Whats the future of generative AI? An early view in 15 charts
Generative AI: 7 Steps to Enterprise GenAI Growth in 2023
As the development and deployment of generative AI systems gets under way, a new value chain is emerging to support the training and use of this powerful technology. After all, of the six top-level categories—computer hardware, cloud platforms, foundation models, model hubs and machine learning operations (MLOps), applications, and services—only foundation models are a new addition (Exhibit 1). The breakneck pace at which generative AI technology is evolving and new use cases are coming to market has left investors and business leaders scrambling to understand the generative AI ecosystem. While deep dives into CEO strategy and the potential economic value that the technology could create globally across industries are forthcoming, here we share a look at the generative AI value chain composition. Our aim is to provide a foundational understanding that can serve as a starting point for assessing investment opportunities in this fast-paced space. Many organizations began exploring the possibilities for traditional AI through siloed experiments.
As a result, companies can stand up applications and realize their benefits much faster. In fact, the occupational categories most exposed to generative AI could continue to add jobs through 2030 (Exhibit 4), although its adoption may slow their rate of growth. And even as automation takes hold, investment and structural drivers will support employment.
Intelligence as a commodity
When the chief financial officers were asked how they thought generative AI could be most helpful for their organizations someday, the most popular answer was reduced costs — selected by 52% of the respondents. Similarly, the industries that have most rapidly embraced the technology for work and/or outside of work so far are “technology, media and telecom,” at 33%, followed by “financial services” and “business, legal and professional services,” at 24% and 23%, respectively. One way generative AI could help workers is by acting as a “virtual expert” who helps them quickly access internal information, the analysts wrote. One study found that when customer-service agents in the Philippines were given AI assistants, they became happier, more productive, and less likely to quit. The McKinsey analysts said generative AI would have “a significant impact across all industry sectors,” with banking, high tech, and life sciences among the industries that could see the biggest impact as a percentage of their revenues from generative AI.
We estimate that applying generative AI to customer care functions could increase productivity at a value ranging from 30 to 45 percent of current function costs. We then estimated the potential annual value of these generative AI use cases if they were adopted across the entire economy. For use cases aimed at increasing revenue, such as some of those in sales and marketing, we estimated the economy-wide value generative AI could deliver by increasing the productivity of sales and marketing expenditures.
Momentum among workers for using gen AI tools is building
For example, generative AI can improve the process of choosing and ordering ingredients for a meal or preparing food—imagine a chatbot that could pull up the most popular tips from the comments attached to a recipe. There is also a big opportunity to enhance customer value management by delivering personalized marketing campaigns through a chatbot. Such applications can have human-like conversations about products in ways that can increase customer satisfaction, traffic, and brand loyalty. Generative AI offers retailers and CPG companies many opportunities to cross-sell and upsell, collect insights to improve product offerings, and increase their customer base, revenue opportunities, and overall marketing ROI. In other cases, generative AI can drive value by working in partnership with workers, augmenting their work in ways that accelerate their productivity. Its ability to rapidly digest mountains of data and draw conclusions from it enables the technology to offer insights and options that can dramatically enhance knowledge work.
Foundation models can generate candidate molecules, accelerating the process of developing new drugs and materials. Entos, a biotech pharmaceutical company, has paired generative AI with automated synthetic development tools to design small-molecule therapeutics. But the same principles can be applied to the design of many other products, including larger-scale physical products and electrical circuits, among others.
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.
For example, to build a generative model, a company may need PhD-level machine learning experts; on the other hand, to develop generative AI tools using existing models and SaaS offerings, a data engineer and a software engineer may be sufficient to lead the effort. In general, training a model from scratch costs ten to 20 times more than building software around a model API. Larger teams (including, for example, PhD-level machine learning experts) and higher compute and storage spending account for the differences in cost. The projected cost of training a foundation model varies widely based on the desired model performance level and modeling complexity. Those factors influence the required size of the data set, team composition, and compute resources. In this use case, the engineering team and the ongoing cloud expenses accounted for the majority of costs.
Generative AI is both accelerating automation and extending it to an entirely new set of occupations. While this technology is advancing rapidly, other forces are also affecting labor demand. Overall, we expect significant shifts in the occupational mix in the United States through the end of the decade. Total employment hit an all-time high after the pandemic, with many employers encountering hiring difficulties. As of April 2023, some ten million positions remained vacant; labor force participation had ticked up but was 0.7 percentage point below its prepandemic level. That translates into roughly 1.9 million workers who are neither employed nor actively looking for jobs.
Although layoffs in the tech sector have been making headlines in 2023, this does not change the longer-term demand for tech talent among companies of all sizes and sectors as the economy continues to digitize. In addition, the transportation services category is expected to see job growth of 9 percent by 2030. Gen AI’s precise impact will depend on a variety of factors, such as the mix and importance of different business functions, as well as the scale of an industry’s revenue. Nearly all industries will see the most significant gains from deployment of the technology in their marketing and sales functions. But high tech and banking will see even more impact via gen AI’s potential to accelerate software development.
The third example is pharma and medical products, with an estimated total value per industry of $60 billion–$110 billion, and a value potential increase of 15–25% of operating profits based on average profitability of selected industries in the 2020–22 period. Gen AI tools can already create most types of written, image, video, audio, and coded content. And businesses are developing applications to address use cases across all these areas.
Generative AI could add up to $4.4 trillion to the global economy annually, McKinsey report says
Filling the jobs of the future is an opportunity to make the labor market more inclusive. Employers may need to reconsider whether some credential requirements are really necessary. Some 60 percent of US workers have skills gained through experience but lack four-year college degrees. Initiatives like Tear the Paper Ceiling are supporting workers who have experience but not degrees by raising awareness among employers and providing resources. Other trends are affecting the demand for certain occupations, and we expect the employment mix to change significantly through 2030, with more healthcare, STEM, and managerial positions and fewer jobs in customer service, office support, and food services. The changes estimated in our earlier research are happening even faster and on an even bigger scale than expected.
- As a result, a broader set of stakeholders are grappling with generative AI’s impact on business and society but without much context to help them make sense of it.
- Fine-tuning is the process of adapting a pretrained foundation model to perform better in a specific task.
- Although it remains possible that another AI winter could loom (where the tech fails to live up to the hype and falters), it is increasingly looking like an AI tsunami is inevitable.
- So, the key here is to let people drive this transformation, and give them access to generative AI so they can play with it themselves.
To be sure, it’s plausible that the AI boom will bring plenty of positive outcomes for American workers. In addition to helping workers be more productive and spend less time on boring tasks, AI could create jobs, lead to higher wages, and even make a four-day workweek more possible. By 2030, nearly 12 million Americans in occupations with shrinking demand may need to switch jobs, a McKinsey analysis found. AI was deemed a key reason — McKinsey estimated that 30% of hours worked in the US could be automated by 2030. Generative AI is the technology to create new content by utilizing existing text, audio files, or images. With generative AI, computers detect the underlying pattern related to the input and produce similar content.
Generative AI could still be described as skill-biased technological change, but with a different, perhaps more granular, description of skills that are more likely to be replaced than complemented by the activities that machines can do. Banks have started Yakov Livshits to grasp the potential of generative AI in their front lines and in their software activities. Early adopters are harnessing solutions such as ChatGPT as well as industry-specific solutions, primarily for software and knowledge applications.