Comforting Confirmation around Separation of Google’s Search and Privacy

  • Google reported Mar-18 results effectively in-line with the Street. The company does not provide guidance.
  • Most notable on the earnings call was CEO Sundar Pichai’s comment that search (90% of Google’s revenue) relies heavily on keywords and less on private data, reassuring that in a stricter privacy environment, Google’s core business should largely be safe.
  • The company did not comment if they expect a stricter privacy oversight environment in the future.
  • Google stayed heavy on the AI theme for the sixth consecutive quarter, mentioning it 25 times vs. 18 last quarter.
  • While AI can be an abstract concept, it’s currently used in almost every Google product and service today and will be increasingly in the future.

Net revenue growth continues at impressive clip. While investors have largely viewed the Mar-18 quarter as a mixed bag, we believe the key takeaway is that the company continues to defy the law of large numbers when it comes to revenue growth. Specifically, net revenue growth of 23% is in line with growth over the previous six quarters (21-24% range), despite the fact that Google’s revenue run rate has increased by about 73% during that time.

The elephant in the room, GDPR. The anticipation around Google’s earnings call was centered on the company’s reaction to concerns about the use of personal data in the wake of Facebook and, more importantly, how data privacy will impact the Google brand and monetization going forward. The company indicated on the call that they will be fully compliant with GDPR which they have been working toward for the past 18 months. They stopped short of speculation regarding potential changes.

Search as a safe haven. Our biggest takeaway was Sundar confirming that search relies less on personal data and more on keywords. The reason for this is, rather than relying on tracking personal data and inferring, the user offers a query that determines intent. This perspective should provide some insulation to Google’s earnings multiple as investors will continue to brace for a stricter privacy regulatory environment over the next year.

Moving AI from abstract to concrete. Google, more than any other company, throws around the term “AI.” It’s for good reason, given that artificial intelligence enhances almost all of their products and services. Take, for example, a simple search – AI can tie in the location of the search query to yield a more contextually aware answer. What is trending on Google search can influence recommendations on Youtube. Some more obvious AI use cases are natural language processing with Google Home and Assistant, Google Photos’ computer vision that is able to recognize your friends and pets, and Waymo’s self-driving software that can safely operate a vehicle on public roads. In the future, more advanced AI will improve the quality and ubiquity of all of these experiences. At the end of the day, we’re delighted that Google can’t stop talking about their progress in AI.

We believe Waymo will eventually be Google’s third largest business. Google reported respectable progress in the race for autonomous vehicles with its recent disclosure of Waymo’s 5 millionth mile on public roads, with the last million coming in the Mar-18 quarter. It’s hard to quantify who the number two player is in miles driven, but safe to say Waymo’s lead is sizable. We think this lead will eventually translate into a full-scale business of an autonomous ride-hailing network and an operating system that can be used across other platforms. Apparently, Cloud is their third largest business and accounts for about 5% of revenue. While we expect healthy (35-45%) growth over the next several years, we anticipate the Waymo opportunity will surpass Cloud in terms of gross revenue over the next decade. It’s too early to determine how Waymo will stack up in terms of profitability. This suggests that in 2028, Google’s four largest segments will be Search, YouTube, Waymo, and Cloud.

Tracking Google’s AI focus. Over the past six quarters, we have tracked how many times presenters and analysts have made comments about AI including instances of words like AI, artificial intelligence, machine learning, natural language processing, or Tensor Flow.

Over the same timeframe, we have noted Sundar’s opening remarks and the urgency with which he brings up Google’s efforts in AI. In each of the last six quarters, AI is mentioned in the first several sentences. Below is a look at his opening remarks over time.

Disclaimer: We actively write about the themes in which we invest: artificial intelligence, robotics, virtual reality, and augmented reality. 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.

Adrenaline Shots for Apple AI

  • Apple has been criticized for not doing enough in AI. Two recent announcements show the company is closing the gap.
  • In the past two weeks, the company has announced the hiring of Google’s AI head, and an AI partnership with IBM.
  • Google’s AI head (John Giannandrea) brings credibility to Apple AI, critical in recruiting, and is likely work on AI-powered interfaces and Apple’s self-driving car program.
  • IBM partnership allows iOS developers access to IBM Watson’s enterprise machine learning, and use it to make smarter AI apps.

