Category: Uncategorized

  • How to Regulate Big Tech

    There’s been a fair amount of talk lately about proactively regulating — and maybe even breaking up — the “Big Tech” companies.

    Full disclosure: this post discusses regulating large tech companies. I own shares in several of these both directly (in the case of Facebook and Microsoft) and indirectly (through ETFs that own stakes in large companies)

    Source: MIT Sloan

    Like many, I have become increasingly uneasy over the fact that a small handful of companies, with few credible competitors, have amassed so much power over our personal data and what information we see. As a startup investor and former product executive at a social media startup, I can especially sympathize with concerns that these large tech companies have created an unfair playing field for smaller companies.

    At the same time, though, I’m mindful of all the benefits that the tech industry — including the “tech giants” — have brought: amazing products and services, broader and cheaper access to markets and information, and a tremendous wave of job and wealth creation vital to may local economies. For that reason, despite my concerns of “big tech”‘s growing power, I am wary of reaching for “quick fixes” that might change that.

    As a result, I’ve been disappointed that much of the discussion has centered on knee-jerk proposals like imposing blanket stringent privacy regulations and forcefully breaking up large tech companies. These are policies which I fear are not only self-defeating but will potentially put into jeopardy the benefits of having a flourishing tech industry.

    The Challenges with Regulating Tech

    Technology is hard to regulate. The ability of software developers to collaborate and build on each other’s innovations means the tech industry moves far faster than standard regulatory / legislative cycles. As a result, many of the key laws on the books today that apply to tech date back decades — before Facebook or the iPhone even existed, making it important to remember that even well-intentioned laws and regulations governing tech can cement in place rules which don’t keep up when the companies and the social & technological forces involved change.

    Another factor which complicates tech policy is that the traditional “big is bad” mentality ignores the benefits to having large platforms. While Amazon’s growth has hurt many brick & mortar retailers and eCommerce competitors, its extensive reach and infrastructure enabled businesses like Anker and Instant Pot to get to market in a way which would’ve been virtually impossible before. While the dominance of Google’s Android platform in smartphones raised concerns from European regulators, its hard to argue that the companies which built millions of mobile apps and tens of thousands of different types of devices running on Android would have found it much more difficult to build their businesses without such a unified software platform. Policy aimed at “Big Tech” should be wary of dismantling the platforms that so many current and future businesses rely on.

    Its also important to remember that poorly crafted regulation in tech can be self-defeating. The most effective way to deal with the excesses of “Big Tech”, historically, has been creating opportunities for new market entrants. After all, many tech companies previously thought to be dominant (like Nokia, IBM, and Microsoft) lost their positions, not because of regulation or antitrust, but because new technology paradigms (i.e. smartphones, cloud), business models (i.e. subscription software, ad-sponsored), and market entrants (i.e. Google, Amazon) had the opportunity to flourish. Because rules (i.e. Article 13/GDPR) aimed at big tech companies generally fall hardest on small companies (who are least able to afford the infrastructure / people to manage it), its important to keep in mind how solutions for “Big Tech” problems affect smaller companies and new concepts as well.

    Framework for Regulating “Big Tech”

    If only it were so easy… Source: XKCD

    To be 100% clear, I’m not saying that the tech industry and big platforms should be given a pass on rules and regulation. If anything, I believe that laws and regulation play a vital role in creating flourishing markets.

    But, instead of treating “Big Tech” as just a problem to kill, I think we’d be better served by laws / regulations that recognize the limits of regulation on tech and, instead, focus on making sure emerging companies / technologies can compete with the tech giants on a level playing field. To that end, I hope to see more ideas that embrace the following four pillars:

    I. Tiering regulation based on size of the company

    Regulations on tech companies should be tiered based on size with the most stringent rules falling on the largest companies. Size should include traditional metrics like revenue but also, in this age of marketplace platforms and freemium/ad-sponsored business models, account for the number of users (i.e. Monthly Active Users) and third party partners.

    In this way, the companies with the greatest potential for harm and the greatest ability to bear the costs face the brunt of regulation, leaving smaller companies & startups with greater flexibility to innovate and iterate.

    II. Championing data portability

    One of the reasons it’s so difficult for competitors to challenge the tech giants is the user lock-in that comes from their massive data advantage. After all, how does a rival social network compete when a user’s photos and contacts are locked away inside Facebook?

    While Facebook (and, to their credit, some of the other tech giants) does offer ways to export user data and to delete user data from their systems, these tend to be unwieldy, manual processes that make it difficult for a user to bring their data to a competing service. Requiring the largest tech platforms to make this functionality easier to use (i.e., letting others import your contact list and photos with the ease in which you can login to many apps today using Facebook) would give users the ability to hold tech companies accountable for bad behavior or not innovating (by being able to walk away) and fosters competition by letting new companies compete not on data lock-in but on features and business model.

    III. Preventing platforms from playing unfairly

    3rd party platform participants (i.e., websites listed on Google, Android/iOS apps like Spotify, sellers on Amazon) are understandably nervous when the platform owners compete with their own offerings (i.e., Google Places, Apple Music, Amazon first party sales). As a result, some have even called for banning platform owners from offering their own products and services.

    I believe that is an overreaction. Platform owners offering attractive products and services (i.e., Google offering turn-by-turn navigation on Android phones) can be a great thing for users (after all, most prominent platforms started by providing compelling first-party offerings) and for 3rd party participants if these offerings improve the attractiveness of the platform overall.

    What is hard to justify is when platform owners stack the deck in their favor using anti-competitive moves such as banning or reducing the visibility of competitors, crippling third party offerings, making excessive demands on 3rd parties, etc. Its these sorts of actions by the largest tech platforms that pose a risk to consumer choice and competition and should face regulatory scrutiny. Not just the fact that a large platform exists or that the platform owner chooses to participate in it.

    IV. Modernizing how anti-trust thinks about defensive acquisitions

    The rise of the tech giants has led to many calls to unwind some of the pivotal mergers and acquisitions in the space. As much as I believe that anti-trust regulators made the wrong calls on some of these transactions, I am not convinced, beyond just wanting to punish “Big Tech” for being big, that the Pandora’s Box of legal and financial issues (for the participants, employees, users, and for the tech industry more broadly) that would be opened would be worthwhile relative to pursuing other paths to regulate bad behavior directly.

    That being said, its become clear that anti-trust needs to move beyond narrow revenue share and pricing-based definitions of anti-competitiveness (which do not always apply to freemium/ad-sponsored business models). Anti-trust prosecutors and regulators need to become much more thoughtful and assertive around how some acquisitions are done simply to avoid competition (i.e., Google’s acquisition of Waze and Facebook’s acquisition of WhatsApp are two examples of landmark acquisitions which probably should have been evaluated more closely).

