access to IBM Watson or similar. It’s an amazing tool, but in many organisations there’s probably a clever CTO or small team of people who know how to use it, but not many others and consequently only a fraction of its functionality is utilised. The sooner we can move away towards greater accessible, the better.
There’s has been lots of advances in the gaming sector, but that’s a controlled world where there are fewer or more predicable variables.
There are the engineering organisations such as Dassault Systemès, CAE and others that are able to amazing modelling for items like jet engines… engineered systems and products.
I think there’s room to have a step-change in capability, especially for something that democratises the capability. I can’t think of who’s done that recently – I’m sure there’s something I’m just not thinking of at the moment, but suspect it’s still niche. What I’m excited about next is seeing broader access to simulation and seeing it applied to everyday human interaction. To do this in a meaningful way introduces a whole new range of new variables and challenges that haven’t been addressed yet.
David: I think the fact you couldn’t name any top innovators is more enlightening than if you threw out a couple of names
With any technology a huge part of it is getting the timing right and no one can predict when that timing is going to hit. Until a piece of technology has enough widespread adoption to make it self-sustainable, you’re constantly making a bet on it.
Thought Leaders and Innovators can spin a story of a technology’s capability but this story is based upon eventual truths rather than current capabilities. They’re under pressure knowing that they’ve got a certain amount of funds and therefore a certain amount of time to get the technology adopted depending on how fast you work.
This results in situations where you think something is coming sooner than it is and other times where you think it’s going to be adopted later than it is. All you can do from a funding perspective is make the best possible decision you can with information you have the time.
The US drives a huge portion of VC spending as they have the scale to justify continued valuation increase. Consequently, they can look at a proposal and have the ability to invest $10million at an early stage or $100 million at a super early stage because the reality is that even if you get a small fraction of the market, you stand to make your money back.
The UK is a smaller market: people’s worlds and networks tend to be smaller in a lot of instances. It goes without saying that the UK plays a great role internationally, but there’s a tendency to want to prove the concept on UK soil first, which isn’t necessarily wrong, and then go abroad. But that is a scaling issue as opposed to a proving issue. In the UK it feels like 2 separate investments: One to prove it and one to scale it, whereas that process is more likely to get mashed together in the US.
The institutions in the UK are great at producing ground-breaking research and high-end innovations, from breakthroughs in Pharmaceuticals and antibiotics to the steam engine then later the jet engine and of course computing itself. A more recent example would be Deep Mind being brought by Google and the advances that have come from that in the area of AI and machine learning.
There is a greater appetite for risk in the US: People can take the risk, win big and fail big, and move on with their life. Failure can be viewed positively as learning experience. Culturally there’s a different appetite in the UK, but the output around the creative innovations historically been tremendous. Obviously there’s loads of great innovations coming out of the US but if I’m generalising on a spectrum, then I would say that the UK leans more towards that ground-breaking, whereas the US is better at commercialising.
From the VC perspective the more data that is out there, the more people can trust the system, the more that people can use the system and come up with value approaches intuitively, the better.
This is helped by most people now subscribing to network theory: in most cases, it’s better to have an innovation out in the market and being used rather than guarding it too closely as it means you can rely on the market to explore all possible options with the data available. This allows for different and unpredictable responses and applications.
It’s often said that plans are wrong from the day that you agree them, but what you hope it is that it’s taking you enough in the right direction that there’s 10 different but equally good things might happen.
I don’t believe the data itself is inherently valued, the value lies within what you do with it.
Unlike with mined materials where it’s pretty well known what they can be used for and what to do with it, giving it good value upfront. With data I think we’re still figuring how to create value from it once we have it:
You need to know what to do with it, how to process it, interact with it, and how to mix and match it with other forms of data. Do this and you will come out with something that’s more meaningful to a decision making party, whether that’s in an automated or strategic setting.
However, it is understandable that some data might want to be kept private for intelligence or engineering research data purposes however, the more you keep data hidden, you are immediately limiting its potential impact.
There is an element of power when you’ve got exclusive data, but ultimately you can have all the datasets you want, but they’re not worth diddly squat unless you’re able to turn them into something meaningful. It’s that action primarily that is making digital twins so exciting because all of a sudden if you can get that tool into the hands of the right people, you’re able to allow so many more interesting minds to interact with these datasets productively.
A Living Model
In a normal model you can test and do different things and that’s great, but a Digital Twin can take in live data or real data and adapt to change in real time and that allows for storytelling. The human mind is attracted to change, and as a Digital Twin is ‘living’, it can show that change. Storytelling shapes change into a narrative. Tools that enable story telling make data much more valuable because they bring the information to life and impact the human mind differently than traditional data mediums.
There’s a famous Stanford experiment where they tested subjects’ recall abilities. And the results showed that people ‘s recall was much better when they’ve received it in the form of a story as opposed to a list of facts or other means. There’s impact in that. There’s so much research, and interesting information out there, stuck in datasets or sitting in reports and journals that can only be accessed by certain people who tend to see it in the single context. There’s a lot of potential if you can bring storytelling to that information. Digital Twins allows a whole new level of accessibility. The impact is in being able to get the right stories in front of the right people. It can also take away layers. If experts have tools to tell the story better, they can have more impact and if there is enough trust in the tools, people can reduce suspicion, and perhaps more easily unify around evidence and reject conspiracy.
Because Digital Twins makes room for change, you can play with scenarios and that opens up a whole new level of creativity and allows for a new wave of design as a result because suddenly you can see and engage with information in a way that you couldn’t before and I’m excited to see what comes out of that capability.
One of the biggest opportunities is increased accessibility, bringing in new users invites new forms of creative thinking.
On the flip side, adoption remains a challenge: creating solutions that actually help rather than hinder the people who use the system on a daily basis. We need to move away from putting amazing tools into the hands of people who either don’t want something new, don’t have the capability to use the tool, or who use systems that aren’t designed to take on that tool. Don’t just give a hammer to someone who uses the screwdriver the whole time.
One of the issues we are experiencing globally is a decline in trust. People are less likely to trust authorities to provide them with truthful and meaningful information. Simple example, there are protests in the US where people are arguing about their right to not wear a mask where it might be required despite clear evidence that masks help prevent spread. What if there were more compelling ways to communicate that evidence that would help to re-establish trust.
Perhaps is possible to use Digital Twin technology to provide a central point of truth. To present the data in a way that unquestionably tells the story, by visualizing the datasets, that says to people: “Look: These are all the different data streams and here’s what we think was happening and why”, as opposed to asking them to trust a third part that could be argued as biased. Can we give experts better tools to help them communicate with general public and re-establishes trust, allow people to have confidence in the experts again.
It’s a living model that you can play with.