Nearly every day, Grant Lee, a notable entrepreneur situated in Silicon Valley, is approached by eager investors looking to put their money into his ventures. Some are even sending personalized gift baskets to him and his co-founders in an attempt to win them over.
For someone like Lee, who is 41, this attention would normally be quite flattering. Traditionally, a rapidly growing start-up like Gamma—the artificial intelligence enterprise he co-founded in 2020—would be continuously seeking additional funding.
Yet, in today’s Silicon Valley landscape, many budding companies, including Gamma, are taking a different path. They’re leveraging AI tools to boost productivity across various departments like customer service, marketing, coding, and customer research.
As a result, Gamma, known for creating software that aids in developing presentations and websites, isn’t in the market for more capital, according to Lee. Remarkably, his company has achieved “tens of millions” in annual recurring revenue and attracted nearly 50 million users with just 28 staff members. And on top of that, Gamma is a profitable venture.
“If we belonged to the previous generation, our team would easily be 200 people strong,” Lee remarked. “We’ve got the opportunity to rethink the game plan, essentially rewriting the rulebook.”
Historically, the Silicon Valley approach emphasized raising significant capital from venture investors and expanding the team as quickly as possible, with profits being a distant goal. The focus was on headcount and funding, with bigger often equating to better.
However, Gamma is part of an emerging group of start-ups, primarily those developing A.I. products, that are leveraging artificial intelligence to optimize efficiency. They’re generating revenue and experiencing rapid growth without the need for extensive funding or workforces that were once considered necessary. Today, the ultimate bragging rights among these companies come from achieving substantial revenue with minimal personnel.
The trend of “tiny team” triumphs is gaining momentum, with tech enthusiasts eagerly sharing examples. Take Anysphere, for instance. This start-up, which offers a coding tool called Cursor, reached $100 million in annual recurring revenue in under two years with only 20 employees. Similarly, ElevenLabs, an A.I. voice company, achieved comparable success with around 50 staff members.
The potential for A.I. to allow start-ups to do more with less has sparked speculation about the future. Sam Altman, CEO of OpenAI, envisions the possibility of a one-person enterprise being worth $1 billion someday. OpenAI itself, known for developing a high-cost A.I. concept called a foundational model, employs over 4,000 people and has raised over $20 billion in funding, with ongoing discussions to secure even more capital.
Some start-ups are embracing A.I. tools with a commitment to capping their employee count. Runway Financial, a finance software enterprise, plans to stop hiring once they reach 100 employees, contending that each worker can achieve the output of 1.5 people. Similarly, Agency, focused on A.I.-driven customer service, also plans to limit its workforce to 100.
“It’s about cutting out roles that aren’t necessary in smaller teams,” explained Elias Torres, founder of Agency.
The notion of A.I.-driven efficiency received a boost last month when DeepSeek, a Chinese A.I. start-up, demonstrated its ability to develop A.I. tools at a fraction of the typical cost. Their breakthrough, building on open-source tools available online, has spurred a wave of companies eager to replicate these cost-effective methods.
“DeepSeek was a big turning point,” mentioned Gaurav Jain, an investor with the venture firm Afore Capital, a backer of Gamma. “The cost of computing is going to drop remarkably fast.”
Jain compared the rise of new A.I. start-ups to the surge of companies in the late 2000s following Amazon’s introduction of affordable cloud computing services, which substantially reduced the costs associated with launching a company, sparking a flurry of new enterprises.
Traditionally, start-ups would burn through $1 million just to reach $1 million in revenue. According to Jain, achieving that milestone now costs one-fifth of what it once did, with the potential to approach one-tenth, as seen in an analysis of 200 start-ups by Afore.
“This time, we’re automating human tasks rather than just data centers,” Jain noted.
Yet, if start-ups can turn profits without heavy spending, it could pose challenges for venture capitalists who allocate vast sums to A.I. start-ups. Last year alone, A.I. companies raised $97 billion in funding, representing 46 percent of all U.S. venture investment, according to PitchBook.
“VCs thrive when they invest in the winners,” remarked Terrence Rohan, an investor with the Otherwise Fund, targeting early-stage start-ups. “If the successful enterprises of the future require significantly less capital due to a smaller workforce, what does that mean for V.C.?”
For the moment, investors are fiercely vying for spots in the most promising companies, many of which are not seeking additional funding. Last year, Scribe, an A.I. productivity start-up, witnessed overwhelming interest from investors who far exceeded the $25 million they intended to raise.
“It was a negotiation to determine the absolute minimum we could take,” recounted Jennifer Smith, Scribe’s CEO. Investors were astounded when they learned the company employed a mere 100 individuals while serving three million users and experiencing rapid growth.
Some investors remain hopeful that this wave of A.I.-driven efficiency will lead to the birth of numerous enterprises, resulting in more investment opportunities. They anticipate that upon reaching a certain stage, these firms will revert to the old model of large teams and substantial funding.
Some youthful companies, such as Anysphere behind Cursor, are already embracing this shift, securing $175 million in funding with plans to expand their workforce and conduct research, as shared by Oskar Schulz, the company’s president.
However, other founders have experienced the challenges of the traditional start-up approach, which often left companies trapped on a fund-raising treadmill. Hiring more personnel led to increased costs beyond just salaries.
Larger teams required management, comprehensive human resources, and back-office support. This translated into a need for specialized software, larger office spaces, and added perks, inevitably depleting start-ups’ cash reserves and compelling founders to seek more funding continuously. Many companies from the 2021 funding boom eventually downsized, shut down, or sought mergers.
Achieving profitability ahead of time can help avoid such outcomes. At Gamma, staff members employ around ten A.I. tools to enhance efficiency, including Intercom for customer service, Midjourney for generating marketing images, Anthropic’s Claude chatbot for data analysis, and Google’s NotebookLM for customer research. Engineers leverage Anysphere’s Cursor to streamline their coding processes.
Gamma’s offering, built on tools from OpenAI and others, isn’t as costly to develop compared to similar A.I. products. (The New York Times has sued OpenAI and Microsoft, alleging copyright infringement of news content in relation to their A.I. systems, which both companies have denied.)
Other efficient start-ups are adopting similar strategies. Thoughtly, a 10-person provider of A.I. phone agents, turned a profit within 11 months through its use of A.I., co-founder Torrey Leonard shared.
The payment processor Stripe developed an A.I. tool that helps Leonard analyze Thoughtly’s sales, a task he previously would have hired an analyst for. Without this and other A.I. tools to streamline operations, Thoughtly would need at least 25 people and remain unprofitable, he explained.
Thoughtly plans to seek additional funding eventually, Leonard added, but only when the timing is right. Not being overly concerned about running out of cash is “a huge relief,” he expressed.
Gamma, meanwhile, intends to roughly double its workforce this year, bringing on board expertise in design, engineering, and sales. Lee aims to recruit a different type of employee, favoring generalists who can handle a variety of tasks over specialists. He also seeks “player-coaches” instead of managers, who can mentor newer staff while actively contributing to daily work.
According to Lee, the A.I.-driven model has freed him from the time-consuming responsibilities of managing and recruiting personnel, enabling him to focus on engaging with customers and refining the product. In 2022, he created a Slack channel for feedback from Gamma’s top users, often leaving them pleasantly surprised when the CEO responded to their insights.
“That’s really every founder’s dream,” Lee said.
