東京の AI 企業 Sakana AI、製品開発責任者インタビュー

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Sakana AI, a Tokyo-based AI company, has released four products in rapid succession: starting with Sakana Chat in March 2026, followed by Sakana Marlin, Sakana Fugu, and Sakana Translate. Why is a company long known for research and development shipping products at this pace? And what vision lies beyond it?

We spoke with Sota Omura, who leads the product team as head of product development. The conversation ranged from how the team came into being, to the reasons behind the unusual release pace, a vision of a future in which humans and AI make decisions together, and an organizational philosophy of turning diversity into strength.

Interviewee

Sota Omura

Head of Product Development

Previously engaged in strategy formulation and business transformation consulting at Deloitte Tohmatsu Consulting LLC. Later served as Business Lead at Coincheck, Inc., managing multiple businesses including crypto asset trading and staking services. Joined Sakana AI in July 2025 as a Product Manager, leading the planning, design, and implementation of AI products.

From Consulting and a Startup to an “Industry That Leans Forward”

— To start, could you walk us through your career so far?

My career began in consulting. I joined Deloitte Tohmatsu Consulting straight out of university, on a team serving companies that provide services close to everyday life — supermarkets, railway companies, and the like. Most of my work involved projects to rebuild aging IT systems.

As I gained experience as an IT consultant, I wanted to know what it really means to build and grow a product with my own hands. So I moved to Coincheck, a company that operates an exchange app for buying and selling crypto assets such as Bitcoin. Everything was developed in-house: product managers, designers, engineers, and customer support worked as one to refine a single service. Eventually I came to lead the entire product as head of the business.

— What led you from there to Sakana AI?

My time at Coincheck coincided with the period when the blockchain industry suddenly captured the world’s attention. The prices of Bitcoin and other crypto assets swung between surges and crashes; NFTs and Web3 filled the news day after day. Debates over regulation moved forward, and large corporations entered the space one after another. We were in the middle of society deciding, in real time, how to receive this technology. Money and talent were pouring in, and the whole industry was racing to claim a future several years ahead of its time.

“There are views you can only see by placing yourself in an industry that leans forward.” My boss at the time told me that, and it proved true. Inside an industry swaying between what a technology makes possible and how society responds, there was a fascination you could find nowhere else. Those words remain my compass whenever I think about where to go next.

In 2025, when I had time to step back and look at my career, I reconsidered where to base myself next. Measured against my old boss’s words, the industry leaning furthest forward was, without question, generative AI.

— There are plenty of AI companies. Why Sakana AI?

There were many reasons, but one deciding factor was that the headquarters is in Japan. At the Japan office of a foreign tech company, a division of roles tends to emerge with the home country, where the decisions are made — and the work tends to center on delivering products that already exist, through sales and marketing. When a customer says, “We’d like it to work more like this,” you can find yourself escalating the request to headquarters and spending a long time coordinating.

Sakana AI, by contrast, is run from Japan, so the core of its product-making is entirely here. For someone based in Japan competing in the global market, I saw a company born in Japan as a major advantage.

A Product Team Born from Members’ Grassroots Discussions

— We understand you initially joined the Applied team. How did the product team come about?

At the time, Sakana AI had two main teams: the Research team, which studies a wide range of technologies including the development of foundational AI models, and the Applied team, which puts those results to work solving problems for enterprise customers. I joined the Applied team; a product team did not yet exist. The Applied team’s work — walking closely alongside each client, one company at a time — creates value nothing else can. At the same time, from the Applied team’s earliest days there had been a discussion: couldn’t a product deliver the value of the same technology to many more customers at once?

So from around the summer of 2025, I reached out to younger members of the Applied team who were interested in building products, and on our own initiative we set up sessions to discuss what shape product work could take at this company.

Amid all this, Ren Ito, our co-founder and president, approached me: he wanted to discuss how to treat model development as part of our own business. I presented a plan for growing a product business, model development included. As a result, the effort began moving as a partly independent offshoot of the Applied team. When the release of Sakana Chat was decided, we became a fully independent team to make the lines of responsibility clear, and I have led it since February 2026. Sakana AI, which began as a research and development company, is coming to wear the face of a product company as well. I feel I am standing at that turning point.

— How do the three teams — Research, Applied, and Product — relate to one another?

The basic division of labor is that the Research team creates new technology, and the product team delivers those results to customers as products and monetizes them. On top of that, the two teams are in everyday contact. Sometimes we tell Research, “This is the kind of model the business front lines are asking for”; sometimes a request arrives from Research: “We’d like this used in such-and-such a way.” That is exactly what happened when the model Sakana Fugu was born.

It is the same with the Applied team. From the customer’s point of view, both are services of the same Sakana AI. If an Applied customer takes an interest in Sakana Marlin, we provide it; if a customer who came in through a product wants more hands-on support, we pass the baton to the Applied team.

