Unleashing Artificial Intelligence's true potential › Chapter 3
The double-edged sword of AI: Will we lose our jobs or become extremely productive?
July 2023
Sai Satkriti Menon, Jonah Trenker, Timothy Owens, Ozan Tas, Christoph Blumtritt
Bringing together data-driven intelligence, expert perspectives, and extensive research to provide insights and future outlooks on current trends, market data, and key player strategies in the Artificial Intelligence market.
What to learn in this chapter?
• How did the COVID-19 pandemic accelerate transformative technologies like AI?
• Why is a general profitability decline per resource and digital overload at work a chance for AI?
• How and where is AI potentially reducing costs and increasing productivity?
• How does this specifically relate to the healthcare and semiconductor industries?
• Which jobs are going to be automated and how will our workforce be restructered?
• What does the future job skillset look like?
The pandemic accelerated the fourth industrial revolution, leading to the widespread and fast adoption of transformative technologies such as AI
The COVID-19 pandemic has had a significant impact on the world of work, with changes in remote work, eCommerce, and automation being accelerated. The shift to eCommerce and other digital transactions is likely to stick in the on-site customer interaction arena, while the leisure and travel arena may experience a reduction in labor demand due to the shift to remote work and related reduction in business travel, as well as automation of some occupations.
According to the Future of Jobs Survey, technology adoption is expected to drive business transformation in the next five years. Big data, cloud computing, and AI are among the top technologies that companies are likely to adopt, with approximately 75% of businesses planning to incorporate them by 2027. This trend highlights the increasing importance of technology in shaping the future of work and the need for upskilling to keep up with these advancements.
Sources: Statista Market Insights, Mckinsey, World Economic Forum
Despite increased hiring in the tech industry, generally it is observed that profitability per resource is on the decline
The tech industry has been one of the most resilient sectors in the face of the global pandemic and economic downturn. Many tech companies have reported increased revenues, profits, and market shares. However, this growth has also come with a cost: hiring more and more workers to meet the demand. While hiring more workers to expand the business and capture more opportunities, it also poses a challenge for tech companies: how to maintain or increase their profitability per resource? There are several factors that contribute to this decline, such as:
• Increased competition and price pressure from new entrants and existing rivals
• Higher wages and benefits for tech workers, especially in high-demand areas such as cloud computing, artificial intelligence, and cybersecurity
• Higher costs of acquiring and retaining customers, especially in saturated or mature markets
• Greater costs of innovation and research and development, as tech companies need to constantly invest in new products and services to stay ahead of the curve
• Increased costs of compliance and regulation, as tech companies face more scrutiny and oversight from governments and regulators
These factors suggest that tech companies need to rethink their hiring strategies and find ways to optimize their profitability per resource. Besides massive layoffs that happend in the tech industry in 2023, some possible solutions include the use of AI to leverage productivity per resource.
Sources: Statista Market Insights; Company information; Macrotrends
The inflow of data, emails, meetings, and notifications are outpacing humans’ ability to process, for which increasing AI adoption could be a solution
Sources: Microsoft work trend index
One of the top use cases for gaining a competitive edge is harnessing AI for cost reduction, increased productivity, and revenue growth
Businesses are leveraging AI across multiple functions to achieve cost reduction, increased productivity, and revenue growth. While AI adoption is at an early stage, there are opportunities for emerging markets to leapfrog more developed counterparts. The impact of AI on business is already evident, with significant cost reductions in business functions such as service operations, strategy and finance, and manufacturing.
In recent times, the integration of AI coding assistants like GitHub Copilot has led to a remarkable increase in productivity for software engineers. These intelligent tools have proven to be invaluable assets, enabling engineers to complete coding tasks in record time, often surpassing expectations by finishing them in less than half the usual duration. This significant boost in coding efficiency has revolutionized the software development process, allowing engineers to allocate more time to higher-level problem-solving and innovation. Another example of AI application lies in the field of graphic design. By leveraging AI algorithms, it is now possible to generate stunning graphic designs within minutes at astonishingly low cost. In fact, our findings indicate, that AI can create a graphic design for as little as US$0.08, a negligible expense compared to the average cost of US$150 for human labor. This substantial cost reduction has opened up new opportunities for businesses and individuals, providing them with access to high-quality graphic design services at a fraction of the traditional cost.
Sources: ARK Invest; AI Index 2023
AI can be used in a variety of areas, including healthcare to accelerate the creation of new treatments and reduce costs, or the semiconductor business, where up to US$95 billion in revenue could be additionally generated
The integration of AI in the pharmaceutical industry is transforming multiple facets of the business. AI applications are being utilized from target discovery to post-approval activities, automating processes, extracting insights from large-scale data, and facilitating stakeholder engagement. These solutions rely on diverse data sources, including chemical, biological, patient data, and literature. Companies like InSilico Medicine, BenevolentAI, Atomwise, and XtalPi specialize in leveraging scientific data for drug discovery, while others like Antidote and BullFrog AI optimize clinical processes using patient data. AI continues to play a crucial role in patient monitoring, compliance monitoring, and marketing optimization even after the development stage. Innovations such as CardioDiagnostics' wireless heart monitoring tools, AiCure's medication adherence smartphone app, and Eularis' sales and marketing analysis tools exemplify the wide range of AI applications in the pharmaceutical industry. Additionally, AI-powered chatbots like Novo Nordisk's Sofia are being employed to provide first-level responses to patient inquiries, enhancing efficiency and learning from each interaction.
