Video now dominates how we capture information—across social media, education, marketing, and surveillance. Yet, the way we manage video is stuck in the past. Most workflows rely heavily on human labor: people watching screens in real-time, reviewing footage after incidents, or at best using basic object or motion detection.
Despite millions of cameras capturing billions of hours of footage, these outdated methods make it nearly impossible—or prohibitively expensive—to handle video at scale without cutting corners. The sheer volume of data is overwhelming, leaving much of it untapped and underutilized.
But if history is any guide, computing has always evolved through distinct eras, each defined by revolutionary capabilities and greater accessibility.
In the 1930s, computers depended entirely on humans for data entry. Early data storage systems relied on punch cards, where operators manually punched holes into stiff paper to encode information. Sorting and retrieving data was just as labor-intensive, requiring rooms full of clerks managing cabinets of punch cards. The bottleneck of human labor limited what computers could digitize and understand. They were powerful tools in theory but constrained by the slow, manual processes of the time.
By the 1970s, a revolution began as computers themselves started doing their own data entry. Banks adopted optical character recognition (OCR) to automatically process handwritten check numbers, transforming financial workflows. Retailers embraced barcode scanners to streamline inventory management and accelerate checkout processes. In manufacturing, sensors started tracking machine performance and output in real time, paving the way for modern automation. This leap unlocked new possibilities for efficiency and scale, as machines began shouldering the repetitive tasks once performed by humans.
Now, in 2024, we stand on the brink of another transformation: the era of AI. Computers are no longer limited to storing and processing information—they now perceive, interpret, and understand our world. These new capabilities enable machines to automatically analyze vast amounts of video data with unprecedented precision and speed.
The evolution from traditional video workflows to richer, more intuitive, and efficient systems is already underway. That’s why we created tl;dw, with a humble but ambitious mission: to empower developers to build software that perceives, interprets, and acts on information from both the virtual space and the physical world.
Our first release is an API that seamlessly integrates into any project, providing state-of-the-art video understanding technology. tl;dw enables teams to go from concept to prototype in a single afternoon and move to production in just days, not weeks. Extracting meaningful insights from video has never been easier or more cost-effective.
This transformation is most evident in two critical domains: security and media production.
In security, traditional approaches are rapidly becoming obsolete. While the last generation of companies focused on cloud storage and seamless networking gateways—important steps forward—the real revolution lies in advanced computer vision. Security systems of the future won’t just record incidents; they’ll actively prevent them. Cameras equipped with AI can detect suspicious behavior, identify potential threats, and send real-time alerts, enhancing safety in ways previously unimaginable.
In media production, AI is redefining efficiency. Automated scene detection, object classification, and natural language search make massive footage libraries searchable and contextualized. A process that once took hours—like finding a specific moment in a video—can now happen in seconds. Imagine a content creator using AI to identify the exact clips needed for a montage or a production team instantly locating scenes with specific objects or environments. Netflix’s use of AI to personalize recommendations and optimize content production is just one example of this shift in action.
Beyond these domains, the applications of AI-driven video understanding extend across industries:
Healthcare: Continuous, non-intrusive patient monitoring powered by AI enables early detection of anomalies and improves care outcomes.
Manufacturing: AI-powered cameras identify defects, track items, and optimize assembly lines in real-time, ensuring quality control and boosting efficiency.
Retail: AI can analyze foot traffic, detect product interactions, and measure engagement, allowing retailers to adapt dynamically to consumer behavior and optimize store layouts.
Video, once locked away as a cumbersome data source, is transforming into an active stream of structured insights. The shift from passive video recording to active video intelligence represents one of the most significant technological advancements of our time. Just as digitizing text and numbers reshaped businesses in the 20th century, AI-driven video understanding is poised to redefine organizations in the 21st.
The technology is ready. The cost barriers have fallen. The future of intelligent video understanding is here—and it’s reshaping how we interact with the world around us. The question is no longer whether to embrace this transformation, but how quickly and effectively organizations can adapt to this new paradigm.
