Home » Steve Blank’s Corner (Page 4)

  • Why Real Learning is Outside the Building, Not Demo Day

    Steve Blank 2011 PhotoOriginally posted Sept. 5, 2013, at www.steveblank.com

    By Steve Blank

    Over the last three years our Lean LaunchPad / NSF Innovation Corps classes have been hundreds of entrepreneurial teams a year how to build their by getting out of the building and testing their hypotheses behind their business model.  While our teams have mentors, socialize a lot and give great demos, the goal of our class final presentations is “Lessons Learned”  – about product/market fit, pricing, acquisition/activation costs, pricing, partners, etc.  We think teaching teams a formal methodology around the Lean Framework (Business Model design, and Agile Engineering) is a natural evolution of how successful incubators/accelerators will build startups.

    Here’s the story of one such team; Jonathan Wylie, Lakshmi Shivalingaiah and the Evoke team.

    —–

    Imagine if, in the course of ten rollercoaster weeks, your customer segment changed from executives on corporate campuses to moms on playgrounds, a tool that was just part of your product turned into the killer product, and the value of the problem you were solving went from number 47 to customers trying to give you money when you demo’d.  Here’s how that happened.

    We came to the Lean LaunchPad class wanting to build a mobile/web research management system aimed at helping qualitative researchers better manage the media they captured in the field. We were ready to learn, but pretty confident we would end the journey in the same market space in which we started.  We had a killer team and all the right skillsets.  I was a consultant and ethnographer, another teammate was a market researcher, and two others had the software engineering skills to build what the market needed.  And what the market needed would, of course, be exactly what we had envisioned. After all, there must be a huge number of researchers struggling with the exact same problems we had, right?  Not quite…

    Out of the Building
    In the first 4 weeks, our team got out of the building and spoke with employees at 42 different companies.  We spoke with people at all levels, from front line user experience researchers at large tech firms to the CMO of a fortune 500 consumer goods company. Discover X WorkflowFrom the first 10 interviews, we learned that video is a big problem for researchers who use that medium.  It takes an average of 4 hours to mine every hour of video for the relevant 10 seconds of insight that matters.  Thus, we focused our early minimum viable product on helping researchers save money and time in finding insight in market research videos.

    Wireframes
    We built wireframes as a Minimum Viable Product to elicit feedback and began showing them to customers during our interviews.  At this point, things got real…and a bit ugly.  Given something tangible, customers were able to start gauging their willingness to use and pay.  Discover X wireframeTurns out, researchers were “just not that into us.”  We heard consistently that the product looked good and solved a problem, but it was not an important problem.  It was number 47 on their list, and there was no way they could justify paying to solve that problem.

    First Pivot
    As disappointing as this was, we dug deeper with our questioning.  To our surprise, customers started offering ideas on where there might be a true need; one of which was the legal market, specifically the deposition process. We thought this would be perfect for our product. There is a lot of video being recorded, and attorneys need to be able to pull out the insights quickly. After a solid week of speaking with lawyers and attending webinars on real-time deposition software, we had mapped both the technology and the buying relationships.  What we learned was that, we would just be an incremental feature to the incumbents and would need to integrate our solution with theirs. This, combined with regulation from the courts, a 2-year sales cycle, and the realization that e-discovery groups are not early adopters, made this an unattractive market.

    Technology in search of a market
    By this point, we were a technology in search of a market…not a good place to be.   The next customer segment we tried was startup founders.  After all, they are just like us – researching their markets and needing a way to share insights and keep their teams connected to customers. However, we found that most just assume that what they are building will have a market. The few who did get it felt uncomfortable using video during the interview process.

    Pivot Two
    While at times we felt like we wanted to give up, we began to hear a positive signal in the noise of all the customer rejections. Evoke BrainstormingAt first it was faint.  While customers in all three markets were lukewarm for use at work, they got visibly excited telling us that it would definitely solve a problem at home. Say what??  They told us “too bad we weren’t making a consumer product so they could document their kids… they would pay a lot of money for that product.”  Whoah…were customers telling us we are a consumer product rather than B-to-B??

    We settled on a small-scale experiment to test the consumer market. We decided to speak with 10 parents over the course of a week. If 5 had a similar problem, we would dive deeper. What we got was a landslide of interest.  All 10 parents had the problem.  Even more amazing to us, 9 of them liked our solution!

    We learned that parents capture moments with their families to:

    1. remember and relive later
    2. share with those closest to them
    3. pass along a memoir to their kids

    To our surprise, it turns out that none of these are being accomplished well with existing products, and parents are stressed because they feel like they are failing in an important responsibility.

    Eureka!
    Since that initial experiment in class, we’ve validated these findings (and many others) during over 200 hour-long interviews.

    1st evoke wireframes

    We even partnered with the university on a 112-person design workshop to learn more about how photos and videos fit into people’s lives.  It’s always an incredible experience to be invited into someone’s home to learn about how they capture their most precious family moments.  Sometimes, the learning is immediate and conclusive. Other times, we have to do multiple rounds before we arrive at an answer to an important question.

    The result of all this effort is that we have found a large and underserved market in hidden in plain sight, right in the middle of an area that gets a lot of attention – photos and videos!

    Lessons Learned
    There’s no way we would have learned any of this unless we were out of the building and in the trenches, with parents over an extended period.

    Knowing our customers and their problems first hand has given us a huge head start and a competitive advantage. Most seem to just make this stuff up for a pitch deck or to please stakeholders, but the validated learning that we gained through these interviews and other methods of business model experimentation is not something that can be easily replicated.

    As for our current status, we are building the product, continuing customer development, exploring and validating other aspects of our business model, and…oh yeah…hitting the pavement to raise our first round of funding!  If you want to talk to us about that, or if you know parents that we should be speaking to, please feel free to reach out.

    For all the parents out there, relief (and much more) is on its way… http://www.evokeapp.com

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  • How to Get Meetings with People Too Busy to See You

    Originally published Aug. 12, 2013, at www.steveblank.com

    By Steve Blank

    Steve Blank 2011 PhotoAsking, “Can I have coffee with you to pick your brain?” is probably the worst possible way to get a meeting with someone with a busy schedule.  Here’s a better approach.

