Inference demand seems to keep

Inference demand seems to keep rising with each new generation of AI models. It feels like the ultimate Jevons Paradox in action: better AI capabilities lead to more usage, more token demand, and more workloads shifting from humans to machines. As models improve, companies don't just use less AI; they find more ways to deploy it. Now, it looks like OpenAI is facing this same trend of accelerating inference demand.

From where I stand, the winners might not only be the model creators but also the infrastructure providers powering the AI economy. I'm watching a few names in this space: $Microsoft(MSFT)$ for its AI ecosystem and cloud infrastructure, $Oracle(ORCL)$ for AI cloud capacity, $NVIDIA(NVDA)$ for accelerated computing, and $Broadcom(AVGO)$ for AI networking.

To me, the AI cycle appears to be moving from training scale to inference scale. Long-term demand for compute remains one of the biggest trends to watch.

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It feels like the ultimate Jevons Paradox in action: better AI capabilities lead to more usage, more token demand, and more workloads shifting from humans to machines. As models improve, companies don't just use less AI; they find more ways to deploy it. Now, it looks like OpenAI is facing this same trend of accelerating inference demand.\nFrom where I stand, the winners might not only be the model creators but also the infrastructure providers powering the AI economy. I'm watching a few names in this space: <a href=\"https://ttm.financial/S/MSFT\">$Microsoft(MSFT)$&nbsp;</a> for its AI ecosystem and cloud infrastructure, <a href=\"https://ttm.financial/S/ORCL\">$Oracle(ORCL)$&nbsp;</a> for AI cloud capacity, <a href=\"https://ttm.financial/S/NVDA\">$NVIDIA(NVDA)$&nbsp;</a> for accelerated computing, and <a href=\"https://ttm.financial/S/AVGO\">$Broadcom(AVGO)$&nbsp;</a> for AI networking.\nTo me, the AI cycle appears to be moving from training scale to inference scale. Long-term demand for compute remains one of the biggest trends to watch.\n<img src=\"https://community-static.tradeup.com/news/5936f6be61ba42ce8d3f8b41b950b503\" tg-width=\"2048\" tg-height=\"1138\"></body></html>","htmlText":"<html><head></head><body>Inference demand seems to keep rising with each new generation of AI models. It feels like the ultimate Jevons Paradox in action: better AI capabilities lead to more usage, more token demand, and more workloads shifting from humans to machines. As models improve, companies don't just use less AI; they find more ways to deploy it. Now, it looks like OpenAI is facing this same trend of accelerating inference demand.\nFrom where I stand, the winners might not only be the model creators but also the infrastructure providers powering the AI economy. I'm watching a few names in this space: <a href=\"https://ttm.financial/S/MSFT\">$Microsoft(MSFT)$&nbsp;</a> for its AI ecosystem and cloud infrastructure, <a href=\"https://ttm.financial/S/ORCL\">$Oracle(ORCL)$&nbsp;</a> for AI cloud capacity, <a href=\"https://ttm.financial/S/NVDA\">$NVIDIA(NVDA)$&nbsp;</a> for accelerated computing, and <a href=\"https://ttm.financial/S/AVGO\">$Broadcom(AVGO)$&nbsp;</a> for AI networking.\nTo me, the AI cycle appears to be moving from training scale to inference scale. Long-term demand for compute remains one of the biggest trends to watch.\n<img src=\"https://community-static.tradeup.com/news/5936f6be61ba42ce8d3f8b41b950b503\" tg-width=\"2048\" tg-height=\"1138\"></body></html>","text":"Inference demand seems to keep rising with each new generation of AI models. It feels like the ultimate Jevons Paradox in action: better AI capabilities lead to more usage, more token demand, and more workloads shifting from humans to machines. As models improve, companies don't just use less AI; they find more ways to deploy it. Now, it looks like OpenAI is facing this same trend of accelerating inference demand. From where I stand, the winners might not only be the model creators but also the infrastructure providers powering the AI economy. I'm watching a few names in this space: $Microsoft(MSFT)$ for its AI ecosystem and cloud infrastructure, $Oracle(ORCL)$ for AI cloud capacity, $NVIDIA(NVDA)$ for accelerated computing, and $Broadcom(AVGO)$ for AI networking. To me, the AI cycle appears to be moving from training scale to inference scale. 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