Turning Technology into Business Impact | Meet AIEEV Business Lead Karen Kim

The Story of Hyunkyung(Karen) Kim, AIEEV's Business Lead

Good technology doesn't automatically become good business. Between solving a customer's problem and turning that solution into real-world adoption and sustainable revenue, someone has to fill in countless blanks.

Karen Kim is the person at AIEEV who finds those blanks and connects them all the way through. After working in telecom B2B and business development at an AI semiconductor company, she's now building a new market: distributed AI cloud. From partner meetings to product planning, customer proposals, government projects, overseas business, and operations, her work has no clear boundaries.

We sat down with Karen to hear how she translates technology into her customers' language, and turns possibility into the next step of execution.

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Hi, could you introduce yourself briefly?

Hi, I'm Hyunkyung(Karen) Kim, and I lead the business side at AIEEV. 😊

If I had to describe myself in one sentence, I'd say I'm someone who connects technology to real business. I worked in B2B sales and IT consulting at KT, then moved into business development at SAPEON Korea, an AI semiconductor company. There, I experienced the full journey of a project, starting as a technical proof of concept, becoming a product, getting adopted in the field, and eventually turning into revenue.

At AIEEV, I now cover a wide range: business strategy, partnerships, sales, product planning, government projects, and global business. In a startup, I think the question “what does the company need most right now to move forward” matters more than any job title. So when it's needed, I write proposals, build product demos, organize customer feedback, and handle operations myself.

Were you interested in AI infrastructure from the start?

Not really. I majored in Russian in college and studied information systems in graduate school. My career started in telecom B2B. On the surface, these might look like unrelated paths, but looking back, I've always been connecting technology with people, and technology with business.

Working on business development for AI semiconductors at SAPEON Korea taught me something important: no matter how great the technology's performance is, it's hard to create a market unless it's connected to a product and operating model that customers can actually use. On the flip side, I also saw that when you truly understand a customer's problem and build a solution together with the technical team, even unfamiliar technology can make its way into real industry settings.

That's when I became more interested in how technology gets used, rather than the technology itself.

What made you decide to join AIEEV?

As AI services spread rapidly, I saw that operating cost and infrastructure would become just as important as model performance. Since 2024, working at an AI semiconductor company, I met a huge number of AI companies, and it was obvious that the inference market was going to grow. That raised a question for me: is there a way to improve this quickly and directly through software, rather than hardware? More companies are building great AI models, but to keep serving those models, they still have to solve very real problems like GPU cost, deployment, scaling, and security.

This becomes an even bigger burden as more services, like AI agents, call a model multiple times to complete a single task. In the end, even a team with a great idea and a great model can find itself unable to scale its service because of infrastructure cost.

AIEEV solves this problem with distributed infrastructure, connecting a wide range of existing GPU resources and letting companies use them as an API, a container, or a private environment, whichever fits their needs. I resonated with the idea of “letting more companies run AI at a cost they can actually afford,” and joined early on to help connect the technology with the market.

🤔 Building the Business at AIEEV

What's your current role at AIEEV?

It's probably fair to say I own almost every touchpoint related to the business. I find what the market and customers need, shape that into a product and business model, validate it together with partners, and connect it to contracts and recurring revenue.

On the surface, sales, marketing, business planning, product planning, and partnerships look like separate jobs, but at an early-stage startup I think they're really one continuous flow. A problem discovered in a conversation with a customer has to make it into the product roadmap, and a product's strengths have to be turned into a proposal customers can actually understand. And a PoC isn't just a technical demo, it has to be designed with the criteria and timeline that will lead to the next contract.

So I care more about “what's the next action” than “we had a good meeting.” A business only starts moving once concrete next steps are in place, things like an NDA, sharing technical materials, connecting the right teams, or agreeing on PoC scope and success metrics.

What does a typical day look like for you?

