Almost every day Grant Lee, an entrepreneur in Silicon Valley, hears from investors who try to persuade him to take their money. Some have even sent him and his co-founders personalized gift baskets.
Mr. Lee, 41, would normally be flattered. In the past, a fast-growing start-up such as Gamma, the artificial intelligence start-up that he set up in 2020, would constantly look forward to more financing.
But like many young startups in Silicon Valley today, Gamma follows another strategy. It uses artificial intelligence tools to increase the productivity of its employees in everything, from customer service and marketing to coding and customer research.
That means that Gamma, which makes software with which people can make presentations and websites, does not need more money, Mr Lee said. His company has hired only 28 people to get “tens of millions” in annual return and nearly 50 million users. Gamma is also profitable.
“If we were to the generation earlier, we would easily be among 200 employees,” said Mr. Lee. “We get the chance to reconsider that, in fact rewrite the Playbook.”
The old Silicon Valley model dictated that start-ups should raise a huge sum of money from investors in risk capital and hire an army of employees to scale up quickly. Profit would come much later. Until that time, the number of heads and fundraising were honorary badges among founders, who philosophized that was bigger.
But Gamma is one of a growing cohort of start-ups, most of work on AI products, which also use AI to maximize efficiency. They earn money and grow quickly without the financing or employees they needed before. The biggest bragging for these start-ups are to generate the most income with the least employees.
Stories about the success of the “Tiny Team” have now become a meme, in which techies share excited lists that show how Anysphere, a start-up that makes the coding software-cursor, $ 100 million in annual recurring income in less than two years with only 20 employees with only 20 employees with only 20 employees, and How Elevenlabs, an AI-voice start-up, did the same with around 50 employees.
The potential for AI to let start-ups do more with less, has led to wild speculation about the future. Sam Altman, the Chief Executive of OpenAi, has predicted that one day there could be a single company worth $ 1 billion. His company, which builds up a cost -intensive form of AI that is called a fundamental model, has more than 4,000 people employed and has raised more than $ 20 billion in financing. It is also in conversation to raise more money.
With AI tools, some start-ups now explain that they will stop hiring at a certain size. Runway Financial, a financial software company, has said that it is planning to fill 100 employees because each of his employees will do the work of 1.5 people. Agency, a start-up using AI for customer service, is also planning to hire no more than 100 employees.
“It's about eliminating rolling that are not necessary if you have smaller teams,” said Elias Torres, founder of the agency.
The idea of AI-driven efficiency was reinforced last month by Deepseek, the Chinese AI-start-up that showed that the AI tools could build for a small part of the typical costs. The breakthrough, built on open source tools that are available freely online, caused an explosion of companies that build new products using the cheap Deepseek techniques.
“Deepseek was a turning point,” said Gaurav Jain, an investor at the daring company during the capital that Gamma supported. “The cost of calculation will fall very quickly very quickly.”
Mr Jain compared new AI startups with the Gulf of companies that occurred in the late 2000s, after Amazon started offering cheap Cloud Computing Services. That reduced the costs of starting a company, which led to a flurry of new start-ups that could be built cheaper.
Before this AI-boom, startups generally burned $ 1 million to get $ 1 million in income, Mr Jain said. Now $ 1 million in income costs a fifth so much and could eventually fall to a tenth, according to an analysis of 200 start-ups performed by earlier.
“This time we automate people unlike only the data centers,” said Mr Jain.
But if startups can become profitable without spending a lot, it can become a problem for investors in venture capital, who assign dozens of billions to invest in AI startups. Last year, AI companies collected $ 97 billion in financing, making 46 percent of all venture investments in the United States, according to PitchBook, that keeps startups.
“Daring capital only works if you get money in the winners,” said Terrence Rohan, an investor at Anders Fonds, who focuses on very young startups. He added: “If the winner of the future needs much less money because they have far fewer people, how does that change?”
For now, investors continue to fight to get to the most popular companies, many of which do not need more money. SCRIBE, an AI productivity start-up, struggled last year with much more interest from investors than the $ 25 million that wanted to pick it up.
“It was a negotiation of the smallest amount that we could possibly assume,” said Jennifer Smith, CEO of Scribe. She said that investors were shocked about the size of her staff – 100 people – compared to the three million users and rapid growth.
Some investors are optimistic that AI-driven efficiency will encourage entrepreneurs to create more companies, leading to more opportunities to invest. They hope that as soon as the start-ups reach a certain size, the companies will take over the old model of large teams and a lot of money.
Some young companies, including AnSphere, those behind Cursor, are already doing that. Anysphere has collected $ 175 million in financing, with plans to add and conduct research, according to the President of the Company, Oskar Schulz.
Other founders have seen the dangers of the old start-up playbook, which kept companies on a fundraising treadmill where hiring more people created more costs that went beyond their salaries.
Larger teams needed managers, more robust Human Resources and Back Office support. Those teams then needed specialized software, together with a larger office with all the benefits. Etc., which caused startups to burn in cash and forced founders to constantly raise more money. Many start-ups of the financing tree of 2021 eventually confused, closing or clambering to sell themselves.
Making a profit early can change that result. At GAMMA, employees use around 10 AI tools to help them be more efficient, including intercom customer service for dealing with problems, the image generator of Midjourney for Marketing, Claude Chatbot from Anthropic for data analysis and Google's NotebookLM for analyzing customer research. Engineers also use Anysphere's cursor to write code more efficiently.
The product of Gamma, which is built on top of Tools from OpenAi and others, is also not as expensive to make as other AI products. (The New York Times has sued OpenAi and his partner, Microsoft, and claims infringement of the copyright of news content with regard to AI systems. The two companies have denied the claims of the suit.)
Other efficient start-ups follow a similar strategy. Thought, a 10-person provider of AI telephone agents, has made a profit in 11 months, thanks to the use of AI, said his co-founder Torrey Leonard.
The payment processor Stripe created an AI tool that helps Mr. Leonard to analyze well -considered sale, something he would have hired an analyst to do before. Without and AI tools of others to streamline its activities, thoughtful at least 25 people would need and be far from profitable, he said.
Thought will eventually raise more money, Mr. Leonard said, but only when it is ready. Don't worry about standing out cash is “a huge relief,” he said.
At Gamma, Mr Lee said that he was planning to roughly double the workforce this year to 60, to take for design, engineering and sales. He is planning to recruit a different type of employee from earlier, looking for generalists who perform a series of tasks instead of specialists who do only one thing, he said. He also wants “player coaches” instead of managers people who can guide less experienced employees, but can also pitch on daily work.
Mr Lee said that the AI-efficient model had made time for time that he would have spent differently in managing people and recruiting. Now he focuses on talking to customers and improving the product. In 2022 he created a weak space for feedback from Gamma's top users, who are often shocked to discover that the Chief Executive responded to their comments.
“That is actually the dream of every founder,” said Mr. Lee.
