McKinsey releases its Technology Report 2023!

After a tumultuous 2022 for technology investment and talent, the first half of 2023 has seen renewed enthusiasm for the potential of technology for business and social progress. Generative AI (AI) has been instrumental in this renaissance, but it is just one of many advances that can drive sustainable, inclusive growth and solve complex global challenges.

 

To help business executives keep track of the latest developments, McKinsey's Technology Council has once again identified and interpreted today's most important technology trends; on July 20, 2023, McKinsey released its latest report, McKinsey's Technology Trends Outlook 2023.

 

 

In the report, the team analyzed and examined quantitative metrics of interest, innovation, and investment to measure the momentum of each trend. The underlying technologies, uncertainties, and issues surrounding each trend are also examined in depth, taking into account the long-term and interdependent nature of these trends.

 

Compared to the past, some trends have seen momentum and investment accelerate, while others have declined. One of these trends, Generative AI, is a new trend that has emerged with great momentum and has shown the potential for transformative business impact.

 

This new trend represents the next frontier in AI. Building on existing technologies such as applied AI and machine learning industrialization, generative AI has tremendous potential and applicability across most industries. Interest in this topic (in terms of news and internet searches) triples from 2021 to 2022. Generative AI and other foundational models are game-changers for AI by taking assistive technologies to the next level, reducing application development time, and bringing powerful capabilities to non-technical users. Generative AI promises to add up to $4.4 trillion in economic value by increasing productivity through a combination of specific use cases and broader uses, such as assisting with draft emails. Despite the enormous value that generative AI can unlock, companies should not underestimate the economic significance and growth potential that the industrialization of underlying AI technologies and machine learning can bring to a wide range of industries.

 

Investment in most tech trends has tightened year-over-year, but the potential for future growth remains high, as further demonstrated by the recent rebound in tech valuations. In fact, absolute investment in 2022 remains strong, totaling more than $1 trillion, demonstrating confidence in the value potential of these trends. Of the 14 trends over the last year, trust architectures and digital identities have seen the greatest growth, up nearly 50%: as security, privacy and resilience become increasingly important across a wide range of industries. Investment in other trends, such as applied AI, advanced connectivity, cloud and edge computing, declined, but this may be due, at least in part, to their maturity. More mature technologies may be more sensitive to short-term budgetary dynamics than emerging technologies with longer investment time horizons, such as climate and mobile technologies. In addition, as some technologies become more profitable, they can often be scaled up further with lower marginal investments. Given that these technologies are used in most industries, mainstream adoption will continue to grow.

 

Companies should not focus too much on the most talked about trends. Focusing only on the hottest trends may miss the tremendous value potential of other technologies and hinder purposeful capacity building. Instead, companies seeking long-term growth should focus on portfolio-oriented investments in the technology trends that are most important to their business. Innovations in technologies such as cloud computing, edge computing, and future bioengineering have grown steadily and continue to expand their use cases across industries. In fact, more than 400 edge use cases have been identified across industries, and edge computing is expected to earn double-digit growth globally over the next five years.

 

Additionally, emerging technologies such as quantum are evolving and show great potential for value creation. The latest analysis for 2023 shows that four industries - automotive, chemicals, financial services, and life sciences - may be the first to be economically impacted by quantum computing, and could be worth up to $1.3 trillion by 2035.

 

 

The McKinsey team described each trend by scoring innovation and interest, and also tallied investments and rated organizations on their level of adoption.

 

By carefully assessing the changing landscape and considering a balanced approach, organizations can leverage both established and emerging technologies to drive innovation and achieve sustainable growth.

 

The importance of talent as a key source of developing competitive advantage cannot be overemphasized. The lack of talent is the number one constraint to growth.

 

A survey of 3.5 million job postings in these technology trends reveals that many of the most in-demand technology areas have less than half the global average of qualified practitioners per job posting. Companies should be at the forefront of the talent market, ready to respond to significant changes and offer a strong value proposition to the tech professionals they want to hire and retain.

 

For example, recent layoffs in the tech sector may offer a silver lining for other industries that have struggled to win the attention of attractive job seekers and retain senior technical talent. Moreover, some of these technologies will accelerate the pace of workforce transformation. Over the next decade, 20 to 30 percent of a worker's work time could be altered by automated technologies, leading to a major shift in the skills needed to succeed. Organizations should continue to look at how they can adapt roles or improve individual skills to meet tailored job requirements. between 2021 and 2022, job postings in areas related to technology trends grew a very healthy 15%, despite an overall 13% decrease in job postings globally. between 2018 and 2022, there were nearly 1 million job postings in applied artificial intelligence and next-generation software development. million jobs. Next-generation software development has seen the most significant growth in the number of job postings.

