As the leader in both the smartphone and the TV set segment and one of only a few large memory producers, Samsung Electronics has considerable influence in the CE space.  They tend to be a leader in CE technology, particularly in the display space and produce both components and end end-user products.  As such they can be both an indicator and a lightning rod for change in the CE space, making them a valuable tool in understand the current an future status of consumer electronics.
Last night Samsung Electronics (005930.KS) reported 1st quarter 2025 results of 79.14 trillion won ($55.25b US) in sales, ↑4.4% q/q and ↑10.0% y/y and 1.3% above consensus with this quarter being the best in the company’s history.  Operating profit was 6.7 trillion won ($4.68b US), ↑3.1% q/q, ↑1.4% y/y, and ↑4.7% above consensus.  In order to better understand the table below, which breaks down sales and operating profit by Samsung division, we note:
DX (Device Experience) includes – TV sets & Monitors, Appliances, Smartphones and tablets, Network Equipment, and Health Products
DS (Device Solutions) – Memory, Processors & Sensors, Logic, foundry
SDC (Samsung Display) – small panel OLED and large panel QD/OLED displays
Harman – Automotive & Consumer audio

​Samsung also breaks out some components of its divisions.  The division formerly known as MX, which now is comprised of smartphone and tablet products (part of the DX division), the TV segment, also part of the DX division, and Memory, a part of the DS division.  While operating income is not given sales can be computed, as well as the share of sales of the company total,

The results were mixed, with some product categories outperforming and sone underperforming, essentially the way Samsung is supposed to operated in difficult times, although we expect many investors were hoping for a more positive quarter from the semiconductor segment, and a more optimistic feel for 2Q and 2H, which they did not get.  Samsung overall was careful to hedge any optimistic view of this year with the caveat of tariff uncertainty, and the division leaders were even more so, although the company was a bit more forthcoming about product rollouts and timelines than usual.
As a number of Samsung’s major products have been exempted from US reciprocal trade tariffs, they have only moderate exposure to direct import costs, but as a component supplier, they were cautious about raw material costs impacting component prices and how that would follow through the supply chain.  That said, without any hard US trade policy or realistic negotiations with major trading partners, and the prospects of another nuclear option in early July, they have little choice but to forge ahead as originally planned. 
As seems to be the case with many larger CE companies who have the option, they are considering shifting production from countries that have onerous tariff requirements to less onerous ones, but seem to be in no rush to make those changes.  We expect there will be lots of talk about how negotiations are progressing and how many deals have been agreed upon by July, but we also expect little confirmation, little detail, and even less about timelines for balancing trade.  While the full impact of tariffs has yet to be felt by consumers, the peripheral impact, such as a weak equity market, has already put consumers on edge, something mentioned by Samsung a number of times on the call, we believe a big part of Samsung’s cautious stand on 2Q and the rest of the year.
Getting all Samsung divisions to operate effectively and profitably is a complex task and one drenched in global macroeconomics, but a bit less ‘unknown’ might be helpful in getting the planets to align.  Below are our quick notes.  Comments in red are our own.

​General Comment Summary

• DX strength came from flagship smartphone strength (Galaxy S25 series) and high-end appliances
• DS weakness came from demand deferrals from High Bandwidth memory customers
• Capex – 12t won with 0.5t won for display (down substantially), as the Gen 8.6 IT OLED fab is completed and 10.9t won for the DS division overall.
• Repurchased 3t won of common and preferred as part of 10t won 2025 program, which continues in 2Q.

Memory - General

• Ai Server demand remained strong
• Some PC/Mobile demand improvement (Small, possibly China driven)
• SSD demand weak
• Some data center projects delayed

DRAM

• Bit growth higher than expected
• HBM export controls on AI chips and customers waiting for HBM3E release caused deferrals. 

NAND

• Bit growth down ~10% but above expectations as perception that market has bottomed sets in. More, less downside pressure than upside pressure.

Memory Outlook – 2Q

• AI Server demand to remain strong
• “Preemptive Purchasing” after tariff pause
• Memory for PC & Mobile inventory now normal because of China subsidy inclusion
• Overal,l expect some incremental demand but tariff remains a question

Memory Outlook – 2H

• Ai server demand to remain strong (Said a number of times)
• SSD to recover as deferred projects begin
• Incremental PC demand from Win10 end AI (Win10 replacement cycle theory seems dead – Ai better bet but still an unknown)
• Mobile demand improves due to AI

 
System LSI – General

• Weakness from delayed SoC adoption by major customer (Samsung)
• Strong demand for image sensors

LSI – 2Q Outlook

• Expect image sensor volume to decline
• SoC increase to offset sensor volume decline

