20VC: Chips, Models or Applications; Where is the Value in AI | Is Compute the Answer to All Model Performance Questions | Why Open AI Shelved AGI & Is There Any Value in Models with OpenAI Price Dumping with Aidan, Gomez, Co-Founder @ Cohere
The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
It stays true on Scaling Up Models
Scaling up models by increasing their size and compute is a reliable method for improving performance, but it is often inefficient and costly. While larger models like GPT-4 with 1.7 trillion parameters may outperform smaller models, innovations have shown that significantly smaller models, such as those with 13 billion parameters, can achieve similar or better performance. This raises questions about the sustainability of scaling advantages and whether they plateau over time. Continuous exponential input is necessary to maintain linear gains in model intelligence; however, practical economic constraints limit the accessibility and adoption of their largest and most expensive versions. As companies face pressure to manage costs, there is a growing emphasis on developing smaller, more efficient models that leverage better data and algorithms rather than solely relying on increased scale.
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