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.
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.
This weekend, I paid a visit to The Henry Ford. Its a combination of multiple venues — a museum, an outdoor “innovation village”, a Ford Motors factory tour — which collectively celebrate America’s rich history of innovation and manufacturing and, in particular, the legacy of Henry Ford and the Ford Motors company he built.
While ambitious super-CEOs like Larry Page (Google), Elon Musk (Tesla), and Jeff Bezos (Amazon) with their tentacles in everything sometimes seem like a modern phenomena, The Henry Ford shows that they are just a modern-day reincarnations of the super-CEOs of yesteryear. Except, instead of pioneering software at scale, electric vehicles, and AI assistants, Ford was instrumental in the creation of assembly line mass production, the automotive industry (Ford developed the first car that the middle class could actually afford), the aerospace industry (Ford helped develop some of America’s first successful passenger planes), the forty hour workweek, and even the charcoal briquet (part of a drive to figure out what to do with the lumber waste that came from procuring the wood needed to build Model T’s).
In the same way that the tech giants of today pursue “moonshots” like drone delivery and self-driving cars, Ford pushed the frontier with its own moonshots: creating cars out of bioplastic, developing biofuels, and even an early collaboration with Thomas Edison to build an electric car.
It was a striking parallel, and also an instructional one for any company that believes they can stay on top forever: despite the moonshots and the technology advantages, new technologies, market forces, and global shifts come one after the other and yesterday’s Ford (eventually) gets supplanted by tomorrow’s Tesla.
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.
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 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).
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).
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:
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.
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.
Technology in the 1990s and early 2000s marched to the beat of an Intel-and-Microsoft-led drum.
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.
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.
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.
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.
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! (also available from Google Books)
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:
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.
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.
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.
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!
The chart above shows how Renaissance Capital’s US IPO index (prospectus), which tracks major IPOs in US markets, has performed versus the broader market (represented by the S&P500) over the past year. While the S&P500 hasn’t had a great year (down just over 10%), IPOs have done even worse (down over 30%).
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:
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.
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.
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.
is powered by a responsive design (so the page will look good even on the smaller screens of smartphones and tablets)
natively supports social links
takes advantage of WordPress’s menu-ing system (so I can more easily customize menu’s without going into a theme’s custom menu setup flow)
takes visitors to my homepage directly to the About Me page which also now features one of the few photos that doesn’t make me look terrible (taken by the wonderful Jennifer Gong) instead of drops users into a confusing, touch-unfriendly link carousel
I’ve also made a couple of other tweaks and customizations to update information / fix formatting to fit better with the new theme, but if anyone spots any bugs, please let me know in the comments! 🙂
Better late than never, but a few weeks ago, I got the chance to attend Google I/O — this time, not just as a fan of the Android platform but representing a developer. Below are some of my key takeaways from the event
Google‘s strategic direction – there were three big themes that were emphasized
Next Billion – a lot of what Google is doing (like making Google Maps / YouTube work without internet) is around making Chrome/Android/Google Search the platforms of choice for the next billion mobile users — many of whom will come from Brazil, India, China, Indonesia, etc. Its important for us to remember the US/Western Europe is not the totality of the world and that there’s a big chance that future major innovations and platform will come from elsewhere in the world.
Machine Learning – I was blown away (and a little creeped out!) by the machine learning tech they showed: Google Now on Tap (you can hold the home button and Android will figure out what’s on your screen/what you’re listening to and give you relevant info), the incredible photo recognition tech in the new Photos app (which you should all try! unlimited storage!), innovations Android is making in unlocking your phone when it knows its been in your pocket and not your desk. Every company should be thinking up where machine intelligence can be used to enhance their products.
Everything Connected – it reminded me of Microsoft’s heyday: except instead of Windows everywhere, its now Android/Chrome everywhere: Android Wear, Chromecast, Android TV, Android Auto, Brillo/Weave, Cardboard for VR, Nest/Dropcam for the home, things like Jacquard & Soli enabling new user interfaces, etc.
