Tag: Google

  • Google’s Quantum Error Correction Breakthrough

    One of the most exciting areas of technology development, but that doesn’t get a ton of mainstream media coverage, is the race to build a working quantum computer that exhibits “below threshold quantum computing” — the ability to do calculations utilizing quantum mechanics accurately.

    One of the key limitations to achieving this has been the sensitivity of quantum computing systems — in particular the qubits that capture the superposition of multiple states that allow quantum computers to exploit quantum mechanics for computation — to the world around them. Imagine if your computer’s accuracy would change every time someone walked in the room — even if it was capable of amazing things, it would not be especially practical. As a result, much research to date has been around novel ways of creating physical systems that can protect these quantum states.

    Google has (in a pre-print in Nature) demonstrated their new Willow quantum computing chip which demonstrates a quantum error correction method that spreads the quantum state information of a single “logical” qubit across multiple entangled “physical” qubits to create a more robust system. Beyond proving that their quantum error correction method worked, what is most remarkable to me, is that they’re able to extrapolate a scaling law for their error correction — a way of guessing how much better their system is at avoiding loss of quantum state as they increase the number of physical qubits per logical qubit — which could suggest a “scale up” path towards building functional, practical quantum computers.

    I will confess that quantum mechanics was never my strong suit (beyond needing it for a class on statistical mechanics eons ago in college), and my understanding of the core physics underlying what they’ve done in the paper is limited, but this is an incredibly exciting feat on our way towards practical quantum computing systems!

    The company’s new chip, called Willow, is a larger, improved version of that technology, with 105 physical qubits. It was developed in a fabrication laboratory that Google built at its quantum-computing campus in Santa Barbara, California, in 2021.

    As a first demonstration of Willow’s power, the researchers showed that it could perform, in roughly 5 minutes, a task that would take the world’s largest supercomputer an estimated 1025 years, says Hartmut Neven, who heads Google’s quantum-computing division. This is the latest salvo in the race to show that quantum computers have an advantage over classical ones.

    And, by creating logical qubits inside Willow, the Google team has shown that each successive increase in the size of a logical qubit cuts the error rate in half.

    “This is a very impressive demonstration of solidly being below threshold,” says Barbara Terhal, a specialist in quantum error correction at the Delft University of Technology in the Netherlands. Mikhail Lukin, a physicist at Harvard University in Cambridge, Massachusetts, adds, “It clearly shows that the idea works.”

    ‘A truly remarkable breakthrough’: Google’s new quantum chip achieves accuracy milestone
    Davide Castelvecchi | Nature News

  • Why Intel has to make its foundry business work

    Historically, Intel has (1) designed and (2) manufactured its chips that it sells (primarily into computer and server systems). It prided itself on having the most advanced (1) designs and (2) manufacturing technology, keeping both close to its chest.

    In the late 90s/00s, semiconductor companies increasingly embraced the “fabless model”, whereby they would only do the (1) design while outsourcing the manufacturing to foundries like TSMC. This made it much easier and less expensive to build up a burgeoning chip business and is the secret to the success of semiconductor giants like NVIDIA and Qualcomm.

    Companies like Intel scoffed at this, arguing that the combination of (1) design and (2) manufacturing gave their products an advantage, one that they used to achieve a dominant position in the computing chip segment. And, it’s an argument which underpins why they have never made a significant effort in becoming a contract manufacturer — after all, if part of your technological magic is the (2) manufacturing, why give it to anyone else?

    The success of TSMC has brought a lot of questions about Intel’s advantage in manufacturing and, given recent announcements by Intel and the US’s CHIPS Act, a renewed focus on actually becoming a contract manufacturer to the world’s leading chip designers.

    While much of the attention has been paid to the manufacturing prowess rivalry and the geopolitical reasons behind this, I think the real reason Intel has to make the foundry business work is simple: their biggest customers are all becoming chip designers.

    While a lot of laptops and desktops and servers are still sold in the traditional fashion, the reality is more and more of the server market is being dominated by a handful of hyperscale data center operators like Amazon, Google, Meta/Facebook, and Microsoft, companies that have historically been able to obtain the best prices from Intel because of their volume. But, in recent years, in the chase for better and better performance and cost and power consumption, they have begun designing their own chips adapted to their own systems (as this latest Google announcement for Google’s own ARM-based server chips shows).