 

 

Between 2021 and 2022, job postings in fields related to tech trends increased by 400,000 jobs. Generative artificial intelligence is the fastest-growing field, with an increase of 400,000 jobs between 2021 and 2022.

 

The bright outlook for practitioners in most fields underscores the challenges employers face as they struggle to find enough talent to meet demand. Shortages of qualified talent have been a limiting factor in many high-tech fields, including artificial intelligence, quantum technology, space technology, electrification and renewable energy. The talent shortage is particularly acute in the context of trends such as cloud computing and the industrialization of machine learning, which are in demand in most industries. Talent shortages are also a major challenge in areas that require highly specialized talent, such as the future of mobility and quantum computing.

 

 

The supply of skilled talent needed in most of the areas associated with these technology trends is low, and there is a surplus of talent in only a few areas.

 

The report lists considerations for all 15 technology trends, and they can be grouped into five broad categories: the AI revolution, building the digital future, computing and connectivity frontiers, cutting-edge engineering, and a sustainable world. There is, of course, tremendous power and potential across these categories when considering combinations of trends.

 

1) Quantum Technology

 

Quantum technology promises to utilize the unique properties of quantum mechanics to perform specific types of complex calculations with several times greater efficiency than traditional computers, secure communications networks, and provide a new generation of sensors that can be dramatically more sensitive than traditional sensors. In principle, quantum technologies can perform simulations and solve problems, thereby driving significant advances in a wide range of industries, including aerospace and defense, automotive, chemical, financial, and pharmaceutical. However, potential users of quantum technology should be prepared for an uncertain adoption roadmap, as there are still technical challenges to realize fully error-correcting quantum computers and scalable quantum communication networks.

 

Despite research advances over the past few years, quantum technology is still in its infancy and has received less attention than more mature technologies.

 

 

Quantum-based technologies can enable exponential growth in computational performance for certain problems and transform communication networks by improving the security of these networks. Despite research advances over the past few years, quantum technology is still in its infancy and has received less attention than more established technologies.

 

Here are some recent developments involving quantum technology:

 

- Hardware continues to advance.1 A few months ago, Google announced the first experimental demonstration that the quantum error rate can be reduced by increasing the number of physical quantum bits to form logical quantum bits. Many scientists see this as further proof that errors can be reduced enough to allow quantum computers to perform large-scale computations.

 

- The talent gap is still huge, but it may be shrinking. The shortage of quantum experts who can build devices and solutions using quantum technology could hinder its adoption. However, the situation improves slightly in 2022: McKinsey's research suggests that nearly two-thirds of open positions in the industry could be filled with new master's degrees in quantum technology, compared to only about one-third in 2021. Looking ahead, this gap is set to narrow further: the number of universities offering master's programs in quantum technologies nearly doubles in 2022.

 

- Increased attention to information security In July 2022, after a six-year competition, the National Institute of Standards and Technology (NIST) published the first set of four quantum-resistant encryption algorithms. Meanwhile, papers published in 2022 suggest that 2048-bit RSA encryption is increasingly risky, and that the number of quantum bits required to break RSA encryption has dropped by several orders of magnitude since 2015. As quantum hardware and algorithms improve, players are investing in quantum key distribution (QKD) and post-quantum security to keep data safe.

 

- Quantum scientist wins Nobel Prize in Physics. Quantum scientists Alain Aspect, John Clauser and Anton Zeilinger have been awarded the 2022 Nobel Prize in Physics for their work on entangled photons in the 1970s and 1980s. Their discoveries were applied to quantum communications, and several companies have since utilized similar technology to securely transmit information.

 

While the labor market for quantum technology is small, demand for talent has quadrupled since 2018. Due to the technology's nascent nature, the number of graduates in quantum programs is low. As a result, talent is coming from broader fields such as physics, math, electrical engineering, chemistry, biochemistry and chemical engineering.

 

Hiring is increasing for applied roles in quantum technology, such as business development managers and data engineers. If this trend follows the same pattern as more mature technologies such as artificial intelligence, we may see a rise in specialized positions such as quantum software engineers. Where the supply of technical talent is much lower, specialized skills such as cryptography and cryogenics are in shortest supply.