    Wrap-Up

    Source: OECD Forum Network

    This is hardly a complete set of rules and policies needed to approach growing concerns about “Big Tech”. Even within this framework, there are many details (i.e., who the specific regulators are, what specific auditing powers they have, the details of their mandate, the specific thresholds and number of tiers to be set, whether pre-installing an app counts as unfair, etc.) that need to be defined which could make or break the effort. But, I believe this is a good set of principles that balances both the need to foster a tech industry that will continue to grow and drive innovation as well as the need to respond to growing concerns about “Big Tech”.

    Special thanks to Derek Yang and Anthony Phan for reading earlier versions and giving me helpful feedback!

  • Why Tech Success Doesn’t Translate to Deeptech

    Source: Eric Hamilton

    Having been lucky enough to invest in both tech (cloud, mobile, software) and “deeptech” (materials, cleantech, energy, life science) startups (and having also ran product at a mobile app startup), it has been striking to see how fundamentally different the paradigms that drive success in each are.

    Whether knowingly or not, most successful tech startups over the last decade have followed a basic playbook:

    1. Take advantage of rising smartphone penetration and improvements in cloud technology to build digital products that solve challenges in big markets pertaining to access (e.g., to suppliers, to customers, to friends, to content, to information, etc.)
    2. Build a solid team of engineers, designers, growth, sales, marketing, and product people to execute on lean software development and growth methodologies
    3. Hire the right executives to carry out the right mix of tried-and-true as well as “out of the box” channel and business development strategies to scale bigger and faster

    This playbook appears deceptively simple but is very difficult to execute well. It works because for markets where “software is eating the world”:

    Source: Techcrunch
    • There is relatively little technology risk: With the exception of some of the most challenging AI, infrastructure, and security challenges, most tech startups are primarily dealing with engineering and product execution challenges — what is the right thing to build and how do I build it on time, under budget? — rather than fundamental technology discovery and feasibility challenges
    • Skills & knowledge are broadly transferable: Modern software development and growth methodologies work across a wide range of tech products and markets. This means that effective engineers, salespeople, marketers, product people, designers, etc. at one company will generally be effective at another. As a result, its a lot easier for investors/executives to both gauge the caliber of a team (by looking at their experience) and augment a team when problems arise (by recruiting the right people with the right backgrounds).
    • Distribution is cheap and fast: Cloud/mobile technology means that a new product/update is a server upgrade/browser refresh/app store download away. This has three important effects:
    1. The first is that startups can launch with incomplete or buggy solutions because they can readily provide hotfixes and upgrades.
    2. The second is that startups can quickly release new product features and designs to respond to new information and changing market conditions.
    3. The third is that adoption is relatively straightforward. While there may be some integration and qualification challenges, in general, the product is accessible via a quick download/browser refresh, and the core challenge is in getting enough people to use a product in the right way.

    In contrast, if you look at deeptech companies, a very different set of rules apply:

    Source: XKCD
    • Technology risk/uncertainty is inherent: One of the defining hallmarks of a deeptech company is dealing with uncertainty from constraints imposed by reality (i.e. the laws of physics, the underlying biology, the limits of current technology, etc.). As a result, deeptech startups regularly face feasibility challenges — what is even possible to build? — and uncertainty around the R&D cycles to get to a good outcome — how long will it take / how much will it cost to figure this all out?
    • Skills & knowledge are not easily transferable: Because the technical and business talent needed in deeptech is usually specific to the field, talent and skills are not necessarily transferable from sector to sector or even company to company. The result is that it is much harder for investors/executives to evaluate team caliber (whether on technical merits or judging past experience) or to simply put the right people into place if there are problems that come up.
    • Product iteration is slow and costly: The tech startup ethos of “move fast and break things” is just harder to do with deeptech.
    1. At the most basic level, it just costs a lot more and takes a lot more time to iterate on a physical product than a software one. It’s not just that physical products require physical materials and processing, but the availability of low cost technology platforms like Amazon Web Services and open source software dramatically lower the amount of time / cash needed to make something testable in tech than in deeptech.
    2. Furthermore, because deeptech innovations tend to have real-world physical impacts (to health, to safety, to a supply chain/manufacturing line, etc.), deeptech companies generally face far more regulatory and commercial scrutiny. These groups are generally less forgiving of incomplete/buggy offerings and their assessments can lengthen development cycles. Deeptech companies generally can’t take the “ask for forgiveness later” approaches that some tech companies (i.e. Uber and AirBnb) have been able to get away with (exhibit 1: Theranos).

    As a result, while there is no single playbook that works across all deeptech categories, the most successful deeptech startups tend to embody a few basic principles:

    1. Go after markets where there is a very clear, unmet need: The best deeptech entrepreneurs tend to take very few chances with market risk and only pursue challenges where a very well-defined unmet need (i.e., there are no treatments for Alzheimer’s, this industry needs a battery that can last at least 1000 cycles, etc) blocks a significant market opportunity. This reduces the risk that a (likely long and costly) development effort achieves technical/scientific success without also achieving business success. This is in contrast with tech where creating or iterating on poorly defined markets (i.e., Uber and Airbnb) is oftentimes at the heart of what makes a company successful.
    2. Focus on “one miracle” problems: Its tempting to fantasize about what could happen if you could completely re-write every aspect of an industry or problem but the best deeptech startups focus on innovating where they won’t need the rest of the world to change dramatically in order to have an impact (e.g., compatible with existing channels, business models, standard interfaces, manufacturing equipment, etc). Its challenging enough to advance the state of the art of technology — why make it even harder?
    3. Pursue technologies that can significantly over-deliver on what the market needs: Because of the risks involved with developing advanced technologies, the best deeptech entrepreneurs work in technologies where even a partial success can clear the bar for what is needed to go to market. At the minimum, this reduces the risk of failure. But, hopefully, it gives the company the chance to fundamentally transform the market it plays in by being 10x better than the alternatives. This is in contrast to many tech markets where market success often comes less from technical performance and more from identifying the right growth channels and product features to serve market needs (i.e., Facebook, Twitter, and Snapchat vs. MySpace, Orkut, and Friendster; Amazon vs. brick & mortar bookstores and electronics stores)

    All of this isn’t to say that there aren’t similarities between successful startups in both categories — strong vision, thoughtful leadership, and success-oriented cultures are just some examples of common traits in both. Nor is it to denigrate one versus the other. But, practically speaking, investing or operating successfully in both requires very different guiding principles and speaks to the heart of why its relatively rare to see individuals and organizations who can cross over to do both.

    Special thanks to Sophia Wang, Ryan Gilliam, and Kevin Lin Lee for reading an earlier draft and making this better!

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

  • Advice VCs Want to Give but Rarely Do to Entrepreneurs Pitching Their Startups

    Source: Someecards

    I thought I’d re-post a response I wrote a while ago to a question on Quora as someone recently asked me the question: “What advice do you wish you could give but usually don’t to a startup pitching you?”

