The economic potential of generative AI: The next productivity frontier

Generative AI brings knowledge to our immediate access with large language models (LLM) like ChatGPT or Bard.ai. They answer questions, generate content, and translate languages, making information retrieval efficient and personalized. Moreover, it empowers education, offering tailored tutoring and personalized learning experiences to enrich the educational journey with continuous self-learning. For example, the NSF’s Regional Innovation Engines and the EDA’s Regional Tech Hubs (as well as other new programs) are not just sources of R&D funding—they are designed to accelerate ecosystem-based tech development. Both programs provide planning grants for the design of in-depth industry strategies to promote advanced-sector growth. Both also hold out competitive opportunities to win large-dollar implementation grants.

It then covers why generative AI applications and large foundation-model companies look very different, and what that may mean for our industry. Inaccurate AI outputs raise concerns about the authenticity AI-generated content. While existing regulatory frameworks primarily focus on data privacy and security, it’s difficult to train models to handle every possible scenario.

CNIL Report – The ethical matters raised by algorithms and artificial intelligence

In the coming year, the GenAI space will settle into a new paradigm for enterprises, one in which  they deploy just a handful of GenAI-powered applications in production to solve specific use cases. Frontier technology solutions are innovative applications of emerging technologies such as blockchain, artificial intelligence, machine learning, data science, and augmented reality. These solutions have the potential to address some of the most complex and pressing challenges facing children and youth in the world, especially those who are vulnerable, marginalized or excluded. Conversational AI chatbots represent a significant leap in the realm of artificial intelligence, particularly in their application as AI-enabled virtual assistants. These sophisticated systems are designed to engage in natural, intuitive conversations, handling tasks ranging from answering inquiries to performing rule-based operations like password resets.

This content is for informational and educational purposes only and not intended as investment advice or recommendation to buy or sell any security. Investment advice and recommendations can be provided only after careful consideration of an investor’s objectives, guidelines and restrictions. In this episode of William Blair Thinking Presents, equity analysts Jason Ader, Arjun Bhatia, and Ralph Schackart explore the disruptive, ubiquitous subject of generative AI and its broad implications across the technology sector, the economy, and society.

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For AI models, it is possible that entirely novel architectures could emerge that are not as dependent on the chips the West dominates today. Even if China remains the world’s number-two AI power, China will remain a formidable competitor to the US and to the US-led technology ecosystem. For countries to lead in AI, they need national strategies that foster and direct innovation, as well as world-leading AI companies and research institutions. The generative AI ecosystem will empower incumbent enterprises and also likely define the next generation of Big Tech companies. Private AI investment globally is considerable and growing, and is forecast to increase to more than $160 billion by 2025, according to Goldman Sachs Research. However, sustained growth and innovation requires a system that promotes property rights and entrepreneurship, and that provides predictable rules of the road to startups and mega-cap technology companies alike.

Employer-provided training is an important determinant of economic outcomes, yet our understanding of its extent and distribution is well out of date—with the most recent national survey being from 2008. Add in the “winner-take-most” dynamic of digital economies, and it’s likely that the growing geographical divergence of the AI sector could easily become entrenched, even as the generative AI gold rush holds out potential opportunity for new firms in new places. Economist Nicholas Bloom and his colleagues studied 29 disruptive technologies from the last 20 years and found that the distribution of those jobs remained highly concentrated, with long-lasting advantages for the “pioneer” locations. A companion to the earlier Brookings paper that warned about the uneven geography of AI activity, the discussion here reviews the AI location problem and highlights key federal, state, and local policy moves that could counter it.

Podcast: Global Spotlight

This was apparent in a recent Stanford and MIT study, in which call-center reps who used gen AI were 14% more productive on average than those who didn’t. The gains were even greater among workers who had been on the job for less than a few months. This new way of interacting with a digital system compels us to question whether traditional apps and websites will even be necessary in the future. As generative AI becomes more advanced, it could usher in an era where digital interaction is far more intuitive, immediate and tailored to individual needs, going beyond what traditional apps and websites can offer. The true revolution of generative AI is in opening doors to these previously unimagined possibilities.

For example, it may take an investment of $20 million to build a robot that can pick cherries with 80% accuracy, but the required investment could balloon to $200 million if you need 90% accuracy. Not only is that a ton of upfront investment to get adequate levels of accuracy without relying too much on humans (otherwise, what is the point?), but it also results in diminishing marginal returns on capital invested. Many AI products need to ensure they provide high accuracy even in rare situations, often referred to as “the tail.” And often while any given situation may be rare on its own, there tend to be a lot of rare situations in aggregate. This matters because as instances get rarer, the level of investment needed to handle them can skyrocket.

FPF: How to Operationalize Data Protection by Design for Machine Learning

Each country is pushing the technology further, developing AI champions, and finding the most relevant use cases and advantageous areas for AI adoption. The US and China are, in different ways, seeking to advance their absolute and relative positions, protect their interests, and secure leverage as they each follow distinct strategies. We believe that this is the time to take stock of where we are, and to identify some of the inflection points that will shape our AI future. This paper takes up the key changes in the generative AI field over the last year in the domains of geopolitics, technology, and markets. We discuss what trends are emerging, what debates remain unsettled, and how the generative world order is being defined. Artificial Intelligence is transforming how we work by accelerating innovation, optimising processes, and enhancing human capabilities, thereby increasing productivity and efficiency across all industries.

Similarly, generative AI tools excel at data management and could support existing AI-driven pricing tools. Applying generative AI to such activities could be a step toward integrating applications across a full enterprise. The application of machine learning (ML) to operational data is becoming increasingly important with the rapid development of artificial intelligence (AI). We propose a model where incumbents have an initial advantage in ML technology and access to (historical) operational data.

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