TwelveLabs just secured $100 million to solve a problem that sounds boring but could reshape how enterprises handle video: making footage that already exists searchable and understandable to software. While competitors like Kling AI and Runway chase the flashier goal of generating new video from text prompts, TwelveLabs is betting that the harder, more valuable problem sits one layer underneath: turning raw footage into structured data that machines can reason over.

Why Is Video Understanding Getting Serious Funding Now?

The funding landscape for AI video has split into two distinct races in 2026. On one side, generation companies are consolidating around massive rounds. Kling AI, the video generation unit of Chinese tech company Kuaishou, just confirmed a round valued at more than $2 billion at an $18 billion valuation. Runway closed a $315 million Series E at a $5.3 billion valuation earlier this year. These numbers dwarf TwelveLabs' $100 million raise, which might look like a rounding error on the surface.

But the real story isn't the check size. It's what Amazon did alongside it. AWS committed to a multiyear deal to optimize TwelveLabs' video processing workloads specifically on its own Trainium chips, custom silicon designed for machine learning inference. More importantly, TwelveLabs agreed that its new frontier models will launch on AWS first, rather than being available everywhere at once. That's the kind of commitment a cloud provider makes when it believes a category is about to explode, not when it's hedging a small bet.

TwelveLabs' funding journey shows how seriously investors are taking this shift. The company raised $5 million in its initial seed round in March 2022, then a $12 million seed extension later that year. A $50 million Series A followed in mid-2024, co-led by NEA and NVIDIA's NVentures. The Series B brings total funding to roughly $167 million across three rounds.

What Does TwelveLabs Actually Do Differently?

Most AI systems that work with video today take a shortcut: they sample video into a sequence of still images, like taking screenshots every few frames. That approach throws away motion, timing, and continuity, the very things that make video different from a photo album. TwelveLabs built its models from the ground up to handle video natively.

The company's technical approach relies on two core layers. Marengo 3.0, released in late 2025, is described as the world's most powerful video embedding model. It converts sound, speech, and motion across time into a single machine-readable representation rather than a stack of still frames. On top of that sits Pegasus 1.5, which turns raw video into structured data: scene boundaries, entities, temporal segments, and semantic context that language models can reason over directly.

TwelveLabs describes Pegasus as functioning like a domain-specific language for video, one that makes raw footage parseable by any intelligent system built on top of it. The company also builds persistent memory into the system, so the more video an organization indexes, the more capable its own archive becomes to query, rather than starting from zero with every new clip.

How to Put Video Understanding to Work in Your Organization

• Sports Broadcasting: Pull every match-winning goal scored in the final minutes out of years of unlabeled game tape without manually reviewing footage.
• Security Operations: Search surveillance footage by description instead of scrubbing through timestamps by hand, dramatically reducing response time to incidents.
• Media Library Management: Index enterprise video archives that have accumulated more footage than any human team can watch, making content discoverable and reusable.
• Automotive Development: Analyze test footage and real-world driving data to extract patterns and insights at scale.
• Advertising and Content: Identify scenes, objects, and moments across video libraries for licensing, rights management, and content discovery.

The use cases follow directly from TwelveLabs' architecture. Security, advertising, sports, and automotive are the primary industries the company points to today, alongside broader enterprise media libraries that have accumulated more footage than any human team can watch, let alone index.

Why Is Generation Getting Commoditized So Fast?

The generation layer has spent 2026 consolidating around a handful of extremely well-funded labs. OpenAI's decision to shut down Sora earlier this year offers a cautionary tale: the consumer app burned roughly $1 million a day against $2.1 million in lifetime revenue. Generation is getting commoditized fast. Cheaper, more capable models are shipping constantly, and the money chasing that layer is now a scale game between a handful of giants.

TwelveLabs is the first meaningfully funded bet that the harder, more durable problem sits one layer over, in making all that newly abundant video legible to software in the first place. The company's co-founder and CEO Jae Lee framed the company's thesis around durability and lasting value.

"Video is the data understanding has to answer to. Models commoditize. The intelligence layer that composes them does not," said Jae Lee, co-founder and CEO of TwelveLabs.

Jae Lee, Co-founder and CEO at TwelveLabs

Investors backing the round echoed that logic. NEA partner Tiffany Luck noted that TwelveLabs' models "are purpose-built to turn millions of hours of footage into intelligence that compounds over time." NAVER Ventures general partner YJ Park added that "video is the modality that matters most, and TwelveLabs is the team building that capability".

The company has grown rapidly to support this expansion. TwelveLabs had around 58 employees a year ago and has grown to roughly 178 as of June 2026. The company plans to put the new funding into research and development and into opening offices in New York and London, adding to its existing San Francisco and Seoul bases.

The distinction between generation and understanding may seem technical, but it carries real implications for how enterprises will build with AI video. As generation becomes cheaper and more accessible, the companies that own the layer that makes video searchable, queryable, and actionable will control a more defensible, longer-lasting advantage. TwelveLabs' $100 million raise and Amazon's chip commitment suggest that serious infrastructure capital is now backing that second race too.