The AI Paradox

Nobel Prize-winning Economist Robert Solow once quipped that “You can see the computer age everywhere but in the productivity statistics”. This famous observation has become known as the Productivity Paradox – the conundrum that economists face when trying to explain how is it that productivity in the US slowed down just as investments in information technology exploded in the 1980s and 1990s.

Today, we face a similar paradox, the AI Paradox. Just as corporations are investing billions of dollars in AI infrastructure, the actual impact and absorption of AI in the enterprise seems relatively muted.

Earlier this month, the MIT Technology Review published an excellent article entitled “This is why AI has yet to reshape most businesses”.  Some of the themes rhymed with our recent post about the potential for Applied AI companies, but also the real adoption barriers. It is striking that, although AI has been a central area of focus for may enterprises for years, actual adoption has lagged the hype. PWC recently conducted a survey of 1000 enterprises that are currently implementing or investigating AI and learned that only 20% plan for enterprise-wide adoption in 2019. Many projects are thus still stuck in pilot purgatory.

This dichotomy between promise and reality points to the power of Applied AI startup companies that focus intensely on the path to the adoption, and absorption, of AI into the enterprise.  Internally, we are calling this type of startup “AAA” grade – Absorbable, Applied AI.

Here’s why the approach of AAA startups matters so much in driving AI adoption::

  • First, as the article points out, the “initial payoff is often modest” of AI projects. Thus, we think it is important for Applied AI companies to “manage expectations along the way, as many Applied AI use cases falter based on overselling the potential and customers expecting too much when in reality improvements come incrementally over time”.
  • Second, the article mentions how if “companies don’t stop and build connections between such systems, then machine learning will work on just some of their data”, which strikes us as an approach a company focused solely on developing narrower, application-specific solutions can take as they can often derive insights gleaned from data that reflects “the real-world variance and dimensionality of the problem space” and can work aggressively with “input from domain experts to identify the logical gaps that exist and how to fill those gaps”.
  • Third, as “Ninety percent of the work is actually data extraction, cleansing, normalizing, wrangling”, this provides an advantage to third-party Applied AI companies, as opposed to internal DIY AI efforts, as the third party application provider–which are typically cloud-based platforms that scale across customers—is able to leverage this data wrangling cost across a wide swath of enterprises.  
  • Finally, the article points to several aspects that impede adoption including how end users need to be “attuned to how AI works and where its blind spots are” and how “In order for them to trust its judgments, they needed to have input into how it would work” which is why we feel it is critical for Applied AI companies to combine domain expertise with technical expertise, carefully collect the domain-specific requirements and use cases, use those requirements to “instantiate the models into a broader application that fits into customer’s broadly defined workflows”, and make transparency and explainability a core feature of the application, not an afterthought.  

If AI startups can apply these AAA techniques, they are going to continue to achieve superior customer traction as compared to their competitors. And, in doing, attract plenty of attention from AI-focused venture capital firms like Flybridge!

Applied AI: Beyond The Algorithms

One of the primary areas of focus for Flybridge over the years has been to be the first institutional investor behind companies looking to transform the enterprise technology landscape with modern software.  Given the explosion in the volume of data being generated globally, this theme has led to investments in companies such as MongoDB (databases) and Nasuni (storage) that operate at the data infrastructure layer of the enterprise tech stack.

More recently, we have been investing in further advances in data management, analytics, machine learning, and artificial intelligence.  While the potential for artificial intelligence has been written about extensively, what is less well understood is that the algorithms and underlying tools are only a fraction of the value and are unlikely to be a source of long-term differentiation.  Fully realizing the power of AI requires a deep understanding of the domain and the specific workflows that AI will seek to improve and optimize. In other words, the application layer of AI ultimately drives the business value. And we believe the window of opportunity for the AI application layer is now.

This evolution from platform to applications is not an uncommon one: when a new technology platform is immature and not well understood, there is a lot of room for innovation at the underlying technology layer, but as the platforms mature the application layer is where value accrues.  For example, in the PC era, once Windows and its associated tools were well developed, apps accumulated a huge amount of value; and similarly in the early Internet era, once the browser, server, and app server infrastructure were well established, apps became super valuable.

