Defining the Future of Human Information Consumption

Human evolution depends on an ever-increasing rate of information creation and consumption. From communication to entertainment to education, the more information we create and consume, the stronger our society in total. Communication enhances community. Entertainment encourages creativity. Education builds knowledge. All of these elements build on top of one another like an upside-down pyramid, each new layer built a little bigger on top of the prior. It’s no coincidence that the Information Age of the last several decades has marked both the greatest period of increased information creation and consumption as well as, arguably, the greatest period of human progress.

The explosion of information created in the Information Age came with the tacit understanding that more information was good. The volume of information available to us is unprecedented, so the firehose is beneficial even if some (or perhaps much) of the water misses its target. The advancements of our technological devices that convey information have endeavored to bring the firehose closer and closer to us; from the non-portable PC, to the semi-portable laptop, to the nearly-omnipresent smartphone, to the emerging omnipresent wearable.

Now we’re continuously drowning in information.

The average global consumer spends 82 hours per week consuming information. Assuming an average of seven hours of sleep per night, this means that 69% of our waking hours are engaged in consuming information. For many consumers in developed markets, that number is likely closer to 100%. It’s not often many people disconnect from information sources. Even when we’re not in front of a screen, the nearest one is always in our pocket, or there’s music or a podcast playing in the background. As a result, we consume almost 90x more information in terms of bits today than we did in 1940 and 4x more than we did less than twenty years ago.

Source: Carat, Loup Ventures

Not only are we maxing out time available to spend with information, we’re creating so much information that it’s impossible to keep up. The ratio of the amount of information consumed per year by the average person to the amount created per year in total is the lowest it’s ever been by our analysis. There’s always more information to consume, and the trend toward autonomous systems is only going to amplify that.

Source: Loup Ventures

These two observations don’t paint a very positive picture of our relationship with information or our prospects for continued evolution, but in every challenge there’s an opportunity. We’re now preparing to exit the Information Age and enter two separate eras: The Automation Age and the Experiential Age. The Automation Age represents the natural continuation of the Information Age where artificially intelligent systems act on the vast amounts of information produced and quantity continues to dominate. The Experiential Age will define how the human relationship with information changes, creating new paradigms for communication, education, and entertainment. Out of evolutionary necessity, we will demand information with greater relevance, density, and usefulness than ever before. Meaning and efficiency, not quantity, will be the metrics on which we will measure information for human use over the next several decades.

Reintroducing Meaning + Information

Our current relationship with information is measured in quantity. Internet speeds are calculated in bits per second, as are hard drive speeds. Processor speeds are based on how many instructions they can perform per second, an instruction being a manipulation of bits. We even talk about how many messages we process a day, whether email, text, Snaps, etc. as a derivative of bits. We expect all of these quantitative measurements to improve over time, so we can consume more information faster.

One of the reasons we think about information in quantity is because of how information theory evolved. Claude Shannon developed information theory, built on work from Boole, Hartley, Wiener, and many others (upside down pyramid), to establish a method for accurately and efficiently transferring information in the form of a message from creator to receiver. His work on information theory yielded the bit and created the basis for modern computing. As Shannon developed his theory, he realized that the meaning of a message (the semantic problem) was irrelevant for establishing the efficient transfer of the characters that created the message, and therefore focused on the quantifiable aspects of the message instead.

It’s time to reintroduce meaning to information.

All meaning is established as a consumer’s interpretation of the creator’s intent (i.e. the creator’s version of meaning). The meaning of the same picture or movie or article may differ from person to person, even if the creator of the message might intend a single meaning. Since all interpreted meaning is some derivative of intended meaning, to contemplate “meaning”, you must contemplate both sides.

To help demonstrate the flow of meaning, it’s helpful to overlay the transfer of meaning on Shannon’s diagram from his groundbreaking piece on information theory:

The flow of meaning is analogous to the flow of information as diagrammed

by Shannon in “A Mathematical Theory of Communication”

A creator (human or machine) intends meaning in some message, which is conveyed to the consumer (human for our purposes) for interpretation via information transmission channels. A message is a container of information, which can be digital (e.g. a file) or physical (e.g. a book).