 

 

Real-world use cases for using quantum technology include the following:

 

- JPMorgan Chase, Toshiba, and Ciena demonstrated the feasibility of a metropolitan area QKD network that can detect and defend against eavesdroppers. JPMorgan Chase utilizes the QKD secure channel on the network to deploy and protect blockchain applications.

 

- Researchers at Amazon Web Services (AWS) implemented a peer-to-peer quantum security network in Singapore. The team connected two QKD devices using a production-grade fiber optic network.

 

- In early 2022, Robert Bosch GmbH, a major sensor developer, announced the creation of an internal division dedicated to the development and commercialization of quantum sensing technologies. The in-house startup will utilize existing quantum research to create new products with potential applications in the medical field.

 

- Quantum computing solved a complex design problem for a German automotive OEM in record time (6 minutes). The challenge was a 3854-variable optimization problem of configuring sensors for a specific vehicle to provide maximum coverage (i.e., detecting obstacles in different driving scenarios) at minimum cost.

 

Major uncertainties affecting quantum technology also remain: technical challenges include the ability to manage a sufficient number and quality of quantum bits over a long enough period of time to arrive at meaningful computational results; cost-effectiveness may take time. Most calculations required by businesses can be done well by conventional supercomputers at a much lower cost; this is expected to change once quantum dominance is achieved and general-purpose quantum computers take center stage.

 

For now, advances in quantum technology paint a promising future, but there are potential barriers to adoption (e.g., regulatory, technical, and financial) that may not yet be apparent.

 

The ecosystem is also just getting started. Only a handful of proven hardware platforms are commercially available on a small scale, and the pool of people skilled in quantum computing is extremely sparse; this may change as the technology matures and adoption increases.

 

Businesses and leaders may want to consider a few questions as they move forward with quantum technology:

 

- Will quantum technology be ready within the next decade?

- Will quantum technologies realize their full disruptive potential?

- How should organizations prepare for quantum technologies?

- Will the supply of talent keep pace with demand?

- What levers are available and how can companies help fill the talent gap?

 

2) Applied Artificial Intelligence

 

With AI capabilities such as machine learning (ML), computer vision, and natural language processing (NLP), companies across industries can use data and insights to automate processes, add or enhance capabilities, and make better decisions.

 

McKinsey research estimates the potential economic value of applied AI (AI) at between $17 trillion and $26 trillion, and the percentage of companies pursuing this value has been increasing. McKinsey's annual global survey on the state of AI shows that the percentage of surveyed companies adopting AI has more than doubled from 20 percent in 2017 to 50 percent in 2022. the 2022 survey also suggests that adopting AI can have significant economic benefits: 25 percent of respondents attribute 5 percent or more of their company's EBITDA to AI. However, there are organizational, technical, ethical and regulatory issues that need to be addressed before companies can realize the full potential of the technology.

 

 

Demand for talent in applied AI is growing rapidly, with job postings more than tripling since 2018. demand for data scientists and software engineers grows sharply in 2021, and shows moderate growth in 2022.

 

Demand for machine learning, data science, NLP and some related tools practitioners is high compared to supply.

 

 

Examples of real-world applications of AI include:

 

- Emirates Airline New Zealand used AI to train "digital twins" to test designs in a simulated environment, which greatly accelerated hydrofoil design and testing. By using AI to eliminate the bottleneck of testing by human sailors, the team reduced costs by 95 percent and was able to test ten times as many designs.

 

- Freeport-McMoRan deployed a customized AI model loaded with three years of operational data to optimize the copper plant's production process and total output. As a result, it increased production by 10 percent while reducing capital expenditures for a planned expansion.

 

- Telkomsel built a new data analytics platform complemented by AI-driven tools to better understand its customers across thousands of market segments. The company uses 9,000 data points per customer across more than 50 models to drive personalization by determining the right way to interact with customers and offer the most relevant products and services.

 

Regarding the future, there are several questions companies and leaders may want to consider as they move forward with applying AI:

 

- How can companies better determine which AI applications will best benefit the company and its stakeholders?

- What features make AI trustworthy and accountable?

- What checks should companies put in place to guard against AI risks related to data privacy and security, fairness, equity, and compliance?