    • Person X on your team reflects poorly on your company — This is tough advice to give as its virtually impossible during the course of a pitch to build enough rapport and get a deep enough understanding of the inter-personal dynamics of the team to give that advice without it unnecessarily hurting feelings or sounding incredibly arrogant / meddlesome.
    • Your slides look awful — This is difficult to say in a pitch because it just sounds petty for an investor to complain about the packaging rather than the substance.
    • Be careful when using my portfolio companies as examples — While its good to build rapport / common ground with your VC audience, using their portfolio companies as examples has an unnecessarily high chance of backfiring. It is highly unlikely that you will know more than an inside investor who is attending board meetings and in direct contact with management, so any errors you make (i.e., assuming a company is doing well when it isn’t or assuming a company is doing poorly when it is doing well / is about to turn the corner) are readily caught and immediately make you seem foolish.
    • You should pitch someone who’s more passionate about what you’re doing — Because VCs have to risk their reputation within their firms / to the outside world for the deals they sign up to do, they have to be very selective about which companies they choose to get involved with. As a result, even if there’s nothing wrong with a business model / idea, some VCs will choose not to invest due simply to lack of passion. As the entrepreneur is probably deeply passionate about and personally invested in the market / problem, giving this advice can feel tantamount to insulting the entrepreneur’s child or spouse.

    Hopefully this gives some of the hard-working entrepreneurs out there some context on why a pitch didn’t go as well as they had hoped and maybe some pointers on who and how to approach an investor for their next pitch.

    Thought this was interesting? Check out some of my other pieces on how VC works / thinks

  • The Four Types of M&A

    I’m oftentimes asked what determines the prices that companies get bought for: after all, why does one app company get bought for $19 billion and a similar app get bought at a discount to the amount of investor capital that was raised?

    While specific transaction values depend a lot on the specific acquirer (i.e. how much cash on hand they have, how big they are, etc.), I’m going to share a framework that has been very helpful to me in thinking about acquisition valuations and how startups can position themselves to get more attractive offers. The key is understanding that, all things being equal, why you’re being acquired determines the buyer’s willingness to pay. These motivations fall on a spectrum dividing acquisitions into four types:

    • Talent Acquisitions: These are commonly referred to in the tech press as “acquihires”. In these acquisitions, the buyer has determined that it makes more sense to buy a team than to spend the money, time, and effort needed to recruit a comparable one. In these acquisitions, the size and caliber of the team determine the purchase price.
    • Asset / Capability Acquisitions: In these acquisitions, the buyer is in need of a particular asset or capability of the target: it could be a portfolio of patents, a particular customer relationship, a particular facility, or even a particular product or technology that helps complete the buyer’s product portfolio. In these acquisitions, the uniqueness and potential business value of the assets determine the purchase price.
    • Business Acquisitions: These are acquisitions where the buyer values the target for the success of its business and for the possible synergies that could come about from merging the two. In these acquisitions, the financials of the target (revenues, profitability, growth rate) as well as the benefits that the investment bankers and buyer’s corporate development teams estimate from combining the two businesses (cost savings, ability to easily cross-sell, new business won because of a more complete offering, etc) determine the purchase price.
    • Strategic Gamechangers: These are acquisitions where the buyer believes the target gives them an ability to transform their business and is also a critical threat if acquired by a competitor. These tend to be acquisitions which are priced by the buyer’s full ability to pay as they represent bets on a future.

    What’s useful about this framework is that it gives guidance to companies who are contemplating acquisitions as exit opportunities:

    • If your company is being considered for a talent acquisition, then it is your job to convince the acquirer that you have built assets and capabilities above and beyond what your team alone is worth. Emphasize patents, communities, developer ecosystems, corporate relationships, how your product fills a distinct gap in their product portfolio, a sexy domain name, anything that might be valuable beyond just the team that has attracted their interest.
    • If a company is being considered for an asset / capability acquisition, then the key is to emphasize the potential financial trajectory of the business and the synergies that can be realized after a merger. Emphasize how current revenues and contracts will grow and develop, how a combined sales and marketing effort will be more effective than the sum of the parts, and how the current businesses are complementary in a real way that impacts the bottom line, and not just as an interesting “thing” to buy.
    • If a company is being evaluated as a business acquisition, then the key is to emphasize how pivotal a role it can play in defining the future of the acquirer in a way that goes beyond just what the numbers say about the business. This is what drives valuations like GM’s acquisition of Cruise (which was a leader in driverless vehicle technology) for up to $1B, or Facebook’s acquisition of WhatsApp (messenger app with over 600 million users when it was acquired, many in strategic regions for Facebook) for $19B, or Walmart’s acquisition of Jet.com (an innovator in eCommerce that Walmart needs to help in its war for retail marketshare with Amazon.com).

    The framework works for two reasons: (1) companies are bought, not sold, and the price is usually determined by the party that is most willing to walk away from a deal (that’s usually the buyer) and (2) it generally reflects how most startups tend to create value over time: they start by hiring a great team, who proceed to build compelling capabilities / assets, which materialize as interesting businesses, which can represent the future direction of an industry.

    Hopefully, this framework helps any tech industry onlooker wondering why acquisition valuations end up at a certain level or any startup evaluating how best to court an acquisition offer.

    Thought this was interesting? Check out some of my other pieces on how VC works / thinks

  • Snap Inc by the Numbers

    A look at what Snap’s S-1 reveals about their growth story and unit economics

    If you follow the tech industry at all, you will have heard that consumer app darling Snap Inc. (makers of the app Snapchat) has filed to go public. The ensuing Form S-1 that has recently been made available has left tech-finance nerds like yours truly drooling over the until-recently-super-secretive numbers behind their business.

    Oddly apt banner; Source: Business Insider

    Much of the commentary in the press to date has been about how unprofitable the company is (having lost over $500M in 2016 alone). I have been unimpressed with that line of thinking — as what the bottom line is in a given year is hardly the right measure for assessing a young, high-growth company.

    While full-time Wall Street analysts will pour over the figures and comparables in much greater detail than I can, I decided to take a quick peek at the numbers to gauge for myself how the business is doing as a growth investment, looking at:

    • What does the growth story look like for the business?
    • Do the unit economics allow for a path to profitability?

    What does the growth story look like for the business?

    As I’ve noted before, consumer media businesses like Snap have two options available to grow: (1) increase the number of users / amount of time spent and/or (2) better monetize users over time

    A quick peek at the DAU (Daily Active Users) counts of Snap reveal that path (1) is troubled for them. Using Facebook as a comparable (and using the midpoint of Facebook’s quarter-end DAU counts to line up with Snap’s average DAU over a quarter) reveals not only that Snap’s DAU numbers aren’t growing so much, their growth outside of North America (where they should have more room to grow) isn’t doing that great either (which is especially alarming as the S-1 admits Q4 is usually seasonally high for them).

    Last 3 Quarters of DAU growth, by region

    A quick look at the data also reveals why Facebook prioritizes Android development and low-bandwidth-friendly experiences — international remains an area of rapid growth which is especially astonishing considering how over 1 billion Facebook users are from outside of North America. This contrasts with Snap which, in addition to needing a huge amount of bandwidth (as a photo and video intensive platform) also (as they admitted in their S-1) de-emphasizes Android development. Couple that with Snap’s core demographic (read: old people can’t figure out how to use the app), reveals a challenge to where quick short-term user growth can come from.

