The Creation Of Biotech Startups: Evolution Not Revolution

Posted August 15th, 2019 in Biotech financing, Biotech investment themes, Capital efficiency, Drug discovery, Talent | Leave a comment

The startup paradigm for the creation and funding of new biotech companies has evolved enormously over the past two decades. Recently there’s been a pair of articles from Tech VCs about applying alternative models of company creation (here, here) to the world of biotech, so thought it was worth reflecting on the prevailing venture formation landscape and offering up some counterpoints.

Back in 2015, I shared my perspective on five changes to the biotech venture model since 2005 – and to a large extent those same dynamics remain at work today. I’ll start with a quick review for context.

When I first started in the venture business fifteen years ago, founding biotech entrepreneurs would often prepare pitch decks and then shop them around to different VCs as part of the typical “dog and pony” show. Sometimes those were academic professors or post-docs, sometimes they were entrepreneurs leaving existing biopharma jobs to do the startup thing. They’d often propose to build or lease a standalone bricks-and-mortar lab, hire 15-20 scientists, and advance an academic observation into bona fide drug discovery during the Series A round. Sometimes it would take $15M or more just to figure out whether the initial academic insight was reproducible and generalizable as a therapeutic approach. Often the initial premise wouldn’t survive these killer experiments, but because “real” capital had already been invested these companies would frequently try to “pivot” to other programs or approaches. Sometimes, albeit rarely, these pivots worked in biotech. But with fixed infrastructure and sunk cost, it was hard to just walk away. Returns suffered in this model.

Our 2006 vintage “prove-build-scale” model of biotech investing emerged from this world and was an attempt to change the paradigm. During the “prove” seed phase, the aim was to demonstrate the scientific robustness of the founding concept as cheaply as possible. This model was largely enabled by the emerging “virtualization” of the ecosystem. Virtual drug discovery startups that leverage CROs and partners from around the world, assembling expertise on an as needed basis, became a realistic operating model about a decade ago. With the rise of biotech wet-lab launchpads like LabCentral, hybrid models with both in-house labs and heavy virtual outsourcing emerged, and are a common feature today. For novel areas of biology, some internal lab footprint is often critical.

Enabling technologies certainly helped (and continue to help) create the virtual infrastructure that powers up many biotech startups: computational drug design with partners like Schrodinger, in vitro safety screening, cheaper DNA sequencing and bioinformatics, lab automation, etc… Even virtual team management platforms like Slack for global remote project efficiency have helped.

I’d also argue the post-merger dismantling of Big Pharma’s R&D footprints has had as much to do with the rise of virtualization as these enabling technologies: when large and small R&D sites around the world were shut down to reduce R&D footprints, many of these teams/facilities formed into new specialty CROs or were absorbed into existing ones. Europe and the Midwest have a huge number ex-Pharma sites that became CROs. Further, the massive expansion of offshore medicinal chemistry services as China and India liberalized their business approaches also played a key role.

The venture-backed biotech ecosystem was embracing this virtually-enabled drug discovery model a decade ago, and that trend has just accelerated since – and will likely continue to.

We’ve evolved our strategy at Atlas over the past 15 years to what we call our seed-led venture creation model. I’ve written much about it in the past (here, here) so will quickly sum it up. During the setup and creation “prove” phase, we partner with our scientific and business co-founders to create new companies. We generally write small checks initially (from $100Ks to a few $1Ms) to do the seed-stage validation work, deliver the killer experiments, or assemble the right science/IP. But we also look for other signals – like whether the entrepreneurs involved in the startup have the savvy execution and strategic thinking required to launch and scale a young drug company (i.e., a “talent signal”). Another important variant of that talent signal is if experienced skilled entrepreneurs raise their hand to want to put their careers behind the idea during the seed phase. We also socialize the concepts with downstream Pharma and future funding partners during the seed to characterize the market interest in the approach (i.e., a “market signal”). This seed-led approach, and integrating these various signals, helps to bend the risk curve in our favor by weeding out the ideas/startups earlier with a high degree of signal stringency.