This “Applied AI” investment thesis resulted in four new investments by Flybridge in 2018: Aiera, which is using AI to drive fundamental equity analysis; Kebotix, which is using AI to discover and create advanced chemicals and materials; Looka, which is an AI-powered graphic design platform; and Proscia, an AI-powered digital pathology solution.  We also made follow-on investments in our existing, scaling, Applied AI portfolio companies Bitsight, Bowery Farming, Datalogue, and DataXu.  

Given the breadth of this emerging portfolio, we thought it would be helpful to expand on what we look for in Applied AI companies and some of the keys to success in building a company in this exciting field.  We believe that it is important to:

  1. Go Old School. Opportunities for Applied AI companies lie outside of the markets targeted by traditional web-scale companies.  As Andrew Ng recently observed, “a lot of the stories to be told next year [2019] will be in AI applications outside the software industry”.  Many of these markets, such as Real Estate, Finance, Healthcare, Oil & Gas, Agriculture, Manufacturing, and Logistics, have the advantages of being A) extremely large, B) where innovative AI driven approaches can drive massive levels of improvement versus the status quo, C) where potential customers may not be able to access AI talent on their own, such that build versus buy is less attractive, and D) not being an area of focus of the Googles and Baidus of the world where their massive troves of data can be a source of significant competitive advantage.  
  2. Combine Talents. The most successful teams in Applied AI will have a unique combination of an understanding of the domain and the technical capabilities to realize the vision.  Even more specifically, it is doubly helpful if one of the founders was formerly a practitioner in the field. For example, Dawson Whitfield, the founder of LogoJoy, was previously a top-notch graphic designer himself; Ken Sena, was a top-ranked equity analyst before founding Aiera, and Kebotix co-founder Professor Alan Aspuru-Guzik holds a Ph.D. in Chemistry and is a leader in the field of computational chemistry. In other words, AI experts will do better when teamed up with someone that comes from the field in which they are seeking to operate.
  3. Drive continued technical innovation.  Given how quickly the field is advancing, a deeply technical co-founder who is up to speed on, and willing to continually learn about the latest advances in AI, and see the application of new approaches to the problems their company is seeking to solve is essential.  Whether it is “few-shot” learning approaches, ensemble models, GANs, CNNs, transfer learning, explainability, and a myriad of other developing techniques, knowing and understanding the strengths, weaknesses, and applicability of different approaches is critical. We often see the domain expert mentioned in point 2) renting or borrowing their AI expertise in the form of advisors and part-time experts, but this approach is not good enough given the need to have a tight feedback loop between market-driven customer needs and the AI-driven technology insights and art of the possible.
  4. Create Data Network Effects. The most successful companies will have a clear understanding and angle on how to start and continue to spin the data network effects flywheel.  Generally, this requires having access to initial datasets that can begin the model building process, and a well thought out and focused strategy on how to increase the quantities of data available for analysis.  The initial data sets, which Proscia refers to as “inorganic data”, might be acquired, and are used to overcome the cold start problem of training a new model from scratch. In contrast, “organic data” that comes from the ongoing use of the platform can help hone and refine the algorithms over time.  Taken together, this means the cost of data should decline over time because organic data is typically free (or even negative if you can get users to pay you for the service delivered while the data is collected). In the pursuit of data, it is important to remember that the sheer volume is not always inherently better. Yes, size matters, but quality matters more.  The data should reflect the real-world variance and dimensionality of the problem space and a data strategy should incorporate input from domain experts to identify the logical gaps that exist and how to fill those gaps. Further, when assessing a technical team (per point 3), we believe it is important that they know how to build an AI infrastructure that can be monitored for changing performance and updated accordingly as the scale and scope of the datasets increase.  For example, when Aiera first started making buy-sell calls on stocks, they only did so on 16 companies based on a model that analyzed 10,000 documents a week from 300 data sources. Today, they cover nearly 2,000 securities with a model that analyzes 500,000 documents a week from 22,000 data sources. Perhaps not surprisingly, the accuracy, breadth, and duration of their buy-sell calls increased significantly over this time.
  5. Absorb The Algorithm. The specific algorithms and AI techniques themselves are not the sources of defensible value so the most successful AI companies will instantiate the models into a broader application that fits into customer’s broadly defined workflows.  We call this “Absorbable AI”, which means customers can incorporate the AI into their business and realize the benefits of the operational insights. Successful AI Applications need to not only explain why the model is generating certain results (and, importantly, explainability also helps understand and highlight bias such as gender-based or racial bias), but also integrate into the customer’s business in a logical and systematic way.  It’s also important to manage expectations along the way, as many Applied AI use cases falter based on overselling the potential and customers expecting too much when in reality improvements come incrementally over time. These application and process skills are often found coming from the more traditional application development space in fields such as UX, visualization, workflow/BPM, and integrations.
  6. Craft the Business Model. Thinking through the business model of a company is critically important.  Depending on the domain, the openness of customers to new approaches, their willingness to pay for innovation, and the scale of the level of AI being incorporated, the best way to realize the value of an Applied AI company could be by selling an application that makes human work more efficient and accurate–an end-to-end automation stack that replaces humans–or it could be by selling a complete product.  For example, our indoor farming portfolio company, Bowery, leverages a significant amount of AI to drive efficiency and quality in their operation, but they decided the best way to realize the value of that AI was to sell incredibly tasty, locally grown, pesticide-free green vegetables versus selling an AI-powered Farm Operating System to other growers. A similar example would be Tesla, where the vision is the sell a complete, AI-powered, autonomous vehicle, as opposed to say Cruise, which chose to realize the value in selling the application (and the company) to other automobile producers.  