    As a result, Snap’s growth in the near term will have to be driven more by path (2). Here, there is a lot more good news. Snap’s quarterly revenue per user more than doubled over the last 3 quarters to $1.029/DAU. While its a long way off from Facebook’s whopping $7.323/DAU (and over $25 if you’re just looking at North American users), it suggests that there is plenty of opportunity for Snap to increase monetization, especially overseas where its currently able to only monetize about 1/10 as effectively as they are in North America (compared to Facebook which is able to do so 1/5 to 1/6 of North America depending on the quarter).

    2016 and 2015 Q2-Q4 Quarterly Revenue per DAU, by region

    Considering Snap has just started with its advertising business and has already convinced major advertisers to build custom content that isn’t readily reusable on other platforms and Snap’s low revenue per user compared even to Facebook’s overseas numbers, I think its a relatively safe bet that there is a lot of potential for the number to go up.

    Do the unit economics allow for a path to profitability?

    While most folks have been (rightfully) stunned by the (staggering) amount of money Snap lost in 2016, to me the more pertinent question (considering the over $1 billion Snap still has in its coffers to weather losses) is whether or not there is a path to sustainable unit economics. Or, put more simply, can Snap grow its way out of unprofitability?

    Because neither Facebook nor Snap provide regional breakdowns of their cost structure, I’ve focused on global unit economics, summarized below:

    2016 and 2015 Q2-Q4 Quarterly Financials per DAU

    What’s astonishing here is that neither Snap nor Facebook seem to be gaining much from scale. Not only are their costs of sales per user (cost of hosting infrastructure and advertising infrastructure) increasing each quarter, but the operating expenses per user (what they spend on R&D, sales & marketing, and overhead — so not directly tied to any particular user or dollar of revenue) don’t seem to be shrinking either. In fact, Facebook’s is over twice as large as Snap’s — suggesting that its not just a simple question of Snap growing a bit further to begin to experience returns to scale here.

    What makes the Facebook economic machine go, though, is despite the increase in costs per user, their revenue per user grows even faster. The result is profit per user is growing quarter to quarter! In fact, on a per user basis, Q4 2016 operating profit exceeded Q2 2015 gross profit(revenue less cost of sales, so not counting operating expenses)! No wonder Facebook’s stock price has been on a tear!

    While Snap has also been growing its revenue per user faster than its cost of sales (turning a gross profit per user in Q4 2016 for the first time), the overall trendlines aren’t great, as illustrated by the fact that its operating profit per user has gotten steadily worse over the last 3 quarters. The rapid growth in Snap’s costs per user and the fact that Facebook’s costs are larger and still growing suggests that there are no simple scale-based reasons that Snap will achieve profitability on a per user basis. As a result, the only path for Snap to achieve sustainability on unit economics will be to pursue huge growth in user monetization.

    Tying it Together

    The case for Snap as a good investment really boils down to how quickly and to what extent one believes that the company can increase their monetization per user. While the potential is certainly there (as is being realized as the rapid growth in revenue per user numbers show), what’s less clear is whether or not the company has the technology or the talent (none of the key executives named in the S-1 have a particular background building advertising infrastructure or ecosystems that Google, Facebook, and even Twitter did to dominate the online advertising businesses) to do it quickly enough to justify the rumored $25 billion valuation they are striving for (a whopping 38x sales multiple using 2016 Q4 revenue as a run-rate [which the S-1 admits is a seasonally high quarter]).

    What is striking to me, though, is that Snap would even attempt an IPO at this stage. In my mind, Snap has a very real shot at being a great digital media company of the same importance as Google and Facebook and, while I can appreciate the hunger from Wall Street to invest in a high-growth consumer tech company, not having a great deal of visibility / certainty around unit economics and having only barely begun monetization (with your first quarter where revenue exceeds cost of sales is a holiday quarter) poses challenges for a management team that will need to manage public market expectations around forecasts and capitalization.

    In any event, I’ll be looking forward to digging in more when Snap reveals future figures around monetization and advertising strategy — and, to be honest, Facebook’s numbers going forward now that I have a better appreciation for their impressive economic model.

    Thought this was interesting or helpful? Check out some of my other pieces on investing / finance.

  • 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.

  • Why VR Could be as Big as the Smartphone Revolution

    Technology in the 1990s and early 2000s marched to the beat of an Intel-and-Microsoft-led drum.

    Source: IT Portal

    Intel would release new chips at a regular cadence: each cheaper, faster, and more energy efficient than the last. This would let Microsoft push out new, more performance-hungry software, which would, in turn, get customers to want Intel’s next, more awesome chip. Couple that virtuous cycle with the fact that millions of households were buying their first PCs and getting onto the Internet for the first time — and great opportunities were created to build businesses and products across software and hardware.

    But, over time, that cycle broke down. By the mid-2000s, Intel’s technological progress bumped into the limits of what physics would allow with regards to chip performance and cost. Complacency from its enviable market share coupled with software bloat from its Windows and Office franchises had a similar effect on Microsoft. The result was that the Intel and Microsoft drum stopped beating as they became unable to give the mass market a compelling reason to upgrade to each subsequent generation of devices.

    The result was a hollowing out of the hardware and semiconductor industries tied to the PC market that was only masked by the innovation stemming from the rise of the Internet and the dawn of a new technology cycle in the late 2000s in the form of Apple’s iPhone and its Android competitors: the smartphone.

    Source: Mashable

    A new, but eerily familiar cycle began: like clockwork, Qualcomm, Samsung, and Apple (playing the part of Intel) would devise new, more awesome chips which would feed the creation of new performance-hungry software from Google and Apple (playing the part of Microsoft) which led to demand for the next generation of hardware. Just as with the PC cycle, new and lucrative software, hardware, and service businesses flourished.

    But, just as with the PC cycle, the smartphone cycle is starting to show signs of maturity. Apple’s recent slower than expected growth has already been blamed on smartphone market saturation. Users are beginning to see each new generation of smartphone as marginal improvements. There are also eery parallels between the growing complaints over Apple software quality from even Apple fans and the position Microsoft was in near the end of the PC cycle.

    While its too early to call the end for Apple and Google, history suggests that we will eventually enter a similar phase with smartphones that the PC industry experienced. This begs the question: what’s next? Many of the traditional answers to this question — connected cars, the “Internet of Things”, Wearables, Digital TVs — have not yet proven themselves to be truly mass market, nor have they shown the virtuous technology upgrade cycle that characterized the PC and smartphone industries.

    This brings us to Virtual Reality. With VR, we have a new technology paradigm that can (potentially) appeal to the mass market (new types of games, new ways of doing work, new ways of experiencing the world, etc.). It also has a high bar for hardware performance that will benefit dramatically from advances in technology, not dissimilar from what we saw with the PC and smartphone.