This in-house venture creation model helps bring the right elements together that were hard to assemble in traditional “pitch-deck” venture formation: rigorous and objective exploration of the founding science, enough capital to do the key early work, and, most importantly, the ability to attract and retain the right talent. Experienced drug R&D veterans and senior business talent can often better stomach the career risk of jumping out of bigger companies by joining not just one isolated high-risk startup in a random office park, but by joining an in-house startup community of other entrepreneurial executives within an incubator-like model.  Most are mid-career by definition – in order to have assembled the requisite experience set. To align interests and motivations, founder-EIRs know that if the scientific premise doesn’t reproduce or validate, that we’ll do well by them – often by recruiting them to other projects. As company creators, we often shared a piece of the founding stock pool, which financially aligns us with the entrepreneurial team (as our ownership goes down, not up, with larger capital raises and lower valuations).

Today there are many flavors of this in-house venture creation model at different firms: some more artisanal and bespoke, some more systematic and formulaic. There used to be only a few firms doing this type of work 10 years ago – but the venture creation model has delivered stellar returns in the last decade for founders, employees, investors, and patients. We went from tombstones hailing the death of life science venture capital in 2011 to being one of the star-performing sectors in the venture capital industry. And with that success, today we’re seeing lots of VC firms talking about doing their version of in-house company creation. And seeing lots of tech VCs move into biotech. As they say, imitation is the sincerest form of flattery.

But as the biotech venture ecosystem continues to change, we will need to continue to evolve our model, of course.

A big change today is that the world is awash in capital. The tsunami of funding into private biotech firms in the last few years has led to the rise of the Series A mega-round: go big or go home. Some of these have been successful, but many others won’t be. Along with more funding at the company level, VC fund sizes have also gotten bigger across the board, which means more capital has to be put to work per deal, worsening the venture capital math problem. Series A sizes are way up, as are later rounds, and it seems like every week there’s another $100M+ mega-round. There’s also just more capital going into the same number of startups, in general. According to Pitchbook’s data, the number of therapeutic biotech firms getting their first financing remains largely flat for the past 5+ years despite overall venture funding numbers being 2-3x greater.  I’ve written on this paradox many times in the past (here is one example), which differs greatly from the tech space where the number of startups exploded with more fund flows. This concentrated allocation of capital is potentially troubling, as it could presage that smart de-risking decisions aren’t being done with discipline. Over-funding and crowding in ‘hot’ spaces like I/O and gene modification will likely dampen returns. I worry a great deal that in a world of capital abundance we will lose stringency as a sector. Two truisms come to mind today: more startups have died of indigestion than starvation; and, the average fitness of the herd goes down with abundance. We are at risk for this in biotech today.

At Atlas, we’ve tried to stick to our capital efficient startup model of seed-led venture creation, augmented by what we think is an optimal fund size configuration, without being dogmatic or formulaic about it. Some science deserves to be in an asset-centric focused startup, and other science requires a full and expansive platform build. We added an Opportunity Fund to power up our more capital intensive companies, and to secure additional positions in follow-on financings. But in general we’re still starting companies that have a “prove” seed phase: whether it’s a standalone seed investment, or a first “seed-tranche” of a more significant Series A, we remain firm believers that de-risking should be done before a large amount of capital gets deployed. It’s the essence of the value-creating, positive aspects of equity capital efficiency.

In the past month, proponents of a “new” model of biotech startup creation have opined on the subject: tech entrepreneur Jared Friedman of Y-Combinator (here) and Jorge Conde of Andreessen Horowitz (here). They both propose to apply elements of the tech VC approach to the world of biotech.

As a matter of first principles, I welcome their engagement in the biotech ecosystem (and that of other tech investors) as I’m sure there are things we can learn from alternative approaches, and vice versa. And I truly hope they are successful – which means more innovative drugs will make it to patients, and that’s a good thing.

