Advice From 5 IA40 Leaders

We polled the founders of five companies from our inaugural Intelligent Application 40 list to bring together some of the best advice they have from hard lessons they’ve learned throughout their journey or the best advice they received during that time.

When setting out to found a company, advice is never hard to come by. Mentors, advisers, professors, investors, Meetup groups, entrepreneur networks – there is no shortage of places to go to seek advice. The problem is – that advice can be as varied as the types of companies one can found.

We polled the founders of five companies from our inaugural IA40 list to bring together some of the best advice they have from hard lessons they’ve learned throughout their journey or the best advice they received during that time.

Cristobal Valenzuela, CEO of RunwayML, which is one of the Intelligent Application 40 winners
Listen to our podcast with Cristobal here.

Cristobal Valenzuela is the Co-founder and CEO of Runway, which offers web-based video editing tools that utilize machine learning to automate what used to take video editors hours if not days to accomplish. He says he learned a lot when he was just starting out — spinning his company out of this thesis project at NYU. Still, he says the most important thing to always keep in mind is your rate of learning — entrepreneurs should never stop learning.

“How fast you are learning as a company, as a team and as a product, how fast you are learning about your customers, how fast you are learning about the industry, about the competition, about the market, about technology. That rate of learning and how fast you can do something you’ve never done before, experiment, learn as much as possible, and then adapt is really, really, really, really important. It’s easy to get stuck and not be able to adapt. So, just have that mentality that you’re always learning. And then everything else will come.”

Justin Borgman, CEO of Starburst Data, which is one of the Intelligent Application 40 winners
Listen to Justin’s IA40 podcast here.

Justin Borgman is the Co-founder and CEO of Starburst, which provides a query and analytics engine to unlock the value of distributed data. Justin says the advice he gives any entrepreneur at any stage in their journey, but particularly to those just starting out, is to look inside themselves and consider whether they have the perseverance required because that is the single most important attribute to being an entrepreneur.

“You have to have a high pain threshold and a willingness to push through that pain because it is not for the faint of heart. It is not easy. I think some people are just built for that. They have the stubbornness, the drive to push through that when others get overwhelmed by it and bogged down.

One piece of advice I will share that I heard myself — I actually asked a now public company CEO founder, ‘Does this ever get easier?’ Because as you’re building, you always think, ‘Okay, at some point, it’s just going to get easy, right? Like I’m going to be relaxing on the beach, this thing’s going to run itself.’ And he said, ‘No, it’s just different kinds of hard.’ And that stuck with me because particularly as you scale, every new chapter has been a new challenge and in a totally different way. That’s part of what’s amazing about startups, I think, just from a personal growth perspective. You are always having to improve yourself and scale to the next level. And so that really stuck with me. It never gets easier, just different kinds of hard.”

Anoop Gupta, CEO of SeekOut, which is one of the Intelligent Application 40 winners
Listen to Anoop’s podcast here.

Anoop Gupta is Co-founder and CEO of SeekOut, which provides the Talent platform companies use to find, hire, grow, and retain talent. Anoop spent much of his career at Microsoft, but as he’s transitioned into the world of entrepreneurship and helping others evolve in their own careers, he said he’s started to better understand the importance of setting a company culture – and how it needs to be foundational for any entrepreneur.

“Throughout my career, I have worked with incredible people and was lucky enough to be at a place with a culture that really invested in people. In a larger organization, you kind of take culture for granted — in the sense that it is already baked in. In starting SeekOut, my appreciation and conviction that people and culture are paramount has grown. Having the right people and creating a culture of gratitude, humility, and empathy is foundational to success. My advice for others starting their own companies is to be proactive about defining your culture and to stay true to that culture as you grow.”

Clem Delangue, CEO of Hugging Face, which is one of the Intelligent Application 40 winners
Listen to our podcast with Clem here.

Clem Delangue is Co-founder and CEO of Hugging Face, an AI community and platform for ML models and datasets, which just landed $100 million in financing this year. He thinks the beauty of entrepreneurship is owning one’s own uniqueness and building a company that plays to each entrepreneur’s individual strengths. He shared his biggest learning during his early days was to always take things one step at a time.

“You don’t really know what’s going to happen in three years or five years. So just deal with the now. Take time to enjoy your journey and enjoy where you are now because when you look back at the first few years, at the time you may have felt like you were struggling, but at the end of the day, it was fun. Also, trust yourself as a founder. You’ll get millions of pieces of advice, usually conflicting. For me, it’s been good to learn to trust myself, to go with my gut and usually it pays off.”

Luis Ceze, CEO of OctoML, which is one of the Intelligent Application 40 winners
Listen to our podcast with Luis here.