    Source: Forbes

    The ultimate proof will be whether or not a compelling ecosystem of VR software and services emerges to make this technology more of a mainstream “must-have” (something that, admittedly, the high price of the first generation Facebook/Oculus, HTC/Valve, and Microsoft products may hinder).

    As a tech enthusiast, its easy to get excited. Not only is VR just frickin’ cool (it is!), its probably the first thing since the smartphone with the mass appeal and virtuous upgrade cycle that can bring about the huge flourishing of products and companies that makes tech so dynamic to be involved with.

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

  • 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!

  • What Happens After the Tech Bubble Pops

    In recent years, it’s been the opposite of controversial to say that the tech industry is in a bubble. The terrible recent stock market performance of once high-flying startups across virtually every industry (see table below) and the turmoil in the stock market stemming from low oil prices and concerns about the economies of countries like China and Brazil have raised fears that the bubble is beginning to pop.

    While history will judge when this bubble “officially” bursts, the purpose of this post is to try to make some predictions about what will happen during/after this “correction” and pull together some advice for people in / wanting to get into the tech industry. Starting with the immediate consequences, one can reasonably expect that:

    • Exit pipeline will dry up: When startup valuations are higher than what the company could reasonably get in the stock market, management teams (who need to keep their investors and employees happy) become less willing to go public. And, if public markets are less excited about startups, the price acquirers need to pay to convince a management team to sell goes down. The result is fewer exits and less cash back to investors and employees for the exits that do happen.
    • VCs become less willing to invest: VCs invest in startups on the promise that future IPOs and acquisitions will make them even more money. When the exit pipeline dries up, VCs get cold feet because the ability to get a nice exit seems to fade away. The result is that VCs become a lot more price-sensitive when it comes to investing in later stage companies (where the dried up exit pipeline hurts the most).
    • Later stage companies start cutting costs: Companies in an environment where they can’t sell themselves or easily raise money have no choice but to cut costs. Since the vast majority of later-stage startups run at a loss to increase growth, they will find themselves in the uncomfortable position of slowing down hiring and potentially laying employees off, cutting back on perks, and focusing a lot more on getting their financials in order.

    The result of all of this will be interesting for folks used to a tech industry (and a Bay Area) flush with cash and boundlessly optimistic:

    1. Job hopping should slow: “Easy money” to help companies figure out what works or to get an “acquihire” as a soft landing will be harder to get in a challenged financing and exit environment. The result is that the rapid job hopping endemic in the tech industry should slow as potential founders find it harder to raise money for their ideas and as it becomes harder for new startups to get the capital they need to pay top dollar.
    2. Strong companies are here to stay: While there is broad agreement that there are too many startups with higher valuations than reasonable, what’s also become clear is there are a number of mature tech companies that are doing exceptionally well (i.e. Facebook, Amazon, Netflix, and Google) and a number of “hotshots” which have demonstrated enough growth and strong enough unit economics and market position to survive a challenged environment (i.e. Uber, Airbnb). This will let them continue to hire and invest in ways that weaker peers will be unable to match.
    3. Tech “luxury money” will slow but not disappear: Anyone who lives in the Bay Area has a story of the ridiculousness of “tech money” (sky-high rents, gourmet toast,“its like Uber but for X”, etc). This has been fueled by cash from the startup world as well as free flowing VC money subsidizing many of these new services . However, in a world where companies need to cut costs, where exits are harder to come by, and where VCs are less willing to subsidize random on-demand services, a lot of this will diminish. That some of these services are fundamentally better than what came before (i.e. Uber) and that stronger companies will continue to pay top dollar for top talent will prevent all of this from collapsing (and lets not forget San Francisco’s irrational housing supply policies). As a result, people expecting a reversal of gentrification and the excesses of tech wealth will likely be disappointed, but its reasonable to expect a dramatic rationalization of the price and quantity of many “luxuries” that Bay Area inhabitants have become accustomed to soon.

    So, what to do if you’re in / trying to get in to / wanting to invest in the tech industry?

    • Understand the business before you get in: Its a shame that market sentiment drives fundraising and exits, because good financial performance is generally a pretty good indicator of the long-term prospects of a business. In an environment where its harder to exit and raise cash, its absolutely critical to make sure there is a solid business footing so the company can keep going or raise money / exit on good terms.
    • Be concerned about companies which have a lot of startup exposure: Even if a company has solid financial performance, if much of that comes from selling to startups (especially services around accounting, recruiting, or sales), then they’re dependent on VCs opening up their own wallets to make money.
    • Have a much higher bar for large, later-stage companies: The companies that will feel the most “pain” the earliest will be those with with high valuations and high costs. Raising money at unicorn valuations can make a sexy press release but it doesn’t amount to anything if you can’t exit or raise money at an even higher valuation.
    • Rationalize exposure to “luxury”: Don’t expect that “Uber but for X” service that you love to stick around (at least not at current prices)…
    • Early stage companies can still be attractive: Companies that are several years from an exit & raising large amounts of cash will be insulated in the near-term from the pain in the later stage, especially if they are committed to staying frugal and building a disruptive business. Since they are already relatively low in valuation and since investors know they are discounting off a valuation in the future (potentially after any current market softness), the downward pressures on valuation are potentially lighter as well.

    Thought this was interesting or helpful? Check out some of my other pieces on investing / finance.

  • An “Unbiased Opinion”

    I recently read a short column by gadget reviewer Vlad Savov in The Verge provocatively titled “My reviews are biased — that’s why you should trust them” which made me think. In it, Vlad addresses the accusation he hears often that he’s biased:

    “Of course I’m biased, that’s the whole point… subjectivity is an inherent — and I would argue necessary — part of making these reviews meaningful. Giving each new device a decontextualized blank slate to be reviewed against and only asserting the bare facts of its existence is neither engaging nor particularly useful. You want me to complain about the chronically bloopy Samsung TouchWiz interface while celebrating the size perfection of last year’s Moto X. Those are my preferences, my biased opinions, and it’s only by applying them to the pristine new phone or tablet that I can be of any use to readers. To be perfectly impartial would negate the value of having a human conduct the review at all. Just feed the new thing into a 3D scanner and run a few algorithms over the resulting data to determine a numerical score. Job done.”

    [emphasis mine]

    As Vlad points out, in an expert you’re asking for advice from, bias is a good thing. Now whether or not Vlad has unhelpful biases or is someone who’s opinion you value is a separate question entirely, but if there’s one thing I’ve learned — an unbiased opinion is oftentimes an uneducated one and tend to come from panderers who fit one of three criteria:

    1. they think you don’t want them to express an opinion and are trying to respect your wishes
    2. they don’t know anything
    3. they are trying to sell you something, not mutually exclusive with (2)

    The individuals who are the most knowledgeable and thoughtful about a topic almost certainly have a bias and that’s a bias that you want to hear.

