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Emerging technologies outlook: Chapter-4

In this chapter we will review emerging technologies into IT and Material & Resource sectors:


4D printing

4D printing utilizes special materials and sophisticated designs that are programmed to prompt the 3D printed object to change its shape post-production. Companies in this space are developing 4D printing technologies and relevant materials such as chemicals, electronics, particulates or nanomaterials. 

Example: Autodesk


AIOps refers to the use of big data analytics and artificial intelligence (AI) to provide visibility into the state and performance of the IT systems that businesses rely on. ​​​​​​

​​​​​​​Example:​​​​​​​ Broadcom, IBM

Autonomous delivery

Autonomous delivery involves the use of ground and air-based drones to facilitate last mile delivery in urban areas or other confined areas. These companies hope to save costs on human capital by managing a fleet of autonomous robots to complete deliveries.

​​​​​​Example:​​​​​​​ Nuro

Autonomous vehicle simulation

Autonomous vehicle simulation focuses on virtually simulating road, traffic and vehicle conditions in order to develop self-driving cars.

Example: Lyft

Cloud gaming

Cloud gaming is the hosting of video game-related content for a subscription fee, effectively decentralizing the local hardware normally required to play video games into the cloud. Though the technology requires a stable and fast internet speed, it has the potential to expand the gaming market.

Example: PlayGiga

Cloud workload protection

Cloud workload protection platforms enable threat detection and codebase hardening for applications in container-based, serverless and virtualized environments.


Cognitive computing

Cognitive computing companies develop platforms built on self-learning algorithms that use data mining, pattern recognition and natural language processing to mimic the way the human brain works.

Example: Afiniti

Computational storage

Computational storage is a new IT architecture whereby compute functions are added to the data storage layer, rather than moving the data up to the host CPU for processing as in traditional computing. 

Example: Xilinx

Crowdsourced testing

Crowdsourced testing is an emerging type of software testing that leverages a large group of participants to remotely test websites, mobile apps and software. Using this approach, software can be tested from a variety of perspectives which makes it more reliable, cost-effective, fast and bug-free. 

Example: Applause

Database sharding

Database sharding is a type of relational database architecture that partitions databases into smaller chunks that can be spread across multiple servers and stitched together more easily than other monolithic databases. 

​​​​​​​​​​​​​​Example: NuoDB


DevSecOps encompasses platforms that enable software developers to embed security protections within their code, test their code’s vulnerabilities on a regular basis and deploy application updates securely.

​​​​​​Example:​​​​​​​ Snyk

Edge computing semiconductors

Edge computing semiconductor companies are developing novel semiconductor architectures enabling artificial intelligence and machine learning (AI and ML) inferencing in edge devices. Edge computing semiconductors can be used in battery-powered and wired edge devices, as well as some datacenter applications.

Example: Kneron


FinOps is the integration of financial measuring software into the application development process. Companies in this space develop platforms designed to access and analyze the costs of cloud services, enabling firms to better plan, budget and forecast consumption-based spending on cloud resources.

​​​​​​Example:​​​​​​​ Cloudability

Generative AI

Generative AI is the use of artificial intelligence (AI), statistics and probability in applications to produce a representation or abstraction of observed phenomena. This space is expected to make contributions auto programming, content development, visual arts, and other creative, design, and engineering activities.

​​​​​​​Example:​​​​​​​ Deeptrace

IoT security

IoT Security includes platforms that are designed to safeguard connected devices and networks. Platforms in this space increase the visibility of distributed assets and enable security policy enforcement at the network level.

Example: Vectra


LiDAR is a surveying method that measures distance by sending out beams of laser light and measuring the reflections with a sensor. Autonomous vehicles are a key end-market use case for this technology.

Example: Velodyne LiDar

Next-gen network security

Next-gen network security encompasses software-based secure networks that protect expanding enterprise perimeters. Companies in this space develop platforms for software-defined wide-area networking security, browser isolation and secure web gateways.



Post-quantum cryptography

Post-quantum cryptography (PQC) techniques use software algorithms to encrypt messages on standard computers in a manner that is resistant to being broken by quantum computers. Companies in this space develop software and devices with encryption protocols that do not rely on the use of discrete logarithms.

Example: ISARA

Quantum computing

Quantum computers utilize quantum-mechanical phenomenon to encode information into quantum states and process vast numbers of calculations simultaneously. Companies in this space develop and produce quantum computers, components and related emerging technologies.

Example: D-Wave

Robotic process automation

Robotic process automation is when an algorithm or computer software performs actions usually carried out by a human to complete rule-based tasks. 

Example: UiPath

Security orchestration, automation and response (SOAR)

SOAR platforms utilize disparate technologies to gather data and security alerts from different sources and automate responses to security log data to remediate breaches. SOAR replaces slow, manual analyst intervention in conventional incident response processes with machine-speed decision making.

Example: Splunk

Service mesh

Service mesh is a software infrastructure solution that allows firms—who face increasing complexity in monitoring and securing independent services—to better manage microservices.

​​​​​​​Example:​​​​​​​ HashiCorp

Swarm AI

Swarm AI companies algorithmically analyze a group of real-time human inputs to optimize decisions or make predictions. The approach is based on swarm/hive mind behaviors in nature.

Example: ArkRobot


TinyML refers to the development of machine learning algorithms capable of performing on-device sensor data analytics at extremely low power. Today, much sensor data is ignored due to cost, bandwidth, or power constraints. Some applications include mobility sensors ingesting real-time traffic data to reduce congestion, monitoring retail shelves and sending immediate alerts for restocking and monitoring livestock health to prevent disease.

Example: Xnor.ai


V2X (connected-vehicle-to-everything communication) technologies allow vehicles to communicate with the traffic system around them. It represents the next evolution of autonomous vehicles, where vehicles are not just observing their surrounding environment—but also communicating with it.

Example: Valens

Materials and Resources

Cellular agriculture

Cellular agriculture is the production of agriculture products from cell cultures to design and create new methods of producing proteins, fats, and tissues that would otherwise come from traditional agriculture. 

Example: Impossible Foods

Desalination tech

Desalination technologies focus on increasing efficiency or cost-effectiveness of desalinating ocean water into fresh water. With the increasing scarcity of access to fresh water, desalination technologies may be crucial to supporting populations in drought-prone areas.

Example: Gradiant

Livestock health

Livestock health companies support farmers and livestock caretakers through solutions dedicated to animal monitoring, genomics, breeding, feeds and pharmaceuticals.

Example:​​​​​​​ Caribou Biosciences

Smart waste management

Smart waste management companies develop tech-oriented solutions that improve the efficiency and effectiveness of traditional waste management, including waste bins with sensors, database management and logistics platforms, and robots and computer vision systems that sort trash and recycling.

Example: Olio

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