Asobo’s CEO says smaller teams don’t need gen-AI to compete, and the industry should listen

Asobo does not believe smaller teams need generative AI tools to compete with bigger development studios. That blunt framing comes from the French developer itself, the same company behind the Plague Tale series and Microsoft Flight Simulator. In other words, the studio is treating gen-AI adoption as a strategic choice, not a competitive survival requirement.

If you are running a product, a studio, or a portfolio of game companies, this matters because “everyone is using gen-AI” has become a kind of boardroom default. Once a technology becomes the presumed answer, teams start spending before they know the question. Asobo is pushing back on that psychology. Their position suggests that smaller teams can still win on fundamentals like craft, pipeline discipline, and creative direction without leaning on generative systems as a must-have layer.

Zoom out a bit and the competitive landscape starts to look like two fights at once. One is the traditional race for talent, time, and iteration, where bigger studios can spread costs across multiple teams and projects. The other fight is about workflow advantage. Gen-AI is often pitched as a way to accelerate parts of development, from early concepting to drafts of text or assets. Even if the output quality is uneven, speed can look like a competitive edge in quarterly metrics. That is why investors and executives are increasingly asking not just “can you use AI,” but “are you falling behind if you do not.”

Asobo’s counterpoint reframes the decision. If a studio of Asobo’s profile, operating in the same general market pressure cooker, does not believe smaller teams need gen-AI to compete, then the real question becomes: what part of your process is bottlenecked, and what kind of improvement would be worth changing the culture for? The Plague Tale and Microsoft Flight Simulator franchises also signal something else: these are not tech demos. They are games where player experience, pacing, and production values are expected. In such settings, a tool that speeds one step but introduces rework elsewhere can be more expensive than it looks on day one.

There is also a governance angle that often gets ignored in the rush to adopt. Gen-AI systems are tied to data, training, provenance, and content risk, even when they are used internally. While the source here does not list specific regulations, the broader industry reality is that regulators and rights holders are paying attention to how AI systems generate or transform content. Executives do not need a moral lecture to justify governance. They need to understand the operational risk. If generated assets or text cannot be cleanly traced, or if they require additional review to satisfy legal or platform requirements, then “speed” can degrade into “additional overhead.”

Boards and CFOs, in particular, should notice the implicit financial logic in Asobo’s position. When a studio adopts gen-AI, it usually does not stop at software purchases. It tends to require training, changes to pipeline tooling, new roles or responsibilities, and review processes to maintain quality. Those costs hit the ledger whether the outputs end up being useful or not. Saying gen-AI is a choice, not a requirement, is effectively saying: do not treat AI spend as a subscription to competitive destiny. Treat it like any other investment. Show the measurable value, or keep the budget for the things you can already defend, like production, art direction, engineering, and testing.

Second-order effects follow quickly from that stance. If smaller studios feel less pressure to adopt gen-AI just to “keep up,” the market may see a divergence: some teams will integrate gen-AI aggressively and push for speed, while others will focus on human-led workflows and selective automation. The competitive advantage then shifts from “who adopted first” to “who matched the tool to the bottleneck.” That could also influence hiring. Instead of building headcount around AI tooling alone, studios may prioritize creative problem-solving and production execution, while using AI only where it supports existing strengths.

For peers in similar roles, Asobo’s message is a reminder that competition does not always reward the newest capability. Sometimes it rewards the ability to say no to fashionable pressure. In a sector where resources and schedules are tight, the most dangerous move is buying a new workflow before you have proven that it improves your product, reduces risk, and helps you ship. Asobo is essentially arguing that smaller teams can compete on the fundamentals, and that generative AI should be evaluated on its merits, not on the fear of falling behind.