    ——

    Jason, an entrepreneur I’ve known for over a decade, came out to the ranch today. He was celebrating selling his company and just beginning to think through his next moves. Since he wasn’t from Silicon Valley, he decided to use his time up here networking with other meetings with VC’s and company executives.

    I get several hundred emails a day, and a good number of them are “I want to have coffee with you to bounce an idea off.” Or, “I just want to pick your brain.” I now have a filter for which emails get my attention, so I was curious in hearing what Jason, who I think of as pretty good at networking, was asking for when he was trying to set up meetings.

    “Oh, I ask them if I can have coffee to bounce an idea off of them.”…Sigh.foot in the door

    I realized most don’t know how to get meetings with people too busy to see you.

    Perfect World
    Silicon Valley has a “pay-it-forward” culture where we try to help each other without asking for anything in return. It’s a culture that emerged in the 60’s semiconductor business when competitors would help each other solve bugs in their chip fabrication process. It continued in the 1970’s with the emergence of the Homebrew Computer Club, and it continues today.  Since I teach, I tend to prioritize my list of meetings with first my current students, then ex-students, then referrals from VC firms I’ve invested in, and then others.  But still with that list, and now with a thousand plus ex-students, I have more meeting requests than I possibly can handle. (One of the filters I thought would keep down the meetings is have meetings at the ranch; an hour from Stanford on the coast, but that hasn’t helped.)

    So I’ve come up with is a method to sort out who I take meetings with.

    What are you offering?
    I’m not an investor, and I’m really not looking for meetings with entrepreneurs for deal flow. I’m having these meetings because someone is asking for something from me – my time – and they think I can offer them advice.

    If I’d had infinite time I’d take every one of these “can I have coffee” meetings. But I don’t.  So I now prioritize meetings with a new filter: Who is offering me something in return.

    No, not offering me money.  Not for stock.  But who is offering to teach me something I don’t know.

    The meeting requests that now jump to the top of my list are the few, very smart entrepreneurs who say, “I’d like to have coffee to bounce an idea off of you and in exchange I’ll tell you all about what we learned about xx.”

    get into my head

    This offer of me something changes the agenda of the meeting from a one-way, you’re learning from me, to a two-way, we’re learning from each other.

    It has another interesting consequence for those who are asking for the meeting – it forces them to think about what is it they know and what is it they have learned – and whether they can explain it to others in a way that’s both coherent and compelling.

    Irony – it’s
    While this might sound like a, “how to get a meeting with Steve” post, the irony is that this “ask for a two-way meeting” is how we teach entrepreneurs to get their first customer discovery meetings; don’t just ask for a potential customers time, instead offer to share what you’ve learned about a technology, market or industry.

    It will increase your odds in any situation you’re asking for time from very busy people – whether they are VC’s, company executives or retired entrepreneurs.

    • Lessons Learned
    • Wanting to have coffee is an ask for a favor
    • Offering to share knowledge is a different game
    • Try it, your odds of will increase
    • And the meetings will be more productive
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  • Reinventing Life Science Startups – Evidence-based Entrepreneurship

    Steve Blank 2011 PhotoOriginally published Aug. 21, 2013, at www.steveblank.com

    What if we could increase productivity and stave the capital flight by helping Life Sciences build their companies more efficiently?

    We’re going to test this hypothesis by teaching a Lean LaunchPad class for Life Sciences and Health Care (therapeutics, diagnostics, devices and digital health) this October at UCSF with a team of veteran venture capitalists.

    Part 1 of this post described the issues in the drug discovery. Part 2 covered medical devices and digital health. This post describes what we’re going to do about it.  And why you ought to take this class.

    ——

    When I wrote Four Steps to the Epiphany and the Startup Owners Manual, I believed that Life Sciences startups didn’t need . Heck how hard could it be?  You invent a cure for cancer and then figure out where to put the bags of money. (In fact, for oncology, with a successful clinical trial, this is the case.)

    Pivots in Life Sciences Companies

    But I’ve learned that’s not how it really works. For the last two and a half years, we’ve taught hundreds of teams how to commercialize their science with a version of the Lean LaunchPad class called the National Science Foundation Innovation Corps.  Quite a few of the teams were building biotech, devices or digital health products.  What we found is that during the class almost all of them pivoted - making substantive changes to one or more of their business model canvas components.

    In the real world a big pivot in life sciences far down the road of development is a very bad sign due to huge sunk costs.  But pivoting early, before you raise and spend millions or tens of millions means potential disaster avoided.

    Some of these pivots included changing their product/service once the team had a better of understanding of customer needs or changing their position in the value chain (became an OEM supplier to hospital suppliers rather than selling to doctors directly.) Other pivots involved moving from a platform technology to become a product supplier, moving from a therapeutic drug to a diagnostic or moving from a device that required a PMA to one that required a 510(k).

    Some of these teams made even more radical changes.  For example when one team found the right customer, they changed the core technology (the basis of their original idea!) used to serve those customers. Another team reordered their device’s feature set based on customer needs.

    These findings convinced me that the class could transform how we thought about building life science startups.  But there was one more piece of data that blew me away.

    Control versus Experiment – 18% versus 60%
    For the last two and a half years, the teams that were part of the National Science Foundation Corps wanted to learn how to commercialize their science, applied to join the program, fought to get in and went through a grueling three month program.  Other scientists attempting to commercialize their science were free to pursue their startups without having to take the class.

    Both of these groups, those who took the Innovation Corps class and those who didn’t, applied for government peer-reviewed funding through the SBIR program. The teams that skipped the class and pursued traditional methods of starting a company had an 18% success rate in receiving SBIR Phase I funding.

    The teams that took the class  – get ready for this – had a 60% success rate. And yes, while funding does not equal a successful company, it does mean these teams knew something about building a business the other teams did not.

    The 3-person teams consisted of Principal Investigators (PI’s), mostly tenured professors (average age of 45,) whose NSF research the project was based on. The PI’s in turn selected one of their graduate students (average age of 30,) as the entrepreneurial lead. The PI and Entrepreneurial Lead were supported by a mentor (average age of 50,) with industry/startup experience.

    This was most definitely not the hoodie and flip-flop crowd.

