Generative AI (Gen AI) is a type of artificial intelligence designed to generate new content without human intervention, such as text, images, and even music. This technology uses complex algorithms and machine learning models to memorize patterns and rules from existing data and generate new content similar in style and structure.

Generating new content based on cumulative data input makes gen AI worthwhile in many industries. The speed with which this technology can create content can help employees develop more content in less time and/or work more efficiently. This can reduce the need for human labor, raising concerns about job displacement and income inequality.

Gen AI’s impact on consumption patterns has made it easier for companies to personalize their marketing and advertising efforts. This has led to a more targeted approach to advertising, which can be beneficial but also problematic from a privacy perspective.

Applications of Generative AI

Gen AI has increased accuracy and productivity while lowering costs in various industries, including:

Healthcare

In the healthcare industry, gen AI is used to analyze medical images and assist doctors in making diagnoses. According to a report by the World Health Organization (WHO), up to 50% of all medical errors in primary care are administrative errors. Gen AI has potential to increase accuracy, but the technology also comes with vulnerabilities, as its trustworthiness depends heavily on the quality of training datasets, according to the World Economic Forum.

Further, the WHO anticipates a shortfall of 10 million health workers by 2030. Gen AI is expected to help address this shortage through increased efficiency, allowing fewer workers to serve more patients.

Finance

In the financial industry, AI algorithms detect fraud and identify investment opportunities. Generative AI has shown the potential to automate routine tasks, enhance risk mitigation, and optimize financial operations.

The use of gen AI in finance is expected to increase global gross domestic product (GDP) by 7%—nearly $7 trillion—and boost productivity growth by 1.5%, according to Goldman Sachs Research. Gen AI is a good fit with finance because its strength—dealing with vast amounts of data—is precisely what finance relies on to function.

Transportation

In the transportation industry, self-driving vehicles are powered by generative AI, enabling them to navigate roads and make real-time decisions. The applications of gen AI in transportation include much more than that, however.

Artificial intelligence can solve many problems that humans can’t, such as traffic congestion, parking shortages, and long commutes. Gen AI is expected to play a role in improving the quality, safety, efficiency, and sustainability of future transportation systems that do not exist today.

Manufacturing

Gen AI has the potential to revolutionize manufacturing. With its ability to leverage vast amounts of data and predict outcomes, AI can significantly improve decision making, optimize production, enhance product quality, and reduce waste.

Generative AI is improving operations and ensuring employees are following the proper steps. It can also enhance performance visibility across business units by integrating disparate data sources.

Entertainment

In the entertainment industry, gen AI creates personalized recommendations for movies, TV shows, and music based on individual preferences. This technology can foster the same efficiency and accuracy that it does in other industries, making it a potential cost-saver for media companies.

On the less innocuous side, generative AI’s ability to replace some of the work done by human writers, artists, photographers, and other creative professionals was part of the reason for the Writers Guild of America (WGA) strike that began in May 2023.

Retail

Optimizing inventory management and recommending products to customers based on their purchase history and browsing behavior is only part of the value of gen AI in the retail industry. Generative AI can also help retailers increase sales and optimize operations.

For example, generative AI can help retailers with inventory management and customer service, both cost concerns for store owners. Gen AI can also help retailers innovate, reduce spending, and focus on developing new products and systems.

Case Studies and Reports About AI

Numerous case studies and reports have pointed to AI’s impact on various industries, the economy, and the workforce.

Accenture

A study by Accenture found that artificial intelligence could add $14 trillion to the global economy by 2035, with the most significant gains in China and North America. The study also predicted that AI could increase labor productivity by up to 40% in some industries.

Johns Hopkins Medicine System

A trial conducted at five Johns Hopkins Medicine System-affiliated healthcare facilities found that using AI algorithms to analyze medical images led to a 20% reduction in sepsis deaths in hospitals. Sepsis, which happens when the response to an infection spirals out of control, is responsible for one out of three in-hospital deaths in the United States. According to the Centers for Disease Control and Prevention, about 1.7 million adults in the U.S. develop sepsis each year, and about 350,000 of them die.

McKinsey & Company

A report by McKinsey & Company found that AI could automate up to 45% of the tasks currently performed by retail, hospitality, and healthcare workers. While this could lead to job displacement, the report also noted that just because AI could automate a job doesn’t necessarily mean that it will, as cost, regulations, and social acceptance can also be limiting factors.

World Economic Forum

A study by the World Economic Forum found that adopting AI could lead to a net increase in jobs in some industries, particularly those that require higher levels of education and skills. However, the report also warned that the benefits of AI could be unevenly distributed, with some workers and regions experiencing more significant job displacement than others.

Advantages and Disadvantages of Generative AI

Whether the benefits of generative AI outweigh the drawbacks is not always clear. Consideration of both outcomes is imperative.

Increased Productivity vs. Required Technical Expertise

Pro: AI-powered machines and robots can perform repetitive tasks with greater accuracy and speed, increasing productivity and efficiency in various industries. These, in turn, can lead to lower overall manufacturing costs and, eventually, lower inflation.

Con: The development and implementation of generative AI algorithms require significant technical expertise, which may be challenging to find or afford for some businesses. For those left behind, catching up and keeping up can become a real challenge.

Implementation Cost Savings vs. Investment Costs

Pro: Gen AI can save business costs by reducing the need for human labor in certain areas. The need to hire fewer paid workers and the ability to replace them with unpaid machines can lower costs significantly.

