Emerging Trend: The Potential Future Role of Virtual Assistants

A fascinating trend I recently came across is the humanization of virtual assistants that seems to be emerging in Japan and South Korea. Line, through its subsidiary Vinclu last year released a holographic virtual assistant called Gatebox that initially seems to have sold rather well. It’s basically a limited virtual assistant that has a 3D avatar in a box you keep in your house that can interact with you. The video below explains it better than words could. The company right now markets it as “living with characters”.

Currently virtual assistants are seen as tools or utilities, but it is a fascinating thought that eventually they might be seen as friends or confidants. The loneliness epidemic, especially among Generation’s Y and Z is very well documented and publicized with data showing 13% of the country has 0 close friends. This leads to all sorts of negative health effects from additional stress, substance abuse, suicide, etc. This might be a technological answer to coping with the issue, especially as the algorithms become better and more lifelike.

I am curious to see how this type of technology/interface develops and especially if AI virtual assistants become a health tool and as common in a household as a television is.

Big Trends for 2019

For the last 2 years ARK Invest has put out an interesting look ahead for the year focused on what they see as the big technology trends. You can find the latest for 2019 here. Some points I found particularly interesting this year are:

  • This has been apparent for several years but barely talked about in the media, but the cost of lithium ion batteries is dropping rapidly, allowing a transformation in how we consume energy and our electrical grid systems. Due to this they forecast electric vehicles will be cheaper by early 2020s than any comparable internal combustion engine vehicle.

  • They might be cherry picking their data but they do make the case that bitcoin and crypto demand is accelerating in emerging markets with unstable currencies.

  • The rise of digital wallet apps in the US grabbing market share, just like they’ve long been dominant in China with WeChat. They specifically call out Venmo and Square as evolving into the center of a consumers financial life.

AI and Cybersecurity

The AI Now Institute recently released their annual review looking at AI and security. Their annual report for 2018 can be seen here. The main theme this year is that governments need to start regulating AI yesterday, especially the use of facial recognition technologies. The institute believes that the biggest near term risk is the use of AI in surveillance technologies and automated decision making, especially by the state on their people.

The report is a fascinating read on an area of AI most people haven’t thought about so take a look.

Trim

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Overview

Trim is a fintech startup that offers an AI personal assistant that improves the financial health of its users. Services include saving automation, spending analysis and automatic budgeting. Its hottest offering is an automated service that negotiates and lowers a user’s bills. Trim does this with subscriptions such as Comcast cable by using AI to look at billing and pricing trends regionally. It’s customers tend to be younger and more tech savvy individuals, mostly in the millennial demographic. Their next set of offerings will be focused on helping solve debt challenges people have, especially around student loans and credit card debt.

Trim has an interesting business model that takes a percentage of what they save their users by negotiating their cable and internet bills.

The team is currently less than 20 people and is based in San Francisco, CA.

Why I like Them

Automation - the team is hyper focused on automating the personal finances of its users. A long term thesis of mine is the growth of more automatic personal finance since the vast majority of people don’t understand and hate dealing with their finances. Trim recognizes this and is investing heavily in R&D to ultimately become a platform that improves user’s financial health.

The team is also laser focused on their users’ needs and their mission of solving people’s financial problems. In finance and fintech in general to often firm’s are offering a service, but not helping the end user actually improve their financial well being. Trim talks to their user’s weekly to target the next products to build that directly helps them solve an issue they need. As expected with this focus, they have strong traction and growth. Even more interesting, they find that their service is very sticky as they deliver a newsfeed of transactions and information via SMS to their users that is very engaging, so much so that most users stop using their banks app.

Disclosure:  I have spoken to members of the team.

Investor Discussion Series: Evangelos Simoudis of Synapse Partners

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Evangelos Simoudis is the founder of Synapse Partners, a VC firm that focuses on AI and Big Data investing.

How do you really identify and due diligence AI companies, versus companies just doing statistics or have vaporware?

There is a lot of hype in the space about what is possible with the technology available today.  Anytime there is hype in a sector you get pretenders along with the startups that are developing important IP. 

At Synapse we partner with large corporations in automotive and transportation, financial services, and telecommunications. We work with Sr. Executives from corporations in those industries to understand what they view as strategic problems for their companies and we then determine which of these can be addressed via data and AI.

