Yesterday Samsung (005930.KS) announced that the new Galaxy S25 flagship line would be based on the new Qualcomm (QCOM) Snapdragon 8 Elite chipset, the first mobile device to use this processor.  This chipset competes directly with Mediatek’s (2454.TT) Dimensity 9400 chipset, which is already the basis for 3 smartphone models from Vivo (pvt) and 3 from Oppo (pvt) that were released in October of last year.  The characteristics of each of those chipsets are listed below, along with a number of benchmarks that compare performance and details on what those benchmarks capture.  However while there are benchmarks that score the AI related performance of smartphones, there does not seem to be a recognized benchmark that rates the performance of the chipsets themselves in terms of AI performance. 
We believe this is due to the integration of the AI into each phone’s hardware and software, which can influence AI performance, along with the fact that the Snapdragon 8 Elite is quite new.  Using just the AI benchmarks for the few models that are based on the Dimensity 9400 (average) and comparing that to the previous Snapdragon 8 Gen 3 chipset, it shows that the Dimensity has just under twice the AI performance of the older Snapdragon, so we have to assume that the 8 Elite Ai benchmark will at least match that once the Galaxy S25 line is benchmarked.  In the interim, the chipset comparisons are the only benchmarks to compare the performance of the processors.

Test Bench Criteria

• GeekBench 6 – Real-world tasks
  • Image Processing – Applying filters, resizing images, etc.
  • Machine Learning – Training & running ML models.
  • Augmented Reality – Processing & rendering AR.
• AnTuTu 10 – Combined Workloads
  • Image Processing – Rendering 3D scenes
  • Video Editing – Processing & encoding video files
  • Multitasking – Switching between apps & performing multiple tasks
• Wild Life Extreme (GPU)
  • Frame Rate – Higher frame rate implies smoother graphics
  • Graphics Quality – Details, texture, lighting, effects
• AI-Benchmark
  • Object Recognition – Object, facial and scene recognition
  • OCR – Character recognition
  • Effects – Deblur, Denoise, Bokeh

While the structure of these two chipsets are different, there performance on the test bench is very close, and that seems to indicate that Mediatek is getting close to closing the gap in terms of phone performance.  However, as AI functions are added to these devices, the need for larger die size, higher densities, and more memory affect the cost and yield as the NPU becomes a larger part of the chipset.  Ideally the NPU should have a dedicated core with an instruction set specifically designed for the NPU but the complexity of the design and production of such a chipset is not economically feasible yet.  For now chipset NPUs share the cores with device processes, but the current drive toward on-device Ai processing intensifies the impact on overall device performance.  This leads to eventual chipset designs with a dedicated NPU core or a hybrid approach with a dedicated NPU core and main core sharing the load.  With each of these improvements comes more manufacturing complexity and cost, but if AI is going to drive a mobile device replacement cycle, these improvements will have to continue with each chipset iteration.
Each of the chipsets below contained an NPU, and while NPU ‘size’ is a function of a number of factors, the NPU affects cost.  We expect it will continue to be an increasingly large component of the overall cost of subsequent chipsets, and, if the Samsung Galaxy line is any indication, smartphone brands have yet to try to recoup the incremental cost.  We expect that, at the smartphone level, consumers will assume that the added functionality that AI adds is already built into the cost of the device.  They have learned over the years that the incremental changes in each smartphone generation (50MP camera to a 100MP camera, or 6.7” display to a 6.8” display) do not seem to add to the device cost, so why should they think that AI is different, especially as AI looks to the user as software, not a visible physical change…  Another slice out of smartphone brand margins.

