From Block's Massive Layoffs to "SaaSpocalypse": How AI Is Reshaping the White-Collar Job Market?

In February 2026, the global tech industry experienced a profound shakeup regarding “human-machine relations.” Jack Dorsey, the former Twitter co-founder, led the payments company Block (formerly Square) to announce layoffs of over 4,000 employees, accounting for 40% of its workforce. Unlike layoffs driven by poor performance, Dorsey candidly stated in an all-staff email, “Our business remains strong, gross margins are growing, our customer base is expanding, and profitability is improving. But the world has changed. The intelligent tools we are creating and using, combined with smaller, flatter teams, are opening up a whole new way of working.”

This event is not isolated. Almost simultaneously, discussions about “AI replacing white-collar workers” swept through global capital markets. Mustafa Suleyman, head of Microsoft’s AI division, issued a warning in an interview: all computer-based professional jobs—accounting, law, marketing, project management—will be fully automated by AI within 12 to 18 months. Meanwhile, a report titled “The 2028 Global Intelligence Crisis” circulated wildly in Silicon Valley and Wall Street, projecting an unsettling future: as AI boosts corporate efficiency, it systematically destroys high-paying white-collar jobs and could trigger chain reactions of economic deflation.

The “decentralization” and “flattening” of organizational structures are shifting from theory to reality. Whether it’s Perplexity, an AI-native company valued at $14 billion with only 247 employees, or Block’s decisive “downsizing,” both point to the same trend: AI is no longer just an auxiliary tool but a core variable in restructuring corporate power and human value.

Timeline and Key Milestones of AI Reshaping Organizations

To understand the current impact of AI on white-collar jobs, it’s essential to review key developments over the past year:

• Early 2025: Generative AI adoption in enterprises explodes. A McKinsey global survey shows 78% of companies use AI, with 71% frequently deploying generative AI in at least one business function. At this stage, mainstream narratives still emphasize “AI augmenting humans,” not “AI replacing humans.”
• Q4 2025: Corporate financial data begins reflecting structural shifts. Gartner reports that over 30% of tech companies worldwide will initiate organizational streamlining due to AI applications in 2026, with more than half cutting jobs despite profit growth—primarily because AI fundamentally changes labor demand structures.
• February 17–24, 2026: Anthropic launches Claude Cowork, an AI tool capable of automating legal reviews, customer relationship management, and data analysis. Within 48 hours, global software stocks experience a “SaaSpocalypse”: Atlassian plunges 35%, Intuit drops 34% quarterly, Thomson Reuters down 16%, LegalZoom falls 20%, wiping out hundreds of billions in market value.
• February 24, 2026: Citrini Research releases “The 2028 Global Intelligence Crisis,” a fictional scenario projecting how AI’s “intelligent substitution spiral” could cause white-collar income collapse and mortgage defaults.
• February 26, 2026: Block announces a 40% layoff, with Jack Dorsey explicitly attributing it to AI-driven organizational flattening, marking the overt emergence of “profit and employment decoupling” as a clear trend.

These events show that AI’s impact on white-collar jobs is not a gradual infiltration but a nonlinear acceleration driven by technological breakthroughs (like Claude Cowork) and corporate decisions (such as Block’s aggressive layoffs).

Data and Structural Analysis: Who Is Being Replaced, and What Is the Logic?

Current data and case studies indicate that AI’s replacement of white-collar roles is not uniform but follows specific functional logic.

First, middle-management and process-oriented roles are most vulnerable. In traditional bureaucratic organizations, middle managers primarily transmit instructions and supervise progress—functions centered on information coordination. When digital dashboards provide top executives with panoramic oversight, and AI agents can automatically track workflows and performance metrics, the value of middle managers is algorithmically replaceable at zero cost. Similarly, roles heavily reliant on information processing—such as basic data analysis, standardized reporting, and initial contract review—are being mass-automated. The US IT employment from its 2022 peak to early 2026 has declined by 8%, a drop unseen in the past decade.

Second, the logic of replacement is task-based rather than role-based. 36Kr’s analysis points out that AI is unlikely to fully replace an entire role at once but will gradually automate specific tasks within that role. For example, lawyers and auditors, despite deploying AI for document review, have seen limited productivity gains and are far from complete role automation. However, once the proportion of automatable tasks in a role exceeds a critical threshold, companies are motivated to merge functions and reduce staffing.

Third, AI-native organizations are becoming new benchmarks. Valued at $14 billion, Perplexity has only 247 employees; Cursor AI, valued at about $9 billion, has roughly 30 staff. These “AI-native organizations” operate by encapsulating workflows into networks of AI agents, with humans mainly defining problems, setting goals, and verifying outputs. Once traditional large firms adopt this model, massive layoffs are likely.

Public Opinion: Optimists, Pessimists, and Realists

Three main narratives shape current market discourse on AI and white-collar employment:

Pessimists: AI-driven deflation and employment collapse. Citrini’s “The 2028 Global Intelligence Crisis” argues that AI uniquely replaces the “demand creators” in human history. When high-salary white-collar workers are laid off, they flood the gig economy, suppressing overall wages, leading to reduced consumption and mortgage defaults—forming a “spiral of intelligent substitution.” The report envisions scenarios where just 5% white-collar unemployment could cause far more than 5% decline in consumption, as a $150,000/year product manager losing their job might only earn $40,000, a 70% income drop.

