Interest in AI Startups Exploded in 2017

Artificial Intelligence (AI) technology startups seem to be in the news all the time. Is that hype, or are AI startups truly seeing major interest from investors? Using our research platform, we can conclude that interest in AI absolutely exploded in 2017.

We come to this conclusion from the following takeaways, each of which tracks a different metric for interest in AI startups:

  • AI funding grew exponentially in 2017
  • The number of AI deals is increasing
  • Investor interest in AI is growing

We’ll illustrate these takeaways with a series of graphs to show the extensive growth in AI.

AI Funding Grew Exponentially in 2017

Let’s start off by looking at AI funding trends over the past 6 years. Here is the annual amount of AI startup funding, stacked by quarters.

Q4 2017 AI Funding - Annual Funding

AI funding has been on an upward trend over these past few years, with a rapid explosion in 2017. Total funding in 2017 was over 3 times the size of 2016 funding. The Compound Annual Growth Rate (CAGR) in funding grew by a whopping 85% in the timeframe of interest (2012-2017).

This funding growth is substantial news on its own, but are there other metrics showing a growing interest in AI? Let’s see what the total number of deals looks like.

AI Deal Numbers Are Up Over 5 Years

The following graph shows us the annual number of AI startup funding deals, stacked by quarters.

Q4 2017 AI Funding - Annual Funding Count

Over the past five years, this graph shows an increased interest in AI via the number of deals in that startup ecosystem. Whereas the rapid growth in funding occurred in 2017, the rapid growth in deals occurred in 2014. The six-year CAGR on the number of deals stands at 23%. 2017 is also the highest year on record, beating the previous high in 2015 by around 2%.

So, we’ve seen that total funding and the number of deals are expanding, how about investor interest?

Investor Interest in AI is Growing

To gauge how investors are feeling, let’s look at the total number of AI investors who participated in each financing round. For example, if 5 investors participated in funding one company, and 4 investors participated in funding another, the total investor interest metric would be 9.

Q4 2017 AI Funding - Investor Participation

This graph clearly shows increased investor interest over the past few years. The six-year CAGR is 31%, and the 2017 total is 21% larger than in 2016 (the previous annual high-water mark).

Conclusion: Interest in AI is Exploding

In summary, we’ve seen significant growth in the total amount of AI funding. At the same time, the total number of AI funding deals has been steadily growing. Moreover, the investor interest in AI seems to be on a linear upward trend line. Taken together, we can say the AI sector is experiencing a rapid growth in interest over these past few years. It’ll be interesting to see if this trend continues into 2018, or if we travel into the “trough of disillusionment”.

What are your thoughts on this? Let us know in the comments section below.

To learn more about our complete Artificial Intelligence report and research platform, visit us at www.venturescanner.com or contact us at info@venturescanner.com.

Real Estate Technology Startup Highlights  – Q4 2017

Here is our Q4 2017 summary report on the Real Estate Technology (proptech) startup sector. The following report includes an overview, recent activity, and a category deep dive.

To learn more about our complete real estate technology (proptech) report and research platform, visit us at www.venturescanner.com or contact info@venturescanner.com

Insurance Technology Startup Highlights  – Q4 2017

Here is our Q4 2017 summary report on the Insurance Technology (insurtech) startup sector. The following report includes an overview, recent activity, and a category deep dive.

To learn more about our complete insurtech research platform, visit us at www.venturescanner.com or contact info@venturescanner.com

Artificial Intelligence Startup Highlights  – Q4 2017

Here is our Q4 2017 summary report on the AI startup sector. The following report includes an overview, recent activity, and a category deep dive.

To learn more about our complete AI research platform, visit us at http://www.venturescanner.com or contact us at info@venturescanner.com

Financial Technology Startup Highlights  – Q4 2017

Here is our Q4 2017 summary report on the financial technology startup sector. The following report includes an overview, recent activity, and a category deep dive.

To learn more about our complete financial technology research platform, visit us at www.venturescanner.com or contact us at info@venturescanner.com

Retail Tech Market Overview and Innovation Quadrant – Q3 2017

The following post highlights how Venture Scanner categorizes the Retail Technology startup landscape, and presents our Innovation Quadrant showing how those categories compare to one another. The data for this post is through September 2017.

Retail Tech Q3 2017 Logo Map

The above sector map organizes the sector into 22 categories and shows a sampling of companies in each category.

Retail Tech Q3 2017 Innovation Quadrant

Our Innovation Quadrant provides a snapshot of the average funding and average age for the different Retail Tech categories and how they compare with one another.

  • Heavyweights: These categories are comprised of companies that have reached maturity with significant financing.
  • Established: These categories are comprised of companies that have reached maturity with less financing.
  • Disruptors: These categories are comprised of companies that are less mature with significant financing.
  • Pioneers: These categories are comprised of companies that are less mature with earlier stages of financing.