    Obviously there’s lots of bias built into the data – those who volunteered might be the better teams, the peer reviewers might be selecting for what we taught, funding is no metric for successful science let alone successful companies, etc.  – but the difference in funding success is over 300%.

    The funding criteria for these new ventures wasn’t solely whether they had a innovative technology. It was whether the teams understood how to take that idea/invention/patent and transform it into a company. It was whether after meeting with partners and regulators, they had a plan to deal with the intensifying regulatory environment. It was whether after talking to manufacturing partners and clinicians, they understood how they were going to reduce technology risk. And It was after they talked to patients, providers and payers whether they understood the customer segments to reduce market risk by having found product/market fit.

    Scientists and researchers have spent their careers testing hypotheses inside their labs. This class teaches them how to test the critical hypotheses that turn their idea into a business as they deal with the real world of regulation, customers and funding.

    So after the team at UCSF said they’d like to prototype a class for Life Sciences, I agreed.

    Here’s what we’re going to offer.

    The Lean LaunchPad Life Sciences and Health Care Class

    The goal of the Lean LaunchPad Life Sciences class at UCSF is to teach researchers how to move their technology from an academic lab into the commercial world.UCSF Logo

    We’re going to help teams:

    • assess regulatory risk before they design and build
    • gather data essential to customer purchases before doing the science
    • define clinical utility now, before spending millions of dollars
    • identify financing vehicles before you need them

    We’ve segmented the class into four cohorts: therapeutics, diagnostics, devices and digital health.  And we recruited a team of world class Venture Capitalists and to teach and mentor the class including Alan MayKarl HandelsmanAbhas Gupta, and Todd Morrill.

    The course is free to UCSF, Berkeley, and Stanford students; $100 for pre-revenue startups; and $300 for industry. – See more here

    The syllabus is here.

    Class starts Oct. 1 and runs through Dec. 10.

    Download the all three parts of the Life Science series here.

    Watch my fireside chat at the recent Health Innovation Summit here.

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  •  
  • Reinventing Life Science Startups – Medical Devices and Digital Health

    Steve Blank 2011 PhotoOriginally published Aug. 20, 2013, at www.steveblank.com.

    What if we could increase productivity and stave the capital flight by helping Life Sciences build their companies more efficiently?

    We’re going to test this hypothesis by Lean LaunchPad class for Life Sciences and Healthcare (therapeutics, diagnostics, devices and digital health) this October at UCSF with a team of veteran venture capitalists.

    In this three post series, Part 1 described the challenges Life Science companies face in Therapeutics and Diagnostics. This post describes the issues in Medical Devices and Digital Health.  Part 3 will offer our hypothesis about how to change the dynamics of the Life Sciences industry with a different approach to commercialization of research and .  And why you ought to take this class.

    ——–

    Medical devices prevent, treat, mitigate, or cure disease by physical, mechanical, or thermal means (in contrast to drugs, which act on the body through pharmacological, metabolic or immunological means). They span they gamut from tongue depressors and bedpans to complex programmable pacemakers and laser surgical devices. They also diagnostic products, test kits, ultrasound products, x-ray machines and medical lasers.

    Incremental advances are driven by the existing medical device companies, while truly innovative devices often come from doctors and academia. One would think that designing a medical device would be a simple engineering problem, and startups would be emerging right and left. The truth is that today it’s tough to get a medical device startup funded.

    Life Sciences II – Medical Devices

    Regulatory Issues
    In the U.S. the FDA Center for Devices and Radiological Health (CDRH) regulates medical devices and puts them into three “classes” based on their risks.

    Class I devices are low risk and have the least regulatory controls. For example, dental floss, tongue depressors, arm slings, and hand-held surgical instruments are classified as Class I devices. Most Class I devices are exempt Premarket Notification 510(k) (see below.)

    Class II devices are higher risk devices and have more regulations to prove the device’s safety and effectiveness. For example, condoms, x-ray systems, gas analyzers, pumps, and surgical drapes are classified as Class II devices.FDA approvals

    Manufacturers introducing Class II medical devices must submit what’s called a 510(k) to the FDA. The 510(k) identifies your medical device and compares it to an existing medical device (which the FDA calls a “predicate” device) to demonstrate that your device is substantially equivalent and at least as safe and effective.

    Class III devices are generally the highest risk devices and must be approved by the FDA before they are marketed. For example, implantable devices (devices made to replace/support or enhance part of your body) such as defibrillators, pacemakers, artificial hips, knees, and replacement heart valves are classified as Class III devices. Class III medical devices that are high risk or novel devices for which no “predicate device” exist require clinical trials of the medical device a PMA  (Pre-Market Approval).Life Science Decline

    • The FDA is tougher about approving innovative new medical devices. The number of 510(k)s being required to supply additional information has doubled in the last decade.
    • The number of PMA’s that have received a major deficiency letter has also doubled.
    • An FDA delay or clinical challenge is increasingly fatal to Life Science startups, where investors now choose to walk away rather than escalate the effort required to reach approval.

    med device pipeline

    Business Model Issues

    • Cost pressures are unrelenting in every sector, with pressure on prices and margins continuing to increase.
    • Devices are a five-sided market: patient, physician, provider, payer and regulator. Startups need to understand all sides of the market long before they ever consider selling a product.
    • In the last decade, most device startups took their devices overseas for clinical trials and first getting EU versus FDA approval
    • Recently, the financing of innovation in medical devices has collapsed even further with most Class III devices simply unfundable.
    • Companies must pay a  medical device excise tax of 2.3% on medical device revenues, regardless of profitability delays or cash-flow breakeven.
    • The U.S. government is the leading payer for most of health care, and under ObamaCare the government’s role in reimbursing for medical technology will increase. Yet two-thirds of all requests for reimbursement are denied today, and what gets reimbursed, for how much, and in what timeframe, are big unknowns for new device companies.

    Issues

    • Early stage Venture Capital for medical device startups has dried up. The amount of capital being invested in new device companies is at an 11 year low.
    • Because device IPOs are rare, and M&A is much tougher, liquidity for investors is hard to find.
    • Exits have remained within about the same, while the cost and time to exit have doubled.

