“These technologies are foundation models, which are vast systems based on deep neural networks that have been trained on massive amounts of data and can then be adapted to perform a wide range of different tasks. Because information and knowledge work dominate the U.S. economy, these machines of the mind will dramatically boost overall productivity.”
— Erik Brynjolfsson Professor and Senior Fellow Stanford Institute for Human-Centered AI
to collaborate with AI tools, seeing them as partners that enhance their capabilities, not replacements,” he says. “The goal is to elevate human potential, not just automate existing jobs.”
If human-like AI leads machines to automate rather than augment human labor, Brynjolfsson warns in The Turing Trap that the concentration of wealth and power could increase. That concentration could then lead to an equilibrium where smaller companies or individuals without power have no way to improve their outcomes, a situation the paper defines as the “Turing Trap.”
As Brynjolfsson sees it, the biggest challenge of the coming era will be to reap the unprecedented benefits of AI, including its human-like manifestations, while avoiding the Turing Trap. If that can be done, he writes that both automation and augmentation will boost labor productivity — the ratio of value-added output compared to labor- hours worked. “As productivity increases, so do average incomes and living standards, as do our capabilities for addressing challenges from climate change and poverty to health care and longevity,” he writes. “Mathematically, if the human labor used for a given output declines toward zero, then labor productivity would grow to infinity.” Ensuring the advancement of AI results in greater outcomes for employees and companies of all sizes will require eliminating or reversing the excess incentives for
22 FEDA News & Views
automation over augmentation, Brynjolfsson writes. “In concert, we must build political and economic institutions that are robust in the face of the growing power of AI,” he concludes. “We can reverse the growing tech backlash by creating the kind of prosperous society that inspires discovery, boosts living standards and offers political inclusion for everyone. By redirecting our efforts, we can avoid the Turing Trap and create prosperity for the many, not just the few.”
Dissecting the AI-Powered Productivity Boom Over the past few years, large language models (LLM)
such as ChatGPT and Google’s Gemini that can interact using human language have emerged as one of those AI-driven tools that can make workers more productive through augmentation. As Brynjolfsson and his two co- authors discuss in Machines of Mind: The Case for an AI-Powered Productivity Boom, LLMs are increasing the rate of innovation and laying the foundation for a significant acceleration in economic growth. “The potential of the most recent generation of AI systems is illustrated vividly by the viral uptake of ChatGPT, an LLM that captured public attention by its ability to generate coherent and contextually appropriate text,” Brynjolfsson writes. “This is not an innovation that is languishing in the basement. Its capabilities have already captivated hundreds of millions of users.” Although LLMs are best known for generating plain language text, Brynjolfsson points out that generative AI can do much more. Generative AI systems such as Midjourney, Stable Diffusion and DALL-E are already able to create images based on a text prompt. Multimodal systems go even further by combining text, images, video, audio, and — in some cases — robotic functionality. “These technologies are foundation models, which are vast systems based on deep neural networks that have been trained on massive amounts of data and can then be adapted to perform a wide range of different tasks,” Brynjolfsson writes. “Because information and knowledge work dominate the U.S. economy, these machines of the mind will dramatically boost overall productivity.” The recent advances in generative AI have been driven
by progress in software, hardware, data collection, and growing investment in cutting-edge models. As explained in Machines of Mind, the generative AI system capabilities have grown in tandem, effectively allowing systems to “perform many tasks that used to be reserved for cognitive workers, such as writing well-crafted sentences, creating computer code, summarizing articles, brainstorming ideas, organizing plans, translating other languages, writing complex emails, and much more.”
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