The economic potential of generative AI: The next productivity frontier

October 31, 2023

Big Data Industry Predictions for 2024

GenAI has the potential to fundamentally change the marketing function – from storyboarding to creative content to customization for different media channels and audiences. Our analysis shows that GenAI could increase marketing productivity by about 9 percent. First, entrepreneurs should consider that challenging incumbents in the era of ML will be more difficult since incumbents’ use of ML for proprietary data makes them more formidable competitors. This fact implies that entrepreneurs need to become riskier and more creative in the future to obtain a competitive edge. They may therefore seek support from angels or venture capital firms and use their financing and experience to become more novel in their ventures.

The UK has a strong history of scientific discovery and AI innovation, including at its world-leading research universities like Oxford and Cambridge, and AI companies like DeepMind, now a part of Google. The UK ranks as third in private AI investment, behind only the United States and China. The UK AI market is currently valued at $21 billion and projected to scale by orders of magnitude over the next decade. The UK government has also announced $1.3 billion for supercomputing and AI research, on top of $2.8 billion in prior AI investments. France has invested at the national level, including EUR 1.5 billion from 2018 – 2022 and an additional EUR 500 million for AI “champions” in 2023. Most importantly, Paris is betting on open-source firms to win the technology debate and provide room to innovate within existing and pending EU regulatory frameworks.

From specialized to generalized intelligence: The pathway paved by generative AI models

While leading cloud providers’ newest data center chips use 60% less power than the previous generation, cutting-edge GPUs have increased power consumption in every successive release. Geopolitically, the pivotal question will be whether adoption trends toward “scaled-up” or “scaled-down” models. Many of the most important debates about access and control of AI systems are downstream of the scale-up vs. scale-down debate, including the debate about open-source vs. closed-source AI.

Economists at the National Bureau of Economic Research found a 5% increase in the number of openings for highly skilled jobs that had been considered vulnerable to AI, such as white-collar office work. The timeframe for the study was 2011 to 2019, the period when businesses started using deep learning to automate tasks. The researchers concluded that new technology can increase demand for more skilled workers even when it replaces those who do routine work.

Report on responsible AI and privacy governance – discussion of findings

However, the progress in AI technology means e-commerce companies can now leverage AI on the front end for virtual photoshoots, 3D product catalogs, and automated product descriptions in an effort to enhance their business performances. Content creation is another function sprung into the disruptive arena with the rise of generative AI. With the help of these advanced models, creative tasks can now be automated, diversified, and customized, leading to an overall enhancement of quality. This could have massive implications for all forms of digital content creation, from social media and user generated content to digital movie or game production. If the benefits of scaling up AI models reach the point of diminishing returns, then the restrictions on China’s AI development may become less effective. For example, in the domain of military hardware, legacy, trailing-edge chips are often sufficient for today’s strategic use cases, including missile guidance systems.

The US is the world’s preeminent AI power, thanks to its world-leading universities and companies. Working alongside a global network of allies and partners on everything from research to export controls, Washington is concerned with keeping its advantages and accelerating the pace of domestic AI innovation. Varian (2018) notes that in the traditional form of learning by doing, learning is passive, but in practice, learning requires active investment in ML machinery and human capital. Thus, human and financial capital quality likely affect ML applications and the R &D process.

Quantum AI: A catalyst for AGI advancement

Grounded in responsible technology, USC will accelerate innovation with novel and robust educational and research opportunities across all disciplines. Sustainability investors are turning to AI solutions to help achieve their ESG objectives and financial performance, while considering potential risks. Generative AI will likely affect a wide range of professions and create new occupations.

Generative AI’s natural-language capabilities increase the automation potential of these types of activities somewhat. But its impact on more physical work activities shifted much less, which isn’t surprising because its capabilities are fundamentally engineered to do cognitive tasks. With the acceleration in technical automation potential that generative AI enables, our scenarios for automation adoption have correspondingly accelerated. These scenarios encompass a wide range of outcomes, given that the pace at which solutions will be developed and adopted will vary based on decisions that will be made on investments, deployment, and regulation, among other factors. But they give an indication of the degree to which the activities that workers do each day may shift (Exhibit 8).

As a result, they were developed primarily by a few tech giants, startups backed by significant investment, and some open-source research collectives (for example, BigScience). However, work is underway on smaller models that can deliver effective results for some tasks and more efficient training. Some startups have already succeeded in developing their own models—for example, Cohere, Anthropic, and AI21 Labs build and train their own large language models. A published scholar in the fields of artificial life, agent-oriented software engineering and distributed artificial intelligence, Babak has 31 granted or pending patents to his name. He is an expert in numerous fields of AI, including natural language processing, machine learning, genetic algorithms and distributed AI and has founded multiple companies in these areas.

