Investor Discussion Series: Ben Narasin of NEA

Ben Narasin is a Venture Partner at NEA where he focuses on investing in early stage emerging markets and technologies.  His overarching focus in seeking new investments is in his words, “to find founders who make me say wow”.  

Author’s note, Ben is one of the most engaging VC’s I’ve ever been fortunate enough to speak with. If you get a chance to see him speak I highly recommend making it a priority. 

What trends are you currently investing in, especially any that are more under the radar?

There isn’t much overlooked today with so much money and people in the space.  For example, Artificial Intelligence and Machine Learning is popular but every startup claims they have that so you have to figure out who has that for real.

Industrial IoT is a big, interesting category including robotics.  The way I think about the world is different.  Some investors look at one category of things.  I’m more centered around the entrepreneur and finding a vision that makes me say wow.  They show me a new vision of the future.  Its not a category investing method but one based around human beings.

Areas I don’t focus on are crypto, security, cannabis, and medtech.  You need a very specialized expertise to get really good at investing in these spaces.

Blockchain will create a ton of value, but at this point there is so much hype.  Its currently speculation, not investing.  Right now you are over paying because something is on the blockchain.

Right now you want to look at enormous industries that are antiquated and need to be disrupted.  For example, one of my investments, Transfix is disrupting trucker brokerage which is an enormous but antiquated industry.

If something is hot now it’s to late.  You need to be in the business of what’s hot tomorrow.  What’s next is where venture makes its money.   

Where and how do you source your investments?

You find these entrepreneurs everywhere but as an investor we have to see a stunning number of pitches to get there.  Active investors will see over 1,000 pitches a year.  From that you will make 1 or 2 investments.

The best opportunities come from referrals from entrepreneurs you already invested in.  I also make sure I speak at every opportunity I get and stick around afterward for entrepreneurs to talk to me.  

I actually found LendingClub while driving to work listening to the local NPR.  Spent time tracking the founder down.  Pushed until I got to the founder. 

What do you look for when Investing?

I look for 5 things: People, People, People, Great Ideas, Enormous market

When I was a seed investor I funded roughly 80 companies over a decade, half of which raised Series A from tier 1 firms.  The company is proving a thesis off a seed round - Series A is what allows you to build a business

What’s Overhyped today from an investment standpoint?

ICOs and coin based things.   

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

The decision making process is the most important part in venture investing - the outcome is less important than that you had a good process. Luck matters and influences this. 

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

Go to a lot of conferences and trade shows.  Learn from every meeting.  Even in a horrific pitch there may be some nugget you take away such as a trend or competitor they are up against. I read from books in the library across sci fi, fiction, non-fiction. Read the newspapers.  You do everything.

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

I was an institutional seed investor, never an angel investor.  You need to take it very seriously.  Never lose your fear when making an investment.  You do all the work you can to get to a point of comfort.

People obsess excessively over valuation (what you pay and what you sell for are all that matter).   Exercise tremendous discipline.  Its ok to go with your gut but your gut needs to be some level of training, knowledge and experience. 

Any predictions for the next year or two?

Deflation is much more likely than a pop.  No idea how long this will continue.  I don’t invest for something short term.  We invest in companies that can be world class so assume a 7-10 year journey.  Can’t imagine in the next 7-10 years we don’t see at least one correction.

Your job is to wait for the right ball to swing at, wait for the right pitch.  As an investor you aren’t penalized for when you don’t swing but what you strike out at. 

Anything else you think investors or entrepreneurs should know?

It comes down to trusting your gut as long as your gut is based on experience. It’s both science and art.  Early stage is more art than science. Later stage is science. 

Emerging Trends: EMG, the Upcoming Human-Machine Interface

One of the coolest areas of frontier tech that is starting to emerge is Electromyography (EMG).  It’s part of what some firms are coining neurotechnology which is a set of technologies designed to digitize nerve and brain activity. 

EMG is hardware that uses sensors to record electrical activity from muscles.  A more thorough explanation of the science can be found here.  The technology was initially invented for medical applications but lately has been used in computer technologies by using nerve signals from muscle and hand gestures to interact with computers.  On the consumer side this type of technology is being coined intention capture because it interprets electrical impulses from nerves in a user’s muscles. The user doesn’t even need to make the motion but simply think it for their intention to be captured. Seeing is believing as the videos show. 

