With only three of the 11 months of Chinese mobile phone shipments in 2024 being above the monthly 5-year averages, one might think that the Chinese mobile phone market continued the long-term decline it has been in since 2017.  While 2024 Chinese mobile phone shipments will not wind up anywhere near the heady levels seen eight or ten years ago, we expect 2024 to be the second year of improvement after hitting a low in 2022 (see Figure 1).  Based on our expectation for mobile phone shipments in China for December, we estimate that 2024 will see shipments increase by 6.9% y/y.  This is a bit better than shipment growth in 2023, which was 6.5%, but comes after the extremely weak 2022, which saw y/y Chinese mobile phone shipments down 22.6% y/y.  

In isolation (see Figure 2), the last three years of Chinese mobile phone shipments present a picture of growth, however the shipment level for 2024, the better of the three years shown in Figure 2, are only slightly above the level of shipments in 2020 and are still below all other years, other than the previous two.  Given the strength of 4Q of 2023, much of the y/y growth in 2024 came in late 2Q and 3Q.  That said, while we would expect 2025 to again see some y/y growth in Chinese mobile phone shipments, we would expect overall y/y growth to be lower than in the two previous years as the comparisons become more difficult throughout the year.  However there are two factors that could influence shipments in 2025.
First, AI.  The Chinese market has had a number of AI enabled models available to consumers, with the Vivo (pvt) X100, released in November of 2023, the first we can find that had advanced generative AI features, and also the first to use the Mediatek (2454.TT) Dimensity 9300 Ai chipset.  By 3rd quarter of 2024 ~22% of all Chinese smartphone shipments were AI capable and Chinese brands held a roughly 92% share of the 28m Ai capable smartphones that were shipped in that quarter in China (Samsung (005930.KS)) had a 4% share along with ‘others’, also at 4%).  This data points to Chinese smartphone brands looking to use AI in 2025 as a way to push the upgrade cycle on the mainland, in what has been a relatively weak retail environment. 
While the success of this strategy is far from certain, Chinese consumer seem to be more enthusiastic about AI, we believe, in part, because China itself has been quite aggressive in AI development, and started from a point similar to others, rather than having to play catch-up.  While trade restrictions on semiconductor development could slow that growth a bit, we expect a continued push from Chinese smartphone brands to offer AI features as a selling point this year.
Second, the Chinese government has recently included smartphones in its ‘New for Old’ consumer subsidy program, which helped to spur sales for a number of CE products in 4Q ’24.  Details of the smartphone subsidies are not yet available, but other products covered under the program typically garner a subsidy between 10% and 20% of the retail price, which we believe is substantial enough to move the needle, at least at the onset.
With these two potential factors as influencers early in the year, and an early Chinese New Year (1/29), we expect 1Q ’25 could see continued y/y growth, but we expect that it will lessen as the year progresses, as comparisons get more difficult and the enthusiasm behind the subsidies wears thin.  All in we expect Chinese smartphone shipments to stay in the low 300-million-unit range for the year, with much of the y/y growth in the 1st half, and, barring any unforeseen trade issues, we see similar results for 2026 at this time. 
As Chinese smartphone brands have a small share of the US market, potentially onerous trade restrictions on Chinese phones would have little effect on US consumers, with Xiaomi (1810.HK) having the largest share, albeit a small one (~1.2%).  However the implications for non-Chinese brands, particularly US smartphone brands in China could face a bit of additional nationalistic pride from Chinese consumers, who tend to opt for Chinese brands during periods of trade tension with the US.  2025 presents a fair number of variables that can have an influence on the Chinese mobile phone market, but we believe the longer-term picture is one where it become similar to other more mature markets, finding a shipment level that remains fairly constant over the years.

With only three of the 11 months of Chinese mobile phone shipments in 2024 being above the monthly 5-year averages, one might think that the Chinese mobile phone market continued the long-term decline it has been in since 2017.  While 2024 Chinese mobile phone shipments will not wind up anywhere near the heady levels seen eight or ten years ago, we expect 2024 to be the second year of improvement after hitting a low in 2022 (see Figure 1).  Based on our expectation for mobile phone shipments in China for December, we estimate that 2024 will see shipments increase by 6.9% y/y.  This is a bit better than shipment growth in 2023, which was 6.5%, but comes after the extremely weak 2022, which saw y/y Chinese mobile phone shipments down 22.6% y/y.  