LSI – 2H Outlook

• Limited mobile momentum
• SoC steady
• Will add product (Automotive sensors)

 
Foundry – General

• Seasonal weakness for mature nodes
• Inventory adjustments due to China trade tensions (meaning order reductions)
• New advanced node starts in 2H

Foundry - 2Q Outlook

• Subdued demand
• Ramping production for US automotive products (Tariff issues?)
• Tariffs could have big impact
• 2nm GAA production starts in 2Q, but small

Foundry - 2H Outlook

• Geopolitical Risk expected to increase
• Demand for PC & Mobile expected to weaken
• AI & HPC momentum still strong (advanced nodes)

 
Samsung Display – General

• Improvement in demand from major customers
• Favorable exchange rate
• But seasonally weak quarter
• Double digit monitor sales growth (QD/OLED share?)

SDC - 2Q Outlook

• Mobile – Conservative view due to tariff situation
• Will launch new ultra-high refresh rate monitors (gaming)

SDC - 2H Outlook

• Increasing uncertainty due to trade issues
• Competition increasing
• Weak consumer sentiment
• Mobile driver is AI
• QD/OLED will expand monitor line w. lower-priced models (Good news – when?)

Visual Display (TV) – General

• Demand was down q/q but up slightly y/y w. premium and ultra-large TVs driving growth.
• Raised prices and lowered material costs but…
• Overall TV demand remained weak, and the cost of competition was high

TV - 2Q Outlook

• TV demand flat y/y
• Expanding AI TV (Will consumers care?)

TV - 2H Outlook

• Demand for high-value products (Ultra-large and OLED) will remain

 
Q&A
Tariffs

• Semis, phones, tabs currently exempt
• Reviewing other products
• Potential to ‘manage’ global production

Stock Buyback

• Will cancel 2.5t won shares of 3t won current buyback

TV

• Intense entry-level competition
• Will add to 98”+ lineup

Memory

• NAND Bit growth up mid-teens in 2Q
• DRAM Bit growth up low 10%
• NAND for PC & Mobile price decreases to end – flat going forward

Foldables

• Differentiated AI for each foldable type

All in, we thought the quarter was just about as expected, although we were a bit surprised at the comments about data center deferrals, which was mentioned a number of times.  We were concerned that Samsung was as cautious about 2H, but we expect given the volatility of the situation, they have little choise.  The fact that they were able to find a way to the exemptions that will allow them to not have a disastrous year is a bit of an offset, but it is better to under-promise and overachieve than the opposite.

AI is unusual in that while we (humans) develop architectures and algorithms that make models work, we are not really sure how they do what they do.  But when we ask a model, in this case ChatGPT (OpenAI) to explain how they work, the model seems to be able to step back a bit in order to explain details.  This step back puts the LLM we are talking with in the unusual position of describing how it works as if it were not a model but an observer, although sometimes it seems odd when a model describes how it works by saying “models do this…” sort of ignoring the fact that it is  model, but we digress…
What we were trying to understand when we started our conversation with ChatGPT was how models represent information for each token as it learns.  We understand that the model (software called a tokenizer) breaks down text into tokens, typically a token for each word, although in many cases it can be a sub-word, such as a syllable or even a single character.  Each token gets assigned an ID number which goes into a master token ID list.
Example:
“The cat was running away from the dog.”

              
The list of unique tokens for a large model is fixed at ~100,000 tokens.  No matter how much data the model sees it only uses tokens from this list, breaking down unknown words into smaller known sub-word pieces, so the corpus of data the model sees could be 300 billion tokens.  The token ID list remains with the model after training, but the large list of tokens processed during training does not need to be stored, as the model learns from the tokens but does not need them later.
The part that is difficult to visualize comes as the tokens are first encountered by the model.  The model looks up the token in the token list and matches it to another list that contains that tokens vectors.  Think of vectors as a string of numbers (768 numbers for each token in a small model)

​ On the first run through, the dimensions for each token are set to random numbers, essentially ‘noise’, then the token sequence is passed to the first layer of the model.  These vectors are used to begin to ‘classify’ each token. If the model ‘sees’ that ‘cat’ and ‘dog’ appear in the same sentence often, it will adjust a particular dimension slightly for both the cat and dog token, and with each layer will further adjust that dimension, which we might call the “animal” dimension.  By the time the token has been cycled through all the layers, the ‘animal’ vector for both dog and cat will be close to each other, but not exactly the same.  That is how the model ‘knows’ that both dog and cat have the ‘animal’ relationship but are still different from each other.  If that vector was the same for both, the model would not know that while both are animals, they are different animals.
While this is a very simplistic look at how an LLM learns, one should understand that the model is always looking at the relationships between tokens, particularly in a sequence, and with over 700 vector dimensional ‘characteristics’ for each token, the model can develop lots of connections between tokens.  It is hard not to think of the dimensions as having specific ‘names’ as the semantic information that the dimensions contain is quite subtle, but it is all based on the relationships that the tokens have to each other, which is ‘shared’ in token vectors.
All in, this is just the tip of the iceberg in terms of understanding how models work and their positives and negatives, although even the best of LLNs still has difficulty explaining how things work internally when the questions are highly specific.  Sometimes we think its because it doesn’t really know how it works and other times it seems that it just doesn’t want to give that proprietary detail.  But we will continue to dig and pass on what we find out and how it affects AIs and their use in current society.  More to come…
 

