Although still in its early stages, the adoption of artificial intelligence (AI) by businesses is progressing quickly, even when compared with previous information technology innovations like personal computers and smartphones. In part, this has been due to the availability and user-friendly nature of generative AI (genAI) applications like ChatGPT, which are driven by large language models (LLMs). Their ease of use has encouraged companies in many industries and sectors to experiment with early adoption, driven more by the rational recognition of potentially large efficiency gains than by an irrational FOMO (fear of missing out).

In the May 2024 edition of the McKinsey Global Survey on AI, 65% of respondents reported that their organizations regularly use genAI, nearly double the percentage revealed in McKinsey’s 2023 survey. For the past six years, adoption of any type of AI by survey respondents’ businesses had hovered at about 50%. But over ten months, that figure jumped to 72%.

Organizations that have adopted AI in at least 1 business function (as % of respondents)
Source: McKinsey

McKinsey survey respondents claimed their expectations for genAI’s impact remain undimmed, with three-quarters predicting that the technology will lead to significant or even disruptive change in their industries in the years ahead.

GenAI is a tool that can help increase speeds and efficiencies in nearly any role, and is already redefining the world of work. It is a bit like digging a hole when there is a large rock in the way. GenAI is the shovel that transforms into a pickaxe to break down that rock, then goes back to being a shovel to finish the digging.

Personal experience with generative AI tools by industry in 2023-2024 (as % of respondents)
Source: McKinsey

Today, AI, generative or otherwise, is everywhere. Many people have encountered customer service chatbots, which are relatively simple AI-driven applications. But this development went even further with much more complicated applications, like the driverless taxis (‘robotaxis’) that are starting to appear in the streets of the US and China.

The technology is also widespread in online search engines and social media platforms such as Google and Facebook where, respectively, it ranks and summarizes search results and creates lists of topics tailored to users’ perceived preferences and interests.

In contrast with the early stages of computerization in business and commerce, the adoption of genAI has been helped by widespread support and engagement among many businesses. For them, the process is both top-down (i.e., led by management) and bottom-up (i.e., driven by employees).

4 Sectors to Be the Most Impacted by GenAI Adoption

Some industries are more amenable than others to the benefits of AI. Accenture, one of the leading consultancy groups, for instance, envisions banking, insurance, and similar sectors to reap the most rewards, whether through augmenting existing processes or automating them completely by means of AI.

Share of working hours in selected industries in the US that could be automated/augmented by the use of AI
Source: Accenture

Let’s look at how four of these key industries are likely to be affected, and possibly even transformed.

Industry #1: Healthcare

According to Boston Consulting Group (BCG), generative AI has the potential to transform the healthcare sector. For each segment – providers, pharmaceutical firms, payers, medical technology, services and operations, and public health agencies – BCG grouped the options into three categories: validated solutions already on the market, early-stage or conceptual use cases, and possible future use cases not yet in development.

Validated, early state, and conceptual genAI use cases across all healthcare segments
Source: BCG

The following are just some examples of how AI might be used to benefit staff and patients:

  • 1. Administrative Workflow Optimization

    Healthcare workers spend a great deal of time on paperwork and other administrative tasks. According to a 2016 report by the World Health Organization (WHO), up to 50% of all medical errors in primary care are administrative. Generative AI can help with many of these mundane tasks, minimizing errors and enabling employees to focus on core activities and spend more time with patients.

  • 2. Virtual Nursing Assistants Implementation

    64% of patients are comfortable with the use of AI for around-the-clock access for answers to their questions. Virtual nurses – AI-powered chatbots, apps, and even devices – can help answer questions about medications, forward reports to doctors, and help patients schedule visits with physicians.

  • 3. Dosage Error Reduction

    Mingmin Zhao, et al., as published in Nature Medicine, found that up to 70% of diabetics don’t take insulin as prescribed. An AI-powered tool that sits in the background (much like a Wi-Fi router) can help monitor patient dosage and send timely reminders.

Already, AI is used for imaging analysis in CT scans, MRIs, and X-rays, and has the potential to help medical professionals in clinical trials and diagnostics. It is revolutionizing the notoriously lengthy, expensive, and failure-prone process for researching and developing new drugs. As Bain & Company states: “The technology could lower administrative costs, speed biomedical research and drug development, improve claims management, and help develop next-generation diagnostic equipment.”