Points 5 and 6 can be better visualized in the following matrix:

Slide1

With the continued explosion of data availability, advances in AI techniques, and the accessibility and performance of computing (GPU) cycles, we believe the trend of AI as the next great application enabler will continue for some time, and we look forward to finding more Applied AI companies with passionate domain experts and technical founders to invest behind in the coming year.

Thanks to my partner Jeff Bussgang, our advisor Harini Suresh, David West of Proscia, and Bryan Healey of Aiera for their input and insights in developing these thoughts.

Reflections on MongoDB’s IPO

MongoDB_Gray_Logo_small

A month ago our portfolio company MongoDB went public. The stock priced at $24.00 per share and closed the first day of trading at $32.07 per share. While an IPO is merely a financing event in the trajectory of a successful company and, in the words of the company’s super talented CEO, Dev Ittycheria, “NOT an end but rather a new beginning for MongoDB”, it’s still an important milestone and one worth celebrating and reflecting upon.

My association with MongoDB has been an absolute pleasure, having served on the board since Flybridge led a $3.4M financing in the company at a $12M post-money valuation ($0.66 per share) in October of 2009. In that month, MongoDB had 2,563 downloads for its nascent product. Today, it has had over 30 million. When we announced the investment, I wrote about what led to our decision to support the company: a phenomenal team, a large market with trends in the company’s favor, and a great product that customers loved. What strikes me upon re-reading that post 8 years later, is how simple, but true, that analysis was.

Since the beginning, under the leadership of co-founders Eliot Horowitz and Dwight Merriman, MongoDB has had one of the most talented, creative and driven technical teams I have had the pleasure of working with. Further, throughout the company-building process, the company benefited tremendously from the advice and guidance of Kevin Ryan, the company’s third co-founder and Chairman. Our market thesis was that the database market was large, growing (at the time, $30B in annual revenue; in 2016, $44.6B) and trending towards more special purpose solutions rather than the legacy, one-size fits all relational database model. This market insight has played out broadly. In addition to MongoDB, alternative datastores like Hadoop and its derivatives in the analytics market have thrived in the market. Finally, on the product front, MongoDB has continued to be loved by developers for it’s simplicity, flexibility, scalability and the fact it can run in any environment from the company’s database as a service offering, Atlas, to the cloud, on-premise or in hybrid environments.

What we did not write about publicly, but discussed in our internal analysis, were three additional observations:

  • MongoDB is a classic disruption story. When we were conducting diligence on the company, many of our friends and experts with deep expertise in the relational database market were quick to point out all the product’s shortcomings and the features it lacked. These objections failed to recognize that user’s desires to (1) develop software in a more agile, iterative manner; (2) deploy databases in horizontally scalable cloud architectures; and (3) utilize a product that was easy to access and allowed for immediate productivity gains all created benefits that more than compensated for the product’s supposed shortcomings at the time. Today, this “nice toy” of a database, as one of these experts called it, sees 30% of it’s new paying customers come from applications migrating off of relational databases (the other 70% comes from net new applications).
  • Developers are the new King of IT. When we first invested, it becoming apparent that the proliferation of software applications across all enterprises coupled with the rise of of the cloud, and more distributed architectures, was making the developer the new “King of IT”. This allowed, and continues to allow, MongoDB to go-to-market with a very developer-centric approach and then leverage this grassroots adoption into paying customers over time. I have written much about this developer adoption strategy in general, and as it applied to subsequent investments we made in companies such as Firebase, Stackdriver and Crashlytics (all successful investments and interestingly, all now owned by Google), but the approach of building a passionate user base prior to selling into a large enterprise has proven to be a successful one.
  • Land and Expand is a powerful business model. The flip side of a grassroots adoption first model is that when you do land a paying customer, you often land them for relatively small dollars. But, with a recurring revenue model and a product that delivers on its promises, over time these small customers renew and expand and this can build a large and growing recurring revenue base as shown in the chart below. In the case of MongoDB, this led to annual revenues that grew from $40.8M in FY 2015 (Jan) to $101.4M in FY 2017; in almost 300 customers that now spend in excess of $100K per year, up from 110 in early 2015; net ARR expansion rate of over 120% for each of the last ten quarters; and, annual cohorts that show, in the case of 2013 for example, 4.1x expansion over 4 years (i.e $5.3 million in FY13 grew to $22.1 million in FY17).

Of course getting the investment thesis right only matters if the company is able to execute. And the team at MongoDB has executed exceptionally well. Along the way, the founders were joined by Dev Ittycheria, who to no one’s surprise given his track record of success, has proven to be a remarkable, strategic, focused and results oriented, leader. He is also an exceptional recruiter and under his guidance the company has added well over 400 employees, including Michael Gordon, an extremely adept CFO (who after the IPO process is in need of a good night’s sleep), Carlos Delatorre, CRO who has built a world-class sales team, and Megan Eisenberg, CMO who has the unique talent in a marketing executive of being to drive both high level corporate marketing and a demand generation machine. Under Eliot’s leadership as CTO the company has also built a world-class, deeply technical, enterprise software team in New York City (which many thought was not possible, but it turns out to be a distinct advantage), including Cailin Nelson, SVP Cloud Engineering, Dan Passette, SVP Core Engineering, and Richard Kreuter, SVP Field Engineering. A hat tip to all of these executives, plus the 800+ other employees at MongoDB, on what they have built together.

Mongo celebration

It has been my distinct pleasure to work with such a talented team for the last 8 years. But, again, the IPO is just a financing. The company feels like it’s just getting started in its quest to disrupt this massive database market. I look forward to remaining on the company’s Board and continuing the journey for many years to come, building an important, anchor public technology company in New York City.

Fired Up By a Flybridge Family Reunion

crashfirebase-photo

Last week there was an interesting piece of news in the tech world when Firebase/Google acquired the Fabric product line and team from Twitter. Over 20+ years in the venture industry and hundreds of companies, this was a first for me: two companies we had invested in merged post their respective acquisitions by larger players. While it was unusual, nothing could make me happier than to see Crashlytics, which was acquired by Twitter in 2013, and Firebase, which was acquired by Google in 2014, join forces and continue the missions they held from their founding, and our original seed investments, of improving the lives and effectiveness of mobile developers. Huge congrats to Andrew, James, Jeff and Wayne and many thanks to both Twitter and Google for their support of both companies and allowing them to deliver on their common goals.

Rewind the clock to 2011. At the time my core investment focus was on developer-driven cloud platforms and an insight that companies that went to market with products that delighted developers could achieve significant adoption and break down many of the barriers seen by companies that focused on more traditional enterprise sales models. Within this broader theme (that also led to our investments in companies such as MongoDB, Stormpath and Apiary), it was clear at that time that the mobile developer was the new rock-star and thought leader and that most new application development spend was for mobile apps. But while mobile developers were leading the way, it was still too hard and technically challenging to quickly and easily get high quality apps into the market. Over the next year, this thesis led us to make seed investments behind two phenomenal teams. Both companies started out focused on very different markets – Crashlytics on crash reporting and Firebase on a platform to allow developers to easily build serverless back-end platforms. But both had a common goal of creating innovative technologies to help developers create amazing apps.