    Are these chips as good as Intel’s across every dimension? Almost certainly not. It’s hard to overtake a company like Intel’s decades of design prowess and market insight. But, they don’t have to be. They only have to be better at the specific use case Google / Microsoft / Amazon / etc need it to be for.

    And, in that regard, that leaves Intel with really only one option: it has to make the foundry business work, or it risks losing not just the revenue from (1) designing a data center chip, but from the (2) manufacturing as well.

    Axion processors combine Google’s silicon expertise with Arm’s highest performing CPU cores to deliver instances with up to 30% better performance than the fastest general-purpose Arm-based instances available in the cloud today, up to 50% better performance and up to 60% better energy-efficiency than comparable current-generation x86-based instances1. That’s why we’ve already started deploying Google services like BigTable, Spanner, BigQuery, Blobstore, Pub/Sub, Google Earth Engine, and the YouTube Ads platform on current generation Arm-based servers and plan to deploy and scale these services and more on Axion soon.

    Introducing Google Axion Processors, our new Arm-based CPUs
    Amin Vahdat | Google Blog

  • Geothermal data centers

    The data centers that power AI and cloud services are limited by 3 things:

    • the server hardware (oftentimes limited by access to advanced semiconductors)
    • available space (their footprint is massive which makes it hard to put them close to where people live)
    • availability of cheap & reliable (and, generally, clean) power

    If you, as a data center operator, can tap a new source of cheap & reliable power, you will go very far as you alleviate one of the main constraints on the ability to add to your footprint.

    It’s no small wonder, then, that Google is willing to explore partnerships with next-gen geothermal startups like Fervo in a meaningful long-term fashion.

    But Google is hoping the road to commercialization for next-generation geothermal will mimic the early days of the tech industry’s solar procurement efforts, Maud Texier, global director of clean energy and decarbonization development, told Latitude Media.

    “While there are some important differences between solar and geothermal technologies, we would like to see geothermal power follow a similar trajectory as solar has over the last few decades in terms of rapid cost declines and performance improvements,” Texier said.

    A geothermal plant

    Google: Geothermal could follow ‘similar trajectory’ to solar
    Maeve Allsup | Latitude Media

  • The IE6 YouTube conspiracy

    An oldie but a goodie — the story of how the YouTube team, post-Google acquisition, put up a “we won’t support Internet Explorer 6 in the future” message without any permission from anyone. (HT: Eric S)

    IE6 had been the bane of our web development team’s existence. At least one to two weeks every major sprint cycle had to be dedicated to fixing new UI that was breaking in IE6. Despite this pain, we were told we had to continue supporting IE6 because our users might be unable to upgrade or might be working at companies that were locked in. IE6 users represented around 18% of our user base at that point. We understood that we could not just drop support for it. However, sitting in that cafeteria, having only slept about a few hours each in the previous days, our compassion for these users had completely eroded away. We began collectively fantasizing about how we could exact our revenge on IE6. One idea rose to the surface that quickly captured everyone’s attention. Instead of outright dropping IE6 support, what if we just threatened to? How would users react? Would they revolt against YouTube? Would they mail death threats to our team like had happened in the past? Or would they suddenly become loud advocates of modern browsers?

    A Conspiracy to Kill IE6
    Chris Zacharias

  • Pixel’s Parade of AI

    I am a big Google Pixel fan, being an owner and user of multiple Google Pixel line products. As a result, I tuned in to the recent MadeByGoogle stream. While it was hard not to be impressed with the demonstrations of Google’s AI prowess, I couldn’t help but be a little baffled…

    What was the point of making everything AI-related?

    Given how low Pixel’s market share is in the smartphone market, you’d think the focus ought to be on explaining why “normies” should buy the phone or find the price tag compelling, but instead every feature had to tie back to AI in some way.

    Don’t get me wrong, AI is a compelling enabler of new technologies. Some of the call and photo functionalities are amazing, both as technological demonstrations but also in terms of pure utility for the user.

    But, every product person learns early that customers care less about how something gets done and more about whether the product does what they want it too. And, as someone who very much wants a meaningful rival to Apple and Samsung, I hope Google doesn’t forget that either.

