Bonus Weekend AI Newsletter: AI and Work
A rec list of my weekly readings on AI! This week, in a bonus edition, I am focusing on reports that cover AI’s impact on the future of work.
Workforce Transformation
Oliver Wyman Forum has dedicated a big part of its research towards AI and Work, resulting in a large 2025 report on Workforce Transformation In The AI Era. The key message of the report is that AI is being integrated into specific job functions but not replacing whole occupations. From an optimistic lens, that means that teams are already using AI to automate repetitive tasks, freeing up time for strategic and creative work. Yet, it also means that AI is rapidly narrowing what “safe” professions and assignments are and skill requirements are shifting rapidly. Some stats that caught my eye in the report:
The largest group of organizations, at about 46%, are implementing AI selectively, particularly in regulated industries or those with legacy systems.
~15%– 50% of all business tasks will be automated or augmented due to generative AI by 2030.
76% of employees report changes to their job description or required skills due to AI disruption.
Index of AI Usage Across Industries
Anthropic put out a comprehensive Economic Index along with a paper where they outline their framework for measuring AI usage patterns across the economy to add to the discourse about AI’s impact on the future of work. Some of the statistics from the Index:
An overwhelming number (37.2%) of Claude’s queries are for assignments that can be classified under the “computer and mathematical” category.
They do not see AI taking up over 75% of tasks in any occupation, but they do see a widespread moderate use (at least 25% of tasks).
Mid-to-high median salary ranges are among the heaviest users of AI but the highest and lowest-paid positions see equally low AI penetration.
Regulatory framework
An IMF blog: AI Will Transform the Global Economy. Let’s Make Sure It Benefits Humanity argues that governments and universities need to actively steer AI’s impact through reskilling, regulation, and inclusion. While the two reports above report the status quo about the market “figuring it out” with AI implementation, IMF is calling for a more regulatory framework. Here are some of the most notable points that come from Ithe MF:
About 60 percent of jobs may be impacted by AI in the new future.
The IMF is making a distinction between high complementary vs low complementary AI implementation: positions that will increase productivity and even income with AI tools and others where AI can fully put people out of work and worsen global inequality.
The IMF developed a comprehensive AI Preparedness Index that they explain in this paper. The index measures digital infrastructure, human capital, and labor-market policies across 174 countries to assess the intersection of innovation and regulations.
Negative examples
Here are some less unfortunate news and updates in the realm of AI and work without going too deep into summarizing them:
Shopify CEO says staffers need to prove jobs can't be done by AI before asking for more headcount: the CEO of Shopify sent out a controversial memo that departments can’t request new hires until they have confirmed that AI can't do that job. In that same memo, Tobi Lutke highlighted that he wanted to see people at Shopify lean into the use of AI: “as a thought partner, deep researcher, critic, tutor, or pair programmer.”
An AI investor Ethan Batraski shared on his Substack that the “elite” professions that many college graduates try to get (finance, wealth management, consulting, etc.) are easily swept away by AI. Yet he also notes that clients of these companies will embrace and hire AI-native services. Here is a narrated podcast of this story with the same catchy title, “Why AI Will Take Over the $20T Professional Services Industry.”
General Thoughts for Students and Educators
Here are general insights and thoughts for students and educators—the demographic I most interact with currently—I have after diving into this topic for a few weeks:
🎓 For Students: AI will be your co-worker, collaborator, and even maybe boss
Don’t give into anxiety: Learn how to use AI in daily tasks to increase your efficiency and productivity because our work will require it. The reports above show that there is already a visible shift in KPIs and productivity metrics. AI won’t replace jobs wholesale and probably even won’t have a super-deep penetration (75%+), but they already do include managing AI tools. Check out Ethan Mollik’s Co-Intelligence I wrote about before to identify AI’s roles in your life: teammate, coach, editor, etc.
Double down on human skills: Many positions will be highly complementary with AI tools and AI literacy can potentially lead to higher wages. However, those positions, from junior to senior, require skills beyond technical: creative ideas, critical thinking, self-motivation, and others.
Broaden your foundation. Even if you’re (and me :) a humanities major, we need to learn and understand prompt engineering, AI ethics, or automation tools. I believe that one of the most effective skills will be the ability to translate between tech and non-tech people while ethically implementing AI in teamwork.
🏛️ For Educators and Indicators: We need to learn and teach the skills together
Integrate AI fluency into all disciplines: Students need exposure to real-world tools—not just theory. Even non-tech majors should engage with GenAI tools, data-driven thinking, and automation frameworks. Classes can and should be the first ethical and effective collaboration with GenAI tools to understand which tasks will be made more efficient and which we are better off developing as our key specialties and skills.
Rethink assignments. In the Critical AI Learning Community, we have discussed GenAI tools being cited and used as collaborators. This collaboration can help produce better analysis, writing, or problem-solving—without practicing, students won’t know if that collaboration is fruitful and effective. We also collaboratively need to establish boundaries between cheating, and plagiarism vs tech literacy.
Address the inequality gap: Data shows that women and underrepresented populations rely upon and use GenAI tools less. We need to create creative spaces where all student populations have equal access to upskilling opportunities. The digital divide will get worse without an intentional design of curriculum and assignments.