Category Archives: Insights

A Q&A with Camille Samuels

Camille is a healthcare veteran and joined Venrock in 2014. Although she is based in Venrock’s Palo Alto office, Camille is a New Yorker at heart. Growing up, her father’s relentless pursuit of his dreams inspired her, and today she partners with entrepreneurs in areas like longevity science to achieve novel solutions to big problems.

We asked Camille a few questions to learn a little more about her.

Q: Who do you most admire? Why?

My dad.

Before I explain why, I have to say that my father died when I was a kid – so he remains on the pedestal he was supposed to be kicked off of when I became a bratty teenager!

My dad grew up a poor Jewish kid in upstate NY.  The way he described it, he eked his way into MIT.  He would also say that his company, Kordite Corporation, was the product of a failed senior thesis on plastic-coated clotheslines.  The company ultimately became the purveyor of baggies and Hefty garbage bags.

Before he founded Kordite (in an abandoned school house that he rented for $20/month), dad worked his way to Lt. Colonel in Patton’s Third army, mainly by being clever about finding ways to get fuel to Patton’s resource-starved forces.  In my mind, both the stories of dad’s start-up and his success in the army are about overcoming substantial obstacles – and doing so with cleverness, creativity, selling skills and grit. Today, as a VC, I tend to really gravitate toward entrepreneurs who rely on grit and hustle – instead of overwhelming resources – to achieve their goals, probably as a result of my upbringing.  I also love stories of creating something from next-to-nothing – particularly based on cutting edge technology.  I suppose it’s not surprising that I love early-stage VC.

Dad’s lifelong dream, never realized, was to be Governor of NY state.  He ran unsuccessfully four times on a platform of bringing a businessman’s management skills to government – a topical theme today, but probably not a great fit for 1960’s and 70’s New York.  The fact that he kept running and losing is inspirational to me – again, themes of perseverance. I hope to become someone who sticks her neck out and risks failure that much – I am not yet.

On a hidden wall in my house is my most prized possession: an article from the Amsterdam News (NB: a publication that describes itself at the oldest Black newspaper in the country) that was written the week my dad died.  There are 8 or so loving vignettes about my father from civil rights and community leaders.  In the piece, Ken Auletta describes dad as “the youngest adult I’ve ever known” because of dad’s infectious enthusiasm and his unwavering belief in people.  He added that dad was “a better man and visionary than a politician.”

Q: What area are you most excited about?

Tony Evnin (who is Emeritus in name only!) and I share a passion for longevity science, a growing area of interest in biotech.

Longevity science has uncovered 8 or so pathways that contribute to aging – and emerging evidence that modulating these pathways can positively impact diseases of aging.  As one player in this field recently stated, “the data is now pretty clear that aging is a disease that can be modulated with therapy.”  The goal of the more responsible companies in this field is to extend human healthspan, not necessarily lifespan, although aging science is achieving both goals for mice and has seen the earliest data in people.

I have the privilege of sitting on the Board of company called Unity that we made a seed investment in.  Unity recently emerged from “stealth mode” alongside a major publication of its science in Nature. The company is led by Ned David, an entrepreneur whom I have invested with three times previously, and Keith Leonard (as Executive Chair).  Keith was CEO of Kythera, a company Keith, Ned, and I made the founding investment in (at a $1M pre-money valuation!) that sold for $2.1B this September.  I hope that Unity does even better for Venrock!

Q: What’s your most recent investment?

At Venrock, we like to be non-consensus with our investments, and my most recent investment, Spirox, is certainly that!  Spirox is a pre-commercial medical device company.  The company has a simple implant that treats a condition called nasal valve collapse (floppy nostrils).  Nasal valve collapse is an important part of an overall condition called nasal obstruction that impacts almost three million Americans.  If you’ve ever known anyone who had their septum or turbinates treated, they very likely had nasal obstruction.  These patients have difficulty breathing and often get sequelae like heart disease and sleep apnea.  Spirox is rendering floppy nostrils, a cause of nasal obstruction that was previously untreatable by ENTs (ear, nose, throat doctors), – treatable with a simple in-office procedure.