Con: Adopting gen AI requires a significant investment in technology and infrastructure, which may be prohibitively expensive for some businesses.

New Job Creation vs. Job Displacement

Pro: While gen AI may displace some jobs, new jobs may be created in fields such as data analysis and software development.

Con: As gen AI automates specific tasks, some workers may find themselves out of work or in lower-paying positions, which could lead to increased economic hardship and social unrest.

Improved Decision Making vs. Bad Data and Bias

Pro: Gen AI algorithms can analyze vast amounts of data and identify patterns and insights that humans may miss, leading to improved decision making in various industries.

Con: Gen AI algorithms rely on vast amounts of data to learn and improve, but if that data is biased or incomplete, it can lead to inaccurate or unfair outcomes.

Personalization vs. Ethical Considerations

Pro: Gen AI-powered marketing and advertising can lead to more personalized messaging and product offerings, improving customer satisfaction and loyalty.

Con: Gen AI raises critical ethical questions about privacy, bias, and accountability, which must be carefully considered and addressed.

Enhanced Safety vs. Regulatory and Legal Considerations

Pro: In industries such as transportation and manufacturing, gen AI-powered machines and robots can perform dangerous or hazardous tasks, improving workers’ safety.

Con: As gen AI becomes more pervasive in various industries, there may be a need for new regulations and legal frameworks to ensure that it is used responsibly and ethically.

The table below illustrates how the pros and cons of generative AI must be compared to determine whether the use of gen AI is beneficial.

Generative AI Pros and Cons
Pros		Cons
Increased productivity	vs.	Required technical expertise
Cost savings	vs.	Cost of development
Job creation	vs.	Job displacement
Improved decision making	vs.	Impact of poor data
Personalization	vs.	Ethical concerns
Enhanced safety	vs.	Legal considerations

Economic Impact of Gen AI: Expert Opinion

Asked about the potential overall economic impact of generative AI on the economy, , Ph.D., professor of economics at the Darden School of Business at the University of Virginia in Charlottesville and nonresident fellow at The Brookings Institution, an economic think tank, sees productivity growth as the primary impact of gen AI on the overall economy.

“This includes increasing the level of productivity through direct efficiency gains as well as accelerating the rate of innovation and future productivity growth,” Korinek says.

“The effect on the labor market will be more uncertain,” he adds. “In some sectors, there will almost certainly be job losses and downward wage pressures as gen AI automates certain tasks. However, if the economy-wide productivity effects are strong enough, it could spur overall labor demand. The distributional impacts will depend on whether gen AI primarily substitutes for or complements different types of workers.”

As for possible solutions to the labor issue, Korinek says, “Economic policymakers will need to focus on facilitating the rollout and adoption of gen AI throughout the economy to maximize the productivity benefits. They must also update policies around job training, social welfare, and taxes to help workers adjust to labor market disruptions.”

Korinek also suggests long-range planning now that the age of generative AI is upon us. “Economic policymakers should stress-test existing institutions against a range of AI scenarios that may play out in coming decades, including the possibility of artificial general intelligence,” he says. “By that, I mean AI that can perform all intellectual tasks at human levels. We can no longer rule out such a scenario and must prepare our institutions and social insurance systems to ensure that the benefits of continued AI progress are broadly shared.”

Which Companies Make Generative AI?

The list of companies creating gen AI technology is growing. Some of the more well-known names include:

• Alphabet (GOOGL and GOOG) has developed several generative AI models, including Bard for natural language processing and Studio Bot for coding.
• Hugging Face is a startup specializing in creating AI models for natural language processing, including GPT-2.
• IBM (IBM) has developed several AI models, including Watson for natural language processing and the IBM Research AI system for computer vision.
• Microsoft (MSFT) has developed several AI models, including Copilot, a productivity assistant, and Azure AI Vision for computer vision.
• NVIDIA (NVDA) is a technology company specializing in creating graphics processing units (GPUs) that power AI algorithms, including generative image and speech recognition models.
• OpenAI is a research organization that develops advanced AI technologies, including generative models for natural language processing and computer vision. OpenAI released ChatGPT, one of the best-known chatbots, in November 2022.
  

Which Companies Are Using Generative AI?

The innovative uses and potential business upside is driving many companies to employ this technology in consumer-facing and internal tools. Some of the more well-known companies include:

• Amazon (AMZN) uses generative AI in its recommendation engines and voice-activated assistant, Alexa.
• Google uses generative AI in its search engine and advertising products, as well as in its voice recognition and natural language processing tools.
• IBM’s use of generative AI is primarily in its Watson platform.
• Microsoft uses generative AI in its Azure cloud computing platform and in its Bing search engine.
• Netflix (NFLX) uses generative AI in its recommendation engine, which suggests movies and TV shows to users based on their viewing history and preferences.
• Tesla (TSLA) uses generative AI in its self-driving cars, which use AI-powered sensors and algorithms to navigate roads and make real-time decisions.
  

The Bottom Line

The adoption of generative AI is expected to significantly impact various industries and job markets, including manufacturing, healthcare, retail, transportation, and finance. While it is likely to lead to increased efficiency and productivity, it is also expected to lead to job displacement for some workers.

Several studies and analyses have examined the impact of generative AI on the economy, with estimates ranging from $14 trillion to $15.7 trillion in economic contribution by 2030. The potential economic benefits of generative AI include increased productivity, cost savings, new job creation, improved decision making, personalization, and enhanced safety. However, there are also important questions about the distribution of those benefits and the potential impact on workers and society.