- How can companies use generative AI in conjunction with applied AI to maximize potential synergies or differentiate when it makes more sense to use one approach over the other

 

3) Machine Learning Industrialization

 

Machine Learning (ML) Industrialization, often referred to as ML Operations or MLOps, refers to the engineering practices required to scale and sustain ML applications in an enterprise. These practices are supported and aided by an ecosystem of technology tools that is rapidly improving in both functionality and interoperability.MLOps tools can help organizations transition from pilot projects to viable business offerings, accelerate the scaling of analytics solutions, identify and solve problems in production, and improve team productivity. Experience has shown that organizations that successfully industrialize ML can reduce the time to production (from proof of concept to product) of ML applications by approximately 8 to 10 times and reduce development resources by up to 40%.

 

In this regard, scores for News, Search, Publications and Patents have increased significantly, while demand for talent has almost quadrupled over the same period. These increases suggest that the use of the ML Industrialization approach is likely to expand in the coming years.

 

Large-scale talent acquisition is a key factor in achieving scalable growth and implementing ML and AI. As AI adoption increases, job postings for related positions are also on the rise, nearly quadrupling since 2018 and growing 23.4% from 2021 to 2022.

 

Key positions needed to develop and implement industrialized technologies include data scientists, software engineers, data engineers, and ML engineers.

 

 

Companies that are expanding their ML initiatives need professionals with many technical skills, and technical skills such as Kafka and Hive are often in short supply. In addition, these professionals now require more software engineering (SWE) skills than ever before (e.g., data scientists must have stronger SWE skills to perform MLOps than they do to perform research experiments).

 

 

Businesses and leaders may want to consider several questions as they move forward with the industrialization of AI:

 

- Establishing industrialized ML in the enterprise requires upfront investment and resources.

- Processes and accountability are critical to maintaining industrial-scale ML solutions.

- Rapidly evolving markets require organizations to avoid vendor lock-in in order to realize value from newer offerings from companies outside the existing vendor ecosystem.

- Avoiding the possibility of mismatched capabilities requires ensuring that organizations are investing at the right level and in the right solution for their specific use case needs.

 

4) Generative AI

 

Generative AI is a turning point in AI. Unlike previous AI, it can create new, unstructured content such as text, audio, video, images, code, simulations, and even protein sequences or consumer journeys based on information learned from unstructured data in similar formats. Its core technology, the underlying model, can be applied to a variety of tasks; such as summarizing, categorizing, and drafting. In contrast, previous generations of AI models typically performed only one task.

 

In a business environment, generative AI can not only unlock new use cases, but also accelerate, extend or otherwise improve existing ones. Generative AI has the potential to redefine the enterprise and the value chain, facilitate the development of new products and revenue streams, and enhance the customer experience. However, its greatest impact is expected to be in improving employee productivity and experience.

 

In these early stages, we are seeing companies in many industries using generative AI primarily as an assistive technology to create first drafts, generate hypotheses, or assist experts in performing tasks faster and better. All of these uses have two things in common: there are experts in the loop to check the output, especially for illusions (inaccurate content generated by applications) and intellectual property (IP) issues, and they are used within existing workflows, which simplifies adoption and change management. It may be some time before organizations advance generative AI-based applications from assistive to fully automated for high-risk use cases.

 

Talent demand for generative AI has seen strong and accelerating growth since 2018. Hiring demand is likely to increase significantly in 2023 due to increased interest and investment.

 

 

Global enthusiasm for this trend has paved the way for companies to pilot it. Generative AI is witnessing significant investment activity. For instance, venture capital investments have increased by 425% over 2020, and Microsoft has signed a multi-year agreement with OpenAI for an investment of $10 billion. With nearly 80% of AI research now focused on generative AI, it's no surprise that companies in a wide range of industries, from financial services to life sciences, are beginning to experiment with enterprise use cases. We've also seen a range of startups that have successfully developed their own models - for example, companies such as Anthropic and AI21 have built and trained their own large-scale language models (LLMs).

 

In addition, others in the space, such as Cohere, have been able to offer higher levels of intellectual property protection, consumer privacy protection, and lower costs for LLMs that larger companies may wish to have in their environments. many others are building on the LLM platforms provided by others, or extending the open source modeling. In addition to these startups, tech giants such as Google are making strides.2023 In May, Google announced several new features powered by generative AI, including a search generation experience and a new LLM called PaLM 2, which will power Google products such as its Bard chatbot. We've also seen software providers like Salesforce invest heavily in integrating generative AI features into their existing products.