All intentionally transmitted messages must have some intended meaning, or the creator wouldn’t be able to conceive the message. Even a blank message is a reflection of the creator’s current mindset. Likewise, a message of random characters might imply boredom, or that a machine was programmed to do it, or it was a byproduct of some machine-learned behavior, etc. Messages transmitted by accident are free of this rule.

Since every message must be created with some initial meaning, all messages must therefore be interpreted because there is some underlying meaning to contemplate. A blank message might be interpreted as a mistake or a sign of trouble, a scrambled message might be a pocket dial, etc. This interpretation establishes the meaning from the message to the consumer.

In the process of transferring meaning, holistic noise impacts the interpretation of the intended meaning. It represents the natural disturbance between what a message creator intends and what the message consumer interprets. Holistic noise is created by differing contexts, that is, differing experiential, emotional, and physiological states between the creator and consumer.

Exploring meaning is a philosophical abyss — its precise characterization is engaging to discuss, but extremely hard to define. Nonetheless, the definition we’ve arrived at thus far is sufficient for establishing information utility, and further debate would distract us from the focus of defining utility.

Meaning/Time — The New Measure of Information

Thanks to Shannon, the quantity of information we can transfer is no longer a gating factor to the human consumption of information; the quantity we can process is. Therefore, the amount of meaning we derive from a message is now paramount, as is the time it takes to consume the message. To this end, we would propose that the value of a message in the Experiential Age should not be measured by bits in any way, but by meaning divided by time. We call this measure information utility:


Utility (U) of information is equal to the value of the meaning interpreted by the consumer of the information (m) divided by time it takes to consume the information (t).

Of course, meaning is abstract. How could you ever quantify it?

The closest we may be able to get is to borrow from marginal utility in economics. Just as we all have some innate scale with which we rank the value of consumer goods and that scale theoretically determines where we spend our money, we have a similar innate scale relative to the meaning of messages that should determine where we spend our time. We therefore measure meaning by time to help determine where best to spend our scarce asset of time just as we measure marginal utility by money to determine where best to spend our money.

Any individual’s scale for valuing meaning will likely be different, but that doesn’t matter. It only matters that we all have a scale that can consistently be used to compare the “meaningfulness” of the meaning we consume. In other words, we interpret the meaning of a message and then rank the perceived satisfaction or benefit we got from consuming it on an innate scale based on the aggregate of our prior experiences and expectations therefrom.

In reality, the scale is likely to be fuzzy (ordinal) rather than precise (cardinal). Most of us in any given moment wouldn’t be able to say message A is 11.7625% more meaningful than message B, but we would be able to say message A has more meaning than message B. That said, an information consumer can assign theoretical cardinal values to different messages based on the fuzzy scale, just as in marginal utility. Those cardinal values can be used to create a mathematical determination of information utility for the consumer.

Relevance and Compression: improving Information Utility

The next logical question is: How do we improve information utility?

The underlying intent of a message will always be the greatest factor in utility because all interpreted meaning (m) is a derivative of intent. A message of minimally useful or irrelevant intent will rarely be interpreted to have great meaning and thus usually have minimal utility. The answer may seem to be to improve the intent of the message; however, if you change the intent of a message, you change the message entirely, which is like telling the message creator to just say something else, ignoring their desire to convey their intended meaning.

Improving information utility should not rely on changing the intent of a message and instead focus on matching a consumer’s need or want with a creator’s intent and then optimizing the conveyance of that intent. This leaves us with two core opportunities to improve utility: relevance and compression. Relevance ensures that a consumer is engaging with useful messages while compression enhances the presentation of a specific message to increase meaningfulness. To maintain our hose analogy: Relevance improves the hydrant that the firehose is attached to and compression distills the water itself.

One may argue that relevance is just another measure of meaning since by definition for some message have meaning, it must be relevant to the consumer. However, to argue that relevance is meaning assumes that it can only be applied post message consumption. The problem is that once a message has been consumed, its determined relevance (and by transference, meaning m) is immutable for that given point in time; utility cannot be changed after the fact. Therefore, leveraging relevance to improve utility must happen prior to the message consumption.

We define relevance as the reduction of the total pool of potential messages available for consumption (the consideration set) prior to consumption.