Further, the World Health Organization (WHO) anticipates a global shortfall of 10 million health workers by 2030. Generative AI is expected to help address this shortage, mainly through increased efficiency that allows fewer workers to serve more patients. As a result, the worldwide market for genAI in healthcare is projected to reach an astonishing USD 21.74 billion by 2032, growing at a CAGR of 35.14%.

Generative AI in healthcare market size from 2022 to 2023 (in USD billion)
Source: Precedence Research

Industry #2: Energy

The computational power AI requires doubles roughly every 100 days. To achieve a tenfold improvement in AI efficiency, the computational power demand could surge by up to 10’000 times from current levels. The energy required to run AI tasks is already accelerating, with an annual growth rate between 26% and 36%. By 2028, this means AI could be consuming more power than all of Iceland used in 2021.

To meet these needs, the world will need to shift to clean energy including solar, wind, hydroelectric, nuclear, and hydrogen power generation. According to McKinsey, trillions of dollars’ worth of investment will be needed to achieve these goals, but AI could help.

Correctly applied, AI could be a powerful tool for meeting the ambitious targets, established in last year’s United Nations Climate Change Conference (COP28), of tripling renewable energy capacity and doubling energy efficiency by the end of the decade.

There’s no way to get there without a breakthrough. We need [nuclear] fusion or we need like radically cheaper solar plus storage or something at massive scale.
— Sam Altman

AI can help climate and energy transition efforts by helping to develop new materials for clean energy, optimize solar and wind farms, boost energy storage capacity, improve carbon capture processes, enhance climate and weather predictions for better energy planning, and achieve breakthroughs in the development of green energy sources such as nuclear fusion.

The value of AI to the energy transition is already being proven in several areas, driving measurable improvements in renewable energy forecasting, grid operations and optimization, coordination of distributed energy assets, demand-side management, and materials innovation and discovery.

However, while AI’s application in the energy sector has proven promising so far, innovation and adoption remain limited. That presents a tremendous opportunity to accelerate the transition toward zero-emission, highly efficient, and interconnected energy system that the world needs now.

Industry #3: Manufacturing

From the Industrial Revolution over 250 years ago to this day, modern manufacturing has had a profound impact on our lives that has been made possible by unrelenting innovation. With the advent of genAI, the industry is facing one of the most exciting and daunting transformations in its history.

AI promises to reduce time to market, drive innovation, improve asset lifecycles through predictive maintenance, and enhance decision-making. According to findings from Boston Consulting Group, one automotive supplier who deployed genAI solutions realized a 21% boost in productivity; the technology reduced waste and also limited the need for quality control staff by 65% percent while improving the accuracy of inspections.

According to a 2023 KPMG survey, industrial manufacturing leaders highlighted increasing productivity (80%) and changes to the way people work (63%) as the main positive effects of genAI on their business. Moreover, 95% agreed that AI will enhance employees' creativity and thoughtfulness, and 78% believed the technology will reduce burnout. In addition, a joint McKinsey and Statista survey suggests genAI could add from USD 170-290 billion in revenue to the various segments of the manufacturing industry.

Potential impact of generative AI on industry revenues (in USD billion)
Source: Statista

Industry #3.1: Semiconductor Manufacturing

According to Franklin Templeton, a leading investment management group, the computing requirements for training LLMs are driving demand for graphics processing units (GPUs). Relative to the central processing units (CPUs) that run the operating systems for computers, GPUs are optimized for the kind of high throughput, massively parallel operations used in training generative AI models.

The surge in usage of genAI is accelerating demand for GPUs manufacturing and benefits the companies behind. Currently, the dominant manufacturer of GPUs is NVIDIA, with an estimated share of more than 90% of the market. As genAI demand increases, the company is expected to retain its market leadership due to its full-stack computing platform, the high performance of its processors, its cost advantage over competing chips, and its head start in software that facilitates enterprise adoption, such as industry-specific libraries and pre-trained models.

Heightened AI demand also benefits semiconductor makers serving leaders in cloud technology with other products related to AI infrastructure. These leaders include developers of custom chips and networking solutions, semiconductor foundries, and semiconductor equipment makers critical to producing the leading-edge chips required for AI. In the end, McKinsey estimates that the application of these technologies could add USD 85 billion to USD 95 billion to the economy in the long term.

