Google (GOOG) has now (almost) joined the list of smartphone brands that have released foldable smartphones, with the announcement of the Google Pixel Fold, the company’s first foldable model.  With Samsung on its 4th yearly iteration, and foldables from 7 other major smartphone brands, Google, and Apple (AAPL) were among the few that have not joined the foldable craze.  The Pixel Fold itself, is as close as one might come to a Galaxy Z Fold 4 clone, with only fractional differences in size and features, and, not unsurprisingly, the same price as the Z Fold 4, although if you pre-order, you can get a Google watch for free.
As the Pixel Fold is still to be delivered there is little real-world data to see how consumers find the software and applications, but there is one feature that seems somewhat noteworthy.  When using the main screen to make a presentation, the presenter is able to see the same images on the back screen, giving the ability to make sure the verbiage is in sync with the display presentation.  It’s a simple application, but a helpful one that gives some indication that Google is thinking more about applications than hardware, which is likely a better direction for them to go, rather than battle Samsung point for point.
Growth expectations are considerable for foldable smartphones this year, with estimates ranging between 30% and 51% growth in unit’s y/y, but the absolute numbers are still relatively small, and even smaller looking when compared to the overall smartphone market.  Even at the top end of the growth estimates, foldable smartphone unit volume is likely to be less than 2%, but with the average smartphone selling for ~$300, foldables sell at a huge premium.  Samsung Electronics, who has been the leader in the foldable smartphone space for years, had an over 80% share last year and with such premium pricing, and a weak smartphone market, we expect more of the same this year, and likely a new model (aside from the Z Fold/Flip 5 series) for the holidays, which will reinvigorate the hardware competition between brands,  and potentially give those who have passed on foldables thus far, a reason to become more interested.

In the pre-GPS world, day-to-day navigation was done with a combination of landmarks, maps, signs, and a good sense of direction, a struggle for many who do not have that last item, but with the advent of GPS systems, the necessity for a good sense of direction has become almost moot, other than keeping you from driving into a lake when the navigation system is incorrect.  Life without GPS is difficult for those that have grown up with it, and the fact of not having your smartphone with you when traveling almost anywhere is testament to our dependency on GPS navigation, but there are alternatives on the horizon.
In VR systems it is absolutely necessary for the user (and the system) to know where they are spatially, not only for the safety of the VR user but for others that might be negatively affected by wildly flailing arms or other body parts, or for the possible destruction of nearby furniture or pets.  Kidding aside, there are a number of systems that are used to gather position information for VR systems, primarily the requirement to mark absolute boundaries before a VR session, or the system’s ability to sense object information around the user using RF, although more sophisticated sensing systems are needed for the advancement of VR into society.  That said, the characteristics needed for positioning in AR systems are far more related to everyday location information, as the evolution of AR systems into daily life will necessitate precise positioning information in addition to the visual information seen by the user.
Without location data the AR system will not be able to point you toward a particular destination or help you pick out your rideshare in an airport waiting line, and more importantly, it could point you in the wrong direction if being used in a driving situation.  We have noted that Google (GOOG) has been collecting visual data for its ‘Street View’ database since 2007, which it incorporates in Google Maps and Google Earth, and more recently made the 20+m GB of data and 10+m miles of roadway imagery available to developers under the “Live View” API, and while Google is the leader in non-GPS location data, they are certainly not alone.  Apple’s geo-anchors use the company’s “Look Around” data, movement indicators, and user imagery to develop a global 3D map, while Facebook (FB) focuses on “Live Maps”, a collection of physical and geometric information, along with a host of other social media oriented companies that are looking for ways to generate location data without using GPS.
The problem stems from the fact that the GPS system relies on signals from at least 4 of the 30+ GPS satellites that orbit our world.  There are instances when atmospheric conditions or signal blockage can compromise GPS signals and smartphone GPS data has a 4.9m (16 feet) radius, which could make it a bit difficult to pinpoint a specific parked vehicle or an item in a large warehouse.  Niantic (pvt) a small company that was spun out of Google, and financed by Google, Nintendo (7974.JP), and The Pokemon Company (pvt), recently bought 8th Wall (pvt), a company that created an interactive AR development platform that is browser based rather than a standalone application.  Niantic’s system is based on geometrics and the system’s understanding of its surroundings along with an understanding of objects themselves, essentially ‘does that object look like something in my database that is defined as a…’, with some of its data collected from Pokemon Go users, a vast network of users who play individually but are now able to play ‘in-network’.  As much of the game is based on finding Pokémon hidden in various locations, the visual data that is collected during the games is added to the Niantic database to build out the 3D maps.
While all of the players in the non-GPS location space are approaching it from different angles, the important factor is that the data is robust, giving object recognition and spatially oriented systems more information on which to rely, making their ability to recognize an exact location more precise.  As noted Google has a huge amount of data from which its systems can match,  and certainly has an advantage over smaller data sets, but tapping into social media or other image sources can build maps quickly with sophisticated algorithms, so there is still no foregone conclusion that one system will rule.  The good news is that will all of this data collection, and more socially acceptable hardware for AR, the idea that you could walk down a street wearing AR glasses that tell you how to get to your destination by painting a red dotted line on the sidewalk or indicating which way to go to find that food truck that used to be nearby, is getting to be more of a reality.