Back in 2017 a paper on the use of AI in video generation written by two computer science professors spurred two European entrepreneurs two form Synthesia (pvt), a company that began operation with a AI dubbing tool that used computer vision to create natural mouth movements that could be adapted to a variety of languages.  While the Ai dubbing tool generated sales, the company shifted gears in 2020 toward video creation.  The video tools they have developed are based on the concept that a 16-year old should be able to create a Hollywood level video using only a device in his or her bedroom.  Considering the cost of a short (1-2 min.) video can run anywhere from $1,000 to $10,000 depending on resources and quality, that was a tall order.
The company used the same computer vision system to create ~159 avatars that could be placed in templates with the ability to easily change features, such as style, skin color, gender, age, etc., all on a smartphone or laptop.  Given the previous focus on voice, the avatars can take the user’s script and can ‘perform’ that script with accurate and realistic mouth movements and facial expressions, and a synthetic voice.  But it does not stop there.  If the user is willing to record 5 to 10 minutes of audio, using text script provided by the company, the AI can clone your voice and transfer it to the avatar in over 140 languages.   The system can generate a video from a pdf, a PowerPoint presentation, or even a website, all on a laptop or phone, and the most recent version gives the user the ability to edit the video, match a screenshot, zoom, or transcribe voiceovers, so they seem to have realized their goal.
Synthesia says they have over 200,000 users, ranging from individuals to companies like Dupont (DD), Heineken (HEIA.NL), and Reuters (TRI), most of whom use the application to create inexpensive training videos.  Heineken used the service to create videos that trained employees in the basic concepts of continuous improvement, replacing PowerPoint presentations that were uninspiring.  Given that Heineken has over 90,000 employees in 170 countries, the cost of producing even simple videos in each language was enormous.  Using the Synthesia application, it took only a few minutes to duplicate the videos into any language. 

The original concept of low-cost, easy to use video creation comes with three price tiers, a free (trial) that allows 3 minutes of video to be created but does not allow it to be downloaded, the  Starter tier, for $18/month, that allows for 120 minutes of video creation and does allow downloading, and the creator/business tier, for $64/month that allows for 360 minutes of video and all the bells and whistles.  That said, should one search the internet for Synthesia, a number of competitors show up that provide a similar service for a dollar or two more or less each month. 
The competition has not stopped Synthesia from raising money as can be seen in the table below, and with Nvidia (NVDA) and Kleiner Perkins on the investor list, the company has VC credibility.  While some say the valuation placed of AI application companies remains low (in comparison to the AI hardware space), Sythesia’s valuation at its Series C round was $1b and for it’s Series D round was $2.1b, making it the most valuable media AI company in the UK.  While we do not know the details of Synthesia’s financials for 2024, we know that the generated ₤8.6m in sales in 2022 and ₤25.7m in 2023 and posted losses of ₤4.5m and ₤23.5m in those years.  What’s it really worth?