Luis Ceze is the Co-founder and CEO of OctoML, an ML model deployment platform that automatically optimizes and deploys models into production on any cloud or edge hardware. Luis is an entrepreneur and tenured professor at the University of Washington. He said as a professor, you can have impact by writing papers that people read and then do something as a result. And you can directly impact your students – what they go on to learn, research – maybe even become a professor themselves. But getting into company building – where you actually put a product into the hands of a consumer has been a new and exciting experience for him. One of the most important lessons he’s learned, he said, has been to surround himself with people that he genuinely likes to work with because it creates a more supportive, trusting environment.

“People who are supported, they can count on people around them and feel like there is a very trusting relationship with the folks that you work closely with. I have no worries about showing weaknesses and always having to be right. I think it’s great when you say, ‘You know what, I was wrong, I’m going to fix it.’ It’s much better to admit when you’re wrong and fix it quickly than trying to insist on being right.”

Investing in Intelligent Applications – 2020 and beyond

We have been writing about the Intelligent App Application Stack for over five years – which means we’ve been investing it in for even longer. And that has not changed in 2020. We continue to believe that applications will be intelligent. What has changed over that time is both the rate of adoption and the availability of underlying infrastructure and technology to support these applications. In this post we detail four areas that we are continuing to see innovation around intelligent applications.

We define application intelligence as the process of using machine learning to create apps that use historical and real-time data to build a continuous learning system and make predictions and deliver rich, adaptive, personalized experiences for users. Intelligent applications typically have a modern user experience; a cloud-native, microservices architecture, and integrations to other systems and cloud services. We believe that every successful new application built today and, in the future, will be an intelligent application.

What benefits do intelligent applications deliver? We believe that next generation intelligent apps will allow us to:

  • Create custom workflows to automate any process
  • Process data in real-time across multiple systems of records to deliver insights and predictive capabilities
  • Build digital-first go to market and customer service models
  • Provide better services with lower delivery and customer service costs
  • Become the new systems of intelligence on top of legacy transactional systems

What are the types of intelligent applications that we are seeking to invest in at Madrona? Here are four broad categories where we see immense opportunities:

1. Automation

Many of the most impactful “intelligent apps” today focus on identifying repetitive, time-consuming processes and creating new ways to handle these workflows in a way that allows customers to focus more of their time on high-value synthesis and cognitive work. This is a cornerstone of the digital transformation that every enterprise around the world is currently going through.

The largest companies in this space today are the robotic process automation (RPA) vendors, such as Madrona portfolio company, UiPath. These companies have built horizontal platforms that allows companies to automate individual steps of a workflow, such as opening up a PDF document, extracting key data, entering that data into another system, and combining these steps into an automated workflow.

Despite the success of RPA, it is only scratching the surface of what is possible with AI and automation. With innovations in computer vision, natural language understanding, and other machine learning techniques, we are also seeing more and more companies build “RPA”-like automation into new products to create end-to-end workflows for specific use cases in industries such as legal services, financial services, healthcare, and real estate.

Some of the most interesting companies in this space go beyond automating one workflow to automating multiple workflows and creating a new integrated workflow. For example, a company like Zeitworks uses machine learning to map out a customer’s workflows in order to help understand which processes can be automated and to track how they perform over time. Madrona funded Zeitworks’ seed round in June of 2020 recognizing both the need for discovery of workflows prior to applying automation and the fact that automation for small to medium sized firms is particularly needed as the workforce and resources they need are not co-located any longer.

2. Next Generation Business Applications

Many of today’s key business systems for finance, HR, sales, and customer support were built decades ago, with software architectures that have not changed for the last twenty years. While these companies have built large businesses around certain types of customer behavior, they are often unable to innovate at the same pace that modern companies need.

We believe the most successful next-gen business applications will compete with their legacy alternatives by attacking a small portion of what their legacy competitors offer today or completely reimagining a business process that can only be enabled with modern software architectures.

For example, in the travel and expense space, Concur was founded in 1993 and built a massive business digitizing a manual process where paper receipts and expenses were passed from employees, to managers, to FP&A teams. Modern startups are transforming this process by reorienting around purchase data instead of receipts and forms. Rather than waiting for a month after a purchase is made, modern tools like Center deeply integrate credit cards with enterprise grade software to process expenses as they occur to give managers real-time insights into employee purchasing behavior and budgets.

HighSpot, a Madrona portfolio company, is another example of an intelligent application that uses integrations and data from multiple systems to help sales teams find the right content and relevant guidance for each conversation. By using data from CRM systems, email, and other workflow tools, their system is able to score content and understand what engages customers and drives revenue.

These types of workflows and systems are possible today because of modern microservices architectures that can process data in real-time, stream data to and from other systems, and convert data and insights into immediate actions. While many of these modern platforms start with a small feature like better insights, better UI, or better data, we believe they have the potential to eventually replace legacy systems.

3. “Avant Garde” Applications

Photo of an Amazon Go cashierless store.

“Avant garde” applications create completely new experiences and products by using machine learning – services that just weren’t possible before the combination of low-cost cloud computing, massive amounts of data, and new machine learning algorithms.