The definitions of the Retail Tech categories are as follows

Automated Personalization Platforms: Companies that work with retailers to deliver custom ads, marketing messages, and dynamically optimize site pages for different users. Examples include platforms that allow A/B testing and platforms that tailor websites to each individual user’s specific tastes.

Coupons: Companies that focus on both traditional and digital merchant coupons.

Data and Analytics: Companies that help with the acquisition, organization, and distribution of data that companies can then utilize to enhance their applications and service offerings. Includes inventory management software.

Infrastructure and Enablers: Companies that provide tools designed to help developers increase functionality in their existing products. Examples include payment integration, native smartphone applications, and website development.

In-Store Experience: Companies that enable brick and mortar retailers to enhance the customer journey through digital engagement, mobile-first initiatives, gamification, and more.

In-Store Management: Companies that aim to improve the productivity of brick and mortar sales associates. Examples include productivity apps that track their effectiveness in-store as well as apps that provide them with insights to better do their jobs.

Last Mile Logistics: Companies that are innovating on the last phase of the supply chain, from the store/warehouse to the consumer.

Local Advertising Technology: Companies that alert the consumer of a retail product or service. The advertising models in the O2O market often center around targeted ads, real-time mobile ads, retargeting, dynamic ads based on proximity to clear inventory, ads targeted based on check-ins or social comments, and in-store up-sell ads.

Local Daily Deals: Companies that sell locally available, pre-paid vouchers for steeply discounted goods and services. This category also includes daily deal aggregators.

Local Incentives: Companies that help stores increase loyalty, customer base, and revenue from both new and repeat customers through deals, local offers, discounts, frequency rewards, gamified badges, and other techniques.

Loyalty Programs: Products that provide or power a merchant’s reward / loyalty program. Examples include digital frequent shopper cards, and tailored rewards based on spending.

Made-to-Measure Customization: Companies that use proprietary technologies and supply chain processes to enable shoppers to create custom goods. Examples include clothing fitted to exact specifications.

Marketing Platforms and Customer Relationship Management: Companies that enable merchants / brands to engage with their customers across social media channels, and execute and manage marketing campaigns. This category also includes customer relationship management tools used to improve customer communication, tracking, and overall relations.

Online to Offline Payments: Companies that are changing the way we pay for goods. In addition to payment execution, this also includes companies that provide consumers with a mobile wallet (e.g. payment information, loyalty cards) or other digital storage functionality (e.g. receipts).

Physical Store Analytics and Indoor Mapping: Companies that use sensors, cameras, and mobile devices to provide retailers more data about customer behavior in-store such as window conversion rate, customer dwell time, optimal shelf placement, and ideal store hours. These companies help retailers optimize the customer experience to increase revenue.

Point of Sale Payments: Companies centered around payment acquirers, providing physical payment solutions for brick-and-mortar businesses and organizations. Examples include mobile point-of-sales (POS) systems and POS innovations (e.g. QR code, palm scanners).

Price and Feature Comparison: Companies that empower consumers to compare product prices at different outlets or compare features across similar products (e.g. scan and engage capabilities for QR codes, bar codes, or physical items to bring up product information and comparisons in real-time).

Product Recommendation: Companies that use crowdsourced data, individual stylists, and/or automated algorithms to determine the best products for a given shopper based on their individual preferences.

Retail Augmented Reality: Companies that enable consumers to interact with products using augmented reality (e.g. virtual manipulation).

Retargeting: Companies that use cookie data to follow online users and serve dynamic, relevant ads all over the web.

Search and Local Availability: Companies that provide the means by which consumers can search and/or compare local availability of products and prices. This includes innovations such as store-level inventory searches and local comparisons.

Social Discovery: Companies that allow for discovery of products through social sharing and location check-ins. Examples include discovery social networks as well as platforms with integrated ecommerce functions.

We are currently tracking 1,670 Retail Tech companies in 22 categories across 58 countries, with a total of $51 Billion in funding. Click here to learn more about the full Retail Technology market report.

Internet of Things Funding Trends – Q3 2017

The following graphs highlight recent trends in Internet of Things (IoT) startup funding activity. The graphics include data through August 2017.

IoT Q3 2017 Funding by Year

The above graph summarizes the total funding raised by IoT startups for each year. 2014 has the most funding to date at just under $7B.

IoT Q3 2017 Vintage Year Funding

The above graph summarizes the total amount of funding raised by IoT companies founded in a certain year. Companies founded in 2012 have raised the most funding around $3.6B.

We are currently tracking 1,998 IoT companies in 20 categories across 52 countries, with a total of $39 Billion in funding. Click here to learn more about the full Internet of Things market report.