 

Significant advances have been made over its predecessor, the widely anticipated release of the GPT-4 heralded improvements in functionality and performance over previous models, such as improved scores on more than 30 academic and professional exams. the GPT-3 ranked in the bottom 10% of bar exam takers, while the GPT-4 ranked in the top 10%. the GPT-4 is now available in a variety of languages, including English, French, Spanish, Spanish, and Chinese. In addition, the GPT-4 can now process up to 25,000 words (compared to 4,000 on the GPT-3) using images and text as input, increasing the likelihood of generating accurate answers by 40 percent. Sophisticated applications have also been enabled, such as the use of multimodal input (e.g., text and images) and the coordination of a series of actions to accomplish a task, such as designing a new recipe (e.g., through applications such as AutoGPT and BabyAGI).

 

Large cloud computing and technology companies are beginning to be active in the area of hardware gas pedal design. For example, Google has developed a fourth-generation Tensor Processing Unit (TPU v4), which improves system performance by a factor of about ten compared to previous versions.

 

5) Next Generation Software Development

 

Next-generation software development technologies are transforming the capabilities of engineers at all stages of the software development life cycle (SDLC): from planning and testing to deployment and maintenance, and enabling more non-technical people to create applications. They can help simplify complex tasks and streamline others into a single command.

 

These technologies include AI pairing programmers; low-code and no-code platforms; infrastructure-as-code; automated integration, deployment, and testing; and emerging generative AI tools. Adoption of these technologies is likely to be slow due to technical challenges, the need for massive retraining of developers and test engineers, and other organizational barriers.

 

However, the dramatic productivity gains seen in early trials suggest that widespread adoption is just around the corner.

 

 

Real-world examples of the use of next-generation software development are listed below:

 

- Netflix built Netflix Test Studio (NTS) to support a seamless streaming experience across multiple types of devices.NTS is a cloud-based automation framework for internal and external developers to deploy and execute tests. It abstracts device differences and has a standard set of tools for evaluating performance.NTS runs more than 40,000 long-term tests per day and allows remote testing of Netflix-ready devices.

 

- Citi is investing in several tools for next-generation software development. For example, the company is a customer and investor in Genesis, a low-code software development company whose platform handles direct automation use cases, including end-user computing (EUC) and customer service portals. Citibank has also been working with Temenos, a provider of core banking systems, for more than a decade, whose software has improved time-consuming, repetitive accounting and reporting tasks.

 

- Ticketmaster's mobile development team began using GitLab's CI tools because small software changes were taking too long to implement. With GitLab's CI tool, the team reduced build time from two hours to eight minutes.

 

6) Trust Architecture and Digital Identity

 

Digital trust technologies enable organizations to manage technology and data risks, accelerate innovation, and protect assets. In addition, building trust in data and technology management increases organizational performance and improves customer relationships. Foundational technologies include Zero Trust Architecture (ZTA), digital identity systems, and privacy engineering. Other technologies help build trust by incorporating principles of interpretability, transparency, security, and bias minimization in AI design. However, the adoption of digital trust technologies is hindered by a number of factors, including integration challenges, organizational silos, lack of talent, and limited consideration of them as a key component of the value proposition.

 

Building a comprehensive 'trust first' risk mindset and capability will require top-down leadership and thoughtful change across multiple areas of activity, from strategy and technology to user adoption.

 

While investment in trust architecture and the digital identity enterprise grows approximately fivefold from 2018 to 2022, and demand for talent grows dramatically, other momentum scores are mixed.

 

 

Hiring positions increased by 16 percent from 2021 to 2022 and by an average of 39 percent from 2018 to 2022. security analysts are in highest demand between 2021 and 2022, while network engineers and software engineers have the highest growth rates in demand.

 

Computer security, risk and regulatory compliance are the most in-demand skills. Specific branches of trust architecture, such as interpretable artificial intelligence, will require skills from the specialized branch of artificial intelligence.

 

 

 

7) Web 3

 

Web3 includes a variety of platforms and applications that aim to enable a shift to a future decentralized Internet through open standards and protocols while protecting digital ownership. This is not simply a cryptocurrency investment, but a transformative way of designing software for specific purposes. This shift has the potential to provide users with greater ownership of their data and give rise to new business models.

 

Web3 goes beyond the typical understanding of cryptocurrency investment: it refers more importantly to a future Internet model that decentralizes and redistributes power to users, potentially giving them more control over how their personal data is monetized and stronger ownership of digital assets. In addition, it offers a range of possible business opportunities: new business models managed by decentralized autonomous organizations (DAOs) and eliminating intermediaries through secure (smart contract) automation, new services involving digitally programmable assets, and new data storage and management using blockchain technology. web3 has attracted significant funding and engineering talent, but new ventures are still testing and scaling viable business models. Existing organizations also continue to explore best use cases for Web3.