Core ML 101. At WWDC 2017 Apple unveiled Core ML, a platform that allows developers to integrate machine learning into an app. The AI model runs locally on iOS and does not need the cloud. At the time of the announcement, Apple outlined 15 domains for which they have created ML models, such as face detection, text summarization, and image captioning.

IBM Watson and Apple announcement. Two weeks ago Apple and IBM announced they will integrate IBM Watson with Apple Core ML. Previously, developers could convert AI models built on other third-party platforms, like TensorFlow (Google) or Azure ML (Microsoft) into Core ML, and then insert that model into an iOS app. Now developers will be able to use Watson to build the machine learning model, convert it to Core ML, and then feed the data back to Watson’s cloud. The reason why this is important is it allows iOS developers to leverage Watson’s capabilities and ultimately improve the AI in iOS apps.

Watson works locally on iOS and improves apps. What’s unique about Core ML is it runs locally on mobile devices, meaning it doesn’t have to send data back to a server. This is different than other mobile AI approaches. Running locally is an advantage when the speed of AI is important, like image recognition in AR or natural language processing. What’s new is Watson will be able to “teach” Core ML to run the AI model built with Watson. Basically, Watson does the hard work of getting a usable AI model built and then teaches it to Core ML, who can then run the model locally on its own. The app can then send data on the model’s performance back to Watson, at any time, to be analyzed for available improvements.

Recent history of Apple and IBM. In July 2014, Apple and IBM partnered to create enterprise applications on iOS devices, leveraging IBM’s big data and analytics and Apple’s hardware-software integration. IBM started selling iPhones and iPads to clients that came with software and applications for enterprise designed with Apple’s help.

Summary of big tech’s machine learning services. 

Disclaimer: We actively write about the themes in which we invest: artificial intelligence, robotics, virtual reality, and augmented reality. 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.

Apple’s AI Coup

  • Apple has hired John Giannandrea who formerly served as Google’s head of AI and Search.
  • Given the industry’s shortage of AI talent, Giannandrea brings expertise along with credibility, critical in recruiting.
  • Giannandrea will likely be working on AI-powered interfaces that will replace the touchscreen and iOS, like augmented reality wearable. Separately, AI related to Apple’s self-driving car program (PAIL) will likely fall under Giannandrea.

What this means for Apple, recruiting more AI talent. It’s a win. Talent follows talent, and John Giannandrea will no doubt help to build Apple’s AI brand and enhance future recruiting efforts. His shared vision on privacy is good news for a company who claims to be the vanguard of user security. In the meantime, Google will maintain its strength in AI, given they are still an “AI first company” and have tremendous AI and deep learning horsepower with their Google Brain and DeepMind teams. Jeff Dean, the founder of Google Brain, has taken over as the head of their AI department in a “reshuffling” making AI a more central part of their business. Will Google employees follow in Giannandrea’s footsteps? There will probably be a few, but the competition is fierce, and this will not be the last major AI trade.

Why did Giannandrea come to Apple? Most likely – projects, pay, and privacy. As one of the most senior experts in arguably the most in-demand field in the world, the conversation around compensation was probably short. Giannandrea may be given freedom to work on projects he is more passionate about and have the chance to build something new. In an email obtained by the New York Times, Cook praised Giannandrea saying, “John shares our commitment to privacy and our thoughtful approach as we make computers even smarter and more personal. Our technology must be infused with the values we all hold dear.” That affinity for privacy may have steered him to Apple at a time when concerns have never been higher.

What will he do? It’s easy to think about how Google uses AI (search, image rec., voice, etc.) but Apple’s use cases are more abstract. If you consider the user interfaces that will replace the touchscreen and iOS, like augmented reality wearables, it becomes more clear why AI is critical. Just as multi-touch was a core technology enabling the iPhone, AI will be a core technology enabling the operating systems of the future. For example, wearables like AR glasses or even AirPods will heavily rely on AI-driven functionality like image recognition, ambient listening, and smart notifications. In other words, these devices need to know what you want and when you want it. With our phones, we directly control the information that we want when we want it; in the future of computing, AI will anticipate the same information. We expect Giannandrea to address these opportunities as well as bolster Apple’s overall AI prowess, overseeing AI initiatives like Siri, Core ML, and the deliberately under-the-radar autonomy project.

