IBM’s Post

Following IBM’s earnings announcement earlier today, I had a lot of conversation and without fail, DeepSeek came up. It’s clear this moment is resonating across the industry, so I wanted to share a few thoughts directly here. For too long, the AI race has been a game of scale where bigger models meant better outcomes. But there is no law of physics that dictates AI models must remain big and expensive. The cost of training and inference is just another technology challenge to be solved. We’ve seen this play out before. In the early days of computing, storage and processing power were prohibitively expensive. Yet, through technological advancements and economies of scale, these costs plummeted. AI will follow the same path. At IBM, we’ve been on this journey for years. We’ve been steadfast in our belief that efficient, fit-for-purpose models can deliver exceptional value and that open is key to innovation. Our work has already led to up to 30-fold reductions in inference costs. This is promising for businesses. Technology becomes truly transformative when it becomes more affordable. As more companies embrace this shift, we’ll see an AI landscape that is both more powerful and accessible.

Following IBM’s earnings announcement earlier today, I had a lot of conversation and without fail, DeepSeek came up. It’s clear this moment is resonating across the industry, so I wanted to share a few thoughts directly here. For too long, the AI race has been a game of scale where bigger models meant better outcomes. But there is no law of physics that dictates AI models must remain big and expensive. The cost of training and inference is just another technology challenge to be solved. We’ve seen this play out before. In the early days of computing, storage and processing power were prohibitively expensive. Yet, through technological advancements and economies of scale, these costs plummeted. AI will follow the same path. At IBM, we’ve been on this journey for years. We’ve been steadfast in our belief that efficient, fit-for-purpose models can deliver exceptional value and that open is key to innovation. Our work has already led to up to 30-fold reductions in inference costs. This is promising for businesses. Technology becomes truly transformative when it becomes more affordable. As more companies embrace this shift, we’ll see an AI landscape that is both more powerful and accessible.

Following IBM’s earnings announcement earlier today, I had a lot of conversation and without fail, DeepSeek came up. It’s clear this moment is resonating across the industry, so I wanted to share a few thoughts directly here. For too long, the AI race has been a game of scale where bigger models meant better outcomes. But there is no law of physics that dictates AI models must remain big and expensive. The cost of training and inference is just another technology challenge to be solved. We’ve seen this play out before. In the early days of computing, storage and processing power were prohibitively expensive. Yet, through technological advancements and economies of scale, these costs plummeted. AI will follow the same path. At IBM, we’ve been on this journey for years. We’ve been steadfast in our belief that efficient, fit-for-purpose models can deliver exceptional value and that open is key to innovation. Our work has already led to up to 30-fold reductions in inference costs. This is promising for businesses. Technology becomes truly transformative when it becomes more affordable. As more companies embrace this shift, we’ll see an AI landscape that is both more powerful and accessible.

Following IBM’s earnings announcement earlier today, I had a lot of conversation and without fail, DeepSeek came up. It’s clear this moment is resonating across the industry, so I wanted to share a few thoughts directly here. For too long, the AI race has been a game of scale where bigger models meant better outcomes. But there is no law of physics that dictates AI models must remain big and expensive. The cost of training and inference is just another technology challenge to be solved. We’ve seen this play out before. In the early days of computing, storage and processing power were prohibitively expensive. Yet, through technological advancements and economies of scale, these costs plummeted. AI will follow the same path. At IBM, we’ve been on this journey for years. We’ve been steadfast in our belief that efficient, fit-for-purpose models can deliver exceptional value and that open is key to innovation. Our work has already led to up to 30-fold reductions in inference costs. This is promising for businesses. Technology becomes truly transformative when it becomes more affordable. As more companies embrace this shift, we’ll see an AI landscape that is both more powerful and accessible.

Following IBM’s earnings announcement earlier today, I had a lot of conversation and without fail, DeepSeek came up. It’s clear this moment is resonating across the industry, so I wanted to share a few thoughts directly here. For too long, the AI race has been a game of scale where bigger models meant better outcomes. But there is no law of physics that dictates AI models must remain big and expensive. The cost of training and inference is just another technology challenge to be solved. We’ve seen this play out before. In the early days of computing, storage and processing power were prohibitively expensive. Yet, through technological advancements and economies of scale, these costs plummeted. AI will follow the same path. At IBM, we’ve been on this journey for years. We’ve been steadfast in our belief that efficient, fit-for-purpose models can deliver exceptional value and that open is key to innovation. Our work has already led to up to 30-fold reductions in inference costs. This is promising for businesses. Technology becomes truly transformative when it becomes more affordable. As more companies embrace this shift, we’ll see an AI landscape that is both more powerful and accessible.

