The next unicorn could be run by a solo entrepreneur with AI
With a $200-a-month LLM subscription now able to replace entire departments, could the next unicorn be a team of one?
It took five funding rounds spread over five years for the software powerhouse to hit a billion-dollar valuation, a feat that legacy enterprise software giants like SAP and Oracle had taken decades to achieve.
With AI, companies are scaling within months and with leaner teams.
These AI-native startups—think AI image generator startup Midjourney and coding platform Cursor—are posting eye-popping annual recurring revenue, ranging anywhere from $10 million to well over $100 million.
While it sounds unreal, a new-age coder with a rack of GPUs and co-pilots could, in theory, spin up a product, ship globally, and sprint to $100 million ARR before a traditional SaaS giant can even finish its next headcount planning cycle.
Last year, OpenAI CEO Sam Altman predicted we would soon see the first one-person, billion-dollar company. Co-founder and CEO Dario Amodei went a step further, arguing this feat could arrive even earlier.
That future seems to have arrived. Solo entrepreneurs are building companies using AI agents and plug-and-play APIs, flipping the traditional organisation chart.
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Doing more with less
For software companies, valuation— a number that investors believe the company is worth—often hinges on Annual Recurring Revenue (ARR), the predictable subscription income that flows in year after year.
ARR is a crucial metric of SaaS businesses because it represents stable, contracted revenue that can be counted on, making it easier to forecast growth.
Recently, OpenAI itself shot past $10 billion in ARR as it rode the wave of ChatGPT, the viral AI chatbot that it launched less than three years ago.
According to Gian Segato, a founding data scientist and engineer at Replit, building the next unicorn doesn’t just involve skill or capital, but requires individual agency and a willingness to let AI “just do things” at scale.
Several AI-focused startups have already shown this willingness and have reached ARR in the eight or nine figures with leaner teams.
“The rise of lean, AI-native startups hitting $10 million to over $100 million ARR with tiny teams isn’t a blip, it’s a structural shift. AI has replaced scale with smarts. Small teams can now out-execute giants,” Jaspreet Bindra, CEO of AI&Beyond, a tech policy think tank.
Midjourney, a generative AI art service, achieved an estimated $200 million ARR with only 11 employees in 2023. As of 2024, it was reportedly nearing $500 million in annual revenue with just 40 employees.
The firm has no formal sales or marketing arm. Its growth has been entirely community-driven via its popular Discord channel.
Midjourney says it has not taken external capital and has been profitable from day one, scaling to hundreds of millions of users by relying on AI infrastructure and community-driven adoption.
According to Cornerstone Ventures’ Managing Partner Abhishek Prasad, a shift to smaller teams is primarily visible in the US, where a handful of foundational model startups have reshaped the AI sector.
“It’s a lot like what happened with Big Tech,” says Prasad.
“You're not going to get another Apple or Google. The incumbents, such as Microsoft and Meta, are too deeply entrenched. And in AI, OpenAI, Anthropic, xAI (Grok), and Perplexity are already emerging as the next dominant players. All of them are on their $100 million-plus trajectories,” he explains.
For instance, one such company is Fireflies.ai, which identifies as an AI-native startup. The AI productivity app hit unicorn status earlier in June by doing one thing well, i.e., automating meeting notes. Now, it plans to build AI voice agents that can attend meetings on a user’s behalf to grow on the same momentum.
“We’re 100% AI-native today, but when we started, we were maybe one or two years early—there was no ChatGPT, no LLMs at the time,” recalls CEO Krish Ramineni. “So we had to do a lot of the hard work ourselves.”
A key turning point came in 2022, when their investor Vinod Khosla, the first institutional backer of , introduced Fireflies to Altman. That early access to foundational models gave the startup a major edge, says Ramineni.
“We were able to incorporate those LLMs into our product, and all of a sudden, we saw incredible acceleration in 2023,” he says. “Everything we promised during our Series A—the AI could actually do it. It blew my mind. I don’t think any of us really thought AI could take better notes than a human.”
Despite the scale, Fireflies has kept things lean. “What we’ve been able to support millions of users…we’ve done that on a headcount of just 120 people,” he adds.
Echoing the same trend, Prasad argues that India is primed for a similar narrative of having AI native firms that scale rapidly.
“Our big opportunity lies in what we’ve historically been great at—building applications. From the IT services boom to the rise of SaaS, Indian tech has excelled at developing end-to-end solutions. And that strength will play a crucial role again. As new AI-native applications emerge, a lot of the innovation will happen at the application layer, built on top of these foundational models,” he adds.
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Higher revenue per employee
AI-native business models are also rewriting metrics. Cursor, the AI code editor built by just 20 engineers, soared to $100 million ARR in under a year and, by 2024, was reportedly near $300 million.
With AI handling development and support, the company relies on a product-led strategy that scales without heavy sales overhead. Every employee focuses on code or model quality, pushing ARR per employee into the millions.
Its close competitor, Windsurf, has followed a similar path. Though slightly larger in headcount, the platform also hit around $100 million in ARR early with a similar approach. The model is so attractive that OpenAI is reportedly considering acquiring the firm in a $3 billion deal.
“High ARR per employee shows focus, but moats today lie in proprietary data, vertical depth, and fast iteration. India, with its talent and cost advantage, is well-placed to lead this wave. The next iconic AI company could easily come from a 10-person team in Bengaluru,” says Bindra.
Recently, early-stage VC firm BoldCap in their AI Manifesto said that it is specifically targeting AI-native companies built around core principles such as context-aware systems that understand business environments, multi-modal interfaces that work across text, voice, and visual inputs.
The AI-focused fund is targeting startups building around agentic architectures that can act independently rather than just respond to commands, and intelligence that replaces human decision-making.
“Small teams are now doing in months what used to take years. Firms can achieve a lot today with just a handful of people. But it's also important to recognise survivorship bias. For every one-person unicorn, there are dozens who ship clever demos and then flame out,” says Poorvi Vijay, Vice President, Elevation Capital.
“On the exits front, the most common path we’re seeing is strategic M&A—and that’s already started happening, such as OpenAI’s Windsurf acquisition or Salesforce acquiring Informatica. Legacy companies are racing to add as many AI capabilities, which is why even early-stage AI startups are getting acquired quickly. DevTool companies and vertical SaaS firms, especially, are looking to acquire AI-native teams with novel IP or deep technical insight,” she adds.
Edited by Affirunisa Kankudti