 

Early adopters face a number of challenges, including unclear and evolving regulations and immature emerging technology platforms whose user experience is often inferior to existing Web2 utilities. However, organizations are beginning to experience success with Web3 pilots, including new user engagement models and financial products.

 

8) Advanced-connectivity technologies (Advanced-connectivity)

 

Improvements in advanced-connectivity technologies will enhance the user experience for consumers around the world and increase productivity in industries such as mobile, healthcare and manufacturing. Enterprises have already rapidly adopted advanced-connectivity technologies that build on existing deployment and connectivity standards, but some upcoming newer technologies, such as low-Earth orbit (LEO) connectivity and dedicated 5G networks, face a number of hurdles that will need to be addressed to increase adoption.

 

Recent developments include:

 

- Increasing integration of various connectivity technologies. With a variety of connectivity solutions, such as Wi-Fi, cellular, and satellite, available for different use cases, attention is beginning to focus on how they can be integrated into a seamless customer experience. Large companies such as Apple and T-Mobile are investing in integrating satellite connectivity into existing products (e.g., integrating emergency distress features into the iPhone 14).

 

- Telcos are struggling to monetize 5G for consumers, and industrial applications are growing slower than expected. While 5G's Application Programming Interfaces (APIs) give telcos the ability to monetize 5G for consumers, adoption has been slow because consumer use cases that rely on advanced connectivity have not yet reached scale. Many industrial companies have chosen to wait for 5G private network adoption for reasons that include complexity, lack of understanding of the benefits and management of cellular technology, deployment costs, and the nascent state of end-to-end use cases. the 5G private network market is picking up, with lighthouse deployments taking place in a wide range of industries, such as manufacturing, logistics, utilities, and a number of other sectors.

 

- The fiber market is growing and beginning to consolidate. Following the initial success of fiber optic networks in the 2010s, deal activity and company valuations have increased over the past few years. A number of smaller fiber companies have been formed in recent years, but the market is now moving towards consolidation with a significant increase in M&A activity, particularly in Europe.

 

9) Immersive Virtual Technology

 

Immersive virtual technologies utilize spatial computing to interpret physical space; simulate the addition of data, objects, and people to real-world environments; and enable interaction in virtual worlds through the varying degrees of immersion provided by augmented reality (AR), virtual reality (VR), and mixed reality (MR).

 

In 2021, venture capital investors provided approximately $4 billion in funding for AR and VR startups, making it the second most successful funding year behind 2018. While total investment in AR and VR declined in 2022, investors are still showing strong interest in the trend: at least seven investment rounds of $100 million or more were completed last year.

 

Research suggests that by 2030, the emerging "meta-universe" could be worth as much as $4 trillion to $5 trillion in consumer and enterprise use cases. While interest and investment levels have remained steady over the past few years, innovation and demand for talent have been on the rise.

 

Job postings for immersive virtual have more than doubled since 2020, with slight growth between 2021 and 2022. The sector brings together a wide range of technical, creative and management professionals, with high demand for positions in software, hardware, design, program and project management, and scientists.

 

 

While skills such as graphic design, computer vision and 3D modeling are more available in the market, the supply of product design, product engineering and video game development professionals is lower.

 

10) Cloud and Edge Computing

 

In the future, businesses will leverage infrastructure footprints involving compute and storage at multiple points of location, from on-premises to closer to on-premises (edge), and from small regional data centers to remote hyperscale data centers. Edge computing unleashes a variety of new use cases by providing organizations with greater flexibility to process data faster (ultra-low latency) and closer to home, as well as enabling data sovereignty and enhanced data privacy, compared to cloud computing. Shorter distances to end-users will reduce data transfer latency and costs, and enable faster access to more relevant data sets, which helps organizations comply with data residency laws. Public clouds will continue to play a key role in the future of the enterprise, executing non-time sensitive computing use cases with better economies of scale. The continued integration of cloud and edge resources will allow users to extend the innovation, speed and agility of the cloud to edge and real-time systems to accelerate innovation, increase productivity and create business value.

 

Cloud and edge computing have become core technologies for many digital solutions, and attention is growing on all fronts from 2018 through 2022.