Disclaimer: We actively write about the themes in which we invest: artificial intelligence, robotics, virtual reality, and augmented reality. 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.

AI Is Pulling Off Its Own March Madness Upset

In what is becoming an annual tradition, we entered a bracket chosen by artificial intelligence into the Loup Ventures March Madness pool. (See our 2017 results here) Given this year has proven especially challenging to predict due to the uncanny number of upsets, Microsoft Bing Predicts’ bracket is in surprisingly good shape. Heading into the Final Four this weekend, it is tied for 2nd place out of 13 entrants. Here’s a look at its picks.

Results. To date, Bing has chosen 38 out of 64 games correctly, including the opening round. Bing was 3 of 4 in the opening round, 22 of 32 in the 1st Round, 8 of 16 in the 2nd Round, 3 of 8 in the Sweet Sixteen, and 2 for 4 in the Elite Eight.

It’s also important to note that if you look at Bing’s bracket now, it will show a different story because it re-picks winners for matches after each round. Even with this adjustment, it only picked 48 of 64 games correctly (75%, compared to 2017’s 72%) leading into tonight’s games.

Here are Bing’s results this year compared to last:

In 2018, Bing performed slightly worse in the 1st, 2nd, and Sweet Sixteen rounds. More importantly, Bing correctly picked 2 of the Final Four teams – no small feat for anyone, man or machine.

Methodology. The Bing Predicts algorithm factors in millions of data points in an effort to create the best predictive model. The algorithm looked at every college basketball game played in the last 16 years in an attempt to analyze correlations between measurable statistics and wins. The algorithm will give an output of the likelihood a team will win the game. It’s not meant to choose a certain winner, but the higher the percentage, the greater the disparity amongst the teams.

Walter Sun, an architect of the Bing Predicts algorithm, was asked by Wired Magazine about some of the important considerations in the algorithm. Defensive efficiency, strength of schedule, coaching rankings, and miles traveled were a few of the metrics that the algorithm measures.

Who will win the NCAA Championship? Below are the algorithm’s estimations for this weekend’s matchups.

Bing pits Michigan vs Villanova in the National Championship game on Monday night. This year has stumped even college basketball fanatics, and the AI bracket is no exception. At the end of the day, the algorithm is predicting the percentage chance each team has to win the game. It may be entirely accurate, but if Villanova has a 69% chance of winning, they also have a 31% chance of losing – thus is the nature of predicting something binary like the outcome of a sports game. What’s interesting, however, is that Bing picked these two teams to meet in this round at the beginning of the tournament with Kansas winning. Looking at it now, the algorithm gives Villanova the higher probability of winning. That’s quite the change of heart.

After a last-place finish in our pool last year, Microsoft’s algorithm has performed much better against us, whether by luck or by real improvement. If Kansas wins the NCAA tournament, there is a chance the AI bracket could win our pool. We’ll be watching this weekend to see if man will prevail over machine for another year.

Disclaimer: We actively write about the themes in which we invest: artificial intelligence, robotics, virtual reality, and augmented reality. 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.

How To Think About Recent Volatility in Tech

Market decline does not change the mega growth opportunities. The heart rate of the market increased the past week because of fears of a trade war, Facebook data privacy, and broken market technicals, but the health of the market is unchanged and the health is good. Core underlying tech trends including artificial intelligence, robotics, big data, and autonomous transportation, will support continued growth.

Hold tech for the long-term. We believe that tech is essentially taking over the rest of the economy; therefore, investors should hold tech long term. Just as every company is now an internet company to some degree, we believe that eventually every company will be an AI company.

Market undervalued. From a valuation perspective, our view is undervalued. The market has rallied back to the old highs, but the S&P is up only 3% per year over the past 17 years, compared to the previous 17 years (1983-2000) when it was up 17% per year.

