Can you build a billion-dollar business with only AI agents (yet)? This author thinks so

If you are seeking an alternative to corporate life, self-employment — sometimes called “solopreneuring” — may be the answer for you. You can ply your software development, design, or business skills as an independent contractor — and make a decent living. 

Or, taking it a step further, you can build your own company for even larger returns. However, building a scalable business requires financial investment, product ideas, market research, marketing acumen, sales abilities, technology solutions, and likely a team of skilled people to help make it happen. It takes money and resources.

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Until now. Tim Cortinovis, in his recent book, Single-Handed Unicorn: How to Solo Build a Billion-Dollar Company, claims you can build a huge company with just AI tools and freelancers. The online and AI tools now available make it possible to rapidly build and scale a company by yourself, or at least with a really small team. The only missing ingredient is the entrepreneur’s spirit of innovation and ability to identify and serve a market.

What is needed to scale a one-person business is the right mindset, toolset, and business model, Cortinovis explained in a recent podcast. With the emergence of AI agents, “you can handle everything,” he said. 

“They work on nearly all platforms the same. You have one managing agent, the brain of the others. You have subagents on the way down, and you can give the subagents access to client information, client addresses and so on. And invoices and another subagent. This is accessed through your email client, like Gmail, Outlook, or a database. Connect to WhatsApp, and you can handle incoming customer queries and customer messages automatically. And the management agent just supervises all the processes and is responsible for that person to be successful.”

Is a one-person mega-business realistic? Frankly, it still sounds like a lot of work and worry. Industry trailblazers who have scaled their own businesses have mixed reactions to this notion. 

For starters, it may depend on a few factors — especially the nature of one’s industry. It’s hard to imagine, for example, operating a one-person natural-gas refining facility or bank.

“The real question isn’t whether one person can scale something large — it’s which industries make that feasible,” said Cassie Kozyrkov, CEO of Kozyr and Google’s first chief decision scientist. “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.”

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In high-risk industries such as healthcare, finance, or law, “the primary limitations aren’t technical, they’re operational: security, compliance, regulation, and auditability are all essential to deploying solutions responsibly and passing enterprise scrutiny,” Kozyrkov explained.

“There are already some impressively sized solopreneur companies at the so-called bleeding edge,” said Nic Adams, co-founder and CEO at Orcus. “A solopreneur or micro-team can build and scale a billion-dollar operation by weaponizing automation, data pipelines, 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 — such as org charts and headcounts.”

Achieving scale “no longer depends on headcount; it depends on leverage,” agreed Arvind Rongala, CEO of Edstellar. “Today’s solopreneur doesn’t need to do everything — just needs to architect a system where technology, global talent, and automation do the heavy lifting.”  

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Still, there are limits as to how far a solopreneur can go building a mega-company.

 “I don’t think what Cortinovis suggests is feasible,” said 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 there are companies that have achieved something similar — like Midjourney with a $1 billion valuation with 11 employees — those were developing an AI product rather than using AI to develop a product,” Chatzipapas continued. “AI has an impressive breadth of knowledge, but limited depth. It can be a better programmer than most people, but a much worse one than your average software developer.”

This is the opposite of what’s needed to build a successful business, he pointed out. “Businesses thrive on deep domain expertise, flawless execution, and being the absolute best at one specific thing, like image generation for Midjourney or search for Google. That’s not something AI is capable of yet.”

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Chatzipapas said proof of AI’s inability to support scale across many domains “is the number of people still involved in writing content, which should’ve been AI’s raison d’être. As an AI company, we still employ multiple writers to generate content for our site because AI can still not perform at their level.”

AI can generate, automate, and predict — “but it still struggles with abstract judgment, strategic storytelling, and enterprise trust,” said Rongala. “These remain the domain of human intuition.”

Adams added that “a few gaps remain in seamless AI orchestration and secure autonomous decision-making at scale. True autonomous agents that handle complex, multi-domain workflows end-to-end without human intervention are still nascent. Additionally, there’s a need for ultra-low-latency AI threat detection to defend the platform itself from adversarial exploitation, a black hat arms race in itself.”  

What are the best tools and technologies to scale and sustain a large one-person or small-team business? 

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Adams recommended “cloud compute platforms for elastic scale; container orchestration such as Kubernetes for modularity; advanced large language models tuned for domain-specific tasks; AI orchestration frameworks like LangChain or custom pipelines; and state-of-the-art observability tooling to monitor emergent behaviors. In essence, a full-stack AI ops environment with self-healing, self-optimizing capabilities.” 

The best tools are those that remove bottlenecks, said Rongala. “AI copilots, intelligent CRMs, global payment platforms, and modular APIs.”  Still, he added, “What truly drives success is clarity — knowing what not to do, and letting the system run the rest. It’s not about being a one-person army, it’s about being a systems thinker.”

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