The AI Gold Rush
Tags: Venture Capital, Startup, Artificial Intelligence, Business, Funding

We are now living in what can be called the AI Gold Rush. There are thousands of innovative AI startups popping up every day and investors are quick to claim stake. Venture capital funding of AI companies soared 72% last year, hitting a record of $9.3 billion.

The increase in AI investment partly reflects the frothy funding environment overall. But there is an elevated excitement with AI technology as it has matured in recent years. College students last year enrolled in introductory AI and machine learning classes in record numbers and US officials mentioned the technology in more than 70 meetings of the Congress.

The object detection and tracking technology developed by SenseTime Group is displayed on a screen at the Artificial Intelligence Exhibition. Photo from Fortune.com

Some of the notable AI companies in the US are Dataminr, Crowdstrike, Tanium, Cyclance Inc., Pony.ai each raising hundreds of millions of dollars. But none of these companies come close to the incredible funding numbers accomplished by AI startups in China. The startup Sensetime is a prime example and they focus on innovative computer vision and deep learning. The Chinese company raised not one but two rounds of $600M in 2018. It now claims to be the world’s most valuable AI startup at $4.5 billion. There have also been successful funding rounds by AI companies in Europe like Dataiku, which received $100M in 2018.

Riding The Wave

Like any gold rush, there are plenty of prospectors who are making grandiose claims to the AI name. In a recent AI report, over 40% of 2,830 so-called AI startups in Europe are not really using AI. Tech companies benefit from the perception that they’ve built sophisticated automation and AI, rather than a system that relies on manual labour. Startups have realized this, so many of them will use the AI brand to describe their company even when their company has little or nothing to do with the technology. There are even claims that tech giants, like Microsoft and Google, are not always honest about their reliance on humans to power their “AI solutions.”

The biggest harm that comes out of all of this is that consumers will not able to tell the difference between companies that make legitimate claims and companies that make false claims. With companies both big and small riding the so-called AI wave it becomes increasingly difficult for consumers to know what companies they can trust to deliver the very best product/service using AI technology. The plausible solution to this issue is for investors to filter out the startups that misuse the AI name right from the get-go by denying them funding. That way genuine AI startups will be able to flourish and enrich this emerging tech ecosystem.

Tech Giants Dominate

While AI startups provide plenty of vertical industry solutions, deep-pocketed technology giants like Google, Microsoft, and Amazon dominate the rest of the AI value chain. They are the picks and shovels of this gold rush as they offer the chips, cloud services, and algorithms needed to propel AI technology forward.

It takes at least $100M for a startup to design, build and distribute hardware chips optimized for AI processes like machine learning. In addition, they are facing competition from the likes of Google, Facebook, and Microsoft who are introducing their own AI optimized chips.

Tech giants are also in heavy contention to see which of their cloud service can run the millions of AI applications that will be everywhere. The primary contenders are AWS Startups, Google Could, Microsoft Azure or Chinese Alibaba. The battle is heating up as the overall cloud market is estimated to be worth a whopping $400 billion in 2020. And the increasingly the cloud market competition will be over the AI enable cloud.

Lastly, tech giants are battling to provide the best underlying AI algorithms and cognitive services to power millions of AI applications that will be built. If we look at it today, computer programmers can write a few lines of code and insert it into really powerful AI services through application program interfaces (APIs). Aforementioned companies like Google, Amazon, Microsoft, IBM are all offering machine learning and cognitive services in the cloud. This new category of AI-as-a-Service (AIaaS) will power a wealth of conversational agents and chat-bots, speech, natural language processing (NLP) and semantics, vision, and enhanced core algorithms programs.

Where Startups Fit In

Illustration from Fortune.com

Knowing all of this begs the question: What makes a successful AI startup?

Well, there are a few key principles that have allowed certain companies to raise such large sums of money.

The secret sauce of these companies is that they provide valuable point solutions to enterprises and are succeeding as they have access to (1) large and proprietary data training sets, (2) domain knowledge that gives them deep insights into the opportunities within a sector, and (3) a deep pool of talent around applied AI.

Even with all the right elements, sometimes it can be difficult for AI startups (or any startup for that matter) to gain momentum and raise significant capital. As an AI startup ourselves, we know the human element of operating a startup holds just as much importance as the service that we provide.

Here at Corl, our dogma is that entrepreneurs should have access to the capital they need to successfully grow their businesses at their own pace. While we may leverage sophisticated machine intelligence software to analyze entrepreneurs’ growth businesses, we are more ardent about the human side of entrepreneurship. We take the extra step to communicate founders on funding decisions. We take the time to get to establish relationships with founders and to cultivate a deep understanding of their company, their product and to cater to their individual needs.

Corl strives to make access to growth capital fast, competitive, flexible, and human. For more information about Revenue Sharing model, Corl put together a breakdown on “Revenue Sharing? Cool! What the heck is that?”