It’s always interesting to look back on your investment successes to see what if any common traits they shared. For Firebase and Crashlytics, there were many:

  • Founded by young, passionate entrepreneurs[i] who had strong technology backgrounds, startup experience and an innate understanding of their target customer who were
  • Creating platforms that addressed large and expanding markets with a
  • Special “developer first” approach to working with the developer community that led to rapid adoption of their platforms which led them to
  • Blow away their seed round metrics within less than a year and in turn raise Series A Rounds led by their initial investors and ultimately to being
  • Acquired by leading technology companies less than a year after Series A rounds and these
  • Acquirers provided significant incremental resources, let the teams run independently and continue to innovate under their own brands post the acquisitions

I could not be more proud of both of these teams. With millions of apps and hundreds of thousands of developers now using their technologies, what each has built is exceptional and I am 100% sure, will only get better as they join forces.

[i] It should also be noted in this time of anti-immigrant sentiment that in each company one of founders was born outside the US

Full-Stack Analytics: The Next Wave of Opportunity in Big Data

This post orginally appeared on KDnuggets in May of 2014 and came out of a panel discussion at Analytics Week in Boston that was moderated by Gregory Piatetsky of KDnuggets.  On the panel, I was asked to discuss where we see investment opportunities in the Big Data landscape and this post will expand on my comments.  The lens through which I make these observations is from our role as a seed and early stage venture capital investor, which means we are looking at where market opportunities will develop over the next 3-5 years, not necessarily where the market is today.

Over the past few years, billions of dollars of venture capital funding has flowed into Big Data infrastructure companies that help organizations store, manage and analyze unprecedented levels of data.  The recipients of this capital include Hadoop vendors such as Cloudera, HortonWorks and MapR; NoSQL database providers such as MongoDB (a Flybridge portfolio company where I sit on the board), DataStax and Couchbase; BI Tools, SQL on Hadoop and Analytical framework vendors such as Pentaho, Japsersoft Datameer and Hadapt.  Further, the large incumbent vendors such as Oracle, IBM, SAP, HP, EMC, Tibco and Informatica are plowing significant R&D and M&A resources into Big Data infrastructure.  The private companies are attracting capital and the larger companies are dedicating resources to this market given an overall market that is both large, ($18B in spending in 2013 by one estimate) and growing quickly (to $45B by 2016, or a CAGR of 35% by the same estimate) as shown in the chart below: 

BigDataMarketForecast2013

While significant investment and revenue dollars are flowing into the Big Data infrastructure market today, on a forward looking basis, we believe the winners in these markets have largely been identified and well-capitalized and that opportunities for new companies looking to take advantage of these Big Data trends lie elsewhere, specifically in what we at Flybridge call Full-Stack Analytics companies.   A Full-Stack analytics company can be defined as follows:

  1. They marry all the advances and innovation developing in the infrastructure layer from the vendors noted above to
  2. A proprietary analytics, algorithmic and machine learning layer to
  3. Derive unique, and actionable insights from the data to solve real business problems in a way that
  4. Benefits from significant data "network effects" such that the quality of their insights and solutions improve in a non-linear fashion over time as they amass more data and insights. 

A Full-Stack Analytics platform is depicted graphically below:

Full Stack Analytics Graph

Two points from the above criteria that are especially worth calling out are the concepts of actionable insights and data network effects.  On the former, one of the recurring themes we hear from CIOs and LIne of Business Heads at large companies is that they are drowning is data, but suffering from a paucity of insights that change decisions they make.  As a result, it is critical to boil the data down into something that can be acted upon in a reasonable time frame to either help companies generate more revenue, serve their customers better or operate more efficiently.  On the latter, one of the most important opportunities for Full-Stack analytics companies is to use machine learning techniques (an area my partner, Jeff Bussgang, has written about) to develop a set of insights that improve over time as more data is analyzed across more customers – in effect, learning the business context with greater data exposure to drive better insights and, therefore, better decisions.  This provides not only an increasingly more compelling solution but also allows the company to develop competitive barriers that get harder to surmount over time.  In other words, this approach creates a network effect where the more data you ingest, the more learning ensues which leads to better decisions and opportunities to ingest yet even more data.