Having been in a diversified venture fund for nearly a decade (before we went biotech-only in 2014), I appreciate the cultural value of the cross-fertilization of ideas. Prove-Build-Scale was a mantra both the tech and biotech side of Atlas embraced for years. But I also appreciate how vastly different our ecosystems are and caution against naïve assumptions to the contrary.

Let me start with what I agree with in Jared and Jorge’s approach: I fully embrace the value of seed-stage de-risking of novel therapeutics companies (the essence of our 15+ year biotech strategy) and how the ecosystem and enabling technologies are continuing to support virtual and efficient biotech operating models. This is certainly true, and is at the heart of our investment model.

But I’d like to share a few counterpoints.

  1. The cost curve of drug R&D hasn’t meaningfully changed with virtualization or new enabling technologies. Drug R&D is hard, and the typical program costs a lot to bring it through initial hit finding, hit-to-lead, lead optimization, preclinical, and early clinical testing. Those phases – what I would call drug discovery and translational research – cost on the order of $25-50M per program, take 5+ years, and face high failure rates (with expensive false positives accruing spend over time). Sadly, more virtual R&D models, even those enabled by computation, in our experience aren’t fundamentally cheaper or faster over this entire multi-year translational period. However, they are often more strategically flexible to respond to changes in the program (the benefit of variable vs fixed costs), and that flexibility allows for less expensive “down time” as well as earlier kill decisions on programs (and companies) than in prior biotech operating models. Unfortunately, as I’ve written before, translating novel biology into real drugs is complex and still consumes everything else in the R&D process; it’s the biology that takes so long and costs so much to unravel. While the cost to get a startup formed and initial experiments going may be cheaper than it was 15 years ago (when you had to build your lab), that’s generally not the big cost over the 5+ years of moving from taking a “hit” into patients. And, frankly, we’ve been doing this model for a while: it’s not a novel idea to do lean seed-stage experimentation in the process of starting biotech companies.
  2. Seed-stage capital in biotech doesn’t bring a product forward, it just starts the long journey. In the tech world, a seed stage investment can help a startup assess product-market fit questions and perhaps even get early revenue traction with consumers/users; that’s just not the case with therapeutics. This model may fit with other areas of “biotech” like research tools, synbio service companies, or B2B life science businesses, but making drugs for patients isn’t weeks or months but many many years away from revenues. Given the timelines, and the reality that a biotech will burn cash without any top line for 10+ years, you can’t “just” be a seed investor. You have to keep investing capital until real inflection points happen, typically human proof-of-concept in the clinic (leading to an M&A event or an IPO). If you are only a seed investor, you will undoubtedly be massively diluted in the process. That’s why we, and most other biotech investors, tend to participate in all the private financings of our companies – you typically have to be there for the entire journey to make returns in biotech.
