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EMERGING TECH RESEARCH

Artificial Intelligence & Machine Learning Overview

Industry and taxonomy update with latest VC activity

2023

REPORT PREVIEW

The full report is available through the PitchBook Platform.

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Vertical overview 3
VC activity 5
AI & ML landscape 6
AI & ML VC ecosystem market map 7
Segment overview 11
Horizontal platforms 12
Vertical applications 17
Autonomous machines 23
AI & ML semiconductors 26
Appendix 31
Institutional Research Group
Analysis
Brendan Burke Senior Analyst, Emerging Technology
brendan.burke@pitchbook.com
pbinstitutionalresearch@pitchbook.com
Data
Matthew Nacionales Data Analyst
Publishing
Report designed by Julia Midkiff and Joey Schaffer
Published on April 10, 2023

AI & ML landscape

Horizontal platforms

Vertical applications

Semiconductors

Autonomous machines

AI & ML VC ecosystem market map

Click to view the interactive market map on the PitchBook Platform.

Market map is a representative overview of venture-backed or growth-stage providers in each segment.
Companies listed have received venture capital or other notable private investments.

Horizontal platforms

Vertical applications

· Al automation platforms

Al core

Consumer

Computer vision

· Financial services

Healthcare

Natural language technology

Autonomous vehicles

Autonomous machines

Industrial

Information technology

Transportation

Intelligent robotics

Semiconductors

Edge Al software

\ SiMa

NeuroBlade

celona

Clobotics

decl。

三 EDGE IMPULSE

DLITMUs

· Intelligent sensors & devices

dreame

TERMINUS 特斯联

LeddarTech'

Aqara

V Verkada

人 Density ECHODYNE

sense

Processor design

紫光展锐

GRAPHCORE

BITM∧IN

壁仞科技 TECHNOLOGY

Cerebras

LEntame 健原科技

groq

VC activity

2022 challenged the highly funded AI vendors that are unlikely to drive the field’s future. VC funding for the vertical declined more than for IT overall, falling 34.9% in VC deal value to \$78.0 billion. The median venture growth valuation step-up declined below the overall VC median, to 1.3x, demonstrating the prevalence of flat rounds and outlying down rounds faced by leading vendors such as Dataiku and Noodle.ai. As with global IT, 2022 deal value exceeded 2020’s total. Deal count also declined more than for IT, at 13.2%, as early-stage deal count regressed even as late-stage deal count remained high. The sector experienced deal count declines of over 50% in autonomous vehicles, processor design, automation platforms, and intelligent sensors as investors avoided hardware- and labor-intensive businesses. Consumer AI proved to be the most resilient vertical application, declining only 20.5% in VC funding year-over-year, as consumer interest in AI applications emerged.

VC exit count continued to decline in Q4, though deal value rebounded due to significant listings in China. We tracked \$7.0 billion in VC exit value in Q4—up from Q3, but the second-lowest value we have tracked since Q2 2020. Exit count has remained above pre-pandemic levels. Among tech giants, active acquirers included Meta, Oracle, and Spotify. PE found opportunities in generative AI, with growth equity PSG using generative copywriting startup Copywriter AI as a platform to add on two writing startups in Frase and Rytr, forming a new platform called Copyrytr. This platform play gives an early look at the synergies of generative AI startups as they proliferate in different use cases. AI copywriting leader Jasper has also become an acquirer as part of the company’s strategy to deliver a unified content suite across formats.

Median AI & ML VC pre-money valuation step-up by stage

Source: PitchBook | Geography: Global | *As of December 31, 2022

Source: PitchBook | Geography: Global | *As of December 31, 2022

Vertical applications

Overview

Vertical applications in AI & ML address specific problems within industries and are not always AI first. Many startups in this category design a solution to an industry problem using software and integrate AI & ML to optimize some part of their product. These solutions typically differentiate based on the quality of the dataset used to train the industry-specific model and the industry expertise of the data scientists identifying decision-making areas that can be enhanced by AI & ML models. As a result, many of these startups help automate specific functions within their industry but have limited ability to cross apply their AI & ML to other industries.

Current subsegments include:

• AI & ML in financial services: Includes technologies that embed AI & ML into existing financial services via advanced analytics, process automation, robo-advisors, and self-learning programs. Product categories include financial chatbots, intelligent banking, lending analytics, payment optimization, predictive underwriting, and robo-advisors.

• AI in healthcare: Includes technologies that leverage AI & ML to improve medicine and the provision of care. Product categories include AI-based drug discovery, clinical decision support, genetic analytics, healthcare administration, and personal health.