Which brings me to a theme of mine: I have noticed over the years that physicians only notice those “nails” for which they possess a “hammer.”  In doing diligence on Spirox, I spoke to countless MDs who said some version of “Wow!  Before I learned about Spirox, I didn’t notice all the patients in my practice with nasal valve collapse – now that I know there is a treatment for it, I am seeing the problem in 10 patients a week.”  Some pretty big therapies have been created for those previously lonely “nails” – from Viagra to Botox.

On December 4, 2015 Spirox learned that FDA approved the implant.  The company plans to launch within a month.

Should You Have To Buy A New Car to Get New Features?

Introducing our investment in Pearl, the first direct-to-consumer products company bringing safety, convenience, and eventually, autonomous driving features to your existing car.

With more than 1.2 million fatalities and up to 50 million injuries globally per year, driving is dangerous. It takes a tremendous toll on our society — more than $1.9 trillion in direct economic costs each year. Reducing and eventually eliminating most of the dangers of driving is actually a technology problem for which we have the answer. Humans should drive less and computers should drive more.

There are 1.2 billion cars on the road today. Even with the popularity of ride-sharing services like Uber and the potential Millennial trends away from car ownership, there will be two billion cars on the road by 2035. Today, none of them can drive themselves. And only about 70,000 of them (most Tesla Model S and X units) have autopilot features—the beginning of autonomous driving. The automotive industry offers us only one way to get new safety and convenience features—buy a new car.

The problem with this approach is that it is just too slow. The average life of car ownership is about 11 years and the average life of a car is about 17 years, so if we wait for everyone to buy a new car in order to get new important safety features and autonomous driving capabilities, it will take at least 40 years for those features to reach about 90% of existing cars. This is true even if every single new car came with all of these features—but that’s not how the auto industry works—they tend to offer advanced features only in higher-end packages on select models. If autonomous cars are offered for sale in 2020, and we assume these features are in many of the new cars available, by 2030 there will still be 1.6 billion cars on the road without any autonomous features. This is not ideal.

Thankfully, a team of more than 50 enormously talented engineers and designers from Apple thought there should be a different path towards the autonomous driving future. Together, they have designed and launched more than 25 different iPods and iPhones currently in use by a billion people. With this unparalleled consumer products expertise, they created a company dedicated to offering the world’s most elegant and beautifully functional products for your existing car, intending to pave a path to autonomous driving for everyone.

To start, one of the most useful safety features for cars is the backup camera. According to the NHTSA, in the U.S. alone, 210 fatalities and 15,000 injuries are caused each year by backwards-moving car accidents. But today, despite being introduced in 1956 and appearing in the first production car in 1991, only 1 in 4 cars has a backup camera, and most of them are terrible — poor quality optics, low-resolution displays, limited sight distance and field of view, no intelligence, and to add one to an existing car, someone has to rip open your car to install it.

Welcome to Pearl RearVision.

Pearl RearVision is self-installable in just a few minutes. It’s solar-charged and fully wireless. It has two of the most advanced stereo-optic cameras ever put in a car and can see, day or night, with nearly 180º field of view. It sees things we humans cannot see. The system is built with deep learning intelligence and will be able to auto-identify different types of objects behind and to the side of your car. It provides both audible and visual alerts as you near certain objects and is contextually aware of your surroundings, using GPS to know the difference between a driveway and a parking lot, for example. What about the screen? Pearl RearVision streams wirelessly to your phone. And the picture is gorgeous.

Most importantly, it get’s better over time through automatic software updates. As the system learns, every user benefits with smarter image sensing and better object identification. And the company will add more and more features to all units through these updates—truly a product that improves over time.

This is only the first product from Pearl. Over time, they will deliver more products for your car, built with the same outstanding quality and premium features found only in the most expensive luxury automobiles, plus many features not found inany automobiles. Pearl will pave the road to autonomous driving for the more than one billion cars on the road today. Because everyone should have life-saving technology in their car, not just the people who buy high-end new cars.