  3. Disruptions in regulated businesses where lives are at stake necessitate deliberate evolution not revolution. Mark Zuckerberg’s idea that success requires startups to “move fast and break things” doesn’t apply to therapeutics companies. We are making bioactive substances to put into patients, maybe even permanently engineer their genes. This isn’t to be taken lightly or haphazardly. Regulators need to be brought along with new innovations. Iteration cycles are long. Safety and toxicology studies need to be thorough and take a certain immutable amount of time. Patients need to understand the risks, as people die because of drugs in clinical trials. Further, as an animal lover, think about the hundreds of animals we expose in our preclinical experiments in each drug program: the idea of animal models as things to “break” by moving programs faster doesn’t seem right. Doing well-controlled, scientifically-justified experiments is not only smart business, but it’s the right moral thing to do. Drug R&D, unlike many of the areas that tech VCs invest in, isn’t the place for rapid iteration where it’s ok to have a sloppy beta-test product hit the market (or go into the clinic, by analogy). Deliberate, thoughtful advancement of optimized drug candidates that could go the full distance to approval is not really a choice but an axiom. The therapeutics R&D model has evolved over the past few decades, and I’m sure it will continue to do so. But it won’t change overnight as “do no harm” (i.e. “don’t break things”) is an important guiding principle in bringing new medicines to market.
  4. Experienced executive talent is critical to success in therapeutics. CEO inexperience, naivety, and poor judgment have destroyed a ton of value over the past 40 years in biotech. In light of this history, we and other veteran VCs generally favor executives who have honed their skills running R&D programs or closing BD deals, and already demonstrated some level of judgment, credibility, and, frankly in light of Theranos, business ethics. I’ve written and tweeted on this extensively, so won’t rehash it here – but the idea that a recent PhD or post-doc who has studied a narrow piece of biology for 5+ years is ready to run a multi-disciplinary drug R&D organization powered with $10Ms in their first job out of academia seems far-fetched. We do, however, back first-time CxO’s all the time (most of our CEOs are first-timers), including lots of folks who’ve never “made money” for themselves or investors before. We are constantly adding talent to our network from biotech, pharma, and academia. We’ve recruited folks from all over the country, and our executive ranks are full of immigrants from all over the world. We pride ourselves on a portfolio that’s not a “club” of only insiders, but are open to working with great folks from around the ecosystem. Our in-house venture creation model does generally bias us towards folks who are willing to live in or commute to Boston, but even that’s not always the case.
  5. Founders, entrepreneurs, and early stage company-creation VCs frequently have the same interests. As company formation focused investor-entrepreneurs, we are hurt by onerous VC terms too. In general, we try to increase the alignment between our interests and that of our other co-founders – and most VC financial terms don’t tend to do this. Large liquidation preferences sitting on top of the cap table affect my early stakes much like a founding management team. I often have proposed “call-able” future tranches of funding if milestones are met, rather than “put-able” future tranches (the latter enable investors to buy up at their discretion at pre-agreed lower prices vs keeping options open for better cost-of-capital outside financings). One big difference in alignment arises with anti-dilution: generally early founder-investors don’t get reloaded with fresh stock or options the way management teams do (without investing more capital). I’m very sensitive to teams that are indifferent to dilution, just as most founders are. As for “control” protections and the most important decision of a Board – which is the hiring and firing of a CEO – I do think investors who’ve significantly financed the enterprise should have a big say in this decision. Further, as a general observation, biotech VC terms have actually become more startup-friendly in recent years (here).