• Consumer AI: Includes technologies that use AI & ML to enhance B2C business models. Product categories include AI in media & entertainment, AI & ML advertising technology, digital avatars and gaming, e-commerce recommendation engines, education technology, intelligent price optimization, and smart retail.

• Industrial AI & ML: Includes technologies that automate industrial processes and unlock industrial data to find new efficiencies. Product categories include crop maximization, energy grid automation, geospatial analysis, heavy industry automation, IoT predictive analytics, supply chain optimization, and telecommunications optimization.

• AI in IT: Includes enterprise software tools that optimize specific functions typically administered by IT departments, including both back-end and front-end use cases. Product categories include human resource automation, information security (infosec) automation, IT infrastructure management, legal automation, property technology, sales & marketing automation, and software development tools.

• AI in transportation: This category includes intelligent fleet management and driver assistance. It excludes autonomous vehicles.

Industry drivers

Growing availability of industrial datasets: Startups can build preliminary models based on data and frameworks from sources including Google Dataset Search, Kaggle, UCI Machine Learning Repository, Microsoft Coco, and GitHub. Governments support data sharing, as evidenced by President Biden’s Executive Order on Enhancing Safeguards for United States Signals Intelligence Activities.5 This order codifies the EU-US Data Privacy Framework for secure data sharing for AI training.

Enterprise customers are achieving ROI with AI solutions: McKinsey’s State of AI survey found that over 40% of AI adopters in service operations, manufacturing, human resources, risk management, and sales & marketing achieved cost savings as a result of their AI projects in 2021.6 Only around 50% of these adopters reported cost savings the prior year.

5: “Executive Order on Enhancing Safeguards for United States Signals Intelligence Activities,” The White House, October 7, 2022.
6: “The State of AI in 2022—and a Half Decade in Review,” McKinsey Institute, December 6, 2022.

VERTICAL APPLICATIONS

Market size

We forecast that the vertical applications market reached \$94.O billion in 2O22,with a 31.2% CAGR out to 2O25,resulting in a \$212.2 billion market. The segment is weighted heavily toward enterprise IT applications,which we view as a \$28.6 billion market,led by sales,infosec,and human resources.We forecast IT to grow to a \$65.4 billion market by 2O25 at a 31.7% CAGR. We estimate industrial Al to be the second-biggest category,at \$16.3 billion, focused primarily on manufacturing automation,supply chain optimization,and predictive maintenance. Both the consumer Al and Al in financial services markets exceed \$1O billion. Healthcare is a smaller market,at \$4.1 billion,given barriers to adoption for third-party Al solutions. This estimate has been revised upward due to new data on spending for digital assistants and media augmentation.

Business model

The business models of enterprise Al vendors can vary widely and have considerable influence on their cash flow profiles.Software Al startups employ several key business models,including:

·SaaS: A monthly subscription that bundles data engineering,model building,and deployment. Additional setup and maintenance fees may be layered on top of a base package. Applications can be delivered as a cloud service through channel partners.In most cases, managed services are required via the vendor or a third-party consulting firm.

·Codevelopment agreements: An Al-first vendor partners with an industry specialist to codevelop an Al solution to be used by the industry specialist as a customer and licensed to the industry specialist for distribution. Often,this includes an upfront payment to the Al-first vendor

Source: PitchBook Emerging Tech Research | Geography: Global | *As of March 31, 2023

AI & ML VC deal value (\$M) by stage

Source: PitchBook | Geography: Global | *As of December 31, 2022

AI & ML VC deal count by stage

Source: PitchBook | Geography: Global | *As of December 31, 2022

About PitchBook Emerging Tech Research

Independent, objective and timely market intel

As the private markets continue to grow in complexity and competition, it’s essential for investors to understand the industries, sectors and companies driving the asset class.

Our Emerging Tech Research provides detailed analysis of nascent tech sectors so you can better navigate the changing markets you operate in—and pursue new opportunities with confidence.

©2023 by PitchBook Data, Inc. All rights reserved. No part of this publication may be reproduced in any form or by any means—graphic, electronic, or mechanical, including photocopying, recording, taping, and information storage and retrieval systems—without the express written permission of PitchBook Data, Inc. Contents are based on information from sources believed to be reliable, but accuracy and completeness cannot be guaranteed. Nothing herein should be construed as any past, current or future recommendation to buy or sell any security or an offer to sell, or a solicitation of an offer to buy any security. This material does not purport to contain all of the information that a prospective investor may wish to consider and is not to be relied upon as such or used in substitution for the exercise of independent judgment.

PitchBook Data, Inc.

John Gabbert Founder, CEO Nizar Tarhuni Vice President, Institutional Research and Editorial Paul Condra Head of Emerging Technology Research

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