  • 3D Printing as Disruptive Innovation

    Last week, I attended a MIT/Stanford VLAB event on 3D printing technologies. While I had previously been aware of 3D printing (which works basically the way it sounds) as a way of helping companies and startups do quick prototypes or letting geeks of the “maker” persuasion make random knickknacks, it was at the event that I started to recognize the technology’s disruptive potential in manufacturing. While the conference itself was actually more about personal use for 3D printing, when I thought about the applications in the industrial/business world, it was literally like seeing the first part/introduction of a new chapter or case study from Clayton Christensen, author of The Innovator’s Dilemma (and inspiration for one of the more popular blog posts here :-)) play out right in front of me:

    • Like many other disruptive innovations when they began, 3D printing today is unable to serve the broader manufacturing “market”. Generally speaking, the time needed per unit output, the poor “print resolution”, the upfront capital costs, and some of the limitations in terms of materials are among the reasons that the technology as it stands today is uncompetitive with traditional mass manufacturing.
    • Even if 3D printing were competitive today, there are big internal and external stumbling blocks which would probably make it very difficult for existing large companies to embrace it. Today’s heavyweight manufacturers are organized and incentivized internally along the lines of traditional assembly line manufacturing. They also lack the partners, channels, and supply chain relationships (among others) externally that they would need to succeed.
    • While 3D printing today is very disadvantaged relative to traditional manufacturing technologies (most notably in speed and upfront cost), it is extremely good at certain things which make it a phenomenal technology for certain use cases:
      • Rapid design to production: Unlike traditional manufacturing techniques which take significant initial tooling and setup, once you have a 3D printer and an idea, all you need to do is print the darn thing! At the conference, one of the panelists gave a great example: a designer bought an Apple iPad on a Friday, decided he wanted to make his own iPad case, and despite not getting any help from Apple or prior knowledge of the specs, was able by Monday to be producing and selling the case he had designed that weekend. Idea to production in three days. Is it any wonder that so many of the new hardware startups are using 3D printing to do quick prototyping?
      • Short runs/lots of customization: Chances are most of the things you use in your life are not one of a kind (i.e. pencils, clothes, utensils, dishware, furniture, cars, etc). The reason for this is that mass production make it extremely cheap to produce many copies of the same thing. The flip side of this is that short production runs (where you’re not producing thousands or millions of the same thing) and production where each item has a fair amount of customization or uniqueness is really expensive. With 3D printing, however, because each item being produced is produced in the same way (by the printer), you can produce one item at close to the same per unit price as producing a million – this makes 3D printing a very interesting technology for markets where customization & short runs are extremely valuable.
      • Shapes/structures that injection molding and machining find difficult: There are many shapes where traditional machining (taking a big block of material and whittling it down to the desired shape) and injection molding (building a mold and then filling it with molten material to get the desired shape) are not ideal: things like producing precision products that go into airplanes and racecars or printing the scaffolds with which bioengineers hope to build artificial organs are uniquely addressable by 3D printing technologies.
      • Low labor: The printer takes care of all of it – thus letting companies cut costs in manufacturing and/or refocus their people to steps in the process which do require direct human intervention.
    • And, of course, with the new markets which are opening up for 3D printing, its certainly helpful that the size, cost, and performance of 3D printers has improved dramatically and is continuing to improve – to the point where the panelists were very serious when they articulated a vision of the future where 3D printers could be as widespread as typical inkjet/laser printers!

    Ok, so why do we care? While its difficult to predict precisely what this technology could bring (it is disruptive after all!), I think there are a few tantalizing possibilities of how the manufacturing game might change to consider:

    • The ability to do rapid design to productionmeans you could do“fast fashion” for everything – in the same way that companies like Zara can produce thousands of different products in a season (and quickly change them to meet new trends/styles), broader adoption of 3D printing could lead to the rise of new companies where design/operational flexibility and speed are king, as the companies best able to fit their products to the flavor-of-the-month gain more traction.
    • The ability to do customization means you can manufacture custom parts/products cost-effectively and without holding as much inventory; production only needs to begin after an order is on hand (no reason to hold extra “copies” of something that may go out of fashion/go bad in storage when you can print stuff on the fly) and the lack of retooling means companies can be a lot more flexible in terms of using customization to get more customers.
    • I’m not sure how all the second/third-order effects play out, but this could also put a damper on outsourced manufacturing to countries like China/India – who cares about cheaper manufacturing labor overseas when 3D printing makes it possible to manufacture locally without much labor and avoid import duties, shipping delays, and the need to hold on to parts/inventory?

    I think there’s a ton of potential for the technology itself and its applications, and the possible consequences for how manufacturing will evolve are staggering. Yes, we are probably a long way off from seeing this, but I think we are on the verge of seeing a disruptive innovation take place, and if you’re anything like me, you’re excited to see it play out.

  • The Marketing Glory of NVIDIA’s Codenames

    While code names are not rare in the corporate world, more often than not, the names tend to be unimaginative. NVIDIA’s code names, however, are pure marketing glory.

    Take NVIDIA’s high performance computing product roadmap (below) – these are products that use the graphics processing capabilities of NVIDIA’s high-end GPUs and turn them into smaller, cheaper, and more power-efficient supercomputing engines which scientists and researchers can use to crunch numbers. How does NVIDIA describe its future roadmap? It uses the names of famous scientists to describe its technology roadmap: Tesla (the great American electrical engineer who helped bring us AC power), Fermi (“the father of the Atomic Bomb”), Kepler (one of the first astronomers to apply physics to astronomy), and Maxwell (the physicist who helped show that electrical, magnetic, and optical phenomena were all linked).

    Source: Rage3D

    Who wouldn’t want to do some “high power” research (pun intended) with Maxwell? 

    But, what really takes the cake for me are the codenames NVIDIA uses for its smartphone/tablet chips: its Tegra line of products. Instead of scientists, he uses, well, comic book characters. For release at the end of this year? Kal-El, or for the uninitiated, that’s the alien name for Superman. After that? Wayne, as in the alter ego for Batman. Then, Logan, as in the name for the X-men Wolverine. And then Stark, as in the alter ego for Iron Man.

    Source: NVIDIA

    Everybody wants a little Iron Man in their tablet.

  • 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

  • Standards Have No Standards

    Many forms of technology requires standards to work. As a result, it is in the best interest of all parties in the technology ecosystem to participate in standards bodies to ensure interoperability.

    The two main problem with getting standards working can be summed up, as all good things in technology can be, in the form of webcomics. 

    Problem #1, from XKCD: people/companies/organizations keep creating more standards.