    Life Sciences III – The Rise of Digital Health
    Over the last five years a series of applications that fall under the category of “Digital Health” has emerged. Examples of these applications include: remote patient monitoring, analytics/big data (aggregation and analysis of clinical, administrative or economic data), hospital administration (software tools to run a hospital), electronic health records (clinical data capture), and wellness (improve/monitor health of individuals). A good number of these applications are using Smartphones as their platform.digital health flow

    Business Model Issues

    • A good percentage of these startups are founded by teams with strong technical experience but without healthcare experience. Yet healthcare has its own unique regulatory and reimbursement issues and business model issues that must be understood
    • Most of these startups are in a multisided market, and many have the same five-sided complexity as medical devices: patient, physician, provider, payer and regulator.  (Some are even more complex in an outpatient / nurse / physical therapy setting.)
    • Reimbursement for digital health interventions is still a work in progress
    • Some startups in this field are actually beginning with while others struggle with the classic execution versus search problem

    Regulatory Issues

    • Digital Health covers a broad spectrum of products, unless the founders have domain experience startups in this area usually discover the FDA and the 510(k) process later than they should. 

    Venture Capital

    • Seed funding is still scarce for Digital Health, but a number of startups (particularly those making physical personal heath tracking devices) are turning to crowdfunding.
    • Moreover, the absence of recent IPOs and public companies benchmarks creates uncertainty for VCs evaluating later investments too

    Try Something New
    The fact that the status quo for Life Sciences is not working is not a new revelation. Lots of smart people are running experiments in search of ways to commercialize basic research  more efficiently.

    Universities have set up translational R&D centers; (basically university/company partnerships to commercialize research).  The National Institute of Health (NIH) is also setting up translational centers through its NCATS program.  Drug companies have tried to take research directly out of university labs by licensing patents, but once inside Pharma’s research labs, these projects get lost in the bureaucracy.  Realizing that this is not optimal, drug companies are trying to incubate projects directly with universities and the researchers who invented the technology, such as the recentJanssen Labs program.

    But while these are all great programs, they are likely to fail to deliver on their promise. The assumption that the pursuit of drugs, diagnostics, devices and digital health is all about the execution of the science is in most cases a mistake.

    The gap between the development of intriguing but unproven innovations, and the investment to commercialize those innovations is characterized as “the Valley of Death.”valley of death

    We believe we need a new model to attract private investment capital to fuel the commercialization of clinical solutions to todays major healthcare problems that is in many ways technology agnostic. We need a “Needs Driven/Business Model Driven” approach to solving the problems facing all  the stakeholders in the vast healthcare system.

    We believe we can reduce the technological, regulatory and market risks for early-stage life science and healthcare ventures, and we can do it by teaching founding teams how to build new ventures with Evidence-Based .

    ——

    Part 3 in the next post will offer our hypothesis about how to offer our hypothesis how to change the dynamics of the Life Sciences industry with a different approach to commercialization of research and innovation in this sector. And why you ought to takethis class.

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  • Reinventing Life Science Startups –Therapeutics and Diagnostics

    Steve Blank 2011 PhotoOriginally posted on August 19, 2013, on www.steveblank.com

    It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of Light, it was the season of Darkness, it was the spring of hope, it was the winter of despair, we had everything before us, we had nothing before us, we were all going direct to Heaven, we were all going direct the other way.

    Charles Dickens

    Life Science (therapeutics- drugs to cure or manage diseases, diagnostics- tests and devices to find diseases, devices to cure and monitor diseases; and digital health –health care hardware, software and mobile devices and applications streamline and democratize the healthcare delivery system) is in the midst of a perfect storm of decreasing productivity, increasing regulation and the flight of .

    But what if we could increase productivity and stave the capital flight by helping Life Sciences build their companies more efficiently?

    We’re going to test this hypothesis by teaching a Lean LaunchPad class for Life Sciences and Healthcare (therapeutics, diagnostics, devices and digital health) this October at UCSF with a team of veteran venture capitalists and angels.

    It was the best of times and the worst of times
    The last 60 years has seen remarkable breakthroughs in what we know about the biology underlying diseases and the science and engineering of developing commercial drug development and medical devices that improve and save lives. Turning basic science discoveries into drugs and devices seemed to be occurring at an ever increasing rate.

    Yet during those same 60 years, rather than decreasing, the cost of getting a new drug approved by the FDA has increased 80 fold.  Yep, it cost 80 times more to get a successful drug developed and approved today than it did 60 years ago.Overall efficiency

    75% or more of all the funds needed by a Life Science startup will be spent on clinical trials and regulatory approval. Pharma companies are staggering under the costs.  And medical device in the U.S. has gone offshore primarily due to the toughened regulatory environment.

    At the same time, Venture Capital, which had viewed therapeutics, diagnostics and medical devices as hot places to invest, is fleeing the field. In the last six years half the VC’s in the space have disappeared, unable to raise new funds, and the number of biotech and device startups getting first round financing has dropped by half. For exits, acquisitions are the rule and IPOs the exception.

    While the time, expense and difficulty to exit has soared in Life Sciences, all three critical factors have been cut by orders of magnitude in other investment sectors such as internet or social-local-mobile.  And while the vast majority of Life Science exits remain below $125M, other sectors have seen exit valuations soar.  It has gotten so bad that pension funds and other institutional investors in venture capital funds have told these funds to stay away from Life Science – or at the least, early stage Life Science.

    WTF is going on?  And how can we change those numbers and reverse those trends?

    We believe we have a small part of the answer.  And we are going to run an experiment to test it this fall at UCSF.

    In this three post series, the first two posts are a short summary of the complex challenges Life Science companies face; in Therapeutics and Diagnostics in this post and in Medical Devices and Digital Health in Part 2.  Part 3 explains our hypothesis about how to offer our hypothesis how to change the dynamics of the Life Sciences industry with a different approach to commercialization of research and innovation in this sector.  And why you ought to take this class.

    ——-

    Life Sciences I—Therapeutics and Diagnostics

    It was the Age of Wisdom – Drug Discovery
    There are two types of drugs. The first, called small molecules (also referred to as New Molecular Entities or NMEs), are the bases for classic drugs such as aspirinstatins or high blood pressure medicines. Small molecules are made by reactions between different organic and/or inorganic chemicals. In the last decade computers and synthesis methods in research laboratories enable chemists to test a series of reaction mixtures in parallel (with wet lab analyses still the gold standard.) Using high-throughput screening to search for small molecules, which can be a starting point (or lead compound) for a new drug, scientists can test thousands of candidate molecules against a database of millions in their libraries.