Relevance reduces the information consideration set, compression enhances the information itself

Today, relevance is optimized largely by data companies like Google or Facebook. These platforms reduce some large consideration set of messages that may be relevant to the user into something more focused to help reduce the user’s time seeking meaning. The more these systems know about our preferences, the more relevant information they can funnel to us. Of course, this creates a double-edged sword related to privacy, but all technology comes with tradeoffs. While it seems the data wars have been won by the information giants, the privacy wars have just begun. The hidden opportunity here may be for some platform to put privacy first and balance the careful line between keeping consumer data safe while still presenting the most relevant information.

Less time searching for relevant information means more meaning divided by time by avoiding less useful messages in the consideration set. The time saved can be spent consuming other relevant information, creating a greater aggregate value utility given time spent. Note that our version of relevance does not influence m/t for any specific message, only the set of messages considered; only compression can do that.

We define compression as a direct improvement to a given message that either enhances the meaning intended by the creator and/or decreases the time spent consuming it.

While the interpretation of the consumer is what ultimately establishes meaning for calculating utility, the compression of meaning depends on enhancing the intention of the creator and its consequent effect on the consumer’s interpretation. Compression doesn’t change the intent of a message, but makes it clearer and more useful, usually reducing holistic noise in the process. An example might be a set of written directions to a store vs a map. The underlying intent is the same, but the latter message should offer more utility provided the map is legible and accurate.

Historically, compression has happened via transitions to different information formats. With the advent of the written language, we went from the imprecise spoken word to the more precise written word. Writing a book or a letter is a form of compression relative to the spoken word because more thought and contemplation generally goes into writing vs discourse. Greater contemplation of the message should result in greater meaning of the message. More recently, the shift from written word to video is another example of relative compression. Video conveys relatively more meaning with less holistic noise than written words because the images help an information consumer see body language, emotion, and other factors that can be difficult to ascertain through words alone. This doesn’t mean that video is the perfect medium of transmission for every message — the written word can be effective for certain purposes — but there is relative compression when comparing writing as a message format vs video as a message format.

Compressing relatively more meaning into less time will be a prominent factor in the Experiential Age. If we can get more meaning in less time, we will spend more time with the technology that delivers that benefit. To that end, there are several emerging opportunities around compression:

Stacking Information Purposes. Content formats like GIFs and emoji combine communication and entertainment by replacing text. When a message creator picks a specific GIF or emoji to express themselves, it carries not only the underlying intended message, but an added element of emotion and entertainment. These added elements not only increase the meaning transferred, but also reduce the holistic noise between the message creator and consumer. Most obviously, when someone sends a GIF of a shaking fist or a coffin emoji, they probably aren’t seriously mad.

Platform-Enforced Time Restriction. While some content platforms are moving away from time restriction, like Instagram and Twitter, placing an artificial cap on the length of a message will always compress meaning. When an information creator is forced to deliver his or her message in limited time like a 140-character tweet or a six-second Vine (maybe now v2), it demands creativity and precision that results in greater meaning for the consumer. This incurs an additional cost of time to the creator to make their message succinct but still meaningful; however, the burden of quality in a message is always on the creator, and perhaps many messages aren’t worth the time to send in the first place.

Consumer-Enforced Time Restriction. In some cases, information consumers put the restrictions on time they’re willing to spend consuming a creator’s message. This is most apparent in the shift to digital assistants. When an assistant answers a consumer’s query, the consumer is not willing to listen to a painfully long list of ten possible answers. They expect one answer; the right answer. In this sense, the consumer shifts the burden of time he or she previously spent deciding the best answer from a set of options back to the creator that established the options in the first place (i.e. Google, Amazon, Apple, etc.). Relevance has always been important in the consumer Internet, but increasingly so here. Companies with extremely large customer datasets are the only ones able to play in this game because data allows the digital assistant companies to infer the context of the consumer and tailor messages appropriately.

Information Layering. The emergence of augmented reality (AR) has enabled the concept of information layering. Now a picture or video can be enhanced by stickers or lenses that add additional information related to the original purpose of the content, most commonly entertainment today. The layered information acts somewhat similarly to stacking information purposes in reducing holistic noise by adding context. As AR continues to evolve, the layered information can become more educational or instructional. For example, AR can compress knowledge about how to do something from a book or 2D video that suffered from the friction of transferring that meaning to the real world, to overlaying that meaning on the real world itself; another example of reducing the holistic noise between creator and consumer and improving meaning in time.

