Thought leadership from Newmark Group
by Joe Biasi, David Bitner, Jessica Morin, and Ray Whelan
Executive Summary
Artificial intelligence (AI) will be transformative and disruptive for the labor market and future of office, with far‑reaching implications for commercial real estate demand and workplace design. This report examines AI’s potential impact on office‑using employment and explores how AI‑driven workforce shifts could shape U.S. office demand by 2030.
Public opinion on AI is internally conflicted, with excitement about its potential running alongside persistent concerns about its disruptive impact on jobs. There is broad consensus that AI’s impact on employment will vary across industry sectors, occupations and skill levels but disagreement on the scope of those impacts. Today, the rapid expansion of established and emerging AI and AI‑enabled firms is driving new office demand in select tech hubs, most notably the San Francisco Bay Area. Over the next five years, as adoption accelerates, AI is likely to moderate labor‑driven office demand by enabling greater output with fewer employees. Over time, we expect new firms, product and occupations to emerge, offsetting the initial negative demand shock from AI. This pattern is consistent with earlier waves of labor‑saving technological change (for example, personal computers replacing typists, automobiles replacing horse‑drawn transport and telephones displacing telegraph and mail carriers).
There are reasons to be optimistic: the recent surge in new business formation suggests that the lag between labor substitution and the emergence of new patterns of trade may be shorter this time. AI will likely amplify the office market transformation set in motion by hybrid work, intensifying the flight to quality and refining how and when employees engage with the workplace. It will influence which space types deliver the greatest value for in‑person collaboration and guide the design of more efficient, adaptable work environments. For occupiers, AI offers a powerful tool for optimizing workplace strategy and sharpening their competitive edge. For investors, it will create targeted opportunities, particularly in markets and assets aligned with emerging AI‑driven demand patterns.
This report is divided into two parts: Part I: Labor Trends and AI’s Workforce Impact and Part II: Quantifying the Impact—Our Modeling Approach. Readers interested in the quantitative analysis may wish to begin with Part II.
Key Findings:
- Office job transformation, but without growth: AI will likely act as a headwind to labor‑driven office demand through 2030. In our base case forecast, office-using employment growth will be essentially flat (+0.3%) in the 2026-2030 period. This is more remarkable than it first appears. Since at least 1944, office-using employment has rarely been flat or declined in a five-year period—the Great Recession being the notable exception—and has never done so without an associated recession, which our forecast does not entail.
- New AI-driven office demand hubs: In the immediate term, AI and adjacent industries such as cloud and data infrastructure, semiconductors and specialized hardware are generating new office demand. The surge is concentrated in the San Francisco Bay Area and is spreading into major talent markets including, but not limited to, Manhattan, Seattle, Los Angeles and Austin.
- Entry level knowledge roles most exposed: Near‐to medium‑term displacement risk is concentrated in entry‑level and highly automatable office‑using roles, heightening exposure for back‑office functions. Conversely, higher‑skill and relationship‑driven office roles are more likely to be augmented by AI rather than replaced. As AI is more likely to dampen overall office space utilization rather than trigger wholesale upheaval, high‑quality, collaboration‑oriented office settings will be comparatively resilient, while commodity space will be more vulnerable.
- No (broad) office recovery but no collapse: The anticipated slower pace of hiring is expected to push office vacancy up by about 10 basis points from year‑end 2025 to 21.5% in 2030, in our base case scenario. To capture uncertainty around the speed and scale of AI adoption, productivity gains and workforce restructuring, we present four alternative scenarios that bracket this outlook under varying assumptions. In our moderate upside case, vacancy declines to 19.5% by 2030 whereas in our severe downside case vacancy rises to 23.5%.
- Strategic imperative for CRE stakeholders: Occupiers will need flexible, purposefully designed workplaces that prioritize collaboration, culture, wellbeing and talent attraction as AI reshapes job structures and space needs. For owners, portfolio resilience will depend on curating high‑quality assets in prime locations with durable, innovation‑aligned tenant mixes that drive long‑term outperformance and limit downside risk.
Part I: Labor Trends and AI’s Workforce Impact
AI, Fear and Familiar Patterns of Disruption
Intense hype around AI‑driven productivity gains has been accompanied by increasing concerns about job displacement, echoing anxieties from past technological transitions. Ultimately, the outcomes will depend on several factors, including the pace of AI adoption, the balance between task augmentation and automation for various occupations, how firms choose to apply productivity gains and how demand for goods and services evolves.
