The micro-multinational: could AI make the one-person unicorn a reality?

The traditional corporate hierarchy, with its sprawling departments and specialised silos, grew out of a simple necessity: one person cannot be everywhere at once. Scaling a business used to require a massive headcount to manage the growing friction of daily operations, from customer support to complex data analysis. But that fundamental constraint is beginning to dissolve as autonomous systems take over the cognitive heavy lifting that previously demanded an entire team of people. We are entering an era where the threshold for global reach no longer correlates with the size of an office building.

Solo business founders now have access to a synthetic workforce capable of executing sophisticated workflows across multiple time zones. Instead of hiring a marketing firm or a logistics team, an entrepreneur can deploy a constellation of specialised AI agents to handle market research, code generation, and localised outreach. We are seeing the rise of lean, hyper-efficient entities that compete directly with established firms without the burden of overhead or internal bureaucracy. This trend raises some practical questions worth exploring: How does one person manage complexity without becoming a bottleneck? What kinds of businesses would benefit most from this model, and which would still resist it? And would this democratise entrepreneurship by lowering barriers to entry, or would it simply concentrate more economic output in even fewer hands?

“In the past, companies relied on massive teams. Today, AI makes that model obsolete. It’s not needed anymore. Not when you have AI.”

Tim Cortinovis, tech visionary

Bigger is (not) better

The recent proliferation of companies that went on to achieve billion-dollar valuations with a small number of employees is challenging our assumptions about corporate success.

For decades, the standard metric for corporate success was the size of a company’s payroll. Massive corporations like General Motors, which employed over 600,000 people at the height of its power, set the standard for what a global powerhouse should look like. Even today’s tech giants like Google and Meta still maintain vast organisational structures with over 100,000 employees each. Bigger meant better. More employees signalled more capability, more reach, more staying power. Yet recent history tells a different story. Some of the most valuable technology companies of the past decade were built with remarkably small teams. Microsoft spent US$2.5bn to acquire Mojang when the studio had only 40 employees. Facebook’s acquisition of WhatsApp for US$19bn happened while the team stood at just 55 people, while their US$1bn purchase of Instagram occurred when only 13 people were on the books. These figures prove that an astronomical value doesn’t necessarily require a proportional workforce size.

AI has pushed this trend into overdrive. Over the last couple of years, AI startups have broken every historical record for scaling, hitting milestones like 100 million users or billion-dollar valuations at speeds that defy traditional business logic. “A startup’s inherent advantage over an incumbent is its ability to move quickly, experiment faster, perform data-driven decision-making, and test through a bunch of different hypotheses on their way to product-market fit,” explains Alex Gurevich, a managing director at Javelin Venture Partners. “Generative AI puts these inherent advantages on steroids.” This raises an obvious question: how long before we see a company run by a single person reach unicorn status?

Chasing the unicorn

Traditionally, building a billion-dollar company required massive manpower, complex infrastructure, and deep pockets. However, that may not be the case anymore. According to tech visionary Tim Cortinovis, anyone can now build a successful company on their own. But instead of starting with the technology, they first need to identify a problem in need of a solution and then deploy the right AI tools to tackle said problem. “In the past, companies relied on massive teams. Today, AI makes that model obsolete. It’s not needed anymore. Not when you have AI,” argues Cortinovis. “The biggest challenge isn’t technology. It’s thinking like a problem-solver.” OpenAI’s chief executive, Sam Altman, agrees that we will see a one-person company become a unicorn pretty soon. “In my little group chat with my tech CEO friends, there’s this betting pool for the first year there is a one-person billion-dollar company, which would’ve been unimaginable without AI,” he says. “And now it will happen.”

This newfound efficiency can largely be attributed to AI’s ability to automate work that previously required entire departments. There is now a growing number of AI tools designed to handle tasks across various business functions, including marketing, customer support, legal work, and writing code, allowing companies to come up with thousands of product variations, marketing angles, and cost scenarios in a fraction of the time without expanding the headcount. “I think we’re living in one of the most exciting areas to be building companies,” says Benjamine Liu, chief executive of AI drug development company Formation Bio. “We have PhD-level intelligence in our pockets, and we’re beginning to see AI systems do the work of entire teams. I think in that world, AI-native companies have a pretty significant advantage.”

“A solopreneur or micro-team can build and scale a billion-dollar operation by weaponising automation, data pipelines, and self-improving agents.”

Nic Adams, co-founder and chief executive at Orcus

Going solo

How long will it be before we see a company run by a single individual reach unicorn status?

