The Significance of Proprietary Data in AI.
The Data Appetite of Large Language Models:
GPT-4 and Gemini Ultra, trained on 4-8 trillion words, highlight the insatiable appetite for data in large language models.
Anticipating a Data Drought:
EpochAI predicts a potential shortage of high-quality training data as soon as next year, prompting a need for strategic data acquisition.
Unlocking Proprietary Data Reservoirs:
To maximize quality training data, collaborations like Axel Springer and OpenAI's recent partnership showcase the value of accessing proprietary data reservoirs.
The Moat of Proprietary Data:
Leveraging proprietary data is questioned for its role in building a moat between foundational models. Open source, relying on open datasets, may face a gap in accessing the best data.
BloombergGPT's Domain-Specific Success:
BloombergGPT's success, built on proprietary financial data, exemplifies how domain-specific proprietary data can significantly impact model performance.
Financial Commitments for Data Access:
OpenAI's willingness to invest eight figures annually for historical data access underscores the importance of proprietary data, potentially creating a gap between open source and proprietary models.
Commercial Realities and Alternatives:
Despite substantial revenue, Meta's focus on cloud providers limits their interest