Trends Watch: VC Investing in AI-Enabled SaaS Companies

EisnerAmper’s Trends Watch is a weekly entry to our Alternative Investments Intelligence blog, featuring the views and insights of executives from alternative investment firms. If you’re interested in being featured, please contact Elana Margulies-Snyderman.  

This week, Elana talks with David Evans, Managing Partner, Sentiero Ventures. 

What is your outlook for VC investing in AI-enabled SaaS companies?

We are at the very beginning of the cycle of adoption of artificial intelligence (AI) in software as a service (SaaS).  Most technologies have a 60+ year cycle from discovery thru commercialization. Eliza, one of the earliest chatbots, was developed at Bell Labs in the 1960s, but most people don’t know that.  Growth and adoption of a technology only goes parabolic around the time the public becomes aware of the technology, which we’d mark as the release of ChatGPT last year.  We are excited about the prospects of investing in companies that will be at the forefront of that explosion.

Where do you see the greatest opportunities and why?

There are six major trends we are tracking as AI becomes an integral part of our day-to-day business lives.  Those are:

• Knowledge Assembly Line - Humans and machines working together in concert to "manufacture" knowledge products. In effect, delivering the same level of automation to knowledge work that the assembly line delivered for manufacturing at the turn of the   20^th  Century.
• Next Generation of User Experience - Using voice- and language-based commands to interact with software and systems in a more natural way. It’s not voice and text only, but using the right interface at the right time for the right purpose. Point and click isn’t dead. It’ll just be one piece of the puzzle.
• Digitization of the Physical - Algorithms and approaches that replicate and understand complex natural processes and systems better than humans can because they can ingest vast amounts of data in real-time.
• Truly Smart Devices - Devices that use real-time intelligence from sensors and cameras to make decisions on their own.
• Novel Computational Approaches - New methods of training AI that reduce the amount of data or computation to achieve results. Current methods of developing AI models require vast amounts of computational resources, and the companies working in this space will help reduce the cost to develop new models.
• Accessibility of Machine Intelligence - Platforms and tools that increase the accessibility of artificial intelligence in the enterprise.

What are the greatest challenges you face and why?

The biggest challenge is balancing the hype with the outsized potential. On one hand, AI will touch every aspect of our business lives, so it deserves much of the hype it’s getting.  However, in some regards, it’s being over-hyped as being nearly omniscient.  Reconciling massive potential with unrealistic expectations is a constant challenge.

What keeps you up at night?

As a parent, trying to predict how the nature of work is going to change is the most challenging. Now, we don’t believe AI is going to take away jobs. It will create new jobs as well as help to fill gaps in the labor force where work will get done.  However, we’re still very early in the evolution of work.  I remember having to learn how to type letters and set margins on a typewriter because that’s how lots of business correspondence was still conducted in the late 1980s.  I’ve never typed a letter because it was replaced by email and instant messaging, and the challenge at the moment is identifying exactly which parts of our current daily work will end up like setting margins on a typewriter.

The views and opinions expressed above are of the interviewee only, and do not/are not intended to reflect the views of EisnerAmper.