​There is a war going on that rarely makes it into the press as the battlefield is not on the ground, in the air, or in space, but inside the guts of massive processing nodes that are used to understand the nuance of language.  The algorithms that direct these processor are based on a subset of Ai that deals directly with syntax, expression, and a host of other language variables that make understanding other languages a challenge for humans and a monumental task for digital entities.  The opponents here are companies rather than political entities, and well-known ones at that, with Microsoft (MSFT), Google (GOOG) and Meta (FB) all pitted against each other with the focus of being the dominant force in the digital translation market.
There are no casualties or battle lines in this war, as it is fought with processing metrics and advertising, and that makes it hard to know who is winning, but the participants seem to have taken on a particular metric to make the public aware of who might be in the lead.  That metric is the number of languages a system can translate, which seems to be used a s a gauge toward whose system is the most advanced.  While the number of languages that a system can translate is certainly important, especially to those languages that might be considered secondary, but by far the most important metric for translation services is accuracy. 
Recently Meta indicated that its NLLB-200 Ai model has increased the number of languages it can translate to 200, which it accomplished in two years, while Google Translate is only able to work with 133 languages and Microsoft’s system translates only 111 languages, although that includes two Klingon dialects, and is clearly staking a claim as the world’s most advanced translation tool.  While the number of languages a system is able to detect is certainly easily understood by the general public, the quality of the translation, which is based on the algorithm and the sample base, is far more important and there are two ways in which that can be evaluated, by humans or by machines.  Using humans to evaluate translation quality throws subjectivity into the mix, while automated machine evaluation does not, but a machine evaluates a translation by averaging words and sentences against a human evaluation, with the idea that the closer the machine score is to a human score, the better the translation is.
With all of this being beyond the scope or desire of the general population, translation giants will continue to use the simplest metrics to give credence to their systems, but will have little correlation to real world results, as the ability of the AI to understand nuance and what to do with that understanding is really the key.  All three companies mentioned here have access to vast stores of speech, which certainly goes toward the ability of an AI to learn, but the algorithm is the key and that is something that not only grows with more resources but must change as humans better learn how to convert those subjective views into language that a machine can understand.  So the question is, does the AI need more language to read or does it need more human evaluations in order to take the subjectivity out of their evaluation?  The only way to know is…
E ninau i ka Mīkini - Hawaiian
Spurðu vélina - Icelandic
Faighnich dhan Inneal – Scottish Gaelic
quaeritur machina – Latin
Ask the Machine - English
 
 
 
 
 
 
 