 

 

Here are some of the latest developments involving cloud and edge computing:

 

- While organizations continue to migrate to the public cloud, ballooning costs and issues related to data privacy and latency have caused the migration to slow down. In some cases, however, organizations are "migrating back" from the cloud, and a recent study by the Uptime Institute Global Data Center found that approximately 33 percent of respondents had already migrated from the cloud to a data center or co-location facility. However, only 6% of these "back-movers" have abandoned the cloud altogether. The majority of respondents are taking a hybrid approach - using both on-premise and public cloud options.

 

- Edge computing continues to attract investment. Edge computing continues to attract investment, with more than 400 identified edge use cases across industries and double-digit growth expected in edge computing globally over the next five years. With edge computing, data can be processed where it is generated, allowing for efficient data analysis and business decisions to be made with greater speed and accuracy.

 

- Cloud computing continues to see increased adoption in high performance computing and artificial intelligence/machine learning (ML). To capitalize on the growth of these workloads and optimize hardware, cloud providers are not only relying on partnerships, but also investing in in-house silicon designs (e.g., Google's Tensor Processing Unit and Amazon Web Services' Nitro system).

 

- Hyperscale organizations are increasingly focused on sustainability. Google has announced a full transition to 24/7 carbon-free energy by 2030, and Microsoft has committed to a 100% renewable energy supply by 2025.

 

After being nearly flat from 2018 to 2020, cloud computing jobs see significant growth in 2021, but slow down in 2022. Cloud and edge job postings are particularly high for software engineers and network engineers, while non-technical project and program jobs see moderate growth relative to 2021; there is a relative shortage of most of the technical skills required for cloud computing, such as DevOps, Kubernetes, and Terraform.

 

 

11) The Future of Transportation

 

More than a century after automobiles began mass production, mobility is at its second major inflection point: the shift to autonomous driving, connectivity, vehicle electrification, and shared mobility (ACES) technologies.

 

This shift promises to disrupt markets while improving the efficiency and sustainability of the transportation of people and goods by land and air.ACES technologies have seen increasing adoption over the past decade, and the pace is accelerating as sustainability measures tighten, consumer preferences evolve, and innovations advance. For example, automated driving technologies are expected to generate up to $400 billion in revenue by 2035.

 

However, challenges remain in the near term as innovators grapple with technical, regulatory, and supply chain issues, such as delays of up to six months in the introduction of certain vehicles.

 

Interest, investment, and innovation initiatives in ACES technology have roughly doubled in the past four years, and demand for talent has grown even higher, signaling advances in new solutions and broader adoption of existing ones.

 

 

These are some of the latest developments involving the future of transportation:

 

- Leading companies are scrambling to be the first. Robotaxi and Roboshuttle have announced that they will expand their operations in 2022 and make strategic deals to "acquire" talent. Meanwhile, the microtransit and mini-transit4 industries are expected to reach $440 billion and $100 billion, respectively, by 2030.

 

- The global automotive software market is also expected to grow at a rapid CAGR of 5.5% from 2019 to 2030. Urban and advanced air transportation is similarly moving forward, with leading electric vertical take-off and landing (eVTOL) players aggressively pursuing significant certifications by the mid-2020s.

 

- Automotive suppliers face continued margin pressure. in 2022, margin pressure on automotive suppliers accelerates. Nearly all suppliers are impacted by rising utility, natural gas and power costs and shortages, with 50 percent of them severely affected. In addition, semiconductor shortages, supply base consolidation, rising raw material and freight costs, and sporadic fluctuations in vehicle production are also concerns.

 

We believe that the future of efficient, sustainable transportation will be defined by ACES and neighboring technologies such as the following:

 

- Autonomous technologies. Automated systems with sensors and artificial intelligence can make independent decisions based on collected data.

 

- Connected Vehicle Technologies. Devices, applications and systems utilize vehicle-to-vehicle communication technologies to improve safety and efficiency. These solutions replace vehicle components that use traditional energy sources with components that use electricity.

 

- Shared Transportation Solutions. Hardware and advanced digital solutions, as well as new business models and social adoption, enable the use of alternative shared transportation in addition to, or instead of, privately owned vehicles.

 

- Materials Innovation. The use of new materials (e.g., carbon fiber and other lightweight materials) and new processes (e.g., engine miniaturization) can improve efficiency and sustainability.

Value chain decarbonization. In addition to electrification, technology levers (e.g., green primary materials) can reduce emissions from material production processes and can increase the use of recyclable materials.