    But while Google can call itself an AI company all it likes, people ultimately just want phones filled with useful features. At a certain point, it risks putting the AI technology cart in front of the feature horse. 

    Google’s Pixel 8 launch was a parade of AI
    Jon Porter | The Verge

  • Mea Culpa

    Mea culpa.

    I’ve been a big fan of moving my personal page over to AWS Lightsail. But, if I had one complaint, it’s the dangerous combination of (1) their pre-packaged WordPress image being hard to upgrade software on and (2) the training-wheel-lacking full root access that Lightsail gives to its customers. That combination led me to make some regrettable mistakes yesterday which resulted in the complete loss of my old blog posts and pages.

    It’s the most painful when you know your problems are your own fault. Thankfully, with the very same AWS Lightsail, it’s easy enough to start up a new WordPress instance. With the help of site visit and search engine analytics, I’ve prioritized the most popular posts and pages to resurrect using Google’s cache.

    Unfortunately, that process led to my email subscribers receiving way too many emails from me as I recreated each post. For that, I’m sorry — mea culpa — it shouldn’t happen again.

    I’ve come to terms with the fact that I’ve lost the majority of the 10+ years of content I’ve created. But, I’ve now learned the value of systematically backing up things (especially my AWS Lightsail instance), and hopefully I’ll write some good content in the future to make up for what was lost.

  • Dr. Machine Learning

    How to realize the promise of applying machine learning to healthcare

    Not going to happen anytime soon, sadly: the Doctor from Star Trek: Voyager; Source: TrekCore

    Despite the hype, it’ll likely be quite some time before human physicians will be replaced with machines (sorry, Star Trek: Voyager fans).

    While “smart” technology like IBM’s Watson and Alphabet’s AlphaGo can solve incredibly complex problems, they are probably not quite ready to handle the messiness of qualitative unstructured information from patients and caretakers (“it kind of hurts sometimes”) that sometimes lie (“I swear I’m still a virgin!”) or withhold information (“what does me smoking pot have to do with this?”) or have their own agendas and concerns (“I just need some painkillers and this will all go away”).

    Instead, machine learning startups and entrepreneurs interested in medicine should focus on areas where they can augment the efforts of physicians rather than replace them.

    One great example of this is in diagnostic interpretation. Today, doctors manually process countless X-rays, pathology slides, drug adherence records, and other feeds of data (EKGs, blood chemistries, etc) to find clues as to what ails their patients. What gets me excited is that these tasks are exactly the type of well-defined “pattern recognition” problems that are tractable for an AI / machine learning approach.

    If done right, software can not only handle basic diagnostic tasks, but to dramatically improve accuracy and speed. This would let healthcare systems see more patients, make more money, improve the quality of care, and let medical professionals focus on managing other messier data and on treating patients.

    As an investor, I’m very excited about the new businesses that can be built here and put together the following “wish list” of what companies setting out to apply machine learning to healthcare should strive for:

    • Excellent training data and data pipeline: Having access to large, well-annotated datasets today and the infrastructure and processes in place to build and annotate larger datasets tomorrow is probably the main defining . While its tempting for startups to cut corners here, that would be short-sighted as the long-term success of any machine learning company ultimately depends on this being a core competency.
    • Low (ideally zero) clinical tradeoffs: Medical professionals tend to be very skeptical of new technologies. While its possible to have great product-market fit with a technology being much better on just one dimension, in practice, to get over the innate skepticism of the field, the best companies will be able to show great data that makes few clinical compromises (if any). For a diagnostic company, that means having better sensitivty and selectivity at the same stage in disease progression (ideally prospectively and not just retrospectively).
    • Not a pure black box: AI-based approaches too often work like a black box: you have no idea why it gave a certain answer. While this is perfectly acceptable when it comes to recommending a book to buy or a video to watch, it is less so in medicine where expensive, potentially life-altering decisions are being made. The best companies will figure out how to make aspects of their algorithms more transparent to practitioners, calling out, for example, the critical features or data points that led the algorithm to make its call. This will let physicians build confidence in their ability to weigh the algorithm against other messier factors and diagnostic explanations.
    • Solve a burning need for the market as it is today: Companies don’t earn the right to change or disrupt anything until they’ve established a foothold into an existing market. This can be extremely frustrating, especially in medicine given how conservative the field is and the drive in many entrepreneurs to shake up a healthcare system that has many flaws. But, the practical reality is that all the participants in the system (payers, physicians, administrators, etc) are too busy with their own issues (i.e. patient care, finding a way to get everything paid for) to just embrace a new technology, no matter how awesome it is. To succeed, machine diagnostic technologies should start, not by upending everything with a radical solution, but by solving a clear pain point (that hopefully has a lot of big dollar signs attached to it!) for a clear customer in mind.

