From the perspective of funding for generative AI, players in the foundational model space have essentially become the dominant force. Investors have shown limited interest in funding a new wave of players in the AI sector. In the early stages of this cycle, capital investment in foundational models has been very concentrated, while the capital required for AI applications is comparatively lower, which may explain the decline in absolute dollar funding.
Many investors attribute the overall reduction in AI investment to growth falling short of expectations. The initial enthusiasm for AI investment has given way to reality: AI faces significant challenges, some technical and some related to market entry, which will take years to address and fully overcome.
Yeah I think a lot of people have made the mistake of seeing rapid progress in ai capability, and assuming that adoption would be as rapid. I don’t think it works that way.
As a founder using AI as part of a solution, I have the perspective of utilizing AI for enhancing a use case solution that solves client pain points. By doing this, we can incorporate larger open source LLM code that improves over time on someone else’s investment. Meanwhile, our vision is to continually enhance our internal model as we grow in user count by taking advantage of the data passing through our solution. This allows us to keep our ROI where we need it to be, but roll out a better product as our internal model becomes more robust.
It puts our model’s enhancement more in the hands of our sales/marketing efforts, so our ROI doesn’t have a massive upfront investment to overcome before becoming profitable.
Also, by utilizing blockchain storage technologies, we can keep cost of storage down over using more common cloud storage solutions. Not completely there yet, but our goal is to eliminate cloud storage requirements as quickly as possible.
Overall we keep our revenue more in line with customer growth instead of trying to put the cart before the horse so to speak.
From the perspective of funding for generative AI, players in the foundational model space have essentially become the dominant force. Investors have shown limited interest in funding a new wave of players in the AI sector. In the early stages of this cycle, capital investment in foundational models has been very concentrated, while the capital required for AI applications is comparatively lower, which may explain the decline in absolute dollar funding.
Many investors attribute the overall reduction in AI investment to growth falling short of expectations. The initial enthusiasm for AI investment has given way to reality: AI faces significant challenges, some technical and some related to market entry, which will take years to address and fully overcome.
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Yeah I think a lot of people have made the mistake of seeing rapid progress in ai capability, and assuming that adoption would be as rapid. I don’t think it works that way.
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As a founder using AI as part of a solution, I have the perspective of utilizing AI for enhancing a use case solution that solves client pain points. By doing this, we can incorporate larger open source LLM code that improves over time on someone else’s investment. Meanwhile, our vision is to continually enhance our internal model as we grow in user count by taking advantage of the data passing through our solution. This allows us to keep our ROI where we need it to be, but roll out a better product as our internal model becomes more robust.
It puts our model’s enhancement more in the hands of our sales/marketing efforts, so our ROI doesn’t have a massive upfront investment to overcome before becoming profitable.
Also, by utilizing blockchain storage technologies, we can keep cost of storage down over using more common cloud storage solutions. Not completely there yet, but our goal is to eliminate cloud storage requirements as quickly as possible.
Overall we keep our revenue more in line with customer growth instead of trying to put the cart before the horse so to speak.