The perfume and scents industry is not one that makes headlines often.  Perfume ads with celebrities tend to come and go but over the last 25 years there have been some scents for both men and women that have sustained themselves on the best-seller list and generated millions of dollars in revenue.  As an example in 2022 Dior (CDI.FR) Sauvage was selling at the rate of $4.6m/day for much of the year and last year the perfume market was estimated to be between $50.5b and $55.5b US, with an expected CAGR of between 4.7% and 5.9%[1].


[1] Sources: Estee Lauder, VMR.com, CB Insights

Scent developers start their process with an idea.  It could come from examining current popular scents to capitalize on a trend, or it could come from a creative point of view, maybe recalling  a travel destination or personal experience.  The process then moves to the selection stage where the perfumer, based on expertise, selects fragrances that they believe will represent their concept.  What follows is an extended trial and error process where the scents are blended to form a ‘top note’, the basis for the overall scent, ‘accords’ that push the scent in a particular direction (rose, marine, etc.), while making sure that the materials have a consumer-oriented longevity and projection (how far away the scent can be noticed), all of which are developed by trial and error.
There is software that can help perfumers, even AI based software like Philyra, developed by Symrise (SY1.XE) and IBM (IBM) and released in 2018.  The software contains a database of 3.5m legacy formulas and 2,000 raw materials, and, according to the company “…is able to guide perfumers towards exciting and surprising solutions, explore new combinations and materials without human bias, and help perfumers update and improve upon iconic fragrances.” In particular, Philyra helps perfumers to work toward using sustainable materials in their development.
While software platforms like this help the scent development process, it is a long and arduous process that takes many months or years until the right combination of scent and materials is reached.  Even with software providing assistance to perfumers and the expertise of a professional and experienced ‘nose’ (1st tier perfumers can make over $400K/year) commercial success is certainly not guaranteed and the cost of development, materials, and advertising can be quite financially burdensome, even for a large company.
But fear not perfumers, as a group of Japanese scientists have taken the idea of AI scent development further and created a Generative Diffusion Network for creating scents.  This new model uses mass spectrometry data from 166 essential oils to isolate 9 ‘odor descriptors’ that can be used to form scent combinations which are then tested for accuracy in a double-blind (human) process where participants had to match the AI aroma with the appropriate descriptors.
To illustrate: “As an illustration of the procedure, for the first sample of the sensory test two odor descriptors, Wood and Spicy, were selected. A random 201-dimensional vector of Gaussian noise was chosen as the seed for the OGDiffusion network. The network was then run in inference mode, generating a mass spectrum as the output. This mass spectrum was subsequently analyzed using non-negative matrix factorization to identify the essential oils required for the mixture. The analysis determined the following essential oils and proportions: Cypress (0.10), Angelica root (0.07), Cuminum cyminum (0.05), and Trachyspermum ammi (0.78). The specified amounts of each essential oil were pipetted into 5 mL vials and diluted with alcohol at a 2:1 ratio. The resulting mixtures were prepared for sensory evaluation in odor vials. Table S1 shows the essential oil recipes used in all sensory experiments.”
​

The idea behind this model is to eliminate problems that exist with current AI scent development systems.  Such systems are based on proprietary data and require expert human intervention, along with results being hard to reproduce.  While they are considered helpful to those in the scent profession, they are not automated and that is where this new model goes.  The system learns without needing prior chemical composition knowledge and is able to generate precise results that can be reproduced exactly, and mass spectrometry data can be easily represented as weighted sums, a function commonly used in LLMs.
So, will those wishing to become perfumers or scent specialists be out of a job?  In some ways the answer is yes, as there will be less need for the trial and error development system used today and that means less learning situations for those coming up in the industry, but again humans are essential, even in this automated scenario, as there must be someone who can test the combinations created by the AI, even if they were created without human assistance.  Without a ‘nose’ to smell the combinations there is no subjective point to attach to the scent.  So, in this case, such an AI system will reduce the amount of work associated with the development of scents but will still require a high-quality professional to make sure that the scent is a pleasant or exciting as expected.  The nose knows.