Optimists: Lessons from history and new job creation. A recent Morgan Stanley cross-asset research report states AI will not cause large-scale permanent unemployment. Past technological revolutions—electrification, the internet—reshaped labor markets but did not eliminate overall employment. For instance, the spread of spreadsheets automated bookkeeping tasks but also created new roles in financial modeling and analysis. Future roles like Chief AI Officer, AI Governance & Compliance Expert, and AI Personalization Strategist will emerge. Citadel Securities also counters the “AI destroys jobs” narrative, noting a recent surge in software engineering job postings, suggesting AI is more of a supplement than a substitute.

Realists: Productivity paradox and organizational adaptation challenges. Based on frontline management practices, surveys by Fudan University and 36Kr reveal that while individual productivity improves with AI (e.g., Boston Consulting’s experiment shows GPT-4 users complete tasks 25% faster), organizational value realization remains difficult. MIT reports that only about 10% of firms see significant financial gains from AI, mainly due to organizational learning, process reengineering, and human-AI collaboration deficiencies. This indicates that large-scale replacement is not inevitable but depends on whether companies can bridge the gap from individual efficiency to organizational capability.

Evaluating the Narratives’ Credibility

In the mixed discourse, it’s crucial to assess the factual basis of each narrative.

On the “apocalypse” scenario: Alap Shah, co-author of “The 2028 Global Intelligence Crisis,” emphasizes that the report is a “long-term stress test” based on hypothetical models, not a prediction. Its value lies in exposing logical vulnerabilities, not foretelling the future. In reality, large-scale AI deployment faces constraints like power supply, computing costs, organizational change speed, and regulatory approval. The San Francisco Standard notes that disruption speed is limited by the slowest link; technological iteration is rapid, but organizational change lags.

On “historical analogy”: Optimists’ historical comparisons have blind spots. Citrini notes that past revolutions (computers, internet) mainly enhanced human efficiency, whereas AI directly automates workflows. Nobel laureate Daron Acemoglu warns that AI may differ qualitatively, with pure automation potentially devaluing human expertise and further decoupling profits from employment.

On “task automation” versus “role automation”: Suleyman’s “12-18 months replacement” claim sparks debate. Scholars point out that he confuses “task automation” with “role automation”—a single role often involves multiple inseparable functions. Automating one task (like dishwashing) does not eliminate the chef profession, which relies on creativity, quality control, and menu design that AI cannot replace.

Industry Impact: From Enterprises to Financial Systems

AI’s impact on white-collar jobs propagates through three pathways, reshaping broader industry landscapes:

Path 1: Reconfiguring corporate valuation logic. Capital markets are beginning to price “AI replacement capability.” After Block’s layoffs, its stock rose 5.2% the next day, reflecting investor recognition of efficiency gains. Meanwhile, traditional labor-intensive companies face valuation pressure, while AI and computing tool sectors attract capital. This divergence indicates markets see AI as both an efficiency enhancer and a potential disruptor of information-dependent business models.

Path 2: Dissolving “friction” in business models. Citrini highlights that many traditional firms profit from exploiting “human weaknesses”—banks from fees, intermediaries from information asymmetry, SaaS from subscription churn. AI agents act as “frictionless” tools: 24/7 price comparison, negotiation, supplier switching—costs that were once intermediaries’ margins can now be optimized away. This shift threatens revenue models in insurance, travel booking, financial advising, food delivery, and more, prompting layoffs and restructuring.

Path 3: Credit risk transmission in the financial system. The “Prime Crisis” discussed in “The 2028 Global Intelligence Crisis” raises concerns. High-credit-score, high-income professionals—like those with 780+ scores and $200,000 annual salary—are ideal borrowers. But if AI-driven layoffs cause income drops, mortgage defaults could spike. Although China’s financial system is bank-led and differs from the US, sustained weakening of white-collar employment and income expectations would dampen housing demand and consumer spending, impacting real estate and related sectors.

Scenario Evolution and Projections

Based on current information, three potential scenarios for AI’s impact on white-collar jobs emerge:

Scenario 1: Gradual restructuring (baseline). AI replaces some roles while creating new ones (e.g., AI governance specialists, human-AI collaboration designers). Organizational reforms and workforce retraining gradually improve individual and organizational productivity, leading to a balanced transition. Success depends on policy support, retraining systems, and social safety nets.

Scenario 2: Spiral of intelligent substitution (pessimistic). Firms, under competitive pressure, rapidly replace human labor with AI, causing displaced workers to flood gig markets, suppress wages, and reduce consumption. This triggers a feedback loop: declining corporate revenues lead to further layoffs, reinforcing the “profit-employment decoupling.” Conditions include sustained low AI marginal costs, lagging policy responses, and slow creation of new roles.

Scenario 3: Regulatory intervention and redistribution (interventionist). Governments implement policies like “compute taxes” or “AI prosperity funds” to mitigate structural unemployment. Policies promote human-AI complementarity—subsidizing core jobs, investing in sectors hard to automate (healthcare, education, infrastructure)—aiming for a more equitable transition.

Conclusion

Block’s 10,000 layoffs serve as a stark warning: the old consensus that “profitability ensures job stability” is failing. In the wave of AI-driven organizational restructuring, the real risk of displacement lies not in “white-collar” as a whole, but in functions that are standardized, process-driven, and intermediary. Both optimistic and apocalyptic narratives have their biases; the future trajectory depends on the pace of technological iteration, organizational learning, and policy responses. For practitioners, rather than succumbing to anxiety over replacement, it’s more productive to reassess one’s unique value—those tasks requiring creativity, judgment, ethics, and handling exceptions will remain human core competencies in the AI era.