Samsung (005930.KS) announced its flagship smartphone line for 2025, the Galaxy S25 series today.  While the event detailed a number of hardware improvements, the real focus was on (not surprisingly) AI.  It seems that while Samsung continues to upgrade hardware to maintain a competitive stance against Chinese smartphone brands, we believe they understand that the incremental hardware improvements made each year are not enough to stimulate consumer to upgrade unless their phone begins to age out.  Foldables represent a new mobile category but due to their high price, volumes are relatively low, so it is essential that major brands maintain a flagship line to offset mid-range, lower margin lines.
There are times when hardware improvements can be a driver for consumers, as were OLED displays when they were new to the mobile world, and multiple cameras back a few years ago. But at this juncture, even OLED displays cannot be much larger and there is no new display technology on the near-term horizon that is appreciably different from what is available today.  Higher resolution cameras will always be possible as semiconductor technology improves, as will chipsets, CPUs, and GPUs, but other than foldables, mobile phone hardware will improve slowly and slowly does not excite consumers.
Smartphone software is in a similar position.  Smartphone applications have changed little over the past few years and do almost nothing to convince consumers to upgrade their mobile devices, but Ai for mobile devices is developing quickly and represents a platform where smartphone brands can compete and attract attention.  Ai does need processing power but Qualcomm (QCOM), Mediatek (2454.TT), Apple (AAPL), Google (GOOG), and Samsung continue to adapt their chipsets to the needs of AI, and while in the true sense, AI is a hardware-based system, on a mobile device it appears to consumers as software and requires relatively little hardware or mechanical changes or design restrictions.  However AI represents change and change in the CE market is something every marketing department looks toward to sell more devices.
Samsung seems to understand the fact that there are two AI’s.  One, answers your questions and interacts directly with users, while the other works silently in the background.  But they also seem to understand that the two should be working together and if there was anything to be taken away from the Samsung S25 event, it was that Samsung is interested in merging those two AI processes.  This not only improves the user’s experience with the phone but lessens the need for breakthrough hardware or software application improvement to attract consumers.  By leveraging Ai to allow applications on the phone to work together, the applications seem to be improved, even if they are not, and the ability of the AI to control or direct applications without the user having to open an application for each task is an improvement worth buying a new phone for.
Samsung’s multi-modal Ai allows the user to speak to the Ai directly (voice or even audio) and gives the AI the ability to create ‘agents’ that perform tasks that the user would typically have to do by pausing what they are doing to open a separate application.  Here’s an example.  The user is listening to a conference call which happens to be in Korean.  The Ai translates the call in real time but also compiles a transcript of the call.  When the call is finished the user tells the AI to summarize the call and reviews the AI summary.  The user tells the AI to change the 3rd paragraph to be more concise and reviews the change.  The user then tells the Ai to write a cover letter describing the circumstances of the call and to send the cover letter and the transcript summary to those in the ‘Level 2 Client’ list. 
Rather than having to open a number of applications to complete each part of the full task, the user either read or listened to the AI during each step and opened no applications.  The AI interfaced with the necessary applications and completed each task.  The system also gives the option to allow the Ai to collect information from other applications and devices in the Samsung ecosystem that can help it build a detailed profile of the user in order to make better or more personalized suggestions.
This can only happen when the AI is integrated into applications and Samsung has the advantage of having a recognizable enough brand that users are willing to use Samsung applications on its phones, along with a variety of externally developed applications.  Samsung offers external developers a number of tools to give them access to Samsung’s One UI 7, the user interface that sits on top of Android and provides the hooks to the AI, but there is nothing better than having that interface and the applications themselves developed in-house.  Only Samsung, Apple, and Google have the ability to tie their hardware and AI to such large application bases, with both Apple and Samsung concentrating on processing AI on device whenever possible.
As noted Samsung said all the right things about its AI at the Galaxy S25 event but everyday use can be much different from well produced event videos.  The Galaxy S25 family is now on pre-order and will be available in stores on February 7, at which point we should be able to get a better idea as to whether the phones live up to their marketing pitch.  The good news is that if they do, there is no premium being charged for the AI capabilities as the phones are priced the same as last year’s models, a plus for consumers.  Below we show only the differences between the hardware in the Galaxy S25 and last year’s Galaxy S24.

⁎ The 4th main camera (ultrawide) in the S25 Ultra is 50 MP f/1.9 while the 4th mani camera in the S24 is 12MP f/2.2.  The other three cameras are the same.
​

​While headlines are oriented toward incoming President Trump’s tariff policies that have yet to be formulated or implemented, we noticed that a Day 2 EO has called for reviews of a wide variety of trade practices by various agencies in the Executive Suite, with reports due by either April 1 or April 30.  They range from evaluating the US/Canada/Mexico Trade agreement to the potential for building an ‘External Revenue Service’ to collect tariffs.  Also mentioned under the auspices of the Secretary of the Treasury review is an evaluation of the $800 De Minimis duty exemption available to foreign retailers selling to US citizens.  That exemption allows for the duty-free importation of goods valued at $800 or less per person per day and is the driving force that allows companies like Temu (PDD) and Shein (pvt) to sell low-priced goods in the US without paying tariffs.
Many consider this exemption a way for China to bypass tariff regulations, giving them an advantage over US companies like Amazon (AMZN), WalMart (WMT), and Target (TGT) in a broad sense, although the impact is greater on discount retailers like Burlington (BURL), TJ Maxx (TJX), or Ross (ROST).  The exemption was raised from $200 to $800 in 2016 to facilitate trade and boost economic activity but also to speed up shipments and reduce the burden on customs processing. According to the US Customs & Border Protection Service, over 4m de minimis shipments enter the US each day, up substantially after the 2016 change, so the elimination of the exemption, while reducing the advantage that low-cost Chinese goods providers have in the US, it would also force an increase in processing time and likely require additional hiring for the customs service.  While that aspect of the exemption will go relatively unnoticed should it be eliminated or reduced, the exemption is certainly political fodder and is already the subject of Congressional debate.  With a report from the Treasury on the exemption due at the end of April, we expect at least some change in that rule in 2Q.  It’s low hanging fruit that plays into anti-China sentiment and has only minor consequences.