    Its reasons like this that I eagerly follow the development of companies with initiatives in applying machine learning to healthcare like Google’s DeepMind, Zebra Medical, and many more.

  • Laszlo Bock on Building Google’s Culture

    Much has been written about what makes Google work so well: their ridiculously profitable advertising business model, the technology behind their search engine and data centers, and the amazing pay and perks they offer.

    Source: the book

    My experiences investing in and working with startups, however, has taught me that building a great company is usually less about a specific technical or business model innovation than about building a culture of continuous improvement and innovation. To try to get some insight into how Google does things, I picked up Google SVP of People Operations Laszlo Bock’s book Work Rules!

    Bock describes a Google culture rooted in principles that came from founders Larry Page and Sergey Brin when they started the company: get the best people to work for you, make them want to stay and contribute, and remove barriers to their creativity. What’s great (to those interested in company building) is that Bock goes on to detail the practices Google has put in place to try to live up to these principles even as their headcount has expanded.

    The core of Google’s culture boils down to four basic principles and much of the book is focused on how companies should act if they want to live up to them:

    1. Presume trust: Many of Google’s cultural norms stem from a view that people are well-intentioned and trustworthy. While that may not seem so radical, this manifested at Google as a level of transparency with employees and a bias to say yes to employee suggestions that most companies are uncomfortable with. It raises interesting questions about why companies that say their talent is the most important thing treat them in ways that suggest a lack of trust.
    2. Recruit the best: Many an exec pays lip service to this, but what Google has done is institute policies that run counter to standard recruiting practices to try to actually achieve this at scale: templatized interviews / forms (to make the review process more objective and standardized), hiring decisions made by cross-org committees (to insure a consistently high bar is set), and heavy use of data to track the effectiveness of different interviewers and interview tactics. While there’s room to disagree if these are the best policies (I can imagine hating this as a hiring manager trying to staff up a team quickly), what I admired is that they set a goal (to hire the best at scale) and have actually thought through the recruiting practices they need to do so.
    3. Pay fairly [means pay unequally]: While many executives would agree with the notion that superstar employees can be 2-10x more productive, few companies actually compensate their superstars 2-10x more. While its unclear to me how effective Google is at rewarding superstars, the fact that they’ve tried to align their pay policies with their beliefs on how people perform is another great example of deviating from the norm (this time in terms of compensation) to follow through on their desire to pay fairly.
    4. Be data-driven: Another “in vogue” platitude amongst executives, but one that very few companies live up to, is around being data-driven. In reading Bock’s book, I was constantly drawing parallels between the experimentation, data collection, and analyses his People Operations team carried out and the types of experiments, data collection, and analyses you would expect a consumer internet/mobile company to do with their users. Case in point: Bock’s team experimented with different performance review approaches and even cafeteria food offerings in the same way you would expect Facebook to experiment with different news feed algorithms and notification strategies. It underscores the principle that, if you’re truly data-driven, you don’t just selectively apply it to how you conduct business, you apply it everywhere.

    Of course, not every company is Google, and not every company should have the same set of guiding principles or will come to same conclusions. Some of the processes that Google practices are impractical (i.e., experimentation is harder to set up / draw conclusions from with much smaller companies, not all professions have such wide variations in output as to drive such wide variations in pay, etc).

    What Bock’s book highlights, though, is that companies should be thoughtful about what sort of cultural principles they want to follow and what policies and actions that translates into if they truly believe them. I’d highly recommend the book!

  • Web vs Native

    When Steve Jobs first launched the iPhone in 2007, Apple’s perception of where the smartphone application market would move was in the direction of web applications. The reasons for this are obvious: people are familiar with how to build web pages and applications, and it simplifies application delivery.