In the Flybridge Capital portfolio, we have supported, among others, Full-Stack Analytics companies such as DataXu, whose Full-Stack Analytics programmatic advertising platform makes billions of decisions a day to enable large online advertisers to manage their marketing resources more effectively; ZestFinance, whose Full-Stack Analytics underwriting platform parses through 1000s of data points to identify the most attractive consumers on a risk-adjusted basis for its consumer lending platform; and Predilytics, whose Full-Stack Analytics platform learns from millions of data points to help healthcare organizations attract, retain and provide higher quality care to their existing and prospective members. 

Each company demonstrates important criteria for success as a Full-Stack Analytics company:

  1. identify a large market opportunity with an abundance of data;
  2. assemble a team with unique domain insights into this market and how data can drive differentiated decisions and have the requisite combination of technical skills to develop and;
  3. manage a massively scalable learning platform that is self-reinforcing.

If your company can follow this recipe for success, you will find your future as a Full-Stack Analytics provider to be very bright!

A return to the clouds

I was at the GigaOm Structure conference last week which provided me with an opportunity to reflect on trends in the cloud computing world and how my thoughts on the market opportunities and trends have changed since I last wrote about this market in depth almost three years ago.

Before looking ahead, it is worth revisiting that old post, which can be found here, as not surprisingly i got some things right and missed some things.  Observations that in hindsight I got right include:

  1. The advent of cloud computing is a fundamental architectural shift that will create numerous opportunities up and down the enterprise IT landscape.  This has, and continues, to come to pass in spades.
  2. Competing as a company in the public Infrastructure as a Service (IaaS) market is a fool's errand.  One only needs to look at the pace of innovation at AWS, which is awe inspiring, and their dominant market share, to realize that this too was correct.
  3. The cloud will be a huge enabler of innovation.  This in hindsight, is the most fundamental impact and has changed how entrepreneurs think about starting and building companies, not to mention how venture capitalists fund them.
  4. Latching on to developer communities, which I also discussed in depth here, has also proven to be a successful go-to-market strategy for many companies.
  5. Some of the specific ideas I discussed such as tools to better manage cloud environments (NewRelic, OpsCode come to mind) and SaaS applications for vertical markets (Jive, SuccessFactors) have also proven to be fruitful market opportunities for talented entrepreneurs.

Observations that, with the benefit of hindsight, I missed include:

  1. Enabling enterprise data centers to be more cloud like has not proven to be as fertile an area as I suspected.  Yes, VMware is seeing success with their software defined data center strategy, but as of yet none of the private cloud  platforms has broken out of the fray with significant revenue momentum.
  2. Bridging the cloud enterprise boundary has also not really developed into a significant market opportunity.
  3. The impact of mobility on cloud adoption.  No use case has driven cloud adoption the way the explosion of mobile devices has, and in hindsight, even three years ago this trend should have been identified more explicitly.
  4. The impact cloud scale data centers would have on networking technologies.  One only needs to looks at the >1,000 people that attended the recent Open Networking Summit to realize that the trend to Software Defined Networking and OpenFlow is gaining steam, although in my defense this was less evident in 2009.

So where does this leave entrepreneurs and investors on a go forward basis as we collectively look for opportunity in the cloud computing market?  A few thoughts:

  1. The public cloud is going to win.  Due to a combination of innovation at large companies such as Amazon, the OpenStack initiative that will enable Amazon's competitors and the fact that the vast majority of young companies developing innovative new solutions will be focused on the public cloud, it is going to be tough for private clouds other than at the largest of companies (JP Morgan and Zynga come to mind, or perhaps some highly regulated industries) to compete with the public cloud on a cost, performance or features basis.  This will, in turn, increasingly drive usage to the public cloud, in turn furthering the pace of innovation and differentiation for the public cloud creating a virtuous cycle.
  2. Driving efficiency will remain critical.  As cloud adoption continues apace, driving the efficiency of these large scale systems will be of paramount importance for cloud operators and their customers.  This means there is significant running room for companies not only that effect the labor side of the equation by driving efficiency in DevOps but also across other operating costs, especially power consumption (both in aggregate and in terms power usage effectiveness, or PUE.  As the CIO of Zynga said at Structure, "we need more innovation on power").
  3. Performance and response time demands from consumers will continue to increase.  The threshold of patience from any consumer of cloud services, whether they be delivered over the web or a mobile device, continues to decline rapidly.  Technologies that offer greater performance, or a path to developing applications with greater performance, to address this trend will see great demand.
  4. Leveraging developers to drive adoption remains critical.  As fundamental infrastructure architectural decisions get delegated into the cloud, developers increasingly will make critical application level decisions, so tools that increase their efficiency and/or offer new functionality that speed application development have the potential to see rapid uptake in the market.
  5. Look for ways to leverage the cloud to bridge data and applications onto mobile devices.  Just as Dropbox did for consumer file access, there are a myriad of applications and use cases that can harness the combined power of mobile and the cloud to provide unique user experiences.

