Morgan Stanley analyst Adam Jonas asked investors at the bank’s annual Technology, Media, and Telecom Conference what question they were hearing most often. The answer wasn’t about AI stocks or infrastructure plays or which lab was winning the capabilities race.
It was: “What will our kids do?”
The question hung over San Francisco this week as Wall Street’s biggest AI-focused gathering delivered a message that should concern anyone with a job to lose: the disruption is no longer theoretical, the timeline is accelerating, and the people building these systems are telling us to brace for impact.
The Survey Results
A Morgan Stanley survey of roughly 1,000 executives across five countries found an average net workforce reduction of 4% over the past 12 months - directly attributable to AI adoption. On average, these companies reported an 11.5% increase in productivity alongside that headcount decline.
The U.S. was actually the optimistic outlier, with a 2% net gain in jobs as AI-related hiring outpaced cuts. But automotive companies in the survey reported a 10% net loss. The displacement is real and it’s uneven.
Multiple executives at U.S. LLM labs warned that near-term AI progress would “surprise, and potentially shock, investors.” OpenAI CEO Sam Altman suggested models are becoming “extremely capable” faster than he originally thought. He floated a future where “one or five people” could run entire companies.
The Breakthrough Timeline
Morgan Stanley’s researchers predict a major leap in AI capabilities will become visible between April and June of this year. The basis for this prediction: scaling laws are holding firm, and an unprecedented accumulation of compute at America’s top AI labs is about to pay off.
The report cites Elon Musk’s belief that applying 10x the compute to LLM training will effectively double a model’s “intelligence.” OpenAI’s recently released GPT-5.4 “Thinking” model scored 83.0% on the GDPVal benchmark - placing it at or above the level of human experts on economically valuable tasks.
xAI co-founder Jimmy Ba delivered the conference’s most dramatic warning: “Recursive self-improvement loops likely do live in the next 12 months. 2026 is gonna be insane and likely the busiest and most consequential year for the future of our species.”
The Layoff Wave Continues
The first quarter of 2026 has already seen over 45,000 tech layoffs, with more than 9,200 attributed to AI and automation. According to RationalFX analysis, if cuts continue at this pace, total reductions could reach 265,000 by year-end - exceeding 2025’s total of 245,000.
The latest example: Atlassian announced it would cut 1,600 jobs - 10% of its workforce - to “self-fund further investment in AI and enterprise sales.” The company expects the restructuring to cost between $225 million and $236 million in severance and office closures. Its stock rose 2% after the announcement.
Block cut 4,000 jobs in February, reducing headcount from 10,000 to 6,000. CEO Jack Dorsey predicted most companies would reach the same conclusion within the next year. Pinterest, Dow, HP, Amazon, and others have made similar moves.
The Three Growth Areas
Morgan Stanley’s analysts argue the picture is more nuanced than pure displacement. Three areas of the labor market are actually experiencing AI-driven demand growth:
Skilled trades: The massive infrastructure buildout for AI is creating unprecedented demand for electricians, electrical engineers, and construction workers. CoreWeave reported a shortage of “thousands of skilled-trade workers” for data center construction. Nvidia CEO Jensen Huang cited electrician shortages in Texas as a constraint on expansion.
Education and reskilling: Coursera reported AI-content enrollments reached 15 per minute in 2025, nearly double the 8 per minute in 2024. Corporate buyers are increasingly driving this demand as companies race to upskill employees.
AI supervisors and orchestrators: A new category of knowledge work is emerging around managing AI systems rather than performing operations directly. C.H. Robinson told the conference that “future jobs will involve managing standard operating procedures and context for AI agents.” Salesforce introduced “Agentic Work Units” as a productivity metric for humans directing AI systems.
The Economics Problem
Morgan Stanley’s “Intelligence Factory” model projects a net U.S. power shortfall of 9 to 18 gigawatts through 2028 - a 12% to 25% deficit in the energy needed to run AI infrastructure. Companies are converting Bitcoin mining operations and deploying fuel cells to fill the gap.
An emerging “15-15-15” dynamic shows 15-year data center leases generating 15% yields with $15 per watt in net value creation. Nvidia CEO Jensen Huang summarized the economics simply: “Compute equals revenue.”
Morgan Stanley predicts “Transformative AI” will become a powerful deflationary force as AI tools replicate human work at a fraction of the cost. The bank warns this could create a consumption wedge: high-income consumers with AI-driven portfolio gains will spend more, while middle-income workers exposed to automation will spend less.
What This Means
The Morgan Stanley conference crystallized a shift in how Wall Street talks about AI. The question is no longer whether AI will displace workers but how fast and how many. The survey data shows the displacement has already begun. The executive statements suggest it’s about to accelerate.
The conference also revealed the uncomfortable tension at the heart of the AI economy. The same companies announcing record AI investments are simultaneously announcing workforce reductions to pay for them. The “self-funding” model that Atlassian described - cutting jobs to invest in AI that will cut more jobs - is becoming standard practice.
Whether the three growth areas Morgan Stanley identified can absorb the displaced workers remains an open question. Electricians and AI orchestrators require different skills than the software engineers and knowledge workers being let go. The reskilling gap is real, and it’s not clear the education market can close it fast enough.
The Bottom Line
Wall Street’s biggest AI conference left investors with a question they couldn’t shake: what will our kids do? The answer from the people building these systems was unsettling in its honesty. They don’t know either - but they’re building anyway, and they expect the results to shock us.