In 2017 Foxconn (2354.TT) announced that it was thinking about coming to Wisconsin, and, as all good politicians do, local bureaucrats quickly cobbled together a $2.85b tax credit plan to help convince Foxconn to choose their site in the tiny (27,732 in 2020) village of Mt. Pleasant for the $10b Gen 10 LCD fab that Foxconn was going to build, along with ‘thousands of jobs to the state’ as per the company.  The deal, which had tax incentives that were 10 times larger than any deal ever made in Wisconsin with a foreign entity was approved by Foxconn in 2018.  Houses were purchased under the threat of eminent domain and razed, and then President Trump, along with Terry Gou, chairman of Foxconn and the governor of Wisconsin, broke ground at the 1,200-acre site on a sunny day in June, promising at least 13,000 jobs.

However, as has been the case with a number of Foxconn projects, things got off to a slow start, although local government spending on roads and infrastructure continued, and Foxconn did not meet qualifications for subsidies in 2018, falling short of the target of 260 hires for the year by 104.  Foxconn decided to change its plans in early 2019, then promising a Gen 6 LCD fab, due to ‘market conditions’ and by 2021, with little project development, a new administration signed a ‘modified’ agreement with Foxconn that reduced the number of jobs created by the project from 13,000 (“…probably 50,00 or more…” – Donald J. Trump) to 1,454, reduced the tax credits to $80m (paid by taxpayers) if conditions were met, and lowered Foxconn’s capital commitment from $10b to $672m (by 2026), while giving Foxconn the option to produce items other than LCD displays at the site.
Over the years since the original plan was formulated a number of buildings have been erected on the site (Figure 2); a guardhouse, a 120k ft2 storage facility, a 1m ft2 factory, and a dome.  During the pandemic the manufacturing center was used to produce respirators, face masks, and coffee kiosks, and more recently servers.  No displays were ever built on the site, but Foxconn began leasing about 1/3 of the site to Microsoft (MSFT) for $50m in 2023.  Microsoft is building a $1b data center on the site that is thought to eventually employ between 300 and 400 people.  Phase 1 of the Microsoft project must begin by July 2026 and phase 2 by July 2033 to qualify for tax breaks. 
All in, promises were made by Foxconn, the Governor of Wisconsin, and President Trump that the Foxconn project would be “…the Eighth Wonder of the World”, bringing jobs and prosperity to the region.  In this case taxpayers footed much of the bill for infrastructure, but few realize that the person who introduced the Chairman of Foxconn to President Trump which began this sad tale, was none other than Masayoshi Son, the Chairman of Softbank (9984.JP),  who is the financial spearhead of the just announced ‘Stargate Project’ that promises to spend $500b over the next  4 years to build power efficient Ai data centers across the US. 
While we expect at least the initial $100b funding that the partners in the project are promising will be privately funded, with little taxpayer involvement other than infrastructure and lost tax revenue (We note that the taxpayers footed the bill for the Foxconn site to the tune of $426m by 2023 and an additional $257m for high-voltage power lines through electricity rate increases), but actual funding details have yet to be worked out.  Hopefully, taxpayers are not going to be required to be involved in this massive project, as its long-term benefits will likely accrue to the partners, but more important are the timelines and how the project is going to provide itself with power without tapping into the national grid or further destroying the environment with fossil fuel power plants.  Once the politicians and plutocrats move on to the next headline, it will be interesting to see whether Project Stargate becomes another ‘Foxconn Wisconsin’ or actually does anything other than making its wealthy partners more wealthy. 