AGI or Artificial General Intelligence is a term that gets tossed about regularly, particularly by those in the AI business.  According to them, we are closing in on a point at which AI systems would have the capacity to reason, solve problems, understand complex ideas, and adapt to tasks that were not explicitly programmed.  This is both an exciting and scary prospect that is difficult to quantify, but we are sure that those in the Ai field will let us know as soon as it happens, or when they think it does.
There is only one problem, something called a world view.  A world view is a human being’s image of how the world works.  It’s not a single image but a collection of information that you starting amassing the second you were born.  From those first moments the human mind builds a world model by using sensory information to draw conclusions. A baby eventually understands that if you hold something and then let go, it falls to the ground.  The baby doesn’t know what the concept is called, but after a few (or many) things falling to the floor, the baby understands that if I let something go or push it off a table, it will fall to the floor.  Simply, our world model is experience based
Humans are also subjective.  Baby A will learn that if I let go of something it will drop to the floor and people will come over and make funny noises which are frightening, while Baby B learnsd that if I let go of this it will drop to the floor and people will come over and make funny noises which is funny.  Much of what human learn in implicit. It does not always require conscious effort.  It doesn’t’ take conscious effort to realize that your feet are going to blister if you walk barefoot on a hot surface.  Once it happens you don’t forget, but we also update our world model every second we are alive as our sensory input continues until death.
We are lucky to have the capacity to create a world model that helps us interact with our environment, as without it humans as a species would never have survived.  This is also true for animals who have to navigate through their environment by building a world model, albeit a much different one than we might have, although it is based on sensory input and a subjective interpretation of same, learned implicitly, and updated consistently.  While animal world models are different for each animal because of their sensory capabilities, they acquire the information and process it the same way we do.
AI systems don’t work the same way.  While many in the industry believe AI’s build their own internal world models, they are certainly unlike our own.  AI world models are quantitative not qualitative.  They are not based on sensory data but are based on numbers that have been labeled (mostly by humans) making the information explicit, and they are limited by the data they are trained on.  Of course, the typical response is, if you give them more data to learn on, they will get smarter, but we do not believe that is true because Ai systems do not have the ability to be subjective.  If two AIs are based on the exact same algorithms and taught with exactly the same data, they will arrive at the same answers, while humans will not.   AIs will certainly find patterns and relationships that we cannot, but unless they are told that a set of numbers represents an object falling to the ground, it is meaningless information.
Credit were due, AI systems are very good at finding relationships, essentially similarities that are extremely subtle.  In that way they can recognize that Dr. Seuss used certain words, certain rhyming patterns, certain letters, parts of speech, and other conventions that we don’t recognize.  In that way an AI can write ‘in the style of’ Dr, Seuss’, while we need some sort of sensory input to know that hearing “Sam I am” makes us think of Dr. Seuss.  But it doesn’t stop there.  The AI spits out an 8 line paragraph about a small environmentalist and moves on to the next task, while when we hear or see the word ‘Lorax’ we think of the happy times when that story was read to us as children or when we read that story to our own children.  That points to the difference in world models.:
In a world of bright hues, lived young Tilly True, who cared for the planet, the whole day through! With a Zatzit so zappy, and boots made of blue, she’d tell grumpy Grumbles, "There's much we can do!"
"Don't litter the Snumbles, or spoil the sweet air, Let's plant a big Truffula, with utmost of care!" Said Tilly so tiny, her voice like a chime, "For a healthy green planet, is truly sublime!"
We are not criticizing AIs here.  They are machines, essentially super calculators that have an almost infinite ability to follow instructions and find patterns but giving them more data doesn’t allow them to build a subjective world model.  While AIs can note that the color difference between two pixels in an image are different by 1 bit in a 16 bit number, our sensory (visual) input fits that color into our world view, and we say “Wow, those are beautiful flowers”. 
AGI, in our view, would require a huge amount of sensory input and the ability to place that input into a world view that is subjective, and at the moment, we don’t believe that is possible for any AI.  AIs can be better ‘pattern recognizers’ than humans and don’t get annoyed or tired, but they cannot ‘see’ or ‘hear’ or ‘touch’ anything and that is what keeps AGI from becoming a reality.  JOHO.
Side Note: Here is the image that we got when we asked Gemini, “How about you come up with an image that represents a human world view on one side and an AI world view on the other?”  That has to tell you something, right?