AI servers relying on GPUs
Source: Franklin Templeton

Industry #4: Banking and Finance

In the financial services industry, AI algorithms are used both to detect fraud and identify investment opportunities. According to Deloitte, genAI can automate routine tasks, enhance risk mitigation, and optimize financial operations.

Jamie Dimon, CEO of JPMorgan Chase, has stated that his firm already has over 300 use cases for AI, ranging from risk management to marketing automation. In his 2022 annual update, he described continued investment in AI as a “necessity,” a sentiment that most banks likely share.

According to Goldman Sachs Research, the use of genAI in finance is expected to increase global gross domestic product by 7%, or nearly USD 7 trillion, and to boost productivity growth by 1.5%. The technology is an especially good fit with finance because its main strength, handling vast amounts of data, is precisely what the industry relies on to function well. Indeed, this is the main reason that 26% of private-equity fund managers are already using genAI and 51% more are planning to do so in the future.

Generative AI can enhance private equity dealmaking by making it easier for analysts to navigate through reams of data when generating an investment thesis.
— John Stecher, Chief Technology Officer at Blackstone

Currently, genAI is used to automate manual tasks in the financial sector. However, AI cannot yet handle end-to-end processes without human intervention. For example, it cannot discover an early trend, devise a strategy on how to use that to a company’s advantage, and execute the strategy. Nonetheless, there are four significant benefits genAI already brings to the financial sector.

  • 1. Lower costs

    According to a recent MIT report, the real value of genAI in the financial sector lies in cost reduction. The majority of these reductions will come from automating manual tasks and freeing employees to do higher-value work.

  • 2. Higher productivity

    Accenture suggests LLMs can benefit 90% of all working hours in the banking sector, while BCG claims that companies that deploy genAI tools can increase productivity by up to 20%.

  • 3. Better customer experience

    GenAI is good at personalization; Magnifi, for instance, offers an investment platform that uses ChatGPT and other software to give users personalized investment advice. GenAI can also support employees in their communications with clients, helping them locate information faster and reduce waiting time.

  • 4. Greater resilience and risk management

    GenAI can minimize risks associated with financial products and services. For instance, one North American bank relies on genAI models to analyze loan applicants’ financial data, which helps minimize the risk of customers defaulting on loans.

GenAI adoption in financial services in the form of an S-curve
Source: BCG

According to McKinsey, the integration of genAI in the banking sector is poised to create a seismic shift in how financial services operate, offering possible additional revenue of between USD 200 billion and USD 340 billion annually. This is significant, representing a 2.8% to 4.7% increase in total industry revenue, primarily driven by enhanced productivity.

On the other hand, according to UBS, corporate deployment of genAI in financial services is still largely nascent. The most active use is in cutting costs by freeing employees from low-value, repetitive work. Companies have begun deploying genAI tools to automate time-consuming and tedious jobs, which previously required employees to assess unstructured information.

Legacy technology and skills shortages may slow the financial sector’s adoption of genAI, but only temporarily. Many companies, especially large banks and insurers, still have antiquated IT and data structures, probably unfit for use in modern applications. In recent years, however, the problem has eased with widespread digitalization and will continue to do so. That said, according to Precedence Research, the global generative AI in banking and finance market is expected to reach around USD 12.34 billion by 2032, a CAGR of 33% over 10 years.

Value created by AI at stake by segment and function (in USD billion)
Source: McKinsey

More on the Road: Robotics and Data Collection

In addition to these four major industries, genAI is also bringing significant advances to several more specialized sectors, notably, robotics and data collection.

In robotics, AI has helped create sim-to-real transfer, where robots are trained extensively in simulated environments before deployment in the real world. This allows rapid and comprehensive training without the risks and costs associated with real-world testing. For instance, OpenAI's Dactyl robot learned to manipulate a Rubik's Cube entirely in simulation before successfully performing the task in reality.

In data collection, genAI offers augmentation, where generative models create synthetic training data to overcome challenges associated with acquiring real-world information. This is particularly valuable when collecting sufficient and diverse empirical data is difficult, time-consuming, or expensive.

For example, NVIDIA uses generative models to produce varied and realistic training datasets for autonomous vehicles. The models simulate a range of lighting conditions, angles, and object appearances, enriching the training process and enhancing the reliability and versatility of AI systems. They ensure that systems can adapt to differing real-world scenarios by generating new and varied datasets continuously, thereby improving overall performance.