​AR or Augmented Reality needs one very important piece of information if it is to provide a service to consumers, and that is where it is.  While you can see where you are through the AR lenses, if the system is to project other images or data onto your actual image, it has to know where in space the glasses are to position things correctly, especially directional information, which we expect to be an integral part of AR for consumers.  Without location data the AR system will not be able to point you toward a particular destination or help you pick out your rideshare in an airport waiting line, and more importantly, it could point you in the wrong direction if being used in a driving situation.  At a recent conference, Google (GOOG) highlighted its work in what it calls Google Live View for developers, which is based on Google’s Street View database, a 20m+ GB collection that comprises satellite imagery, geological surveys, municipal maps, and the 10m+ miles of roadway taken by its Street View cars, all combined to produce accurate maps, turn-by-turn navigation, business locations and real-time  traffic information, much of which is sourced from GPS enabled smartphone users, all of which will be licensed (API) to developers who wish to use this incredibly accurate mapping data.
While we expect Google will certainly be a player in the AR application market, the company is limited in what applications they can develop, both by resources and by the necessity not to undercut their client base, so by licensing this vast data resource, the API gives developers direct access without having to build their own interface which might include built-in sensors or ‘anchors’ that would have to be pre-defined and expensive and completed to develop on their own.  Google seems to have stepped forward as the de facto supplier of the geospatial information needed to make AR work, and is already using the API as part of its Live View mode inside the Google Maps application and as the AR Places filter in Google Lens.  The idea of the API is to free developers from having to build out their own spatial information interfaces by just using (and licensing) the Geospatial API.
GPS is commonly used for location data, but the accuracy of that data is not always accurate, especially in densely built-out areas, leading to between 5 and 10 meters of positional accuracy and 35 to 40⁰ of rotational accuracy, which could lead to AR images being out of view or the necessity for consumers to place anchors at a particular location to allow the system to have a point of reference, which might work well in a small space but not in a larger venue  By using the Google Geospatial API all of the amassed location and image data that Google has collected over the last 15 years becomes available to the developers application, at least anywhere where Google has StreetView data (not in Germany, North Korea or China and a number of other countries), which gives almost pinpoint accuracy and an easy way to guide users to location where they might spend some money.
After spending some time understanding how the Google API works from a technical standpoint, we were even more impressed than just listening to the company’s promo.  Without going into detail, the API links the user’s mobile camera to the company’s servers and matches the cameras image to the Google data.  Given that the StreetView data has been filtered by Google AI to eliminate non-stationary objects like cars and people, the algorithm matches the camera image by comparing against the buildings in its database, which includes buildings all over the world.  Once a match is made, the detailed coordinates are sent to the API from the Google servers and the AR application can then accurately overlay the new information on the camera image.  Considering the billions of buildings the algorithm has to ‘look at’ to match the camera image, the technology is quite incredible
While it might seem that we are favorable toward Google, as we have singled out their AR translation application previously and the API mentioned above, Google has amassed such a large volume of disparate data that it seems able to coordinate, that they can easily create applications and data for developers and customers that is unavailable from other sources.  In reality our favorable view of Google from an AR point of view comes from the fact that the company not only collects user information, as many social platforms do, but has been collecting other, sometimes seemingly strange data, for so many years that its databases are the most fertile grounds for AI learning, whether it be image related or data related, so the edge that it gives the company for things like the positional data mentioned above or the translation data we have mentioned in the past is a distinct advantage over other data collectors that have not been doing so for an extended period or to the extent Google has. 
As the globes primary search engine, the company has had access to so much consumer data that would likely be unavailable to others, it puts the company in the sights of those concerned with anti-competitive behavior.  But the fact that the company has had the foresight to spend the time and resources to collect what might have seemed irrelevant data is more the reason why it was collected and not as much for the world domination that some consider Google’s goal.  They had the opportunity and were willing to allocate the resources likely without the ultimate goal of using it for such specific applications.  That’s smart thinking, and while it might lead to world domination in data resources, it was good planning years ago that made it possible. 

While much of the press after Google’s (GOOG) I/O conference was focused on the potential for Google’s entry into the Smartwatch market and its potential to challenge Apple (AAPL), we noted that the company teased conference viewers with a quick look at a prototype of new AR glasses that are about as natural looking as one might hope and still provide the features of current AR glasses.  While the idea of ‘regular’ looking AR glasses was originally Google’s idea with the ill-fated “Google Glass” product concept, what underlies the style of the prototypes is their focus on utility, with Google looking to promote AR by making it more than a way to fit furniture into your living room.  The company ‘focused’ on language translation as an application ripe for AR, where all of Google’s work on translation technology is incorporated in the AR glasses, meaning you get an instant transcription of the conversation you are having if you speak the same language, and a fully translated, real-time transcription in your language if the speaker is not speaking your language.
While this is a prototype, the effect of having a direct translation of a conversation is enormous in our view and while these glasses and the applications associated with them are not an actual product yet, the implications for AR for just this application are enormous.  Merely by looking at a person during a conversation you can see the translated words in front of your eyes, which makes any conversation, regardless of the participant’s ability to translate, incredibly rich.  Of course there will be detractors that will say you will miss the nuance that speaking other languages might give, but for everyday conversations, including ASL, the short video below opened our eyes to this game changing application for AR.  Hopefully Google is far enough along to actually produce such a product sometime during the next few years as we believe it will have a last effect on both the AR world and those that face the challenges of speaking a single language in a multi-language world.  If you are linked to a browser check on YouTube when you click on the link below, click on ‘browse Youtube’ to see the actual video.
https://youtu.be/lj0bFX9HXeE
​