Scarcity. By limiting the number of times someone can send a message or components of a message, content platforms can create artificial scarcity similar to a time restriction. In this case, the restriction applies to the meaning side of the equation. If a message creator chooses to leverage a limited resource in a message (other than time), it should confer greater meaning to the consumer because it comes at an opportunity cost to the creator. An example might be some of the frameworks emerging around the ownership of unique digital goods, which could extend into transference of meaning through those goods. When a creator includes a one-of-a-kind component to a message, the intended meaning increases, which should also increase interpreted meaning.

Time Manipulation. Since the majority of content we consume is digital, much of it can be sped up or slowed down according to the consumer’s preference (i.e. videos, audiobooks, podcasts, etc.). Time manipulation requires no additional effort on the side of the creator; it only requires the consumption tool to enable it. While time manipulation techniques are effective on an individual level, they’re less interesting than the other opportunities because they’re optional to the consumer where the others are common across all consumers.

Brain-Computer Interface (BCI). The development of brain-computer interfaces will, in theory, create a direct way to receive information from and transmit information to the brain. A deeper understanding of how we create meaning from a neurological perspective is embedded in this future, thus BCI should reduce holistic noise and enhance meaning. An example may be that a message transmission mechanism could understand the contextual state of both the message creator and consumer and alter the message as appropriate in an effort to match states. While BCI is exciting technology, it has a long clinical path that is just beginning before we can consider using it for meaningful augmentation.

It bears repeating the message creator’s intent is paramount to utility. A useless message is still useless no matter how you dress it up. However, a more compressed version of a given message should always be more useful than a less compressed one given the same baseline intent, so we should strive to compress messages as much as possible.

The current opportunities in compressing information should be relevant for the next 5-10 years or more, particularly in the case of BCI. That said, the aforementioned concepts and technologies compress information relative to how we consume it today. As they become the standard for future information transmission, the bar must move correspondingly higher, and we will need to find new ways to compress information even more. Therefore, while the human desire for increasing information utility is eternally valid, how we compress more meaning into less time must always evolve.

Embracing Information Utility

It’s popular to lament our overexposure to information, ever-shortening attention spans, and superficiality of what we create and consume, but technology has always been a Pandora’s Box. Now that we’ve gone down the path of near-constant connectivity, it’s impossible to walk it back. While it can be beneficial to spend less time with information when possible, we have to embrace the path we’ve created; embrace the unending flow of information. Discovering ways to leverage new content formats and technologies in ways to transmit useful information is our future. Demanding more meaning from that information we choose to spend time with is the next evolutionary step for humanity.

Improvements in relevance will continue to be driven by technology, but as we think about the opportunities in compression, it’s clear that utility will not be driven purely by hardcore technologists, but also by hardcore creatives. Injecting more creativity into how we convey meaning, something that is unique to the human experience, make up some of the biggest opportunities in improving information utility. After all, the humans that consume information determine the utility of it, not robots.

Some people fear the coming Automation Age will devalue humanity, removing purpose and meaning from our lives. Luckily, we have this survival instinct — this innate need for more information, more knowledge. That curiosity means there will always be a purpose for us. As automation frees us from the constraints of labor, the Experiential Age will bring about a golden era of exploring what it means to be human, and information utility will guide the way.

Disclaimer: We actively write about the themes in which we invest: virtual reality, augmented reality, artificial intelligence, and robotics. From time to time, we will write about companies that are in our portfolio. Content on this site including opinions on specific themes in technology, market estimates, and estimates and commentary regarding publicly traded or private companies is not intended for use in making investment decisions. We hold no obligation to update any of our projections. We express no warranties about any estimates or opinions we make.

Investing in Paradromics

Neurotechnology has long been a passion for Loup Ventures because we think it represents a vast and open opportunity to improve human life via both restoration and augmentation. Brain-computer interfaces are one of the most compelling areas in the neurotech space and we’re excited to invest in Paradromics as they work to capitalize on that opportunity.

To date, many of the BCI solutions have been relatively low bandwidth, leveraging surface electrodes or other non-invasive methods to collect signals from the brain. Paradromics, along with Neuralink (Elon Musk), is one of only a few companies that are attacking an emerging theme around high-bandwidth, invasive brain-computer interfaces.

