Convened by the National Academies’ Action Collaborative on Education and Workforce Trajectories in Tech, The Impact of Artificial Intelligence on Education and Workforce Trajectories in Tech is a one-day exploratory workshop examining how AI is altering the value of human expertise, organizational workforce structures, and career pathways in the tech sector. Bringing together leaders from higher education, industry, and research institutions, discussions will consider how education and workforce systems can prepare individuals with the ethical, technical, and analytical capabilities needed to adapt and thrive in an AI-impacted landscape.
What do professional software developers need to know to succeed in an age of Artificial Intelligence?
Generative AI is showing early evidence of productivity gains for software developers, but concerns persist regarding workforce disruption and deskilling. We describe our research with 21 developers at the cutting edge of using AI, summarizing 12 of their work goals we uncovered, together with 75 associated tasks and the skills & knowledge for each, illustrating how developers use AI at work. From all of these, we distilled our findings in the form of 5 insights. We found that the skills & knowledge to be a successful AI-enhanced developer are organized into four domains (using Generative AI effectively, core software engineering, adjacent engineering, and adjacent non-engineering) deployed at critical junctures throughout a 6-step task workflow. In order to "future proof" developers for this age of AI, on-the-job learning initiatives and computer science degree programs will need to target both "soft" skills and the technical skills & knowledge in all four domains to reskill, upskill and safeguard against deskilling.
Artificial intelligence, cognitive offloading and implications for education
This report investigates the challenge driven by the rapidly expanding use of artificial intelligence (AI) in schooling: the risk that students will outsource too much of the cognitive work that is crucial to establishing knowledge, skill and ‘thinking infrastructure’. The report includes specific recommendations for policy and teaching and learning strategies.
AI Economy Institute - Microsoft Research
The AI Economy Institute (AIEI) is Microsoft’s flagship think tank dedicated to shaping an inclusive, trustworthy AI economy. We building a network of scholars and convening that network with our subject matter experts to explore how artificial intelligence is transforming work, education, and productivity – and making this knowledge base available to policy-makers, educators, and […]
Is AI ruining our skills? Early results are in — and they’re not good
Reliance on artificial-intelligence tools degrades the abilities of physicians and software engineers, studies show.
Bridging the gap: inequalities that divide those who can and cannot create sustainable outcomes with AI
The widespread use of AI technologies impacts individuals, organisations, and societies in ways that warrant more comprehensive investigation. Inequalities in benefiting from AI advancements may le...
AI Gender Pay Gap - Emerging Tech - I by IMD
As AI investment grows, unequal access to emerging tech skills is widening the AI gender pay gap. New research shows how organisations can reverse it.
Can the adoption of AI technology widen the gender gap in science? postdocs' use of AI technology
There is a known gender gap in science. Some studies suggest that this gap is decreasing, others that it is not. Although many studies have been conducted on gender equity in science, none have yet focused on the possibility that generative artificial intelligence (AI), a technology that promises to revolutionize research and academic work can have on widening or narrowing the gender gap in science. Postdoctoral researchers are a particularly important population to study in this context, as they have already demonstrated scientific maturity and independence and are poised to become the next generation in science and academia. Thus, this paper focuses on the gendered use of AI technology by postdocs. By using data from the 2023 Nature Post-Doctoral Survey, this paper investigates how postdocs are adopting AI and whether the usage of AI technology is likely to affect the gender gap in science and academia. The results show that females are less engaged with AI than male postdocs by 34%. This disparity may put female researchers at a disadvantage, potentially causing the gender gap to increase or be maintained. The study also reveals variations in AI adoption within gender groups. Younger female postdocs tend to use AI more than their older counterparts. Compared to female postdocs without promising job opportunities, those with good job prospects tend to use AI more frequently. Additionally, female postdocs working outside their native country are more engaged with AI than those working within their native country. For male postdocs, the results indicate that employment is an important factor in AI adoption. Regardless of age, male postdocs tend to use AI as part of their postdoc: they start using it once they begin their postdocs positions and use it more if they are satisfied with their postdoctoral experience.
Artificial intelligence tools expand scientists’ impact but contract science’s focus
Nature - Artificial intelligence boosts individual scientists’ output, citations and career progression, but collectively narrows research diversity and reduces collaboration, concentrating...
How AI Is Fueling the Gender Pay Gap in Tech
A new Wharton study finds that too few women are working with emerging tech, and that exclusion is driving a growing divide in pay.
A Collectivist, Economic Perspective on AI
Information technology is in the midst of a revolution in which omnipresent data collection and machine learning are impacting the human world as never before. The word ``intelligence'' is being...
The AI Roles Continuum: Blurring the Boundary Between Research and...
The rapid scaling of deep neural networks and large language models has collapsed the once-clear divide between "research" and "engineering" in AI organizations. Drawing on a qualitative synthesis...
Women in AI: Numbers Behind the Gender Gap in Tech
How is AI changing the game for women in 2026? From job shifts and personal life-hacks to the rise of female AI founders, explore the unique impact of women in tech today.
Software engineering may no longer be a lifetime career
I don’t think there’s compelling evidence that using AI makes you less intelligent overall1. However, it seems pretty obvious that using AI to perform a task means you don’t learn as much about performing that task. Some software engineers think this is a decisive argument against the use of AI. Their argument goes something like this:
70 years of AI hype
Quoting from Olivia Guest et al. (2025) "Against the Uncritical Adoption of AI Technologies in Academia."
Towards Critical Artificial Intelligence Literacies
Critical Artificial Intelligence Literacies (CAILs) is the collection of ways of thinking about and relating to so-called artificial intelligence (AI) that rejects dominant frames presented by the technology industry, by naive computationalism, and by dehumanising ideologies. Instead, CAILs centre human cognition and uphold the integrity of academic research and education. We present a selection of CAILs across research and education, which we analyse into the following non-orthogonal dimensions: conceptual clarity, critical thinking, decoloniality, respecting expertise, and slow science. Finally, we note how we see the present with and without a wider adoption of CAILs — a fundamental aspect is the assertion that AI cannot be allowed to drive change, even positive change, in education or research. Instead cultivation of and adherence to shared values and goals must guide us. Ultimately, CAILs minimally ask us to contemplate how we as academics can stop AI companies from wielding so much power.