Samsung Electronics (005930.KS) and BOE (200725.CH) are rivals, not quite directly but Samsung Electronics’ affiliate Samsung Display (pvt) competes head-to-head with BOE in the small panel display market and to a lesser degree in the large panel TV space.  As we have noted, Samsung Display has been at loggerheads with BOE over IP issues and after a recent partial win concerning BOE’s misuse of Samsung trade secrets and IP, Samsung Display continues to fight BOE in the courts.  That said, Samsung Electronics also has issues with BOE.  As the largest TV set producer, Samsung Electronics, requires that those who use “We supply to Samsung Electronics” in their advertising, pay a royalty.  In 2022 BOE, who was the second largest supplier of TV panels to Samsung in 2021, refused to pay and Samsung has reduced BOE’s share as a TV panel supplier considerably since that time as they continued to battle over the royalty situation.
It seems that the President of Samsung Electronics TV division is scheduled to visit China in the middle of May, and BOE officials are expected to visit Samsung in Korea, with both expected to try to negotiate an agreement between the two on both panel prices and royalties.  The idea is that BOE can either lower panel prices to compensate Samsung or can leave panel prices the same and pay the royalty. 
While this seems reasonable, it might not be to BOE, who is also a major supplier to LG Display (LPL), Samsung’s local rival.  LGD has recently sold it’s last LCD TV panel plant (Guangzhou, China) to Chinastar (pvt), also a supplier to both Samsung and LG (066570.KS).  The large panel product that was being purchased from the LGD Guangzhou fab before the sale, would now become purchases from Chinastar.  Samsung has an internal requirement that no supplier of key materials can represent more than 30% share, and that means that it will have to maintain that 30% restriction, keeping it from purchasing panels from the Guangzhou fab now under the Chinastar banner.  While there are other large panel producers, such as AUO (2409.TT), Innolux (3481.TT), HKC (248.HK), and CHOT (pvt) that Samsung already buys panels from, Samsung tends to go with suppliers that have large capacity, leading to a secure supply, without violating the share limit..
At least to a degree, this puts BOE in the catbird seat or at least gives it some room to negotiate with Samsung, as Samsung Display is out of the large panel LCD business and supplies only QD/OLED TV panels to its parent which make up only a small portion of Samsung’s TV panel needs.  This leaves Samsung Electronics to outsource all of its LCD TV panel purchases.  As they cannot increase purchases from Chinastar without overstepping their limit, BOE is the obvious choice if they can come to an agreement over royalties.

In front of April 2, CE brands rushed to get containers into the US, only to find that ‘reciprocal’ tariffs proposed by the administration were to be postponed for 90 days.  One would expect brands to take a less aggressive approach to building US inventory levels given the lack of follow-through on these additional tariffs, yet it seems the postponement has done little to stem the inventory build in the CE retail space.  Given the exemptions the administration placed on a number of CE goods it can be seen that the administration is particularly aware of the impact that these potential tariffs would have on the US economy and the US consumer.  Yet brands don’t seem to be convinced that they will be at least partially and possibly fully exempt from new tariffs and continue to build inventory further as ‘protection’.
Sales for electronics & appliance retailers were down on a y/y basis in January and February and up in March (seasonally adjusted) and were above 5-year averages for February and March as shown below, so there was a bit of pre-buying by consumers.  That said, since the April 2 deadline and the subsequent postponement, inventory levels, at least in the PC space, have continued to increase. 
Typically 6 to 8 weeks of inventory are held across the CE retail supply chain, yet it seems that the average currently is about 10 weeks, with some mid and low price tier products in the 12 to 16 week range according to data from primary suppliers.  As we still have over 60 days before the next deadline, there seems to be little panic among brands, but more of a steady stream of additional product, with most traffic through standard shipping containers and little air transport.  But for containers to reach the US and pass through customs, they need to be on the water in early June and container prices to date are remaining steady, a good indication of the anxiety level of shippers at this point in time.  While they are not in a panic, they are not taking any chances.
We are a bit surprised that CE brands are continuing to build inventory after the postponement and exclusions, but it seems to indicate a distrust of the administration’s exemption fortitude and leans toward a worst case scenario in July.  We believe that the impact of reciprocal tariffs should they be implemented on CE products and therefore consumers, would be quite onerous and politically burdensome, making such an enactment very short-term (days/weeks) before exemptions come to the surface again.  While there are lots of countries that could see massive tariff increases, the primary US trading partners will likely be exempted again.  The harder question to answer would be how much the potentially high tariff status of countries like Vietnam, Indonesia, and the Philippines will affect prices if those tariffs are imposed, and whether India will be exempted as these are a key manufacturing centers for CE products.
In the interim, China remains the primary target for tariffs, and to offset the expectations for lower trade with the US, China will continue its efforts to increase local consumption.  We expect they will add additional consumer appliances to the subsidy programs that have been in place for months and will cooperate with provincial governments to add ‘smart’ home-oriented devices to the subsidy list, paid for with another round of bond sales of ~$41b US.  There has been some talk about the central government’s desire to consolidate the semiconductor equipment industry in China by merging China’s ~200 semi equipment companies into 20 primary suppliers.  While this will save subsidy capital and improve cost efficiency, it will take some time to implement and to see results, although it has been done in other industries in the past, typically through M&A financed with government support..

