Irving Wladawsky-Berger

The Economic Impact of Generative AI at Work

“The emergence of generative artificial intelligence (AI) has attracted significant attention for its potential economic impact,” wrote economists Erik Brynjolfsson, Danielle Li, and Lindsey Raymond in a recently published article, “Generative AI at Work.” “Although various generative AI tools have performed well in laboratory settings, questions remain about their effectiveness in the real world, where they may encounter unfamiliar problems, face organizational resistance, or provide misleading information.”

Their article studied the impact of generative AI (GenAI) on productivity and worker experience in the customer-service sector, an industry with one of the highest rates of AI adoption. It analyzed survey data from 5,172 customer-support agents working for a Fortune 500 firm that sells business-process software in order to quantify the effect of deploying GenAI at scale in the workplace. The AI system used in the study was designed to monitor customer-service chats and provide the agents with real-time suggestions for how to respond. While the AI system is intended to assist the agents, the agents remain responsible for the overall conversation and are free to ignore or edit the AI's suggestions.

“Computers and software have transformed the economy with their ability to perform certain tasks with far more precision, speed, and consistency than humans,” said the authors. “To be effective, these systems typically require explicit and detailed instructions for how to transform inputs into outputs: a software engineer must program computers. Despite significant advances in traditional computing, many workplace activities, such as writing emails, analyzing data, or creating presentations, are difficult to codify and have therefore defied computerization.”

The State of Open Standards

“Technical standards define guidelines and specifications across products, services, or systems to ensure consistency, compatibility, and quality,” said “The State of Open Standards,” a recent report by “Linux Foundation Research” in its Introduction. “They are fundamental in facilitating trade, ensuring product safety, and enabling interoperability between different technologies and systems. With such an interconnection of objectives, requirements and stakeholders, approaches to create technical standards tend to vary along a continuum of characteristics. The Linux Foundation supports hundreds of projects that work to achieve standardization across various levels, from agreement within a single project’s ecosystem to globally-adopted ISO Standards.”

Open standards have played a central role in fostering innovation, ensuring interoperability across systems and applications, and driving the growth of the digital economy. For example, in the early decades of the IT industry, proprietary systems from different vendors didn’t interoperate with each other. Just sending an e-mail across two different proprietary networks and applications was quite complicated, as was sharing information across such disparate systems.

This all finally changed in the 1990s when the Internet and the World Wide Web brought a badly needed culture of standards to the IT industry. Open network protocols (e.g., TCP/IP), were widely embraced across the marketplace, making it possible to interconnect systems from any vendors. Internet e-mail protocols, — SMTP, MIME, POP, IMAP, — enabled people to easily communicate with anyone on any system. And the Web’s open standards, — HTML, HTTP, URLs, — enabled any PC connected to the Internet to access information on any web server anywhere in the world. Open standards have also played a central role in the explosive growth of open source software and collaborative innovation.

In addition, despite the major technology advances of the past few decades, the lack of open, interoperable standards has been a major obstacle to progress in critically important industry sectors like healthcare systems and global supply chains.

The Future of Agentic AI Ecosystems

Wikipedia defines Agentic AI as “a class of artificial intelligence that focuses on autonomous systems that can make decisions and perform tasks without human intervention.” A recent IBM website, “What is Agentic AI,” adds that: “Agentic systems provide the flexibility of LLMs, which can generate responses or actions based on nuanced, context-dependent understanding, with the structured, deterministic and reliable features of traditional programming.”

“Broadly speaking, agentic systems refer to digital systems that can independently interact in a dynamic world,” explained a McKinsey report published in July of 2024.  “While versions of these software systems have existed for years, the natural-language capabilities of gen AI unveil new possibilities, enabling systems that can plan their actions, use online tools to complete those tasks, collaborate with other agents and people, and learn to improve their performance. Gen AI agents eventually could act as skilled virtual coworkers, working with humans in a seamless and natural manner. … In short, the technology is moving from thought to action.”

“Billions of dollars of investment by some of the largest firms on the planet are flowing into tools that will make it easy to build autonomous agents,” wrote senior technology executive Eric Broda in “Agentic Mesh: The Future of Generative AI-Enabled Autonomous Agent Ecosystems,” a November 2024 article in the Medium platform. “And if this huge investment, and the recent headlines, are any indication, we will soon have many, many autonomous agents collaborating in a dynamic ecosystem.”

Is AI a Normal Technology?

Hard Fork is a weekly podcast from the NY Times that examines the impact of the latest developments in technology on the economy and society. The podcast is hosted by Kevin Roose, NY Times technology journalist, and Casey Newton, founder of the Platformer newsletter. A few weeks ago the podcast discussed Is AI a ‘Normal' Technology? with Princeton computer science professor Arvind Narayanan, co-author of AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference with Princeton PhD candidate Sashay Kapoor, a book that was published in September of 2024.

Roose and Newton started the podcast by noting that the previous week their guest at the podcast had been AI researcher Daniel Kokotailo, founder and executive director of the AI Futures Project, where they discussed their new article, AI 2027, which predicted that “the impact of superhuman AI over the next decade will be enormous, exceeding that of the Industrial Revolution.” Narayanan is highly skeptical of the fast takeoff predictions of AI 2027. In a recent essay also co-authored with Kapoor, “AI as a Normal Technology,” they laid out an alternative vision of AI, “basically one that treats AI not as some looming superintelligence that’s going to go rogue and take over humanity, but as a type of technology like any other, — like electricity, like the internet, like the PC, — that have taken a period of years or even decades to fully diffuse throughout society.”

This is a very important question I’ve been thinking a lot about. Will AI’s impact on economies and societies be similar to those of previous technological revolutions of the past two and a half centuries, — that is, Is AI a normal technology?, — or is AI in a class by itself, destined to exceed the impact of the Industrial Revolution in just one decade?

The 2025 AI Index Report: AI’s Influence Across Society Continues to Transform the Way We Live and Work

The 2025 AI Index Report was released in early April by the Stanford Institute for Human-Centered Artificial Intelligence, — it’s eigth annual analysis of the impact, progress, and trends of AI. Led by an interdisciplinary group of experts from across academia and industry, the AI Index report aims to be the world’s most authoritative source for data and insights about AI.

“What a year 2024 has been for AI,” wrote the report’s co-directors Yolanda Gil and Raymond Perrault. “The recognition of AI’s role in advancing humanity’s knowledge is reflected in Nobel prizes in physics and chemistry, and the Turing award for foundational work in reinforcement learning. The once-formidable Turing Test is no longer considered an ambitious goal, having been surpassed by today’s sophisticated systems. Meanwhile, AI adoption has accelerated at an unprecedented rate, as millions of people are now using AI on a regular basis both for their professional work and leisure activities. As high-performing, low-cost, and openly available models proliferate, AI’s accessibility and impact are set to expand even further.”

“AI is no longer just a story of what’s possible — it’s a story of what’s happening now and how we are collectively shaping the future of humanity,” Gil and Perrault added. At over 450 pages and eight chapters, the 2025 AI Index is recognized globally as one of the most comprehensive and authoritative resources on artificial intelligence, referenced in hundreds of academic papers and used by policymakers and government agencies around the world.