Investing In Artificial Intelligence

Artificial intelligence is one of the most exciting and transformative opportunities of our time. From my vantage point as a venture investor at Playfair Capital, where I focus on investing and building community around AI, I see this as a great time for investors to help build companies in this space.

     
There are three key reasons.

First, with 40 percent of the world’s population now online, and more than 2 billion smartphones being used with increasing addiction every day (KPCB), we’re creating data assets, the raw material for AI, that describe our behaviors, interests, knowledge, connections and activities at a level of granularity that has never existed.

Second, the costs of compute and storage are both plummeting by orders of magnitude, while the computational capacity of today’s processors is growing, making AI applications possible and affordable.

Third, we’ve seen significant improvements recently in the design of learning systems, architectures and software infrastructure that, together, promise to further accelerate the speed of innovation. Indeed, we don’t fully appreciate what tomorrow will look and feel like.

We also must realize that AI-driven products are already out in the wild, improving the performance of search engines, recommender systems (e.g., e-commerce, music), ad serving and financial trading (amongst others).

Companies with the resources to invest in AI are already creating an impetus for others to follow suit — or risk not having a competitive seat at the table. Together, therefore, the community has a better understanding and is equipped with more capable tools with which to build learning systems for a wide range of increasingly complex tasks.

How Might You Apply AI Technologies?

With such a powerful and generally applicable technology, AI companies can enter the market in different ways. Here are six to consider, along with example businesses that have chosen these routes:

There are vast amounts of enterprise and open data available in various data silos, whether web or on-premise. Making connections between these enables a holistic view of a complex problem, from which new insights can be identified and used to make predictions.
        
Leverage the domain expertise of your team and address a focused, high-value, recurring problem using a set of AI techniques that extend the shortfalls of humans (e.g., Sift Science or Ravelin for online fraud detection).

Productize existing or new AI frameworks for feature engineering, hyper parameter optimization, data processing, algorithms, model training and deployment (amongst others) for a wide variety of commercial problems.
    
Automate the repetitive, structured, error-prone and slow processes conducted by knowledge workers on a daily basis using contextual decision making.

Endow robots and autonomous agents with the ability to sense, learn and make decisions within a physical environment.

Take the long view and focus on research and development (R&D) to take risks that would otherwise be relegated to academia — but due to strict budgets, often isn’t anymore.

There’s more on this discussion here. A key consideration, however, is that the open sourcing of technologies by large incumbents (Google, Microsoft, Intel, IBM) and the range of companies productizing technologies for cheap means that technical barriers are eroding fast. What ends up moving the needle are proprietary data access/creation, experienced talent and addictive products.

I see a range of operational, commercial and financial challenges that operators and investors closely consider when working in the AI space.

Here are the main points to keep top of mind:

Operational
    
How to balance the longer-term R&D route with monetization in the short term? While more libraries and frameworks are being released, there’s still significant upfront investment to be made before product performance is acceptable. Users will often be benchmarking against a result produced by a human, so that’s what you’re competing against.
            
The talent pool is shallow: few have the right blend of skills and experience. How will you source and retain talent?
    
Think about balancing engineering with product research and design early on. Working on aesthetics and experience as an afterthought is tantamount to slapping lipstick onto a pig. It’ll still be a pig.
    
Most AI systems need data to be useful. How do you bootstrap your system w/o much data in the early days?

Commercial
    
AI products are still relatively new in the market. As such, buyers are likely to be non-technical (or not have enough domain knowledge to understand the guts of what you do). They might also be new buyers of the product you sell. Hence, you must closely appreciate the steps/hurdles in the sales cycle.
    
There are two big factors that make involving the user in an AI-driven product paramount. One, machines don’t yet recapitulate human cognition. To pick up where software falls short, we need to call on the user for help. And two, buyers/users of software products have more choice today than ever. As such, they’re often fickle (the average 90-day retention for apps is 35 percent).

Consider the digitally connected lifestyles we lead today. The devices some of us interact with on a daily basis are able to track our movements, vital signs, exercise, sleep and even reproductive health. We’re disconnected for fewer hours of the day than we’re online, and I think we’re less apprehensive to storing various data types in the cloud (where they can be accessed, with consent, by third-parties). Sure, the news might paint a different story, but the fact is that we’re still using the web and its wealth of products.

On a population level, therefore, we have the chance to interrogate data sets that have never before existed. From these, we could glean insights into how nature and nurture influence the genesis and development of disease. That’s huge.

Artificial intelligence is one of the most exciting and transformative opportunities of our time
I’m bullish on the value to be created with artificial intelligence across our personal and professional lives. I think there’s currently low VC risk tolerance for this sector, especially given shortening investment horizons for value to be created. More support is needed for companies driving long-term innovation, especially considering that far less is occurring within universities. VC was born to fund moonshots.

We must remember that access to technology will, over time, become commoditized. It’s therefore key to understand your use case, your user, the value you bring and how it’s experienced and assessed. This gets to the point of finding a strategy to build a sustainable advantage such that others find it hard to replicate your offering.

Aspects of this strategy may in fact be non-AI and non-technical in nature (e.g., the user experience layer ). As such, there’s renewed focus on core principles: build a solution to an unsolved/poorly served high-value, persistent problem for consumers or businesses.

Finally, you must have exposure to the US market, where the lion’s share of value is created and realized. We have an opportunity to catalyze the growth of the AI sector in Europe, but not without keeping close tabs on what works/doesn’t work across the pond.

Techcrunch: http://tcrn.ch/1JzqP2v