While not announced officially, it seems Google (GOOG) has bought a small California start-up, Raxium (pvt), that specializes in the development of micro-LED displays, particularly for AR applications.  The company’s technology is based on light field displays, which can produce 3D images without the optics normally associated with 3D viewing.  In a normal display the image is scanned by its volume (picture the slices of a loaf of bread), while in light field displays scan radially (picture layers of a cake), allowing at least two ‘layers’ to reach each eye.  Since those images are from slightly different locations on a horizontal plane, you are able to ‘see’ around an object just by moving your position.
One issue concerning light field imaging is that they require a large number of ‘elements’ in order to capture the ‘cake slices’, some 2 to 3 times what might normally be used in volumetric imaging, and that is where Micro-LEDs come into the picture, as they are considerably smaller than typical pixels and sub-pixels, allowing designers the ability to place a greater number of imaging elements in the same space as more typical self-emitting display systems.  That said, producing micro-LEDs and placing them on control circuitry is a difficult task and makes such a process an expensive one, which is reflected in the high cost of Micro-LED displays, and those are using much larger Micro-LEDs than would be needed here.  Raxium is also developing a process for producing ‘monolithic’ Micro-LEDs (see Figure 5), essentially LEDs that are produced directly on a silicon substrate, as opposed to sapphire substrates from which they have to be transferred.
The application that we expect has interested Google here is the use of light field imaging in an AR device, which would allow someone using such a device to see a far more realistic image overlaid on the real world, and in theory, the user should be able to see more than just a ‘flat’ image overlay, being able to look ‘around’ the object to get a better perspective on how it fits with the actual image he sees through the glasses.  The example might be an engineer wearing such glasses, who was replacing a part on a mechanical device, could bring in the image of a replacement part to see what other parts might need to be removed to install it, but would also be able to see around the part just by shifting his view, rather than having to load static images of the sides or back of the part. 
This is only one application of such an AR device and there are so many more that large company’s like Google, Apple (AAPL), Microsoft (MSFT), Meta (FB), and a slew of smaller companies have been trying to develop micro-display technology that will enable AR since ~2013, when Google announced the abortive ‘Google Glass’ device.  We expect that Google sees Raxium as another step toward such a device and we expect the valuation for Raxium might be high, but if it advances Googles efforts toward a ‘revolutionary’ AR device, the cost is worth it. In the long run.
 