    Yet in under a year, Apple changed course, shifting the focus of iPhone development from web applications to building native applications custom-built (by definition) for the iPhone’s operating system and hardware. While I suspect part of the reason this was done was to lock-in developers, the main reason was certainly the inadequacy of available browser/web technology. While we can debate the former, the latter is just plain obvious. In 2007, the state of web development was relatively primitive relative to today. There was no credible HTML5 support. Javascript performance was paltry. There was no real way for web applications to access local resources/hardware capabilities. Simply put, it was probably too difficult for Apple to kludge together an application development platform based solely on open web technologies which would get the sort of performance and functionality Apple wanted.

    But, that was four years ago, and web technology has come a long way. Combine that with the tech commentator-sphere’s obsession with hyping up a rivalry between “native vs HTML5 app development”, and it begs the question: will the future of application development be HTML5 applications or native?

    There are a lot of “moving parts” in a question like this, but I believe the question itself is a red herring. Enhancements to browser performance and the new capabilities that HTML5 will bring like offline storage, a canvas for direct graphic manipulation, and tools to access the file system, mean, at least to this tech blogger, that “HTML5 applications” are not distinct from native applications at all, they are simply native applications that you access through the internet. Its not a different technology vector – it’s just a different form of delivery.

    Critics of this idea may cite that the performance and interface capabilities of browser-based applications lag far behind those of “traditional” native applications, and thus they will always be distinct. And, as of today, they are correct. However, this discounts a few things:

    • Browser performance and browser-based application design are improving at a rapid rate, in no small part because of the combination of competition between different browsers and the fact that much of the code for these browsers is open source. There will probably always be a gap between browser-based apps and native, but I believe this gap will continue to narrow to the point where, for many applications, it simply won’t be a deal-breaker anymore.
    • History shows that cross-platform portability and ease of development can trump performance gaps. Once upon a time, all developers worth their salt coded in low level machine language. But this was a nightmare – it was difficult to do simple things like showing text on a screen, and the code written only worked on specific chips and operating systems and hardware configurations. I learned C which helped to abstract a lot of that away, and, keeping with the trend of moving towards more portability and abstraction, the mobile/web developers of today develop with tools (Python, Objective C, Ruby, Java, Javascript, etc) which make C look pretty low-level and hard to work with. Each level of abstraction adds a performance penalty, but that has hardly stopped developers from embracing them, and I feel the same will be true of “HTML5”.
    • Huge platform economic advantages. There are three huge advantages today to HTML5 development over “traditional native app development”. The first is the ability to have essentially the same application run across any device which supports a browser. Granted, there are performance and user experience issues with this approach, but when you’re a startup or even a corporate project with limited resources, being able to get wide distribution for earlier products is a huge advantage. The second is that HTML5 as a platform lacks the control/economic baggage that iOS and even Android have where distribution is controlled and “taxed” (30% to Apple/Google for an app download, 30% cut of digital goods purchases). I mean, what other reason does Amazon have to move its Kindle application off of the iOS native path and into HTML5 territory? The third is that web applications do not require the latest and greatest hardware to perform amazing feats. Because these apps are fundamentally browser-based, using the internet to connect to a server-based/cloud-based application allows even “dumb devices” to do amazing things by outsourcing some of that work to another system. The combination of these three makes it easier to build new applications and services and make money off of them – which will ultimately lead to more and better applications and services for the “HTML5 ecosystem.”

    Given Google’s strategic interest in the web as an open development platform, its no small wonder that they have pushed this concept the furthest. Not only are they working on a project called Native Client to let users achieve “native performance” with the browser, they’ve built an entire operating system centered entirely around the browser, Chrome OS, and were the first to build a major web application store, the Chrome Web Store to help with application discovery.