We’re investing in Paradromics as a play on two of our thematic focus areas: AI and experience.

AI. Paradromics is a hardware and software company. The company creates a device that consists of an implantable microwire array that allows for the high-density collection of neuronal data. The brain has ~100 billion neurons. The current standard in implantable BCI devices, the Utah Array, has about 100 channels (connection points to the brain) and each channel connects with a handful of neurons. Paradromics’ solution currently has over 65,000 channels, and the company’s goal is to connect with over a million neurons.

The collection of such a large array of data presents a unique challenge in that tens of thousands of connection points with the brain yields massive amounts of data to process (tens of GB/sec). To put that in perspective, average high-speed Internet connections are around 20 MB/sec or more than 1,000 times slower. To address this, Paradromics has developed a proprietary tool that allows them to more efficiently process certain elements of the signal to allow for high-resolution analysis of a neuronal signal while compressing the data transferred to more manageable levels.

Experience. In our Manifesto, we outlined the future of computer interfaces moving from phones, to wearables, to implants. Paradromics is also a bet on the last of the three stages. To the extent that we can read (and eventually write to) the brain, we can achieve our long-term vision of a world where it’s possible to create life-like experiences directly via brain-computer interface. This augmentative future is far away, and initial use cases will center on therapeutic outcomes, but we believe the groundwork will be established now to achieve this goal.

Paradromics is attacking the true frontier of frontier technology. Their service has the potential to change human lives near-term for those afflicted with disorders of the brain, as well as long-term as an augmentative tool. We believe the therapeutic market is likely several billion dollars in opportunity, while the augmentative opportunity may be significantly larger.

Disclaimer: We actively write about the themes in which we invest: virtual reality, augmented reality, artificial intelligence, and robotics. From time to time, we will write about companies that are in our portfolio.  Content on this site including opinions on specific themes in technology, market estimates, and estimates and commentary regarding publicly traded or private companies is not intended for use in making investment decisions. We hold no obligation to update any of our projections. We express no warranties about any estimates or opinions we make. 

WWDC 2018: The Customer Is Always Right

  • Today, Apple said loudly and clearly that the customer is always right.
  • There were more new features for users than there were new tools for developers: screen time limits, monitoring, and reports, grouped notifications, Do Not Disturb at bedtime, Siri shortcuts, new Safari privacy features, performance improvements for previous generation iPhones, and even third-party navigation apps on CarPlay. Apple is forgoing near-term benefits for developers and themselves in favor of a better user experience.
  • Apple is drawing a hard line between itself and other companies that rely on consumer data.
  • There are now 20M registered iOS developers building applications for 1.3B active devices.

Source: iMore

Democratizing Machine Learning. Apple spent the bulk of its limited developer-centric discussion on new ML tools. Specifically, Create ML on the Mac and Core ML 2 for iOS, which make it easy for developers to build ML techniques into their apps. When thinking about the ML announcements at WWDC, it’s important to note that the keynote is designed to inspire developers and the “state of the union” session (which followed the keynote) shows developers how to actually use these tools. As investors in the space, we’re excited to see Apple making it easy for entrepreneurs to harness the power of ML on Apple’s 1.3B+ device ecosystem.

As investors in the space, we’re excited to see Apple making it easy for entrepreneurs to harness the power of ML on Apple’s 1.3B+ device ecosystem.

No New Hardware. Apple will take some heat for the lack of hardware product announcements, but we did a quick check and found that 11 of the past 19 WWDC keynotes have not included any hardware announcements. We didn’t see a new iPad (which we continue to expect in the coming months), and any discussion of new Beats hardware with Siri integration was nowhere to be found 🤦‍♂️.

At the beginning of the keynote, Cook declared that WWDC 2018 was “all about software.” And he kept his promise. iOS, macOS, tvOS, watchOS – each one saw improvements that make those product lines more appealing. In many ways, Apple finished what they started with the software updates announced at WWDC 2017.

ARKit 2: a measurable step forward, but we’re not there yet. We are still believers that AR will transform human interaction, but it will take time. ARKit 2 is a measurable step forward, making it easier for developers to build compelling experiences with the additions of multiplayer sessions (allowing two or more people to share an AR experience) and a new file format (USDZ), which allows you to add AR content to existing media formats. While these two additions will clearly streamline AR development, mobile AR tech still lacks “persistence” (the ability of a virtual object to remain in place after a session has ended), as well as the mapping of the AR cloud (managing virtual data and privacy).