This happens again in deployment phases with processes like CI/CD, where they must consider security while deploying. It’s hardly surprising that the most popular use cases for gen AI have been around natural or conversational language interfaces for tasks ranging from query to coding. We expect that data discovery and governance will be a major target for gen AI augmentation in the coming year.

Pharma companies that have used this approach have reported high success rates in clinical trials for the top five indications recommended by a foundation model for a tested drug. This success has allowed these drugs to progress smoothly into Phase 3 trials, significantly accelerating the drug development process. Generative AI tools can facilitate copy writing for marketing and sales, help brainstorm creative marketing ideas, expedite consumer research, and accelerate content analysis and creation.

We are currently at the beginning of a journey that will lead us to understand the power, scope and capabilities of this technology. That is why it is important that we invest time in getting to know and understand Generative AI well in order to get the maximum benefit and predict its impact. Generative AI holds transformative potential across diverse sectors such as education, entertainment, health care, manufacturing, marketing, and research. By automating or enhancing tasks that demand human creativity or intelligence, generative AI can elevate the quality and quantity of outputs, cut costs and errors, and unlock new avenues for expression and discovery.

This material is intended only to facilitate discussions with Goldman Sachs and is not intended to be used as a general guide to investing, or as source of any specific investment recommendations. Certain information contained here may constitute “forward-looking statements” and there is no guarantee that these results will be achieved. Goldman Sachs has no obligation to provide any updates or changes to the information herein. Goldman Sachs is not providing any financial, economic, legal, accounting, or tax advice or recommendations. This material does not purport to contain a comprehensive overview of Goldman Sachs products and offering and may differ from the views and opinions of other departments or divisions of Goldman Sachs and its affiliates.

The EU Artificial Intelligence Act: A look into the EU negotiations

With gen AI, the gains will also come from innovation, as this new technology supercharges humans’ ability not only to make and create, but to think. Many businesses are hesitant about incurring a major security or ethics breach—not unlike the early days of PCs, the internet and mobile computing. But like those technologies, gen AI will move through its current era of vast disruption to become an unquestioned part of the fabric of work. With due diligence, governance and a phased implementation, these new tools can, and should, be safely deployed without constraining the potential gains in innovation, efficiency and productivity. In the sectors where the technologies were widely implemented, productivity increased, much as it did after the first Industrial Revolution, when humans stopped digging trenches and turned instead to steam shovels.

Today’s computing ecosystem – and, by extension, its energy consumption – is centralized in large cloud data centers. From 2010 to 2018 global computing output in data centers jumped six-fold while energy consumption rose only 6%. This relative efficiency reflects a concerted effort by cloud computing players and data center operators to optimize energy usage and performance. Following Moore’s Law, the processing power of today’s most advanced chips is exponentially greater than the most advanced chips of a decade ago.

With more than 25 years of experience in initiatives, Chander is pronounced with a passion for delivering sustainable 10X impact through inspiring, engaging & enabling people. The technological advances that have been developed as a result of this Fourth Industrial Revolution present a window of opportunity for states and international organizations to address global problems in a much more effective and coordinated manner. Artificial Intelligence will integrate and analyze diverse data and models to make farming recommendations for more bountiful harvests in Ethiopia. It has one of the most diverse, innovative and creative populations found anywhere, positioning Southern California to become a turbocharged innovation incubator. Through USC Frontiers of Computing, USC will prepare society for a more tech-intensive world of work, spark new technological advances to improve people’s lives and shape responsible policy.

The emergence of foundation models in generative AI dramatically lowers the barriers to AI adoption, simplifying labeling requirements and enhancing the accuracy and efficiency of AI-driven automation. This means that more companies can now deploy AI in a range of critical operations, signaling a new era of AI integration across industries. Generative AI represents a class of advanced deep-learning models that can process and “learn” from massive datasets, such as the entire content of Wikipedia or the collective works of artists like Rembrandt. These models abstract the training data into a simplified form and use it to create new, unique outputs that are similar but not identical to the original data. This significant development is largely driven by advancements in generative AI models. Narrow AI has become a cornerstone of technological innovation, offering unparalleled specialization across numerous fields.

• An important phase of drug discovery involves the identification and prioritization of new indications—that is, diseases, symptoms, or circumstances that justify the use of a specific medication or other treatment, such as a test, procedure, or surgery.
• In the future, DePriest envisions scenarios where developers will receive suggestions, tips, and even auto-completions for more secure methods as they work.
• Right on cue to improve sustainability practices, a key part will be to make supply chain practices more efficient.
• Artificial neural networks, inspired by the structure and function of the human brain, and hybrid models that combine neural networks with rule-based systems are at the forefront of AGI research.

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