Generative AI in robotics market size by component from 2022 to 2032 (in USD billion)
Source: MarketResearch.biz

GenAI and Investment – The Exponential Effect

Whether in terms of its adoption by companies or in terms of public and private portfolio management, investment in AI has been exploding. The May 2024 McKinsey survey, already cited, revealed that most respondents (67%) expect their organizations to invest more in AI over the next three years.

Share of organization’s digital budget spent on generative AI (as % of respondents)
Source: McKinsey

Unsurprisingly, the investing public has been taking notice. The surge of interest in AI fueled a major rally in technology stocks in 2023, with a concentrated group of large US companies leading the market. These so-called “Magnificent 7” collectively drove 70% of the absolute performance of the Nasdaq Composite Index and about two-thirds (62.2%) of S&P 500 annual gains in 2023.

Magnificent 7 performance compared to S&P 500 index and S&P 493 (index performance deducting seven largest companies by market cap)
Source: Goldman Sachs

Meanwhile, the S&P 500 Index returned 26% in 2023 but, without those seven technology leaders, it would have risen by only 8%. The dominance of those names, primarily due to large AI-driven increases in earnings (and the expectations of even more to come), continued throughout the first half of 2024.

The first-order beneficiaries of AI are what Goldman Sachs calls the “enablers.” This category includes companies building necessary AI infrastructure, such as semiconductor and semiconductor capital equipment (‘semi-cap’) manufacturers. The advent of commercially accessible LLMs, such as ChatGPT, require significant computing power and memory. This has led to heightened demand for high-powered chips that only a handful of companies can currently design or manufacture.

The most prominent of these enablers is NVIDIA, as discussed above, whose market value first surpassed USD 1 trillion in 2023 and, in 2024, reached USD 3 trillion. It would be no exaggeration to describe the rise in its shares as vertigo-inducing.

Months it took for companies to hit USD 2 trillion market capitalization after breaching USD 1 trillion
Source: Reuters

As a result, according to Fortune, the “AI stock frenzy” helped the number of American millionaires grow by 600’000 in 2023, to 7.5 million, far outnumbering any other region. Even so, the excitement surrounding AI has yet to run its course on Wall Street, with Goldman Sachs, Citi, and Barclays all raising their stock market forecasts.

Private Markets and Generative Artificial Intelligence Boom

Out of the public eye, the world of private equity and venture capital has also been caught up in the excitement. Findings from CB Insights reveal that private market AI commitments shot up fivefold in 2023 compared to the previous year, while the number of transactions increased by 66%. 2023 was a breakout year for investment in generative AI start-ups, with equity funding topping USD 21.8 billion across 426 deals. As a result, there are already 36 private genAI companies that have reached unicorn status.

Generative AI investment funding and deals through 2023
Source: CB Insights

Both individual and institutional investors, such as private equity firms, have been increasingly invested. Generative AI investments by private equity firms reached USD 2.18 billion in 2023 compared to USD 1 billion in the prior-year total, according to S&P Global Market Intelligence data. The capital surge came despite total private equity-backed M&A across all industries plunging in 2023.

Global PE/VC-backed investments in generative AI from 2018 to February 2024
Source: S&P Global Market Intelligence

Some two-thirds (67%) of genAI companies are still in their early stage, according to CB Insights. That gives discerning investors a wide range of opportunities in private markets. Meanwhile, several tech giants, such as Alphabet (the owner of Google) and Amazon, as well as many smaller players, are increasing their investment in private companies in order to gain exposure to specialist technology and boost their own development.

Percentage of private genAI companies by their latest disclosed round
Source: CB Insights

Final Thoughts

According to analysts at T. Rowe Price, “Generative AI is not a bubble. It is a genuine breakthrough, and a business upcycle due to AI is already underway.” Moonshot shares that view, which is why our investment portfolio already includes 3 prominent AI companies – OpenAI, Anthropic, and xAI – as well as a well-diversified portfolio of AI investment opportunities available for accredited members of our community. Options are continually growing, offering Moonshot Circle members exclusive access to the most prominent private genAI companies’ ground floor.

Evolution of both input and output from 1980 to present day
Source: T. Rowe Price

In the current, rapidly changing environment of increasing genAI adoption, seasoned investors are taking advantage of the wide range of exciting public and private market AI opportunities. The future is already here; the only thing left is not to miss out.

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