The Economics of Generative AI

Every new technology is considered disruptive and sets a new operational pattern, bringing additional economic value to the enterprise. Generative AI is the application of artificial intelligence capable of generating text, images and other data using generative models to respond to the prompts. The models learn the patterns and structure of their prompts (inputs) and generate new data with similar characteristics. Traditional AI, by practice, also uses predictive models and, by analysing historical data, can make future quantitative predictions. Generative AI allows machines to produce brand-new outcomes, which are often found to be indistinguishable from human-generated content.

One of the popular generative AI is ChatGPT, which helps create content and retrieve information. Generative AI is an application built on foundation models. These models contain expansive artificial neural networks inspired by the millions of neurons connected to our brains. These foundation models are part of deep learning as they explain the many layers within neural networks. Deep learning has powered many of the recent advances in the AI area. The foundation model powering the generative AI applications is a significant change within the deep learning practices. These model applications can process large and varied databases that include unstructured data and can multitask.

The foundation models have enabled newer capabilities across various modalities, including images, video, audio and computer codes. AI trained on these foundation models can perform many functions like classification, editing, summarization, responding to questions and drafting new content.

The impact of generative AI on business performance and productivity can add millions to the business and economy. Generative AI can add 2.6 trillion USD to 4.4 trillion USD annually to the global economy. The generative AI can increase the impact of all AI applications by 15-40%. Let’s look at the embedded AI in the existing software practices and modules used for other tasks. The value addition of the same can almost double. The current estimations are based on the 63 used cases of generative AI.

There are four key areas where we can see the potential of the application of generative AI, and these four areas can cater to almost 75% of the new market. These include customer operations, marketing and sales, software engineering and Research and Development. For example, generative AI can support customer interactions, generate creative content for marketing and sales and develop computer codes based on natural language prompts. The impact will be significant in the banking, finance, high-tech, and life sciences sectors. The technology can deliver value equal to an additional 200-350 billion USD annually if all the use cases are fully implemented. The potential impact on the retail and consumer packaged goods industry can be 400-660 billion annually.

Beyond the potential impact of generative AI is an additional factor in how it will influence the anatomy of work by augmenting the capabilities of individuals by automating some of their activities and actions. The emerging technologies include generative AI, which can automate work-related activities that consume almost 60-70% of the employees’ time. This acceleration in the potential for technical automation of the work is mainly due to the application of generative AI’s enhanced abilities to understand natural languages, which is essential for work activities that consume 25% of the total work time. The generative AI will have a higher impact on the knowledge work associated with high-paying jobs and qualification requirements.

As there will be an increase in the potential for technical automation, the pace of workforce and workplace transformation will increase in the current adoption scenarios, including technology development, economic feasibility, and diffusion timelines. Between 2030 and 2060, half of today’s work activities are expected to be automated.

The application of generative AI can significantly increase labour productivity across the economy; this will need investments to support workers as they shift work activities or change their current jobs. The generative can enhance labour productivity between 0.1-0.6 % annually till 2040. Of course, this will depend on the technology adoption rate and worker time redeployment into other activities. All technologies, including generative AI, can add 0.5-3.4 percentage points annually to productivity growth. This also warrants workers learning new working skills and changing jobs. Suppose the workers are willing to transit, and other risks can be managed. In that case, generative AI can contribute significantly to economic growth and create more sustainable economies worldwide.

Generative AI has a more significant potential as we are at the beginning of the adoption curve. The results of pilots and business cases are positive, indicating that we will experience faster adoption of generative AI in business. There are other immediate challenges that business leaders need to pay attention to: Risk management in applying generative AI to business processes. Estimating what new skills and capabilities the workers will need and allocating resources. Rethinking the core business processes, including retention and development of new business skills.

Technology companies have invested decades in building ChatGPT, GitHub Copilot, Stable Diffusion, and other generative AI tools with billions of dollars in investments. ChatGPT was launched in November 2022. Only after four months, Open AI released a new large language model or LLM called GPT-4 with more extensive capabilities. In May 2023, an Anthropic generative AI called Claude could process 100,000 tokens of text, equal to almost 75,000 words in a minute (nearly the size of a novel), compared to 9000 tokens when it was launched in May 2023. In May 2023, Google announced several new features powered by generative AI, including a Search Generative Experience and a new Large Language Model called PaLM2, which will power its Bard Chatbot and other products. So, companies invest millions to build robust generative AI systems with potential commercial applications.

These investments have helped in the advancement of machine learning and deep learning. We can understand that these applications have embedded the product and service offerings for us. The applications include: tech powering smartphones, autonomous driving features on cars and tools used by retailers to surprise customers.

People are also realizing the value of generative AI as it’s in public memory how AlphaGo, an AI-based program developed by Deepmind, defeated a world champion go player in 2016. Catching the public imagination can only make a technology run deeper and get a quicker adoption.

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