ML breakthroughs in fields like robotics and computer vision have created self-driving automobiles, which enable completely new vehicle form factors, business models, and services. Alexa, Siri, and Google Home’s voice assistants enable new interaction models that would not have been possible without advancements in natural language processing.

Many of the companies in this category are pioneers in bringing important new technologies such as computer vision, deep learning, robotics, and NLP to market, so it is a very dynamic space to watch because it sits at the intersection of massive markets, cutting-edge technologies, and novel business models.

For example, Amazon Go has created a completely new shopping experience by using computer vision technology to reimagine the shopping workflow. This allows for the construction of stores with new layouts that don’t require cash registers at the exit and may one day allow for retail stores to adopt new business models as well.

4. Intersection of Innovation spanning Life Science and Data Science

This is a vertical specific intelligent application category. However, given the potential opportunity size and impact, we have called it out seperately.

Whether it is in the field of diagnostics, therapeutics, or healthcare operational efficiencies, the availability of massive data sets combined with applied ML/AI is revolutionizing what is possible in the fields of life science and healthcare. For example, a company such as Adaptive Biotechnologies leverages decades of research on the immune system, next generation sequencing, and machine learning in order to detect changes in the immune system to diagnose disease.

While these companies can become massive winners, they may also be harder to measure and monetize in the short term. However, as early stage investors, we are excited to continue exploring investments in this category.

Over the next decade, we believe that every successful new application will be an intelligent application, and this will lead to many opportunities to build enduring software companies. If you are working on building an intelligent app in one of these categories, we would love to meet you and learn more! Our contact info is linked in our byline!

Welcoming Zeitworks To The Madrona Family!

Today, we are thrilled to announce a $4.5 million seed financing in Zeitworks, a company incubated at Madrona Venture Labs, that is automating process discovery, mapping and measurement. We are also excited to partner again with Ryan Windham – who previously was the CEO of Cedexis in our portfolio – and welcome our new co-investors JAZZ Venture Partners, and entrepreneur Spencer Rascoff, founder of Zillow.

Most investments we make at Madrona follow the time-tested model in which an entrepreneur pitches us his/her vision of how to change the world and we end up partnering because we believe that is the right team taking on an important market opportunity for which the time has come. And we also need to believe that we can directly contribute to their success. Occasionally, we have bent that model and incubated a company at Madrona via Madrona Venture Labs, that has a full fledged incubation program. Zeitworks is the outcome of one such collaboration in a space that we have a deep conviction in.

Intelligent applications have been at the core of our investing strategy for years. The proliferation of data, and the ability to process it and derive insights has changed both consumer facing and enterprise facing experiences. In the last couple of years we have seen this extend to automating tasks and we have become more involved with companies looking to change how work gets done, as evidenced by our investment in UiPath, the leader in RPA. But understanding the work and the process is a required element to actually automating.

Businesses in every industry execute hundreds of repetitive business processes for wide-ranging use cases such as claims processing, employee onboarding, order processing, returns management, etc. to name just a few. The success of such processes, and in turn of the businesses executing those, critically depend on the efficiency of those processes and the ability to improve their efficiencies. However, in an overwhelming majority of cases, the processes are not documented – or, at least poorly documented – and not measured accurately enough. As a result, businesses struggle to understand the true costs of their processes, identify the bottlenecks and make improvements that would have the highest ROI.

Traditionally, process discovery and modeling has been largely manual – serviced by consulting firms such as Accenture, Deloitte, PWC, etc. – and as a result, costly and time-consuming. However, with every business undergoing digital transformation, automated discovery and measurement of processes is increasingly becoming key to success.

Zeitworks is building a process discovery/mining product to automatically map, measure, and improve business processes across all applications, without IT integrations, consultants, interviews, or workshops. The operative word here is “automatically:” Zeitworks collects data on user activity and events via desktop sensors, applies Machine Learning to automatically discover and map processes, and analyzes and measures those processes to understand, optimize and automate those.

While process discovery and mining is not new, what makes Zeitworks possible today is the convergence of three macro trends – (a) the availability of “infinite” compute thanks to cloud computing, (b) the ability to collect large volumes of high-fidelity data, and (c) the maturity of ML/AI techniques to identify patterns and extract unique insights. Capitalizing on advanced ML algorithms and the computing ability to process vast amounts of data, Zeitworks can help identify repetitive processes and provide insights into how they are being accomplished now and how they can be completed more efficiently. Equally importantly, Zeitworks requires no deep technology integrations, enabling teams to deploy the software and realize value in a matter of hours.

From a market need standpoint, the focus on digital transformation and increasing efficiencies is driving business users’ awareness of the benefits of analyzing and understanding their own processes. We believe that Zeitworks will be a key enabler in that inevitable digital transformation of enterprises.

