Mercor CEO & Co-Founder, Brendan Foody: How They Grew from $1M to $500M in 17 Months

Macaw (McCore) Growth and Metrics

• The company achieved a revenue run rate of $500 million within 17 months, scaling from $1 million to $500 million, marking the fastest revenue growth of all time (one month faster than Cursor).
• Since the acquisition by Scale AI, Macaw's revenue has quadrupled, with growth accelerating to a rate higher than any previous point in the company's history.
• The business maintains an average marketplace pay rate of $95 per hour, significantly higher than the ~$30 per hour typical for competitors like Scale AI and Search.
• Current demand is sufficient to double the business overnight if capacity constraints regarding talent supply are resolved.
• The company operates with positive growth and net margins, utilizing a capital-efficient model rather than aggressive burn rates.

Founder Background and Philosophy

• Brandon Foody, age 22, launched Macaw in January 2023, having previously generated hundreds of thousands of dollars in high school through side hustles like sneaker reselling and consulting for AWS credits.
• Foody's initial side hustles (selling donuts) led his mother to enroll him in Catholic high school to instill values and prevent him from "getting into trouble."
• He advises against college for most young people due to the abundance of free educational content online, though he acknowledges social value in the university experience.
• Foody rejected a $100,000 sponsor offer and a law professor's salary of $82,000 early in his career to pursue entrepreneurship full-time.
• He describes his leadership style as focusing on "impact" and "mission" over strict hours, rejecting the "996" work culture as a mandate while acknowledging its historical correlation with early team output.

Market Strategy and Supply Side

• Macaw differentiates itself by shifting from the "crowdsourcing" paradigm (low-skilled labor) to a "sourcing and vetting" paradigm (high-caliber experts like Goldman Sachs bankers, McKinsey analysts, and top engineers).
• The company operates as a research partner for top AI labs, focusing on creating "RL environments" (Reinforcement Learning) rather than simple text generation tasks.
• Foody asserts that the total addressable market is limited by the specific tasks humans can perform better than models, not just by the availability of smart people.
• A proprietary advantage exists in matching the top 10–20% of contributors to complex tasks, which drives the majority of model improvement and value creation.
• The company intends to expand market share in "RL environments," which Foody estimates currently represent 50–60% of the human data market and are expected to subsume the entire economy as AI adoption grows.

Competitive Landscape and Industry Trends

• Foody disputes the notion that the AI data market is merely a "body shop," emphasizing the strategic depth of Macaw's engagement with frontier labs.
• While competitors have emerged, Macaw maintains that the "outcomes of data" and the ability to access top-tier talent create defensible moats that are difficult to replicate.
• Customers often start with multi-vendor strategies but tend to consolidate spend with Macaw as model performance requirements increase and the trade-offs of diversification become apparent.
• Foody believes scaling laws are not plateauing; instead, the industry is shifting from ingesting low-quality data to curating high-quality data from expert sources.
• He predicts a future with both specialized and generalized models, noting that recent breakthroughs in generalization (e.g., GPT-5) have made foundation models structurally efficient investments.
• Sovereignty and regional focus (e.g., Mistral in Europe) may create scoped advantages, but Foody expects the largest winners to be general-purpose model providers.

Valuation and Future Outlook

• Macaw recently raised a Series B valuation of ~$200–250 million against a run rate of $20 million (100x multiple), and current offers suggest a valuation significantly higher despite the company not needing capital.
• The founder views a $10 billion valuation as "cheap" given the growth trajectory and intends to pursue financing soon to signal market leadership.
• Macaw plans to stay private to maintain long-term orientation and avoid quarterly earnings pressure, citing Jack Dorsey's advice.
• Foody predicts that in 10 years, models will still require human input for training because superintelligence has not yet been reached, and "RL environments" will become the primary method for training models on real-world tasks.
• The company is actively turning down projects to focus on the "best customers" and is currently constrained by supply capacity rather than demand.
• Foody identifies "vibe spending" on AI applications as a risk, advising investors to prioritize retention metrics and marginal utility over pilot success rates.
• He believes the current market is not yet frothy like 2000, comparing the current cycle more favorably to 1996–1997 with a 10-year positive ROI outlook for durable businesses.

Final Thoughts and Corrections

• Foody disputes the widely held belief that superintelligence superior to humans in all tasks will arrive within three years.
• He suggests OpenAI could improve by focusing more on model customization and API bundling to increase switching costs and pricing power.
• The company is heavily penetrated into the spend of foundation model labs, particularly in the shifting "RL environment" data buckets.
• Foody's mother, initially upset by his college dropout status, has since come around to his success.
• He identifies a need for better evaluation metrics that bridge the "real-to-sim" gap, moving away from academic benchmarks (Olympiad math) toward practical, task-based assessments (e.g., building financial models or using tools).