Beyond the AI boom: Why the data gold rush is just beginning

Many would think that the AI boom immediately ignited a huge demand for public web data. After all, those models are trained on data, and plenty of that is on the Internet. It has some truth, but it is not the whole story.

When tools like ChatGPT started rolling out one after another, the AI models they were based on were already trained. The data was already acquired from various sources and used to create the tools being introduced to the consumers. Of course, these tools were always improving with the help of additional data. However, much of this data was collected through interactions with users or by the developers of these tools via their internal methods. At first, this was enough.

Things started to change when these solutions were given the power of search engines to access data in real time. The need for web data skyrocketed. Even that was just the warmup compared to the demand for web data that is accelerating right now.

A bridge over the knowledge gap

Progress fast in the age of AI. But if you think back to when the first conversational AI tools were released, you might remember that they had one noticeable weakness compared to traditional search engines – a knowledge cutoff.

They could only know what happened until the date they were released or last updated. Thus, there was a gap between the reality you were living in and that last update. Tools like ChatGPT failed you when you wanted to explore recent events or get updated and relevant information.

That changed with the advancement of AI-powered search engines. In order to provide relevant and reliable generative search results, these tools must have access to real-time online data. A bridge was needed between the models and the Internet, over which information could travel instantaneously.

Many parts, such as vast proxy networks, scraping APIs, and other tools for seamless integration and open access to websites, combine to create the web data collection infrastructure – that necessary bridge.

And that is only the beginning. The impact of generative search on how we navigate the Internet will almost certainly be the greatest since Google search arrived in 1998. As we witness its unfolding, companies, from established classical search engines to emerging and hungry startups, are racing to carve out their space in the future of search. That race largely depends on how reliable a bridge they are running on.

AI goes multimodal

The AI models we are most familiar with operate in a limited space. Chatbots can read and respond to text-based prompts. Even the more advanced tools that can generate images based on natural language prompts have quite strict limits.

A natural next step in AI evolution, multimodal AI uses multiple types of data to provide more versatile, insightful, and well-based outputs. Training multimodal AI requires large volumes of video, audio, text, speech, and other data types. These models will also allow next-level AI-based video generation, resulting in higher quality and internal consistency of generated footage.

As the competition intensifies with new players like DeepSeek emerging suddenly and seemingly out of nowhere, the question is which companies are ahead in developing multimodal tools behind closed doors. Whichever they are, those companies need data scraping capabilities, which are unprecedented even in the age of big data.

To create effective multimodal tools, especially video generators, developers must scrape a lot of video data. Scraping videos is not like scraping the HTML of text-based webpages. The size and complexity of the task are completely different. Firstly, video datasets are thousands of times bigger than HTML datasets. Secondly, you need to get the imagery, the sound, the transcriptions – all aspects of a video, to make your tool competitive in the exploding market.

Thus, companies need a steady stream of data that is both huge and diverse. Aside from the vastness, the required infrastructure must possess advanced data processing capabilities to handle this flow without errors. Some companies might opt for ready-made data sets or solutions to avoid even the slightest delays that can be very costly in the fast-paced market.

Multimodal meets multilingual

The demand for reliable multilingual AI is huge. It can make life a lot easier by removing language barriers in everyday situations, as well as streamlining international business operations. Most large-language models were trained to operate primarily in English, and while they are improving, there is still a long way to go.

This is another area of competition that might be especially attractive to AI startups that cannot compete in the dominant English-based AI model markets. The Internet speaks all languages and is looking at another wave of data extraction by developers racing to build multilingual or non-English language prioritizing tools.

And as this already considerable demand couples with the demand for video generation in other languages, one can easily see why before was just a warmup for AI. A lot in AI development was put off for later, after the basics can be mastered. That later has arrived. Now, AI wants to create anything in any media and speak all languages. To achieve this, a lot of untapped data still needs to be extracted.

Evergreen data

To sum up, even in the age when web data scraping is crucial for dominating the technological landscapes of the future, a lot of data is yet to be scraped. Those with the tools to get that data first will position themselves to lead the next stage of AI development.

However, even after next-generation multimodal tools are trained and released, and the need for video data sets for training subsides, there will always be one kind of data in high demand – real-time data. The best AI tools will be those able to provide relevant information and understand the current context.

Thus, what AI developers need even more than large data sets that will eventually age is the integration with the web that enables a steady flow of data, newly generated every second. Building that integration and making it reliable is the challenge that will define the future of AI markets.