Apple Watch Improvements. Apple Watch is running away with the wearable space. Today, Tim Cook announced Apple Watch grew units by 60% last year (2017). While Apple Watch had a slow start in 2015, it appears to be picking up momentum. Apple doesn’t disclose the number of watches sold, but we estimate, in 2015, the company sold 5.7M, compared to 10.2M in 2016, and 16.1M in 2017. We believe that number will increase by 44% in CY18. We expect the Apple Watch business to grow in the mid-to-low 20% range through 2020, which implies Apple Watch will account for 6% of revenue in 2020 compared to 3% in 2017. Apple Watch is gaining momentum because Apple created the computer-on-your-wrist category allowing for significantly more advanced functionality compared to other wearables. For example, today, Apple announced walkie-talkie, new personal and group fitness features, Siri’s accelerometer integration, and a handful of Universities enabling student IDs on Apple Watch. Apple Watch’s measurable utility lead in the wearable space gives us confidence that the product can account for 31M units in 2020, nearly double the units sold in 2017.

Expect $32B in Apple Developer earnings in 2018. Apple announced that developers have earned over $100B since the App Store launched in 2008. That compares to $86B in earnings at the end of 2017, and $70B a year ago (June 2017).  While Apple reported that developer earnings grew just over 30% in 2017, we expect that growth to be closer to 20% in 2018, in line with the overall growth of Services. This implies that developers will earn about $32B this year, a number that we believe is big enough to continue to entice world-class developers to continue to code on iOS and macOS.

Disclaimer: We actively write about the themes in which we invest: virtual reality, augmented reality, artificial intelligence, and robotics. From time to time, we will write about companies that are in our portfolio. Content on this site including opinions on specific themes in technology, market estimates, and estimates and commentary regarding publicly traded or private companies is not intended for use in making investment decisions. We hold no obligation to update any of our projections. We express no warranties about any estimates or opinions we make.

WWDC Preview; Siri, AR, AI, Digital Health

  • Apple’s annual developer conference starts next Monday, June 4th.
  • Consistent with past WWDC’s, announcements will be software heavy. Most notably a preview of iOS 12 and the upcoming MacOS.
  • We expect Monday’s keynote to be highlighted by extending the reach of Siri (most likely adding new domains, opening HomePod to more capabilities, and integrating Spotlight), along with additional AI tools (new Core ML extensions).
  • We also anticipate new features around digital health (privacy and device management) and ARKit (development tools).
  • Expect Siri integration with Beats.
  • Collectively, these announcements advance the ease of use and intelligence of Apple’s mobile and desktop experiences.

Consistent with past WWDC’s, announcements will be software heavy. We expect the tone of this year’s developer conference to be similar to past years. Since 2000, we counted 47 software related announcements made at WWDC, 11 new hardware announcements, and 8 hardware update announcements. That compares to the past five years with 19 software, 3 new hardware, and 6 hardware updates. This year we are expecting 5 software announcements and 1 hardware-related announcement. 

New Siri domains. In our testing of Siri over the past two years, we found the product lags measurably behind Google Home and marginally behind Alexa and Cortana. In our December-17, 800 question Siri test, she was able to correctly answer 75% of questions compared to 66% in April-17. Siri would have been able to answer about 85% correct if she was more competent within commerce and information. That 85% would essentially be on par with Google Assistant. Siri on HomePod is more limited in the number of domains, so adding support for things like navigation and email would quickly improve the experience. Siri can also improve the information utility by simply integrating Spotlight Search.

AI extensions. At the 2017 WWDC Apple announced Core ML. Core ML is a machine learning framework that sits beneath apps and third-party, domain-specific AI models, but above processing hardware inside of a Mac, iPhone, iPad, Apple Watch, or Apple TV. Core ML allows app developers to easily incorporate third-party AI models into their apps. App developers don’t need to be experts in ML to deliver an experience powered by the technology within their app. In other words, Apple will take care of the technical side of incorporating ML, which allows developers to focus on building user experiences. Core ML currently has the 15 domains listed below. We expect new domains to be announced at this year’s WWDC. Digital Health. Apple has been a leader in the privacy movement. We expect further announcements related to new features that notify users when their data is being shared with developers. Additionally, iOS 12 will likely have new device management features to curb screen time and digital anxiety.