Pace of Adoption
AI’s impact on jobs will depend on how quickly and how deeply organizations move from AI pilots to fully redesigned workflows. Today, the barrier to realizing AI’s potential lies not just in the technology itself, which is improving at a rapid pace, but in the slow pace of scaled adoption, due to factors including distrust, regulatory complexity and unclear governance and risk standards. McKinsey’s 2025 global AI survey finds that while 88% of organizations use AI in at least one business function, 62% remain in experimentation or pilot mode, and only about one‑third have begun to scale AI across the enterprise, primarily larger firms with more than $5 billion in revenue. Similarly, a recent Anthropic report comparing theoretical task exposure with observed usage on its Claude platform finds a wide gap between theoretical capability and adoption. In computer and math occupations, for example, large language models could theoretically perform 94% of job tasks in that category, yet current usage covers only about 33%. Despite this early stage, business leaders are betting on fast and far-reaching change. The 2025 World Economic Forum (WEF) Future of Jobs report reveals that 86% of employers expect AI and information-processing technologies to transform their business by 2030, suggesting today’s pilots are laying the foundation for large‑scale change.
Augmentation vs. Automation
AI will affect jobs through a mix of automation and augmentation. Automation occurs when AI fully takes over tasks previously performed by workers, with limited human oversight. Augmentation, on the other hand, involves AI complementing and enhancing a human’s work, allowing workers to focus on their areas of comparative advantage, thereby increasing the quality and volume of output through greater specialization. Occupations with a higher degree of automatable tasks have a greater displacement risk than jobs that are more highly augmented.
Based on survey responses from over 1,000 global employers, the WEF report estimates that in 2025, an average of 47% of work tasks across occupations were performed solely by humans, 22% by technology and 30% by a combination of both. By 2030, employers expect these shares to be nearly evenly split (Figure 3). However, these values vary substantially by sector and occupation.
Evidence suggests that current AI use leans more towards augmented rather than automated work. Anthropic’s January 2026 Economic Index found that 52% of Claude conversations were classified as augmentation compared with 45% categorized as automation, with augmentation especially prevalent in complex, knowledge‑intensive tasks. If this pattern holds as adoption deepens, many more jobs are likely to be restructured through augmented workflows than replaced by automation, which would temper but not eliminate displacement. Achieving this outcome will require sustained public‑ and private-sector investment in upskilling workers and training a new pipeline of talent to work effectively alongside AI.
Reallocating AI Efficiency Gains
What companies, and society, do with AI‑driven efficiency gains will be critical for labor‑market outcomes. For our purposes, these gains come in two forms: time and money. Historical experience shows that redeploying human capital and the real income generated by positive productivity shocks can be powerful. Each major wave of new production technology—steam power, electrification, mechanization, computing and the internet—ultimately gave rise to new industries and occupations. Despite near‑term disruption, these advances lifted overall employment and standards of living over time. As Citadel Securities observed in a recent report “A scenario in which productivity surges but aggregate demand collapses while measured output rises, violates accounting identities.”
A 2024 McKinsey survey finds that most organizations reallocate time saved by automation into new activities or higher‑value work. Larger employers, however, are more likely to reduce headcounts as efficiencies build—and those reductions are strongly associated with greater bottom-line gains from generative AI. The balance between reinvestment and role redesign versus layoffs will likely be an important factor in shaping net employment outcomes.
These efficiency gains will likely spur new firm creation, which has already surged over the past few years. Annual business applications in the U.S. are well above pre‑pandemic norms and more than double the 2018–2019 average. By lowering the cost, time and expertise required to start and run a business, AI can further accelerate new business creation.
Office Sector Exposure to AI
AI’s impact on employment will be uneven across industries and occupations. Knowledge‑based roles—especially repetitive or entry‑level positions typically performed in offices or remotely, are among the most exposed, as AI increasingly conducts basic administrative, analytical and communication tasks with little human oversight. Traditional “office‑using” sectors— professional and business services, information and financial services—are heavily composed of these knowledge roles (telemarketers, insurance claim and policy processing clerks, credit authorizers, payroll staff, travel agents, tax preparers, bill collectors). In contrast, service and field‑based occupations that take place primarily outside the office (athletes, paramedics, landscapers, roofers, lab technicians, barbers) are expected to be less affected given their emphasis on in‑person, manual or context‑rich work that is harder to fully automate.
Figure 5 illustrates select industries and their expected shift in the human share of work‑task delivery in total firm output between 2025 and 2030, distinguishing between changes driven by automation and those driven by augmentation, based on WEF survey data. The steepest declines in human‑only task share are concentrated in office‑based sectors. In professional and business services, for example, employers estimate that the human-only task share will drop from roughly one‑half today to about one‑third by 2030, with approximately two‑thirds of that decline attributable to outright automation rather than augmentation. We incorporate this sector‑level exposure to automation and augmentation into our broader model of AI’s impact on employment and office demand, discussed later in this paper.
