In the first half of 2023, the emergence of generative AI/ChatGPT ignited our enthusiasm for a new generation of technology. Of course, there is constant good news for mankind in many fields of science and technology. Generative AI, together with many other advanced technologies, is expected to promote sustainable and inclusive growth and solve various complex challenges around the world.
Last month, McKinsey & Company released the "McKinsey Technology Trends Outlook 2023" report.
To assess the momentum of each trend, the report examines quantitative indicators such as interest, innovation and investment . Given the long-term nature and interdependence of these trends, McKinsey also An in-depth look at the underlying technology, uncertainties, future challenges, and more behind each trend.
Different from previous years, cloud and edge were selected into the McKinsey report, and McKinsey added an important new analysis dimension—talent.
The importance of talent goes without saying – talent shortage is the number one enemy of innovation and growth.
The data shows that compared with 2021, global job postings will overall decrease by 13% in 2022, but job postings in fields related to the 15 technology trends mentioned in the article will increase by 15% in 2022 . McKinsey surveyed 3.5 million job postings across these technology trends and found that for many of the most in-demand skills, the number of qualified practitioners per role is less than half the global average↓
Purple indicates a shortage of talent supply, the size of the bubble indicates the degree of shortage, and blue indicates a surplus.
Next, let’s look at these 15 trends. The 15 trends can be divided into five broader categories: Artificial Intelligence Revolution, Building the Digital Future, Frontiers of Computing and Connectivity, Cutting-Edge Engineering and Sustainability
Part 1 Artificial Intelligence Revolution
1
Generative AI
Generative AI
Generative AI marks a turning point in artificial intelligence.
OpenAI, Google, Microsoft, Facebook, Salesforce, IBM, etc. are all investing heavily in the research and development of large-scale language model technology LLM to promote continuous innovation and improvement of models. Unlike previous artificial intelligence, generative AI can generate new unstructured content such as text, audio, video, images, code, simulations and even protein sequences or consumers based on information learned from similar unstructured data formats. journey. Moreover, its core technology, the base model, can be adapted to various tasks.
In a business context, generative AI can not only unlock new use cases, but also accelerate, extend or improve existing ones. Generative AI has the potential to redefine businesses and value chains by facilitating the development of new products and revenue streams and improving customer experience. However, its impact is most expected to be in improved employee productivity and experience.
At this early stage, we see companies in many industries using generative AI primarily as an assistive technology to create first drafts, generate hypotheses, or assist experts in completing tasks faster and better.
Of course experts are also needed to check the output, especially for ghosting (inaccurate content produced by the application) and intellectual property (IP) issues.
In high-risk applications, it may be some time before generative AI gradually transitions from assistance to full automation based on the application.
2
applied artificial intelligence
Applied AI
Through artificial intelligence technologies such as machine learning (ML), computer vision, and natural language processing (NLP), businesses across all industries can leverage data and derive insights to automate processes, enhance capabilities, and make smarter decisions.
McKinsey research estimates that the potential economic value contained in applied artificial intelligence is between US$17 trillion and US$26 trillion, and the proportion of companies pursuing this value is increasing.
McKinsey's annual Global State of AI survey shows that adoption of artificial intelligence in organizations will double from 20% in 2017 to 50% in 2022. What’s more, the 2022 survey also showed that adopting AI could lead to significant financial benefits: 25% of respondents attributed 5% or more of their company’s net profits to AI.
However, organizational, technical, ethical and regulatory issues will need to be addressed before companies can realize the full potential of this technology .
3
Industrial machine learning
Industrializing
machine learning
Industrialized machine learning, often called machine learning operations (ML operations), or simply MLOps, refers to the engineering practices required to scale and sustain machine learning applications in the enterprise. These practices are supported by a rapidly evolving ecosystem of technology tools that have significantly improved in functionality and interoperability.