At Madrona Venture Labs (MVL), the founders Ryan Windham, Ben Elowitz and Matthew Holloway tested the idea behind Zeitworks extensively, with input from hundreds of prospective customers and us, while assembling a world-class team of product and technology leaders to go execute that vision. MVL continues to be core to our work with early stage founders – the MVL process includes both ideating and testing as well as partnering with a wide variety of technical and business founders – and Zeitworks is a great example of a world-class founding team taking on a market opportunity we have a deep conviction in.

We could not be more excited to partner with Ryan and team and we look forward to helping them build the next billion-dollar business in enterprise software!

 

 

 

Our Journey With Snowflake

We first met the Snowflake team three years and three months ago. At the time, Snowflake was at a sub-$10M revenue run rate, and we were skeptical that the world needed another data warehouse, given the number of other data warehouses from both the cloud providers and legacy on-prem competitors.

However, after meeting the team and speaking with early customers, we realized that Snowflake was a must-have product for next generation intelligent applications. By rebuilding the data warehouse from the ground up with cloud-first design principles, modern enterprises can benefit from both higher throughput and speed as well as better concurrent queryability, and for any data-driven company, Snowflake’s product is a must-have, not a nice-to-have.

At the time, Snowflake also wanted to take a bet on the Seattle ecosystem to build stronger relationships with the cloud providers and to tap into the local talent pool of systems and database engineers.

So given the combination of technically superior product, early but strong customer traction, the perfect team for the space, and our ability to support their growth in Seattle, we decided to invest in the company.

Today, we are excited to announce Snowflake’s $479M funding round, led by Dragoneer Investment Group and Salesforce Ventures.

Despite Snowflake being the fastest growing enterprise company we have ever seen at Madrona, it still feels like it’s early days for Snowflake, and we are looking forward to the next chapter of their journey.

Tesorio, Applying AI to the Office of the CFO

Today, we are thrilled to announce leading Tesorio’s $10m Series A funding round. As a career CFO, I am always looking for ways to automate the back office and to apply modern technologies, such as ML/AI and RPA to the office of the CFO. When you are managing a company that is growing quickly, it is imperative that processes scale and do not break as the organization changes. That is why I was excited when I met Tesorio and saw a practical application of new algorithms and technology in a space that I have been involved in my entire professional life.

Throughout my career at both private and public companies I was constantly frustrated by how many important analyses happen in a bespoke excel spreadsheet. In today’s modern era, it is amazing how many crucial decisions are made, key conclusions are formed and key metrics are created with spreadsheets that are on the brink of breaking – too many links, formulas, dependencies and worksheets!

The ultimate financial metric for a company is Cash. Not just the current balance, but the trajectory of the balance. In the vast majority of companies this analysis is performed on a spreadsheet. One containing many links, often circular references, and pulling in data from multiple sources. The risk of an error, a break, is high. Equally importantly, a spreadsheet is not exactly a living, breathing thing even though we might pretend otherwise. Changes to data sitting in different silos do not flow easily into spreadsheets without complex processes and significant human involvement.

When I met the Tesorio team, it was exciting to be able to quickly dive into a product that was replacing the spreadsheet and adding an intelligent layer to the cash flow forecasting process. By pulling actual transactions from back-office systems, adding ML/AI to that history and allowing the user to add in unique transactions, the system uses a 3-part process to generate a cash flow.

In addition, Tesorio enables their clients to impact and improve their cash flow. The building blocks of cash flow – the inputs and outputs–are addressed in the Tesorio offering. Their AR Automation offers the ability to streamline the collection of AR (Accounts Receivables) by understanding when customers typically pay, automating customer contact to speed payment, and delivering a dashboard for finance teams to manage the workflow and communication that is core to successful collections. The same is true with the management of AP (Accounts Payable) and the forecasting and planning of hedging strategies. The result of all these areas is that much of the Finance and Accounting teams spend their day in the Tesorio application – all ultimately feeding the cash flow forecast.

The founding team, Carlos Vega and Fabio Fleitas together bring a unique combination of technical and financial expertise. They partnered together at UPenn, where Carlos was studying analytics at Wharton after spending nearly a decade in finance, and Fabio was studying computer science in the School of Engineering where he founded PennApps Fellows. Together they have brought to market a product that has already been adopted and used by an impressive list of companies including Veeva Systems, Box, WP Engine, Instructure, and Couchbase.

Finally, Tesorio squarely fits our Intelligent Applications thesis that we at Madrona have been focused on for several years. As we have discussed here, we expect intelligent applications to disrupt every business process by collecting data across different silos and applying ML/AI to that data to extract unique insights, automate workflows and even obliterate obsolete processes in some instances. In Tesorio, we believe we have finally found a product that provides the CFO and her team unique insights into the business and optimizes its finances like never possible before.

