Infrastructure Opportunities for Startups

Over the past month I have focused on long term opportunities in Infrastructure Investment. The areas of interest to me outlined here are an invitation for founder-conversations for a venture adventure.

A good qualifier I use is “Is this a 10 year market?” – putting this simple question at the beginning of a discussion and gently raising it during founder pitches for their vision has served to separate chaff from meaningful kernels.

Another good filter is ‘enterprise‘ as the end-customer. Even if a startup is selling to intermediaries like service providers, at some point, most infrastructure “solutions” are consumed by, and hence defined-by enterprise customers.

Following this criteria, I find the following four areas highly interesting:

  • Mobility
  • Cloud & Control
  • Custom Hardware
  • Data Intelligence

Mobility

We’re now definitively entering the era of smartphones, tablets, and other screens enabling mobility for just about every experience. Historically treated via ‘gateways’ or addressed by merely reformatting pixels for the mobile screen, mobility is an aspect that is as much about context as it is about the specific features/information selected for display/interaction on a mobile screen.

In a world of touch interfaces, ‘swipe’ gestures, and easy eye-tracking for display control, the nature of information presentation and interaction changes in a fundamental way. Database, partitioning, load-balancing, caching, front-end serving choices, etc are all up for optimization once you factor in mobility. Unless addressed at every layer of the solution, mobility will break more applications than it enhances and deserves to be treated as a fundamental factor in engineering infrastructure for any service/application.

Thinking further, advances in mobile frameworks like Musubi (Mobisocial Group, Stanford) permit serverless app development by leveraging the smarts in any smartphone while honoring user privacy for all personal/social data. Efforts like these have the potential to reinvent mobility for the next decade.

Cloud & Control

I believe the ‘control’ layer for enterprise, data-center, and service provider infrastructure (private, public, hybrid) is up for grabs for the first time in infrastructure history. None of the large technology vendors: Cisco, EMC/Vmware, Citrix, HP, IBM,… have a defensible position or a compelling solution here. More than merely an OS (open) or resource-aware hypervisor, this layer needs to address policy and compliance as much as it needs to deliver service supervision, quality at the right cost and performance across sub-clouds.

As enterprises begin to consume a rapidly increasing set of software services hosted elsewhere, these services will be required to exchange information between themselves. This exchange of information also needs to occur with compliance and following customer policies. At some point, a TIBCO of cloud(s) also needs to exist. The control layer I envision needs to interact with all the constituent parts of a cloud based, cloud-delivered set of services. Nicira and other’s vision of SDNs are early pointers to the kind of protocol work yet to be done to support service-aware traffic engineering goals over data center and core networks.

In physical infrastructure, at some point, a simple and stupid-fast optical layer in the service of flexible IP networks will eventually happen without jurassic interface definitions like OTN. Physical layer innovations that lead to simplicity will scale – and win.

In the end, the cloud is less about technology – it is about providing the right business solution. Yes, innovative (and open) technology is required but in the end, technology is the easiest part of the stack.

Custom Hardware (Workload optimized Computing)

The most meaningful advances in computing, networking, and storage have come from consumer focused webscale companies in the past decade: Yahoo, Google, Amazon, Facebook, Twitter. Most of these innovations will have applications directly in the enterprise world over the next ten years.

Innovations like the low latency, throughput optimized hi1.4xlarge instance announced by AWS and benchmarked by Netflix in July 2012 are now available on demand without spending a dime on custom engineering, maintenance, or revisions. The software (application) layer needs to do precisely nothing to leverage this hardware innovation – it just runs (faster)!

This is the eventual promise of cloud – ideally it should know no bounds and permit a variety of specialized hardware to be utilized without requiring a change in *any* software. We are approaching the end of commodity hardware machines and entering the era of custom (re)configurable hardware subservient to software.

Data Intelligence (DI)

Moving beyond the first generation of large scale data processing tools (GFS, MapReduce, Hadoop, Pig, Hive,…) we’re now entering phase-2 of wringing intelligent insights from Big Data interactively, and in near real time.

BigQuery (Dremel) at Google, Cassovary at Twitter are examples of easy to use (for programmers) services that scale with ease from tens to thousands of servers and return instantaneous results across diverse data sets. The old/conventional paradigm of batch-processing required Business Analysts, the eventual users of these tools, to work with programmers/IT or learn frameworks like SQL, SAS, R,.. in order to run, modify, and redefine queries of interest. While it won’t turn mere mortal Business Analysts in to data scientists, it meaningfully reduces the ‘process’ friction and conventional-IT cooperation required in order to get the desired intelligence out of data.  Suitable only for developers today given their data models and querying languages, the interactive nature of these tools will make it attractive for startups to build data visualization, manipulation, analytics, and reporting tools on top of these magnificent data processing frameworks. The resultant Agility in business processes directly and positively enables business model innovation.

Thank you for reading this far. Some working notes for this post are here.

On Staying Relevant

Over the years I have put away thousands (approx 12,000) of business cards from people I met since I impulsively moved here in 1996. Among the many engineers, marketeers, investors, and press, only a few have stood out. Not for the CxO or MD or GP designations noted on the cards, but for the quality of the individuals that has held over time.

A few days ago in a conversation with a few founders, the discussion turned to AngelList and Naval and how it was changing the landscape of innovation, financing, and startups. To the amusement of others, I recounted the very first time I met Naval.Naval Ravikant & Elad Gil business cards

The top business card you see here is from a meeting with Naval and Milo, both of @home, in 1998 (I think) at KPCB. We had just been funded by Kleiner and MDV and this was one of the meeting our VCs arranged with a portfolio company of theirs. Milo did most of the talking I remember – about their grand plans for taking over the world through high bandwidth cable broadband. A few years later, Naval was in a meeting again with us – this time as a co-founder of Genoa making and selling semiconductor optical amplifiers. His business card had changed but he was passionate about how that device would change the network despite our concerns about Gain/Noise-figure vs. optical fiber amplifiers. Then a few years later, I heard of him again – this time in a Sand Hill boardroom regarding epinions and the legal wrangling.

In 2011-2012, I joined AngelList and yes, Naval is still relevant – perhaps with more impact than ever. As I meet founders enamored with the latest valley exit still fresh in their mind, I encourage them to ask themselves a simple question – Are you relevant in ten years? How do you plan to stay relevant to yourself and to the community around you? If you haven’t thought about that yet, please consider asking yourselves the question.

The other business card in the scan belongs to Elad Gil. I first met him in 2000 when he was a graduate student who had organized (w/ Pavel) the MIT $50K competition and I was honored to be the opening keynote there. Later that night, he spoke of his research, and his interests in multiple areas including devices and networks. The year after, I met him again – this time along with Gokul Rajaram – as representatives of Onetta – a smarter optical amplifier for networks.  And then in 2004, at Google cafeteria where he talked of Google Mobile and how mobility is the eventual frontier for information. Now, after founding Mixer Labs and a few years at Twitter, he is planning on reinventing himself again while continuously giving back to silicon valley.

Yes, a lot of people in the valley and elsewhere have stayed relevant, learnt constantly but very few have also given constantly – Naval and Elad have openly and continuously shared their learning with others around them and made everyone else a little smarter.  Their impact on our valley is not just measured in dollars or exits, it is compounded through their contributions to amplifying the potential of others.

This is my definition of ‘relevance’. It is not just something you do when you’re successful, it is who you are – for others – constantly. I guarantee that whatever technology or market you happen to be working on will change multiple times in your career.  If you learn and help others learn, you will change too – for the better and you will still be relevant in 10, 15, 20 years – to yourself and to those around you.

 

On Taking Risk

As part of any entrepreneurial pursuit, founders and VCs engage in various mating rituals that are as much about judging risk as they are about judging opportunity.

Over the weeks and months of pitching, founders and VCs manage to assign some subjective meaning to various parts of the risk stack.

Risk stack for startups

During most of this process, your pitch is that each of these is either

  • solved,
  • understood, or
  • will be managed so as not to hurt your chances of success.

However, your reality is that risk along each of these axis, if not taken earliest possibly opportunity will GROW exponentially. Risk is good when quantified early and taken early. Unknown risk equals infinite risk for startups.

The risk stack is contained only by explicitly addressing each component of it as early as possible – before or at least at the first financing.

Another danger is that risk not taken turns toxic in startups. What I mean is the effects of that risk will start to influence other areas. For example, if user related risk is the highest risk not taken, lack of knowledge about how users perceive your product or interact with your product will quickly seep in to other decision-making. In this instance, not knowing the user’s ability to understand the value will invariably lead to sub-optimal decisions across design, features, and operations.

If there is anything I have learnt in the past few years in my experiments in small investments and working with early stage teams, it is that I am learning how to help founders figure out risk and perhaps help them take more risk as soon as possible.

My mission statement as an individual investor is that “Hello, my name is rohit. How may I  help you take more risk.”

p.s. Business model intentionally not part of the risk stack. That is a composite risk which merits more thought before I can say anything meaningful about it.

 

The Entrepreneurial Framework

In the last few months, I had the good fortune of talking to several early-stage entrepreneurs including some in college (Thank you True Ventures Intern Corps) thinking about joining a startup and some others beginning to work on their own startup. Most of these conversations ended up about ‘how to think’ about being a founder, less about how-to-raise money or how-to-build a company.

As I began to think more about this question of “how to learn”, I understood that what we were really talking about was developing a framework for iterative learning. Before a founder or a group of founders begins building the startup stack, they have to build the founder-stack.
Being a founder is not going to happen by osmosis nor accidentally, it is a result of continuous preparation for receiving, understanding, and translating your learnings.

Just like the  best improv is delivered by the most practiced actors and not by someone walking off the street and on to the biggest stages, the best startups are built with constant practice and hard work at learning how to learn, not merely by accident.

The founder stack for learning has three fundamental components:

Ideas: How to think about ideas that will become products
People: You, Your co-founders, your employees
Operations: Everything other than technology, products, people.

Note I do not (yet) mention technology, marketing, or sales as part of the founder stack – that comes later and is part of the complete startup stack. And even though I am going to write about the idea part of the stack first, in the end that is the easiest thing to conceive, build, and deliver.

Ideas

(i)         Thinking with dissatisfaction

One of the first pieces of the entrepreneurial scaffolding is learning to think with some degree of dissatisfaction with the current state of things. As a founder, you should be able to ask yourself ‘does it fit’? And if not, why, and then ask what problem does it leave unsolved.

(ii)       Leverage new platforms

When new platforms emerge (mobile, social,…) behaviors that exist elsewhere may need to exist on these platforms in a new form.

(iii)     Find the voice of many

Amplify your set of thoughts about a problem to be solved by as many other voices defining that problem. Talk to as many as possible – coworkers, friends, family, peers – about the problem – and listen.

In my experience – echoed by many investors and founders I have talked to about this topic – the best ideas emerge from understanding the problem from multiple angles and deriving a rich understanding of the problem, not extending known solutions with new technology or features you can build.

To sharpen your understanding of the problem, quantify it!

–   Is it a productivity problem? (users want to do more with less time, people, and resources)
–   Is it a collaboration problem? (silos exist for capabilities and knowledge but haven’t been interconnected yet)
–   Is it a cultural consumption problem? (users want to be seen/observed/lauded for engaging in this behavior.
–   Is it creation ex-nihilo? (users say wow this is a great/useful new new thing) this may be where no equivalent behavior or product exists in other forms.
–   Is it a connection problem? (users want a connection to other users or to information/knowledge). There is huge and lasting value in solving connection problems – think wheel, trains, books, email, search, social,…
Is it an ‘insights’ problem? (the data sets exist, but infrastructure and applications to deliver insights from data do not yet exist)

People & Values

I believe you will need multiple personalities working with you in the mix of misfits that is a vibrant, healthy startup.  Some of these personality-types are:

The eternal optimists – the problem definers, the eternally dissatisfied with status-quo, the impatient ones who can’t wait for change to come fast enough.
Grumpy Ass Kickers – these are the builders, the craftsmen and women, the hackers for whom nothing exists till it is built and working. They make the wheels go around, not just tell you how the wheels are put together. They take no prisoners, their edges are blunt and effective and always rooted in reality, not an optimist’s imagination of reality as ‘it should be’. They m a k e reality from ideas.
Intuitive humanists – who instinctively and constantly pay attention to the soft-fabric all the time, nurturing the people and enabling them to deliver their best for your startup.

The initial mix can be any one of these, the eventual mix in successful startups is the right mix of all of them.
Not all of them may arrive simultaneously but you can actively look for the missing skill-set as you grow the team (or conversely, think about these when you are thinking of joining a startup – do some of these exist in that team?). But also recognize differences and impedance mismatches between these archetypes. Put in place regular and clear communications to figure out productive working relationships…. Which brings me to:

The hard art of soft skills

We, humans, are a complex organism and everything people do in addition to our skills or craft does not disappear when they start or join a startup. we do not stop being humans when we work.

Variously cast as ethics, values, culture, soft-skills, EQ, or emotional intelligence, people are always an evolving summation of these. If you don’t engage in careful seeding, tending, and pruning of ALL of these sides of a person, you will be signing yourself up for distorted growth where the ignored facets will eventually overcome everything else.

This applies to you (as a founder) as much as to everyone else you contemplate as co-founders or others thinking of you as a co-founder or early employees at a startup.

These soft skills become especially important when you factor in the pace of change of technology & platforms. The hard skills will be changing even faster than the last two decades of tech. Soft skills are the only anchor when everything else is adrift constantly.

Before you build/express your craft, craft-yourself. You are your first creation – make it the best possible on all fronts.

Superstar engineers or hackers are only as valuable as their ability to change, they are only temporarily valuable for their specific hard skills. In superstar teams, it is ok to fail first or fail fast and learn faster IF the people involved are capable of failure… and change.

Many startups and large companies do engineering bootcamps – I advocate an early ‘skills-camp’ where the focus needs to be on:

  • negotiations
  • conflict resolution
  • interviewing skills
  • and communication, communications, communications.

The time to define, build, and grow your values is not after founding or joining, it is before you begin the begin.

Operations

As you develop a framework for evaluating ideas and people, there are multiple other pieces of infrastructure that need to be built – finance, HR, operations,…

There are at least two good reasons to understand non-tech operations as a founder: Either you (or someone as inept as you) will be doing this for some time, or you will be hiring someone or outsourcing them to one or more service organizations (HMC).

Finance is not accountants sailing the chartered seas, it is merely an information system on top of monetary transactions that collects, calculates, and reports information related to money and equity in a startup.

Finance = Terms + Tracking + Planning

Terms = $, contracts, lease
Tracking = Reporting (Burn & Balance, then everything else)
Planning = Monthly, Quarterly, Annual plan. refine all the time – what did you learn in the past month, quarter, year?

If you’re in college, begin by tracking your own or a group’s expenses. Do you already belong to a group or association/non-profit? Find out who does general accounting for that group – buy them a caffeinated drink and bug them to explain how money comes in, on what terms, and how it goes out the door, and how it gets reported to those who care (legally) or those who run that group.

If you’re already in a startup, bug your CFO, CEO, or the hired hands doing accounting, planning, reporting. Make it a monthly objective to learn it bit by bit. Perhaps 1 hour every week?

IF you’re in a big(ger) company, go introduce yourself and what you do to the CFO or controller, ask them that you’d like to understand numbers and logic that go in to a balance sheet and especially estimation/planning. If they ask why? tell them this is a part of your training as a future manager of people and resources. Again, if you don’t take this step, it won’t happen. Trust me that even in a 50 person company, the CFO isn’t going to think “How can I teach accounting to everyone here?”

For new college grads joining startups while they are thinking of starting their own someday, develop this framework further by asking and trying to understand all the micro decisions that get made inside a startup every day – about features, products, markets… and above all – users. Pay special attention to the user-part. Are those around you substituting their best guess for user’s opinions or experience? Have they made a habit of pinging random users for seemingly mundane decisions? If so, insert yourself in this process – not to define a solution for the user, but to understand the problem from a user’s perspective. Volunteer to make a few paper protos to give your startup a reason to get in front of potential or current users and learn learn learn – all about the problem.

Startups are not epiphanies that happen – they are constructed with insights and confirmations via hard work. A simple first step is building a scaffolding for learning – your entrepreneurial framework.

Suggested reading:

Eric Ries, Alex Osterwalder, Steve Blank, Patrick Vlaskovits, Andrew Chen, Hiten Shah

p.s. I hope the following blog posts will be written:

Framework for learning to engineer growth – Andrew Chen

Framework for learning to sell – Noah Kagan

Recommendation driven UI: Startup Challenges

This week our team at Syfto introduced its new UI (www.syfto.com). This UI is essentially an experiment in finding an effective way to present recommendations to the user. Ideally, we would like to present the user with the most relevant content while requiring them to do minimal work. The challenge of finding the right balance between ‘work’ and ‘discovery’ is central to defining the right user experience.

My primary expectations of experience as a user are:

  • Find something I like
  • Browse content that interests me
  • IF I return, I will find more content I like to browse
  • The site helped me wade through a lot of stuff easily

Secondary:

  • I will buy something I found.
  • I will recommend this site/app to a few friends.
  • I want to see what my friends like.
  • I want to see what everyone liked.

The UI challenge is to balance content presentation as well as feature-discovery by the user. A bigger challenge is to quickly take the user from their first-interaction with content to presentation of relevance-filtered content (recommended content). As we begin to think about a UI that presents the quickest path from initial contact with the user to discovery of relevant content, there are a few things we can learn:

Learning from users

There are a number of possibly ways to determine and present recommendations to users. A few we’re working on include:

  • User profile and their social presence (Facebook)
  • User interaction with content on the site (browsing, views, vote up/down,…)
  • Implicit affinities/relationships within content including:
    • category
    • brand
    • designer
    • price/discount
    • natural aggregation by function (a ‘look’, complimentary items in home decor etc)

How

There are a few conventional ways in which user participation with content creates data:

  • Ask the user
  • Data mining user’s interaction with content on site
  • Leverage other data sets

The UI challenge

In order to derive ‘user-style’ or user-DNA from data, there are a few choices though not many:

1. Ask the user a lot of questions – at signup/onboarding as well on an ongoing basis. There are a few sites that do this well but none seems to find the right balance between annoying and helpful.

2. Make users interact with a lot of content. This is easier said than done since overwhelming the user with content is exactly what we’re trying to avoid in the first place.

So neither path seems to be a great way to do it. Over time, a few sites have done this well are:

The Startup Dilemma

For a startup working off a seed financing, not a lot of data exists a priori or can be created in the time permitted by first financing or within a pivot experiment. While social data sets via Facebook (or Twitter) help to some extent, they are typically sparse in order to compute recommendations solely based on that data/mining. Other sources of good data include ‘trending’ content on the site and social proof (what your friends liked on the site).

The challenge to present this information to the users such that we enable guided-discovery is a complex one. The inherent paradox of producing useful recommendations/suggestions to the users while not asking them to do ‘work’ is the one we have to solve. Our current UI stays within conventional representations of ‘recommended items’. Over the next few weeks we will be introducing new ways for users to discover what we find and recommend to them.

If you think you can help us navigate this challenge better, we’d love to hear from you. @rohit_x_ or @syfto or via email (firstname @ syfto.com)