I sat through a panel on how Google does personalized recommendations / search on Google Play — long story short: keywords + ratings matter
Google will now allow A/B testing of Google Play store listings
Google Play console now directly integrates App Install advertising so you can run campaigns on Google Search, AdMob, and YouTube
Google Play console will also track how users get to Play Store listing by channel and how many convert to install
Android M – a lot of tweaks to the core Android app model for developers to pay attention to
Permissions: Android M moves to a very iOS-like model where app permissions aren’t granted when you install the app but when the app first uses them; they’ve also moved to a model where users can go into settings and manually revoke previously granted permissions; all Android developers will need to eventually think about how their apps will work if certain permissions are denied (see: http://developer.android.com/preview/features/runtime-permissions.html)
Doze and App Standby: Applications will now have two additional modes that the OS may enforce — one called Doze that keeps all apps in sleep mode to reduce power drain and Standby where the OS determines an app is “idle” and cuts off network access, syncs, and jobs — apps in both modes can still receive “high priority notifications” (see: http://developer.android.com/preview/behavior-changes.html under Power-Saving Optimizations)
Fingerprint API, Direct Share, and Voice Interactions: universal fingerprint rec API + ability to share specific content with specific favorite users (i.e. send to someone over Facebook Messenger, etc) + new way to build voice interactions in app (see: http://developer.android.com/preview/api-overview.html, starting from Authentication)
For free/automatically: pound on every button / interface on your app that they can see after launch for 1 min and see how many crashes they can get on a variety of Android devices (which helps given the sheer number of them that exist)
Paid: run custom Espresso or Robotium tests on specific devices (so you can get test coverage on a broader range of devices doing a specific set of things)
Places API: a lot of talks promoting their new mobile Places APIs (which will let iOS and Android apps have better mapping and place search capability)
One of the most fascinating things about the technology industry is how the lines between markets and competitors can shift all of a sudden. One day, Nokia is mainly thinking about competing with phone makers like RIM and Motorola on getting influence with carriers and upselling text messaging services / ring tones and, the next, they need to deal with players like Apple and Google, fostering a strong app ecosystem, creating intuitive user experiences, and building a brand that resonates with users.
One interesting case that has emerged in the past couple of days is the electric car company Tesla entering the Home and Industrial energy market. In much the same way that software let Apple and Google build operating systems that could double up as phones, the manufacturing prowess and battery technology which let Tesla take on the electric car market also gives them the ability to offer energy storage solutions for the utility market.
When I was a VC looking at energy storage opportunities, there was a fair amount of discussion in the industry about the future potential for electric cars connected to the grid to themselves to operate as energy storage / load balancing. I never expected this to amount to much for at least a decade — when the penetration of electric vehicles would be high enough to make sense for utilities to invest in this capability. Never would I have imagined the path to anything even remotely like this would be through an electric car company directly making and offering electric batteries to supply the market. While history will judge whether or not Tesla is successful at this (a lot of unanswered questions around the durability of their Li-ion batteries for utility purposes and how they will be serviced / maintained), you can’t fault Tesla for lack of boldness!
I was not impressed when I first saw Google’s vague overly-feel-good marketing materials for their new Inbox product. It seemed like a design refresh for email focused on implementing Google’s Material Design aesthetic rather than something that I absolutely needed. But, thanks to an invite from my buddy David, I’ve been able to use Google’s new take on email for about a week and I have to say this is the email product I’ve been waiting for.
What does it do that has gotten me so excited? There are three core pieces of functionality which make Google Inbox a great fit for the productivity-minded Gmail user:
Auto-categorization that actually works: Google has taken Gmail’s smart inbox functionality (which can tell personal emails apart from social updates, promotions, and forum posts) and have taken this to a whole different level with Inbox. The new categorization tech not only automatically groups common email types like trips vs bills vs social network updates, but it can, for many types of email, recognize the implied rules for the labels I already have and preserve those (i.e. messages to/from my wife).
The ability to snooze/dismiss email: One of Inbox’s most compelling features is a clone of the Dropbox-owned Mailbox app’s snoozing email functionality: moving an email out of your inbox until you’re ready to deal with it (i.e. until later tonight, tomorrow morning, later this week, etc). This feature, thankfully, also extends to the smart categories functionality, which, for instance, lets you snooze all of your promotions-related emails or dismiss all your email receipts in one go.