In isolation (see Figure 2), the last three years of Chinese mobile phone shipments present a picture of growth, however the shipment level for 2024, the better of the three years shown in Figure 2, are only slightly above the level of shipments in 2020 and are still below all other years, other than the previous two.  Given the strength of 4Q of 2023, much of the y/y growth in 2024 came in late 2Q and 3Q.  That said, while we would expect 2025 to again see some y/y growth in Chinese mobile phone shipments, we would expect overall y/y growth to be lower than in the two previous years as the comparisons become more difficult throughout the year.  However there are two factors that could influence shipments in 2025.
First, AI.  The Chinese market has had a number of AI enabled models available to consumers, with the Vivo (pvt) X100, released in November of 2023, the first we can find that had advanced generative AI features, and also the first to use the Mediatek (2454.TT) Dimensity 9300 Ai chipset.  By 3rd quarter of 2024 ~22% of all Chinese smartphone shipments were AI capable and Chinese brands held a roughly 92% share of the 28m Ai capable smartphones that were shipped in that quarter in China (Samsung (005930.KS)) had a 4% share along with ‘others’, also at 4%).  This data points to Chinese smartphone brands looking to use AI in 2025 as a way to push the upgrade cycle on the mainland, in what has been a relatively weak retail environment. 
While the success of this strategy is far from certain, Chinese consumer seem to be more enthusiastic about AI, we believe, in part, because China itself has been quite aggressive in AI development, and started from a point similar to others, rather than having to play catch-up.  While trade restrictions on semiconductor development could slow that growth a bit, we expect a continued push from Chinese smartphone brands to offer AI features as a selling point this year.
Second, the Chinese government has recently included smartphones in its ‘New for Old’ consumer subsidy program, which helped to spur sales for a number of CE products in 4Q ’24.  Details of the smartphone subsidies are not yet available, but other products covered under the program typically garner a subsidy between 10% and 20% of the retail price, which we believe is substantial enough to move the needle, at least at the onset.
With these two potential factors as influencers early in the year, and an early Chinese New Year (1/29), we expect 1Q ’25 could see continued y/y growth, but we expect that it will lessen as the year progresses, as comparisons get more difficult and the enthusiasm behind the subsidies wears thin.  All in we expect Chinese smartphone shipments to stay in the low 300-million-unit range for the year, with much of the y/y growth in the 1st half, and, barring any unforeseen trade issues, we see similar results for 2026 at this time. 
As Chinese smartphone brands have a small share of the US market, potentially onerous trade restrictions on Chinese phones would have little effect on US consumers, with Xiaomi (1810.HK) having the largest share, albeit a small one (~1.2%).  However the implications for non-Chinese brands, particularly US smartphone brands in China could face a bit of additional nationalistic pride from Chinese consumers, who tend to opt for Chinese brands during periods of trade tension with the US.  2025 presents a fair number of variables that can have an influence on the Chinese mobile phone market, but we believe the longer-term picture is one where it become similar to other more mature markets, finding a shipment level that remains fairly constant over the years.

