Economic potential of generative AI

In this section, we highlight the value potential of generative AI across business functions. AI has permeated our lives incrementally, through everything from the tech powering our smartphones to autonomous-driving features on cars to the tools retailers use to surprise and delight consumers. While Jio chairman Akash Ambani announced the launch of JioSpaceFiber at IMC in October, Airtel’s Mittal said that the operator’s satellite-based communications services were to be launched by last month.

• Finally, breakthroughs in software and model architecture design drive and accelerate improvements.
• For its part, north-central Florida and the University of Florida (UF) have been able to establish new relevance in AI by securing powerful new computational tools through a $70 million partnership with NVIDIA and UF alumnus Chris Malachowsky, NVIDIA’s co-founder.
• Our updates examined use cases of generative AI—specifically, how generative AI techniques (primarily transformer-based neural networks) can be used to solve problems not well addressed by previous technologies.

Researchers around the world are now using these structures to accelerate and assist their investigation of diseases and develop new treatments for them. Activities such as bookkeeping, filing, and accounting, much of consumer banking, and the control systems for entire supply chains were partially and sometimes completely automated. In parallel, most information came to be stored and transmitted in digital form, making it cheaper and easier to access and use. An abundance of free and low-cost web-based services also transformed the consumer economy and social interaction. Against these headwinds, the costly clean energy transition—which will require an additional $3 trillion in capital spending each year for several decades, according to projections by the International Energy Agency—will be close to impossible to engineer.

IAPP Summit Keynote Nina Schick on the latest AI developments

It will also require more intentional efforts to build up vibrant AI ecosystems in more and different places. As we have seen, while a dozen or so U.S. metro areas possess the beginnings of unique AI industry clusters, too few of them appear likely to reach critical mass. Which is why the federal government should follow through on its efforts to leverage place-based industrial policy in the acceleration of emerging-sector scale-up in promising but different places. AI and data advances are enabling all three of these buckets, because models for underwriting debt, insurance, and loans are becoming increasingly powerful thanks to AI and increased data exhaust. A number of B2B companies have also been popping up which leverage the growing amount of data available to be sourced or scraped from public sources and package it together to underwrite risk for their customers.

Rather than a pause, some experts have pushed for AI non-proliferation measures akin to the arms-control measures that govern technologies such as nuclear weapons. AI presents significant challenges that likely make comprehensive global governance unrealistic, especially at a time of intense geopolitical competition. The final country that is critical for global semiconductor supply chains is the Netherlands. The Dutch company ASML is the world’s only manufacturer of extreme ultraviolet lithography (EUV) machines, which are necessary for the ever-more sophisticated integrated circuits in semiconductor fabrication, at a cost of $330 million or more for each machine. According to Goldman Sachs Research, EUV could increase the size of the global semiconductor market from $600 billion in 2022 to $1 trillion by the end of the decade.

AI and Biometrics Privacy: Trends and Developments

For our dollar, generative AI holds a similar promise when it comes to the cost and time of generating content—everything from writing an email to producing an entire movie. Of course, all of this assumes that AI scaling continues and we continue to see massive gains in economics and capabilities. As of this writing, many of the experts we talk to believe we’re in the very early innings for the technology and we’re very likely to see tremendous continued progress for years to come.

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. Our updates examined use cases of generative AI—specifically, how generative AI techniques (primarily transformer-based neural networks) can be used to solve problems not well addressed by previous technologies.

Generative AI: Privacy and tech perspectives

It is possible that reductions in the current high costs of AI development and research, as well as competition among the major developers, will lead to affordable AI applications that can be widely implemented, by keeping costs down and spurring entrepreneurial activity. But policymakers must be diligent in creating rules that ensure that such competition results in broad diffusion and use of the technologies. The pace of workforce transformation is likely to accelerate, given increases in the potential for technical automation. Generative AI’s impact on productivity could add trillions of dollars in value to the global economy. Our latest research estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases we analyzed—by comparison, the United Kingdom’s entire GDP in 2021 was $3.1 trillion.

These controversies could arise in the way AI gets regulated globally, as well as in how companies and engineers manage to tackle biases in AI algorithms. We all need to be more vigilant in checking the facts, details, and sources behind any AI-generated content before incorporating it into our work and being clear about which co-workers — bot or human — are contributing to our work and any potential copyright violations. One emerging challenge is to ensure that our workforce knows how to discern what’s real and what isn’t — how to cut through the rubbish. In other words, Gen Zers know that social media is feeding them garbage, yet they’re still eating it. The economist Richard Baldwin commented at the World Economic Forum’s Growth Summit that “AI won’t take your job, but someone who can use AI better than you, just might!

US Senate subcommittee focuses on AI in the workplace

Read more about The Economic Potential of Generative Next Frontier For Business Innovation here.

• While AGI remains in the realm of development and debate, some speculate that models like GPT-4 could be early precursors to AGI, given their extensive language understanding and problem-solving abilities across diverse domains.
• Resource-rich geographies, even remote ones, will have an advantage that was less relevant for CPU data centers, which prioritize proximity to end-users.
• FinTechs, from InsurTech to PayTech, can unlock significant business benefits by adopting generative AI.