At Venrock, we are honored to partner with Bryson, Brian, Joseph and their incredibly talented consumer products team as they undertake one of the most important challenges we face as a society — making us safer on the roads.


Q&A: Talking fintech with Venrock partner Brian Ascher

This post was originally published in PitchBook

Can you tell me your view on fintech and why it’s so important?

The financial services sector is enormous and spans a variety of trillion and multi-hundred billion dollar markets from mortgages and loans, to investments, payments, insurance, and several others. Finance is an intangible concept so well suited to digital technology, yet traditionally financial services have been delivered through massive brick and mortar networks with armies of people and paper intensive processes. Fintech can provide financial services more efficiently through direct to consumer online channels as well as remove the expensive middlemen that take a heavy toll in terms of fees and commissions ultimately born by the consumers, whether those consumers realize it or not. This increased efficiency and transparency means the elimination of mispricing so that consumers pay fairer prices for better services and results, a major reason why online lending and digital wealth management have exploded over the last five years. Cost and waste comes out of the system and benefits both the consumer and the disruptor.

Banks have started to invest in their own fintech apps and services to counter startups entering the space. How will fintech startups continue to compete with large banks?

Financial service markets are generally not winner-take-all (or even “most”) the way they are in social networks, search, or eCommerce. I think we will see some huge fintech companies created that will thrive as large independent companies. But traditional banks, investment companies, and insurance carriers are not going away; instead they are already starting to adapt to the digital consumer and are experimenting with new delivery models to attract Millennial customers. There are also plenty of software companies that want to sell white label technology to financial institutions, and there are FIs that will build good solutions in-house. And of course the incumbents will continue to acquire startups for the teams, skillsets and technology. I believe we will also see more hybrid offerings that blend digital offerings with human service advisors to provide the consumer with the best of both worlds. A great example of melding digital tools with more traditional human interaction is Personal Capital, a Venrock portfolio company. They have found a way to scale the provision of dedicated advisors to clients when they want them, but also give those clients and massive numbers of free users best in class digital tools to stay on track with their personal financial management.

Speaking of portfolio companies, what are some of the criteria you look for in startups when investing in fintech?

Fintech entrepreneurs need a blend of the maverick disruptor mentality balanced with an appreciation for the regulatory compliance, security requirements, transparency and privacy requirements that goes along with handling people’s money. Fintech entrepreneurs often come from outside the financial industry but hire industry expertise into key roles. Other things we look for are a clear business model, ideally one that corrects a mispricing in the market and offers a very different value proposition, and brand experience versus the incumbent FIs.

The growth of fintech has been almost astronomical, largely in part to the amount of VC that has been invested in the space. Can fintech continue the growth we have seen, even if there’s a downturn in venture investing?

We are already seeing VC investments in fintech cool down a bit, especially in online lending where cheap loan capital has become more scarce, consumer acquisition costs have risen due to the huge number of startups funded over the past few years, and there is a sense that the big winners are already out on the field. Investment Management (aka Robo Advisors) may be next in this progression. Insurance is getting a lot of VC investment right now. I still believe that we are early in terms of consumer adoption of fintech and there is massive growth ahead for the industry as a whole.

How has the SEC been able to keep up with the growth of fintech? Is it moving swiftly enough to make sure regulations are put in place before something major happens, or is there a general lack of oversight at the moment?

It’s not just the SEC, but also Federal bank regulators, state regulators, the CFPB (Consumer Financial Protection Bureau), and a host of others. They certainly have their hands full with the sheer number of companies that are emerging and the pace of innovation, but there is plenty of scrutiny. Enough penalties have been levied and examples made that entrepreneurs are generally investing in compliance and seeking to play by the rules. These are very complex operations and rules, so inevitably there will be minor non-compliance incidents here and there, but I don’t see systemic intentional violations nor a lack of oversight.

Cybersecurity is a major concern these days and seems to end up in the headlines with a major breach far too often. Are security concerns seen to be a roadblock in the mass adoption of fintech?

The biggest breaches that have come to light have been across a wide range of traditional retailers, legacy financial institutions, and even large public internet companies. The reality is that fintech startups are not the juiciest targets since they are tiny compared to incumbent FIs. I think security is an issue that every single company has to contend with, even if you are a mostly brick and mortar retailer that accepts credit cards. Fintech startups need to build trusted brands to overcome cybersecurity fears, but also just to get consumers to trust that they are legitimate companies that will provide good service at fair and transparent prices.


The Barbell Effect of Machine Learning

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If there is one technology that promises to change the world more than any other over the next several decades, it is arguably machine learning. By enabling computers to learn certain things more efficiently than humans and discover certain things that humans cannot, machine learning promises to bring increasing intelligence to software everywhere and enable computers to develop ever new capabilities – from driving cars to diagnosing disease – that were previously thought impossible.

While most of the core algorithms that drive machine learning have been around for decades, what has magnified its promise so dramatically in recent years is the extraordinary growth of the two fuels that power these algorithms – data and computing power. Both continue to grow at exponential rates, suggesting that machine learning is at the beginning of a very long and productive run.

As revolutionary as machine learning will be, its impact will be highly asymmetric. While most machine learning algorithms, libraries and tools are in the public domain and computing power is a widely available commodity, data ownership is highly concentrated.

This means that machine learning will likely have a profound barbell effect on the technology landscape. On one hand, it will democratize basic intelligence through the commoditization and diffusion of services such as image recognition and translation into software broadly. On the other, it will concentrate higher-order intelligence in the hands of a relatively small number of incumbents that control the lion’s share of their industry’s data.

For startups seeking to take advantage of the machine learning revolution, this barbell effect is a helpful lens to look for the biggest business opportunities. While there will be many new kinds of startups that machine learning will enable, the most promising will likely cluster around the incumbent end of the barbell.

Democratization of Basic Intelligence

One of machine learning’s most lasting areas of impact will be to democratize basic intelligence through the commoditization of an increasingly sophisticated set of semantic and analytic services, most of which will be offered for free, enabling step-function changes in software capabilities. These services today include image recognition, translation and natural language processing and will ultimately include more advanced forms of interpretation and reasoning.

Software will become smarter, more anticipatory and more personalized, and we will increasingly be able to access it through whatever interface we prefer – chat, voice, mobile application, web, or others yet to be developed. Beneficiaries will include technology developers and users of all kinds.

This burst of new intelligent services will give rise to a boom in new startups that use them to create new products and services that weren’t previously cost effective or possible. Image recognition, for example, will enable new kinds of visual shopping applications. Facial recognition will enable new kinds of authentication and security applications. Analytic applications will grow ever more sophisticated in their ability to identify meaningful patterns and predict outcomes.

Startups that end up competing directly with this new set of intelligent services will be in a difficult spot. Competition in machine learning can be close to perfect, wiping out any potential margin, and it is unlikely many startups will be able to acquire data sets to match Google or other consumer platforms for the services they offer. Some of these startups may be bought for the asset values of their teams and technologies (which at the moment are quite high), but most will have to change tack in order to survive.

This end of the barbell effect is being accelerated by open source efforts such as OpenAI as well as by the decision of large consumer platforms, led by Google with TensorFlow, to open source their artificial intelligence software and offer machine learning-driven services for free, as a means of both selling additional products and acquiring additional data.

Concentration of Higher-Order Intelligence

At the other end of the barbell, machine learning will have a deeply monopoly-inducing or monopoly-enhancing effect, enabling companies that have or have access to highly differentiated data sets to develop capabilities that are difficult or impossible for others to develop.

The primary beneficiaries at this end of the spectrum will be the same large consumer platforms offering free services such as Google, as well as other enterprises in concentrated industries that have highly differentiated data sets.

Large consumer platforms already use machine learning to take advantage of their immense proprietary data to power core competencies in ways that others cannot replicate – Google with search, Facebook with its newsfeed, Netflix with recommendations and Amazon with pricing.

Incumbents with large proprietary data sets in more traditional industries are beginning to follow suit. Financial services firms, for example, are beginning to use machine learning to take advantage of their data to deepen core competencies in areas such as fraud detection, and ultimately they will seek to do so in underwriting as well. Retail companies will seek to use machine learning in areas such as segmentation, pricing and recommendations and healthcare providers in diagnosis.

Most large enterprises, however, will not be able to develop these machine learning-driven competencies on their own. This opens an interesting third set of beneficiaries at the incumbent end of the barbell: startups that develop machine learning-driven services in partnership with large incumbents based on these incumbents’ data.

Where the Biggest Startup Opportunities Are

The most successful machine learning startups will likely result from creative partnerships and customer relationships at this end of the barbell. The magic ingredient for creating revolutionary new machine learning services is extraordinarily large and rich data sets. Proprietary algorithms can help, but they are secondary in importance to the data sets themselves. The magic ingredient for making these services highly defensible is privileged access to these data sets. If possession is nine tenths of the law, privileged access to dominant industry data sets is at least half the ballgame in developing the most valuable machine learning services.

The dramatic rise of Google provides a glimpse into what this kind of privileged access can enable. What allowed Google to rapidly take over the search market was not primarily its PageRank algorithm or clean interface, but these factors in combination with its early access to the data sets of AOL and Yahoo, which enabled it to train PageRank on the best available data on the planet and become substantially better at determining search relevance than any other product. Google ultimately chose to use this capability to compete directly with its partners, a playbook that is unlikely to be possible today since most consumer platforms have learned from this example and put legal barriers in place to prevent it from happening to them.

There are, however, a number of successful playbooks to create more durable data partnerships with incumbents. In consumer industries dominated by large platform players, the winning playbook in recent years has been to partner with one or ideally multiple platforms to provide solutions for enterprise customers that the platforms were not planning (or, due to the cross-platform nature of the solutions, were not able) to provide on their own, as companies such as Sprinklr, Hootsuite and Dataminr have done. The benefits to platforms in these partnerships include new revenue streams, new learning about their data capabilities and broader enterprise dependency on their data sets.

In concentrated industries dominated not by platforms but by a cluster of more traditional enterprises, the most successful playbook has been to offer data-intensive software or advertising solutions that provide access to incumbents’ customer data, as Palantir, IBM Watson, Fair Isaac, AppNexus and Intent Media have done. If a company gets access to the data of a significant share of incumbents, it will be able to create products and services that will be difficult for others to replicate.

New playbooks are continuing to emerge, including creating strategic products for incumbents or using exclusive data leases in exchange for the right to use incumbents’ data to develop non-competitive offerings.

Of course the best playbook of all where possible is for startups to grow fast enough and generate sufficiently large data sets in new markets to become incumbents themselves and forego dependencies on others, as for example Tesla has done for the emerging field of autonomous driving. This tends to be the exception rather than the rule, however, which means most machine learning startups need to look to partnerships or large customers to achieve defensibility and scale.

Machine learning startups should be particularly creative when it comes to exploring partnership structures as well as financial arrangements to govern them – including discounts, revenue shares, performance-based warrants and strategic investments. In a world where large data sets are becoming increasingly valuable to outside parties, it is likely that such structures and arrangements will continue to evolve rapidly.

Perhaps most importantly, startups seeking to take advantage of the machine learning revolution should move quickly, because many top technology entrepreneurs have woken up to the scale of the business opportunities this revolution creates, and there is a significant first-mover advantage to get access to the most attractive data sets.

This post also appeared on TechCrunch


Through the Eyes of One ACO: Deciding on Next Generation ACO

This article first appeared in It is co-authored with Travis Broome.

As the most advanced accountable care organization (ACO) model, Next Generation ACO has its appeal. However, it is the riskiest model, and one ACO explains why it decided to stay with the Medicare Shared Savings Program.