The ability to add todo items/reminders: Inbox also lets you add todo items and reminders directly into the inbox. These todos/reminders are treated as if they were emails — they sit side-by-side with “regular” emails in the interface and, as you probably expected, are also snooze-able (and sync with Google Now’s reminder functionality).
These enhancements let Gmail power users (like myself!) more readily use email as a productivity tool which tracks all the things they need to do (rather than managing a separate email and todo list) across multiple platforms (web, Android, iOS) with functionality built in to make it easier (like auto-bundling related emails and auto-complete as you type out todo items/reminders) as well as being integrated directly into Gmail (so with full support for search and without creating strange new labels/folders the way Mailbox does).
That being said, while the app’s conceptual and usage model are geared for power users, its missing some of the functionality that I (and I’m sure other Gmail power users) have come to depend on such as in-browser push notifications, ability to embed photos in messages, the ability to add things to a bundle/label via keyboard shortcut, support for labs functionality like embedding the calendar widget in the interface or pulling in preview functionality for Yelp/YouTube/Google Maps inline in the email, and the ability to snooze an email/todo (and set the time for the snooze easily) with a keyboard shortcut.
Those small problems aside I’ve taken so much to Inbox that it now pains me to use the regular Gmail interface for my work email account and I can’t wait until Google extends Inbox support for Google Apps accounts. If you have access to an invite, I would 100% recommend giving it a shot for a while.
The barriers to becoming a software engineer are real. People born in technical families, or who were introduced to programming at an early age have this easy confidence that lets them tackle new things, to keep learning — and, in our eyes, they just keep getting further and further ahead. Last year, I saw this gap and gave up. But all we really need is the opportunity to see that it’s not hopeless. It’s not about what we already know, it’s about how we learn. It’s about the tenacity of sitting in front of a computer and googling until you find the right answer. It’s about staring at every line of code until you understand what’s going on, or googling until you do. It’s about googling how-to, examples, errors, until it all begins to make sense.
Everything else will follow.
Practically speaking, nobody can possibly learn or know everything they need to succeed at life. Even the greatest college/graduate education is incapable of teaching you what you need to know two or three years out, let alone the practical ins and outs of the specific situation you may face. As a result, what drives success for knowledge workers today is a mix of three things:
the tenacity to tackle the many problems that you will face
the persistence and skill to figure out the answer — which oftentimes means knowing how to Google well (or Bing or Baidu, if that’s your cup of tea)
(Bear with me a bit, I promise I do get to the title eventually) One of the most formative classes I took in college was a class taught by Professor Doug Melton on stem cells. While truth be told, I’ve forgotten most of what I used to know about the growth factors and specifics of how stem cells work, the class left me with two powerful ideas.
The first is that true understanding requires you to overcome your own intellectual laziness. Its not enough to just take what a so-called expert says at face value — you should question her assumptions, her evidence, her interpretation, her controls (or lack thereof), and only after questioning these things can you properly make up your own mind. While I can’t say I’ve lived up to that challenge to the fullest extent, its been a helpful guide in my coursework and in my career as a consultant, then investor, and now entrepreneur.
The second was about the importance of personal passion as a motivating force. Professor Melton’s research and expertise into stem cells was driven in no small part by the desire to find a cure for diabetes, a condition which one of his kids suffers from. It was something which made him (and his lab) work harder at finding a way to take on the daunting task of taking stem cells and turning them into the beta islet cells in the pancreas that produce insulin. It made him advocate for the creation of the Harvard Stem Cell Institute and to strongly vocalize his opinions on legitimizing stem cell research (something which I had the pleasure of interviewing him on when I worked with Nextgen).
And, its paid off! Very recently, Melton’s lab published a paper in the journal Cell which claims to have devised a way to take stem cells and turn them into functioning beta islet cells capable of secreting insulin into the bloodstreams of diabetic mice that they’re transplanted in and reduce the high blood sugar levels that are a hallmark of the disease! While I have yet to read the paper (something I’ll try to get around to eventually) and this is still a ways off from a human therapy, its amazing to see the lab achieve this goal which seemed so challenging back when I was in college (not to mention, years earlier, when Melton first wanted to tackle the problem!)
Having met various members of the Melton lab (as well as the man himself), I can’t say how happy I am for the team and how great it is that we’ve made such a breakthrough in the fight against diabetes.
“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.”
As Vlad points out, bias isn’t necessarily a bad thing, and, in an expert you’re asking for advice from, its probably 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.
In my experience, “unbiased opinions” tend to come from panderers who fit one of three criteria:
they think you don’t want them to express an opinion and are trying to respect your wishes
they don’t know anything
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.
In the summer of 2010, I made the leap from consulting to venture capital, and for the past four years, I’ve had the privilege of working with some fantastic colleagues and some incredible entrepreneurs. I learned a great deal about startups, about entrepreneurship, about business, and about myself during that time, and its an experience I feel incredibly lucky to have had. But its hard to spend four years working with startups without getting “infected” by the desire to try your own hand at working in a startup :-). So, starting on Oct 1, I will embark on the next chapter of my career as the new Vice President of Product and Business Development for a startup called Yik Yak, which is building a mobile-centric social platform taking advantage of the community and social aspects that come from location and anonymity.
I first found out about the company after looking at the output from a software tool I had built to parse data from mobile app store ranking data provider App Annie to find new mobile applications which were seeing big upticks in downloads. This led to a quick series of back-and-forth emails and phone calls which culminated in my firm investing in their seed round and, a few months later, my firm leading their Series A (complete with me chasing down the founders at San Francisco International airport to get a signature on the term sheet just before their flight). Over the past few months, I’ve gotten the chance to see the two founders build out a product which seems remarkably addictive to its core user base and when they offered me this role, it seemed like a great next step for my career, and I am super-excited to be joining them!
Its hard for a device to get noticed in a world where new phones and tablets and smartwatches seem to come out every day. But one device unveiled back in March did for me: Motorola’s new smartwatch, the Moto 360 (see Motorola marketing video below).
So, being a true Fandroid, I bought a Moto 360 (clarification: my wonderful wife woke up at an unseemly hour and bought one for each of us) and have been using it for about a week — my take?
While there’s a lot of room for improvement, I like it.
This is by far the best looking smartwatch out there. Given how important appearance is for a watch, this is by far the most important positive that can be said of the Moto 360 — it just looks good. I was a little worried that the marketing materials wouldn’t accurately represent reality, but that fear turned out to be unfounded. The device not only looks nice up close, especially since its round design just looks so much better than pretty much every other smartwatch’s blocky rectangular designs, it also feels good: stainless steel body, a solid-feeling glass surface, and a very nice-feeling leather strap.
The battery life is nothing to brag about but will last you a full day. The key here is that the watch display can be used in two modes: (1) where the display is always on (and, from what I’ve read, will get something like 12 hours of battery life which won’t last you a whole day) and (2) where the display only turns on when you’ve triggered it which, in my experience, will get you something more like 20 hours of battery life — enough to get through a typical day. Obviously, I use (2) and what makes this possible is that turning on the screen is quite easy: you can do it by tapping on the touch-sensitive screen, by pushing the side button, or (although this only works 80% of the time) by moving your arm to be in a position where you can look at it. Now, I’d love a watch that could last at least months with the screen on before needing a charge but since I’m already charging my phone every night and since the wireless charging dock makes it easy to charge the device, this is an annoyance but hardly a dealbreaker.
The out-of-the-box experience needs some work. While the packaging is beautiful and fits well with how nice the watch itself looks, the Moto 360 unfortunately ships needing to be charged up to 80% before it can be used. Unfortunately this is not clear anywhere on the packaging or in the Android Wear smartphone app that you’re supposed to use to pair with the device or on the watch display so let me be explicit: if you buy the Moto 360, charge the device up before you download the Android Wear app or try to use it. Otherwise, nothing will happen — something which very much freaked out yours truly when I thought I had gotten a defective unit. Also, while I haven’t heard about this from anyone else, the Moto Connect app that Motorola wanted me to install also failed to provision an account for me correctly, leaving me unable to customize the finer details on the watchface designs that come with the watch. Not the end of the world, but definitely a set of problems a company like Motorola shouldn’t be facing.