Because AI exposure is highest among office-using occupations, and office demand depends on the growth of those jobs, the office sector faces the greatest exposure risk among commercial real estate property types. As AI adoption accelerates, slower job growth in office-using industries could further weigh down future demand for office space. In 2024, Oxford Economics reached the same conclusion, as seen in Figure 6.
Current Evidence of AI-Related Job Displacement
AI has become a defining theme in how companies describe and strategize around their workforces, yet its effects are hard to discern in labor market data. To gauge what’s happening now, we first examine how employers are framing AI’s role in job design, skills and workflows, and then compare those narratives with the underlying employment data.
What Employers Are Saying
Several prominent employers—including Amazon, Walmart, Salesforce, Block, and CrowdStrike—have referenced AI-enabled efficiency gains in public statements related to recent layoffs. Analysts caution against interpreting these disclosures as evidence of AI-driven job displacement, noting that workforce reductions are often influenced by broader factors such as overhiring, slowing demand or margin pressures. To the broader public, however, this framing may lead many to overestimate AI’s immediate labor‑market impact.
The Federal Reserve’s January 2026 Beige Book tempers the narrative of an AI-driven hiring collapse. Multiple contacts across districts reported exploring AI primarily for productivity enhancement and potential future workforce management. They noted incremental productivity gains to date and limited near-term effects on headcount, with more meaningful changes expected over the coming years. Marketing, call center and coding roles were cited as at‑risk, although often in offshore locations, and some firms indicated that modest AI adoption allows them to avoid refilling positions, leaving vacancies unfilled through natural attrition.
Leaders of AI firms have publicly forecast significant white-collar workforce transformation, sentiments that seem quite sober compared to some of the more outlandish prognostications floating around in the discourse. While these perspectives highlight AI’s potential, they also reflect narratives intended to position AI as an essential investment that reduces labor costs. Given this context, it is important to interpret these types of forward‑looking statements alongside empirical labor‑market data and observed adoption trends, recognizing the substantial uncertainty surrounding AI’s long‑term effects on the workforce.
What Labor Data Shows
Next, we need to turn to the labor market data to gauge the impact of AI on jobs. The U.S. labor market has seen relatively weak growth over the past two years, with a low-hire, low-fire dynamic and office‑using employment being essentially flat in 2025 (Figure 7). While the economy has not experienced large-scale layoffs in recent years, companies still trimmed headcount and slowed or paused hiring, and fewer employees chose to leave their jobs voluntarily.
Most net job growth in 2025 came from non‑office‑using, lower‑AI‑exposure sectors—led by education and healthcare, which now employs about 13% more workers than before the pandemic. By contrast, the information sector—an office‑using industry with higher AI exposure that includes many technology workers—still has fewer jobs than in February 2020. This labor market slowdown, outside of healthcare, began before the recent wave of AI adoption and largely reflects structural supply‑side constraints, such as an aging population, tighter immigration and the information sector’s pandemic‑era over‑hiring and subsequent layoffs.
Current data suggests that AI is exerting a modest and diffuse effect on the overall labor market. It appears to be a contributing factor rather than the main driver of recent softness, and its impact remains difficult to disentangle from other underlying forces—an important caveat to keep in mind throughout this discussion.
Early signs of AI’s labor impact have appeared among younger workers in highly AI-exposed roles. A 2025 study by the Stanford Digital Economy Lab found that employment declines were concentrated among 22–25‑year‑olds in exposed occupations such as software development, customer service and clerical work, while employment for older workers in the same occupations, and for workers of all ages in less‑exposed roles has remained stable or continued to grow.
These patterns help explain why overall employment for 22–25‑year‑olds has been relatively flat since late 2022, even as the broader labor market has expanded. In less AI‑exposed jobs, young workers have kept pace with their older counterparts, but in highly AI‑exposed occupations their employment fell 6% from late 2022 to September 2025, while older workers in the same occupations saw gains of 6-9% (see Figure 8).
Replacing entry-level roles with AI automation poses a serious long‑term risk for firms. By shrinking or bypassing early career hiring, companies erode the very talent pipeline they depend on to develop future experienced and leadership roles, a challenge that will be further exacerbated by the aging workforce approaching retirement over the next five to ten years. Without sustained opportunities for early‑career workers to enter, learn and progress, organizations risk facing acute shortages of skilled, promotion‑ready employees in the years ahead[1].
[1]Companies would be wise to consider the fable of the ant and the grasshopper, which teaches that short-term savings can come at the expense of long-term resilience.