Putting the size of tech into perspective. The tech sector’s growing clout is not just a U.S. story. Tech stocks have become so dominant in emerging markets that for the first time since 2004, the industry last year overtook finance as the biggest sector in the MSCI Emerging Markets Index. Tech had a 28% weighting near the end of 2017, more than double its level six years ago, according to data provided by MSCI. Facebook, Amazon, Netflix Inc. and Alphabet together account for a 7.8% weighting in the S&P 500, more than double from five years ago.

Company Updates:

Tesla. We remain positive on TSLA. Shares are down 20% in the past month mostly due to fears of another miss in Model 3 production. The recent stock dive is due to a combination of a Model X accident that is being investigated, Waymo’s partnership with Jaguar, which legitimizes a key competitor (the I-Pace electric SUV), growing concern among all companies testing self-driving vehicles amid the Uber fatality, and news that Moody’s has downgraded Tesla’s bonds to B3 from B2, citing significant shortfall in the Model 3 production rate and a tight financial situation. We continue to believe the Tesla story has the best risk-reward among tech companies over the next 5 years.

  • Model 3 production. We’re expecting another miss in Model 3 production in the March quarter but that does not change the story. There is more demand than supply for the Model 3 (about 400k preorders which is unheard of in automotive). It might take a year, but eventually, Tesla will get the Model 3 production right, and ramp output.
  • Model X accident. We see the recent Model X accident the same as accidents with gas cars. It is unlikely that the battery or Tesla’s advanced cruise control “autopilot” were to blame. Tesla disclosed that the autopilot feature properly functions 200 times a day on the same stretch of road where the accident happened.

Facebook. Limited upside to FB. Given the privacy issues, for the first-time advertisers have to think about Facebook as a liability. Separately, it’s unclear about how the recent privacy changes will impact Facebook’s ability to make money.

Nvidia. We remain positive on NVDA. Shares of NVDA dropped 11% in the past week following the announcement that they temporarily stopped autonomous testing, and in part because of the broader market sell off. While the company did not comment on timing, we expect testing to resume in the next 3 months. The big picture is the company is well positioned to capitalize on four mega trends, AI, autonomous cars, gaming, and blockchain through their dominance of GPU processors.

Apple. We remain positive on AAPL. Concern is emerging that iPhone demand in June will fall below Street expectations. We think iPhone demand over the next two quarters is not important to the story. What’s important is the share buyback, services, and the next iPhone.

  • Share buyback. Apple can add 4% per year to the stock price (assuming they use $40B of the $55B they generate in cash each year to buy back stock). Apple will give an update on the share buyback when they report the March quarter, likely late in April.
  • Bigger screen iPhone this fall. We expect Apple will announce a 25% bigger phone in the fall. This will be a positive for unit demand and average selling price.
  • Services. Services account for about 15% of revenue and are growing at 15-20% year over year. We believe this segment will continue to grow at a 15% or better rate over the next five years. This is important because the earnings multiple on shares of AAPL will likely increase as investors view the predictability of services are more attractive.

Google. We remain positive on GOOG. We expect the next six months to be rough for shares of GOOG as questions emerge about how the company uses data. Despite that negative potential, Google is too tightly woven into the fabric of the internet. The company is one of the best ways to invest in AI, given the company has a stated their intention to move from a mobile-first company to an AI-first company over the next several years. Lastly, the company has a stake in Waymo, the leading autonomous car company. We expect years of positive news to come from Waymo.

Amazon. We remain positive on AMZN. The company is best positioned for the future of retail. We see that future as a combination of both online and offline retail. Online sales account for about 15% of global retail, and in the future, we believe it will eventually reach 55% of sales. We also expect Amazon to do more with physical retail locations and we continue to believe the company will eventually acquire Target (TGT). The company’s AWS web hosting business is only 15% of revenue, but it is growing at greater than 30% for the next several years.

Twitter. Limited upside to TWTR. About 14% of Twitters 2017 revenue came from selling data, growing at 18% y/y, compared to Twitter’s ad business that declined by 6%. Selling private data is a toxic label, and this could limit the upside to shares over the next year.

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.