Almost every day, Grant Lee, a Silicon Valley businessman, listens from investors trying to convince him to get their money. Some have even sent co -founders personalized gift baskets.
Mr. Lee, 41 years old, will normally flatter. In the past, a rapidly growing start -up such as Gamma, the artificial intelligence that helped settle in 2020 would constantly see more funding.
But like many new newly established businesses in Silicon Valley today, Gamma is seeking a different strategy. It uses artificial intelligence tools to increase workers’ productivity in everything, from customer service and marketing to coding and client research.
This means that Gamma, which makes software that allows people to create presentations and sites, does not need more cash, Mr Lee said. His company has only hired 28 people to get “tens of millions” in annual repetitive revenue and about 50 million users. Gamma is also profitable.
“If we were from the generation before, we would be easily at 200 employees,” Mr Lee said. “We have the opportunity to rethink this. We basically rewrite the Playbook.”
Silicon Valley’s old model dictates that newly formed businesses should raise a huge sum of money from business capital investors and spend it to hire an army of workers to increase quickly. The profits will come much later. Until then, the number of funds and the concentration of funds were honor between the founders, who philosophers were better.
But gamma is among a growing group of newly formed businesses, most of them work on AI products, which also use AIs to maximize efficiency. They make money and grow rapidly without the funding or the employees they would need before. The biggest boasting rights for these newly established businesses are to make the most revenue with the least employees.
The stories of the success of “Tiny Team” now became a meme, with technicians who are enthusiastically sharing lists showing how Anysphere, a starting coding coding raw, hit $ 100 million in annual repetitive revenue in less than two years with just 20 employees, how Elevenlabs, a AI voice start, did the same with about 50 employees.
The ability for AI to let newly formed businesses do more with less has led to wild speculations for the future. Sam Altman, Openai’s chief executive, has predicted that he could someday be a company of a $ 1 billion person. His company, which manufactures a form with a strong financial price of AI called a fundamental model, employs more than 4,000 people and has raised more than $ 20 billion in funding. They are also in conversations to raise more money.
With AI tools, some newly established businesses now state that they will stop hiring in a particular size. Runway Financial, a funding software company, said it plans to exceed 100 employees because each of its employees will do the work of 1.5 people. The organization, a start -up that uses AI for customer service, also plans to hire more than 100 employees.
“It is about eliminating roles that are not necessary when you have smaller groups,” said Elias Torres, founder of the service.
The idea of ​​Ai -based performance was reinforced last month by Deepseek, the Chinese start of the AI ​​that showed that it could manufacture AI tools for a small fraction of standard costs. Its discovery, built on open source tools that are freely available on the internet, began an explosion of companies that create new products using the cheap Deepseek techniques.
“Deepseek was a time of the drain basin,” said Gaurav Jain, an investor in the business activity business, who supported Gamma. “The cost of calculation will be reduced very, very quickly, very quickly.”
Mr Jain compared new newly established AI companies with the wave of companies that emerged in the late 2000s, after Amazon began offering cheap cloud computing services. This reduced the cost of starting a company, leading to a turmoil of new newly established businesses that could be cheaper.
Prior to this AI explosion, newly formed operations are generally burning $ 1 million to reach $ 1 million, Mr Jain said. Now reaching $ 1 million in revenue costs one fifth so much and could eventually be reduced to one tenth, according to an analysis of 200 newly established companies carried out by the aforementioned.
“This time we are automating people as opposed to data centers,” Mr Jain said.
But if newly formed businesses can become profitable without spending much, this could be a problem for business capital investors, who distribute tens of billions to invest in newly established AI companies. Last year, AI companies raised $ 97 billion funding, accounting for 46 % of all business capital investments in the United States, according to the Pitchbook, which monitors newly established businesses.
“Business capital works only if you take money to the winners,” said Terrence Rohan, an investor with the other fund, which focuses on very new newly established businesses. He added: “If the winner of the future needs much less money because they will have much less people, how does this VC change?”
At the moment, investors are still struggling to get into the hottest companies, many of which do not need more money. Scribe, a start of AI productivity, fought a lot of interest last year than investors than the $ 25 million he wanted to increase.
“It was a negotiation of what is the smallest amount we could possibly take on,” said Jennifer Smith, chief executive of Scribe. She said that investors were overwhelmed by her staff – 100 people – compared to three million users and rapid growth.
Some investors are optimistic that AI’s performance will drive businessmen to create more companies, leading to more opportunities to invest. They hope that as soon as the newly established businesses arrive, businesses will adopt the old model of large teams and big money.
Some new companies, including Anysphere, one behind the cursor, are already doing so. Anysphere has increased funding of $ 175 million, with plans to add staff and conduct research, according to Oskar Schulz.
Other founders have seen the dangers of the old Playbook, which kept the companies in a capital concentration corridor, where the hiring of more people created more costs that exceeded their salaries.
Larger groups need administrators, more powerful human resources and back office support. These groups needed specialized software, along with a larger office with all the privileges. And so on, which led the newly formed businesses to burn through cash and forced founders to constantly raise more money. Many newly formed businesses since the explosion of funding of 2021 eventually declined, closed or mixed to sell themselves.
Activating a profit early can change this result. In Gamma, employees use about 10 AI tools to help them be more effective, including Intercom customer service tool for handling problems, Midjourney’s Midjourney generator, Anthropic Claude Chatbot for data analysis and analysis Google’s notebooklm to analyze customer research. Engineers also use Anysphere’s runner to write the code more effectively.
Gamma’s product, which is built over tools from Openai and others, is also not as expensive to become as other AI products. (The New York Times has sued Openai and her partner, Microsoft, supporting the copyright violations of the AI ​​-related news content.
Other effective startups receive a similar strategy. Large, a 10 -year -old AI phone supplier, destroyed a profit in 11 months, thanks to the use of AI, said co -founder Torrey Leonard.
The stripe payment processor has created an AI tool that helps Mr Leonard’s sales analyze his sales, which would have previously hired a analyst to do. Without this and AI tools from others to rationalize his activities, he would surely need at least 25 people and are not profitable, he said.
It will eventually raise more money, Mr Leonard said, but only when ready. Don’t worry about exhaustion of cash is “a huge relief,” he said.
In Gamma, Mr. Lee said he was planning to double the workforce this year at 60, hiring design, engineering and sales. He plans to hire a different kind of worker beforehand, looking for generalists who do a series of duties and not experts who only do one thing, he said. He also wants “coaches-coaches” instead of managers who can advise less experienced employees, but can also fight in daily work.
Mr Lee said that the AI-offering model had released time that he would have spent different people management and hiring differently. It now focuses on discussing customers and improving the product. In 2022 he created a loose room for feedback from Gamma’s leading users, who are often overwhelmed to find that the CEO responded to their comments.
“This is actually the dream of every founder,” Mr Lee said.
