Tag: Blockchain

Our Investment in Kush Parikh and Player Tokens

I am pleased to announce Madrona’s seed investment in Player Tokens Inc. (PTI) and our partnership with Kush Parikh, the founder and CEO. Kush joined Madrona as an Entrepreneur in Residence (EIR) in late 2017 and explored several ideas with the Madrona team, including a nascent category called crypto-collectibles. Player Tokens is launching today.

There have been a number of interesting crypto-collectible projects … this Medium post by CryptoKitties, one of the pioneers of the space, does a nice job covering both the definition of crypto-collectibles and some of the earliest experiments. This post is another good 101.

As Kush and we explored the world of crypto-collectibles together, a few things really stood out. First, there is an opportunity to bring together the digital and physical worlds (something my partner Matt lovingly describes as DiPhy) to create an entirely new and compelling collectible experience, powered by blockchain. Second, this intersection of worlds is particularly powerful in professional sports, where fans have the opportunity to encounter and interact with their favorite athletes, at games, on television, and through social media. Third, the crypto-collectible experience as it exists is entirely too complicated for the typical pro sports fan and collector.

These observations and a compelling vision for ways that crypto-collectibles could be the entry point to a whole slew of digital and physical fan experiences is what prompted Kush to found Player Tokens at Madrona, and for us to begin this journey together. Step one on the journey … talking to fans and collectors. Having lived in epic sports towns like Seattle and Chicago, we’ve had no shortage of conversations with sports fans and collectors who are interested in modern ways to buy, sell, and trade, authentic, rare collectibles for their favorite players. Through Madrona’s partnership with the OneTeam Collective, we’ve also had the opportunity to connect with the players, associations, teams and leagues, all of whom are excited about the future of crypto-collectibles and the opportunities they will enable in the professional sports world.

A mix of product vision from Kush and team, and feedback from fans, collectors and athletes alike, have shaped the initial version of Player Tokens launching today, as well as the long-term roadmap for PTI. We are excited to be working with Evan Kaplan and team at the Major League Baseball Players Association, who represent the Major League players, as our first business partner in this journey; we look forward to building great things together for fans and professional athletes across the globe.

Special thanks to our friends Ahmad Nassar, Ricky Medina, Casey Schwab and team at NFL Players Inc. / OneTeam Collective, who have been great thought partners and connectors for PTI at this early stage in the life of the company and the category.

Madrona Expands the Team, Adds Talent Director, Venture Partner and Principal

Veteran Tech Talent Executive Shannon Anderson Joins as Director of Talent, Luis Ceze, a leader in computer systems architecture, machine learning, and DNA data storage joins as Venture Partner; Daniel Li is promoted to Principal

We are so excited to announce today some great additions to the Madrona Team. Each of these people is incredibly talented and will add a significant amount to what we can bring to our portfolio companies and to the greater Seattle ecosystem.

Shannon Anderson is joining us as Director of Talent. We expound on her role here.

Luis Ceze is joining the team as Venture Partner. Luis is an award-winning professor of computer science at the University of Washington, where he joined the faculty in 2007. His research focuses on the intersection of computer architecture, programming languages, molecular biology, and machine learning. At UW, he co-directs the Molecular Information Systems Lab where they are pioneering the technology to store data on synthetic DNA. He also co-directs the Sampa Lab, which focuses on the use of hardware/software co-design and approximate computing techniques for machine learning which enables efficient edge and server-side training and inference. He is a recipient of an NSF CAREER Award, a Sloan Research Fellowship, a Microsoft Research Faculty Fellowship, the IEEE TCCA Young Computer Architect Award and UIUC Distinguished Alumni Award. He is a member of the DARPA ISAT and MEC study groups, and consults for Microsoft.

Luis also has a track record of entrepreneurship. He spent the summer of 2017 with Madrona and has been a vital partner as we have evaluated new ideas and companies for several years. In 2008, Luis co-founded Corensic, a Madrona backed, UW-CSE spin-off company. We are excited to have him on board, continuing and building on Madrona’s long-standing relationship with UW CSE, and working formally with us to identify new companies and work closely with our portfolio companies.

Last but definitely not least, we promoted Daniel Li to Principal. Daniel joined us nearly three years ago and has been an incredible part of the Madrona team. He works tirelessly to not only analyze new markets and develop investment themes that help us envision future companies, but he also dives deeply into his passions. He has built apps that we use internally on a weekly basis at Madrona as well as given a Crypto 101 course to hundreds of people over the past year. He has also proven to be an indispensable partner to entrepreneurs, leading the Madrona investment in fast growing live streaming company, Gawkbox, last year. In addition to digital media and blockchain, Dan has done significant work in investment areas from autonomous vehicles, machine learning, and AR/VR. Dan brings an energy, curiosity and intelligence to everything he does and epitomizes what Madrona looks for in our companies and our team.

We are excited to continue to build the Madrona team to even better help entrepreneurs and further capitalize on the massive opportunity to build world-changing technology companies in the Pacific NW.

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.