Alibaba (BABA) Cloud announced that it was lowering the price of its LLM model for the 3rd time to remain competitive in the Chinese AI market.  The model, known as Qwen-VL has a number of primary features, such as multi-modality (Can accept both text and image input), High-resolution processing (>1m pixels), enhanced image extraction, and multilingual support (Eng, Chinese, Japanese, Korean, Arabic, Vietnamese) and is closest to Google’s (GOOG) Gemini, which has similar features.  The model is part of Alibaba’s cloud-based AI chatbot family, which focuses on enterprise customers rather than the consumer market as a way to differentiate itself from Chinese and other AI competitors.
While much has been said about the competitive nature of Chinese companies, that rhetoric has been typically focused on manufacturing, however it seems that the AI market in China has spurred an even more intense competition to gain share in its own market.  In June of 2024 there were over 230 million AI product and services users in China, according to state-sponsored data, which grew to over 600 million by the end of October, with almost 200 LLM commercially available models to choose from.  While we believe the share of the potential user base that is using an AI on at least a weekly basis is higher currently in the US than in China, and generated more sales in 2023, expectations for industry growth over the next seven years are higher for China (25.6% CAGR for China v. 23.3% for the US[1] ), which is the impetus for the even more aggressive nature of Chinese AI Chatbot brands.
With this intense level of competition among AI Chatbot model providers, we were curious to not only to see if we could quantify the rate of price reductions but also compare those to model price reductions outside of China.  We note that this is an unscientific comparison, as each of the models has its own set of features and characteristics, and the availability of this data is, at best, poor, but we gathered as much data as possible, and converted the Chinese price data to US dollars for comparison.  Most notable is that the price of the most recent Tencent (700.HK) model, which has been available for roughly one month, is now the same as the Alibaba Qwen-VL model, which has been available for well over a year, and while the non-Chinese model prices have come down at a similar rate to the Chinese models, the current prices of the non-Chinese models are appreciably higher.  Overall, one might question the viability of the current business models behind commercial chatbots based on the data.
We note also that Baidu’s (BIDU) ERNIE model is now free, and Google’s BARD has morphed into Gemini.  We can find no specific data on how the MetaAI (FB) models are broken out pricewise and we note also that there are newer models for some (GPT-4 for example) that have much higher performance and cost, but this is as close to a comparison as we could make given the time involved.  The prices are for 1,000 tokens of input data in all cases.


[1] GrandView Research

Alibaba (BABA) Cloud announced that it was lowering the price of its LLM model for the 3rd time to remain competitive in the Chinese AI market.  The model, known as Qwen-VL has a number of primary features, such as multi-modality (Can accept both text and image input), High-resolution processing (>1m pixels), enhanced image extraction, and multilingual support (Eng, Chinese, Japanese, Korean, Arabic, Vietnamese) and is closest to Google’s (GOOG) Gemini, which has similar features.  The model is part of Alibaba’s cloud-based AI chatbot family, which focuses on enterprise customers rather than the consumer market as a way to differentiate itself from Chinese and other AI competitors.
While much has been said about the competitive nature of Chinese companies, that rhetoric has been typically focused on manufacturing, however it seems that the AI market in China has spurred an even more intense competition to gain share in its own market.  In June of 2024 there were over 230 million AI product and services users in China, according to state-sponsored data, which grew to over 600 million by the end of October, with almost 200 LLM commercially available models to choose from.  While we believe the share of the potential user base that is using an AI on at least a weekly basis is higher currently in the US than in China, and generated more sales in 2023, expectations for industry growth over the next seven years are higher for China (25.6% CAGR for China v. 23.3% for the US[1] ), which is the impetus for the even more aggressive nature of Chinese AI Chatbot brands.
With this intense level of competition among AI Chatbot model providers, we were curious to not only to see if we could quantify the rate of price reductions but also compare those to model price reductions outside of China.  We note that this is an unscientific comparison, as each of the models has its own set of features and characteristics, and the availability of this data is, at best, poor, but we gathered as much data as possible, and converted the Chinese price data to US dollars for comparison.  Most notable is that the price of the most recent Tencent (700.HK) model, which has been available for roughly one month, is now the same as the Alibaba Qwen-VL model, which has been available for well over a year, and while the non-Chinese model prices have come down at a similar rate to the Chinese models, the current prices of the non-Chinese models are appreciably higher.  Overall, one might question the viability of the current business models behind commercial chatbots based on the data.
We note also that Baidu’s (BIDU) ERNIE model is now free, and Google’s BARD has morphed into Gemini.  We can find no specific data on how the MetaAI (FB) models are broken out pricewise and we note also that there are newer models for some (GPT-4 for example) that have much higher performance and cost, but this is as close to a comparison as we could make given the time involved.  The prices are for 1,000 tokens of input data in all cases.


