With public data exhausted, AI companies are seeking proprietary datasets. After being rejected by established firms wary of sharing their 'crown jewels,' these labs are now acquiring the codebases of failed startups for tens of thousands of dollars as a novel source of high-quality training data.
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The industry has already exhausted the public web data used to train foundational AI models, a point underscored by the phrase "we've already run out of data." The next leap in AI capability and business value will come from harnessing the vast, proprietary data currently locked behind corporate firewalls.
LLMs have hit a wall by scraping nearly all available public data. The next phase of AI development and competitive differentiation will come from training models on high-quality, proprietary data generated by human experts. This creates a booming "data as a service" industry for companies like Micro One that recruit and manage these experts.
Public internet data has been largely exhausted for training AI models. The real competitive advantage and source for next-generation, specialized AI will be the vast, untapped reservoirs of proprietary data locked inside corporations, like R&D data from pharmaceutical or semiconductor companies.
A key competitive advantage for AI companies lies in capturing proprietary outcomes data by owning a customer's end-to-end workflow. This data, such as which legal cases are won or lost, is not publicly available. It creates a powerful feedback loop where the AI gets smarter at predicting valuable outcomes, a moat that general models cannot replicate.
Cuban identifies a massive, overlooked opportunity: acquiring the intellectual property (patents, data, designs) from millions of defunct businesses. This "dead IP" could be aggregated and sold at a high premium to foundational model companies desperate for unique training data.
As large AI models exhaust public training data, they need novel sources. Crypto provides a powerful solution by creating financial incentives for a global, distributed workforce to collect specific data (e.g., first-person video for robotics). This creates a new market where the demand side from AI companies is nearly guaranteed.
The future of valuable AI lies not in models trained on the abundant public internet, but in those built on scarce, proprietary data. For fields like robotics and biology, this data doesn't exist to be scraped; it must be actively created, making the data generation process itself the key competitive moat.
To build a unique dataset without massive cost, target the aggregated, non-identifiable 'exhaust data' from software, payments, and telematics companies. These firms often undervalue this data, which they may have been deleting, and might provide it cheaply or exclusively.
As algorithms become more widespread, the key differentiator for leading AI labs is their exclusive access to vast, private data sets. XAI has Twitter, Google has YouTube, and OpenAI has user conversations, creating unique training advantages that are nearly impossible for others to replicate.
The initial AI boom was fueled by scraping the public internet. Cuban predicts the next phase will be dominated by exclusive data deals. Content owners, like medical journals, will protect their IP and auction it to the highest-bidding AI companies, creating valuable data silos.