AIEEV joined TechCrunch Disrupt 2026 as a Startup Battlefield 200 company. Notes on our pre-event prep, our two-minute pitch, and talks at our booth.

Ask an AI engineering lead what gives them the most trouble these days and the model itself rarely comes up. The real problem sits one layer below, in the infrastructure, and it comes down to a single question: how much GPU capacity should we actually secure? A March 2026 Akamai survey of 200 AI practitioners put a number on that feeling. 65.9% of AI-native teams named GPU capacity planning the hardest challenge they face when scaling, ranking it above every other operational

There is a GPU in the development workstation, and another one in the lab server. The edge box at a branch office may have an accelerator in it too. But the moment you try to load a model, the story changes. The driver version may not match, the SDK you need may be missing, or a network restriction may block access from outside. The hardware is right there, and yet the service cannot use it. Kitae Yoo of the AIEEV Dev Team builds the distributed infrastructure that connects

If you have been following AI over the past week, you have probably come across at least one of the following stories. Anthropic, which is preparing for an IPO, is reportedly weighing whether to present investors with a potential market of more than $30 trillion The "software apocalypse" argument that AI will dismantle the existing software industry, and the counterarguments to it NVIDIA unveiling an entry level edge AI module that delivers 78 TOPS of compute Capital markets,

A Story from AIEEV Dev Team's Jinbeom Kim Contributing to open source does not automatically make you a better engineer. The skill only sticks once you reproduce the problem, trace the code's flow and the reasoning behind its design, and organize the process so someone else can follow it too. Jinbeom Kim of the AIEEV Dev Team has lived out this process firsthand. On July 4th, he gave his first community talk at AWS Student Community Day 2026's Career track, titled "The Fastes

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 mar

Hi, this is the AIEEV Biz & Strategy Team. When a business grows, it isn't just revenue that goes up. The number of proposals and contracts per client increases, and so does the number of quotes and delivery statements you have to draft. Checking tax invoice dates and payment status, then tracking down and resending issued files, grows right along with it. AIEEV went through the same shift as our B2B projects and Air Cloud platform business grew together. PoCs, build projects

Ever left the air conditioner running all day while you were out? You don't think about it when you walk into a cool apartment. It's only when the electricity bill arrives that you find out exactly what it cost. And staring at that bill, a familiar thought creeps in: "Did I really use that much?" 😅 Right now, infrastructure teams at companies everywhere are having the same thought while staring at their AI bills. The only difference is the number is a lot bigger. Two years a

Hi, I'm CY Lee (이창윤), DevOps/SRE engineer on the dev team. I stopped by once before to talk about the ClawHub plugin — and now I'm back, this time to share a problem we ran into with service alerting and how we worked through it. How many notifications does your team receive in a day? User feedback, payment events, signup events, 500 errors from SigNoz. Each of these was arriving in a separate Slack channel. Keeping channels separated by type made it easy to find a specific c

What did the city of the future look like in your childhood imagination? Maybe it was robots collecting trash in apartment complexes, self-driving vehicles gliding through traffic-free streets, or an AI assistant like Iron Man's J.A.R.V.I.S. handling your every need. In WALL-E, humans drift through life on floating chairs inside a giant spaceship, living almost entirely inside a virtual world. These visions differ wildly in their details, but they share one constant: AI is wo

You're only using 5% of the GPUs you paid for As of April 2026, GPU utilization in enterprise Kubernetes clusters ranges between 5% and 30%. Despite costing $2 to $15 per hour depending on the hardware, most GPUs remain idle for the majority of the time. According to a Cast AI report, companies are spending up to 20× more than what they actually need for GPU compute. In the race to adopt AI, many organizations secure GPU capacity “just in case.”But simply holding onto that ca

Real AI utilization starts with infrastructure — the kind that lets anyone use AI as much as they need, whenever they need it. As AI adoption accelerates, so does the infrastructure market behind it. Providers worldwide are competing across different architectures, and AIEEV is part of that race. Our approach is different: instead of building on centralized data centers, we launched as a distributed cloud that connects idle GPU resources across a decentralized network. Today,

This illustration was created with AI to support the explanation. A few days ago, Meta announced it was extending its AI cloud contract with CoreWeave through 2032, committing an additional $21 billion. Combined with the existing $14.2 billion agreement, the total comes to over $35 billion — roughly $35B locked in for GPU compute, years in advance. CoreWeave, as of the announcement date, became the fastest cloud company in history to reach $5 billion in ARR. The dollar fig

As geopolitical conflicts intensify, the limitations of centralized AI infrastructure are becoming clear. This article explores why distributed infrastructure is emerging as a more resilient and necessary approach.

Google’s TurboQuant reduces KV cache memory usage in LLM inference without sacrificing accuracy. Learn why 80GB GPUs were needed—and why mid-range GPUs may now be enough.

Connecting Idle GPUs to Reduce AI Infrastructure Costs, The Challenge of Distributed Cloud Startup AIEEV

SK AI Summit 2025 Wrapped Up

Air Cloud: From Technology to Brand – Sharing Our Brand Guide, BX GUIDE

기획자에게 PRD란 무엇일까? : Air Cloud의 PRD를 공유해드립니다.

Showcasing the Future of Distributed AI Cloud. We’re thrilled to be part of GITEX EUROPE , the global startup exhibition spotlighting...