Four Products in Rapid Succession: “Standard Practice for the AI Era”

Sakana Chat is a chat service powered by Namazu, our flagship model. It aims to be a chat AI from Japan that feels as natural to use as ChatGPT or Claude. Sakana Translate is a translation service, also powered by Namazu, and a sister product to Sakana Chat.

Sakana Fugu is a model built on “orchestration,” a technology that has multiple AI models work in concert. It handles tasks with higher performance than entrusting them to any single model. It is currently offered to developers through an API.

Sakana Marlin is a service for high-quality research, built on AB-MCTS, a technique for deep reasoning over extended time. We aim to grow it into an AI service that serves as a sounding board for management — a virtual CSO (chief strategy officer).

— Sakana Chat launched in March 2026, followed by Sakana Marlin, Sakana Fugu, and Sakana Translate. Compared with the usual approach — rounds of market research and user interviews before releasing a single product — this pace is extraordinary. Why did you choose to work this way?

First and foremost, there was strong direction from the top. Our CEO, David, had made his intent clear: this would be the year Sakana AI ships products. Release Sakana Chat, then follow with Sakana Marlin, Sakana Fugu, and Sakana Translate. Day after day, the message we received was that making this sequence happen, without fail, was essential for Sakana AI right now.

That said, you are right that this departs sharply from the conventional approach. But I see releasing at this pace as what is becoming standard practice in the AI era. With AI’s help, prototypes that once took months are finished in weeks, sometimes days. Rather than spending a year on research before building, it is faster to ship first and learn from real reactions. Analyze closely what resonates, set the direction from there, and correct course quickly as you learn. That kind of agile decision-making is exactly what will be demanded of players in the AI era.

— None of the four products is specialized for a particular industry. Was that also deliberate?

If we are delivering a service in the form of a product, we want it to be usable across a wide range of fields. Translation, research, consulting over chat — these are tasks performed every day at every company, whatever the industry. If we start by building products that help with this shared work, we can deliver value to that many more people. The size of the market we can target, and the size of the value we can create there: cross those two with our read on where we can win, and the result is the current four products.

Of course, deeper builds specialized for particular industries or use cases very much remain an option going forward. For example, we are actively exploring specialized versions of Sakana Marlin for finance and for manufacturing.

Making Sakana Marlin a Platform for Decision-Making

— Can you tell us about the direction of your products from here?

To set the premise: we do not believe it is desirable for dependence on any single model to grow too strong. Let me be clear that we hold deep respect for the companies polishing excellent models — our own products, and our society, are supported by them. The more good models there are in the world, the greater the value of combining them intelligently. That is how we see it.

At the same time, every model has quirks, strengths, and weaknesses that trace back to its training data and design. Supply chain risk, too, is becoming harder to ignore. Just as an ecosystem with diverse organisms withstands environmental change better, we believe the world as a whole is healthier when diversity remains in how we work with AI.

Sakana Chat and Sakana Translate are products for experiencing Namazu, a model given additional training so that it attunes closely to the Japanese context. Sakana Fugu and Sakana Marlin, on the other hand, are products exploring how to use multiple models intelligently: Sakana Fugu combines several models cleverly to tackle a single task, while Sakana Marlin has them think deeply over extended time.

What we work on lies in territory that is technically possible but that few people have taken up — or likely ever will. We rapidly test the value that emerges from it. We prize this stance of keeping our gaze fixed at an angle askew from everyone else’s.

— You have said before that you want to make Sakana Marlin “a platform for decision-making.”

A key intellectual foundation for Sakana Marlin is hypothesis thinking, proposed by Kazunari Uchida, a management scholar who served as head of the Boston Consulting Group in Japan. When we think something through, we first investigate the situation, form a hypothesis, act on it, then verify the results and revise the hypothesis. Whether weighing a new business or choosing where to work next, people follow these steps without realizing it. We want to implement this cycle as an application.

Today’s Sakana Marlin covers only the “investigate” step. But research is always followed by the steps in which humans form hypotheses and act. Pushed to its essence, every activity in business is made of this cycle. The state we aim for is one where every decision is made in Sakana Marlin, with humans and AI thinking together.

— Concretely, how would it be used?

Take investment as an example. Suppose you are deciding, in light of your company’s portfolio, which fields to invest in. First you look into which industries are growing and where you could expect synergies with your own business, to get a rough fix. This initial research is where today’s Sakana Marlin helps most.

After the initial research produces a list of 100 to 200 companies, the next steps follow: study the ten most promising in detail, and simulate investment schemes and return scenarios. Once you are satisfied, you decide which companies to invest in, how much, in what form, over what recovery horizon, and with what team. Then you look back at how the investments actually performed and set the next year’s plan — which calls for another round of research. Decision-making unfolds within this spiral structure. Today, humans carry most of it.

— True — that territory has traditionally belonged to consulting firms and the like.

Consulting and advisory services persist precisely because this territory is hard to replace with an application. Decision-making is an uncertain endeavor, and which factors a person weighs depends heavily on context. That is exactly why I see great possibility here.