    Ultimately the FDA Center for Drug Evaluation and Research (CDER) is responsible for the approval of small molecules drugs.Drug discovery pipeline

    The second class of drugs created by biotechnology is called biologics (also referred to as New Biological Entities or NBEs.) In contrast to small molecule drugs that are chemically synthesized, most biologics are proteins, nucleic acids or cells and tissues. Biologics can be made from human, animal, or microorganisms – or produced by recombinant DNA technology. Examples of biologics include: vaccines, cell or gene therapies, therapeutic protein hormones, cytokines, tissue growth factors, andmonoclonal antibodies.

    The FDA Center for Biologics Evaluation and Research (CBER) is responsible for the approval of biologicals.

    It was the Season of Light
    The drug development pipeline for both small molecules and biologics can take 10-15 years and cost a billion dollars. The current process starts with testing thousands of compounds which will in the end, produce a single drug.

    In the last few decades scientists searching for new drugs have had the benefit of new tools — DNA sequencing3D protein database for structure datahigh throughput screening for “hits”, computational drug design, etc. — which have sped up their search dramatically.Drug funnel

    The problem is that the probability that a small molecule drug gets through clinical trials is unchanged after 50 years. In spite of the substantial scientific advances and increased investment, over the last 20 years the FDA has approved an average of 23 new drugs a year. (To be fair, this is indication-dependent. For example, in oncology, things have gotten significantly better. In most other areas, particularly drugs for the central nervous system and metabolism, they have not.)

    drugs approved

    It was the Season of Despair
    With the exception of targeted therapies, the science and tools haven’t made the drug discovery pipeline more efficient. Oops.

    There are lots of reasons why this has happened.

    Regulatory and Reimbursement Issues

    • Drug safety is a high priority for the FDA. To avoid problems like Vioxx, Bexxar etc., the regulatory barriers (i.e. proof of safety) are huge, expensive, and take lots of time. That means the FDA has gotten tougher, requiring more clinical trials, and the stack of regulatory paperwork has gotten higher.
    • Additional trials to demonstrate both clinical efficacy (if not superiority) and cost outcomes effectiveness are further driving up the cost, time and complexity of clinical trials.

    Drug Discovery Pipeline Issues

    Drug target Issues

    • In a perfect world the goal is to develop a drug that will go after a single target(a protein, enzyme, DNA/RNA, etc. that will undergo a specific interaction with chemicals or biological drugs) that is linked to a disease.
    • To get FDA approval new drugs have to be proven better than existing ones.  Most of the low-hanging fruit of easy drugs to develop are already on the market.
    • Unfortunately most diseases don’t work that simply. There are a few diseases that do, (i.e. insulin and diabetes, Gleevec -Philadelphia Chromosome and chronic myeloid leukemia), but most small molecule drugs rarely act on a single target (target-based therapy in oncology being the bright spot.)

    Venture Capital Issues

    • For the last two decades, biotech venture capital and corporate R&D threw dollars into interesting science (find a new target, publish a paper in Science,Nature or Cell, get funded.) The belief was that once a new target was found, finding a drug was a technology execution problem.  And all the new tools would accelerate the process.  It often didn’t turn out that way, although there are important exceptions.
    • Moreover, the prospect of the FDA also evaluating drugs for their cost-effectiveness is adding another dimension of uncertainty as the market opportunity at the end of the funnel needs to be large enough to justify venture investment

    drug dev pipeline fundedIn Part 2 of this series, we describe the challenges new Medical Device and Digital Health companies face.  Part 3 will offer our hypothesis how to change the dynamics of the Life Sciences industry with a different approach to commercialization of research and innovation in this sector.  And why you ought to take this class.

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  • How Kevin O’Connor and FindTheBest Got Lean

     By Steve Blank

    Steve Blank 2011 PhotoOriginally posted Aug. 5, 2013, at www.steveblank.com.

    When we started E.piphany there was an equally scrappy startup calledDoubleClick (later acquired by Google for $3.1 billon). Other the years Kevin O’Connor, former CEO and founder of DoubleClick and I got to know each other.  It’s been fun watching a 20th Century entrepreneur learn new tricks as he builds his next startup, FindTheBest using Lean Methodology.  Here’s Kevin’s story to date.

    kevin_oconnor_headshot

    ——–

    You might say Steve and I have lived parallel lives. We’re both serial . We’ve both used a combination of luck, hard work, and mild insanity to get where we are today. We’ve both published bestselling books.*map_of_innovation_kevin_oconnor

    * Okay: my book sold way less copies than Steve’s.

    Steve and I have long held similar beliefs about how to run start-ups, even if we’ve used different names to describe the key principles. He came down a few weeks ago to visit our company, FindTheBest, where we chatted about his lean start-up concept and held a Q&A for the FindTheBest team and others in the Santa Barbara tech community. Here’s how our company is following the Steve Blank blueprint.

    1. An Untested Hypothesis
    Connection: Business Model Canvas

    By 2009, I was fed up. I remember trying to search for the best college for my son and the top ski resort for a family vacation, only to find scam sites promoting a “top 10″ list or “featured” options, meaning they were getting paid to promote them. Visiting each official site took too long, and the information wasn’t always easy to compare (ex: “Net Cost of Attendance” vs. “Resident Tuition per Semester”).

    That’s when it hit me: what if there was a site where you get all the best information in one place, and have access to great research tools to help make a decision? What if you could think like an expert on any topic in a matter of minutes, instead of after hours of inefficient web browsing? Granted, the idea was a little crazy. To really be a game-changer, FindTheBest would have to compete against the thousands of niche sites that focus exclusively on a single market.

    When we started building the site, we had to assume a lot. We figured our key partners would include consumers (to contribute data much like Wikipedia’s users), manufacturers (to keep their product information updated) and the US government (to supply datasets to power our content). We assumed our customers would be any smart Internet users looking to make a decision—granted, an extremely broad customer segment. We guessed that these customers would find value in the ability to make quick decisions on complicated topics. We hoped to start acquiring customers mostly through responsible, focused SEO, where we would target a variety of high-value search terms and provide relevant, useful content to users.