MLOps tools can help enterprises move from pilot projects to viable commercial products, accelerate the scaling of analytical solutions, identify and solve problems in production, and increase team productivity. Experience shows that successfully industrializing machine learning can shorten the production time frame of machine learning applications (from proof of concept to product) by approximately eight to ten times and reduce development resources by up to 40%.
Industrialized machine learning was initially led by a handful of leading companies, but adoption of the approach continues to expand as more companies use AI for a wider range of applications.
In 2021, corporate investment in the field of machine learning industrialization will reach a high of US$4.7 billion, and will reach a cumulative US$3.4 billion in 2022, continuing to maintain a strong momentum. IDC predicts that by 2024, 60% of enterprises will implement MLOps.
Part 2 Building a digital future
4
Next Generation Software Development
Next-generation
software development
Next-generation technologies are changing the capabilities of engineers at every stage of the software development life cycle (SDLC), from planning and testing to deployment and maintenance, and enabling non-technical employees to create applications.
These technologies can help simplify complex tasks and reduce other tasks to a single command. These technologies include AI-assisted programming tools, low-code and no-code platforms, infrastructure as code, automated integration, deployment and testing, and emerging generative AI tools.
Adoption can be slow due to technical challenges, the need for extensive retraining of developers and test engineers, and other organizational obstacles.
By 2026, Gartner predicts that 80% of users of low-code and no-code tools will come from outside traditional IT organizations. AI-enabled tools can also increase traditional developer productivity by automating routine tasks and providing solutions to problems.
Research shows that developers save 35% to 45% of time in code generation and 20% to 30% in code refactoring. They also report feeling happier, more engaged, and gaining more satisfaction when using AI-enabled tools, suggesting that adopting these tools helps companies retain talent in a competitive talent market.
5
Trust architecture and digital identity
Trust architectures
and digital identity
Digital trust technologies enable organizations to manage technology and data risks, accelerate innovation and protect assets. And building trust in data and technology governance can improve organizational performance and improve customer relationships.
Underlying technologies include Zero Trust Architecture (ZTA), digital identity systems and privacy engineering. Other techniques build trust by incorporating principles of explainability, transparency, security, and bias minimization into AI design.
However, the adoption of digital trust technologies is hampered by a range of factors, including integration challenges, organizational barriers, talent shortages, and limited consideration of them as an important part of the value proposition. Building a comprehensive trust-first risk mindset and capability requires top-down leadership and intentional change in multiple areas, from strategy and technology to user use cases and beyond.
Regulation, for example, is driving the industry in the direction of privacy engineering: New regulations around data localization and sharing, as well as the increased use of artificial intelligence and cloud computing, are making privacy engineering even more important. In Europe, regulations like the NIS2 Directive in 2022, mandating increased cybersecurity risk assessments, and the Data Governance Act in 2023, which aims to promote data sharing, make privacy engineering critical. State-level data privacy laws and federal domain-specific data regulations in the US require a range of privacy compliance measures that require automated controls.
6
Web 3.0
Web 3.0 goes beyond the typical understanding of cryptocurrency investing and more importantly refers to a model of the future internet that decentralizes and redistributes power to users, potentially giving them more control over how their personal data is accessed economically value and stronger ownership of digital assets.
Furthermore, it offers a range of business opportunities: new business models governed by decentralized autonomous organizations (DAOs) and eliminating middlemen through secure (smart contract) automation, new services involving digital programmable assets, and the use of blockchain Technology for new data storage and governance.
Web 3.0 has attracted a lot of capital and talent, the underlying technology continues to improve, and applications continue to increase; as of 2023, there are thousands of decentralized applications currently running, compared with only about a thousand in 2018.
But new businesses are still testing and scaling viable business models, while evolving regulations and immature new technology platforms mean the user experience is often inferior to existing Web 2.0 utilities.
Part 3 The Frontier of Computing and Connectivity
7
Advanced Connection Technology
Advanced connectivity
Advanced connectivity improvements will enhance the user experience for consumers around the world and increase productivity in industries such as mobility, healthcare and manufacturing.
