AI is creating a new kind of startup - the one-person unicorn

1 member unicorn model

A unicorn startup is a term used to describe a startup company that is valued at over $1 billion. The term “unicorn startup” was first used by Aileen Lee, co-founder of Cowboy Ventures, in an article published on TechCrunch in 2013.

Aileen Lee wanted to use the term “unicorn” to describe the nature of the group of technology startups that are valued at more than $1 billion and were founded in the US after 2003. At the time of publication, only 39 companies had been found that met this criteria. Currently, unicorn startups that have achieved a $1 billion valuation within 10 years account for only 0.07% of all startups.

The unicorn is an imaginary creature, associated with rarity. (Illustration: KT)

In the past, building a unicorn startup—a billion-dollar company—required a huge, talented team and millions of dollars in venture capital. But a big change is coming thanks to artificial intelligence (AI) applications.

Advances in AI agent systems, OpenAI is allowing single founders to achieve what previously required the collective efforts of a team of collaborators.

OpenAI CEO Sam Altman often thinks about the moment when a founder runs a company that reaches a billion-dollar valuation without hiring a single employee.

“My friends and I, who are tech CEOs, are betting that one day someone will own a billion-dollar company with themselves as their only employee. Something that was then and is now unimaginable without AI,” said Sam Altman.

It’s easy to see how AI can automate many processes that previously required more human workers, said Alex Gurevich, managing director of Javelin Venture Partners. The inherent advantage of a new startup over a traditional company is that it moves faster, experiments faster, and makes data-driven decisions.

AI "shares" the work

OpenAI's agent levels classify AI systems according to their autonomy and decision-making capabilities. At the basic level (Levels 1-2), agents perform narrow tasks: drafting emails, generating code snippets, or summarizing documents. By Level 3, they handle multi-step workflows, like optimizing advertising campaigns or managing customer support channels.

At Level 4-5, AI agents evolve into strategic partners, capable of overseeing departments or even entire organizations — balancing budgets, negotiating contracts, and making high-impact decisions.

While today’s AI tools fluctuate between Levels 2 and 3, their trajectory is clear. It is predicted that by 2028, 33% of enterprise software applications will contain AI agents, enabling 15% of daily work decisions to be made autonomously. These systems not only streamline work, but also compress organizational hierarchies into a single interface.

Illustration: KT

AI becomes co-founder

In the past, large startups had specialized teams for coding, design, marketing, operations... But today, a single founder can do much more thanks to the powerful support of AI.

Code agents can be used to build business plans in a fraction of the time. A full-stack engineer, guided by AI programmers like Github Co-Pilot, can design and deploy functional prototypes at unprecedented speed.

Generative AI can be used to create content on the fly. Tools like MidJourney and Runway ML create social media ads, UGC videos, and brand assets in minutes.

Workflows powered by large language models (LLMs) can handle customer support, SEO, and email marketing. Platforms like Claude 3 or Gemini Advanced craft personalized campaigns, analyze sentiment, and resolve user queries.

Trends in the AI ​​Revolution

Three key trends that will likely dominate the AI ​​revolution in startups are:

Democratizing AI infrastructure: Cloud platforms (AWS, Google Cloud, Azure) and open source models (DeepSeek R1, Llama 3, Mistral) have reduced the cost of AI deployment.

Self-improving reasoning systems: Models like OpenAI’s O1 or DeepSeek R1 allow AI agents to improve their performance incrementally by analyzing past results and adjusting strategies using a Mix of Experts (MoE) architecture. These models engage specialized subsets of their networks to efficiently handle complex tasks like advanced mathematics and coding.

Agent collaboration: AI agents can now delegate tasks to each other. A coding agent can build a feature, pass it to a testing agent for quality assurance, then notify a deployment agent to put it into production — all without human supervision.

Warning about the downsides

The rise of single-member unicorns raises a number of issues to consider:

• Accountability: Who is responsible when an AI agent makes a mistake in a medical transcription tool or a hiring algorithm?

• Discrimination: Will agents trained on biased data discriminate if they are not rigorously audited?

• Displacement: If AI enables solo founders to replace traditional SMEs, how will we retrain the displaced workers?

Current regulatory frameworks have yet to keep pace with the rapid development of AI. The EU AI Act and former US President Joe Biden’s Executive Order on Artificial Intelligence (Executive Order 14110) were initial steps, but global standards for AI have yet to be codified.