We mentioned in our note yesterday that the hardware focus on Apple’s (AAPL) potential AR/VR products is missing the big picture surrounding the long-term viability of such a product’s ecosystem.  For others, especially smaller companies, AR/VR is a hardware game, and there is considerable competition between VR development teams and companies to provide the latest technology to differentiate their products and justify entering a smaller and likely less robust AR/VR environment.  As we have noted previously, we keep an AR/VR database, separating out those products that have been released from those that have been announced, given that some announced products are still unreleased more than a year after being announced.
One AR/VR product from a Prague based company called VRgineers (pvt), who showed their XTAL 3 Mixed Reality and Virtual Reality headsets at CES, and while both are still in pre-order mode, they have a release date of April 2022, only a few weeks away.  These headsets are the next generation following the company’s XTAL 8K headset released in 2020, with much of the design oriented toward the use of VR headset in pilot training.  While gaming VR headsets do have to meet demanding specifications, VR headsets used in pilot training are a bit more specific in use but more demanding in terms of their ability to interface with physical cockpit training systems, and in the case of the XTAL 8K and XTAL 3 headsets, they were designed in cooperation with the USAF and the Royal Air Force and interface with a wide variety of cockpit simulation hardware and software, something not part of most VR headset specifications.
These are not your typical $500 headsets, as the older XTAL 8K (currently on sale) model sells for $4,800, while the two newer versions sell for $8,900 and $11,500, so they are really at the top end of the AR/VR universe, but also not out of the range of some of the AR headsets that have been developed for industrial use.  That said, they have some interesting features that make them a bit different than other VR headsets and justify the price in the right environment, particularly eye and hand movement tracking and 4K resolution.
A number of VR headsets employ eye tracking, a technique that uses internally mounted cameras to measures the position of a reflection on the cornea of the eye (Fig. 1) (red arrow) against the center of the pupil (blue arrow) and calculates where the user is looking, regardless of head movement.  In most VR systems the data is used to move the user’s field of view in game software, so when the user looks to the side, the game view shifts the same way.  In the VR headsets mentioned above, not only does the eye tracking data re-locate the FOV, but the data is recorded and used to measure how long it takes a pilot to notice something appearing in the periphery, or how often they look at controls or other external objects.  Similar data is collected from controllers that evaluates hand motions and can give insight into how quickly a pilot reacts physically to visual stimuli.
Taking eye tracking out of the aerospace environment and into the Metaverse, eye tracking information can be used to give game developers ways to help you improve your gaming ability.  By tracking where you are looking during a game, the eye tracking information can adjust where you are throwing or shooting to more accurately align the shot.  But eye tracking information also gives clues as to emotion and reaction to various situations, which is the kind of data that can help data collectors build a more accurate model of you in the Metaverse, although when we say model, we don’t mean your avatar but more things like your level of excitement when viewing a new smartphone or piece of clothing, data that helps them ‘improve the user experience’ or in real terms produce a better selling environment.
This is just one small aspect of why on-line data collectors like Facebook (FB) and Google (GOOG) are excited about and promoting the Metaverse.  By increasing the amount of information a user generates, the value of the data is also increased, and while there will be much said about selling virtual real estate and other virtual items that don’t exist in the real world, the game remains the same as it is in the 2 dimensional internet, collect more data and sell it to folks so they can sell more stuff, whether its virtual or physical.

​Back in 2020 you had a small surgical procedure to relieve Carpal Tunnel Syndrome on your right hand.  Your insurance paid for the surgery and your 2 weeks of rehab, and you fully recovered, but you suddenly are receiving a bill from a collection agency that says you owe $675 to an anesthesiologist for services performed during the operation.  Since your insurance company was paying the bills directly to the billing hospital, you wonder why a collection agency is sending you a notice only to find out that for some reason the insurance company found that the anesthesiologist used the wrong code when filing the claim and the bill was never paid by the insurance company.  Since you signed a release before the operation, you are ultimately the responsible party and the monthly calls you get from the collection agency are becoming a major nuisance, despite your insistence that the insurance company is responsible for paying the bill.
That’s one kind of ‘collection’ that can become both an annoyance and a detriment to your mental and fiscal well-being, but there are innumerable ‘other’ types of collection agencies that know more about you than you do.  While we stare in horror at scenes of facial recognition systems in China identifying everyone crossing against the traffic lights in Beijing, we routinely allow detailed data collection every time we use social media.  But it goes further, as we habitually agree to ‘Terms of Service” that allows companies to not only monitor everything you do on-line, but to add to that database information collected by almost any company affiliated with that application, meaning any on-line company that uses the Facebook (FB) Pixel application to collect data and monitor their website traffic.
We did a bit of digging to actually try to understand at least some of the data that we give access to when we use social media, in this case Facebook, but we expect such terms and privacy policies are similar for Google (GOOG) and other on-line applications.  By agreeing to Facebook privacy policy you allow the company to collect information about any of the following:

• Information from your sign-up
• Any content you create
• Any content you share
• Any messages you send or receive
• Anything from the Facebook camera app, including being added to their facial recognition system, including people, places, accounts, tags, are groups you are connected with
• How you use content, including type of content, features used, actions, people, time, frequency of use, duration of use, including what posts, videos, content you view and any purchases or transactions you make including credit and debit card numbers, authentication information, billing and shipping addresses, and contracts you agree to
• Information that others provide about you, which can be from computers, phones, TV or other web devices
• Such external information can include device attributes, O/S, hardware & software versions, battery levels, signal strength, available storage, applications, file names, and mouse movements (!).  Information about your Wi-Fi , nearby Wi-Fi networks,  cell tower usage, mobile phone IP addresses is also included, although GPS, Camera, and Photo access can be limited if you choose the option
• Cookie data
• Information from partners that is collected by Facebook partners, even when off the Facebook application, including ‘likes’, ‘dislikes’, sites you visit, purchases you make and ads you see.

