By the time most fintech executives finish reading this sentence, another workflow that once required a human manager will have been automated with AI somewhere in the world. At least, this operational logic is now playing out inside Block Inc., the payments and financial services company co-founded by Jack Dorsey.
Why Block Chooses AI Over Middle Managers
In 2024 and into 2025, Block cut roughly 4,000 positions across its business units, which include Square, Cash App, and Afterpay. The official framing centered on efficiency and focus. But Dorsey’s own statements went further.
In both internal communications and public remarks, Dorsey argued that middle management — the layer of organizational hierarchy responsible for coordinating teams, relaying decisions, and tracking execution, was increasingly redundant in a company that embedded AI into its operating model. He predicted what he called a “middle management collapse”: not a gradual decline, but a structural removal of an entire organizational tier.
Block’s results since the cuts have been notable. The company reported improved margins and faster product iteration cycles. Revenue per employee rose. The organizational structure flattened. Decisions that previously moved through multiple layers now route through smaller, more technically capable teams supported by AI tools. It appears that Block genuinely restructured how work happens and the corporate financial results reflect that.
Why “Middle Management” Is the Right Target for AI Substitution
To understand why Dorsey’s prediction carries weight, it helps to understand what middle managers actually do in financial services organizations.
At most banks, payment processors, and fintech firms, middle management performs several core functions: translating strategy into operational tasks, monitoring team performance, coordinating between departments, managing reporting cycles, and escalating issues upward. These are, bluntly, information-routing and oversight functions.
AI systems, specifically large language models combined with workflow automation and real-time data dashboards, can now handle significant portions of all five. Strategy can be decomposed into tasks automatically. Performance can be monitored continuously without a human reviewer. Cross-departmental coordination can be handled by shared systems rather than scheduled meetings. Reporting compiles itself. Anomalies surface algorithmically.
This does not mean middle managers will disappear overnight, and it does not mean their work holds no value. But it does mean that organizations willing to restructure around these capabilities will carry substantially lower overhead than those that don’t.
The European Fintech Exposure
European fintech faces this pressure with a specific set of structural disadvantages.
Regulatory complexity creates management bloat. Operating across EU member states means compliance with overlapping frameworks — PSD2, GDPR, AML directives, national licensing requirements, etc. That compliance has historically been managed by dedicated teams with significant human oversight. This is exactly the kind of structured, rule-based work that AI handles well. European fintechs that haven’t automated their compliance workflows are now paying for labor that their competitors may soon replace with software.
Funding constraints limit reorganization appetite. The European venture environment, while improving, remains more conservative than its US counterpart. Companies with tighter runways are less likely to absorb short-term disruption costs associated with restructuring. That means the AI transformation that Block executed from a position of relative financial strength may be harder for smaller European players to replicate, even if the long-term economics are favorable.
Labor market regulations create friction. Workforce restructuring in Germany, France, Spain, and several other EU markets involves legal processes, works council consultations, and severance obligations that don’t exist in the same form in the United States. This doesn’t make transformation impossible, but it raises the transition cost and extends the timeline.
Legacy hiring structures reward seniority over output. Many established European financial institutions and the fintechs that modeled themselves on those institutions built compensation and promotion structures around tenure and management scope. Flattening those structures requires cultural change that is slow and politically difficult inside organizations.
What the Block Case Study Actually Demonstrates
Three things stand out from Block’s experience that have direct relevance for European fintech operators.
First, the sequencing matters. Block didn’t cut headcount and then figure out how to operate. The company built and integrated AI tools into its workflows, established that those tools could handle the volume and quality of work previously managed by humans, and then restructured around that new operational baseline. Organizations that try to reverse this, cutting first, integrating later, typically end up with neither the cost savings nor the capability gains.
Second, the gains compound at the team level, not just the firm level. When a product team that previously required a project manager, a data analyst, and two coordination layers can function with engineers who have direct access to AI-assisted analytics and automated task management, the speed of output increases non-linearly. The bottlenecks that slow product development in large fintech organizations usually are coordinative rather than technical. Removing coordination overhead changes the pace of the entire organization.
Third, the talent implications run in two directions. Block’s restructuring created winners and losers, but not along the lines most people expected. Senior individual contributors with deep technical or domain expertise became more valuable, not less. Generalist managers whose primary function was oversight and coordination became redundant. European fintechs need to be honest about which of their current roles fall into which category.
How European Fintech Can Prepare
Preparation here is not about replicating Block’s specific choices. It’s about building the organizational and technical conditions that make transformation possible when the competitive pressure becomes unavoidable … and it will.
Audit your management layer by function, not by title. Which roles exist primarily to route information, coordinate meetings, and track progress? Which exist to exercise genuine judgment, hold client relationships, or develop technical strategy? The former are targets for automation within the next two to four years. The latter are not.
Invest in AI literacy at the individual contributor level. The organizations that will benefit most from AI tools are those where the people closest to the work know how to use them. Training programs that treat AI as a senior leadership concern (something to be understood by executives and implemented by specialists) will produce slower adoption and weaker results than firms that push capability to every team.
Redesign workflows before restructuring teams. Map existing processes. Identify where human effort is spent on coordination, formatting, summarizing, or tracking rather than judgment and execution. Automate those steps first. Once the workflow is lean, the right team structure will become more obvious.
Engage legal and HR early. In European markets, restructuring requires process. Starting that process twelve months before it becomes urgent is not overpreparing. It is the minimum viable approach.
Build the business case around speed, not just cost. The most compelling internal argument for AI-driven restructuring in a fintech context isn’t headcount reduction. It’s product velocity and time-to-market. In a competitive landscape where capital is constrained and customer acquisition costs are high, moving faster than competitors on product development is a durable advantage.
The Uncomfortable Question
The harder issue sits underneath all of this: European financial culture has historically rewarded institutional hierarchy. Banks and fintechs built in that culture have management structures that serve organizational legitimacy functions as much as operational ones. A Director title means something to a corporate client. A layer of management signals stability to a regulator. A promotion structure retains talent.
AI doesn’t dissolve these cultural dynamics on its own. But it does change the economics underneath them. When a flatter, faster competitor can deliver the same product at lower cost with a smaller team, the question isn’t whether hierarchy is culturally comfortable, it’s whether it’s financially sustainable.
Block has provided one answer to that question. The European fintech sector will have to construct its own. Whether it would follow the Block suite, still isn’t clear.
The Bottom Line
Jack Dorsey’s prediction of a “middle management collapse” often reads as provocative. What Block’s operating results show is that it may simply be accurate. At least, for organizations willing to build toward it deliberately.
European fintechs don’t need to move at Block’s speed or scale. What they do need, however, is to take the case study seriously, map their own exposure, and begin the structural and cultural work now, because the companies that start this process last will be competing against those that started it first. That gap compounds over time in ways that are very difficult to close.