Deciding to take on accountability for the total quality and cost for patients is a huge commitment. When physicians form an accountable care organization (ACO) they are not saying “I will do my part,” rather they are saying “I will lead.” All 8 of Aledade’s current ACOs and the physicians at the heart of them are committed to leading the shift from volume to value in healthcare.

ACOs confront physicians with an array of options on how to be compensated for value. The Learning and Action Network created an 18-page white paper just to lay out a framework for categorizing the various options for getting paid. The Medicare Access and CHIP Reauthorization Act (MACRA) includes just a few models as advanced alternative payment models with most of them requiring taking on risk. Nevertheless, MACRA has every ACO, including us, taking another look at risk. To begin, every serious ACO makes major investments in population health. But these are seldom enough. An ACO could do everything right, but if they are in the wrong model, they might receive no compensation for the value they created. When we analyzed our own data and options, we came to a solution that surprised us—that we should not launch a Next Generation ACO even though it is the most advanced ACO model offered today.

The appeal of the Next Generation ACO is that it is the only CMS model that gives the ACO the ability to use the fee-for-service system to drive improvement and negotiate partnerships to reward value. For example, an ACO could pay cardiologists more for treadmill stress tests than a positron emission tomography stress test if they felt that these were higher value. It also offers a high rate of shared savings of 100% after CMS takes a “discount” on the benchmark.

Yet Next Generation ACO is also the riskiest model. An ACO in the Next Generation program is not only responsible for first dollar losses, but will be cutting CMS a check even if it saves somewhere between 0.5% and 4.5%. The Next Generation ACO also sets its benchmark for savings on a singular year, and how that year looks compared to market trends and national efficiency. This is tricky and varies widely across ACOs.

In our case, we looked hardest at our largest ACO as a candidate for Next Generation ACO. Unfortunately, we saw large spikes in overall hospital costs (despite significant declines in admissions) in 2015. This made 2015 a weird year. We expect hospital costs to come down in 2016 (our potential benchmark year), but it is too early to know for sure. If costs revert to normal, 2015 would have been a “good” benchmark year with its uncharacteristically high costs, but 2016 will not be a good benchmark year if costs either revert to normal since those “savings” are lost in the benchmark. If costs stay high, then they could represent a fundamental shift in the local healthcare market that will be tough to overcome.

Through the lens of our ACO, we see a larger truth: using a single year as a benchmark year is bad. What happens in 2016 is less risky in other ACO models since it accounts for 60% instead of 100% of the benchmark. At a policy level, we strongly believe that using a single benchmark year creates too much variability, risk, and chance. Instead, CMS should embrace what is found in other ACO models and use multiple years to craft a baseline that results in a more accurate measure for benchmarking.

The other main consideration for us was looking at how our ACO compared against market trends and national averages. In our case, we are 5% more efficient than the local market, but almost 5% higher compared to the national average. This comes out in our favor, but only modestly with only an estimated 0.25% positive effect on our benchmark.

Now, consider the primary alternative to Next Generation: the Medicare Shared Savings Program (MSSP). MSSP does not reward regional efficiency in the first 3 years; however, regional efficiency is rewarded in the following 6 years. Market-based benchmarking is very important for long-term sustainability of the ACO program since it enables ACOs to generate savings as long as they improve relative to their market so they do not have to keep “beating themselves.” Practically, the differing approaches for creating benchmarks between Next Generation and MSSP leads an ACO that is regionally efficient and playing the long game to prefer MSSP. A regionally efficient ACO looking to be rewarded right away would prefer Next Generation. A regionally inefficient ACO would prefer Next Generation’s lesser focus on regional efficiency. For Aledade, we view population health as the core of a long game so prefer MSSP’s long-term treatment of regional efficiency.

