Unicorn. Cloud. Wearable. Big Data. SaaS. Internet of Things.
These are just a few of the trendy tech buzzwords that have permeated the media, startup pitch decks, and investors’ daily lives over the past few years. It is our job as VCs to look past the hype and sift through the buzzword-bandwagoners to find true innovation and value—and this has never been truer than it is with the hot buzzword of 2017: AI, or Artificial Intelligence.
AI as a concept is nothing new. Ever since Alan Turing developed the Turing test in 1950 to determine a machine’s ability to exhibit intelligent behavior, people have been creating forms of AI. The sophistication of AI has certainly evolved from explicit rules, to statistical models, to machine learning, and now to the age of deep learning—where neural networks are trained to make detections with human-like accuracy. At each stage of advancement, accuracy has been improved at the expense of visibility, meaning that in today’s case we are unable to tell why a neural network behaves the way it does, but given a set of inputs, we get the right outputs, so we don’t actually need to know what goes on inside.
If the application of AI has been around for over sixty years, why is this such a hot area now? In many ways, AI is a natural evolution of the cloud. The pervasiveness of cloud computing and infrastructure have not only made AI better, but also far more accessible due to the low (or even zero) cost of compute and storage. In 1980 the IBM 3380 direct access storage device offered 2.5GB of space for roughly $100,000—that’s $300,000 in today’s money! The storage complexity required for AI applications is significant, so as far as order of operations, the cost and capability of storage solutions had to evolve before AI could be sufficiently enabled. In addition, there’s the sheer abundance of data available to inform AI tools—it’s been reported that over 2.5 quintillion bytes of data are produced globally each day, with 90% of the data available having been created in just the past two years.
If we think about innovation applied to industry, for over twenty years we’ve had the expectation of software being used to automate. Now, there’s a widespread expectation for AI to be used for optimal automation. What used to be viewed as futuristic or even impossible, is now de facto.
You don’t need advanced intelligence to tell you that the sector is on fire. According to Crunchbase, 180 companies in the AI space raised a total of $1.06B in 2016, compared to 44 companies raising $108M in 2012. Not to mention the apparent race for tech giants like Google, Apple, and Twitter to snatch up the most promising young startups in order to stay ahead of the technology curve.
Despite the incredible traction and record-breaking capital pouring in, it’s important to be cautious and thoughtful with a technology trend this hot. In fact, at NEA, we actually don’t view AI as a category on its own, but rather as a competitive differentiator, enabling companies to offer better solutions than incumbents. The term “AI company” is as broad and undefined as “Cloud company,” when really it’s the specific application of cloud or AI that creates the unique value versus the vast technology. This mindset helps us to stay focused on finding entrepreneurs who are applying AI in unique and transformative ways.
Something to be aware of is research projects disguised as companies. On the coattails of AI investment traction, there have been emerging “AI companies” that are actually groups of PhD researchers looking to continue their research using Venture funding in the form of a C-corporation instead of (more appropriately) using academic grant money. The projects lack fundamental business models, but the irresponsible practice of funding them is perpetuated by high-priced, talent-motivated acquisitions from tech giants. This creates an environment where VCs view the “projects” as good investments, and founders see the pursuance of VC money as a lucrative path. This cycle may result in quick returns, but it is not sustainable to fuel innovation and build successful companies.
When it comes to separating signal from noise in the overcrowded, often over-valued AI space, it’s imperative that a startup not only has a strong technology application, but also an assuring business model that solves a real user pain point and drives ROI. The “AI-enabled” portion of the solution should yield an RIO that is greater than could be achieved using any other tools.
AI-focused Startups* with Strong ROI potential
- Aera – Cognitive technology for the self-driving enterprise. Aera understands how your business works, makes real-time recommendations, predicts outcomes, and takes action autonomously
- BloomReach – Open and intelligent digital experience platform (DXP), helping drive customer experience to accelerate the path to conversion, increase revenue and drive loyalty
- Cyence – Technology platform for the economic modeling of cyber risk
- Forter – Fully automated and highly accurate real-time fraud decisions, enabling instant approve or decline decision for every transaction
- Reltio – The first fully featured master data management software platform in the cloud, helping enterprises solve the challenges of managing data silos by providing a central data management hub using API led connectivity that is purpose built for the cloud
- Veriflow – Software-defined networking company that predicts outages before they impact the business, and vulnerabilities before they are exploited
*examples are from NEA portfolio
Still, the ROI of AI technologies can be a bit murky early-on depending on algorithm adjustments and fine-tuning the right mix of data. This may account for the lower than expected adoption rates: Forrester recently reported that despite 58% of businesses being interested in and researching AI, only 12% are currently deploying solutions. As with any “new” technology, it can be risky to buy-in early, but those who get it right will enjoy a valuable jump ahead of the competition. The potential RIO for these new tools cannot be denied.
Investment trends emerge for a reason—there’s something exciting that is changing the way we work and live, and everyone wants in. But the more crowded the space is, the more diligent the scrutiny should be, as AI is not immune to the startup failure rate of 50% by year four.
Don’t be blinded by the buzzwords or wooed by the wow-factor, the AI tools that are going to bring lasting value to customers and investors are those that address real problems within massive markets, and are accompanied by a sustainable, scalable business model.
Ravi Viswanathan is a General Partner at NEA
New Enterprise Associates, Inc. (NEA) is a global venture capital firm focused on helping entrepreneurs build transformational businesses across multiple stages, sectors and geographies. With over $19 billion in cumulative committed capital since the firm’s founding in 1977, NEA invests in technology and healthcare companies at all stages in a company’s lifecycle, from seed stage through IPO. The firm’s long track record of successful investing includes more than 210 portfolio company IPOs and more than 360 acquisitions.
NEA was an early investor in AI technology, with over 50 investments across the full landscape from infrastructure, to enterprise solutions, to consumer and health applications. For additional information, visit www.nea.com