Block cuts 40% of workforce as AI strategy boosts earnings

Block just fired nearly half its employees and Wall Street loved it. The fintech company behind Square and Cash App announced on February 26 that it’s cutting over 4,000 jobs, roughly 40% of its entire workforce, as part of what CEO Jack Dorsey described as an AI-driven operational overhaul.

The company’s headcount will shrink from over 10,000 to under 6,000. Block’s stock surged 20-25% in after-hours trading.

The numbers behind the bloodbath

Block’s Q4 2025 gross profit hit $10.36 billion, a 17% jump year-over-year. Cash App was the standout performer, with its gross profit climbing 33% over the same period.

The company raised its 2026 outlook, forecasting gross profit of $12.20 billion, an 18% increase from the prior year. Block also projected adjusted operating income of $3.20 billion and adjusted diluted earnings per share of $3.66.

Block expects to absorb between $450 million and $500 million in restructuring charges, with the bulk landing in Q1 2026.

Dorsey framed the cuts not as a response to financial trouble but as a strategic bet. Smaller teams armed with AI tools, the argument goes, can outperform bloated departments that were hired during the pandemic-era growth binge.

The crypto connection

Block maintains a corporate Bitcoin treasury. Cash App, which just posted that 33% gross profit increase, offers Bitcoin buying and custody services to its millions of users.

A template for the industry

Block’s CFO indicated that these deep, AI-motivated cuts are likely part of a broader industry trend.

The execution risks are real. Cutting 40% of your workforce is not the same as trimming around the edges. Institutional knowledge walks out the door. Teams that took years to build get dismantled in weeks.

What this means for investors

The $450-500 million in restructuring charges will create noise in the next few quarterly reports. Investors should watch whether Block’s gross profit trajectory actually accelerates post-restructuring or whether the AI efficiency gains plateau once the easy automation targets have been addressed.

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