[1] GrandView Research

Alibaba (BABA) Cloud announced that it was lowering the price of its LLM model for the 3rd time to remain competitive in the Chinese AI market.  The model, known as Qwen-VL has a number of primary features, such as multi-modality (Can accept both text and image input), High-resolution processing (>1m pixels), enhanced image extraction, and multilingual support (Eng, Chinese, Japanese, Korean, Arabic, Vietnamese) and is closest to Google’s (GOOG) Gemini, which has similar features.  The model is part of Alibaba’s cloud-based AI chatbot family, which focuses on enterprise customers rather than the consumer market as a way to differentiate itself from Chinese and other AI competitors.
While much has been said about the competitive nature of Chinese companies, that rhetoric has been typically focused on manufacturing, however it seems that the AI market in China has spurred an even more intense competition to gain share in its own market.  In June of 2024 there were over 230 million AI product and services users in China, according to state-sponsored data, which grew to over 600 million by the end of October, with almost 200 LLM commercially available models to choose from.  While we believe the share of the potential user base that is using an AI on at least a weekly basis is higher currently in the US than in China, and generated more sales in 2023, expectations for industry growth over the next seven years are higher for China (25.6% CAGR for China v. 23.3% for the US[1] ), which is the impetus for the even more aggressive nature of Chinese AI Chatbot brands.
With this intense level of competition among AI Chatbot model providers, we were curious to not only to see if we could quantify the rate of price reductions but also compare those to model price reductions outside of China.  We note that this is an unscientific comparison, as each of the models has its own set of features and characteristics, and the availability of this data is, at best, poor, but we gathered as much data as possible, and converted the Chinese price data to US dollars for comparison.  Most notable is that the price of the most recent Tencent (700.HK) model, which has been available for roughly one month, is now the same as the Alibaba Qwen-VL model, which has been available for well over a year, and while the non-Chinese model prices have come down at a similar rate to the Chinese models, the current prices of the non-Chinese models are appreciably higher.  Overall, one might question the viability of the current business models behind commercial chatbots based on the data.
We note also that Baidu’s (BIDU) ERNIE model is now free, and Google’s BARD has morphed into Gemini.  We can find no specific data on how the MetaAI (FB) models are broken out pricewise and we note also that there are newer models for some (GPT-4 for example) that have much higher performance and cost, but this is as close to a comparison as we could make given the time involved.  The prices are for 1,000 tokens of input data in all cases.


[1] GrandView Research

Becoming a surgeon is a difficult task.  After 4 years at college, typically majoring in scientific specialty, there is another four years of medical school, with even more specialized study, and then a three to seven year residency program depending on the surgical specialty chosen.  Typically neurosurgery requires the longest residency, roughly seven years, while ophthalmology tends to require only three.  Aside from the investment in time and the value of lost wages, the cost of undergraduate college and medical school can be staggering, as seen in the table below, but the demand for surgeons continues to increase as the global population ages, making these financial barriers to entry an ever-increasing problem.

