Each year, roughly a billion people change jobs. Twice as many would like to, but can’t. Those who do make a move often spend six months applying to around 150 roles, only to land somewhere that does not fully fit their strengths or goals. For all the tools and platforms that promise transparency, job search still behaves like a lottery rather than a matching system.
Rethinking Recruiting
The core issue is incentive design. Most of today’s infrastructure was built for employers. Job boards profit from postings and attention, not from fast, accurate matches. Traditional recruiting firms charge companies, not individuals, which can leave candidates as the surplus side of the market. The experience for job seekers is volume and guesswork. The experience for companies is noise and inefficiency.
Refer was founded by Stanford alum Andre Hamra as a Public Benefit Corporation, with a mission written into its charter to enable talented individuals to fulfill their professional potential by helping them pursue their ideal job. The model centers on the person, not the posting.
Instead of asking candidates to blast applications into an abyss, Refer learns their skills, preferences, and purpose, then brings a small number of highly aligned opportunities directly to decision-makers.
Flipping the Incentives
This is a reversal of the usual setup. Companies access the platform for free, while candidates only pay a success fee if they are hired. Shifting the commercial relationship away from employer budgets and toward candidate outcomes removes a major constraint on market coverage. Refer can work across many organizations at once and discard low-fit jobs rather than discarding people. For employers, the result is a short list of relevant introductions instead of thousands of mismatched submissions.
Early traction suggests the approach is resonating. Within months of launching, Refer onboarded more than a thousand companies and began matching candidates with interviews within days. Its network includes technology firms such as Ramp, Rivian, Scale AI, and Moveworks—organizations known for high standards and specialized hiring needs.
Employers that once spent months sourcing qualified candidates now meet them within weeks, sometimes even minutes. One client reported conducting an interview just 15 minutes after signing up. While individual timelines vary, the trend is consistent: fewer steps, more signal, and less waste.
Beyond Automation
Automation is part of the story, but not the point. Recent years have shown that adding more automation to the front of the hiring funnel often increases spam, inflates application counts, and overwhelms review processes. Refer applies AI toward precision rather than volume.
Its system focuses on depth of understanding on the candidate side and constraint-based matching on the employer side. Each interaction generates feedback that helps improve future matches. The process is designed to learn continuously, refining what “fit” means in practice.
Building a Global Labor Market Orchestrator
The company’s long-term vision is to become a Global Orchestrator for Jobs—a neutral layer that allocates the right people to the right opportunities across industries and geographies. By aligning incentives with individuals rather than employers, and by learning from outcomes rather than guesses, Refer aims to reduce the role of chance in career development.
This approach fits a broader cultural shift. Around the world, people are reassessing their relationship with work. Studies like Gallup’s 2024 World Poll continue to show that most employees neither enjoy nor excel at their jobs. The cost is more than economic; it represents a massive loss of human potential.
When people spend their working lives in roles that don’t match their abilities or interests, both creativity and productivity decline. When they are placed where they can thrive, momentum compounds—not just for them, but for the organizations and communities around them.
Toward a More Human System
None of this is without challenges. Labor markets vary widely by sector and geography, and bias in data remains a universal concern. But by aligning the system around people and purpose, and by using technology to understand rather than overwhelm, Refer is addressing structural inefficiencies rather than surface symptoms.
If today’s job market feels like gears grinding, Refer’s model offers a new mechanics: treat the person as the client, use AI to uncover fit instead of noise, and measure success not by how many jobs are filled, but by how many people end up in the right place.
Spencer Hulse is the Editorial Director at Grit Daily. He is responsible for overseeing other editors and writers, day-to-day operations, and covering breaking news.