​As we have noted previously, we find comparisons between humans and AI meaningless as human intelligence is based on sensory input and AI is not.  Yes, Ai systems can sort through vast fields of data far faster than our eyes and brain allow us, but that same Ai system cannot tell whether those numbers mean something other than what it has been told to look for..  It might be able to improve its ability to sort through those numbers, but it has no context other than what it was taught.  Humans, have limitations, but because they have context, are able to feel emotion in a musical piece that an AI would not.  They are able to see the emotion in Edvard Munch’s “The Scream”, without knowing the color value of every pixel, and humans can know not to sit next to someone on the subway whose nose is running.
AI’s need senses if they are ever going to challenge human intelligence and creativity, and all the tokenization of literature, images, videos, and Instagram posts cannot help.  Yes, they might be able to answer a set of test questions better than a high-school student or even a college professor, but Ai art  is not art, its copying without emotion.  That said, what would happen if AI systems wer given senses?  What if they could ‘see’ the outside world in real-time? What if they could hear the sound of a real laughing child and at the same time see what made the child laugh?  Could they begin to develop an understanding of context?  It’s a difficult question to answer and likely would require all of the human senses working together to truly allow the AI to understand context, but we are certainly not there yet, and finding ways to grant AI systems the sense of touch and smell are challenging at best.
There are plans to give AI systems a better sense of sight, by collecting data from moving vehicles.  The data becomes part of a real-world model that strives to identify ‘how things work’, which can then be used to train other models.  However for a model to be ‘real-world’,  it has to be huge.  Humans take in massive amounts of sensory ‘noise’ that has only a minute influence on decisions, but is essential in understanding how the world works.  Much of that ‘noise’ is incomplete, ambiguous, or distracting but is part of the context we need to handle the uncertainty that our complex environment brings.  Of couse, efficiency is also important, and humans have the sometimes dubious ability to filter out noise, while retaining the essence of an situation, something Ai systems would have to be programmed to do, and with all the noise being fed to a real-world model, the storage and processing needed would be astronomical.
Ethics are a hard concept to explain, and building algorithms that contain ethics are prone to bias, Humans are also prone to bias, but they are typically taught lessons in ethics by others around them.  Whither they respond with their own interpretation of those ‘lessons’ or just mimic what they see, is a human issue.  Ai systems form biases only based on their training data and their algoritms, so while a real-world model might tell the AI that driving a vehicle into a concrete wall triggers a rule of physics, it doesn’t tell them that they should feel regret for doing so when they have borrowed that vehicle from their parents.  Humas also continue to learn, at least most do, so Ai real-world models must be ever expanding to be effective and that requires more power, more processing speed, and more storage.
So the idea of a general real-world model has a number of missing parts.  That said, real-world ‘mini-models’ are a bit more feasible.  Rather than trying to model the unbelieveable complexity of the real world, building smaller models that contain sensory data that is relevant to a particular application is, at least, more realistic.  We can use visual (camera) data to control stoplights, but those systems react poorly to anomolies and that is where additional sensory data is needed.  Someone crossing against the light might be looking at the street to avoid traffic, but at the same time can hear (no earbuds) the faint sound of a speeding car that has yet to hit their peripheral vision, and that information, as unsignificant as it might be to someone walking on the sidewalk, becomes very important to the person crossing against traffic. 
Real-world models that try to mimic real-world situations must have sensory information and the ability to filter that information in a ‘human’ way, so the development of real-world models without more complete sensory information will not produce the human-like abilities to react to the endless number of potential scenarions that every second of our lives provides.  Networks could help AI’s gather data, but until the AI is able to feel the stroke of camel hair on canvas, smell the paint and see the yellow of a sunflower, they cannot understand context in the truest sense, something humans begin to learn before their first birthday.  We expect mini-real-world models will be effective for lots of applications but without sensory input, real-world context is a dream.

