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Data Bottlenecks in AI

The AI field continues to expand with better hardware, algorithms, and larger clusters, yet data—the fuel for AI—hasn’t kept pace. Even OpenAI’s Co-Founder, Ilya Sutskever, notes that “compute is growing, but data is not.” Common issues include:

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Key Challenges

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Limited Data Growth

Traditional web-scraping solutions can’t keep up with the exponential demand for real-time, high-quality datasets.

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Walled Gardens

Major platforms monetize user data and restrict access.

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Legal & Ethical Complications

While publicly available data is generally legal to scrape, adversarial approaches run into blocking, IP bans, and privacy concerns.

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Current Approaches Fall Short

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Adversarial Scraping

Standard proxy-based approaches face performance bottlenecks, high costs, frequent IP bans, and detection by websites.

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Centralized Crawlers

Traditional web-crawling services lack the distribution, scale, and flexibility needed to handle dynamic, large-volume data tasks.

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User Disempowerment

People who generate and/or could gather valuable data see minimal to zero returns when they rely on old-school data-mining solutions.