    While it remains to be seen if any of these initiatives will end up successful, this is definitely a compelling view of how the technology ecosystem evolves, and, putting on my forward-thinking cap on, I would not be surprised if:

    1. The major operating systems became more ChromeOS-like over time. Mac OS’s dashboard widgets and Windows 7’s gadgets are already basically HTML5 mini-apps, and Microsoft has publicly stated that Windows 8 will support HTML5-based application development. I think this is a sign of things to come as the web platform evolves and matures.
    2. Continued focus on browser performance may lead to new devices/browsers focused on HTML5 applications. In the 1990s/2000s, there was a ton of attention focused on building Java accelerators in hardware/chips and software platforms who’s main function was to run Java. While Java did not take over the world the way its supporters had thought, I wouldn’t be surprised to see a similar explosion just over the horizon focused on HTML5/Javascript performance – maybe even HTML5 optimized chips/accelerators, additional ChromeOS-like platforms, and potentially browsers optimized to run just HTML5 games or enterprise applications?
    3. Web application discovery will become far more important. The one big weakness as it stands today for HTML5 is application discovery. Its still far easier to discover a native mobile app using the iTunes App Store or the Android Market than it is to find a good HTML5 app. But, as platform matures and the platform economics shift, new application stores/recommendation engines/syndication platforms will become increasingly critical.

    Thought this was interesting? Check out some of my other pieces on Tech industry

  • Why Smartphones are a Big Deal

    A cab driver the other day went off on me with a rant about how new smartphone users were all smug, arrogant gadget snobs for using phones that did more than just make phone calls. “Why you gotta need more than just the phone?”, he asked.

    While he was probably right on the money with the “smug”, “arrogant”, and “snob” part of the description of smartphone users (at least it accurately describes yours truly), I do think he’s ignoring a lot of the important changes which the smartphone revolution has made in the technology industry and, consequently, why so many of the industry’s venture capitalists and technology companies are investing so heavily in this direction. This post will be the first of two posts looking at what I think are the four big impacts of smartphones like the Blackberry and the iPhone on the broader technology landscape:

    1. It’s the software, stupid
    2. Look ma, no <insert other device here>
    3. Putting the carriers in their place
    4. Contextuality

    I. It’s the software, stupid!

    You can find possibly the greatest impact of the smartphone revolution in the very definition of smartphone: phones which can run rich operating systems and actual applications. As my belligerent cab-driver pointed out, the cellular phone revolution was originally about being able to talk to other people on the go. People bought phones based on network coverage, call quality, the weight of a phone, and other concerns primarily motivated by call usability.

    Smartphones, however, change that. Instead of just making phone calls, they also do plenty of other things. While a lot of consumers focus their attention on how their phones now have touchscreens, built-in cameras, GPS, and motion-sensors, the magic change that I see is the ability to actually run programs.

    Why do I say this software thing more significant than the other features which have made their ways on to the phone? There are a number of reasons for this, but the big idea is that the ability to run software makes smartphones look like mobile computers. We have seen this pan out in a number of ways:

    • The potential uses for a mobile phone have exploded overnight. Whereas previously, they were pretty much limited to making phone calls, sending text messages/emails, playing music, and taking pictures, now they can be used to do things like play games, look up information, and even be used by doctors to help treat and diagnose patients. In the same way that a computer’s usefulness extends beyond what a manufacturer like Dell or HP or Apple have built into the hardware because of software, software opens up new possibilities for mobile phones in ways which we are only beginning to see.
    • Phones can now be “updated”. Before, phones were simply replaced when they became outdated. Now, some users expect that a phone that they buy will be maintained even after new models are released. Case in point: Users threw a fit when Samsung decided not to allow users to update their Samsung Galaxy’s operating system to a new version of the Android operating system. Can you imagine 10 years ago users getting up in arms if Samsung didn’t ship a new 2 MP mini-camera to anyone who owned an earlier version of the phone which only had a 1 MP camera?
    • An entire new software industry has emerged with its own standards and idiosyncrasies. About four decades ago, the rise of the computer created a brand new industry almost out of thin air. After all, think of all the wealth and enabled productivity that companies like Oracle, Microsoft, and Adobe have created over the past thirty years. There are early signs that a similar revolution is happening because of the rise of the smartphone. Entire fortunes have been created “out of thin air” as enterprising individuals and companies move to capture the potential software profits from creating software for the legions of iPhones and Android phones out there. What remains to be seen is whether or not the mobile software industry will end up looking more like the PC software industry, or whether or not the new operating systems and screen sizes and technologies will create something that looks more like a distant cousin of the first software revolution.