There's almost no fixed routine. In the morning I might discuss a partnership model with an overseas telecom, and in the afternoon check a customer's deployment issue with the dev team. In between, I validate the numbers in a business plan, polish a proposal for a partner, or test the product screens myself. And sometimes I empty the office water cooler or sweep the floor too, haha.

But the thing I care about most is making sure work doesn't stop at a document or a meeting. If I write a proposal, it needs to actually get used in a real meeting. If we have a meeting, a follow-up needs to get scheduled. If a product improvement request comes up, it needs an owner and a priority.

I also tend to have broad interests. Beyond AI models and GPU infrastructure, I keep studying telecom, edge AI, AI semiconductors, agents, automation, product UX, and overseas markets. I don't see these as separate topics, I see them as one ecosystem that's shaping how AI services will be built going forward.

How deeply do you think you need to understand the technology to do tech business?

I don't think I need to write code the way a developer does. But to relay customer requirements to the technical team, and to explain the technology's possibilities and limits to customers, you absolutely need to make the effort to understand how it's structured.

For example, when a customer asks, “can this be cheaper?”, you can't just talk about a discount rate. You need to look at which GPU and model they're using, their call patterns, whether it's an always-on workload or a temporary one, and whether they need a dedicated environment or data security, before you can offer a real solution.

The same goes the other way: when the tech team builds a great feature, I need to translate what operational benefit it gives the customer. I think of business development as an interpreter between technology and customers, not one who just translates words, but one who understands both sides' constraints and turns them into an actionable agreement.

What principle matters most to you in your work?

The first is separating fact from hypothesis. Startups have to talk about the future, but if you describe something unverified as if it's already been achieved, you lose trust. I try to clearly separate what's possible today, what's being validated, and what we plan to build.

The second is building a verifiable next step rather than a perfect plan. Even big partnerships rarely start as a massive contract from day one. It's far more realistic to design a small PoC with clear success criteria and expand from there based on the results.

The third is confirming that what we've built actually gets used. Making a document or a product isn't enough on its own. I only consider the work done once it's connected to who will use it, when, and in what situation.

I heard you actively use AI tools in your work.

I use AI less like a search or writing tool and more like a small execution team of my own. I connect it across the board: market research, technical document analysis, proposal drafts, product QA, data organization, and automating repetitive tasks.

What matters is not just asking AI a question and getting an answer, but making sure the result actually flows into real work. Research findings turn into a proposal, meeting notes get organized into next actions and owners, and repetitive operational work gets automated.

I got into startups because I love being able to do things with my own hands, and AI has let me do even more that way, I'm experiencing that expanded possibility firsthand. The more people who have this kind of experience, the bigger the stage Air Cloud will have to grow on, I think.

🔎 The AI Infrastructure AIEEV Wants to Build

What problem does AIEEV solve?

Many companies can build AI features, but struggle to run them at a sustainable cost. As users grow and model calls increase, infrastructure cost grows right along with them. Building your own GPU infrastructure means a heavy upfront investment and operational burden, and the big clouds are convenient but can get expensive over the long run.

AIEEV connects distributed GPU resources into a single cloud, so companies can use the AI infrastructure they need more flexibly.

Teams that want to call a model quickly can choose Air API. Companies that want to deploy their own model and service in an isolated environment can choose Air Container. Customers with security or closed-network requirements can choose a hybrid or private setup through Private Cloud. The core idea isn't selling one specific product, it's offering infrastructure options that match how a customer's service grows.

Where do you see AIEEV's biggest opportunity?

AI's center of gravity is shifting from the competition to build models to the competition to actually operate AI as a service. In particular, as agents, multimodal AI, and real-time automation spread, call volume and infrastructure demand are bound to keep growing.

What companies want here isn't just one cheap GPU. They need an environment where they can get started quickly, scale with traffic, meet their data and security requirements, and still keep overall operating costs under control.

AIEEV has a product structure that spans from an API-based service to dedicated containers to hybrid and private environments. If we can connect a wide range of GPUs, NPUs, telecom networks, and edge resources on top of that, I think it can become a new kind of AI infrastructure option that doesn't rely solely on a central data center.

