Aravind Srinivas, CEO and co-founder of Perplexity AI, recently shared valuable insights on building and scaling startup teams during an interview at the Stanford Graduate School of Business. Since co-founding the AI-powered search startup in 2022, Srinivas has developed a distinctive approach to hiring and leadership that prioritizes complementary skills and rapid execution.
In the earliest stages of Perplexity, Srinivas focused on recruiting co-founders and team members with complementary skills rather than similar expertise. “You don’t want to be as good as them in what they excel at — I think they should be a lot better,” he explained. This philosophy extends beyond the founding team, with Srinivas consistently targeting individuals who can “bring in new skills” as the company expands.
A cornerstone of Srinivas’s leadership philosophy is maintaining an “extreme bias for action” that he encourages throughout the organization. This approach has enabled Perplexity to maintain its speed and agility even as it has grown to approximately 100 employees. Srinivas acknowledged receiving advice from another founder warning that “once you get to 100 people, you’re guaranteed to move slow,” but he remains determined to solve the challenges of scaling while preserving the company’s rapid pace.
The Perplexity CEO also emphasized the value of taking chances on individuals with “chips on their shoulders” — people who haven’t yet become established experts but show potential. “Giving people who haven’t necessarily become experts at one thing the opportunity to go do something they’re not yet proven for is something I’ve done a lot,” Srinivas said. He advocates for experimentation and allowing team members to learn through experience rather than exclusively hiring recognized experts.
The interview also addressed controversy surrounding Perplexity’s content practices. Earlier this year, major publications including The New York Times, Forbes, The Wall Street Journal, and Wired alleged that the AI startup was improperly using their content without adequate attribution. Srinivas firmly denied these accusations, stating that Perplexity “never ripped off content from anybody.” He characterized the company’s AI-powered search engine as an aggregator that synthesizes information from diverse sources while providing proper attribution. “We are doing our best to make sure the credit attribution part is clear,” Srinivas reiterated at Stanford, emphasizing the company’s commitment to crediting original sources.
Key Quotes
You don’t want to be as good as them in what they excel at — I think they should be a lot better. Also, you don’t want to step on their toes when they do that.
Aravind Srinivas explained his hiring philosophy for early-stage startups, emphasizing the importance of recruiting team members with complementary skills who excel in areas where the founder may not. This approach helps create a well-rounded team with diverse expertise.
I would say there’s an extreme bias for action that I try to bring in and try to encourage everybody else in the company to adopt. And I think that’s what’s helping us continue to be fast, even when you’ve gotten to about 100 people.
The Perplexity CEO described his leadership philosophy focused on rapid execution and maintaining startup speed even as the company scales. This reflects a common challenge in the AI industry where first-mover advantage can be crucial.
Giving people who haven’t necessarily become experts at one thing the opportunity to go do something they’re not yet proven for is something I’ve done a lot.
Srinivas advocated for taking calculated risks on unproven talent rather than exclusively hiring established experts. This approach of “putting someone in the waters and letting them figure out how to swim” represents his belief in experimentation and growth through experience.
We are actually more of an aggregator of information and providing it to the people with the right attribution.
In response to allegations from major news publications about improper content use, Srinivas defended Perplexity’s practices, characterizing the AI-powered search engine as an aggregator that properly credits original sources rather than a content plagiarizer.
Our Take
Srinivas’s hiring philosophy reveals an interesting paradox in AI startup culture: the need for both specialized expertise and generalist adaptability. His preference for “chips on shoulders” over established experts suggests that hunger and potential may matter more than credentials in fast-moving AI markets. However, this approach carries risks, particularly as AI companies face increasing scrutiny over technical accuracy and ethical practices. The content attribution controversy surrounding Perplexity illustrates how operational speed can sometimes conflict with established norms. While Srinivas frames Perplexity as a responsible aggregator, the pushback from major publishers suggests the AI industry hasn’t yet found a sustainable model that satisfies both innovation imperatives and content creators’ rights. As AI search tools proliferate, this tension will only intensify, potentially requiring new legal frameworks or business models that better balance these competing interests.
Why This Matters
Srinivas’s insights offer a valuable window into how AI startup leaders are approaching the unique challenges of building teams in one of technology’s fastest-moving sectors. His emphasis on complementary skills and rapid execution reflects broader trends in the AI industry, where speed-to-market and diverse expertise are critical competitive advantages. The discussion of scaling challenges is particularly relevant as numerous AI startups face the transition from small, agile teams to larger organizations while trying to maintain their innovative edge.
The content attribution controversy Srinivas addressed highlights one of the most pressing ethical and legal issues facing AI companies today. As AI-powered tools increasingly aggregate and synthesize information from existing sources, questions about intellectual property, fair use, and proper attribution have become central to industry debates. How Perplexity and similar companies navigate these challenges will likely influence regulatory frameworks and industry standards for years to come. This case underscores the tension between AI innovation and traditional content creation, a conflict that will shape the future relationship between AI companies and publishers.
Recommended Reading
For those interested in learning more about artificial intelligence, machine learning, and effective AI communication, here are some excellent resources: