Avoiding Venture Overdose

At its best, venture capital can be an amount of new cash from new advisors, both additive, short-term growth resources. At its worst, venture capital can resemble these benefits but turns out to be an over infusion of cash - potentially crippling to a company - from some new advisors with whom founders were actually misaligned from the jump. In the fall of 2016, Eric Paley wrote a meaningfully transparent piece on the toxicity of overindulging in venture financing titled venture capital is a hell of a drug. In his post, Eric reinforces the point that the outcome for startups that inhale supersized venture rounds early and often is usually the graveyard, not the pantheon. Nonetheless, many startups still fail when blind to the longterm downside of too much venture money. 

It's counterintuitive in a consumer culture to believe More < Less. When it concerns money, this equation runs diametrically against societal perception of value. And so it is for technology startups and venture capital. Offers of larger and larger investment must be a good thing, right? Don't get me wrong, its challenging to argue against all-powerful pots of gold that can be elusive to startup founders.  However, the brutal effects of too much venture investment are often obscured by the same human error that makes us covet more of everything. All parties get cartoon dollar sign eyes.  The press sings your praises for landing a mega round; your employees get excited about more resources and larger salaries; many who only see the headline anoint you as rich and tell everyone they know that you're crushing it. Adoration is a human want and for hardscrabble entrepreneurs its tough not to be drawn to a spotlight and stage that's calling for you. And, of course, in the aftermath of a gigantic venture raise, there are literal benefits to founders as well - notably some form of significantly improved compensation. 

In keeping with long-term value over short-term financial windfall, there are some questions you can ask of any investment offer to help you make a final decision within a sane bounds of benefit to your company:

Do you really need this much money?

This one should be an internal founding team question to discuss. No doubt, it's hard to resist fitting future scenarios to the money offered. But up to a ceiling of the no bs, most immediately achievable forward growth rate, work back to realistic maximum possible operating and capital costs for the 12, 18, 24 months ahead. Give yourselves a 10-15% buffer, or something within reason for your company, to account for unforeseen costs and/or a breakaway growth trigger. 

Do they really believe you need this much money?

Before a specific amount of capital is suggested, it's reasonable to ask the firms offering funding to collaborate with you to understand what amount of capital will achieve an optimal future for your company. After you receive a term sheet with investment numbers defined, it is reasonable to ask the firms involved why, at the stage your business is today, this amount of capital will achieve an optimal future for your company. 

Some of the best firms will have already run through future outcome scenarios based on different amounts of capital invested. In those cases, you should feel more confident in a true alignment with potential new investors around a large cash infusion as you make your decision about taking new money and how much you'll take. The point though is not to push VCs to do more work or show their work done. The point is to help you build conviction in the decision to take a large investment. Firms that cannot provide insight into why their offer is the healthiest choice for your company should give you pause. Sometimes, VCs have too much money to deploy and are eager to record paper valuation markups in their portfolios, sometimes at the expense of sound business decisions for the startups they've backed. Sometimes, firms just get competitive and want to win the deal so they outbid one another and your funding round gets bigger and bigger, for no reason that's related to your company. Pushing potential new investors on this question and related exercise should help provide a fuller picture of the motives behind an outsized venture financing offer. 

Do they really believe in you, your business, and its continued potential? 

This is a more qualitative alignment consideration. Understanding that you only get so many interactions with the people behind the capital before an investment offer is made, you should ask them questions that probe for their excitement and conviction in your specific company and your vision. And its perfectly fine to push them beyond positive overtures, buzzword regurgitation, and cordial niceties. Naturally, you always want investors that believe in you through thick and thin. Often, investors do things like 'chasing heat' or 'taking a flyer' in which they want to invest in you primarily because their peers are (chasing heat) or primarily because they don't want to miss out on some future exit (taking a flyer). If firms are offering you money without discernible interest and belief in what you've built and where its all going, that should give you pause. 

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A common VC advice soundbite is 'raise as much as you can' or 'raise it if you can'. Its easy to have a head-nodding immediate reaction because it makes practical sense at first blush. Upon deeper consideration, its really only valid in a few cases, the most common being when the external financial markets signal that you should or that you have to 'raise as much as you can'. Examples are an impending economic recession or a tech-startup bubble bursting. Here the advice is to raise more now because you might not be able to in the near future. In the many, many other cases when this advice is given its not correct and it has become so generalized as to disregard any one company's specific circumstances. As hard as it can be in practice to rationalize saying no to more, endeavoring to be more pragmatic and honest in consideration of a large venture investment will serve you and your company well for years to come. 

A Streaming Watershed

On Sunday, Netflix dropped a trailer for  'Cloverfield Paradox' during the Super Bowl, with a premiere date of: available to stream that night right after the game. 'Paradox' is the third film in a big budget, CGI-action franchise - the first and second films premiered in theaters and earned a combined $280 million at the box office worldwide. This movie will never play in theaters.

Premiering a movie online is pioneering and Netflix took every opportunity to demonstrate why they got to do it first. Software-based online distribution lets Netflix shrink the 'trailer-to-open' movie promo calendar from a couple of months to three hours. A product with DNA that's direct-to-consumer and on-demand, Netflix can take bold aim at large, live audiences and deliver. The single largest TV audience every year tunes in for the Super Bowl, a last predictable bastion of mass cable viewership for many incumbent broadcast competitors. Regardless of the film's reviews and reception - exceptional movies and shows are platform agnostic - the move is a big, bold notice that our streaming future is at hand. The doors seem wide open at this point for blank-canvas creativity with media distribution-consumption models. The film and TV industry as a whole has arrived at a fascinating, tectonic mega shift as it adjusts to distinctly new consumption behavior and an overall gargantuan demand for more that has manifested. 

A quick look at the state of the streaming-cable viewership tug-of-war in the U.S. shows that cord-cutter and cable subscriber numbers are growing and declining at the same average rate over the past five years. Duh. The more interesting stat is the steeper curve of a newer customer-market segment, individuals who have never paid for or had exposure to traditional cable services: the never cords. In The diagram below, two data points stand out. The first is the point in 2022 where the total number of never cords surpasses the cord cutters and never looks back. The cord debate will be moot as future generations grow up never experiencing a cable cord. 

data source: emarketer.com

The second notable point is actually not graphed, the inevitable date when the total number of non-cable viewers surpasses cable subscribers. My graph is a ten-year projection that keeps the rate of change constant, based on the trailing four-year average delta for each of the three segments graphed. There are a few reasons to expect future periods that significantly accelerate the various rates of change in the graph: 

• The aging of the baby boomers is a double dagger for cable. The generation is both literally a large group as well as a disproportionately large demo of total cable subscribers.

• Yearly growth spikes from successive future generations that increase the growth and size of the streaming-only segment exponentially. 

Below are some more thoughts what else might happen as streaming takes centerstage in film and TV: 

Good Times Ahead for Creators

The sources of funding for film and tv content now include tech's 'big five' - Amazon, Netflix, Apple, Facebook, and Google (via Youtube) - representing a combined market-cap of three trillion dollars. Those new tech dollars combine with the established studios, premium networks, and joint-venture streaming services that are in the Walt Disney or Time Warner umbrellas. These monster acquisition budgets reflect how seriously all of these companies are working to satiate and capture The Binge, or the huge audiences with huge appetites for all the shows and movies, all the time. Such a wealth of financial and distribution partners gives directors, producers, and writers a massive leg-up in an industry that traditionally takes its artists starving. Sharing back-end profit participation and final cut creative control should become table stakes for creators in financing negotiations. And, very simply, across the entire business more original stories will get greenlit, which is an amazing state of things. 

Will 'All-You-Can-Eat' Pricing Survive? 

A forthcoming saturation of great new content available on many different platforms may present a threat the 'all-you-can-eat' monthly subscription model that most streaming services employ. For example, if I hear about an amazing new show on Starz, I definitely want to watch it but I don't necessarily want to add another $8.99 monthly subscription to Starz. So unless I can binge that show during a free trial, I'm stuck. It's unlikely that streaming/internet consumers will end up stacking 5-10 streaming subscriptions that end up costing them the same or more than their old monthly cable bill. That starts to look like just another bundle with inefficient billing. Certain streaming services may need to experiment with ala cart pricing like iTunes in order to capture audiences. There may also be a future in which the streaming industry leaders collaborate on releasing content available on all platforms or offer multiple services in a single subscription bundle. 

Do Streaming Brands Matter? 

Audience loyalty to the services and platforms that deliver us moar shows and movies may become non-existent. I believe Netflix deserves credit for leading the push into streaming and is one of the few examples of a successful counterargument to  The Innovator's Dilemma. However, platforms just like studios should be conscious of a much more direct line between audiences and content creators now. For the past fifty years, audiences had preferred stations, channels, or streaming services because those networks broadcast their favorite shows. That curatorial strength still matters but there's less reason for viewers to have a favorite content distributor when there is fantastic programming everywhere, which can often be seen on a number of different streaming services. The more that creative minds can speak, interact, and build loyalty with audiences directly, the better. However, can the platforms stomach becoming the afterthought access pipes for video content instead of the flashy new faces of online media? 

OTT Ad Placement & a Display Land Grab

I've met a few startups working in the OTT - advertisement space. However, there remains a tremendous opportunity to establish a robust ad ecosystem for streaming and casting programming, both on-demand and live. Today, ads that run during broadcast TV breaks to streaming viewers are at best a mind-numbing loop of the same 2-3 ads for the entire program and at worst are just a static, pixelated network logo for 30 seconds. History would signal a coming land grab for all of the pauses and places within content streaming where companies can put or run ads. I am not one to advocate for ads anywhere, but its very clear that incumbent advertisers haven't done much to serve gigantic streaming audiences that they are losing from cable broadcast. 

Discovery on Ever Smaller Screens

While more new content should be a good thing for the whole industry, without an evolution or expansion of how streaming services market new programming, discovery is limited to the home logged-in page on a laptop or phone screen. That's not much space to squeeze in thumbnail images of new shows or movies. There's lots of experimenting with novel ideas to be done here. Platform recommendation engines should be increasingly well-stocked with individual viewer's tastes. So, a version of the Spotify 'discovery weekly' playlist for tv and film recommendations would be an asset for companies offering streaming content. There is also reason to believe that some industry participants will look to the traditional marketing mechanisms that have traditionally worked - billboards, late night talk show circuit, and live programming spots. Its clear though that the virtually un-marketed  approach that Netflix for example has taken with much of its original programming will be tested as streaming libraries become more and more crowded. 

Creative Forecasting

Last year, I came across what I considered the most accurate and pleasant definition of the vocation of early-stage venture capital. It took me far too long to find it again, but I finally have. Borrowed from an early chapter of the book Originals by Adam Grant and remixed a bit for the purpose of being a stand-alone definition and not a phrase in a chapter in a book, this description of venture capital is one I hope to hang my hat on for decades to come: 

Creative Forecasting

the discipline of predicting the success of novel ideas

Dot v. Mini

While startup twitter continues to be an amalgamation of thirsty AND extra over bitcoin in the run up to the end of the year, there’s another consumer technology that’s low-key (compared to BTC at least) arrived in the mainstream in the second half of 2017: the voice-activated, connected home. Three or four years ago, tech blogs went ham buzzing about IOT – internet of things – and the treasure troves of new data moats and transparent, efficient living that connected home devices would create. All and all, that didn’t really happen, due in some part to the big tech corporations staffing up via acquisitions including Nest to Google, Beats to Apple, and Vesper to Amazon in order to execute on the IOT future.

Data on the total number of IOT home devices sold since 2010 is hard to come by but it’s safe to say that there are many more Echoes and G. Homes in households today then there ever have been internet-connected thermostats. The mass, mega marketing push to get a home device into our homes is in full effect. Barely subliminal commercials that activate any listening Echo or G.Home within range of the TV highlight the surge to saturate. I tweeted a few weeks ago that these intensifying battles are starting to resemble the Sega-Nintendo Console Wars in the 80s and 90s. That tête-à-tête brought us Mario, Sonic, Sega Genesis, Super Nintendo, Dreamcast, and N64. Of course, today e-commerce has replaced big box hardware retailers; consumers, once mesmerized by the novelty of video games and eager to spend for more pixels, are more discerning and demanding of all tech-digital product purchases.

I’ve been conducting an experiment at home recently the pitting the Google Home (mini) against the Amazon Echo (dot). I opted for the smaller versions mostly because they’re cheaper, NYC living is mini and there’s not much lost in the brain of these devices when downsizing. A few observations after an empirical couple of weeks with my two new faceless roommates:

1). The Google Home Mini has a much higher rate of accurately completing requests on first attempt. Neither device is perfect but there have been far fewer I’m so frustrated I’m going to throw this thing out of the window moments with the G.Home compared with the Echo Dot. If the 3rd party integrations are similar for both, the advantage probably has to do with the two corporations’ root technology and existing datasets proprietary to each corporation. Google’s is search; Amazon’s is purchase. Internet users have been searching for decades now so Google has the jump on returning accurate results. Voice-activated requests to a connected home device resemble more closely a proactive Google search then an e-commerce purchase on Amazon. We still want some degree of ability to browse and choose with what we buy, so Amazon’s core data doesn’t translate as well to voice-based requests to smart hardware.

2). The Echo Dot can be manually muted from passive background listening via a button on top of the device. The G.Home Mini cannot be manually muted…(don’t be evil).

3). The Echo Dot has the two best Easter egg features I’ve found so far. The first is the name-change feature that allows you to change your device’s wake-up name from Alexa to either Echo or Computer. The computer is the best. The second is the small collection of celebrity voices that are available alongside the standard ringtone chimes that can be used with Echo Dot alarms. The crown jewel of this collection is the Missy Elliot alarm clip. There’s no better way to end a peaceful night of sleep then Missy yelling at me to get up out of bed.

4). For now, I’m inclined to think that the connected home device falls under the usage threshold necessary to count on consistent revenue from updated hardware sales to existing customers. Regardless of which one you buy, this may be a product category that relies on software-OS improvements inside same hardware body for a significant period of time.

5). At the end of the day, the edge-case variables, semantics, vocal fry, and mumbling in our spoken word are much more complicated to accurately process then the text-based spelling errors via our thumbs and fingers. So it’ll take some time before these devices approach an error-proof state.

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To be clear, I did not put quantitative measurement here. These notes are my subjective observations. I imagine that everyone has had different experiences with these devices and I hope you’ll share them too.

A Timeline of New York City Tech Exits

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Notable NYTech Exits 2002 - 2017

The question of whether New York can create outsized technology startup exits comes up often in discussing startup ecosystems. In order to answer this question — with a resounding yes — for the record, I put together a timeline of the New York Tech’s outsized tech exits since 2002. A couple things:

• Applying geographic boundaries to any industry or a community is arbitrary always. In tech, people tend to say that a company not founded within the five boroughs is not attributable to NYC. However, as Silicon Valley now encompasses some 400 square miles of west coast California, NY Tech = the New York metropolitan area.
• So, welcome to the team Jet.com (HQ in Hoboken, NJ) and Indeed.com (founding biz-ops office in Stamford, CT)!
• Chicagoans might take issue with Seamless.com and Trunk Club appearing on the timeline. Trunk Club was founded in NYC. Regardless of which restaurant delivery startup took the post-merger name, Seamless.com drove the growth explosion of that business.
• I’m obviously biased here and so is everyone who talks about this subject. Contrary to popular belief, there is not an accurate, comprehensive database for this type of private company data. Acquisition amounts are often announced amid bullish speculation. Deciding which companies are tech startups is a slog through subjective grey area and mind-numbing mutual inclusivity.
• New York Tech is amazing and none of the above are an impartial stretch.

ps. the actual line of the timeline is also the $1 Billion exit amount threshold; events listed are either IPOs or acquisitions; I made an eyeball attempt to place events relative to a total dollar amount Y-axis. The X-axis time is laid out on four-year intervals with exponentially increasing spacing to accommodate the higher volume of events since 2010.

Thank you Richard Kerby, marissa, Jeff Tierney, Justin Lintz, Elizabeth Devane, Neil Wehrle, ewu, John Exley for your help!

in restless pursuit of the strange and new

I find these first few stanzas of the Robert W. Service poem “The Men That Don’t Fit In” the most accurate representation of those dedicated to the craft of venture:

There’s a race of (them) that don’t fit in,
   A race that can’t stay still;
So they break the hearts of kith and kin,
   And they roam the world at will.
They range the field and they rove the flood,
   And they climb the mountain’s crest;
Theirs is the curse of the gypsy blood,
   And they don’t know how to rest.

If they just went straight they might go far;
   They are strong and brave and true;
But they’re always tired of the things that are,
   And they want the strange and new.

Raging Thyroid

A year ago, I couldn’t have told you what the thyroid does or where it is in the body. Similarly, my only data point on gluten was that more and more restaurant-goers seemed to hate eating it and were sharing this distaste with their server. In the first half of this year, I’ve learned a great deal, more than I could have imagined I would in a lifetime, about both the tiny, butterfly-shaped gland along the front of the windpipe essential to various metabolic functions and the protein found in pretty much every item at the base of the food pyramid.

At first, I started feeling what I can best describe as off – kind of fatigued, worn out, achy, weak, restless trying to fall asleep. Basically, the same symptoms anyone can and does experience day to day from a head cold, a long work week, the seasons changing, or any other number of normal ailments that Advil or Claritin fix. They didn’t fix me though. Assuming I had some kind of bug or passing allergy, I’d try to carry on and suppress or ignore the symptoms. At first, I did ok-ish. Emphasis on the -ish because as time passed, the unpredictability of these sick periods made piecing together consistent, seven-day weeks much more difficult. For better and worse, I’m not really inclined to raise my own red flag when I’m not operating at 100% and so I limped along.

My first visits to doctors yielded little. From primary to urgent care, it was difficult to get much clarity on what was going on since sometimes my measurable test results didn’t necessarily diagnose a reason for how I described feeling. At various appointments, I was given an IV, recommended bedrest, prescribed antibiotics, and always a healthy dose of ibuprofen.

Gradually, my everyday fatigue and pains gave way to debilitating exhaustion, loss of appetite, insomnia, body temperature spikes and night sweats. Laid up often in bed, I pretty much felt like I was going crazy. I had no explanation for feeling so awful and so struggled to know how to communicate what was happening to anyone. I could still make fun of myself for wondering to myself: Was my body giving up on me? Can I hack it? Maybe I’m just passed my prime? But given such irregular bouts, my expected health outcomes became more and more bleak.

A breakthrough came after I switched primary care physicians (which took longer then I could have ever expected) and upon completing a more comprehensive set of labs screened on the positive range for the presence of celiac disease as well as a potential thyroid dysfunction. These results prompted a number of further acute tests and two endoscopies all of which eventually confirmed that I have both celiac and thyroid disease. My Idiot’s Guide explanation of both are that gluten I eat blocks normal nutrient uptake from anything else I eat and my thyroid gland creates incorrect levels of hormones that regulate energy production in my body.

I soon learned that while both conditions are autoimmune, the treatment is straightforward and symptoms are extremely manageable. With a few different endocrinologists, I explored a variety of treatments ranging from passive diet changes and hormone meds to proactive procedure to remove a part of my thyroid. After a few trial-and-error prescription cycles, we found the correct pill-based meds for my thyroid electing not to have the minor operation and I swore off gluten…

Unfortunately, my saga didn’t reach its peak until a recent Friday evening when I woke up feeling a heaviness and congestion as I was breathing in and out normally. I figured it was some type of intense heartburn but eventually checked myself into the hospital not wanting to sit awake all night waiting for it to subside. I found out in short order that my thyroid was inflamed and it was recommended to do the partial removal we’d previously considered right then. Just like the words disease and autoimmune, surgery and ER set an ominous tone for what was a remarkably painless and fast outpatient procedure. With an access point under my right armpit, the surgery ran a minimally-invasive 90 minutes and I walked out with three tiny stitches later that day once the anesthesia wore off. (I just got the stitches taken out).

While there’s relief in having these health issues under control now, I’ve had to miss, often unexpectedly, an upsetting number of experiences and time-spent with and for family, friends and colleagues who mean everything to me, from casual coffee meetings to weddings and birthdays. I intend to begin to remedy my absences starting with the writing of this post. I hope to see anyone who’s reading this and everyone else too as soon as possible and get on with feeling just fine and being there for everything that’s ahead.  

AllyWatch Interview about VC

I had the opportunity to speak at length with Bart Clareman about early-stage venture, New York tech, and my favorite books:

AllyWatch, Inside The Mind Of A NYC VC: Tim Devane

The Investment Memo

VC evaluation of seed-stage startups can seem arbitrary or imitative at times. Internally, the scarcity of tangible business metrics – product usage or revenue multiples for example – can make an investment decision feel daunting. Presented with various unknowns from any one pitch, VCs often lack an existing information infrastructure to examine in forming an opinion. So, the decision-making process at seed then seems neurotic – a tug-of-war without the rope to champion convictions while pressure testing business mechanics.

Due diligence as a process deserves its own version of a book like Venture Deals and there are a bunch of great blog posts available today. From going with your gut to diligence-ing an idea to death, many tactics with varying degrees of literal rigor exist to bring an investor to a yes or a no. Last week, Rob wrote what we talk about when we talk about companies and as pseudo follow-up, I wanted to break down what comes after those talks and a decision to invest from NextView – our investment memo. To me, the investment memo is essential, a time-stamped artifact that summarizes all of the contributing factors that gave us the conviction to invest. Neither exhaustive financial analysis nor page-turning novel, our memos are no more than two pages, broken into these sections:

Overview: The overview is at most three sentences that explain the company’s concept clearly and concisely.

Market & Background: Depending on how young the company is, this section often has the most work behind it as we will do existing market analysis and map the company against projected product-market-fit via publicly available data and proprietary info from past and present portfolio companies.   

Founding Team: We include three bullet points on each founder’s background as well as any early team members crucial to the then current version of the company.

Business Model & Strategy: Here we outline the approach a company is taking to address a problem and/or break into a market as well as what the current or future revenue and growth drivers are or may be. Any seed stage company’s strategy is a constant work-in-progress. Founders’ employ intuition and rapid reaction to their market(s) to pivot and evolve their model. In this section of the memo, the challenge is in not layering our own opinions into a summary what the actual model is and where the founders want to take it.

Competition: This section is a summary of the existing competitive landscape for the company. We draw largely from publicly available information and in-network, in-portfolio diligence calls here.

Financial Metrics & Financing: This is the most variably-sized section of the memo. Of course, there is always info to add about the current financing that we are leading or participating in. However, given that NextView investments range from incubated ideas that are pre-launch/pre-product/pre-seed to companies that are raising seed rounds having already progressed beyond product-market fit, financial metrics can be detailed or non-existent.

Due Diligence Summary: This is a set of bullet points summarizing everyone’s work in diligence. The process is a highly collaborative one within our team. So these bullets are usually a combination of in-network, in-portfolio calls, individual research, product testing, customer-user interviews, and founder reference calls.

Deal Positives: We include no more than four bullets that highlight the most exciting, high potential aspects of the company.

Deal Concerns: We include no more than four bullets that provide an honest assessment of the downside, highest-risk aspects of the company.

Exit Scenarios:  We present our internal set of projected outcomes for the company – based on a blend of existing and previous comps and our own analysis of probability percentages.

Glimmer of Greatness: Drafting off of the NextView Ethos Point Golazo, the final section of our investment memo is a summary of the beautiful, earth-shattering vision for the company and what it becomes in the most profound future outcome.

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All firms treat investment write-ups differently. At NextView, the memos are an internal discipline that we keep within the team. Union Square Ventures treats their blog posts announcing new investments as the de facto investment memo that’s there for anyone to see. Some firms write lengthy memos, some don’t write them at all.

What I appreciate about the NextView investment memo is the time-stamp of what went into a decision to invest. Most seed stage startups fail, meaning most seed stage investment decisions won’t result in the Glimmer Of Greatness. That reality combined with the lack of existing data around which to anchor an investment decision mean that the investment memo is the only hard evidence of why we did something. The first thing I did when I joined NextView was read all of the investment memos for the existing portfolio (encouraged by Dave). Every month, I block time to go back and do this reading again. It is invaluable in gaining a comprehensive understanding of our evolving investment strategy. Outside of engaging with entrepreneurs, there is nothing more additive in building pattern recognition ability than in reviewing investment memos against the reality of what’s happening or happened with a given company.

Should we completely revamp our investment strategy when we were wrong in a memo? No. A panicked reaction to failure, given its frequency, would induce a heart attack. But we can learn from the memos. Endeavoring to examine what we thought at a given time before an investment had an outcome and without the emotional and psychological influence of subsequent perception, is how we improve as VCs.

The Great Voice Consolidation

Speech is our fastest natural means of communicating. Until we’re all chipped and can talk in wordless brainwaves like Professor X, voice is the human ceiling for external command and expression. With a new market emerging around advances in neural network recognition of vocal inputs, efficiency may well be the core advantage that drives user adoption and so overall maturation of the voice sector.

Its hard to imagine that the voice ecosystem will look anything like the mobile-app layer, or for that matter the early world of early e-commerce marketplaces. Voice as an interface and an experience makes invisible design a reality. There’s still a long way to go to saturate – and potentially replace – software design on the client side. However, all the early Skills (I hate this term FWIW) for the various voice-powered hardware-platforms are 100% invisible once enabled. There’s nothing to look at, click, drag, swipe, post, comment on, delete, log into, or choose to download.

You might think yeah obviously, there’s no screen but shifting away from a base interaction layer that’s visual means the characteristics and design of voice infrastructure will not mirror or even grow out of what’s existed before. There might not be an ‘app store for voice’ for example. Voice-powered products and services will exist and succeed or fail via as yet unknown but fundamentally different looking distribution and discovery mechanisms.

Without visual cues, users will rely on words that they already know today to get what they want from Voice. I think of two general buckets of known ‘wake-up words’ that will drive a voice user’s product and services bias:

Established Internet Brands Today: Sort of like the Sticky Note, we recall certain brands as representative of an entire sector of product or service: Uber and Seamless have held this metonymy distinction. Because voice activation may require users to know both what they want and who provides it, today’s leading brands in any given vertical will have a huge advantage. For better or worse, they are already in our heads as the accompanying solution to what we want, so we’re much more likely to use their names to direct the voice interface.

Real World Category Definitions: An even simpler mental process for new voice users will be calling out the general category of product or service: play music, order lunch, Read news, Schedule doctor’s appointment. Like the Alexa platform today, users may be able to set certain preferences via an or website. But many users may be satisfied if the original request is fulfilled, without caring which internet product or service carried it out. Take streaming media for example, if I say Play Isaiah Rashad or Stream Boogie Nights I really don’t care what service either comes from as long as it does. Even with very specific asks, the value of platform differentiation beyond library volume starts to erode with voice. This reveals another strategic value the move by many media streaming companies to secure exclusive rights to content or create their own.

These two categories of voice-user activation further underscore the future power of dominant voice platforms to develop into independent operating systems. These can then prioritize their own products and services (Amazon already does) or drive the evolution of competitive search monetization and SEO rankings for voice. There are many more open questions to consider about Voice design, user interaction, and market dynamics like: what does a Voice wave without any visuals mean for branding and advertising, which today is driven by measuring clicks, eyeballs, and logo placements? What seems clear is that voice-powered interfaces have the potential to consolidate our product and service selections in the name of efficiency. If wide-open user choice that incites intense competition to acquire those users is a core dynamic of dominant tech ecosystems up to this point, Voice may break from that model.

What competition and choice end up meaning on a voice dominant internet, of course, remains to be seen…

The Blockchain: Ending Data Hacks

Remember when we were scared to buy things online? It wasn’t all that long ago when we swiped plastic cards in person for everything we bought, Black Friday meant toe-to-toe battles with every other holiday shopper in the mall, and the thought of entering your personal financial info into a website to buy something terrified many shoppers.

As a nosy prepubescent, I eavesdropped on these ‘adult’ conversations about internet purchasing on our landline between my parents and relatives or family friends as I eagerly awaited my turn to warp our home phone into a glorious portal transporting me to the land of America Online and A.I.M. Those conversations generally centered on doubt and fear as to whether it was safe and/or reliable to buy stuff over the web. It wasn’t just my parents. In 2002, online commerce accounted for just 1.6% of total retail sales in the U.S. Fourteen years later, consumers in the U.S. spent more than $258 billion online and 80% of the U.S. population has E-bought at least once.

Times have changed to be sure. Although it may now turn out that the safety and security concerns many Web 1.0 consumers felt weren’t as readily dismissed as most AIM screennames from that time (i.e. cybernero or dimtim123). Those internet commerce websites, not to mention social media platforms, online banking accounts, and any other digital destination where we’ve left our identities, personal information and financial access, aren’t foolproof vaults for storing our sensitive data. Whether outspoken or tacit, all parties have acknowledged foreign hacking during the 2016 presidential election. That breach alone demonstrates the premium efficacy of targeted hacking at the high stakes geo-political level. Yet, since 2007 hacks and leaks have exposed, compromised or stolen hundreds of billions of online accounts that belong to regular people, Yahoo, Dropbox, Adobe, Chase, LinkedIn, Ebay, Slack are all listed here.

The point is not to cast endless blame on the companies breached. The proliferation of sensitive data stored online combined with the ever-increasing number of individuals online have produced sprawling, vulnerable databases that we expect to perform in exact accordance with the Terms of Service box we check before confirming a payment or signing into an account. Code is permeable and we’ve dumped entirely too much of our sensitive data on the web for those platforms and institutions to keep safe and sound from bad actors.

I’ve drawn out the preamble here to highlight a circuitous path of online progress. In doubling back to some of the earliest fears of the World Wide Web and demonstrating that some of them were and still are valid, we’ve arrived at a jump off point for a new technological revolution. When we started buying things online and storing our information and identities online, we believed that the merchants and firms who had kept such data safe in the physical world could do so in the digital world just as well. As packages arrived on time and payments went to the right places, we may have begun to believe that a password-protected or 3rd party encrypted online checking account was as safe as a bank vault. But it isn’t. Maybe there isn’t a single entity anywhere that should be expected to perpetually protect the endless mass of sensitive data we produce in leaving our identities all over the place online.

To me, this problem presents an amazing opportunity for widespread adoption of blockchain technology as a solution for all involved individuals and firms. If an explanation or refresher is needed on what that technology is read The Blockchain Application Stack by Joel Monegro or Don Tapscott’s Blockchain Revolution.

In the context of this post on user-level data security, here’s the bitcoin blockchain solution in Before and After diagrams below:

In the second diagram, the bitcoin blockchain infrastructure eliminates the need for any single entity to keep and store your sensitive data (including you). Any business or platform that integrate open-source blockchain APIs for any account verification, access or transaction will provide an innovative level of data security as diagrammed.

Reimagining an exchange fundamental to the known internet is necessary if not inevitable. To date, this exchange has been between internet users and internet businesses, with users providing their data – usage, personal, financial, demographic – in return for information and services. In many cases, users have gladly surrendered personal data for answers, for efficiencies, for stuff, and for access. This accepted internet-user model allowed several companies to achieve profound levels of product efficacy and total enterprise value due to an ever-expanding stockpile of user data they’ve kept and built around. Google, Amazon, Facebook, Uber and Netflix are a few examples. To redirect the current flow of online user data means redefining what is considered valuable on the internet for every entity from the tech titans just referenced to newly founded startups. Network effects and data moats, two tenants underlying some of the most significant web products and overall internet activity to date, are in many ways undone when the blockchain empowers users to keep and control their data.

If that change sounds daunting, that’s because it will be. But add up all the zeros in that awesome data breach visualization from the site Information Is Beautiful and you see how vulnerable we are right now. Which brings me back to the original Web 1.0 concerns about commerce and identity online:

Will it work? Yes indeed.

Can my credit card number get stolen? Most definitely.

Am I being tracked? 100%.

Do they see my profile activity and password? Oh yes.

Is it safe? Well, we thought so…

If the blockchain is a technology that can actually fix these problems, why don’t we all start using it? Of course, many millions of users don’t mind the traditional internet-user model and see so much value and ease of use on the web today that they consider it a fair trade. There are likely many more millions of users who would shift to a blockchain model and yet relinquish their encrypted keys to a blockchain entity to manage and secure. The core process of transaction verification via the bitcoin blockchain needs to continue to progress to a faster and cheaper end product. And internet-wide adoption and integration will require both continued education across the board and tremendous buy-in from today’s large internet companies.

The question with the blockchain has usually been: where’s the tipping point? In theory, its democratic and universally honest; in explanation, its extremely complex technology to understand; in execution, it requires a massive swing up the adoption curve to meaningfully deliver. If I could plot this timeline exactly, I already would have. But if the infrastructure of today’s internet is increasingly at odds with users’ rabid, data-heavy activity and the high-margin business models making internet companies rich, then more and more data breaches could eventually topple the whole system. So, here’s one vote for the blockchain coming to save the day.

Gotham Alpha Podcast Series

A collection of the Gotham Alpha podcast series episodes, exploring the dynamics of NYTech: 

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The 24-Hour Test

As a seed stage investor, I believe I benefit from channeling enthusiasm and excitement. While it’s our responsibility to be thorough and diligent in evaluating entrepreneurs and their ideas, it betrays our job to be too much the cynic or non-believer from the start. Every single company and team we meet can be picked apart in a variety of ways that ultimately would result in passing on the investment. There are always a million reasons why something won’t work and many more than that at seed stage or even earlier.

When my emotional response to a first meeting is excitement, I consider that an important indication of where I’ve ranked a startup concept. I’d argue the genesis of the excitement is born of the right side of my brain and at the very least hasn’t had a negative reaction from the left side. I will usually fixate in the immediate aftermath of the meeting, the idea-pitch-potential sitting prominently at forefront of my mind. The conflict, I’ve found, is in navigating how closely to check or how adamantly to follow that enthusiasm as time passes from that first meeting.

If the literal minutes after are ebullient and positive, what happens with a more gradual passing of hours, days, and inevitable distractions? Did I wake up the next morning with this company as a first thought, the way it was for 90 minutes after the previous day’s meeting? Was I in a rush to tell my mom or a friend or rattle off a opus of a text message to Rob, Dave, Lee, or Jay about such a profound idea? Or has some of the veneer worn off and my enthusiasm abated? Have I lost the spark as a week has progressed and other things have crowded into my brain?

More general measuring stick then precise equation, my home-brewed emotional-reflection filter is one of many indications and data points that impact how we think about things. It may seem too simple, but the 24-hour excitement test has been a telling and intuitive resource for me. Thoughtfully unpacking that emotional response via relevant analysis is the second step in a two-step process that helps determine if we move into formal, deeper diligence with a company - inevitably our largest workload along the timeline of a decision to invest. 

A NYTech Guide

New York City is a dream but can also be a navigational nightmare, equal parts crammed circus and rat race mixed at warp speed. This holds for the city’s tech sector. NYC Tech is bursting at the seams with nightly networking events at floors and floors of co-working spaces. We do not suffer a lack of tech activities. At the same time, the perception of our ecosystem as an insider’s game can often be self-fulfilling and self-perpetuating – creating a walled garden that can’t be breached. People can MeetUp themselves to exhaustion without feeling they’ve made dent.

In order to provide more transparent access and information, today we’re excited to launch the Hitchhiker’s Guide to New York City Tech. The Guide is a living resource intended to equip anyone interested in the New York City startup ecosystem with an information arsenal to help plot his or her own journey.

By no means did we create the NY Guide due to a void in available NYC startup information. There are fantastic sources that have been serving the community for a long time. We’ve tried to list as many of them as we are aware of in our guide. No two people are better at curating the best NY events than Gary and Charlie. Digital.NYC has a deep database of local startups. The New York Tech Alliance is the largest and most significant organization that our ecosystem has ever had. Just to name a few.

In creating our NYTech Hitchhiker’s Guide, we wanted to do our part in a continued effort to make the New York ecosystem transparent and accessible – to newcomers and transplants as much as to local tech OGs. And we need your help. Each of the sections in our guide (Arrive – Learn – Meet – Explore – Work – Raise) has links for open submissions from the community. It would be wonderful to have your additions if you have any to contribute. We know we’ve only just scratched the surface.

Machines Learning In The Wild

Following up on my post about the Bot Craze in machine learning and AI, I wanted to call out a few less obvious areas where increased activity in developing relevant machine learning can have a profound impact:

The Inbox

Google will surely continue to make strides here. We’ve surrounded so much of our information, personal and professional, to Gmail that improving sorting, logging, prioritization and archiving seems inevitable. Given that our inboxes are historical data treasure-troves, there is opportunity for startups to address this complex classification challenge right now.

Genetics & Predictive Diagnosis

While endlessly complex, many of the known cause and effect relationships between symptoms and illness/disease are ideal scenarios for machine learning. With large, existing data sample sets, predicting things like future medical issues or hospital re-admittance is possible. There’s further potential for a more sophisticated neural network where anomaly detection expands our understanding of root causes of disease and illness. There are numerous companies working on this problem, both large and small.

Legal Research

With a growing percentage of historical legal records available online and a trend in courts putting all of their trial information online, ML could save the army of associates that firms employ time and reduce opaque billable hours to clients by pinpointing relevant precedents for any case.

Credit Scores & Alternative Lending

Considering anomaly detection in a different light, a complex neural net should be capable of a more sophisticated and accurate profile of a loan applicant. This deeper and smarter analysis could be a profound lifeline for individuals without credit history or who have had any negative financial issues that in today often disqualify them outright.

Everyday Nutrition

A challenge to machine intelligence for human nutrition is input tracking. We have to be honest and diligent about recording what we consume. However, with a more efficient input, machine-powered nutritional recommendation based on our health, present and future, our dietary restrictions and any fitness or wellness goals could be a transparent knowledge base that we don’t have when deciding what to order at lunch or cook at home.

For many of these examples, machines would clearly be more powerful and efficient than the human brain once they’ve feasted upon the relevant data required. Access to these data stockpiles is not a given. For many sectors, words like scattered, offline and/or highly regulated describe the inhibitors to data access that is pre-requisite for machine learning. This is part of the reason why data network effects have become a hot topic. This status quo also encouraged entrepreneurs to build products with immediate, obvious value for end-users or customers that immediately access and compile data on the heels of delivering upfront value (Ex: 23&Me).

Another challenge to the examples above is the clarity and consistency of data organization. Messy, unpredictable data can be a nightmare to index and process. Depending on the volume of data and types if input sources, entire interstitial systems need to be placed in front of any new product attempting analysis. Even when captured, raw data is tough to translate into uniform sets.

There are a number of companies — startups and corporations — working on bringing machine learning to the sectors outlined above. My intent in describing the specific, potential value in these areas is to express that conversational bots aren’t the sum total of A.I. More likely, they are a major step in increasing mainstream awareness of and comfort with intelligent machines. While for now less marketable than bots, the above ML applications can bring an accuracy and efficiency to industries that is far beyond the capability of the human mind.

Bots In The Wild

Besides Pokémon reclaiming its place in the zeitgeist, bots are the buzzing tech trend of 2016 so far. The big five — Google, Apple, Facebook, Amazon, and Microsoft — have all unveiled or featured bots in their annual product conferences. Consumer startup decks have hurriedly affixed bots to their pitches, for better or for worse. The New Yorker covered conversational bots in June. The New Yorker! Bot has quickly become as common as the word app has been in startup chatter about what people are building: its a bot for ‘X’ (in this case X usually stands for a mundane, predictable task currently carried out by humans). On one hand, bots are a tech craze like many before them. As our attention spans wane and the realization sets in that bots might not change the entire world tomorrow, the hype will begin to die down. The technology powering conversational bots — which are the type of bots driving this particular moment in craze — is still in its infancy and prone to bugs, errors and usability limitations. These errors are akin to why we press zero on our phones through every automated customer service selection tree ever until we reach a person. The bot doesn’t understand the problem, doesn’t have a trained solution, or it can’t hear/read what I’ve said. An even more fatal scenario emerges when consumers go into recorded voice or bot interactions assuming inability and opt for the human without engaging the computer at all. (This is the status quo for recorded voice customer service).

So why the Bot surge? Why would major tech companies and startups decide to release these products now?

A simple answer is that a competitive market forced many companies, large and startup, to announce a bot product — whether or not its available right now — in order to keep up with their peers. Similarly, one bot-focused headline may have rushed other bot announcements or launches, ahead of a previously planned timeline. In this scenario, the buzzwords and echo chamber aren’t far off where suddenly bot is synonymous with success and everyone is building one. However, I think there may have been something strategic in the rapid roll-out of conversational bot technology that is more or less unproven in its marketed product deliverables. Bot technology may become more sophisticated and advanced due directly to its initial launch craze, that traditionally in tech only serves to artificially juice early sign-ups.

The conversational, consumer bots that have sparked this craze are early drafts of machine learning let loose in the wild. In order to deliver the efficiency that the products promise, these ML bots have to be trained largely via supervised learning: feeding tons of specific data relevant to each bot’s function into a set of decision-making conduits over and over. In theory, the machine learns the patterns that determine which input data component elicits which decisions or responses and thus can begin to function with live interactions. One of the core reasons for continuous, relevant data intake is to capture and understand outliers in the set. In experiencing to the often unpredictable syntax, edge-cases, and nuances of any particular data, the bot is equipped to handle as many unique, real-world scenarios as possible once live.

Many impossibly vast and complicated data sets exist today like the index of The World Wide Web or a human genome sequence. Human language and colloquial conversation, especially via text, may be one of the most bizarre, unpredictable, and voluminous data sets in the world. Consider: the number of ways we say hello; the endless words or abbreviations that stand for yes; how emotion (ex: irritation) and expression (ex: irony) can completely change the intended meaning of a phrase (ex: sarcasm) without any grammatical change from the literal meaning; how syntax, spelling errors, and one’s location can impact our conversations. Then consider how much we say. It’s daunting and, as long as we keep talking, impossible to master. The startups and big tech companies that very publicly announced and/or released their early beta bots into live human conversation may have been angling for a jumpstart on this training. The bot craze inevitably has and will continue to draw people into human-bot conversations. Those interactions are the relevant data sets for getting a machine to understand the semantics of human communication just like it does a line of code. Some people may be irritated with the inbox presence of bot assistants Amy or Andrew from x.ai, but you better believe that team has gathered just about every possible way that we express our need to reschedule a meeting over email.

The first wave of bots may well fail to deliver on the promise alluded to at F8 or Google IO. Though if any of these early product releases were strategic in nature, several tech companies may have taken a audacious swing to advance their technology. Early PR and consumer blowback on half-baked products may have been expected and endured in order to compile the conversation data necessary for bots to function consistently at all.

Code Climate, Timber, and NYCode

This coming September, NextView will be hosting NYCode, a half-day conference to recognize and explore the tremendous engineering community in New York City. Below is a brief post to explain why we are doing this. If you’d just like to sign up for early RSVP, click here to do so: NYCode RSVP.

In his recent post about the NYC tech ecosystem, Matt Turck highlighted two developments useful in considering New York’s ability to foster and maintain a local developer community. First, he noted that the talent base here is maturing. For engineering specifically, developers are beginning to leave the New York outposts of tech titans like Google and Facebook to join NY startups or start one here themselves. Second, Matt emphasized ‘Deep Tech’ as an emerging sector in New York. Where once MongoDB stood alone, a cohort of deep tech startups now exists, including growth stage companies like DataDog and Digital Ocean and younger startups like Clarifai and Geometric Intelligence.

To the above evidence of literal tech in New York, I would add the diverse set of schools and courses that now cater to engineering. From Cornell Tech and Flatiron School to the engineering programs at Rutgers and the HackNY Fellows from NYU & Columbia, New York is teeming with avenues to pursue a technical education.These programs and institutions graduate waves of newly minted engineers into the local ecosystems every twelve months, if not every six.

At NextView, we have been bullish on the NYC engineering community for a few years. In the spring of 2014, Dave met Bryan Helmkamp who had founded Code Climate as an automated, static code review product for Ruby. They had a series of conversations about the greater opportunity to build a truly open and extensible platform for a broad set of static analysis, engaging the engineering community to build modules for their language of choice. By that June, NextView was leading the Code Climate seed round. This past winter, I met Zach Sherman and Ben Johnson, two engineers who were leaving SeatGeek to start Timber, a developer tools company focused on user logs. Over a few weeks, we discussed what the two founders saw as an immediate need for a lightweight, real-time logging interface built for application engineers. In February, NextView led a pre-seed investment in Timber, alongside our friends at Notation, Ludlow and Wonder Capital.

What many Deep Tech startups require in a founding location is consistent engineering activity — Meetups to demo their products, other engineering groups or startups to be beta testers, and larger engineering orgs to poach talent from. In short, they need local density. Matt concludes that New York engineering density is finally becoming inevitable and we second that statement wholeheartedly, it is apparent and proving itself to be self-sustaining as the current cycle matures.

As proud advocates of this density development, we want to recognize it. So, NextView is inviting the New York engineering community to NYCode, a half day mini-conference with a couple of panels and fireside chats featuring some of the founders, data scientists, engineers, and investors who are making Deep Tech a reality here. To firmly dispel the rumor that quality, surplus engineering only exists on the west coast, join us for NYCode an afternoon of discussions featuring Eliot Horowitz, Alexis Le-Quoc, Allan Beaufor, Albert Wenger, Bryan Helmkamp, Murat Bicer and more.

Augmented Reality Network Effects?

My friend Jason’s enthusiasm gets me no matter what he’s describing and last night it got me hooked on Pokemon Go. Three quick observations from a relatively late adopter (a crazy statement in and of itself).

Viral Growth — Offline Network Effects:

Rising to 25 million daily active users in its first three weeks in the U.S., the game is the most successful mobile product launch ever. Pure organic viral growth has Pokemon Go breaking app store and OS usage records at the same time. Most remarkable is that the current version of Pokemon Go does not have inherent network effects. That is contrarian compared to the industry-leading consumer products, including games. Snapchat, WhatsApp, Instagram, Draw Something, Farmville, and Words With Friends all benefited from network effects, where new users drove retention. Facebook’s rise is the textbook definition of the same effect. We are in an era where meteoric consumer product growth has often relied on users finding their real-life social networks in-app to drive repeat engagement. Empirically speaking, you play Pokemon Go without any user-to-user interaction. You play the game to find more Pokemon and advance your experience levels; both processes that are born out of and achieved through a virtual world in which you exist largely alone.

Of course, Nintendo can deploy gaming and social features that could achieve a traditional network effect very quickly. It's not hard to imagine what these are. The lures that users can leave to attract Pokemon are an early indication. In-game tips, trading, commerce, battles, and communication — all hallmarks of successful MORPGs — can build network effect moats when given to such a large early user base. I’d like to posit though that there is something else at play with Pokemon Go today that goes beyond buzz. The network effect has already emerged, but it has been achieved offline. Users are communicating, tips are given to one another, real life friends and couples are engaging the game together, commerce is already in place. Its just all happening in the real world. Listen to the conversations you overhear about Pokemon Go, they aren’t all just about the hype. Users are helping each other, recommending what to do, comparing their collections and their avatar designs. Jason literally walked around with me after dinner last night and we played together while he gave me tips. He’s the first person I messaged this morning when I caught a wild Tauros on the train. A product of our first augmented reality network, the social in the Pokemon Go network effect is experienced on the real world side. And its honestly more powerful then if tips and conversation were in-app from the start.

Device Takeover — Single Use:

The Pokemon user interface dominates your device. Certain push notifications can interrupt, but it is by and large a dedicated experience. You have to keep it open in order to keep playing and, even from a pocket, it will run and buzz if there’s something to do nearby. We are clearly demonstrating a desire for this type of UX/UI. Content consumption on Snapchat underscores the same trend. Products that fill up the screen, are easily engaged, and consistently satisfying are winning our attention. We want immersive experiences and may readily sacrifice choice — which app to open, where to read the news — for them.

Nintendo should buy Foursquare:

Just saying…

Authentic Foundation

Last week, I wrote a post referencing Invited Guest, one of the six points that make up our ethos at NextView. Today, I’m calling on another of our ethos points, an adjective that can consistently describe the founders we work with: Authentic. If you’ve had the chance to explore the NextView website recently, you may have found the homepage Easter egg that reveals each ethos point. Authentic is the first one you’ll see:

It may not be surprising then that many of the most successful companies in the NextView portfolio to date were born out of authentic founder experiences.

In the past few months, we’ve invested in three New York-based companies; each hatched by founders whose backgrounds and visions for what they’re building epitomize authenticity. As examples of the types of founding teams that we love to work with and in order to announce some of NextView’s newest investments, I’m thrilled to now shine some light on the founders and founding stories of Dia & Co., Roam, and Timber.

Nadia Boujarwah and Lydia Gilbert met at business school and by the time they graduated were off and running with a startup idea: building a new brand for plus-sized woman’s fashion. And thus Dia & Co. came into being.

The company was not simply the result of a shrewd examination of market opportunities or a spreadsheet-powered analysis of sizing trends in the U.S. Dia exists because their customers have been ignored by the fashion industry. Nadia and Lydia saw a woman forced into the backrooms of brick-and-mortar stores to find her size or resorting to online orders without consideration to her unique fit. They saw a woman with nowhere to shop and no brand she trusted. Nadia and Lydia have since dedicated their waking hours to making life better for this woman.

The Dia mission is to encourage self-confidence and personal agency through ensuring their customers have styles they love in cuts that fit. The whole company values the needs, nuances, delights, and trust of their customer above all else. From profound sizing data that’s collected down to every article of clothing picked and packed for home try-on, Dia is literally built in service to the plus-size woman. Need some proof? Watch this video from Mic.com and then search YouTube for “Dia+Unboxing”. A community is emerging that’s finally getting what it’s deserved. Nadia and Lydia have brought staunch and sincere authenticity to the notion of customer-first. This isn’t a played-out marketing message on a splash page, but the entire reason that their company exists.

Bruno Haid believes that our accepted definition of “the home” is outmoded and restrictive. The 30-year mortgage may not be for everyone and is in fact not viable for many people, yet we’re indoctrinated with the notion that the home we eventually buy as we get older will be both our static, long-term residence and most valuable asset we’ll ever possess. Bruno started Roam as a new living option: a tech-enabled network of communal residences across the globe within which subscribers can move as they so desire.

Who signs up for this? Digital nomads? The freelance workforce? Remote employees? Empty-nesters? Divorced adults? Students taking a gap year? Professors on sabbatical? Regardless of the subscriber demographics, Roam is being built as a conduit for a massive behavioral change. Bruno is the kinetic energy that flows through the conduit, hell-bent on affecting a watershed moment for how we may live. A true nomad, Bruno champions flexible living in practice and conversation; he has opened and resided in communal properties throughout his life. If Roam is a company that exists to offer an alternative to the static homestead, Bruno embodies the early-mover edge to establish this alternative today.   

When founders solve a problem they’ve experienced themselves, they often employ a novel approach that’s so straightforward or simple as to appear an insufficient strategy to outsiders. There is, however, clear authenticity in building a company that you know solves your problem and potentially solves the same problem for many others. For Zach Sherman and Ben Johnson, engineering at SeatGeek meant swimming in a coded sea of user logs all the time. As a major ticketing platform, SeatGeek manages millions of logs every day, and anytime something goes wrong in the code or someone doesn’t get their tickets, those logs have to be called up and poured over.

As a result, Zach and Ben both became intimate with the treasure trove of unique information locked behind the static lists of logged events that were thrown out every seven days or so. For the Timber co-founders, the user log, despite being a huge hassle to access when needed, felt like the lifeblood of the entire company. These logs were the key to everything that was happening on the platform at any given time. It didn’t take long for the duo to unify this notion about user logs into a company focused on unlocking the power of the log.

Timber gives application engineers a lightweight interface for real-time search, filtering, and analysis of clickable, semantic user logs. Born from a painfully sincere toil in accessing logs when there was a problem, Zach and Ben have crafted an original product of which they may well be their own most active beta testers.

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Starting a company for the sake of starting a company is a doomed endeavor. ‘New founder’ is one of the most challenging job titles anyone can take on. authentic, sincere passion for what you’re building – whether born out of a problem you’ve faced, a wider vision for something you’ve experienced, or a need you’ve felt that’s never been answered – can carry a concept through to becoming a company. Launching a startup from origins of authenticity is a more likely road to entrepreneurial success and a characteristic we seek out in the founders we meet with at NextView. 

Passing: A Great Responsibility In VC

Saying no to someone isn’t comfortable or easy because it’s never what a person waiting for an answer about anything hopes to hear. In our industry, venture investors are responsible for passing on an investment in nine out of 10 companies they meet. Theoretically, passing is what we should be doing most often, second only to meeting new companies.

At NextView, one of our ethos points is Invited Guest. We believe we are guests at the tables of the entrepreneurs we work with, in attendance only because we’ve been lucky enough to be asked. From the first time we meet or speak to founders through our entire relationship with a company, they’ve invited us to be there – especially when such a gracious and fortunate invitation needs to be declined.

We endeavor to be efficient, transparent and helpful in our role as investors during evaluation and diligence of a new company. The phrase I’ve learned from my colleague Lee is: it’s a few days to a no and a few weeks to a yes. When it comes time to pass, we intend to do so with a clear explanation as to why we are. The amount of feedback is generally proportional to the time spent with the founders and how much we’ve been able to learn. One phone call with a founder may mean a shorter pass email; several group meetings and other diligence requests to founders will bring more detail on why we’re passing.

We are, of course, human. While we intend no error in our process as described, we miss or drop things at times as anyone does. This is not a prescription for perfection – to write a thoughtful pass message takes time that isn’t always there. But human error aside, we go into every startup meeting with the intention of conducting ourselves as those founders’ invited guests.

The reason we take passing seriously is because we know something that entrepreneurs know all too well: fundraising sucks.

As a process at the seed stage, it’s fundamentally insane. In meeting multiple times with an entrepreneur, asking them to provide references and jump through various other diligence hoops, we take them away from building the very thing that they are asking us to invest in. Their time is better spent doing that herculean task of building their business then having a sixth partner meeting with a fund that isn’t going to invest. Eliminating the distraction of a drawn-out fundraise is the least we can do.

Personally, I keep the following three facts in mind during any pitch process – a mini reflection on why being an invited guest is humbling:  

What you do is harder than what we do.

What you do is more important than what we do.

Your time is more valuable than ours.

When investors don’t pass appropriately, we are willfully negligent. Unfortunately, there’s less accountability here because our performance is not measured by how effectively we say, “no”. Avoiding these conversations because they might be awkward does a disservice to founders who are already attempting the near impossible by starting a company. If we don’t invest 10 times as often as we do, then we should be passing early and often. It’s the right thing to do and, as a service provider in the startup ecosystem, it’s our job.