Madrona’s 2017 Investment Themes

Every year in March, Madrona wraps up what happened in 2016 and we sit down with our investors to talk about our business – the business of finding and growing the next big Seattle companies. First and foremost, our strategy is to back the best entrepreneurs in the Pacific NW attacking the biggest markets. But we also overlay this with key themes and trends in the broader technology market. As part of our annual meeting we present our key investment themes for the year. Below is a snapshot of what we are focusing on:

Business and Enterprise Evolution to Cloud Native

Tim Porter-Madrona-Venture Capital Seattle
Tim Porter

The IT industry is in the early innings of its next massive shift. The transition to “cloud native” is as big or bigger as the move from PC to mainframes, the adoption of hypervisors, or the creation of public clouds. Cloud native at its core refers to applications or services built in the cloud that are container-packaged, dynamically scheduled, and microservices-oriented. Cloud Native enables all companies to take advantage of the application architectures that were once the province of Google or Facebook. Companies like Heptio and Shippable are at the forefront of disrupting how IT infrastructure has traditionally been managed with vastly increased agility, computing efficiency, real-time data, and speed. We firmly believe software that helps applications complete the journey from development on a cloud platform to deployment on different clouds, and running them at scale, will become the backbone of technology infrastructure going forward. As such, we are interested in meeting more companies that are making it easier to network, secure, monitor, attach storage, and build applications with container-based, microservice architectures.

Intelligent Applications

Customers today demand their software deliver insights that are real-time, nimble, predictive, and prescriptive. To accomplish this, applications must continuously ingest data, increasingly using event-driven architectures, coupled with algorithm-powered data models and machine learning to deliver better service and novel, predictive recommendations. The new generation of intelligent applications will be “trained and predictive” in contrast to the old generation of software programs that were created to be “programmed and predictable.” We believe that intelligent applications which rely on proprietary datasets, event-driven cloud-based architectures, and intuitive multisense interfaces will unlock new business insights in real-time and disrupt current categories of software. Investments in intelligent app companies that leverage these trends will likely be our largest area of investment in coming years.

Voice and XR Interfaces for Businesses and Consumers

We believe the shift we are seeing for human computer interactions will be as fundamental as the mouse click was for replacing the command line or touch/text was for the rise of mobile computing. This shift will be as pertinent for the enterprise as it is for consumers, and in fact will serve to further blur the lines between productivity and social communication.

With voice, we are most excited by companies that can leverage existing platforms such as Alexa to create a tools layer, or build intelligent vertical end-service applications.

In the realm of XR (from VR to AR), we believe this is a long game. VR will not be an overnight phenomenon, but will play out over the next 5 years as mobile phones become VR capable and, particularly, as truly immersive VR headsets become less expensive and cumbersome. We are committed to this future and are particularly focused on VR/AR technologies that bring the major innovation of “presence” into a shared or social space, as well as “picks-and-shovels” technology that are needed by the XR community now to start the building process now even in advance of a largescale install base of headsets.

Vertical Market Applications that use proprietary data sets and ML/AI

As algorithms continue to become more accessible by way of access to open-source libraries and platforms such as the one our portfolio company, Algorithmia, provides, we believe that proprietary data will be the bottleneck for intelligent apps. Companies and products with ML at their core must figure out how to acquire, augment, and clean proprietary, workable data sets to train the machine learning models. We are excited about the companies with these data sets, as well as companies, such as Mighty AI, that help build these data sets or work with companies to help them leverage their proprietary data to deliver business value.

One area where we see this is happening is when ML/AI and proprietary data is applied to intelligent apps in vertical markets. Vertical market focus allows companies to amass rich data sets and domain expertise at a far faster pace than companies building software that tries to be omni-intelligent, providing both product and go-to-market advantage. Most industry verticals are ripe for this innovation, but several stand out including manufacturing, healthcare, insurance/financial services, energy, and food/agriculture.

AI, IoT and Edge Computing

Linda Lian

IoT can be an ambiguous term, but fundamentally we see the explosion of devices connected to the Internet creating an environment where enterprise decision-making and consumer quotidian life will be crucially dependent on real-time data processing, analytics, and shorter response times even in areas where connectivity may be inconsistent. Real time response is crucial to success and is difficult to meet in the centralized, cloud-based model of today. For example, instant communications between autonomous vehicles cannot afford to be dependent on internet access or the latency of connecting to a cloud server and back. Edge computing technologies hope to solve this by bringing the power of cloud computing to the source of where data is generated. We are particularly committed to companies building technologies that are focused on solving how to bring AI, deep learning, machine vision, speech recognition, and other compute-heavy services to resource-constrained and portable devices and improve communication between them.

Another facet of IoT where we continue to have investment interest is new vertical devices for consumer (home, vehicle, wearable, retail), healthcare, and industrial infrastructure (electrical grid, water, public safety), along with enabling supporting infrastructure. Opportunities persist for networking solutions that improve access, range, power, discoverability, cost, and flexibility of edge devices and systems management that provide enhanced security, control, and privacy.