    Source: XKCD

    The cartoon takes the more benevolent look at how standards proliferate; the more cynical view is that individuals/corporations recognize that control or influence over an industry standard can give them significant power in the technology ecosystem. I think both the benevolent and the cynical view are always at play – but the result is the continual creation of “bigger and badder” standards which are meant to replace but oftentimes fail to completely supplant existing ones. Case in point, as someone who has spent a fair amount of time looking at technologies to enable greater intelligence/network connectivity in new types of devices (think TVs, smart meters, appliances, thermostats, etc.), I’m still puzzled as to why we have so many wireless communication standards and protocols for achieving it (Bluetooth, Zigbee, ZWave, WiFi, DASH7, 6LowPAN, etc)

    Problem #2: standards aren’t purely technical undertakings – they’re heavily motivated by the preferences of the bodies and companies which participate in formulating them, and like the US’s “wonderful” legislative process, involves mashing together a large number of preferences, some of which might not necessarily be easily compatible with one another. This can turn quite political and generate standards/working papers which are too difficult to support well (i.e. like DLNA). Or, as Dilbert sums it up, these meetings are full of people who are instructed to do this:

    Source: Dilbert

    Or this:

    Source: Dilbert

    Our one hope is that the industry has enough people/companires who are more vested in the future of the technology industry than taking unnecessarily cheap shots at one another… It’s a wonder we have functioning standards at all, isn’t it?

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

  • The “Strangest Biotech Company of All” Issues Their Annual Report as a Comic Book

    This seems almost made for me: I’m into comic books. I do my own “corporate style” annual and quarterly reports to track how my finances and goals are going. And, I follow the biopharma industry.

    Source: United Therapeutics 2010 Annual Report

    So, when I found out that a biotech company issued its latest annual report in the form of a comic book, I knew I had to talk about it!

    The art style is not all that bad, and the bulk of the comic is told from the first person perspective of Martin Auster, head of business development at the company (that’s Doctor Auster to you, pal!). We get an interesting look at Auster’s life, how he was a medical student who didn’t really want to do a residency, and how and why he ultimately joins the company.

    Source: United Therapeutics 2010 Annual Report
    Source: United Therapeutics 2010 Annual Report
    Source: United Therapeutics 2010 Annual Report

    And, of course, what annual report wouldn’t be complete without some financial charts – and yes, this particular chart was intended to be read with 3D glasses (which were apparently shipped with paper copies of the report):

    Source: United Therapeutics 2010 Annual Report

    Interestingly, the company in question – United Therapeutics — is not a tiny company either: its worth roughly $3 billion (as of when this was written) and is also somewhat renowned for its more unusual practices (meetings have occurred in the virtual world Second Life and employees are all called “Unitherians”) as well as its brilliant and eccentric founder, Dr. Martine Rothblatt. Rothblatt is a very accomplished modern-day polymath:

    • She was an early pioneer in communication satellite law
    • She helped launch a number of communication satellite technologies and companies
    • She founded and was CEO of Geostar Corporation, an early GPS satellite company
    • She founded and was CEO of Sirius Satellite Radio
    • She led the International Bar Association’s efforts to draft a Universal Declaration on the Human Genome and Human Rights
    • She is a pre-eminent proponent for xenotransplantation
    • She is also one of the most vocal advocates of transgenderism and transgender rights, having been born as Martin Rothblatt (Howard Stern even referred to her as the “Martine Luther Queen” of the movement)
    • She is a major proponent of the interesting philosophy that one might achieve technological immortality by digitizing oneself (having created an interesting robot version of her wife, Bina).
    • She started United Therapeutics because her daughter was diagnosed with Pulmonary Arterial Hypertension, a fatal condition which, at the time of diagnosis, there was no effective treatment for

    You got to have a lot of love and respect for a company that not only seems to have delivered an impressive financial outcome ($600 million in sales a year and $3 billion in market cap is not bad!) and can still maintain what looks like a very fun and unique culture (in no small part, I’m sure, because of their CEO).

  • The Goal is Not Profitability

    Source: Next Generation Growth

    I’ve blogged before about how the economics of the venture industry affect how venture capitalists evaluate potential investments, the main conclusion of which is that VCs are really only interested in companies that could potentially IPO or sell for at least several hundred million dollars.

    One variation on that line of logic which I think startups/entrepreneurs oftentimes fail to grasp is that profitability is not the number one goal.

    Now, don’t get me wrong. The reason for any business to exist is to ultimately make profit. And, all things being equal, investors certainly prefer more profitable companies to less/unprofitable ones. But, the truth of the matter is that things are rarely all equal and, at the end of the day, your venture capital investors aren’t necessarily looking for profit, they are looking for a large outcome.

    Before I get accused of being supportive of bubble companies (I’m not), let me explain what this seemingly crazy concept means in practice. First of all, short-term profitability can conflict with rapid growth. This will sound counter-intuitive, but its the very premise for venture capital investment. Think about it: Facebook could’ve tried much harder to make a profit in its early years by cutting salaries and not investing in R&D, but that would’ve killed Facebook’s ability to grow quickly. Instead, they raised venture capital and ignored short-term profitability to build out the product and aggressively market. This might seem simplistic, but I oftentimes receive pitches/plans from entrepreneurs who boast that they can achieve profitability quickly or that they don’t need to raise another round of investment because they will be making a profit soon, never giving any thought to what might happen with their growth rate if they ignored profitability for another quarter or year.

    Secondly, the promise of growth and future profitability can drive large outcomes. Pandora, Groupon, Enphase, Tesla, A123, and Solazyme are among some of the hottest venture-backed IPOs in recent memory and do you know what they all also happen to share? They are very unprofitable and, to the best of my knowledge, have not yet had a single profitable year. However, the investment community has strong faith in the ability of these businesses to continue to grow rapidly and, eventually, deliver profitability. Whether or not that faith is well-placed is another question (and I have my doubts on some of the companies on that list), but as these examples illustrate, you don’t necessarily need to be profitable to be able to get a large venture-sized outcome.

    Of course, it’d be a mistake to take this logic and assume that you never need to achieve or think about profitability. After all, a company that is bleeding cash unnecessarily is not a good company by any definition, regardless of whether or not the person evaluating it is in venture capital. Furthermore, while the public market may forgive Pandora and Groupon’s money-losing, there’s also no guarantee that they will be so forgiving of another company’s or even of Pandora/Groupons a few months from now.

    But what I am saying is that entrepreneurs need to be more thoughtful when approaching a venture investor with a plan to achieve profitability/stop raising money more quickly, because the goal of that investor is not necessarily short-term profits.

    Thought this was interesting? Check out some of my other pieces on how VC works / thinks

  • Our Job is Not to Make Money

    Let’s say you pitch a VC and you’ve got a coherent business plan and some thoughtful perspectives on how your business scales. Does that mean you get the venture capital investment that you so desire?

    Source: Toothpaste for Dinner

    Not necessarily. There could be many reasons for a rejection, but one that crops up a great deal is not anything intrinsically wrong with a particular idea or team, but something which is an intrinsic issue with the venture capital model.

    One of our partners put it best when he pointed out, “Our job is not to make money, it’s to make a lot of money.”

    What that means is that venture capitalists are not just looking for a business that can make money. They are really looking for businesses which have the potential to sell for or go public (sell stock on NYSE/NASDAQ/etc) and yield hundreds of millions, if not billions of dollars.

    Why? It has to do with the way that venture capital funds work.

