Irving Wladawsky-Berger

The Impact of AI and Jobs and Skills

Last month I moderated a panel on “The Impact AI on Jobs and Skills” at the annual 2025 MIT Sloan CIO Symposium. “As AI transforms industry after industry, its impact on the labor force is becoming a critical focus for business, educators, and policy makers,” read the panel’s blurb. “What will be the impact of AI on jobs and skills? Will AI mostly automate human jobs or will it augment human skills and make workers more productive? What human capabilities best complement AI shortcomings? How is AI reshaping workforce dynamics, skill requirements, and career paths. The panel will discuss these important questions as well as strategies on how to best prepare for the emerging age of AI.”

The impact of AI on jobs and skills is a very important subject. In 2022 Congress requested a study by the National Academies on the current and future impact of AI on the US workforce. The three year study was conducted by a committee of experts from universities and private sector institutions. The 140-page report, “Artificial Intelligence and the Future of Work” was released in November of 2024. It concluded that “the speed of technological progress is reshaping not just the tools but also the fabric of the workforce and societal structures. … The trajectories that Al-enabled futures might take can lead to outcomes of profound benefit or significant disruption.”

Agent Explainability: The Foundation for Trust in the Agent Ecosystem

What is Agentic AI? There are multiple answers to this question, not surprisingly since we’re still in the early stages of this complex technology. Wikipedia emphasizes the autonomous nature of agentic AI in its definition: Agentic AI is “a class of artificial intelligence that focuses on autonomous systems that can make decisions and perform tasks without human intervention.”

McKinsey noted that AI is now “moving from thought to action” in a July, 2024 report that explained the difference between generative and agentic AI. “We are beginning an evolution from knowledge-based, gen-AI-powered tools — say, chatbots that answer questions and generate content — to gen AI-enabled agents that use foundation models to execute complex, multistep workflows across a digital world. … Gen AI agents eventually could act as skilled virtual coworkers, working with humans in a seamless and natural manner.”

Similarly, IBM answers the question “What is Agentic AI” by writing: “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. This approach allows agents to think and do in a more human-like fashion.”

The Long Term Evolution of AI Economics: Automation vs Human Complementarity

The 2025 MIT Sloan CIO Symposium took place on Tuesday, May 20 at the Royal Sonesta hotel overlooking the Charles River, a short walk from the MIT campus. Not surprisingly, AI was the dominant theme in this year’s Symposium, with a number of keynotes and panels on the topic.

Over the past few years, the Symposium’s closing keynote has been given by a prominent MIT faculty member. In 2023, economics professor David Autor talked about “The Impact of AI on Jobs and the Economy,” where he discussed the potential of AI to reshape the nature of human expertise. In 2024, MIT professor emeritus of robotics Rodney Brooks talked about “What Works and Doesn’t Work with AI,” where he explained what he called the “Seven Deadly Sins of Predicting the Future of AI.”

This year, Daron Acemoglu, Institute professor of economics and 2024 Nobel laureate, talked about “The Long Term Evolution of AI Economics.”

The Key Differences Between Analytical and Generative AI

“Since OpenAI announced ChatGPT in November of 2022, many business executives have focused their attention on generative AI,” wrote Babson College professor Tom Davenport, and technology and business strategist Peter High in “How Gen AI and Analytical AI Differ — and When to Use Each,” an article published in the Harvard Business Review (HBR) issue of December, 2024. “This relatively new technology set off a frenzy around AI and caused companies to pay attention to it for the first time. This is a positive development, since the technology is powerful and important, and enables many new business possibilities.”

“However, many companies have been using AI for years with less visibility,” the authors added. “Those that have recently discovered generative AI are at risk of overlooking an older and better-established form of AI, which we’ll call analytical AI. This form of AI is by no means obsolete and is still an important resource for the great majority of companies.”

I’ve been closely following the evolution of AI since the 1970s, especially the more recent era of data-centric AI systems based on highly sophisticated models trained with large amounts of information and powerful computer technologies. We were wowed when in 1997 Deep Blue won a celebrated chess match against then reigning champion Gary Kasparov, — one of the earliest and most concrete grand challenges of AI. The 2010s saw increasingly powerful deep learning AI systems surpass human levels of performance in a number of tasks  like image and speech recognition, skin and breast cancer detection, and winning at championship-level Go

More recently, the impressive ability of large language models (LLMs) and chatbots to understand our prompts and questions and generate cogent, articulate sentences has given us the illusion that we’re dealing with a well educated, intelligent human, rather than with a sophisticated stochastic parrot that’s been trained with huge amounts of human language but has no human-like understanding of the ideas underlying the sentences it’s putting together.