Commerce Experiences that Bridge Digital to Physical

Retail is in a state of flux and technologies are disrupting traditional models in more ways than e-commerce. First, physical retail isn’t going away, but it has a fresh new look. 85% of shoppers say they prefer shopping in stores due to a variety of factors including seeing the product and the social aspect. This has led the new generation of web-native brands such as Indochino, Warby Parker, Glossier and Bonobos to open stores – but they are very different, carrying little physical inventory and geared towards intimacy with customers and helping find the right product for the buyer.

Second, the decreasing cost of IoT hardware technologies such as Impinj’s RFID, advancements in distributed computing, and intelligent software such as computer vision will fundamentally alter physical retail experiences. Experiments are already underway at Amazon Go where shoppers can pick what they want and casually stroll out without waiting in a check-out line.

Within e-commerce, vertically integrated, direct-to-consumer models remain viable and compelling. They bypass costly distribution channels and can build strong brands and intimate customer experiences like Dollar Shave Club, Blue Apron, or Stitch Fix. Marketplaces that leverage underutilized resources or assets; or the technology that underlies these marketplaces remain relevant and compelling particularly for the millennial generation that prioritize access over ownership.

Security and Data Privacy

While certain security categories have been massively over-funded, new investment opportunities continue to arise. Security and data privacy are areas of massive concern for businesses, particularly in the current macro environment. Internally, enterprises demand full visibility, remediation tools, and monitoring capabilities to guard against increasingly sophisticated attacks. Particularly vulnerable are companies that house massive amounts of customer data such as financial services, big retailers, healthcare, and the government. Externally, the collection and analysis of massive amounts of real-time consumer behavioral and personal data is the bread and butter of sales, marketing, and product efforts. But new privacy laws in the US and imminent from the EU are creating heightened awareness of both the control and security of this data. We continue to be interested in companies and technologies that take novel approaches to protecting consumer data and helping corporations and organizations protect their assets.

Technologies Supporting Autonomous Vehicles

Transportation technology is experiencing a massive disruption. Autonomous driving will be the biggest innovation in automobiles since the invention of the car, impacting suppliers, car makers, ridesharing, and everything in between. Lines are blurring between manufacturer and technology provider. We believe the value creation in AVs will, not surprisingly, shift to software, and the data that makes it intelligent. More innovation is required in areas such as computer vision and control systems. Important advancements also remain to be made in component technologies such as radar, cameras, and other sensors. Indeed, there are billions of edge cases due to construction, pedestrians, weather, and a murky regulatory environment that must be ironed out both at the technology and policy level before the promise of AV is a reality.

Additionally, the rise of AV could massively disrupt current modes of car ownership. Fleet and operations management software will become increasingly important as AV transportation-as-a-service becomes more and more tangible. Software and systems for other vehicles including drones, trucks, and ships will also be huge markets and create new investment opportunities.

Seattle and the PNW are emerging as thought leaders in the area of AV, and we believe a technology center of excellence as well, creating new investment opportunities. We are deeply interested in all the threads that go into this complex and massive shift in technology, the car industry and in social culture.

Well, there you have it – Madrona’s key investment themes for 2017. Thanks for reading. If you are working on a startup in any of these areas – we would love to talk to you. Please shoot any of us a note – our email addresses are on in our bios on our website.

Investing in Suplari – Madrona’s Latest Investment in Intelligent Applications

Suplari, which announced their $3.1 million funding round today, is our newest investment in an area we helped define – Intelligent Applications. Intelligent Apps combine data and machine learning to provide real-time, actionable insight into business and consumer applications. Going back 4-5 years, we were investing in more horizontal machine learning platforms like Dato/Turi, Algorithmia and MightyAI to help companies combine the talents of their business users, data scientists and programmers to design, text and build intelligent apps. More recently we have been focusing on building vertical or functionally focused intelligent applications including Amperity, Saykara, and Placed.com.
Continue reading “Investing in Suplari – Madrona’s Latest Investment in Intelligent Applications”

Technology Trends Changing the World As We Look Ahead

Drones, Cars, Intelligent Apps, Virtual Reality and More – What to expect in 2017
There’s an age old saying that humans tend to overestimate what can be accomplished in one day, but underestimate what can be accomplished in one year. As 2016 comes to a close, it is a good time to zoom out the lens, and get reflective on what has happened this year, and predictive about what we are excited about for the coming 3-5 years.

1. Commercial Drone (UAV) Technology will Turn to Software

The 2015 hype around drones generated over $155M of VC funding in the second half of 2015, but 2016 has seen far chillier attitudes by VCs towards drone startups. However, we believe 2017 will be a year of renewal for investments and innovations in drone technology. For one, the FAA passed the first set of rules in June governing drone fly rules, allowing commercial drones to finally take to the skies without filing for lengthy and cumbersome case-by-case permission. Secondly, over the last year, the hardware war which has spooked many VCs from entering the space has been all but won. Forbes estimates that Chinese drone manufacturer DJI is valued at $8 billion and controls over 70% of the hardware market. Other contenders for this mantle such as 3D Robotics have retooled to focus on vertical software. For 2017, we see the main opportunity for drone technology to be in best-in-class tools and software deployed across platforms such as equipping drones with advanced sensing capabilities, or software for vertical industries such as real estate and farming.