I’m not sure the pedometer or heart rate sensor are super-accurate, but they’ve pretty much killed any need/desire on my part for a fitness wearable. The fitness functionality on the watch isn’t anything to write home about (its a simple step counter and heart rate sensor with basic history and heart-rate goal tracking). I’m also not entirely convinced that the heart rate sensor or the pedometer are particularly accurate (although its not like the competition is that great either), but their availability on a device I’m always going to be wearing because of its other functionality may pose a serious risk to fitness wearable companies which only do step tracking or heart rate detection.
Voice recognition is still not quite where it needs to be for me to make heavier use of the voice commands functionality.
The software doesn’t do a ton but that’s the way it should be. When I first started using Android Wear, I was a little bummed that it didn’t seem to have a ton of functionality: I couldn’t play games on it or browse maps or edit photos (or send my heartbeat or a random doodle to a random person…). But, after a day or two of wearing the device to social gatherings, I came to realize you really don’t want to do everything on your watch. Complicated tasks should be done on your phone or tablet or PC. They not only have larger screens but they are used in social contexts where that type of activity makes sense. Spending your time trying to do something on your smartwatch looks far more awkward (and probably looks far more rude) than doing the same thing on your phone or other device. Instead, I’ve come to rely on the Moto 360 as a way of supplementing my phone by letting me know (by vibrating and quickly lighting up the screen) about incoming notifications (like from an email or text or Facebook message), new alerts from Google Now (like access to the local weather or finding out about sudden traffic on the road to/from work), and by letting me deal with notifications the way I would if they were on my phone (like the ability to play and pause music or a podcast, or the ability to reply using voice commands to an email or text). This helps me be more present in social settings as I feel much less anxiety around needing to constantly check my phone for new updates (something I’ve been suffering from ever since my Crackberry days)
Android Wear’s approach makes it easy to claim support for many apps (simply by supporting notifications), but there needs to be more interesting apps and watchfaces for the platform to truly get mainstream appeal
All in all, I think the Moto 360 is hands down, the best smartwatch available right now (I’ll reserve my judgement when I get a chance to play with the Apple Watch). Its a great indicator of what Google’s Android Wear platform can achieve when done well and I’ve found its meaningfully changed how I’ve used my phone and eliminated my use of other fitness tracking devices. That being said, there’s definitely a lot of room for improvement: on battery life (especially in a world where the Pebble smartwatch can achieve nearly a week of battery life between charges), on voice recognition accuracy, on out-of-the-box setup experience, and on getting more apps and watchfaces on board. So, if you’re an early adopter type who’s comfortable with some of these rough edges and with waiting to see what apps/watchfaces come out and who is interested in some of the software value I described, this would be a great purchase. If not, you may want to wait for the hardware and software to improve another iteration or two before diving in.
I think the industry still needs a good answer to the average person around “why should I buy a smartwatch?” But, in any event, I’ll be very curious to see how this space evolves as more smartwatches come to market and especially how they change people’s relationships with their other devices.
I’ve always been blown away by the richness of the Minecraft “game engine”. While ostensibly a game about breaking and placing blocks (while potentially surviving against monsters and other players depending on the server and the game mode), its “creative mode” as well as widespread user modifications to the game have unleashed an amazing amount of creativity resulting in people building amazing worlds including (but not limited to, HT: Mashable for a lot of these)
Knowing how much kids enjoy Minecraft made me wonder if it would be possible to use the game and these sorts of rich models as an education tool to complement the traditional “blackboard lecture” model of teaching which does a very poor job of imparting intuition and understanding. The beauty of something like Minecraft is that it can be used to produce a visual, modifiable simulation in a format that students are probably already consuming (or can probably learn how in a short amount of time), and as a result, it lends itself to exploration and to students making or modifying things to demonstrate and improve their understanding.
Building a microprocessor or digital storage system may be too difficult for a class assignment (although at a reduced level of complexity, they could become very useful teaching aids), but a digital tour of ancient Rome or an assignment to build an Egyptian pyramid or a basic AND or OR circuit? I think that type of learning could benefit a great deal from some Minecraft-ification :-).