    From day one, we knew our biggest cost would be hiring more employees, but we didn’t know exactly how much it would cost to enter each new market (ex: smartphones, then mountain bikes, then business schools, etc.). By leaning on our technology, we knew we could build cheaply, we just didn’t know how cheaply. We resolved to build out the first dozen comparisons before we started calculating an exact cost per new market.

    Regarding revenue, we made a big bet on “purchase intent.” We looked at social sites—like Facebook and Twitter—with huge user bases, but comparably small revenue. We then looked at wildly profitable ventures—like Google or Kayak—noting that their users were much more likely to make purchases. When you enter a search query for “car insurance,” or submit details for a trip to London, you’re much more likely to end up spending money. We hypothesized that FindTheBest, with its focus on making big decisions, would attract the same purchase-minded users found on Google or Kayak.

    We didn’t need to spend months researching; we just needed to create a viable product to test these hypotheses against. As we went out and started building, many people thought we were insane, stupid, or both. In fact, some probably still do.

    Our First FIndtheBest Wireframe

    2. Validation
    Lean Startup Connection: Customer Development

    Over the next couple of years, several of our hypotheses were confirmed. Thousands, then millions of new customers were coming to the site through SEO, just like we’d guessed with our customer acquisition hypothesis. Our assumptions about cost also proved correct—we were entering new markets incredibly cheaply. Here, we even beat our most optimistic assumptions.

    Once we built out a sales team, revenue also started to grow nicely. We’ve confirmed our ability to harness purchase intent, like Google and Kayak before us, verifying our revenue stream hypothesis.

    That said, a few of our hypotheses were proven at least partially wrong. First, our assumption that consumers would be a key supplier of content (like Wikipedia contributors) was wrong. While user adds and edits grew at a small rate, it wasn’t nearly enough to support the hundreds of topics on the site. Visitors loved using the site to research new topics, but were less likely to add their own listings or consistently update old content. Our customers had identified a flaw in our original plan. We realized our internal team would need to be bigger, and adjusted our business model canvas accordingly .

    Additionally, our value proposition needed adjustment. We had focused on quickdecisions, when really, users wanted a sophisticated research tool for making carefully considered decisions. As a result, we’ve had to adjust our positioning and messaging to better capture the value users see in our product. We’re now promoting FindTheBest as a research hub that helps you think like an expert, much closer to what users were telling us in feedback surveys.

    3. Staying Agile
    Lean Startup Connection: Agile Development

    At FindTheBest, we constantly practice a “test-fail-learn-test-succeed-scale” approach, which is simply another way of describing the philosophy of “agile development.” We’re happy to fail, as long as we do so quickly, learn the appropriate lesson, and move to a new hypothesis. Once we find one that works, we scale the hell out of it.

    For example, we tested out two features early on that didn’t end up as popular as we’d wanted: video guides (a how-to video about researching the topic) and green guides (an environmental report on which products and services were most eco-friendly). Each time, we hoped to capture a new audience by appealing to a specific subset of Internet users. We rolled them out on a limited number of pages to test. Once we saw that these new guides weren’t attracting significant visits or creating increased interaction, we quickly ended the projects.

    On the flip side, we’ve had many major successes that have helped inform our product’s direction. Sister sites FindTheData (a site for researching huge datasets on topics like crime, salaries, and government spending) and FindTheCompany (a tool for finding key information on millions of companies and organizations) have grown user bases of several million visitors per month. We’ve found that people love our platform and use our technology for doing research in a variety of different ways.

    * * *

    At just 3 years in, FindTheBest is real-time proof of Steve Blank’s lean startup blueprint. Even as we make the transition from startup to established company (which I call “company puberty”), we’ll continue to test our hypotheses, seek customer feedback, and test before we scale.

    Hopefully we’ll find that FindTheBest will have a bigger exit than DoubleClick – this time as a Lean Startup.

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  •  
  • An MVP is not a Cheaper Product. It’s About Smart Learning

    Steve Blank 2011 Photo

    A minimum viable product (MVP) is not always a smaller/cheaper version of your final product. Defining the goal for a MVP can save you tons of time, money and grief.

    Drones over the Heartland
    I ran into a small startup at Stanford who wants to fly Unmanned Aerial Vehicles (drones) with a Hyper-spectral camera over farm fields to collect hyper-spectral images. These images would be able to tell farmers how healthy their plants were, whether there were diseases or bugs, whether there was enough fertilizer, and enough water. (The camera has enough resolution to see individual plants.) Knowing this means farms can make better forecasts of how much their fields will produce, whether they should treat specific areas for pests, and put fertilizer and water only where it was needed.drone over farm

    (Drones were better than satellites because of higher resolution and the potential for making more passes over the fields, and better than airplanes because of lower cost.)

    All of this information would help farmers increase yields (making more money) and reduce costs by using less water and fertilizer/chemicals but only applying where it was needed.

    Their plan was to be a data service provider in an emerging business called “precision agriculture.” They would go out to a farmer fields on a weekly basis, fly the drones, collect and process the data and then give it to the farmers in an easy understandable form.

    on Farms
    I don’t know what it is about Stanford, but this was the fourth or fifth startup I’ve seen in precision agriculture that used drones, robotics, high-tech sensors, etc.  This team got my attention when they said, “Let us tell you about our conversations with potential customers.”  I listened, and as they described their customer interviews, it seemed like they had found, that – yes, farmers do understand that not being able to see what was going on in detail on their fields was a problem – and yes, – having data like this would be great – in theory.

    So the team decided that this felt like a real business they wanted to build.  And now they were out raising money to build a prototype minimum viable product (MVP.) All good.  Smart team, real domain experts in hyper-spectral imaging, drone design, good start on customer discovery, beginning to think about product/market fit, etc.

    Lean is Not an Engineering Process
    They showed me their goals and budget for their next step. What they wanted was a happy early customer who recognized the value of their data and is willing to be an evangelist.  Great goal.

    They concluded that the only way to get a delighted early customer was to build a minimum viable product (MVP). They believed that the MVP needed to, 1) demonstrate a drone flight, 2) make sure their software could stitch together all the images of a field, and then 3) present the data to the farmer in a way he could use it.