    • Venture capitalists raise large $100M+ funds. This is a lot of money to work with, but its also a burden in that the venture capital firm also has to deliver a large return on that large initial amount. If you start with a $100M fund, its not unheard of for investors in that fund to expect $300-400M back – and you just can’t get to those kinds of returns unless you bet on companies that sell for/list on a public market for a lot of money.
    • Although most investments fail, big outcomes can be *really* big. For every Facebook, there are dozens of wannabe copycats that fall flat – so there is a very high risk that a venture investment will not pan out as one hopes. But, the flip side to this is that Facebook will likely be an outcome dozens upon dozens of times larger than its copycats. The combination of the very high risk but very high reward drive venture capitalists to chase only those which have a shot at becoming a *really* big outcome – doing anything else basically guarantees that the firm will not be able to deliver a large enough return to its investors.
    • Partners are busy people. A typical venture capital fund is a partnership, consisting of a number of general partners who operate the fund. A typical general partner will, in addition to look for new deals, be responsible for/advise several companies at once. This is a fair amount of work for each company as it involves helping companies recruit, develop their strategy, connect with key customers/partners/influencers, deal with operational/legal issues, and raise money. As a result, while the amount of work can vary quite a bit, this basically limits the number of companies that a partner can commit to (and, hence, invest in). This limit encourages partners to favor companies which could end up with a larger outcome than a smaller, because below a certain size, the firm’s return profile and the limits on a partner’s time just don’t justify having a partner get too involved.

    The result? Venture capitalists have to turn down many pitches, not because they don’t like the idea or the team and not even necessarily because they don’t think the company will make money in a reasonably short time, but because they didn’t think the idea had a good shot at being something as big and game-changing as Google, Genentech, and VMWare were. And, in fact, the not often heard truth is that a lot of the endings which entrepreneurs think of as great and which are frequently featured on tech blogs like VentureBeat and TechCrunch (i.e. selling your company to Google for $10M) are actually quite small (and possibly even a failure) when it comes to how a large venture capital firm views it.

    Thought this was interesting? Check out some of my other pieces on how VC works / thinks

  • I Know Enough to Get Myself in Trouble

    One of the dangers of a consultant looking at tech is that he can get lost in jargon. A few weeks ago, I did a little research on some of the most cutting-edge software startups in the cloud computing space (the idea that you can use a computer feature/service without actually knowing anything about what sort of technology infrastructure was used to provide you with that feature/service – i.e., Gmail and Yahoo Mail on the consumer side, services like Amazon Web Services and Microsoft Azure on the business side). As a result, I’ve looked at the product offerings from guys like Nimbula, Cloudera, Clustrix, Appistry, Elastra, and MaxiScale, to name a few. And, while I know enough about cloud computing to understand, at a high level, what these companies do, the use of unclear terminology sometimes makes it very difficult to pierce the “fog of marketing” and really get a good understanding of the various product strengths and weaknesses.

    Is it any wonder that, at times, I feel like this:

    Source: Dilbert

    Yes, its all about that “integration layer” … My take? A great product should not need to hide behind jargon.

  • 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.

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

  • How to Properly Define a Company’s Culture

    Company culture is a concept which, while incredibly difficult to explain or measure, is very important to a company’s well-being and employee morale. Too often, it comes in the form of vaguely written out “corporate mission statements” or never-ending lists of feel-good, mean-nothing “company values”. Oh joy, you value “teamwork” and “making money” – that was so insightful…

    It was thus very refreshing for me to read the Netflix company culture document (sadly no longer embed-able, but you can find it at this Slideshare link).

    Slidumentation aside, I think the NetFlix presentation does three things extremely well:

    1. It’s not a list of feel-good words, but  actual values and statements which can actually guide the company in its day-to-day hiring, evaluation. Most company culture statements are nothing but long lists of virtues and things non-sociopaths respect. “Teamwork” and “honesty”, for example, are usually among them. But, as the Netflix presentation points out, even Enron had a list of “values” and that wound up not amounting to much of anything. Instead, Netflix has a clear state of  things they look for in their employees, each with clear explanations for what they actually mean. For “Curiosity”, Netflix has listed four supporting statements:
      • You learn rapidly and eagerly
      • You seek to understand our strategy, markets, subscribers, and suppliers.
      • You are broadly knowledgeable about business, technology, and entertainment.
      • You contribute effectively outside of your specialty
      Admittedly, there is nothing particularly remarkable about these four statements. But what is remarkable is that it is immediately clear to the reader what “curiosity” means, in the context of Netflix’s culture, and how Netflix employees should be judged and evaluated. It’s oftentimes astounding to me how few companies get to this bare minimum in terms of culture documents.
    2. Netflix actually gives clear value judgments.  I’ve already lamented the extent to which company culture statements are nothing more than laundry lists of “feel good” words. Netflix admirably cuts through that by not only explaining what the values mean, but also by what should happen when different “good words” conflict. And, best of all, they do it with brutal honesty. For instance, Netflix on how they won’t play the “benefits race” that other companies play:
      A great work place is stunning colleagues. Great workplace is not day-care, espresso, health benefits, sushi lunches, nice offices, or big compensation, and we only do those that are efficient at attracting stunning colleagues.Netflix on teamwork versus individual performance:Brilliant jerks: some companies tolerate them, [but] for us, the cost to teamwork is too high.Netflix on its annual compensation review policy:Lots of people have the title “Major League Pitcher” but they are not all equally effective. Similarly, all people with the title “Senior Marketing Manager” and “Director of Engineering” are not equally effective … So, essentially, [we are] rehiring each employee each year (and re-evaluating them based on their performance) for the purposes of compensation.Within each of the three examples, Netflix has done two amazing things: they’ve made a bold value judgment, which most companies fail to do, explaining just how the values should be lived, especially when they conflict (“we don’t care how smart you are, if you don’t work well with the team, you have to go”), and they’ve even given a reason(“teamwork is more important to delivering impact for our customers than one smart guy”).
    3. They explain what makes their culture different from other companies and why. Most people who like their jobs will give “culture” as a reason they think their company is unique. yet, if you read the countless mission statements and “our values” documents out there, you’d never be able to see that difference. Granted, the main issue may just be that management has chosen not to live up to the lofty ideals espoused in their list of virtues, but what might help with that and make it clearer to employees about what makes a particular workplace special is explaining how and why the company’s culture is different from another’s. Contrast that with the Netflix presentation, which spends many slides explaining the tradeoffs between too many rules and too few, and why they ultimately sided with having very few rules, whereas a manufacturing company or a medical company would have very many of them. They never go so far as to say that one is better than the other, only that they are different because they are in different industries with different needs and dynamics. And, as a result of that, they have implemented changes, like a simpler expense policy (“Act in Netflix’s best interests”) and a revolutionary vacation policy (“There is no policy or tracking”) [with an awesome explanation: “There is also no clothing policy at Netflix, but no one has come to work naked lately”].

    Pay attention, other companies. You would do well to learn from Netflix’s example.