McKinsey: The economic potential of generative AI

The dynamism of the U.S. industrial base depends on increased innovation in more regions, to which AI will be central. Doing nothing will likely leave AI to concentrate even more, and with more concentration will come increased marginalization of the “rest” of the nation. Early indications suggest that generative AI’s most advanced core research and development activities will likely remain concentrated in a few top centers of general and generative AI work.

• For existing operational databases, it was pretty much a no-brainer, as vectors comprise just another data type to add to the mix.
• Will the problems that generative AI creates be solved by the very same technology, or will they prove to be intractable?
• This growth is paralleled by the advancements in AI-driven application security, as highlighted by GitHub, showcasing a symbiotic relationship between technological innovation and regional development.
• 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.
• GenAI has the potential to fundamentally change the marketing function – from storyboarding to creative content to customization for different media channels and audiences.

Today, models are connected to a range of other resources via application programming interfaces, plugins, and more. This direction, which also will necessitate the incorporation of greater and more persistent memory resources, raises the potential for highly capable, personalized, always-on AI assistants which can help us plan and execute instructions. As with past technological revolutions, the nature of different governance systems can enhance or impede progress. Open societies like the US worry about AI risks, including the accuracy of LLM outputs and hallucinations, a phenomenon wherein an LLM provides inaccurate information based on the perception of patterns that do not exist or are otherwise faulty. AI systems are unpredictable, and their black-box structure features inputs that are often invisible and outputs that cannot be determined by government officials and censors. These are particular concerns for closed societies, and have led the Chinese state to impose restrictions that may limit innovation, hold back incumbents, and deter new entrants and entrepreneurs from engaging with this technology.

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Since 2020, the NSF has been building up a distributed network of National Artificial Intelligence Research Institutes, based at universities all across the country. So far, a combined investment of nearly $500 million over five years has established 19 of these institutes, with links to 37 states. Federal leadership will be essential to push against excessive AI sector concentration. The power of digital “superstar” dynamics and the vastness of the U.S. landmass mean that only the federal government has the reach and resources to broaden AI’s geography.

We leverage the SLR findings to provide an updated synopsis of extant scientific work on the focal research area and to develop an interpretive framework which sheds light on the drivers and outcomes of AI adoption for innovation. We identify economic, technological, and social factors of AI adoption in firms willing to innovate. We also uncover firms’ economic, competitive and organizational, and innovation factors as key outcomes of AI deployment. The initiative is also widening data access by building out a shared health data platform that links consented clinical and genomic patient data, as well as developing factory efficiency models and analytics using AI and machine learning (ML) tools. Given that, NAIRR should be established with a special focus on increasing access to critical computation resources and datasets in new communities. Such resources would be designed to make high-performance computing systems, cloud arrays, open and protected datasets, and testing and training resources more broadly available.

Generative AI supports key value drivers in retail and consumer packaged goods

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. The journey toward AGI is filled with scientific intrigue and technological challenges. The current state of AI, with its advancements in large language models and generative systems, demonstrates significant progress but still falls short of the AGI ideal. These systems, impressive in their scope, hint at the potential of AI, yet they don’t fully embody the comprehensive intelligence AGI represents. AGI remains more a subject of speculative fiction and theoretical exploration than a present reality. With GenAI technology evolving rapidly amid many unknowns, industry leaders are racing to understand the full scope of opportunities.

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. However, this view is not universally accepted, with notable figures like Sam Altman, former CEO of ChatGPT, asserting that such models are not close to true AGI. Human intelligence is unique not only in its capability to solve problems but also in its capacity for abstract thinking, emotional understanding, and creative ideation. These elements are integral to the vision of AGI, which aims to replicate such advanced facets of human cognition.

Such availability could be specifically focused on increasing both demographic and geographic access to AI research resources. In that sense, NAIRR should be implemented with a special mission of lowering the barriers to participation in the most dynamic AI research ecosystems for both racially and spatially underrepresented AI researchers. In that vein, NAIRR’s estimated $2.6 billion cost over an initial six-year period appears quite modest for a potentially transformative step toward improved access to core AI research platforms.

The CFS took this as an opportunity to conduct a survey on the prospects of AI in the financial industry. About 75 percent will be created in customer service, marketing and sales, software development, and research and development – and thus in areas that are heavily knowledge- and people-based. Nevertheless, until now, such studies have usually been based only on static customer data obtained before the start of the call, such as demographics and purchasing patterns.

Software Project Estimation Techniques Guide: Types, Importance, Process and More

Big players like Microsoft, Google, AWS and OpenAI will offer customizable, fit-for-purpose models as-a-service. Sam Altman, the CEO of OpenAI, recently explained that while gen AI today is good at doing “parts” of jobs, it’s not very good at all at doing “whole” jobs. When the world was introduced to gen AI through ChatGPT, what stood out initially was its fluid chat interface that users could talk to naturally. The model would reply with seemingly thoughtful answers, scrolling out across the screen as if typed by an excited friend.

Many companies today may be undervalued, as traditional discounted cashflow analyses may overlook the potential for future data licensing revenue. As we reach the limits of publicly available data, private data will likely grow in importance. While proprietary data comes with additional concerns, including around privacy and licensing, companies will be incentivized to find solutions to increase their data pools.

GenAI gives rise to the knowledge-driven enterprise

The concerns raised by various theorists and commentators about AGI potentially threatening human civilization may seem far-fetched today, but they underscore the necessity of caution and responsibility in this field. It is not just about building intelligent machines; it’s about ensuring that these machines embody and adhere to human values and morals. The development of AGI must be guided by rigorous ethical frameworks and safety standards, both at national and international levels. Establishing robust guidelines will be crucial in steering the technology toward beneficial outcomes while mitigating risks. Often referred to as a sub-symbolic method, this approach draws inspiration from the human brain’s structure, utilizing neural networks to foster general intelligence. The hypothesis is that higher-level cognitive functions will naturally emerge from these complex, lower-level neural systems.

In this context, Artificial Intelligence and Machine Learning stand at the forefront of propelling the Metaverse towards new horizons. These technologies are instrumental in crafting sophisticated virtual environments that are increasingly realistic and interactive. Integrating AI and ML within the Metaverse is anticipated to create highly intuitive and responsive virtual spaces where AI-powered chatbots and entities can interact with users seamlessly and naturally. In essence, the continuous development and refinement of NLP technologies, exemplified by the progression from GPT-3 to GPT-4, are critical milestones in the journey towards creating AI that can truly emulate human intelligence. These advances not only broaden the scope of AI applications but also bring us closer to the vision of AGI, where machines can interact, understand, and respond to the world in a fundamentally human way.

Just as the time to 1 million users has been truncated, so has the time it takes for many AI companies to hit $10-million-plus of run-rate revenue, often a fundraising hallmark for achieving product-market fit. Historically, much effort in AI has focused on replicating tasks that are easy for humans, such as object identification or navigating the physical world—essentially, things that involve perception. However, these tasks are easy for humans because the brain has evolved over hundreds of millions of years, optimizing specifically for them (picking berries, evading lions, etc.).

• The opportunity to upsell is extremely attractive, and cloud software players could also improve stickiness with customers.
• Generative AI’s impressive command of natural-language processing can help employees retrieve stored internal knowledge by formulating queries in the same way they might ask a human a question and engage in continuing dialogue.
• It involves constructing a vast knowledge base where symbols represent the physical world’s various elements.
• The upward-sloping curve labeled CGS is the CGS effect and shows the reduction in per-unit profit from a safer project from lower-level consumer willingness to pay and intensified competition from the incumbent.

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