    II. Look ma, no <insert other device here>

    One of the most amazing consequences of Moore’s Law is that devices can quickly take on a heckuva lot more functionality then they used to. The smartphone is a perfect example of this Swiss-army knife mentality. The typical high-end smartphone today can:

    • take pictures
    • use GPS
    • play movies
    • play songs
    • read articles/books
    • find what direction its being pointed in
    • sense motion
    • record sounds
    • run software

    … not to mention receive and make phone calls and texts like a phone.

    But, unlike cameras, GPS devices, portable media players, eReaders, compasses, Wii-motes, tape recorders, and computers, the phone is something you are likely to keep with you all day long. And, if you have a smartphone which can double as a camera, GPS, portable media player, eReaders, compass, Wii-mote, tape recorder, and computer all at once – tell me why you’re going to hold on to those other devices?

    That is, of course, a dramatic oversimplification. After all, I have yet to see a phone which can match a dedicated camera’s image quality or a computer’s speed, screen size, and range of software, so there are definitely reasons you’d pick one of these devices over a smartphone. The point, however, isn’t that smartphones will make these other devices irrelevant, it is that they will disrupt these markets in exactly the way that Clayton Christensen described in his book The Innovator’s Dilemma, making business a whole lot harder for companies who are heavily invested in these other device categories. And make no mistake: we’re already seeing this happen as GPS companies are seeing lower prices and demand as smartphones take on more and more sophisticated functionality (heck, GPS makers like Garmin are even trying to get into the mobile phone business!). I wouldn’t be surprised if we soon see similar declines in the market growth rates and profitability for all sorts of other devices.

    III. Putting the carriers in their place

    Throughout most of the history of the phone industry, the carriers were the dominant power. Sure, enormous phone companies like Nokia, Samsung, and Motorola had some clout, but at the end of the day, especially in the US, everybody felt the crushing influence of the major wireless carriers.

    In the US, the carriers regulated access to phones with subsidies. They controlled which functions were allowed. They controlled how many texts and phone calls you were able to make. When they did let you access the internet, they exerted strong influence on which websites you had access to and which ringtones/wallpapers/music you could download. In short, they managed the business to minimize costs and risks, and they did it because their government-granted monopolies (over the right to use wireless spectrum) and already-built networks made it impossible  for a new guy to enter the market.

    But this sorry state of affairs has already started to change with the advent of the smartphone. RIM’s Blackberry had started to affect the balance of power, but Apple’s iPhone really shook things up – precisely because users started demanding more than just a wireless service plan – they wanted a particular operating system with a particular internet experience and a particular set of applications – and, oh, it’s on AT&T? That’s not important, tell me more about the Apple part of it!

    What’s more, the iPhone’s commercial success accelerated the change in consumer appetites. Smartphone users were now picking a wireless service provider not because of coverage or the cost of service or the special carrier-branded applications  – that was all now secondary to the availability of the phone they wanted and what sort of applications and internet experience they could get over that phone. And much to the carriers’ dismay, the wireless carrier was becoming less like the gatekeeper who got to charge crazy prices because he/she controlled the keys to the walled garden and more like the dumb pipe that people connected to the web on their iPhone with.

    Now, it would be an exaggeration to say that the carriers will necessarily turn into the “dumb pipes” that today’s internet service providers are (remember when everyone in the US used AOL?) as these large carriers are still largely immune to competitors. But, there are signs that the carriers are adapting to their new role. The once ultra-closed Verizon now allows Palm WebOS and Google Android devices to roam free on its network as a consequence of AT&T and T-Mobile offering devices from Apple and Google’s partners, respectively, and has even agreed to allow VOIP applications like Skype access to its network, something which jeopardizes their former core voice revenue stream.

    As for the carriers, as they begin to see their influence slip over basic phone experience considerations, they will likely shift their focus to finding ways to better monetize all the traffic that is pouring through their networks. Whether this means finding a way to get a cut of the ad/virtual good/eCommerce revenue that’s flowing through or shifting how they charge for network access away from unlimited/“all you can eat” plans is unclear, but it will be interesting to see how this ecosystem evolves.