​IDC is out with early predictions for the semiconductor market for 2025, expecting a 16% increase in value this year to $779.8b.  Inside of that number are expectations for 24% growth in the memory market, with DRAM expected to be up 30.2% to $118b, with HBM (High Bandwidth Memory) for AI the driver.  As a contrast, they predict that the smartphone market will grow only 3.3% this year, primarily in 2H as inventory levels are worked down, but the datacenter market will see 23.9% growth, with that jumping to 33.6% if you include the cloud and enterprise markets.
As we have noted previously, Samsung, the leader in the memory market, has been unable to capitalize on that leadership and has been losing ground to competitors, SK Hynix (000660.KS) and Micron (MU) in this very competitive market.  Samsung has had problems with heat and power consumption in its HBM3e 8 and 12-layer products that caused yield issues and qualification delays at Nvidia (NVDA), contributing to the recent weakness, and among the pressures that have lead to Samsung’s recent executive level parings.  The head of Samsung’s memory group made a public apology for the shortfall in 4Q’24, leading one to believe that even more intense pressure continues for Samsung to right the problems in the semiconductor division, and it seems they are taking additional steps toward other issues that might be contributing to the problems with the company’s semiconductor production.
Samsung has been developing a more advanced memory product (D1b) that is based on a 10nm node.  They have been producing D1b memory, which is used in its DDR5 memory products, on a 12nm node since late 2024, which itself was delayed by almost a year.  This node change has raised expectations  about the 10nm product, based on higher density and even higher speeds than the D1b 12nm product.  However it seems that as it continued the development of the 10nm product, Samsung has taken the very unusual step of changing the design of the D1b 12nm product, which has been in production for nearly a year, to improve yield.  This typically means that the production process is also changed, an expensive procedure, with equipment having been ordered in 4Q ’24 (unconfirmed) and production of the D1b memory with the new design scheduled for 2Q or 3Q this year.
This was not a light decision on Samsung’s part, likely based on the need to push ahead of SK Hynix and Micron, both of whom use D1b memory in their HBM product, while Samsung is still using D1a memory in its HBM.  If Samsung is able to meet it goal of producing the new design of D1b memory quickly, without yield issues, they will remain competitive until the D1b 10nm development is completed.  If they face problems with the new design and process, they could lose more ground to their competitors and the idea of a design change mid-stream will be viewed as a mistake.  It’s a big bet, but also indicates the urgency being placed on fixing Samsung’s memory issues and allowing them to capitalize on the strength of the memory business this year, an absolute necessity if they are to remain the leader in the memory space.

When foldables first appeared some in the smartphone world were concerned that foldables were making smartphones inconvenient for users because they were too bulky to comfortably fit in one’s pocket when folded.  Over time those concerns seem to have dissipated as 2024 progressed, it seems that substantial progress was being made on that front.  In the early part of last year foldables were running 15.3mm to 15.8mm when closed and 7.1mm to 7.5mm when open.  Just for reference 25.4mm = 1 inch, so 15.8mm = 0.62”.  Later in the year, it seems that foldable brands took that criticism to heart with the Samsung (005930.KS) Z Fold 6 only 12.1mm when closed and 5.6mm when open.  But the competition did not stop there, with the Huawei (pvt) Mate XT Ultimate coming in at 12.8mm when folded and only 4.8mm when open.  Not to be outdone, Samsung countered with the Z Fold 6 Special at 10.6mm when folded and 4.9mm when open, and we note that the iPhone 16, which is not a foldable, is 7.8mm thick, so the Z Fold 6 Special is only 2.8mm thicker than the iPhone 16 when it is folded.
One would think the concept of thinner foldables would have run its course when a folded phone has come so close to the thickness of a regular phone, but that is not the case.  Chinese brands Oppo (pvt) is said to be about to release (February) the Find N5[1] foldable which is shown to be only 4.0mm thick when open, even though it has a large 5,700mAh battery.  While we don’t have a thickness when closed for the Find N5 yet, given that other foldables are typically 0.8 to 0.9mm thicker closed than open, we can estimate that the Find N5 will be between 9.0mm and 9.3mm thick when closed, just a millimeter and a fraction thicker than an iPhone 16.  Oppo says they were limited in how thin they could make the Find N5 only by the thickness of the Type-C connector used to charge the phone.  Type-C connectors are typically less than 1mm thick but need additional vertical space to be reinforced into the frame of the phone, so unless someone is able to modify the connector, the competition for thinnest foldable might be coming to an end this year, perhaps followed by a focus on multiple fold devices, where only one segment would need a connector, leaving others to be even thinner, or maybe skip the connector altogether in favor of wireless charging, removing the last restriction to thinness.


[1] Will be available in the US as the OnePlus Open 2.