2. Intelligent Applications

Customers nowadays demand their software delivers insights that are real-time, nimble, predictive and prescriptive. We have no doubt that in the future, every application will be an
intelligent application. However, the reality has not caught up to the hype. We believe data, not algorithms are the bottle-neck. Algorithms continue to become commoditized by the way of access to open-source libraries such as Algorithmia, Tensorflow, Hadoop and Cockroach DB. If products wish to do better than commodity performance, companies with machine learning at their core must figure out how to acquire proprietary, unique, clean and workable data sets to train the machine learning models.

Companies with a leg up are also likely to be vertically integrated in such a way that their data, learning models and product are all geared towards developing the best data network effects that will feed the learning loop.

We believe there is a big opportunity for companies focused on a specific industry such as healthcare, retail, legal, construction to build higher quality domain expertise at a faster rate, which facilitates the acquisition and labeling of relevant data critical to building accurate and effective machine problem solvers.

3. Virtual Intelligent Assistants with Focus on a Problem Space Will Succeed

A great example of vertical vs horizontal machine learning applications can be found in chat bots. There are some horizontal chat bot assistants that help you with any and all requests (viv.ai, Magic, and Awesome to name a few). It would seem obvious that building NLP and intelligent capabilities across all conceivable tasks and requests could be a long slow training slog of manual human validation. These companies are also at a heavy disadvantage to incumbent players tackling the horizontal assistant space. Voice enabled platforms like Alexa, Siri, Cortana, or the new Google Assistant still see limited usability despite enormous access to training data bolstered by the distribution platforms of three of the largest companies in the world. Realizing this, Amazon announced at Re:Invent that Lex, the software that powers Alexa, is now available for developers to build their own chat bots. Every developer who designs their conversation on the Lex Console is now feeding Lex’s data model. Microsoft followed suit with a similar announcement of the Cortana Skills Kit and Devices SDK.

Assistants that will be more successful in the short term are bots that are narrowly focused. There is Kasisto for finance, Digital Genius for customer service, or the many virtual assistant/meeting scheduler apps (Meekan, JulieDesk, X.ai’s Amy and later “brother” Andrew, and Clara). What excites us about these vertically oriented chat bot startups is that they are applying machine learning, artificial intelligence and natural language processing in a highly specialized and narrow way. It is far easier to train a bot to recognize and act appropriately on the finite set of lexicon and circumstances around scheduling a meeting, compared to the infinite set of scenarios that could occur otherwise. In machine learning, it is better to be a master of one, than a master of none.

4. Blockchain Will Expand as Enterprise Services Embrace it

2017-01-03-techcrunch-post-blockchain

The technological innovation of Bitcoin, blockchain, seeks to create a global distributed ledger for the transfer of assets (currency, cryptocurrency, music, real-estate deeds etc). This enables peer to peer transactions that bypass traditional intermediaries like banks, credit card companies, and governments whose centralized nature slows down processing speed, increases cost of transaction, and are vulnerable to security threats at the hub-level. Blockchain technology has been heralded by some as being as disruptive to the way people view, share, and interact with their assets as the internet was for information. However, adoption has significantly lagged this envisioned seismic shift.

We believe blockchain’s path to mainstream adoption will be more likely to arise from the enterprise and infrastructure side (creation of APIs and protocols that enable ease of adoption) as opposed to consumer adoption of cryptocurrencies (i.e. Bitcoin). An example is R3 which has gathered a consortium of 42 banks to create the technological base layer for various systems including Bitcoin, Ethereum and Ripple to talk to each other and facilitate global payment transfers.

5. Autonomous Vehicles Have More Validation Work

Aside from machine learning, autonomous vehicles were one of the most hyped technologies in 2016. This year, we saw major product announcements and technology demos from Uber, Lyft, Ford, GM, BMW, Tesla, Cruise, Comma.ai, and many other startups and corporations. Google went so far as to create an entirely new company, Waymo, devoted to their driverless car technology.

Nearly all of the major car manufacturers have announced they will be releasing autonomous vehicles in the next five years, and Lyft has stated that they are planning for the majority of rides to be autonomous within the next five years. Even President Obama said “The technology is essentially here” in a November WIRED interview.

However, despite the hype, there is a tremendous amount of heavy lifting that needs to happen in technology, infrastructure and policy to say the least. Companies still need to solve basic problems related to sensors (e.g., see Tesla Autopilot crash where cameras could not distinguish white truck against bright sky), and billions of edge cases due to construction, pedestrians, and weather, and a murky regulatory environment.