Two startups that are leading in this space are Thalmic Labs and CTRL-Labs.  The Myo armband from Canadian startup Thalmic labs was on the market for $130 per unit before the startup pivoted and discontinued the product. Reports from users were a lack of use cases and accuracy.  CTRL-Labs technology is still in R&D mode but seems to be able to interpret finer muscle impulses allowing users to type by capturing their intention rather than an actual movement.  They are releasing an SDK and hardware soon. So far the interface is via some sort of control armband, like a big watch band.  However, as the technology evolves its easy to see this shrinking in size and locations available to place it.

Challenges of course remain here, with one of the big ones being humans who have more body fat don’t give accurate reading to the sensors. Another is picking up signals precisely among the muscles in your body.

Most neurotech is to early for any sort of commercial application, but EMG is much closer to mainstream commercialization than the others such as direct brain-computer interfaces. This is because there is no need to break the skin-barrier or insert any sort of chip to create the interface.  I expect to see it talked about in the press and by average people in the next ~5 years.  The market and number of applications for this type of application will also be huge.

I will be watching this area develop closely and am extremely curious as to the applications that are created. 

Emerging Trends: The Future of Food

With the world population projected to reach 8.5B people in 2030 by the UN from today's population of 7.3B, a crop of foodtech startups and technologies are under development to tackle this immense challenge of feeding such a huge population.  This challenge is further exasperated by the decline of clean accessible water and depletion of nutrients in crop soils global.  Several technology trends are arising to tackle this challenge in different ways.

Indoor Farming

This set of technologies is based on increasing crop density and yield by having perfect control of growing conditions (humidity, lighting, air composition) as well as making use of vertical farming.  The idea, which has yet to be proven economically viable, is to reduce waste of the inputs (i.e. fertilizer, water, energy, etc) while dramatically increasing the speed of crop growth and completely eliminating plant diseases and rot.  More crops more quickly for less cost then current methods is the goal.  The poster child for this set of technologies is startup Plenty which is backed by a number of big name and deep-pocketed investors.  They advertise yields up to 350 times conventional farming techniques with only 1% of the water.

New Methods of Producing Meat

It is well documented that cattle rearing for the world's ever growing hunger for meat products is horribly inefficient and damaging to the environment releasing high amounts of greenhouse gases.  A number of startups have arisen over the last few years seeking to grow artificial meat from cells in a lab environment.  Of these the one that has received the most press and funding is San Francisco based Memphis Meats.  The technology here is basically to use animal stem cells and bioreactors to grow meats in a lab setting that is indistinguishable from the traditionally raised and processed meat we eat daily.  It's a much more complex scientific challenge than it initially sounds.  The advantage is it likely will be much faster, cheaper and efficient pound for pound to produce any type of meat once the technology is developed and scaled.

Farm 2.0 - The Digital Farm

There are a ton of startups focused on improving traditional farming with everything from using drones and software to analyze crops in the field (i.e. Farmers Edge), robotics to replace human workers, software to better manage farm operations (AgCode), and many other areas.  While not as likely as the above two categories to dramatically increase food yields, this is a much faster to implement and less difficult set of technologies to bring to market.  In a word the Digital Farm is a sustaining set of technologies rather than a revolutionary set of technologies.

Investor Discussion Series: Evangelos Simoudis of Synapse Partners

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.

Looker

Overview

Looker is a data analytics and business intelligence startup.  Their software is used for exploring data and making decisions for everything from marketing, sales, operations, logistics, web, etc.  It is able to pull and digest data in real time from numerous sources including Salesforce, Slack, SQL, Hadoop, web APIs and produce a number of dashboard visualizations and analytics. 

Looker's business model is a standard SaaS subscription model.

The team is based in the San Francisco bay area.

Why I like Them

Companies currently are flooded with data in the "Big Data Age" but understanding that data and being able to use it to make better decisions is something many firm's struggle with.  Today, to make any sort of headway with their data, specialist data scientists with years of advanced education and expertise in tools like SQL, R, etc. are needed.  However, every job function in a company needs to be able to quickly analyze and make decisions off this data without going through the bottleneck of a limited number of data scientists.  Looker provides this ability to every person in the company without the bottleneck and without needing advanced training to use and analyze this data.  In our data driven age this is a critical ability and it's easy to see Looker becoming a standard tool utilized across every levels of a company just like Microsoft Office Suite.

Despite how crowded the Business Intelligence space is, Looker has a lot of tractionI at over 1,000 paying customers and more than 50,000 users.  These include some large names such as Square, IBM, WeWork, Nordstrom, and Amazon. 

Disclosure:  All information is from publicly available sources, I have not had any contact with a member of the company or its investors.