Proprietary LLM builders need to experience a valuation haircut as open source LLMs take share from proprietary LLMs.
Proprietary LLM builders (OpenAI, Anthropic, Google, Microsoft, Amazon), have enjoyed lofty valuations over the past several years. Given the rise of open source competitors - which are on par with proprietary models from a performance standpoint and can be operated at a fraction of the cost - the proprietary model builders should suffer a valuation haircut.
I believe that open source LLM builders such as DeepSeek and META will win the day and that 80% of LLMs and SLMs in production 5 years from now will be open source language models.
https://open.substack.com/pub/tek2day/p/valuation-haircut-is-due-for-proprietary?r=1rp1p&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false
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Proprietary LLM builders need to experience a valuation haircut as open source LLMs take share from proprietary LLMs.
Proprietary LLM builders (OpenAI, Anthropic, Google, Microsoft, Amazon), have enjoyed lofty valuations over the past several years. Given the rise of open source competitors - which are on par with proprietary models from a performance standpoint and can be operated at a fraction of the cost - the proprietary model builders should suffer a valuation haircut.
I believe that open source LLM builders such as DeepSeek and META will win the day and that 80% of LLMs and SLMs in production 5 years from now will be open source language models.
https://open.substack.com/pub/tek2day/p/valuation-haircut-is-due-for-proprietary?r=1rp1p&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false
Watch the video version of this episode here: https://youtu.be/dc68lkZ1Bxo?feature=shared
At some point cost and payback period will factor into frontier LLM building, especially as use cases are not well defined.
We are at the $1 billion LLM level today. $10 billion will likely be the cost of developing frontier LLMs by 2026, $100 billion by 2027 and $1 Trillion by 2028 should the current pace of development continue.
In episode 507 we make the case for smaller, “baseline” language models that are industry domain-specific, trained with opensource data as well as with proprietary enterprise data. These baseline models could power various applications and services and also be used to train third-party models. This scenario would create a natural selection/survivorship process for language models whereby smaller models power well-defined use cases that address specific commercial needs. This path makes more economic sense than developing ever larger monolithic LLMs in a vacuum.
TEK2day Podcast
Proprietary LLM builders need to experience a valuation haircut as open source LLMs take share from proprietary LLMs.
Proprietary LLM builders (OpenAI, Anthropic, Google, Microsoft, Amazon), have enjoyed lofty valuations over the past several years. Given the rise of open source competitors - which are on par with proprietary models from a performance standpoint and can be operated at a fraction of the cost - the proprietary model builders should suffer a valuation haircut.
I believe that open source LLM builders such as DeepSeek and META will win the day and that 80% of LLMs and SLMs in production 5 years from now will be open source language models.
https://open.substack.com/pub/tek2day/p/valuation-haircut-is-due-for-proprietary?r=1rp1p&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false