    And they logically concluded that the way to do this was to buy a drone, buy a hyper-spectral camera, buy the software for image processing, spend months of engineering time integrating the camera, platform and software together, etc.  They showed me their barebones budget for doing all this. Logical.

    And wrong.

    Keep Your Eyes on the Prize
    The team confused the goal of the MVP, (seeing if they could find a delighted farmer who would pay for the data) with the process of getting to the goal.  They had the right goal but the wrong MVP to test it.  Here’s why.

    The teams’ hypothesis was that they could deliver actionable data that farmers would pay for.  Period.  Since the startup defined itself as a data services company, at the end of the day, the farmer couldn’t care less whether the data came from satellites, airplanes, drones, or magic as long as they had timely information.

    That meant that all the work about buying a drone, a camera, software and time integrating it all was wasted time and effort – now. They did not need to test any of thatyet. (There’s plenty of existence proofs that low cost drones can be equipped to carry cameras.) They had defined the wrong MVP to test first. What they needed to spend their time is first testing is whether farmers cared about the data.

    So I asked, “Would it be cheaper to rent a camera and plane or helicopter, and fly over the farmers field, hand process the data and see if that’s the information farmers would pay for?  Couldn’t you do that in a day or two, for a tenth of the money you’re looking for?”  Oh…

    Shortcut

    They thought about it for a while and laughed and said, “We’re engineers and we wanted to test all the cool technology, but you want us to test whether we first have a product that customers care about and whether it’s a business.   We can do that.”

    Smart team.  They left thinking about how to redefine their MVP.

    Lessons Learned

    • A minimum viable product is not always a smaller/cheaper version of your final product
    • Think about cheap hacks to test the goal
    • Great founders keep their eye on the prize
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  • Your Computer May Already be Hacked — NSA Inside?

    By Steve Blank

    Steve Blank 2011 PhotoPosted  July 15, 2013 at www.steveblank.com

    In a time of universal deceit – telling the truth is a revolutionary act.
    George Orwell

    In Russia, President Putin’s office just stopped using PC’s and switched to typewriters.  What do they know that we don’t?

    Perhaps it’s Intel  inside.

    ———

    For those of you who haven’t kept up, the Agency (NSA’s) Prismprogram has been in the news. provides the NSA with access to data on the servers of Microsoft, Google, Facebook, etc, extracting audio and video chats, photographs, e-mails, documents, etc.

    Prism is just a part of the NSA’s larger mass electronic surveillance program thatcovers every possible path someone might use to communicate; tapping raw data as it flows through fiber optic cables and Internet peering points, copying the addresseeson all letters you physically mail, all credit card purchases, your phone calls and your location (courtesy your smart phone.)Slide03

    All hell broke loose when Edward Snowden leaked all this to press.

    Given my talks on the Secret History of Silicon Valley I was interviewed on NPR about the disclosure that the NSA said they had a new capability that tripled the amount of Skype video calls being collected through Prism. Like most Americans I said, “I didn’t remember getting the memo that the 4th amendment to our constitution had been cancelled.”

    But while the interviewer focused on the Skype revelation, I thought the most interesting part was the other claim, “that the National Security Agency already had pre-encryption stage access to email on Outlook.”  Say what??  They can see the plaintext on my computer before I encrypt it? That defeats any/all encryption methods. How could they do that?

    Bypass Encryption
    While most outside observers think the NSA’s job is cracking encrypted messages, as the Prism disclosures have shown, the actual mission is simply to read all communications. Cracking codes is a last resort.

    Slide04

    The NSA has a history of figuring out how to get to messages before or after they are encrypted. Whether it was by putting keyloggers on keyboards and recording the keystrokes or detecting the images of the characters as they were being drawn on a CRT.

    Today every desktop and laptop computer has another way for the NSA to get inside.

    Intel Inside
    It’s inevitable that complex microprocessors have bugs in them when they ship. When the first microprocessors shipped the only thing you could hope is that the bug didn’t crash your computer. The only way the chip vendor could fix the problem was to physically revise the chip and put out a new version. But computer manufacturers and users were stuck if you had an old chip. After a particularly embarrassing math bug in 1994 that cost Intel $475 million, the company decided to fix the problem by allowing it’s microprocessors to load fixes automatically when your computer starts.

    Slide05

    Starting in 1996 with the Intel P6 (Pentium Pro) to today’s P7 chips (Core i7) these processors contain instructions that are reprogrammable in what is called microcode. Intel can fix bugs on the chips by reprogramming a microprocessors microcode with a patch. This patch, called a microcode update, can be loaded into a processor by using special CPU instructions reserved for this purpose. These updates are not permanent, which means each time you turn the computer on, its microprocessor is reset to its built-in microcode, and the update needs to be applied again (through a computer’sBIOS.).

    Since 2000, Intel has put out 29 microcode updates to their processors. The microcode is distributed by 1) Intel or by 2) Microsoft integrated into a BIOS or 3) as part of aWindows update. Unfortunately, the microcode update format is undocumented and the code is encrypted. This allows Intel to make sure that 3rd parties can’t make unauthorized add-ons to their chips. But it also means that no one can look inside tounderstand the microcode, which makes it is impossible to know whether anyone is loading a backdoor into your computer.

    The Dog That Never Barked
    The NSA has been incredibly thorough in nailing down every possible way to tap into communications. Yet the one company’s name that hasn’t come up as part of the surveillance network is Intel. Perhaps they are the only good guys in the entire Orwellian mess.Slide07

    Or perhaps the NSA, working with Intel and/or Microsoft, have wittingly have put backdoors in the microcode updates. A backdoor is is a way of gaining illegal remote access to a computer by getting around the normal security built-in to the computer. Typically someone trying to sneak malicious software on to a computer would try to install a rootkit (software that tries to conceal the malicious code.) A rootkit tries to hide itself and its code, but security conscious sites can discover rootkits by tools that check kernel code and data for changes.

    But what if you could use the configuration and state of microprocessor hardware in order to hide? You’d be invisible to all rootkit detection techniques that checks the operating system. Or what if you can make the microprocessor random number generator (the basis of encryption) not so random for a particular machine? (The NSA’s biggest coup was inserting backdoors in crypto equipment the Swiss sold to other countries.)