”AI will be the most transformative technology since electricity.” (Eric Schmidt); "AI will not replace jobs, but it will change the nature of work." (Kai-Fu Lee); "AI will not replace humans, but those who use AI will replace those who don't." (various); "AI will be the most powerful technology ever created, and it will fundamentally alter the way we live, work, and interact." (Andrew Ng).  These are quotes about how AI will change the world from some very smart/ successful people, although a group that is heavily ‘invested’ in AI technology, giving them a bit of bias.  We certainly don’t denigrate the fact that AI has been able to help understand the complexities of human genetic code, improve weather forecasting, help to develop new materials, and is able to comb through vast amounts of data to find patterns we humans might have missed.  But while the AI community might want consumers to believe that AI is the Mighty Mouse (“Here I come to save the day…”)[1]of the 21st century, its not that easy.
In order for AI to fulfill all the hopes and dreams of its supporters, it not only has to be fast (it is), but it has to be able to work 24/7 (it can), able to learn from its mistakes (sometimes), and has to be correct 99.9% of the time (it’s not).  But the business end of AI does not have the patience to wait until AI is able to meet those specifications and has ushered us into the world of AI as a tool for getting a leg on the competition.  CE companies are among the most aggressive in promoting AI, and the hype continues to escalate, but the reality, at least for the general public, is a bit less enthusiastic, despite initially high expectations.  In a 2024 survey, businesses indicated that 23% found that Ai had underperformed their expectations, 59% said it met their expectations, and 18% said it exceeded their expectations,[2] with only 37% stating that they believe their business to be fully prepared to implement its AI strategy (86% said it will take 3 years), a little less enthusiastic than the hype might indicate.
From a business standpoint the potential issues that rank the highest are data privacy, the potential for cyber-security problems, and regulatory issues, while consumers seem to be a bit more wary, with only 27% saying they would trust AI to execute financial transactions and 25% saying they would trust AI accuracy when it comes to medical diagnosis or treatment recommendations.  To be fair, consumers (55%) do trust AI to perform simple tasks, such as collating product information before making a purchase and 50% would trust product recommendations, but that drops to 44% concerning the use of AI support in written communications[3].  Why is there a lack of trust in Ai at the consumer level?  There is certainly a generational issue that has to be taken into consideration, and an existential fear (end of the world’) from a small group, but there seems to be a big difference between the attitude toward AI among business leaders and consumers, and a recent YouGov survey[4] points to why.
US citizens were asked a number of questions about their feelings toward AI in three specific situations: making ethical decisions, making unbiased decisions, and providing accurate information.  Here are the results:


[1] - [1], Fair use, https://en.wikipedia.org/w/index.php?curid=76753763
 

[2] https://www.riverbed.com/riverbed-wp-content/uploads/2024/11/global-ai-digital-experience-survey.pdf

[3] https://www.statista.com/statistics/1475638/consumer-trust-in-ai-activities-globally/
 

[4] https://today.yougov.com/technology/articles/51368-do-americans-think-ai-will-have-positive-or-negative-impact-society-artificial-intelligence-poll

