Italian Tech Company Bending Spoons Received About 800,000 Resumes Last Year, Hiring Only 286 People
Italian tech company Bending Spoons received about 800,000 resumes last year, hiring only 286 people, making its recruitment selectivity about 100 times that of Ivy League schools.
The company ranks 69th in Fortune's list of the 100 Best Employers in Europe and is one of only two Italian companies on the list. As an acquisition-driven tech group, it has acquired over 50 companies since its establishment in 2013, including Vimeo, AOL, Evernote, and Eventbrite, rebuilding technologies with its own engineers and holding them long-term, rather than exiting in a traditional private equity manner. The company is set to go public on NASDAQ in July 2026, with an estimated valuation of approximately $18.4 billion.
The recruitment funnel is highly selective: about 60,000 applicants passed the initial resume screening and entered an online test lasting up to 6 hours, assessing their ability to solve unfamiliar problems and learn quickly, with some tests monitored. Approximately 3,300 candidates then moved on to the interview stage, with hiring decisions made by a central talent team rather than department managers to reduce personal bias. The performance of each new employee is tracked for up to two years and fed back into the selection model.
The company sends candidates a list of "controversial" work principles, clearly informing them of the high challenges, heavy workload, and strict expectations, with non-committed candidates not being hired. It has developed its own recruitment software called Role Model, which combines test results with an AI model trained on historical performance data to process a massive number of applications. CEO Luca Ferrari has described the company as a "combination of private equity and Google," stating that traditional interviews have almost no predictive power.
The core employee count remains around 620, with revenue per employee rising from $1.12 million in 2023 to $2.57 million in 2025. The proportion of code merged from AI-written or co-written requests increased from less than 10% in Q1 2025 to over 90% in Q1 2026, with about 70% completed independently by AI.
This recruitment behavior is driven by talent scarcity and proactive selection. A large number of applicants voluntarily submit applications, and the company uses algorithms and centralized decision-making to select a very small number of high-learning-speed talents, directing funds and manpower towards internal engineering and acquisition integration rather than large-scale expansion. The beneficiaries are existing shareholders and the core team (through increased per capita output and AI leverage), while job seekers and traditional recruitment models face pressure due to the high selectivity and the possibility of being replaced if performance standards are not met.
Source: Public Information
ABAB AI Insight
Bending Spoons was founded by Luca Ferrari in 2013, after his first startup failed and was liquidated for $40,000. The company chooses to acquire underperforming applications and platforms with a user base, rebuilding technologies with its own engineers and holding them long-term, rather than opting for a quick exit typical of traditional private equity. After going public on NASDAQ in July 2026 with an estimated valuation of about $18.4 billion, the core employee count remains low while significantly increasing the proportion of AI-generated code.
In terms of capital strategy, the company focuses resources on post-acquisition technology integration and internal engineering capabilities rather than large-scale headcount expansion. Recruitment funding and processes are invested in the self-developed algorithm system Role Model, which trains models using historical performance data to screen candidates and feeds new employee performance back into the model over two years. The motivation is to double per capita income by leveraging a very small number of high-learning-speed talents with AI, while maintaining selectivity to support acquisition integration efficiency.
This approach contrasts with the traditional private equity model of layoffs after acquisitions for resale, and differs from the early large-scale recruitment expansions seen in tech companies like Google. The company is currently in a phase of simultaneous acquisition integration and AI leveraging, controlling headcount through selective hiring while amplifying existing team output, rather than entering a purely platform-based or regulation-driven phase.
Essentially, this represents technological substitution and capital concentration. As AI takes on more code production, marginal headcount costs decrease, allowing the company to support higher revenues with fewer core employees while locking in scarce learning capabilities through extreme selection. The mechanism at play is that the existing users and technical debt brought by acquisitions need to be rapidly rebuilt, and AI partially decouples engineering capacity from human scale, making selective hiring a tool for amplifying leverage rather than expanding scale.
ABAB News · Cognitive Laws
The higher the selectivity, the greater the per capita leverage.
Learning speed predicts output better than resumes.
After AI substitution, recruitment becomes filtering rather than expansion.