While the idea of a single-person unicorn might seem like a bit of a stretch, a growing number of solo entrepreneurs running profitable ventures with the help of generative AI are emerging across industries. In fact, there were about 41 million businesses run by a sole individual who served as both the owner and only employee in the US alone in 2025, according to the US Small Business Administration. Dan Mazei is one of them. Having spent years working as a communications and marketing leader for organisations like Reebok, Tinder, Activision Blizzard, and Ford, alongside agency work for major clients including Nintendo and Unilever, he now runs All Tangled Roots, a marketing consultancy for brands, as its sole founder and principal, with AI handling tasks that would typically require a full team of workers.

Samantha Levitin took a similar path when she founded Levitin Collective, a boutique PR firm working across lifestyle, wellness, hospitality, and consumer brands in New York City. According to Levitin, AI helps her manage the mental load that comes with running a business while simultaneously raising a family. “Starting my own firm meant knowing I’d be doing everything myself, and AI helped fill gaps that would normally require a small team,” she says. “I intentionally designed the firm to be small… and AI gives me back time and mental space so I can focus on what matters most in my field: creative thinking, relationship building, and hands-on client work.”

Admittedly, neither firm has reached unicorn status yet, and most solo ventures never will. But as AI systems expand their range of capabilities, the gap between today’s successful solopreneurs and tomorrow’s billion-dollar one-person companies will keep narrowing. “There are already some impressively sized solopreneur companies at the so-called bleeding edge,” says Nic Adams, co-founder and chief executive at Orcus. “A solopreneur or micro-team can build and scale a billion-dollar operation by weaponising automation, data pipelines, and self-improving agents, to name a few. The key is combining real-time adversarial AI with modular, cloud-native infrastructure that scales horizontally without human bottlenecks.”

“The real question isn’t whether one person can scale something large; it’s which industries make that feasible.”

Cassie Kozyrkov, chief executive of Kozyr

Feasible future or Silicon Valley fantasy?

While some industry experts are convinced that a single-person unicorn is inevitable, others are more sceptical of the whole idea.

Not everyone who has built and scaled a company sees the one-person unicorn as an inevitable outcome, though. For many, the feasibility of the whole idea depends primarily on the nature of the industry. While a solo founder could conceivably run a global software product or a content-driven platform, running a natural-gas refining facility, a bank, or a hospital by yourself seems unlikely, regardless of how capable AI becomes. “The real question isn’t whether one person can scale something large; it’s which industries make that feasible,” argues Cassie Kozyrkov, chief executive of Kozyr. “In lower-risk sectors like commerce, content, or productivity, it’s entirely possible for solo founders to build massive businesses. The infrastructure and tooling are in place, and distribution is accessible.” However, that’s not the case with high-risk industries like healthcare, finance, or law, where security, compliance, regulation, and auditability play an important role in determining which solutions are ultimately deployed.

Others express serious doubts about the idea that individuals with limited experience and expertise could scale complex businesses through automation alone. “I don’t think what Cortinovis suggests is feasible,” says Komninos Chatzipapas, founder at HeraHaven AI. “Especially in the context of someone unskilled using AI to scale a business. I think this prediction is a consequence of the Dunning-Kruger effect, which a lot of people experience with AI. This is when limited understanding of AI leads them to vastly overestimate AI’s current abilities.” 

While he agrees that there are companies that managed to achieve billion-dollar valuations with a very small number of employees, he points to an important distinction: those companies were developing an AI product, rather than using AI to develop a product. Although this doesn’t necessarily refute the possibility that one might use AI to build a billion-dollar company, it’s worth bearing in mind when engaging in this discussion. “AI has an impressive breadth of knowledge, but limited depth,” adds Chatzipapas. “It can be a better programmer than most people, but a much worse one than your average software developer.”

The social implications

If one person can create billions in value, what does it mean for the future of work?

Whether the one-person unicorn materialises or not, the very possibility raises difficult questions about how we organise our economy and what the future of work looks like for everyone else. If an individual can generate immense value without a supporting staff, the traditional relationship between corporate growth and job creation starts to break down. We’ve long relied on large employers to provide both income and a sense of purpose for millions of people. But when massive enterprises can operate without a significant human workforce, we have to ask how people will sustain themselves if they can no longer work.

Making sure the gains from AI amplification reach more than a handful of lucky founders is a challenge that goes well beyond technology. Concentrating extreme wealth in the hands of a few solo operators could potentially destabilise society if the broader population has no way to participate in value creation. This could demand entirely new approaches to taxation and wealth distribution. Our current social safety nets were largely built around the assumption of steady employment, and they may not be equipped for an era where value is concentrated this dramatically.

As productivity becomes decoupled from human labour, the way we fund public services will also need a fundamental rethink. Relying on payroll taxes won’t work when the most profitable companies have no payroll to speak of. Exploring different models for economic participation, whether through alternative ownership structures or revised fiscal policies, may prove essential if we want to hold the society together. The definition of ‘earning a living’ itself may need to be completely rewritten.