Conclusion: AI Has Lowered the Bar for Solo Entrepreneurship, but Teams Still Win

Thanks to generative AI, the cost of starting a business alone has dramatically decreased.

Code can be written by Copilot. Landing pages can be created in 30 minutes using Canva and ChatGPT. Customer support can be handled by chatbots. Tasks that once cost 1 million yen to outsource can now be managed with a subscription costing a few thousand yen per month. According to data from ProductHunt, an analysis of over 160,000 product launches after the release of ChatGPT-3.5 shows a clear increase in launches by solo entrepreneurs.

At first glance, one might think, “We don’t need teams anymore.”

However, the data tells a different story. While the number of solo entrepreneurs has increased, it is still teams that produce the best results.

Why is that? There is a structure here that small business owners must not overlook.

The Real Reason for the Increase in Solo Entrepreneurship: “Lower Commitment”

Generative AI has not only lowered the “cost of starting a business” but has also reduced the “cost of withdrawal.”

On weekends, one can shape an idea using ChatGPT and throw it onto ProductHunt. If the response is poor, they can try a different product the following week. This is less about entrepreneurship and more about “experimentation.” While experimentation itself is not bad, simply repeating experiments does not lead to a viable business.

The increase in solo entrepreneurship is largely comprised of these “low-commitment experiments.” Out of 160,000 launches, how many products are still operational six months later? Likely, the number is surprisingly low.

On the other hand, products developed by teams have defined roles. There are salespeople, developers, and individuals who gather customer feedback. What one might casually decide to “quit” as a solo entrepreneur becomes a matter of having to “explain the reason for quitting” within a team. This friction actually enhances the quality of the product.

In other words, while AI has made it easier to “start,” it has not made it easy to “continue and win.”

Can AI Agents Replace Teams?—Not Yet

“So, if I use multiple AI agents, can’t I achieve the same results as a team alone?”

In response to this question, an interesting study has recently emerged. It is a benchmark called “EntCollabBench” that evaluates the collaboration of multi-agents mimicking roles within a company. It measures the ability of specialized agents, such as sales, planning, and development, to work together on tasks.

What were the results?

Current LLM-based agents face significant challenges in replicating corporate workflows. They stumble particularly on three points:

1. Role delegation does not work well. When passing off tasks with “this is now your job,” context is lost. A human can say, “Be careful about this issue because there’s this background” when passing in the hallway. Agents lack this capability.

1. Information deteriorates during context transitions. In long exchanges, the judgment of “what is important and what is trivial” is weak, resulting in irrelevant outputs.

1. Decision-making certainty is low. Judgments in ambiguous situations, evaluating trade-offs, and making decisions like “not doing this now” are areas where current agents struggle the most.

In short, even if you line up AI agents, they do not become a “team.” At least not for now.

This is an important insight for small business owners. The idea that “AI will allow us to reduce staff” is insufficient. The essential question becomes how to design a “human team” that can effectively utilize AI.

Doing the Work of 50 People with 30—Thinking in Terms of Cost Structure

Now, let’s get to the main point. Where does the true value of AI lie for small businesses?

It boils down to being able to “increase workload without increasing staff.”

Let’s consider specific numbers:

• Annual cost per employee (salary + social insurance + indirect costs): approximately 5 million yen
• Annual labor cost for a company with 30 employees: 150 million yen
• Labor cost for doing the same amount of work with 50 employees: 250 million yen

The difference is 100 million yen per year.

If a company with 30 employees can leverage AI to handle the workload of 50, it gains a competitive advantage worth 100 million yen annually. Since this is a “structural cost difference” rather than revenue, it is less susceptible to economic fluctuations.

So, how can we achieve 1.67 times the productivity?

Practical Blueprint—Thinking in Three Layers

Layer 1: Delegate “Tasks” to AI

First, divide internal operations into “judgment” and “tasks.”

• Creating meeting minutes → AI auto-generates. Humans only need to confirm. Time required: 60 minutes → 5 minutes
• Drafting estimates → AI creates a draft from past data. Time required: 30 minutes → 5 minutes
• Aggregating daily and weekly reports → AI summarizes and presents to management. Time required: 30 minutes per day for managers becomes zero
• Screening candidates → AI handles initial processing through condition matching

Just this alone can free up 30 minutes to an hour per person per day. For 30 people, that’s 15 to 30 hours per day. Converted to a month, that’s 300 to 600 hours, equivalent to 2 to 4 full-time positions.

Layer 2: Use AI for “Decision Support”

Automating tasks alone won’t reach 1.67 times productivity. The next area to address is the “speed of decision-making.”

• Creating sales proposals: AI analyzes past successful cases and suggests, “This appeal will resonate with this industry.” This systematizes what was previously dependent on the salesperson’s experience.
• Inventory management: AI provides demand forecasts, improving the accuracy of ordering decisions. Reducing excess inventory alone can enhance cash flow.
• Handling complaints: AI suggests responses based on past handling history, enabling even newcomers to respond at a veteran level.

The key point here is “eliminating dependence on individuals.” By making the know-how that was only in the heads of veteran employees accessible to everyone through AI, this is not just about efficiency. It enhances organizational reproducibility. When people leave, the know-how does not disappear.

Layer 3: Decide What Not to Do

This is often overlooked but has the most significant impact.

When visualizing operations with AI, one can discover tasks that “actually serve no purpose for anyone.” Weekly reporting meetings, monthly reports that no one reads, and outdated approval processes.

Using AI as a catalyst, conduct an inventory of operations. This is the greatest leverage for a company of 30 to handle the workload of 50. If you can eliminate 20 people’s worth of unnecessary work, the remaining 30 can focus on the “meaningful work” that should be done by 50.

A Structure Where Small Businesses Can Win

For large companies to implement AI, there are approvals, security reviews, and plans for company-wide deployment, which can take six months.

In a company of 30, you can start next week.

• Monday: Contract for the ChatGPT Team plan (3,000 yen per person per month, 90,000 yen for 30 people)
• Tuesday: Test automatic generation of meeting minutes
• Wednesday: Have the sales team try drafting proposals
• Friday: Review and keep only what was useful

This speed is the greatest weapon of small businesses. While large companies are establishing “AI utilization promotion offices” and deliberating, small businesses are conducting 10 experiments and finding three successful patterns.

With an investment of 90,000 yen per month, if you can reduce 100 hours of labor per month, that’s equivalent to 900 yen per hour. Cheaper than hiring part-time workers. Moreover, AI works 24/7 without complaints.

“So, what should we do in the end?”

1. First, replace one routine task with AI. It could be meeting minutes or estimates. Start small.
2. Next, systematize one piece of know-how that is dependent on individuals using AI. Translate the judgment criteria of veterans into prompts.
3. Then, eliminate one task that doesn’t need to be done. With AI in place, there will surely be tasks that you realize “we didn’t need this in the first place.”

Cycle through these three steps every month. In six months, six operations will change. In a year, the way the organization operates will fundamentally change.

AI is not a toy for solo entrepreneurs. It only reveals its true destructive power when used by teams. And that team does not need to be 50 people. 30 is sufficient. AI will fill in the gap for the remaining 20.

However, what AI fills in are “tasks,” not “decisions.” The decision-making must be done by humans. Therefore, creating a team where each of the 30 individuals understands “what decisions they should make” will become the core of small business management moving forward.