This barely scratches the surface of what you allow Facebook to collect about you, which makes those collection agency calls seem minor when you realize the huge profile of information you provide to social media companies.  Think of it as a file cabinet full of information about you that they have been accumulating since that time you first signed up, and we have not even begun to explore what they are allowed to do with the information that is collected, although there is a simple stipulation that says if the company is sold, all of that data goes to the new owners.  While the Google and Facebook algorithms use much of that data to offer you ads and suggestions that would lead to more data collection, they are not limited as to what the data can be used for, other than illegal purposes.  In fact much of the data is used for site analytics, but there is no limitation on the sale of such data or its use by affiliated companies. 
While this all might seem a paranoia fest, the US is particularly lax when it comes to personal information, especially when compared to the EU’s GPDR (General Data Protection Regulations) which force companies situated in or doing business in EU member countries to ask for more specific consent as to what they collect and what it is used for, with defaults limiting access to only basic information and operational cookies.  With all the data being collected, the potential for you information to be misused, hacked, or sold is enormous, which could lead to problems that are almost impossible to correct or even discover, and the Meta-verse, if or when it becomes a reality, will just give such collection agencies even more data to collect.  It was much harder to get purchaser information from brick and mortar retailers than it is to get it from on-line retailers.  Just think of how easy that data will be to collect when you wander through hundreds of Meta-verse ‘universes’ or outfit your personal universe with all of those things you can’t afford or could never own in the real world. 

​While all local smartphone markets have their own characteristics, the US smartphone market is quite different from other large markets.  It is expected to garner a ~10% share of the overall smartphone market with a population that represents ~4.2% of the global total, while China, with 18.3% of the global population, is expected to be 26.7% of global smartphone share at the end of this year, although both are expected to see negative growth this year.  In 3Q Samsung took the top share in the global smartphone market, although we expect Apple (AAPL) will take that position in 4Q on sales of the iPhone 13 series, but the number 3 brand globally, Xiaomi, doesn’t appear in the US, unless it falls into the ‘other’ category, which in total came to 7% in the US in 3Q.
In the US Apple was the share leader in 3Q at 42%, growing 3% from last year’s 3Q, while Samsung took a 35% share, up 5% from last year.  LG Electronics, who last year had a 13% share in the US market (3Q) is defunct, with much of that share falling to Apple and Samsung.  There were two Chinese brands that registered in the US smartphone market in 3Q, TCL and OnePlus (pvt), with 5% and 3% respectively, while Samsung did not register in China, although Apple had a 13% share in 3Q.  Oppo (pvt) and Vivo (pvt) were the two brands in China with the largest share in 3Q, at a combined 43%, both of whom are owned by the same private company, and Honor (pvt), formerly a Huawei (pvt) sub-brand, came in just above Apple (13%) and Xiaomi (14%) at 15%.
Last year in 3Q, the Chinese market was dominated by Huawei with a 35.6% share, which is now down to 8% because of trade restriction imposed by the US that limit the company’s ability to access Google (GOOG) store applications. So, while the US is a diverse smartphone market in terms of brand source, China is not, with 89.9% of new models released in 3Q coming from Chinese based brands.  With Apple and Motorola the only US based brands in the US market, there is plenty of room for competition for US smartphone dollars, although based on what is currently available from US carriers, there are really few choices other than Apple, Samsung, and Google, with a smattering of Nokia and last year’s LG phones.  

​Google (GOOG) is expected to release its latest Pixel smartphone, the Pixel 6 and Pixel 6 Pro at the end of this month, and has said to have placed orders for 7m units from suppliers.  While these are small numbers compared to smartphone giants like Samsung and Apple (AAPL), the tally for last year’s Pixel series sales were 3.7m units, putting the current order rate over 89% higher than last year.  Google’s optimism extends further in that they have been said to have placed orders for ~5m Pixel 5 series phones, which were released last year around this time.  The pixel line i(prices are expected to be $600 and $900)  is considered to be a  competitor to high-end flagship brand models at a lower price, but has yet to capture significant share of the smartphone market.  While pre-ordering has begun, we doubt initial order rates have had anything to do with pre-delivery orders and reflects Google’s optimism about its ability to gain share this year.  While they risk ~$1b to $1.5b with such a high order rate, it seems a bit small in perspective given their $182b sales base last year.