​Robotic surgery, an outgrowth of minimally invasive surgery, was approved by the FDA in the US in 2000, allowing surgeons to use the systems by manipulating the device manually, initially for general laparoscopic surgery.  The industry continues to grow, reaching an estimated $10.1b in 2023[1] with an increasing number of surgical procedures able to be done using these tools.  The share of robotic surgery procedures has risen from 1.8% in 2012 to 15.1% in 2018[2], and certain procedures, such as hernia repair, saw growth over that same period, increasing from 0.7% to 28.8%.  Robotic surgery (we know first-hand) has enabled many procedures to move from open surgery to laparoscopic, which typically means small incisions, less patient discomfort, and faster recovery, along with less bleeding and less time in the hospital.
Most hospitals have fellowships available for training in robotic surgery, along with the availability of simulators and continuing education programs that add to the understanding of the procedures by observation of more experienced users.  However the learning curve is particular to the skill level of the surgeon and the difficulty of the procedures, and while simulators and visuals are important, they lack haptic feedback and real-life issues that are absolutely essential for successful robotic surgical outcomes.  Actual surgical time using said tools is most important to gaining expertise, something simulators have difficulty providing.  That said, with over 10 million robotic surgeries having been performed through 2021, there has been a large amount of video and kinematics data recorded during those procedures that can be used for post-operative review and training.
Most surgeons are limited in the amount of time they have available to review video of such procedures, but now that we live in the world of Ai and its ability to build multi-dimensional models from video data, researchers at Johns Hopkins and Stamford have been using this library of robotic procedures to train a robotic surgical system to perform without surgical assistance.  The training procedure is called imitation learning, which allows the AI to predict actions from observations of past procedures.  This type of learning system is, typically used to train service robots in home settings, however surgical procedures require more precise movements on deformable objects (skin, organs, blood vessels, etc.) at times under poor lighting, and while in theory, the videos should provide absolute mechanical information about every movement, there is a big difference between the necessary accuracy and physical mechanics of an industrial robotic arm and a surgical one.
Before AI, the idea of a surgical robot performing an autonomous procedure involved the laborious task of breaking down every movement of the procedure into 3-dimensional mechanical data (x,y,z, force, movement speed, etc.), particular to that specific procedure and was limited to very simple tasks, but it was difficult to adapt that data to what might be called normal variances.  Using AI and machine learning and the AI’s ability to transform the library of video data into training data, in a way similar to how large language models transform text and images into referential data that is used to predict outcomes, the researchers say they have trained a robot to perform complex surgical tasks at the same level as human surgeons, just by watching the robotic surgeries performed by other doctors.
Here is the video of the autonomous surgical robot using the video data for refence:
https://youtu.be/c1E170Xr6BM
The researchers used the video data to train the robot to perform three fundamental tasks, manipulate a needle, lifting bodily tissue, and suturing, without programming each individual step, letting the model decide how to perform the task.  According to the researchers, “the model is so good learning things we haven’t taught it.  Like if it drops the needle, it will automatically pick it up and continue.  This isn’t something I taught it to do.” 
We understand that the idea of enabling a robotic device to pick up a needle without intervention is quite an accomplishment, however it is something that even a 1st year med student does without training, and suturing is a basic skill all surgeons learn in med school.  Good surgeons are able to adapt to the circumstances they find while doing surgery, hopefully adjusting the procedure to a successful outcome regardless of the potential issues.   As their experience increases, good surgeons learn how to cope with the vast number of potential problematic situations that can and do appear during surgery, not all of which relate directly to the procedure itself.
Conversely,  large models draw from their training data to predict what next step in a procedure would be best, with those decisions based solely on the  training data and the algorithms on which the model is based.  Therefore the richness of the training data would determine how successfully the AI predicted the correct step or movement.  But does the AI know that the patient is an 86-year-old male with diabetes, who was a smoker until 5 years ago, and had a single cardiac bypass procedure four years ago?  Hopefully the training data includes that information, but it would also be necessary for the Ai to have been trained to understand the implications of those factors in its move-by-move decision processing.  We expect that a surgeon would know the potential implications of a patient’s history and know to adjust the surgeryaccordingly, but we doubt the AI is that well equipped.
We are not putting down the idea of autonomous robotic surgery through AI, and Ai is certainly able to automate the mechanical functions of robotic devices, but good surgeons are reactive and adaptive, and use all of their senses, experience, and intelligence to make decisions during surgery, especially when performing procedures in which they specialize.  We believe we are many years away from an Ai system that has the input capabilities that a human surgeon has, and while one can say that the Ai system is able to make decisions without the influence of emotion (a questionable positive), it lacks the sensory input of a human surgeon.  Developing a model that has such a level of input ability and the capability to combine those varied inputs and experience information into a general model is beyond anything available today and would likely have to be so specific to the type of procedure that it would have little use as a general model.  This leaves us to rely on humans to do those tasks that can be life or death, although robotics has made those tasks a bit easier.  In the near-term if  we could lower the cost of getting a surgeon educated and certified, that would be a real accomplishment.


