The Contrarian View on Startup Hiring

In the fast-paced world of artificial intelligence startups, conventional wisdom often dictates building teams around specialized roles like product managers and data scientists from the outset. But Edwin Chen, the CEO of Surge AI, a bootstrapped data-labeling firm that’s quietly amassed over $1 billion in annual revenue, begs to differ. Chen, a former data scientist at tech giants like Google and Twitter, argues that early-stage founders are making a critical mistake by prioritizing these positions too soon. “This is just wild to me,” Chen told Business Insider in a recent interview, emphasizing that such hires can dilute focus and drain resources when a startup is still finding its footing.

Chen’s perspective stems from his own experience bootstrapping Surge AI without venture capital, outpacing rivals like Scale AI in revenue while remaining profitable. He believes that in the nascent phases, founders should lean on versatile engineers who can wear multiple hats, from product ideation to data analysis. This approach, he says, fosters agility and ensures that every team member contributes directly to core product development. Surge AI’s success—powering data labeling for AI models at companies like Meta and Google—serves as a case study in this lean methodology.

Why Skip the Specialists?

Delving deeper, Chen critiques the rush to hire product managers, who typically bridge engineering and user needs but can introduce bureaucracy in small teams. “At an early stage, you don’t need someone to manage the product; you need to build it,” he explained. Similarly, data scientists, while crucial for mature AI operations, often spend time on exploratory work that may not yield immediate value. Posts on X from industry observers echo this sentiment, with some noting a shift toward “100x engineers” amplified by AI tools, as highlighted in discussions around Chen’s views.

This hiring philosophy aligns with broader trends in the AI sector, where funding surges—up 75.6% in the first half of 2025, according to a Reuters report—haven’t necessarily translated to efficient team structures. Chen warns that premature specialization can lead to overhiring, a pitfall he’s avoided at Surge AI, which has grown to serve top AI labs without external funding.

Lessons from a Bootstrapped Titan

Chen’s journey offers valuable insights for aspiring founders. Starting Surge AI in 2020, he focused on high-quality data annotation for AI training, differentiating through an intelligent process that discerns nuanced interactions. As detailed in a Inc. profile, Chen bootstrapped the company to billionaire status by prioritizing engineers over managers. This contrasts with venture-backed peers who layer on roles early, sometimes at the expense of innovation.

Industry insiders point to Chen’s strategy as a blueprint for sustainability amid AI hype. A Center for Data Innovation interview from earlier years underscores his emphasis on data quality, which has only grown more relevant as AI models demand precise datasets. Recent X posts amplify this, with users debating the rise of forward-deployed engineers over traditional product roles in AI startups.

The Broader Implications for AI Growth

Critics might argue that Chen’s advice suits only certain niches, like data labeling, but he counters that it’s universally applicable to early-stage ventures. “Founders need to stop hiring product managers and data scientists too soon,” he asserted in the Business Insider piece, urging a focus on builders over planners. This resonates in an era where AI spending is boosting giants like Alphabet, as noted in a Globe and Mail earnings report.

Looking ahead, Chen’s model challenges the startup ecosystem to rethink team composition. With Surge AI’s Meta deals and consistent profitability—surpassing Scale AI’s $870 million in 2024 revenue, per DeepNewz—his approach proves that lean teams can dominate. For industry veterans, this signals a potential shift away from overhyped roles toward engineering prowess in the AI boom.

Evolving Roles in a Dynamic Field

As AI evolves, so too must hiring strategies. Chen’s dismissal of early product managers and data scientists highlights a preference for generalists empowered by tools like Python and TensorFlow, trends echoed in X discussions on upskilling. A Inc. article praises Surge AI’s outpacing of rivals, attributing it to this focused hiring.

Ultimately, Chen’s insights, drawn from building a $1 billion business from scratch, offer a contrarian playbook. In a sector flush with capital but fraught with failures, his emphasis on efficiency could redefine success for the next wave of AI entrepreneurs.