Following IBM’s earnings announcement earlier today, I had a lot of conversation and without fail, DeepSeek came up. It’s clear this moment is resonating across the industry, so I wanted to share a few thoughts directly here. For too long, the AI race has been a game of scale where bigger models meant better outcomes. But there is no law of physics that dictates AI models must remain big and expensive. The cost of training and inference is just another technology challenge to be solved. We’ve seen this play out before. In the early days of computing, storage and processing power were prohibitively expensive. Yet, through technological advancements and economies of scale, these costs plummeted. AI will follow the same path. At IBM, we’ve been on this journey for years. We’ve been steadfast in our belief that efficient, fit-for-purpose models can deliver exceptional value and that open is key to innovation. Our work has already led to up to 30-fold reductions in inference costs. This is promising for businesses. Technology becomes truly transformative when it becomes more affordable. As more companies embrace this shift, we’ll see an AI landscape that is both more powerful and accessible.

Following IBM’s earnings announcement earlier today, I had a lot of conversation and without fail, DeepSeek came up. It’s clear this moment is resonating across the industry, so I wanted to share a few thoughts directly here. For too long, the AI race has been a game of scale where bigger models meant better outcomes. But there is no law of physics that dictates AI models must remain big and expensive. The cost of training and inference is just another technology challenge to be solved. We’ve seen this play out before. In the early days of computing, storage and processing power were prohibitively expensive. Yet, through technological advancements and economies of scale, these costs plummeted. AI will follow the same path. At IBM, we’ve been on this journey for years. We’ve been steadfast in our belief that efficient, fit-for-purpose models can deliver exceptional value and that open is key to innovation. Our work has already led to up to 30-fold reductions in inference costs. This is promising for businesses. Technology becomes truly transformative when it becomes more affordable. As more companies embrace this shift, we’ll see an AI landscape that is both more powerful and accessible.

Following IBM’s earnings announcement earlier today, I had a lot of conversation and without fail, DeepSeek came up. It’s clear this moment is resonating across the industry, so I wanted to share a few thoughts directly here. For too long, the AI race has been a game of scale where bigger models meant better outcomes. But there is no law of physics that dictates AI models must remain big and expensive. The cost of training and inference is just another technology challenge to be solved. We’ve seen this play out before. In the early days of computing, storage and processing power were prohibitively expensive. Yet, through technological advancements and economies of scale, these costs plummeted. AI will follow the same path. At IBM, we’ve been on this journey for years. We’ve been steadfast in our belief that efficient, fit-for-purpose models can deliver exceptional value and that open is key to innovation. Our work has already led to up to 30-fold reductions in inference costs. This is promising for businesses. Technology becomes truly transformative when it becomes more affordable. As more companies embrace this shift, we’ll see an AI landscape that is both more powerful and accessible.

Following IBM’s earnings announcement earlier today, I had a lot of conversation and without fail, DeepSeek came up. It’s clear this moment is resonating across the industry, so I wanted to share a few thoughts directly here. For too long, the AI race has been a game of scale where bigger models meant better outcomes. But there is no law of physics that dictates AI models must remain big and expensive. The cost of training and inference is just another technology challenge to be solved. We’ve seen this play out before. In the early days of computing, storage and processing power were prohibitively expensive. Yet, through technological advancements and economies of scale, these costs plummeted. AI will follow the same path. At IBM, we’ve been on this journey for years. We’ve been steadfast in our belief that efficient, fit-for-purpose models can deliver exceptional value and that open is key to innovation. Our work has already led to up to 30-fold reductions in inference costs. This is promising for businesses. Technology becomes truly transformative when it becomes more affordable. As more companies embrace this shift, we’ll see an AI landscape that is both more powerful and accessible.

Following IBM’s earnings announcement earlier today, I had a lot of conversation and without fail, DeepSeek came up. It’s clear this moment is resonating across the industry, so I wanted to share a few thoughts directly here. For too long, the AI race has been a game of scale where bigger models meant better outcomes. But there is no law of physics that dictates AI models must remain big and expensive. The cost of training and inference is just another technology challenge to be solved. We’ve seen this play out before. In the early days of computing, storage and processing power were prohibitively expensive. Yet, through technological advancements and economies of scale, these costs plummeted. AI will follow the same path. At IBM, we’ve been on this journey for years. We’ve been steadfast in our belief that efficient, fit-for-purpose models can deliver exceptional value and that open is key to innovation. Our work has already led to up to 30-fold reductions in inference costs. This is promising for businesses. Technology becomes truly transformative when it becomes more affordable. As more companies embrace this shift, we’ll see an AI landscape that is both more powerful and accessible.