Buying a pair of glasses can be a traumatic experience.  For many, they are going to be wearing those glasses for most of the day, every day, for years, and a wrong choice can be devastating.  Now choosing glasses has become even more complicated as the glasses you choose that will help you read and keep you from stepping into traffic are not the only ones you need.  Not only do you need those prescription glasses (and maybe prescription sunglasses) but you also need a pair of AI glasses, now the hottest thing in China, where brands large and small are competing to grab consumer attention in this relatively new category.
In fact, similar glasses have been around for a while, but those were AR glasses that allowed you to overlay digital objects or text over what you see through ‘regular’ lenses.  Those are still a thing, typically dominated by Metsa’s (FB) Ray-Ban glasses, but as Ai becomes more embedded in our society, the drift is toward AI over AR, and in some cases both. 
So what are AI glasses?  Typically they look like slightly bulky sunglasses but have an integrated voice assistant that can understand what you want, similar to Siri, Alexa, or Google (GOOG) Assistant.  The voice assistant hears your commands through a number of microphones embedded in the frame and passes it to an LLM that parses speech the same way it parses text queries (actually not the same way, but similar).  The response can either be an answer or an action, typically responding through speakers also embedded in the frame or bone conduction modules that are touching your ear.  Most have some sort of image/video camera that can be activated to record an event or conversation, with some allowing direct livestreams to social media.
Of course, there are the applications that are usually on your phone, which is a necessary part of many AI/AR glasses, that communicate with the glasses, either by wire or wirelessly (typical) and allow the glasses to make calls, receive messages, and give you notifications, but when it really gets down to it, the applications available to each brand of glasses, whether AI or AR or both, are what makes them useful.
The most common application, aside from the basic messaging and notifications, is translation, which can be as complex as sentence by sentence instant translation that appears before your eyes (AR), or voice translation through the speakers.  This is not just for when you are traveling to another country, as anyone living in a metropolitan area is likely to face a few foreign speaking people each day.  They might not be talking to you (think nail salon, bodega, hospital, bus terminal) but it sure is nice to know what people around you are saying.  Existing aural applications like Spotify (SPOT), Apple (AAPL) Music, Amazon (AMZN) Music, or Deezer (DEEZR.FR) can be easily piped to your glasses, so no headphones or earbuds needed if you have glasses, but in the race to outdo other AI/AR glasses brands, there are lots of other applications that are finding their way into said glasses.
Object and scene recognition is one application that garners attention as it can be used for shopping (You see that person’s shoes? Find them for me”) or for navigation (“Tell me where I am -based on these buildings”), and while the navigation application seems to us to be the more important of the two, it is probably the other way around.  There are also health applications, with sensors that measure heart rate or oxygen levels and even some that are set up as hearing aids that use the embedded microphones and conduction systems to avoid having to stick obtrusive devices in your ears to hear.  There is even a set of glasses that can change their tint electronically and some that can read head or hand gestures, making it unnecessary to give a voice command unless a question needs to be answered..
As it is still very early in the ‘smart glasses’ game each new application or feature pushes that device forward into the public eye, only to be surpassed in days, weeks, or months by new features that catch the eye of consumers on another device.  Unlike smartphones however, which typically cost between $500 and $1000, smart glasses are less expensive and there are rumors that Chinese smartphone brand Xiaomi (1810.HK) is going to release their own branded smart glasses this year for just a bit over $200, making it difficult for smaller brands to compete.  While that might limit innovation a bit, it is certainly good for consumers who will benefit from low prices and feature competition similar to the smartphone space.
All in, we expect the smart (AI) glasses segment and the AR/XR segment to merge over the next two years and for new applications and features to drive expansion in the space.  But we also believe that in a relatively short period of time, most smart glasses sales will be based on large CE brands that exist today, with those brands focused on high unit volumes that will augment smartphone sales.  That said, it will be a delicate balance to keep smart glasses from eating into smartphone sales as some of that smartphone functionality shifts to the glasses.  We can also see a scenario where small inexpensive pocket computers, designed specifically for branded smart glasses, could replace smartphones altogether, but it is too early to make that call as consumers are just beginning to see the utility that smart glasses provide and designers are still trying to figure out the best ways to integrate AI functions.  It’s just the beginning of the cycle.
 