[1] Straitsresearch.com – Robotic Surgery Market Size & Trends

[2] Sheetz KH, Claflin J, Dimick JB. Trends in the adoption of robotic surgery for common surgical procedures. JAMA Netw Open. 2020;3(1):e1918911

Becoming a surgeon is a difficult task.  After 4 years at college, typically majoring in scientific specialty, there is another four years of medical school, with even more specialized study, and then a three to seven year residency program depending on the surgical specialty chosen.  Typically neurosurgery requires the longest residency, roughly seven years, while ophthalmology tends to require only three.  Aside from the investment in time and the value of lost wages, the cost of undergraduate college and medical school can be staggering, as seen in the table below, but the demand for surgeons continues to increase as the global population ages, making these financial barriers to entry an ever-increasing problem

Robotic surgery, an outgrowth of minimally invasive surgery, was approved by the FDA in the US in 2000, allowing surgeons to use the systems by manipulating the device manually, initially for general laparoscopic surgery.  The industry continues to grow, reaching an estimated $10.1b in 2023[1] with an increasing number of surgical procedures able to be done using these tools.  The share of robotic surgery procedures has risen from 1.8% in 2012 to 15.1% in 2018[2], and certain procedures, such as hernia repair, saw growth over that same period, increasing from 0.7% to 28.8%.  Robotic surgery (we know first-hand) has enabled many procedures to move from open surgery to laparoscopic, which typically means small incisions, less patient discomfort, and faster recovery, along with less bleeding and less time in the hospital.
Most hospitals have fellowships available for training in robotic surgery, along with the availability of simulators and continuing education programs that add to the understanding of the procedures by observation of more experienced users.  However the learning curve is particular to the skill level of the surgeon and the difficulty of the procedures, and while simulators and visuals are important, they lack haptic feedback and real-life issues that are absolutely essential for successful robotic surgical outcomes.  Actual surgical time using said tools is most important to gaining expertise, something simulators have difficulty providing.  That said, with over 10 million robotic surgeries having been performed through 2021, there has been a large amount of video and kinematics data recorded during those procedures that can be used for post-operative review and training.
Most surgeons are limited in the amount of time they have available to review video of such procedures, but now that we live in the world of Ai and its ability to build multi-dimensional models from video data, researchers at Johns Hopkins and Stamford have been using this library of robotic procedures to train a robotic surgical system to perform without surgical assistance.  The training procedure is called imitation learning, which allows the AI to predict actions from observations of past procedures.  This type of learning system is, typically used to train service robots in home settings, however surgical procedures require more precise movements on deformable objects (skin, organs, blood vessels, etc.) at times under poor lighting, and while in theory, the videos should provide absolute mechanical information about every movement, there is a big difference between the necessary accuracy and physical mechanics of an industrial robotic arm and a surgical one.
Before AI, the idea of a surgical robot performing an autonomous procedure involved the laborious task of breaking down every movement of the procedure into 3-dimensional mechanical data (x,y,z, force, movement speed, etc.), particular to that specific procedure and was limited to very simple tasks, but it was difficult to adapt that data to what might be called normal variances.  Using AI and machine learning and the AI’s ability to transform the library of video data into training data, in a way similar to how large language models transform text and images into referential data that is used to predict outcomes, the researchers say they have trained a robot to perform complex surgical tasks at the same level as human surgeons, just by watching the robotic surgeries performed by other doctors.
Here is the video of the autonomous surgical robot using the video data for refence:
https://youtu.be/c1E170Xr6BM
​


[1] Straitsresearch.com – Robotic Surgery Market Size & Trends

[2] Sheetz KH, Claflin J, Dimick JB. Trends in the adoption of robotic surgery for common surgical procedures. JAMA Netw Open. 2020;3(1):e1918911

Becoming a surgeon is a difficult task.  After 4 years at college, typically majoring in scientific specialty, there is another four years of medical school, with even more specialized study, and then a three to seven year residency program depending on the surgical specialty chosen.  Typically neurosurgery requires the longest residency, roughly seven years, while ophthalmology tends to require only three.  Aside from the investment in time and the value of lost wages, the cost of undergraduate college and medical school can be staggering, as seen in the table below, but the demand for surgeons continues to increase as the global population ages, making these financial barriers to entry an ever-increasing problem.