Since Synapse Partners invests exclusively in early-stage startups developing enterprise applications combining big data with AI, my personal background in AI proves to be very helpful when we consider new investment opportunities.  We also tap into our firm’s advisory board that includes senior AI and data scientists.

 

What kinds of moats do AI companies have from what you’ve seen?  Does it really just come down to the data/all the algos are mostly the same right?

Identifying and gaining access to the right data sets for solving important enterprise problems, selecting the right AI approach to exploit that data, properly preparing the data for processing by the AI system, and finally making sure that the results of this exploitation are correct are the prerequisites for creating such moats.

It is an oversimplification to think that all one needs is data and that more data is always better. The moat is created by the uniqueness of the data and its quality, as well as the ability to exploit  it in a smart way.  It’s a fallacy to think that by taking some open source machine learning software and presenting a large data set means that one can create an important product.  This approach may have been used in the past but the low-hanging fruit has already been plucked. 

For example, properly labeling data before presenting it to machine learning systems turns out to be a difficult and expensive task that today is mostly performed manually. As corporations work with very large data sets, such as those generated by autonomous vehicles, such manual labeling becomes prohibitive. We have invested in a company called Understand.ai that uses AI to automatically annotate that type of data.    

 

What Trends are you currently investing in?

●      For the past couple of years, we have been investing in companies that develop AI-based software to enable autonomous mobility.  More recently, we developed and currently pursue an investment thesis around using big data and AI to monetize autonomy. For example, we are looking at startups developing fleet management and commerce-related AI systems for passenger transportation and logistics where autonomous vehicles will have an advantage. This includes tasks like scheduling, and maintenance of autonomous vehicles aimed at increasing a vehicle’s uptime.   

●      Intelligent software agents (not chatbots) that operate within larger software or hardware systems, for example warehouse robotics. We are interested in systems that understand natural language so that they can collaborate with humans, and can learn from such interactions.

 

What’s Overhyped today from an investment standpoint?

AI, autonomous vehicles, blockchain and cryptocurrencies, augmented and virtual reality are all hot areas but are all overhyped right now in terms of their potential impact and the speed with which this impact will occur. It will take us longer than the popular press talks about to really see the impact from these but I remain optimistic that we will have important changes as a result of using these technologies to address enterprise problems. We will continue to see significant pilots and experiments being done by corporations using these emerging technologies.  However, people need to keep in mind there is a big difference between experimenting with a technology and extracting insights and being able to have broad deployments.

On the other hand, I am very optimistic about the accelerating proliferation of cloud computing in the enterprise. But you can see how long it has taken cloud computing applications to permeate the enterprise and for the market to become as large as it is today. 

What’s the key signal or two you look at when thinking you want to invest in an early enterprise startup, what ultimately convinces you?

I start with the team and I like to see how driven they are for the startup. I pay attention to how complementary the team members are, since I consider well-rounded teams to be an important ingredient to a startup’s success. I don’t want to see a team that consists only of engineers.  I like to understand the team’s background and how they got where they are.  Of course, we always pay attention to the market opportunity, which can often be challenging when the startup is trying to address a brand-new market.  Lastly, at Synapse we always syndicate. It is therefore important to understand who are the other investors.

 

What are some resources you use to stay up to date on a space?

I spend a lot of time reading and working with large corporations. Technology- and startup-related conferences are also important sources of information.  These days I find myself connecting a lot more with PhD and academic colleagues.  The biggest challenge I have is finding good enough filters to discard the overhype of the AI field right now.

 

Any advice to young venture capitalists and angel investors out there in sourcing deals?

It’s not only about writing checks for new investments; it’s about making money for your investors.  Finding a company that wants your money is the easy part, understanding if this is a good company and important investment opportunity to make you want to be part of the company for the next 5-10 years is something you need to focus on. 

Sourcing these days is very difficult. There are so many startups being created and capital is a non-factor.  Having deal flow today is not hard. Having the right deal flow is the important part.  You get the right deal flow by being able to show to the entrepreneur and your co-investors that you have something unique and important to offer.

Today local networks are not as important as they used to be.  You need to be able to tap into global deal flow and be willing to invest globally even as a small investor. 

 

Anything else you think AI  investors or enterprise entrepreneurs should know?

AI is a complex field. It includes several different areas.  Not only machine learning.  People don’t have as much of an understanding of AI as they think.