We are huge believers in the long-term benefits of autonomous vehicles, but 2017 may be a year when autonomous vehicle companies and startups are heads-down solving tough problems rather than continuing to push out flashy tech demos.

6. Augmented Reality and Virtual Reality

We believe there is still a three-year runway before VR and AR sees wide adoption by mainstream audiences. Consumer adoption will be mobile-first and/or low-end tech – think the successful recent launch of Snap Spectacles, and the cheaper price points of Google Daydream, and the Samsung Gear. VR uptake today is still burdened by hardware adoption and ease of use. Prices are still too high for anyone but the hardcore technologist or gamer.

On the enterprise side, we see 2017 as a continuing year of innovation and activity particularly in core applicable industries like engineering, science, medicine, real estate education and manufacturing. However, until the dominant form factor (whether it is glasses, head-mounted-display, or some other yet to be seen hardware) emerges, time spent in VR will still be miniscule compared to time spent in this reality.

Ultimately, if gazing into the future of technology was really so straightforward, there would be no need for speculation and VCs would be out of a job. We’ll be back next year to see assess how many of these predictions hit the nail.

Takeaways from the 5th Annual Data Science Summit

The 2016 Data Science Summit just wrapped up in San Francisco and it was bigger and better than ever. With over 1,300 attendees over two days, the conference combines business and academic leaders in a broad mix of machine learning areas – bringing together the latest in research with the state of the art in the industry. Many of the speakers are both leaders at key technology companies and involved with the top research institutions in the U.S.

Carlos Guestrin, with both Turi (previously Dato) and University of Washington, framed the world of intelligent applications including the opportunities for automating machine learning processes, creating online, closed-loop systems and increasing trust in machine learning applications.

Pedro Domingos, author of The Master Algorithm and also a UW professor, outlined the five schools of machine learning, their underlying philosophical approaches and the types of problems they best address.

Jeff Dean from Google highlighted their powerful new service TensorFlow along with its rapid adoption and independent forks in the open source community. Jeff emphasized that TensorFlow has potential beyond the deep learning area as an end-to-end system for Machine Learning applications.

While Jeff highlighted several Google ML use cases, Robin Glinton from Salesforce.com and Jure Leskovec from Pinterest (and Stanford University) impressed the audience with detailed examples of how to build and continually improve intelligent applications.

Stepping back, there are several observations from this conference that generally confirm and expanded upon learnings from Madrona’s recent AI/ML Summit in Seattle.

  1. Deep Learning is both real and overhyped. Deep learning is very well suited for image recognition problems and is growing in areas like speech recognition and translation. However, deep learning is only one branch of machine learning and is not the best approach for many intelligent application needs.
  1. Greater agility is required for intelligent applications in production. Agility comes in many forms, including automating development processes like data munging and feature engineering. It also applies to model training and ongoing model iterations for deployed intelligent apps. Automated, end-to-end pipelines that continually update production applications are rapidly becoming a requirement. These applications, like the ones consumers experience with Netflix and Spotify recommendations are increasingly referred to as “on line” applications due to their agility in both making real time recommendations and bringing data back to update models.
  1. “Closed” loops and “humans-in-the-loop” co-exist. Many intelligent applications become business solutions by involving humans to verify, enhance or act on machine outputs. These “humans-in-the-loop” cases are expected to persist for many years. However, intelligent applications increasingly require automated, closed-loop systems to meet narrow business requirements for performance and results. For example, product recommendations, fraud predictions and search results are expected to be more accurate and relevant than ever and delivered in milliseconds!
  1. The strategic value of differentiated data grows by the day. Intelligent applications are dependent on data, metadata and the models this data trains. Companies are increasingly strategic about the data they collect, the additional data they seek and the technologies they use to more rapidly train and deploy data models. Google’s internal use cases leveraging data like RankBrain are expanding. And, their decision to “open source” data models for image and speech recognition built on TensorFlow is a leading example of engaging the outside world to enhance a model’s training data.

Overall, I found the conference extremely energizing. There was substantial depth and a diversity of backgrounds, ideas and experiences amongst the participants. And, the conference furthered the momentum in moving from academic data science to deployed intelligent applications.

‘Intelligent Apps’: Seattle Area At Forefront Of Next Big Thing

The Seattle Times 5/11/16 – Chances are the entity managing your favorite smartphone app or Internet service isn’t a person.

Algorithms are setting the price of your airline ticket and hailing your Uber driver. They’re placing the vast majority of stock-market trades.

And we’re only at the beginning of a transition that is going to make the algorithms behind the software people interact with better able to understand and react to humans, technologists at a gathering of Seattle’s burgeoning artificial-intelligence industry said Wednesday.

“Every application that is going to get built, starting today and into the future, is going to be an intelligent app,” said S. “Soma” Somasegar, a venture partner with Madrona Venture Group and a former Microsoft executive.

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