    IV. Contextuality

    There is no better price than the amazingly low price of free. And, in my humble opinion, it is that amazingly low price of free which has enabled web services to have such a high rate of adoption. Ask yourself, would services like Facebook and Google have grown nearly as fast without being free to use?

    How does one provide compelling value to users for free? Before the age of the internet, the answer to that age-old question was simple: you either got a nice government subsidy, or you just didn’t. Thankfully, the advent of the internet allowed for an entirely new business model: providing services for free and still making a decent profit by using ads. While over-hyping of this business model led to the dot com crash in 2001 as countless websites found it pretty difficult to monetize their sites purely with ads, services like Google survived because they found that they could actually increase the value of the advertising on their pages not only because they had a ton of traffic, but because they could use the content on the page to find ads which visitors had a significantly higher probability of caring about.

    The idea that context could be used to increase ad conversion rates (the percent of people who see an ad and actually end up buying) has spawned a whole new world of web startups and technologies which aim to find new ways to mine context to provide better ad targeting. Facebook is one such example of the use of social context (who your friends are, what your interests are, what your friends’ interests are) to serve more targeted ads.

    So, where do smartphones fit in? There are two ways in which smartphones completely change the context-to-advertising dynamic:

    • Location-based services: Your phone is a device which not only has a processor which can run software, but is also likely to have GPS built-in, and is something which you carry on your person at all hours of the day. What this means is that the phone not only know what apps/websites you’re using, it also knows where you are and if you’re on a vehicle (based on how fast you are moving) when you’re using them. If that doesn’t let a merchant figure out a way to send you a very relevant ad, I don’t know what will. The Yowza iPhone application is an example of how this might shape out in the future, where you can search for mobile coupons for local stores all on your phone.
    • Augmented reality: In the same way that the GPS lets mobile applications do location-based services, the camera, compass, and GPS in a mobile phone lets mobile applications do something called augmented reality. The concept behind augmented reality (AR) is that, in the real world, you and I are only limited by what our five senses can perceive. If I see an ad for a book, I can only perceive what is on the advertisement. I don’t necessarily know much about how much it costs on Amazon.com or what my friends on Facebook have said about it. Of course, with a mobile phone, I could look up those things on the internet, but AR takes this a step further. Instead of merely looking something up on the internet, AR will actually overlay content and information on top of what you are seeing on your phone screen. One example of this is the ShopSavvy application for Android which allows you to scan product barcodes to find product review information and even information on pricing from online and other local stores! Google has taken this a step further with Google Goggles which can recognize pictures of landmarks, books, and even bottles of wine! For an advertiser or a store, the ability to embed additional content through AR technology is the ultimate in providing context but only to those people who want it. Forget finding the right balance between putting too much or too little information on an ad, use AR so that only the people who are interested will get the extra information.

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  • Seed the Market

    In my Introduction to Tech Strategy post, I mentioned that one of the most important aspects of the technology industry is the importance of ecosystem linkages. There are several ways to think about ecosystem linkages. The main linkages I mentioned in my previous post was influence over technology standards. But, there is another very important ecosystem effect for technology companies to think about: encouraging demand.

    For Microsoft to be successful, for instance, they must make sure that consumers and businesses are buying new and more powerful computers. For Google to be successful, they must make sure that people are actively using the internet to find information. For Cisco to be successful, they must make sure that people are actively downloading and sharing information over networks.

    Is it any wonder, then, that Microsoft develops business software (e.g. Microsoft Office) and games? Or that Google has pushed hard to encourage more widespread internet use by developing an easy-to-use web browser and two internet-centric operating systems (Android and ChromeOS)? Or that Cisco entered the set top box business (to encourage more network traffic) by acquiring Scientific Atlanta and is pushing for companies to adopt web conferencing systems (which consume a lot of networking capacity) like WebEx?

    These examples hopefully illustrate that for leading tech companies, it is not sufficient just to develop a good product. It is also important that you move to make sure that customers will continue to demand your product, and a lot more of it.

    This is something that Dogbert understands intuitively as this comic strip points out:

    Source: Dilbert

    To be a leading executive recruiter, its not sufficient just to find great executives – you have to make sure there is demand for new executives. No wonder Dogbert is such a successful CEO. He grasps business strategy like no other.

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