​LG Display (LPL) reported 1Q sales of 6.06 trillion won (~$4.24b US), down 22.6% q/q but up 15% y/y.  Operating profit was 33.5 billion won ($23.4m US), making it the 2nd profitable (operating) quarter in a row and the first time in 8 years that the company produced a profit in the 1st quarter, typically a weak quarter for panel sales.  The operating profit far exceeded expectations (loss of 30.7b won) with the outperformamce based on pre-tariff pull-in orders for both TVs and mobile displays.  As LG Display is ending large panel LCD TV panel production, the pull-ins (WOLED TV panels) caused the company’s percentage of OLED revenue to increase from 47% to 55% in 1Q.  Shipment area increased 1% y/y, but as the company closed the sale of its Guangzhou, China LCD panel fab at the end of the quarter, expectations are for a ~20% drop in shipment area in 2Q.  The offset to the shipment area decrease is a roughly 20% increase in selling price/area, as the less profitable LCD fab panel pricing will fall away in 2Q.
While the press focused on the company’s return to profitability in what is usually a weak quarter, we do note that company did report a net loss, although down considerably from the previous quarter.  The sale of the company’s Guangzhou large panel LCD fab to Chinastar (pvt) will improve profitability as (2023) the Guangzhou fab carried a 4.5% net margin.  The proceeds ($1.416b US) will, in part, be used to lower the company’s debt, although the payment schedule has not be revealed (The comment was “…a substantial amount will be received in 1H…”
The Q&A was, as expected, focused on tariff questions, which have little to do with LG Display as they do not ship displays directly to the US, but the company did state that they have not seen any major change in plans from customers and have not seen any increases in component price or any difficulty with availability.  That said, they did point to two display growth drivers that were part of expectations for the 2025 year, the end of Windows™ 10 and an overall replacement cycle for IT products, both of which they indicated have been super ceded by tariff questions.  There was little specific detail about plans for products other than the generic ‘watch carefully and continue to focus on profitability’, although in this environment, we did not expect much detail.
All in it was a relatively benign call with better than expected operating profit, but laden with caveats about the potential for volatility as the US trade situation develops.  Other than very general comments, there was little said about how much of the Guangzhou proceeds will be used to reduce debt, although management did reiterate that their earlier 2025 spending plans (low to mid 2 trillion won) plans were still valid (2.2 t won last year).  We give credit to management for sticking to a strict cost reduction program and getting close to actual net profitability, although we would have liked to hear more about their actual plans for each product segment, rather than the vanilla commentary on the call.

Recently we mentioned that from a carrier perspective the impact of 5G on consumers has been somewhat less than originally expected but has found its way into the business market where it serves as a connection topology for private networks.  These networks can span various business locations but are most effective as part of what are loosely known as ‘smart’ factories.  These facilities can range from small highly automated production centers to vast complexes that encompass everything from business offices to warehouses to large production and assembly operations. 
Before the smart factory concept came into being, production lines were hard wired, meaning that cables were run to various points around the production lines, either in a false floor or overhead.  Not only is this an expensive process, but if the line needs to be reconfigured at a later date, those cables have to be pulled and rerun to new locations, a slow process, and one that essentially limited the number of potential connections for the line.  A wireless network does not have those limitations as reconfiguring a line would mean just moving the sensor or terminal to a different point and reestablishing the network connection, which can take weeks off of a large production line reconfiguration project.
Where the comparison between 5G and a hardwired network on a factory floor becomes more difficult is bandwidth.  Ethernet (hard-wire) theoretically offers higher bandwidth, although 5G continues to offer more adaptable spectrum products. But what balances that give and take is the cost of installing a 5G network at ~50% of a hard-wired one, and is, as noted above, far easier and less expensive to reconfigure.  This weighs the choice more to the wireless side, especially as 5G bandwidth options get closer to matching ethernet.
So, what does a smart factory outfitted with a 5G private network actually look like?  Here’s a practical example:
Pegatron (4938.TT), one of Taiwan’s biggest OEMs, began construction on a greenfield factory in Batam, Indonesia, a few miles from Singapore across the Singapore Strait.  The factory, which is ~915,000 ft2 (23 acres) was built to consolidate the operations of 8 other factories and is Pegatron’s first in Southeast Asia.  The factory recently officially opened (actually started production at the end of last year) and has 26 active production lines with seven more to be built.  There are ~6,000 employees at the complex who produce ~2.5m units/month, primarily CPE equipment (80%) and network products (20%).  The factory has 3 floors that contain ~5,000 machines and 1,000 devices that are interconnected, including automated guide vehicles and cameras that monitor production processes across the floor, with 21 5G radio units as access points across the facility.  Each production line has terminals, cameras, sensors, and process machine output data connections that can be reviewed in almost any configuration.  One might want to see all of the data for a particular line or all of the data for a particular process across all the lines, and as digital wireless data, configuring such information for control panels or dashboards becomes easy.  One can even take camera information for a particular line or process and review it to see what caused a problem that appeared at the end of the line, or use the process review camera information to create training videos that reduce the need for hand-holding during new employee on-boarding.  The camera data and AR can also be used for repair and maintenance, but by far the most important ability for such a network is its cost and reconfiguration ability.
All in, whether consumers recofnize the merits of 5G or feel they should pay a premium seems a smaller issue when compared to the abilities that 5G bring to a smart factory like the Pegatron facility  in Batam.  The flexibility and low installation cost make it a no brainer for those interested in having a flexible and reconfigurable factory that can adapt to new customer demands or new processes.  The LinkedIn video below shows the factory and all the equipment operating under the 5G private network (2:33):
https://www.linkedin.com/posts/pegatron-5g_pegatron-5gs-new-smart-factory-in-indonesia-activity-7301915512660275200-TSaM?utm_source=share&utm_medium=member_desktop&rcm=ACoAAACZQsABSTeutynN9EcgEHCoOEmLOINmF7M
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When reading about phone or e-mail scams it is easy to wonder why someone might fall for such, considering how often we are warned about same and how often they present themselves, but far more people fall for scams than you might think, and they are not only the little old ladies in farmhouses that are usually pictured.  The FBI just released its 2024 Internet Crime Report that shows that US consumers lost a staggering $16.6 billion to-criminals last year, up 33% y/y on 859,532 complaints of which 29.8% had incurred actual losses.  The average loss was $19,372.  Over the last 5 years the FBI received 4.2 million complaints that resulted in $50.5 billion in losses.
While there were more complaints filed by those over 60 years old in 2024, and ~$4.5 billion in losses for that age group, the 40 – 50 age group racked up $2.2 billion in losses and the 30- 40 age group another $1.4 billion.  The 20 – 30 age group was the most careful, losing only $540 million ($7,563/incident report), while the under 20’s lost onl $22 million but averaged over $12,000/ incident report.