Insilico Medicine, a founding member of NVIDIA Inception, has progressed to the next stage of its AI-discovered drug candidate, INS018_055, launching a phase 2 study for the anti-fibrotic small molecule inhibitor. INS018_055 was discovered using Insilico's proprietary Pharma.AI platform, which incorporates AI models trained on massive amounts of data, and its target identification platform, PandaOmics, which discovered a novel target. Insilico's generative chemistry platform, Chemistry42, designed the molecule's structure using machine learning techniques like deep generative models, reinforcement learning, and transformers. The pre-clinical candidate screening process typically takes about 5 years and costs an estimated US$674 million. However, AI technology has the potential to reduce the time taken by 72% and the costs to around a million US$. The use of AI in early-stage drug development could translate into several novel drugs over the next decade. While the data sets for drug development can involve millions of compounds, which can exceed the capabilities of current machine learning tools, AI technology shows immense potential in the drug discovery and screening phase in the drug development process.
According to research from Mckinsey, the semiconductor industry is currently earning between US$5 billion and US$8 billion annually from AI/ML, which is only about 10% of its full potential. However, within the next two to three years, AI/ML could generate between US$35 billion and US$40 billion in value annually. Over a longer time frame, this figure could rise to between US$85 billion to US$95 billion per year, which is equivalent to about 20% of the industry's current annual revenue. While customers will inevitably receive a significant portion of this value, early movers who capture it will have a competitive advantage that cannot be ignored.
Employers expect significant workforce restructuring with several job tasks at the risk of automation
It is evident that the labor market is poised for significant transformations in the coming years. Projections indicate that approximately 23% of current jobs will undergo changes by 2027, leading to the emergence of new job opportunities while also resulting in some job losses. This transition is expected to create approximately 69 million new jobs, but it is accompanied by the loss of around 83 million jobs over the next five years. Notably, automation holds the potential to revolutionize various sectors, with office and administrative support tasks being the most susceptible to automation, at a staggering 46%. Legal tasks closely follow with a potential automation rate of 44%. However, industries such as construction, building, and cleaning tasks exhibit lower possibilities for automation, with an average potential of around 25%. These statistics provide valuable insights into the changing dynamics of the labor market and emphasize the need for individuals and organizations to adapt to the evolving landscape.
Sources: Goldman Sachs; World Economic Forum
The Growing demand for AI expertise also implies a shift towards more social, emotional, and technological skill requirements
The percentage of U.S. job postings that required AI skills increased across various sectors from 2021 to 2022, with a few exceptions. The top three sectors with the highest number of AI job postings in 2022 were information, professional, scientific, and technical services, and finance and insurance. This growth in AI job postings reflects the increasing investment and interest in AI technology. Venture capital firms and large tech companies have invested billions of dollars into AI, leading to massive AI-related job openings in the United States in 2022. The field of AI is expected to continue to grow, with a projected massive increase in AI jobs by 2030.
Mckinsey analysis reveals that the future workforce will undergo significant changes in the skills that will be in demand, driven by automation and AI. The most notable shift will occur in technological skills, encompassing advanced programming, data analysis, and tech design, as well as fundamental digital skills required in an increasingly digital workplace. Additionally, there will be a substantial increase in the demand for social and emotional skills, reflecting the importance of human interaction in a technology-driven world. This shift will also prioritize higher cognitive skills over basic cognitive abilities. On the other hand, the demand for physical and manual skills will continue to decline, although they will still play a significant role in the future workplace. To assess the pace of these skill shifts, historical data from 2002 to 2016 was analyzed in the United States and the trends until 2030 were projected. The results suggest that the demand for technological skills and social and emotional skills will experience accelerated growth during the 2016 to 2030 period. In contrast, basic cognitive skills and physical and manual skills will witness a decline in demand.
Sources: Mckinsey; AI Index 2023; World Economic Forum
As we move into the future, we could see a transition to where AI will perform the majority of human tasks and enhance our capabilities
It is both fascinating and concerning to consider the timeline for the development of machines capable of performing human tasks. Although an optimistic projection suggests that nearly all tasks will be automated by 2060, it is essential to approach this future with a critical eye. There is no doubt that automation and AI have made significant advances in recent years, but the complete substitution of human labor raises many ethical, societal, and economic concerns. There is increasing concern about the potential displacement of millions of workers as a result of the advancement of machines, as well as the social upheaval that may result. Furthermore, it remains an open question as to whether machines are capable of replicating the nuanced skills, creativity, and emotional intelligence inherent in human tasks. Considering the consequences it is essential to ensure that the integration of machines into our society is guided by thoughtful regulations and ethical considerations in order to avoid the blind pursuit of progress at the expense of human welfare.
Sources: dls.tld
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