Indeed, some predicted that the arrival of AI would erode demand for consultants and think tanks. In reality, demand for such experts has held remarkably firm — a measure, I think, of just how uncertain and ambiguous the practice of decision-making is.

Most AI use today stops at piecemeal efficiency: writing Excel formulas, arranging schedules. We want to take on decision-making itself — more uncertain, and further upstream. What I picture is humans and AI each bringing their own hypotheses and evidence, thinking together at the same table. “An AI sat in on the board meeting and, through discussion with the human directors, helped decide where to invest.” How many companies will be able to say that in the years ahead? We intend to translate that practice, carefully, into an application.

An Organization Where Everyone Chases the Same Numbers: Knowledge-Creation Theory and the Roots of Scrum

— As the products multiply, the organization will grow too. What kind of organization do you envision?

There are broadly three ways to structure an organization. A functional structure groups people by specialty — engineers in an engineering division, salespeople in a sales division. A business-unit structure gathers every role under a single service. A matrix structure combines the two. I strongly favor the business-unit structure, because it lets people of every function aim at the same numbers — revenue, user counts — and the same value.

— Yet as they grow, many companies seem to shift toward functional organizations.

I think that is because many people find business-unit organizations hard to run. Divide the organization by business unit, and each manager must grasp every corner of what it takes to run one service; the load on middle management inevitably becomes excessive. So most companies choose functional organizations aligned with individual specialties.

The greatest weakness of the functional organization, though, is that it fragments the process. Developers see only the development stage; quality assurance sees only the QA stage. Work starts falling into the cracks between organizations, and fewer people commit to the final value delivered. Everything becomes someone else’s problem, and you get finger-pointing: “We did hand over the deliverables.” That is the state I want to avoid.

I want everyone to hold responsibility for, and commit to, the whole process. But this is also a way of working that demands a high standard of each individual: take a broad view of your own remit while delivering solid results in your specialty. What we ask for is not just a mindset — it is skill, and professionalism. Because roles deliberately overlap, the team keeps up communication consciously and constantly. It is precisely among professionals working at a high standard that free, emergent exchange arises across role boundaries. I see it happen every day.

— Why choose that difficult path deliberately?

There are two management scholars, Ikujiro Nonaka and Hirotaka Takeuchi, authors of The Knowledge-Creating Company. They argued as follows: new knowledge is born when people with different experiences keep talking, putting into words and sharing the tacit knowledge each carries in their body — experiential knowledge not yet verbalized. That is why Japanese companies deliberately built overlap into roles and information, made things in a way where everyone committed to the final value reaching the customer, and produced innovation.

Knowledge-creation theory is one of the few management theories born in Japan to spread worldwide. It is also the wellspring of Scrum, the emblematic method of agile development. Developers who read the pair’s 1986 Harvard Business Review article, “The New New Product Development Game,” brought its thinking into software development and created Scrum. In other words, at the root of a way of working now taken for granted at IT companies around the world lies a Japanese philosophy of making things.

For those used to organizations with clearly bounded job scopes, working on the premise of overlapping roles can be deeply disorienting. Even so, this Japanese philosophy of making things retains a strength that still carries in the world today. Our founders chose to establish Sakana AI in Japan, and I intend to embody the meaning of that choice in the shape of this organization.

The Ideal Team: Turning Diversity into Strength

— What kind of people do you hope will join the team?

The strongest organization is one that can turn diversity into strength — that is my conviction. There is research behind it, too. In 2004, the economist Lu Hong and the complexity scientist Scott Page, in a study that modeled problem-solving mathematically, proved that under certain conditions a group with diverse ways of seeing and solving problems outperforms a group of individually more capable but similar-minded people. The result is known as the “diversity trumps ability” theorem. The study has drawn many critical responses since, but it remains one of the intellectual endeavors I love most.

I myself have moved fields from consulting to crypto assets to AI, and my own roots include a background outside Japan. Diversity lives not only in a team’s composition but within each individual. I feel that firsthand.

— What does the product team look like today?

We are about fifteen people now, centered on product managers, designers, and engineers, and increasingly diverse in nationality. Functions like marketing and sales are about to be built. The work of designing, from zero, the machinery that carries our products to the world still remains on this team. That is an excitement you find only in the founding period.

— On the other hand, there is the model of a charismatic leader — an Elon Musk, a Steve Jobs — deciding top-down. In fact, that philosophy seems to be what drives today’s tech industry.

True, there is the way of brilliant elites building a tightly commanded organization and changing the world. Raise a strong vision and run the shortest line as a monolith. For sheer speed of execution, no form is stronger. Applied to the AI industry, it is the strategy of polishing one supreme model or application and delivering it to the whole world. But simplify where that line eventually leads, and you get a world where everyone on earth relies on the same “strongest AI.” What is more, how much access anyone has to high-quality models is starting to hinge on external factors — how much they can pay, their nationality, their geography. I want to be cautious about the world becoming that uniform.

The real world is not that simple. The same question has different good answers depending on who is asking — their position and experience, the situation of the moment, the relationships around them. That is exactly why we want