I am sure another 3 years down the road, some of these insights will also prove to be off base, but let me know what you think or other opportunities I should be adding to the list!

 

Driving developer adoption

One of the investment themes we are pursuing here at Flybridge within our Dynamic Computing (which my partner David Aronoff covered in detail here) is the rise of developer driven business models.

Ten years ago architectural standards in large or mid sized companies were set by the CIO or Architecture review boards and the choices were fairly limited (Solaris, Linux or Windows for the OS; Oracle, DB2 or mySQL for the database; Weblogic, Websphere or jBoss for the app server, Java or .Net for languages, etc).  In today's world, on the other hand, the choices have expanded by a couple of orders of magnitude with the rise of open APIS (what Ross Mason calls the ninja slicing of the application stack) open source, virtualization, cloud computing and dynamic scripting languages.  This has empowered the developer to experiment, prototype and ultimately drive the adoption of new technologies in ways that were not previously practical.

As a result many companies are seeing great success in infiltrating large customers not with a top down approach marked by expensive sales people, but rather on with a bottoms up, developer led approach that demonstrates value on a project by project basis before the CIO even knows what is happening.  So what are the keys to success in driving this grassroots adoption?

The most important point is the simple one: your product needs to work and have a clear value proposition.  In other words the age old better, faster, cheaper still applies.  But assuming that, here are ten concrete suggestions for getting the developer driven business model to work for your company:

  1. Rapid time to value: Developers are always stretched and have more to do than time to do it.  Your product needs to show value fast.  Traditionally this meant less than 30 minutes to be up and running, although today this is compressing even further (one of our companies has a 5 minute register, download, install, integrate and run goal).
  2. Free to start: Have a fully functional, high value, free product that allows experimentation.  If your developer customer needs to pay to do anything useful, they wont adopt.
  3. Superior documentation and support: Related to point one, your documentation needs to be clear, easily searched and any support issues need to be addressed quickly and thoroughly.  Get everyone in your company into your support forums providing high value assistance.
  4. Leverage existing communities of interest: As an early stage company it is hard to create a movement on your own.  Tap into existing communities with momentum and where your potential customers can be found.  NewRelic, which out of the gates latched on to the growing Ruby community, has always struck me as one company that did this well.
  5. Buy your engineers plane tickets (or at least subway passes): Find where your developer customers congregate and get your team into the community to attend meet-ups, show off your product and explain what it does and why it solves real problems.  Overtime, generic meet-ups can become more targeted around your specific solution as we have seen withour portfolio company 10gen, and the highly successful events they put together for the mongoDB community.
  6. Facilitate word of mouth: Get your customers to speak on your behalf, either live or via their own blogs.  Share presentations and use cases via slideshare.  Contribute positively to HackerNews & Stackoverflow.  Work twitter.
  7. Have a CEO that can code: Street cred matters both externally and internally and respect in the community will build if the leader of your company has walked in the shoes of your customers.  If you are a CEO and are not a hard-core developer, fix some bugs, work on the front end code, and resolve support issues. 
  8. Don't market:  Developers are smart and cynical.  Slick marketing wont work.  Speak their language, solve their problems, add value first.  That gives your credibility to expand usage and ultimately get paid.
  9. Have a clear pricing model:  Historically people think developers don't pay for anything.  This is true, but their bosses will.  If you are adding value, people will want to pay (and in fact a company with out clear revenue model may actually see slower uptake as Sean Ellis wrote about here), but they will need to know on what basis they will be charged so they can understand the longer-term implications of working with you.
  10. Help your early adopters sell internally: Once you have some champions at a customer, especially in larger organizations they may need to convince their bosses that what they want to do is the right idea.  Be ready to arm them with information on security, performance, integration points and other similar implementations to make this easy for them.  

This is my top ten list, would love to hear other thoughts on what works, or does not work, for you.