It sounds like you're also very interested in global business and telecom partnerships.

A distributed AI cloud has a lot of overlap with telecom companies. Telcos already have networks, data centers, edge locations, and enterprise customer channels, and AIEEV can provide the software and services on top of that to connect and operate a variety of AI resources.

In overseas markets too, I think it makes more sense for us to turn a local telecom's or IT partner's existing infrastructure into a real AI service, rather than compete head-on with the big cloud providers. The partner understands the local customers, regulations, and network, while AIEEV provides the operating layer that lets agent, RAG, and multimodal services actually run.

In global business, a working first case on the ground matters far more than a polished pitch. So I start by looking at each country's industry structure, data regulations, language, and customer channels, and design a concrete PoC that can be validated in a short period of time.

🙋🏻‍♀️ How to Build a Team You Want to Work With

As a business leader, what kind of team culture do you want to build?

I want to build a team that solves shared problems together, rather than one where everyone just stays within their own job description. That doesn't mean everyone has to do everything. Each person's expertise should still be clear, but it's important to have the attitude of taking one more step so that work doesn't stall at the boundary between roles.

I also like a culture where people speak freely, but once a decision is made, execute quickly. At a startup, you can't wait until all the perfect information is in. You have to make the most reasonable decision with the evidence you have, then adjust as you see the results of execution.

Above all, I want it to be a team where people who do the work right get recognized. The contribution of someone who solved an invisible operational problem, who tracked down a customer's pain point to the end, or who built a system that lets a colleague work better, that has to be visible.

What kind of colleagues do you want to work with?

I want to work with people who, when they spot a problem, think “how can we solve this” before they think “that's not my job.”

You don't need to know every answer. What matters more is the ability to quickly learn what you don't know, ask the right person, and produce even a small result to check your direction. In a field where the technology and the market change fast, I believe learning speed and how you execute are a more durable edge than the knowledge you already have. Everyone here, including our CEO, aims for honest, no-hierarchy communication in service of the best possible outcome, and I think the organization gets denser the more people like that keep joining. That's part of why, a little unconventionally, we don't have C-level titles. An organization shouldn't have a ceiling, that's what lets it bring in even better people.

So I'd love someone who can back up their claims with facts and evidence. Someone who can honestly say “I don't know” and quickly correct course when they're wrong is someone you can trust. I believe communicating openly, on the same protocol, is the foundation for better decisions.

What kind of leader do you want to become?

I want to be a leader who can untangle a complicated situation and turn it into a clear next step the team can act on. Talking about a big vision matters too, but a company only moves forward once each person is clear on what they need to do today. I always tell the team that I personally believe in the power of compounding, if tomorrow is even a little better than today, and that adds up over time, growth accelerates faster than you'd expect. My goal is to build an environment where the team can feel that experience for themselves.

I also want to be someone who accurately conveys market context to the tech team, and the essence of the technology to customers. Rather than forcing the two worlds into a compromise, I want to understand what each side values most and help build a better answer together.

Personally, I'd love for the people I've worked with to look back and remember it as, “we did a lot back then, but we grew just as much and got real results.”

Lastly, what kind of company do you want AIEEV to become?

More and more companies will adopt AI, but not every company can build a massive data center or run expensive infrastructure on their own. I want AIEEV to be the company that helps teams with great ideas and products keep growing, without giving up because of infrastructure cost.

Our goal is to be the infrastructure partner that stays with a customer from the moment they think “maybe we should try AI” all the way to the moment they decide, “let's run this as a core feature of our service.”

And someday, I want AIEEV's distributed AI cloud, which started in Korea, to connect telecom networks, data centers, and idle enterprise resources across many countries, so that more people can enjoy the benefits of AI at a reasonable cost.

Technology that isn't just for technology's sake, but that's actually used and runs for the long haul. That's the AI infrastructure AIEEV wants to build.

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