    Rather than risk getting caught messing with everyone’s updates, my bet is that the NSA has compromised the microcode update signing keys  giving the NSA the ability to selectively target specific computers. (Your operating system ensures security of updates by checking downloaded update packages against the signing key.) The NSA then can send out backdoors disguised as a Windows update for “security.” (Ironic but possible.)

    That means you don’t need backdoors baked in the hardware, don’t need Intel’s buy-in, don’t have discoverable rootkits, and you can target specific systems without impacting the public at large.

    Two Can Play the Game
    A few months ago these kind of discussions would have been theory at best, if not paranoia.Slide09The Prism disclosures prove otherwise – the National Security Agency has decided it needs the ability to capture all communications in all forms. Getting inside of a target computer and weakening its encryption or having access to the plaintext of encrypted communication seems likely. Given the technical sophistication of the other parts of their surveillance net, the surprise would be if they haven’t implemented a microcode backdoor.

    The downside is that 1) backdoors can be hijacked by others with even worse intent. So if NSA has a microcode backdoor – who else is using it? and 2) What other pieces of our infrastructure, (routers, smartphones, military computers, satellites, etc) use processors with uploadable microcode?

    ——

    And that may be why the Russian president is now using a typewriter rather than a personal computer.

    Putin's TypewriterUpdate: I asked Intel:

    • Has Intel received any National Security Letters?
    • If you had received a National Security Letter would you be able to tell us that you did?
    • has Intel ever been contacted by anyone in the U.S. government about Microcode Updates or the signing keys?
    • Does anyone outside of Intel have knowledge of the Microcode Updates format or the signing keys?
    • Does anyone outside of Intel have access to the Microcode Updates or the signing key

    Intel’s response from their Director of Corporate and Legal Affairs (italics mine):

    “First, I have no idea whether we’ve ever received a National Security Letter and don’t intend on spending any time trying to find out.  It’s not something we would talk about in any case, regardless of the subject of your blog.

    Second, the questions related microcode and the speculative portion of your blog related to our encryption of microcode and the key all seem to focus around one question:  Do we have backdoors available as a result of our microcode download encryption scheme?
    The answer is NO.  Only Intel has that knowledge.”
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  • Don’t Give Away Your Board Seats

    Steve Blank 2011 PhotoBy Steve Blank

    Published on www.steveblank.com, July 9, 2013

    I had a group of ex-students out to the ranch who were puzzling over a dilemma – they’ve been working hard on their startup, were close to finding product/market fit and had been approached by Oren, a potential angel investor. Oren had been investing since he left Google four years ago and was insisting on not only a board seat, but he wanted to be chairman of the board. The team wasn’t sure what to do.

    I listened for a while as they went back and forth about whether he should be chairman. Then I asked, “Why should he even be on your board at all?”  I got looks of confusion and then they said, “We thought all investors get a board seat. At least that’s what Oren told us.”

    Uh oh.  Red flags just appeared in front of my eyes. I realized it was time for the versus advisors talk.

    Roles for Financial Investors
    I pointed out that there are four roles a financial investor can take in your company: a board member, a board observer (a non-voting attendee of board meetings), an advisory board member, or no active role. I explained that as a non-public company there was no legal requirement for any investor to have a board seat. Period. That said, professional firms that lead a Series investment round usually make their investment contingent on a board seat. And it sounded like if successful, their startup was going to need additional funding past an angel round to scale.

    In the last few years, it’s become more common for angel investors to ask for a board seat, but I suggested they really want to think hard about whether that’s something they need to do now.

    “But how do we get the advice we need? We’re getting to the point that we have lots of questions about strategic choices and relationships. Isn’t that what a board is for?  That’s what we learned in business school.”

    What’s a board for?
    I realized that while my students had been through the theory it was time for some practice. So I told them, “At the end of the day your board is not your friend. You may like them and they might like you, but they have a fiduciary duty to the shareholders, not the founders. (And they have a fiduciary responsibility to their own limited partners.) That means the board is your boss, and they have an obligation to optimize results for the company. You may be the ex-employees one day if they think you’re holding the company back.”Board Fight

    I let that sink it for a bit and then asked, “How long have you worked with Oren?”

    I kind of expected the answer, but still was a bit disappointed. “Well we met him twice, once over coffee and then over lunch.”

    “You want to think hard about appointing someone to be your boss just because they’re going to write you what in the scheme of things will be a small check.”

    Now they looked really confused. “But we need people with great advice who we can help us with our next moves.”

    Advisory Board
    “Do you know what an advisory board is?” I asked.  From the look on their faces, I realized they didn’t so I continued, “Advisors are just like they sound. They provide advice, introductions, investment, and visual theater – (proof that you can attract A+ talent). An advisor that provides a combination of at least two of these is useful.”

    A “board” of advisors is not a formal legal entity like a board of directors. That means that they can’t fire you or have any control of your company. While some founders like to meet their advisors in quarterly advisory board meetings, most companies don’t really have their advisory board meet as group. You can connect with them with them on an “as needed” basis. While you traditionally compensate advisors by giving them stock, I suggest you ask them to match any grant with an equal investment in the company – so they have “skin in the game.”

    shutterstock_70458487Equally important in an advisory board is a great farm team for potential outside board members. It allows you to work with them over an extended period of time and see the quality of their advice and how it’s delivered. If they are world-class contributors, when you raise a Series A round and you need to bring in an outside board member, picking someone you’ve worked with on your advisory board is ideal.”

    Finally I suggested that Oren’s request to be chairman of a five-person startup seemed to be coming from someone looking to upgrade their resume, not to optimize their startup.

    No Outsiders Until a Series A
    As we wrapped up, I offered that there was no “right answer” (see Brad Feld’s post) but they should think about their board strategy as a balance between the amount of control given to outsiders versus the great advice outsiders can bring. I suggested that if they could pull it off they might want to consider keeping the board to the two founders for now, surrounded by great advisors which may include their seed investors. Then when they got a Series A, they’ll probably add one or two professional VC’s on the board with one great advisor as an outside board member.

    As they left they were going through the experienced execs they knew who they were going to take out for coffee.

    Lessons Learned

    • Your board of directors is your boss
    • Your advisory board is your friend
    • Not all investors get board seats, it’s your choice
    • Date advisors, marry board members
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