On balance, the MSSP program comes out significantly better for the work we are doing since the MSSP program uses a multi-year benchmark and rewards regional efficiency over the long term. Even if we did overcome the anticipated headwind in hospital costs, our projected discount (ie, the money CMS get as its portion of the savings) is 2.10% and we usually project 5% savings so the 100% shared savings rate so commonly cited as the shared savings rate becomes a 58% effective shared savings rate in our projections. This is lower than both of the 2-sided risk tracks in MSSP. While it is undoubtedly true the the flexibility of partial capitation and the affiliate model in Next Generation make it more likely to achieve savings and to generate some savings revenue more quickly than in MSSP, it is equally true that higher savings will rapidly increase the effective shared savings thresholds in future years. Since our desire is to be rewarded for our regional efficiency and not be gambling on the uncertainty of a singular benchmark year, we will be sticking with MSSP for 2017.

We hope that CMS will continue to refine the Next Generation ACO model so that it rewards long-term improvement relative to regional competitors and takes into better consideration variability in setting savings benchmarks. We also hope that other payers offer ACOs similar contact options so that incentives can be more fully aligned and simplified.

Taking on risk is a huge commitment by a physician-led ACO, whether it is a full risk such as Next Generation ACO or less risk such as Track 2 of the MSSP. A temporary, 5% bump in fee-for-service payment is not the answer. We need better benchmarks and lower risk. Risk that is tied to the finances of the ACO as we have advocated for represents the best way to move ACOs to 2-sided risk.


Running Through Walls: Mightybells executive Gina Bianchini advises to focus on inputs

In this episode of Running Through Walls, I sit down with Gina Bianchini, CEO and founder of Mightybell. Gina is an expert in community building.

Many also know her as the co-founder of Ning, the largest social platform for communities that grew to 90 million monthly unique visitors and 300,000 monthly active networks.

We discussed the secrets to building healthy communities online, our experiences with “revisionist history” in Silicon Valley and how to build trust among teams. Gina also shares why sponsors, not mentors, helped her the most when first starting out in her career.

The online world is more interesting than the real world (1:14)
How to know when a product isn’t working (3:10)
The difference between mentors and sponsors (6:38)
Why the tech industry doesn’t celebrate failure (11:14)
When hiring, you can’t be half-in on somebody (13:46)

Key links:
NPR 5 Nerds To Watch In 2013

Running Through Walls: Grand Rounds CEO on healthcare startups and hiring

In this episode of Running Through Walls, I caught up with Grand Rounds CEO and co-founder Owen Tripp, one of the first successful technology entrepreneur crossovers to the fight against complexity and confusion in healthcare.

Prior to Grand Rounds, Owen co-founded and grew the company into the worldwide leader in online reputation and privacy management.

We talked about Owen’s goal to make everyone a medical insider, how they maintain a transparent culture and what he loves about Millennials. Owen also revealed his weakness in evaluating candidates and we discussed the prominence of women in healthcare IT. 

Why tech entrepreneurs are starting healthcare companies (3:16)

Why not to hire the overnight-success seeker (7:28)

What’s great about the Millennial generation (8:56)

Gender diversity leads to better decision-making (11:05)

Keys to maintaining transparency in a growing organization (12:22)

Key links:
Gender diversity study
The Boys in the Boat by Daniel James Brown


Running Through Walls: Dynamic Signal CEO Russ Fradin on how good businesses constantly pivot

For the inaugural episode of Venrock’s new podcast, Running Through Walls, I spoke with Russ Fradin, CEO and founder of Dynamic Signal, about his long history as an entrepreneur.

I have known Russ for years and was also an investor in Adify, where he was founder and CEO. Having founded 3 companies, today Russ also advises several other start-ups as a board member.

Our conversation covered a variety of topics, including lessons learned from Dynamic Signal’s early pivot, Russ’s contrarian view on fundraising, and why he’s the only CEO I know who answers every email he receives.

Takeaways from raising 25 venture rounds (3:11)

Why it’s not that challenging to be accessible (7:28)

How to build a good relationship with VCs (12:48)

Why it’s stupid to be stealth in the enterprise world (13:39)

The three things that matter the most about culture (16:43)