Companies are rapidly adopting advanced connectivity technologies that build on existing deployment and connectivity standards, but some new technologies, such as low Earth orbit (LEO) connectivity and 5G networks , face barriers to adoption.
For example, telecommunications companies are struggling to achieve 5G profitability in the consumer sector, and industrial application growth is far below expectations. While 5G's APIs provide telcos with the profitability to deliver 5G services to consumers, adoption has been slow as consumer use cases that rely on advanced connectivity have yet to achieve scale. Many industrial companies are choosing to wait to adopt 5G private networks for reasons including complexity, insufficient understanding of the benefits and management of cellular technology, deployment costs and the early stages of end-to-end use cases. But the 5G network market is growing, and various industries, such as manufacturing, logistics, utilities, etc., are conducting benchmark deployments.
8
Holographic reality technology
Immersive-reality
technologies
Holographic reality technology uses spatial computing to interpret physical space, simulate adding data, objects and people to the real-world environment, and realize virtual reality through augmented reality (AR), virtual reality (VR) and mixed reality (MR). interact.
In 2021, venture capitalists provided around $4 billion in funding for AR and VR startups, making it the second most successful funding year after 2018. Although overall investment in AR and VR has declined in 2022, investor interest in the trend has persisted: at least seven deals worth $100 million or more were closed last year.
Research shows that the future Metaverse is expected to create a value of US$4 trillion to US$5 trillion in consumer and enterprise applications by 2030.
Not long ago, Apple announced that it will release the Vision Pro AR/VR headset in 2024, which will be equipped with technologies such as eye tracking, gesture recognition, and seamless scrolling. Apple has supported Vision Pro with its huge talent and capital, and has more than 5,000 patents. The company believes the headset could offer significant advantages and hopes to set a new standard in mixed reality.
9
Cloud and Edge Computing
Cloud and
edge computing
In the future, enterprises will leverage compute and storage infrastructure at multiple locations, from local to closer to local (the edge), and from small regional data centers to remote hyperscale data centers. Edge computing offers organizations the flexibility to process data closer to its source, enabling faster data processing (ultra-low latency) and enabling data sovereignty and enhanced data privacy compared to the cloud, unlocking a variety of new use cases.
Reducing the distance to the end user will reduce data transfer latency and cost and provide faster access to more relevant data sets, helping enterprises comply with data residency regulations. The public cloud will continue to play a vital role in the future of enterprises, enabling better economies of scale to execute non-real-time computing use cases.
Continuous integration of cloud and edge resources will enable users to extend the innovation, speed, and flexibility of the cloud to edge and real-time systems, thereby accelerating innovation, improving productivity, and creating business value.
However, ballooning costs and issues related to data privacy and latency have slowed enterprise migration to the public cloud. Yet a recent study by the Uptime Institute Global Data Center found that about 33% of respondents had migrated back from the public cloud to a data center or partner facility. However, of those enterprises that moved back, only 6% completely abandoned the public cloud. Most adopt a hybrid approach, using both on-premises and public cloud.
10
Quantum technology
Quantum technologies
Quantum technologies exploit the unique properties of quantum mechanics to perform certain types of complex calculations more efficiently than classical computers, provide secure communication networks, and deliver a new generation of sensors with significantly improved sensitivity over traditional sensors.
In principle, quantum technology can simulate and solve more complex problems, which will lead to major breakthroughs in various industries such as aerospace, defence, automotive, chemicals, finance and pharmaceuticals.
However, technical challenges remain, such as achieving fully error-corrected quantum computers and scalable quantum communication networks.
The talent gap in this area remains significant, but may be shrinking. McKinsey research shows that nearly two-thirds of open positions in industry could be filled with new master’s degrees in quantum technologies, while only about one-third of positions will be filled in 2021. In the future, this gap is likely to narrow further: the number of universities offering master's programs in quantum technologies will almost double in 2022.