​On an overall basis the Internet Crime Complaint Center (IC3) received complaints for a very wide swath of internet crimes, ranging from the most common phishing/spoofing, where personal information is the most typical objective, to SIM Swap scams, where scammers try to transfer user’s account information to another phone.  True Cryptocurrency complaints were given special focus (would have been in the #2 position if included in the list by number of complaints and #1 by loss value), with 4,323 folks notified by the IC3 that they were involved in a crypto scam.  It turned out that 76% did not know they were being defrauded until they were notified, and those notifications  were able to save consumers over $285 million.

Most dangerous were data breach issues and ransomware threats made to critical infrastructure, with manufacturing (primarily data breach) and health care (both) being the biggest targets.  The IC3 was able to successfully freeze $496 million in US assets and $92 million in international assets relating to business e-mail scams for a 66% success rate.
The IC3 also  receives complaints from foreign countries but more relevant is the data on where fraudulent wire transactions were headed, surprisingly China was last on the list.
 
 

• Hong Kong                    27.2%
• Vietnam                         23.5%
• Mexico                            13.4%
• Philippines                    12.7%
• India                                11.8%
• China                              11.4%

 When one looks at the data for loss by age group, there are some obvious standouts, particularly for the 60+ crowd who seem very prone to falling for phishing and tech support scams, but the 40 to 50 year old group topped those falling for credit card scams, while 30 – 40 year old’s topped the list for e-mail scams and employment scams, and almost all groups seem to play along with overpayment scams, where payments are sent to individuals who are asked to forward the amount to another, while keeping a ‘fee’.  When the money is forwarded, the scammers use the account information to drain the scammed account.
The data gets even more granular, with state data showing that California individuals lost over $833 million, followed by Texas ($490m), Florida ($388m) and New York ($258m) and DC ($251m) as the top 5.  As noted earlier, crypto has unusual focus when it comes to internet crime and it is on the rise, with 2024 showing a 29% increase in crypto complaints and a 47% increase in crypto losses in 2024.  US Crypto scam losses amounted to $5.8 billion, with the 40 – 50 and the over 60 age groups hit the hardest, although the ability to access or send crypto via ATMs is beginning to give scammers an easier and less traceable way of getting payment for a number of scheme types.
The IC3 and the FBI generally receive thousands of complaints each day and while the impact on individuals, especially those most vulnerable is considerable, the overall impact of being able to integrate complaint data through the IC3, gives the FBI the ability to connect similar or identical complaints and find patterns that can be investigated, and if necessary, freeze funds for domestic and international entities that are consistent violators.  While scammers continue to evolve tactics to avoid broad detection, at least there is a part of the US government that is working solely for consumers without the influence of lobbyists and special interest groups as scammers don’t typically hire lobbyists…