 

12) Bioengineering the future

 

Breakthroughs in biotechnology, combined with innovations in digital technologies, can help companies meet the needs of sectors as diverse as healthcare, food and agriculture, consumer goods, sustainability, and energy and materials production by creating new products and services.

 

McKinsey's research suggests that 400 use cases for bioengineering - almost all of which are scientifically viable today - could have an economic impact of between $2 trillion and $4 trillion annually from 2030 to 2040.3 The economic impact of bioengineering is also likely to be greater than that of other technologies, such as biotechnology. While certain gene therapies and bioproducts have gained acceptance, ethical, regulatory, and public perception issues need to be addressed to realize the full economic potential of bioengineering.

 

13) Future Space Technology

 

The most important development in the space industry over the past five to ten years has been the decline in the cost of technology, which has made new capabilities and applications more readily available. Reductions in the size, weight, power, and cost of satellites and launch vehicles have strongly contributed to reductions in component costs. These reductions have led to changes in system architecture, such as the shift from individual, large geosynchronous-equatorial orbit (GEO) satellites to small, distributed low-Earth orbit (LEO) satellites, as well as a growing interest in space technology by traditional non-space companies.

 

Currently, there is significant use of space technology and remote sensing and analysis techniques, and analyses indicate that the space market could exceed $1 trillion by 2030.1 The future space economy may include activities that are not currently being pursued on a large scale, such as on-orbit manufacturing, power generation, and space mining, as well as scalable human spaceflight.2 The following are some of the most recent developments involving space technology.

 

Below are some recent developments involving space technology:

 

- The private sector is leading the resurgence of lunar activity. ispace, a Japanese company that went public in April 2023, and the U.S.-based Astrobotic Technology are just a few of the private companies competing to launch lunar landers. Although ispace's December 2022 launch was unsuccessful, the company is already planning another lander launch in 2024. astrobotic recently postponed its May 2023 launch plan. The company that eventually launches a successful lander will be the first to send a private spacecraft to the moon.

 

- A watershed year for launch vehicles is approaching. Several high-profile new launch vehicles are expected to debut in 2023 and 2024, including SpaceX's super-heavy Starship, designed to carry larger payloads; United Launch Alliance's Vulcan Centaur, designed to put satellites into orbit; and Blue Origin's Vulcan Centaur. orbit; and Blue Origin's New Glenn, which will carry some of Amazon's Project Kuiper satellites. These launch vehicles will help cope with the expected increase in demand for launch capacity.

 

- Interest in space is also growing in other sectors. There is increasing consideration of space applications beyond the aerospace industry. Hitachi Energy has developed satellite vegetation management for utilities to better manage and respond to the effects of storms on surrounding vegetation (e.g. wildfires caused by power lines during storms).

 

- Direct-to-Device Satellite Coverage Gets Investment In 2022, Apple added emergency satellite coverage to its iPhone 14 through a partnership with Globalstar. The integration of the emergency distress feature has been in the news for successful rescues. Other companies, including Amazon and T-Mobile, have plans to provide satellite coverage to be used in emergencies or to reach areas of the world not normally covered by the service.

 

14) Electrification and Renewable Energy

 

Electrification and renewable energy can help drive towards net-zero commitments, including solar, wind, hydro and other renewable energy, nuclear, hydrogen, sustainable fuels, and electric vehicle charging.

 

Future growth in energy investment will be driven almost entirely by renewable energy and decarbonization technologies. To accelerate the energy transition, significant investments will be needed in all sectors, and expected returns will depend to a large extent on context, especially in the area of traditional energy sources.

 

 

Global energy investments

 

15) Climate technologies beyond electrification and renewables

 

Climate technologies include carbon capture, utilization and storage (CCUS), carbon removal, natural climate solutions, recycling technologies, alternative proteins and agriculture, water and biodiversity solutions and adaptation, as well as technologies to track net-zero progress.

 

To achieve net-zero emissions, the world will need to innovate, deploy, and scale up technologies at an unprecedented rate.

 

To implement these technologies at scale, policymakers need to be clear about how they work: the science, capital investment requirements, economies of scale, price, regulations, environmental impacts, and more. They need to understand where these factors currently stand and how they may change over time. This is a challenge for this evolving category, where deployments are relatively small and technology advances are occurring at an alarming rate.

 

Reference Link:

https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-top-trends-in-tech#tech-trends-2023