# AI Makes Smaller Teams Possible. What Does the Business Gain — and Lose?

> AI can increase what a small team can produce. A game-development experiment shows why lower production costs do not automatically create commercial success, and why learning capacity still matters.

When a small team can ship a product that once required more people, that is a meaningful opportunity. But as teams shrink, it is worth separating the parts of the work that have become easier from the parts that are now concentrated in the people who remain.

Kaya Genç’s [reporting for Rest of World, based on conversations with game developers in Turkey](https://restofworld.org/2026/ai-video-games-developer-jobs/), makes that shift tangible. It includes people who had worked at larger studios and now develop their own games with AI support. They describe getting further with smaller teams on code, visual production and prototyping. At the same time, educators at Bahçeşehir University describe students struggling to find a way into the profession.

Those are observations from interviews, not the results of a study measuring employment across the whole industry. Even so, they point to a tension businesses should consider: as experienced people can do more alone, there may be fewer places where less experienced people can learn that work.

## I could make a game. That does not mean I learned the business of games

I also built a basic 2D game in Godot with AI support. As a developer who has worked with different programming languages for a long time, I did not find that part especially difficult. Although I already knew C#, I chose to use Godot’s GDScript for this experiment.

The limit was clearer on the visual side. I was not able to create usable 3D assets. Creating 2D visuals was easier. The help AI provided was not equally useful in every part of the work.

The experiment showed me that getting started in game development has become more accessible. But there is still a substantial distance between making a working game and creating a product people want to play, discover and pay for. Games are their own market. My software-development experience does not automatically become knowledge of that market.

## When production becomes easier, the bottleneck to success can move elsewhere

When the cost of making a game falls, more ideas can be tested. A small team can begin a product it previously could not afford to explore. That is a real gain.

But if making the product was not the main problem, faster production will not improve the commercial result by the same degree. Reaching players, giving them a reason to return and earning revenue require other decisions. Competitors can also access the same tools. Shipping products more easily does not by itself create a durable advantage.

The same distinction applies outside games. A marketing employee may be able to produce more ad copy and visuals with AI. If the campaign is offering the wrong proposition to customers, however, more variations will not resolve that problem. Time freed up by the tools may be better spent understanding customer responses.

That is why it is incomplete to judge a small team by the number of files it produces or products it ships. We also need to ask which need it serves more effectively and what changes in customer behaviour.

![A small team can build many game prototypes while a narrow marketplace entrance still decides which product reaches players](/images/inline-ai-smaller-teams/production-market-bottleneck.avif)

## Where did today’s experience come from?

There is an easy-to-miss detail in my game experiment: I already had software experience when I began using AI. I did not acquire that experience during the experiment.

In a company, an experienced employee may take on work that had previously been distributed among several people, with AI support. Yet the judgment that allows that person to do so has often been built through years of working on real problems. New employees also need real responsibility if they are to build that judgment.

When a salesperson prepares a proposal, they learn which condition a customer is likely to challenge. When a junior developer makes a small change, they may discover why a control that looks unnecessary is there. Automating the preparation part of that work does not automatically move the learning elsewhere.

AI can help someone early in their career participate in more difficult work. But they need access to someone who can discuss the reasoning behind a decision and help them understand an error. If the experienced employee spends all their time reviewing the increased output, there may be little room left for that support.

In [protecting the people who hold organizational memory](/protecting-organizational-memory-during-ai-transformation), I discussed why retaining experienced people is not sufficient on its own if a company wants to benefit from what they know. The next question here is who will take on that knowledge, and through which work they will gain their own experience.

![An experienced developer opens a work structure while a junior colleague takes responsibility for a connected section through shared explanation](/images/inline-ai-smaller-teams/experience-transfer.avif)

## Redesigning the work of a smaller team

Headcount may genuinely be able to fall. But the calculation also needs to include the work remaining beyond production: speaking with customers, assessing quality, responding when something goes wrong and developing a new employee still take time.

That is why I care about [AI transformation beginning with redesigning the work](/ai-transformation-redesign-work-not-cut-roles). Deciding where the capacity AI frees up goes is a management decision. It can go into shipping more products, understanding customer needs better or learning within the team. What that gain in speed means for the business depends on that choice.

When assessing this kind of change, I would track the time spent correcting completed work as well as the work completed. I would ask whether a critical task still depends on one person. I would also look at whether people early in their careers are gradually able to take on more responsibility; that is a meaningful signal about the team’s future.

My game experiment showed that starting production has become easier. For a company, the next decision is where to use that ease. Giving the same experienced person more work may help in the short term. Developing someone who can gradually take responsibility alongside them is also an investment in capacity, and it belongs in the calculation.

---

Language: English
License: CC BY 4.0
License URL: https://creativecommons.org/licenses/by/4.0/
Scope: Evren Bal-authored text, unless this article expressly states otherwise.
Excluded: Third-party material, quoted excerpts, logos, and separately marked images retain their own rights.
Attribution: Credit Evren Bal, link to the canonical source and license, and indicate changes.
Source: https://evrenbal.com/ai-smaller-teams-business-capacity