It is not surprising that many Americans do not trust AI to make ethical decisions for them, but over 50% of the US population does not think AI systems are making unbiased decisions, and we expect that is without the more detailed understanding of AIs that might lead one to an even higher distrust.  That said, we were surprised that 49% of Americans believe that AIs were providing accurate information, against 39% who disagreed.  We believe that the push to include AI in almost every CE product as a selling point this early in the development of AI systems that interface with users, will do little to convince users that the information they receive from AI systems is accurate, and has the possibility of reducing that level of comfort. 
LLMs and AI Chatbots have become so important from a marketing standpoint that few in the CE space can resist using them, even if their underlying technology is not fully developed.  Even Apple (AAPL), who tends to be among the last major CE brand to adopt new technology, was forced into providing ‘Apple Intelligence’, a brand product that was obviously not fully developed or tested. While Apple uses AI for facial and object recognition, to assist Siri’s understanding of user questions, and to suggest words as you type, there was no official name for Apple’s AI features until iOS 18.1, when the name ‘Apple Intelligence’ was used as a broad title for Apple’s AI.  The two main AI functions that appeared in iOS 18.1 were notification summaries, and the use of AI to better understand context in Apple’s ‘focus mode’.  iOS 18.2 added Ai to improve recognition in photo selection, gave Siri a better understanding of questions to improve its suggestions, and allowed users to use natural language when creating ‘shortcuts’, essentially a sequence of actions to automate a task, and also enhanced the system’s ability to make action suggestions as the shortcut was being formulated.
None of these functions are unusual, particularly the notification summaries, which are similar to the Google (GOOG) search summaries found in Chrome, but there was a hitch.  It turns out that Apple AI was producing summaries of news stories that were inaccurate, with the problem becoming most obvious when Apple’s AI system suggested that the murderer of United Healthcare’s CEO had shot himself, causing complaints from the BBC.  Apple has now released a beta of iOS 18.3, that disables the news and entertainment summaries and allows users to remove summary functions on an application-by-application basis.  It also changes all AI summaries to italics to make sure that users can identify when a notification is from a news source, or is an Apple Intelligence AI generated summary.
While this is an embarrassment for Apple, it makes two points.  First, AI systems are ‘best match’ systems.  They match queries against what their training data looked like and try to choose the letter or word that is most similar to what they have seen in their training data.  This is a bit of an oversimplification, as during training the AI builds up far more nuanced detail than a letter or word matching system (think “What would be the best match in this instance, based on the letters, words and sentences that have come before this letter, or word, including those in the previous sentence or sentences?”), but even with massive training datasets, AI’s don’t ‘understand’ more esoteric functions, such as implications or the effect of a conclusion, so they make mistakes, especially when dealing with narrow topics. 
Mistakes, sometimes known as hallucinations, can be answers that are factually incorrect or unusual reactions to questions.  In some cases the Ai will invent information to fill a data gap or even create a fictionalized source to justify the answer, even if incorrect.  In other cases the Ai system will slant information to a particular end or sound confident that the information is correct, until it is questioned.  More subtle (and more dangerous) hallucinations appear in answers that sound correct on the surface but are false, making them hard to detect unless one has more specialized knowledge of a topic.  While there are many reasons why AI systems hallucinate, AI’s struggle to understand the real world, physical laws, and the implications surrounding factual information.  Without this knowledge of how things work in the real world, AIs will sometimes mold a response to its own level of understanding, coming up with an answer that might be close to being correct but is missing a key point (Think of a story about a forest without knowing about gravity…” Some trees in the forest are able to float their leaves through the air to other trees…”.  Could it be true?  Possibly, unless there is gravity)
Second, it erodes confidence in AI and can shift consumer sentiment from ‘world changing’ to “maybe correct’ and that is hard to recover from.  Consumers are forgiving folks and while they get hot under the collar when they are shown that they are being ignored, lied to, or overcharged, brands know enough to lay low for a while and then jump back on whatever bandwagon is current at the time, but ‘fooled once, fooled twice’ can take a while to dissipate.  AI will get better, especially non-user facing AI, but if consumers begin to feel that they might not be able to trust AI’s answers, the industry will have to rely on the enthusiasm of the corporate world to support it and given the cost of training and running large models, we expect they will need all the paying users they can find.  Don’t overpromise.

2024 was not a good year for CE companies that are required to ship product by container, with conflicts in the Red Sea and strike threats on the East and Gulf coasts pushing up rates throughout most of the year.  In fact the Global Container Freight Index rose 96.9% during 2024, while the Asia to US East Coast index rose 86.4%.  The global index finished the year down 31.5% from its high but up 85.6% from its low, while the China/East index closed down 40.4% from its high and up 89.5% from its low. 
The good news is that the ILA strike that was scheduled for January 15, which would have shut down ports on both the East coast and the Gulf coast, did not happen, as a last-minute agreement between the ILA and the USMX was reached.  The rank-and-file vote still has to occur, but if ratified it will settle the dispute with a 6-year contract that seems to have satisfied both sides.  As the wage issue had already been settled, the only issue remaining was worker job protection from the automation that the USMX wants to bring in to modernize the ports, which was settled.
With the potential strike settlement and a less frenetic Red Sea situation, one might have expected rates to decline, however Chinese New Year (1/29) comes early this year and demand for containers has increased as the holiday approaches.  In the first two weeks of 2025 the Global index rose 12.7%, while the China/West Coast index rose 22.8%, a bit more of a burden for CE company margins.  To put those numbers in perspective, the 2025 Global index is up 64.1% on a y/y basis, while the 2025 China index is up128.9% y/y.  Not an auspicious start to the year but at least the strike threat is out of the way.