Part 4 Cutting-edge Engineering Technology
11
future travel
Future of mobility
More than a century after mass production of automobiles began, mobility is experiencing a second important turning point: the shift towards autonomous driving, connectivity, vehicle electrification and shared mobility (ACES, Autonomous, Connected, Electric and Shared vehicles) technologies, and even Advanced air mobility technologies such as electric vertical takeoff and landing (eVTOL) are also advancing rapidly.
This shift promises to disrupt the market while improving the efficiency and sustainability of transporting people and goods by land and air. Adoption of ACES technology has increased over the past decade, and the pace is accelerating as sustainability measures strengthen, consumer preferences evolve and innovation advances. For example, autonomous driving technology is expected to generate up to $400 billion in revenue by 2035.
However, challenges remain in the near term, with innovators having to grapple with technological, regulatory and supply chain issues.
12
future bioengineering
Future of bioengineering
Breakthrough developments in biology, combined with innovations in digital technology, are expected to help organizations respond to needs and create new products and services in areas such as healthcare, food and agriculture, consumer goods, sustainability, and energy and materials production.
McKinsey research shows that 400 current bioengineering application cases are scientifically feasible and are expected to have an economic impact of US$2 trillion to US$4 trillion from 2030 to 2040.
For example, the recent success of mRNA vaccines against COVID-19 has triggered an explosion of RNA therapeutics research activity, with more than 50 RNA therapeutics in the global clinical pipeline.
In addition, the FDA approved five new viral vector genes and related therapies in 2022. As viral vector therapy gradually shifts to ultra-rare targets, and mRNA technology becomes a common technology, more attention will be focused on regulating mRNA and gene therapy, developing personalized "n = 1" drugs, and at the same time, higher yields and lower sales costs will be achieved. These personalized medicines will better personalize medical needs.
Of course, while some gene therapies and biological products are already gaining traction, ethical, regulatory and public perception issues will need to be addressed before bioengineering can realize its full economic potential.
13
space technology
Future of
space technologies
The most important development in the space industry over the past five to ten years has been the reduction in technology costs, which has made new capabilities and applications more accessible. The reduction in component costs is mainly due to the reduction in the size, weight, power and cost of satellites and launch vehicles. These degradations have led to changes in system architecture, such as a shift from single large geostationary orbit (GEO) satellites to smaller distributed low earth orbit (LEO) satellites, as well as increasing interest in space technology from traditional non-space companies.
The use of space technology and remote sensing analysis is now very widespread, and analysis shows that the space market may exceed US$1 trillion by 2030. The future space economy could encompass activities that are currently modest, such as in-orbital manufacturing, power generation and space mining, as well as scalable human spaceflight.
Several highly anticipated 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 carry satellites into orbit; and Blue Origin's New Glenn, which will carry some of Amazon's Project Kuiper satellites.
Part 5 Sustainable Development
14
Electrification and renewable energy
Electrification
and renewables
In the future, the energy structure will rapidly shift towards electricity, synthetic fuels and hydrogen, which will account for 32% of the global energy structure by 2035 and 50% by 2050.
Picture screenshot from: mckinsey.com/industries/oil-and-gas/our-insights/global-energy-perspective-2022
Ahead of COP26, a total of 64 countries have committed or legislated to achieve net-zero emissions in the coming decades. Electrification and renewable energy will help deliver on net zero commitments, including solar, wind, hydro and other renewables; nuclear; hydrogen; sustainable fuels and electric vehicle charging.
Affected industries are: Agriculture; Automotive and Assembly; Aviation, Tourism and Logistics; Chemicals; Construction and Building Materials; Power, Gas and Utilities; Metals and Mining; Oil and Gas; and Real Estate.
15
Other climate-related technologies
Climate technologies beyond
Other climate technologies include Carbon Capture Utilization and Storage (CCUS), Carbon Mitigation, Natural Climate Solutions, Circular Technologies, Alternative Proteins and Agriculture, Water and Biodiversity Solutions and Adaptation, and Technologies to Track Progress towards Net Zero Emissions .
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