“The Era of ‘Headcount = Strength’ is Over”

Zuckerberg has cut 8,000 jobs at Meta. Coinbase has reduced its workforce by 14%. Cloudflare has laid off over 1,100 employees.

On the other hand, news has emerged that a company with just 30 engineers has developed an app using AI, completing work that traditionally required a team of 100.

When these two pieces of news are juxtaposed, a simple structural change becomes evident.

“More people = stronger” has collapsed.

And the ones who benefit the most from this structural change are not large corporations but small and medium enterprises.

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What’s Happening in Large Corporations—”Reducing Staff and Replacing with AI”

First, let’s clarify what’s happening on the side of large corporations.

The reduction of 8,000 employees at Meta translates to an estimated cost cut of around 50 billion yen in annual personnel expenses. The total cost per engineer at U.S. tech companies (salary + benefits + office + management costs) is not in the range of 6 to 8 million yen per year, but rather reaches 20 to 30 million yen at Silicon Valley standards. With 8,000 employees at an average of 25 million yen, that roughly amounts to 200 billion yen. In reality, since the cuts are primarily focused on middle management, a more realistic estimate would be in the range of 50 to 100 billion yen.

The 14% reduction at Coinbase and the layoffs at Cloudflare also fall within the same context. AI is writing code, handling customer support, and performing data analysis. This makes “the humans doing that work” unnecessary.

What’s important here is that large corporations are moving in the direction of “reducing staff by implementing AI.” In other words, for them, AI is a “cost-cutting tool” and a means to lighten their organization.

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What’s Happening in a Company of 30—”Doing Big Work with a Small Team”

On the other hand, the actions of a company comprised of 30 engineers are in a completely opposite direction.

They have not reduced their staff. They only have 30 from the beginning. By fully utilizing AI, they completed development that would typically cost 5 million yen in just 1.5 million yen, and in half the usual time.

This difference is decisive.

Large corporations are unable to bear the costs of maintaining “8,000 employees as 8,000 employees” and are replacing them with AI. Small and medium enterprises, on the other hand, can focus on tripling or quintupling individual productivity precisely because they “only have 30 from the start.”

While large corporations are doing “subtraction” with AI, small and medium enterprises are doing “multiplication” with it.

This is where the structural reversal lies.

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What Happens When Cost Levels Change

Let’s think about this with specific numbers.

Traditionally, if you wanted to develop a certain business application, it would take 5 engineers over 3 months, costing 5 million yen just in personnel expenses. Outsourcing would cost 8 to 10 million yen. For small and medium enterprises, this is a serious dilemma of “to do or not to do.”

However, if you have 2 engineers proficient in AI coding tools, it can be done in 1 month for 1.5 million yen. The cost drops to less than a third, and the time required is also reduced to a third.

When this phenomenon of “cost changing by orders of magnitude” occurs, what changes?

“Things you were unsure about making can all be made now.”

Business systems that you previously thought you had to endure with off-the-shelf SaaS due to “lack of budget.” Customer management that you thought you had to continue managing with Excel because “outsourcing is too expensive.” Custom applications you had given up on because “it’s impossible for our scale.”

All of these come within reach.

Large corporations will “reduce staff” due to this change. Small and medium enterprises will “become capable of doing things they couldn’t do before” because of this change. Despite being the same technological innovation, the direction of its effects is completely different. Clearly, the growth potential lies with the latter.

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Three Reasons Why “Small Teams” Become an Asset

Why are small and medium enterprises structurally at an advantage? There are three reasons.

1. Faster Decision-Making

For a large corporation to implement a single AI tool, it can take anywhere from six months to a year due to security reviews, legal checks, internal approvals, PoCs, and company-wide deployment. In a small or medium enterprise, it can simply be, “Let’s start using it next week.”

The speed of AI evolution is such that the landscape can change in six months. Organizations that take a year to make decisions will always be lagging behind. In a company of 30, everyone can start using a new tool the week after it’s released.

2. Everyone Can Operate with an “AI Assumption”

Instilling AI literacy across an organization of 8,000 people incurs enormous educational costs. Ultimately, only certain departments will use it while the rest continue with traditional methods.

In a company of 30, it would take just a week to teach everyone how to use AI tools. The cost of creating an organization where “everyone can use AI” is overwhelmingly low.

3. Lower Fixed Costs Allow for Experimentation

Large corporations have massive fixed costs, making it difficult to take risks on failures. Small and medium enterprises have lower fixed costs, allowing them to “just try it out.”

Utilizing AI is not a world where the right answer is known in advance. You try it, and if it works, you continue. If it doesn’t, you stop. Only agile organizations can rapidly cycle through this “experiment → verification → improvement” process.

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Another Tailwind from Talent Flowing Out of Large Corporations

There’s another change that cannot be overlooked.

With Meta cutting 8,000 jobs, Coinbase reducing by 14%, and Cloudflare laying off 1,100 employees, where do these people go?

Not everyone will find reemployment at GAFAM. Some will go to startups, some will become freelancers, and some will connect with local companies through contracts.

Talent that small and medium enterprises previously thought they couldn’t hire due to salary levels will now be within reach for project-based work or contracts. Since AI tools allow for sufficient collaboration remotely, physical distance becomes irrelevant.

From “Hiring Full-Time” to “Forming Teams on a Project Basis.”

The cost of acquiring talent is also structurally changing.

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So, What Should We Do?

It’s not enough to just conclude that “a tailwind is blowing for small and medium enterprises.” We need to be specific about what to do.

1. First, have someone in the company start using an AI coding tool.

Whether it’s Cursor or GitHub Copilot, it costs between 2,000 and 4,000 yen per month. Have one person start using it and show the rest of the company, “Look what we can do.”

2. Experiment with switching “outsourced work” to AI + in-house production.

There’s no need to change everything at once. Try it out with just one small project. Something that cost 2 million yen to outsource might only cost 300,000 yen if done in-house using AI. The difference can fund the next experiment.

3. Focus on “Increasing Productivity per Person with AI” before “Hiring More People.”

Hiring takes time and costs. Implementing AI tools can start next week. Don’t get the order wrong.

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In a World Where the Meaning of ‘Headcount’ Has Broken Down

Zuckerberg cut 8,000 jobs because “8,000 jobs have been replaced by AI.” A company of 30 was able to create an app because “30 people using AI can do the work of 100.”

The same phenomenon has two sides.

For large corporations, this is “painful efficiency improvement,” but for small and medium enterprises, it’s a “liberation that allows them to do things they couldn’t do before.”

Having a small number of employees is no longer a handicap. In an era where leverage is effective through AI, being small itself becomes a competitive advantage.

The question is simple.

Is your company able to turn this structural change into a tailwind?

If you haven’t started anything yet, I encourage you to try out one AI tool by the end of this week. Six months from now, will you think, “I’m glad I started then,” or “I wish I had started then”? The turning point is today.