ARKit. We remain optimistic regarding AR’s potential. That said, the use cases of AR to date have lagged our expectations, due to a lack of reliable hardware and software to enable developers to build compelling AR experiences. We expect Apple to announce subtle new developer tools to improve the AR development process and ultimately yield more compelling AR applications.

Siri integration with Beats. The knock on HomePod is its $349 price is about 2-3x the price of a typical smart speaker. We believe Apple can advance its digital assistant ambitions with a $250 Beats-branded option that does not compromise HomePod’s $349 price point. We are currently modeling for HomePod to have a low-to-mid teens digital assistant market share, and this Beats integration does not change our market share outlook.

Disclaimer: We actively write about the themes in which we invest: virtual reality, augmented reality, artificial intelligence, and robotics. From time to time, we will write about companies that are in our portfolio. Content on this site including opinions on specific themes in technology, market estimates, and estimates and commentary regarding publicly traded or private companies is not intended for use in making investment decisions. We hold no obligation to update any of our projections. We express no warranties about any estimates or opinions we make.

Autonomy & Apple as a Service

  • The New York Times reported that Apple has signed a deal with Volkswagen to manufacture electric T6 Transporter vans outfitted with Apple’s autonomous sensor suite to be used as self-driving shuttles for employees.
  • This is significant because it plays into the fourth pillar of our Apple as a Service investment thesis: optionality.
  • Autonomy is one component of optionality that is currently not reflected in Apple’s share price along with AR, original content, and health.
  • Coming soon: We’re working on a sensitivity analysis to frame up Apple’s opportunity in autonomous mobility.

As Apple’s market cap approaches $1T, it begs the question: can shares move higher? At Loup Ventures we believe the Apple story is well positioned for future appreciation based on a long-term, sustainable investing paradigm. We call this new paradigm ‘Apple as a Service,’ which includes four pillars: stable iPhone, Services, returning cash to investors, and optionality (AR, content, health, and autonomy). Yesterday’s New York Times report on Apple’s deal with Volkswagen to build autonomous vehicles gives us some clarity regarding the optionality component to Apple as a Service. Investors are currently not giving Apple shares credit, given it’s nearly impossible to model. Eventually, that tide will change, and we expect shares of AAPL to benefit from this opportunity.

What has been said? The Times report detailed Apple’s plans to build a small network of autonomous shuttles for inter-campus employee transport, now with the manufacturing muscle of Volkswagen Group. The report also said this project, which is long overdue, is taking up nearly all the attention of Apple’s car team, so it is reasonable to assume that the project will progress quickly. The T6 Transporter’s frame, wheels, and chassis will remain intact, but Apple will no doubt make serious changes to interior and exterior design elements, along with adding computing power, sensors, and an electric drivetrain (unclear from who).

Why Apple has an interest in autonomy. We believe Apple’s endgame is a software and services platform enabling autonomous mobility fleets. The concept of an autonomous service is a departure from Apple’s current hardware and content services business. Specifically, delivering their experience through third party hardware is a strategy that Apple rarely employs. That said, we believe, given the complexities of manufacturing a car (just ask Tesla) and the size of the opportunity, it makes sense for Apple to partner their way to autonomy.

The fruit of the Volkswagen/Apple partnership will likely yield an Apple-like experience based on the Times’ report that Apple’s talks with other automakers were ended due to disagreements on who would own the customer experience and data. This leads us to believe that Apple will have a considerable amount of input and control over the design and experience of the end product.

Tim Cook has said, “We are focusing on autonomous systems…It’s a core technology that we view as very important…We sort of see it as the mother of all AI projects.” Today those efforts are manifested in an autonomous shuttle for internal employee transportation, but this undoubtedly serves as a controlled proving ground for broader ambitions.

Disclaimer: We actively write about the themes in which we invest: virtual reality, augmented reality, artificial intelligence, and robotics. From time to time, we will write about companies that are in our portfolio.  Content on this site including opinions on specific themes in technology, market estimates, and estimates and commentary regarding publicly traded or private companies is not intended for use in making investment decisions. We hold no obligation to update any of our projections. We express no warranties about any estimates or opinions we make.