Those counterpoints aside, I eagerly await the verdict on whether these new tech-oriented, young founder models for biotech venture formation deliver value for their teams, investors, and patients.

Unfortunately, given the nature of drug R&D, we won’t know that verdict for a long, long time.


Biotech Startups And The Hard Truth Of Innovation

Posted May 20th, 2019 in Bioentrepreneurship, Biotech startup advice, Corporate Culture, Leadership | Leave a comment

Gary Pisano’s recent Harvard Business Review piece, The Hard Truth About Innovative Cultures, beautifully frames up how innovative corporate environments are frequently misunderstood. Innovative startups aren’t just about being cool and nimble, having beer taps in the kitchen, or an endless bounty of swag. Pisano sums up the harder, harsher reality of truly innovative environments: “These cultures are not all fun and games.”

Pisano’s piece struck a huge chord with my own experience working with both large pharma and small startups over the past 20 years.  And many of the observations are in line with my post from July 2017 on Distinctive Biotech Corporate Cultures.

While reading Pisano’s piece, my brain kept saying “Yes!” and “So true!” every few sentences.  I tweeted out my endorsement of the piece back in January but have finally gotten around to blogging on it; in this post, I’d like to add some “practitioner” color-commentary to Pisano’s observations.

Here are his five major attributes about innovative cultures:

  1. Tolerance for Failure but No Tolerance for Incompetence.
  2. Willingness to Experiment but Highly Disciplined.
  3. Psychologically Safe but Brutally Candid.
  4. Collaboration but with Individual Accountability.
  5. Flat but Strong Leadership.

I’m going to share some reflections on each from my vantage point as a biotech investor.

Tolerance for failure requires an intolerance for incompetence. 

We work in a high risk field: less than 5% of drug discovery projects ever make it to market. We have to embrace failure. When projects or investments are killed, we should openly acknowledge it in the light of day, not bury the dead in the night, and celebrate the learnings that can be gleaned: as we did with both the Quartet Medicine and OnQity write-offs.  Innovative cultures have to convey that its “ok to fail” and foster quality learning loops.

But it’s not ok to fail due to sloppy science or poor decision-making. Not asking the tough killer questions, or using the right controls and comparators, can lead to chasing false positives for too long – which is not acceptable. It’s just too easy to keep drug programs going, overhype the data to get the next dollop of funding, or play around but never truly test the science – simply because we can rather than we should. It’s incompetent not to do the killer experiments, and do them as well as possible. If you are waving your hands too much, maybe the science is too early. Try to invalidate the central biological hypothesis early, before mountains of capital put too much weight on the scales of judgement – leading to low stringency and poor decisions biased by the sunk cost fallacy and the practicalities of being “too big to fail”.

All of this requires not only competence, but a shared view that the collective time of truly competent people is the scarcest and most valuable resource in an innovative culture: don’t waste that resource on ideas that are passed their sell-by-date (or never should have had a date!). Chalk up a failure and spend your time on better things.  Move onto the next project, or the next startup, or back to big pharma. But move on nonetheless. Competence, by definition, can’t tolerate complacency around the decision to move on.

There’s another corollary of this observation. We all fail at hiring sometimes, as it’s impossible to hire only truly competent people when you are scaling an organization beyond more than a couple dozen employees. Mistakes in hiring happen, and that’s ok. But there can be no tolerance for keeping underperforming hires around just because we’ve got a “nice” culture. Robust performance management systems need to be in place to weed out those folks who aren’t delivering with high performance. I’m not suggesting it has to be the Jack Welch “fire the bottom 10%” rule – but if you aren’t transitioning some people out of a startup over time than you are almost certainly tolerating too much incompetence. It’s just the practical reality that we all make mistakes in hiring over time. Sadly, I’ve been involved with companies that have tolerated mediocrity for too long. It enables weaker researchers to keep hand-waving around their data, when all the competent folks around them cringe. It feels benign and “nice” not to fire people, but honestly it is enormously corrosive over time.

Nothing reeks worse in a company than tolerance for incompetence: everyone smells it, knows it’s there.  And knows the leadership are allowing it. That stinks for everyone, and negatively affects the overall corporate culture.

Willingness to experiment requires rigorous discipline.

Comfort with ambiguity and a lack of structure, and often process, is frequently key to finding innovative ways of doing things. But in biotech startups this loose structure doesn’t condone doing willy-nilly science – or crazy “Friday afternoon experiments” every day of the week.

In a world where a researcher can do only a few of many possible experiments, which ones should be chosen? The only way to prioritize the experiments for a program is to understand the questions you are asking – and the answers you are likely to get from the data. Once chosen, laying out the goals and milestones of the work is key. And then measure up the outcomes against those. Applying the age-old maxim – “Measure twice, cut once” – is a great rule for scientific exploration.

Experimentation works best in applied R&D when discipline is applied to the thought-process and the decision-tree before actually doing the work: defined a priori, specific go/no-go criteria need to be in place for where to take the project or initiative next.

Further, as Pisano notes, experiments should be selected for their learning value, and designed “rigorously to yield as much information as possible relative to the costs.” Essentially, it’s applying the logic of capital efficiency to innovative experimentation.

Don’t confuse free-ranging scientific exploration of a new modality, or new drug target, with a lack of rigor. Even experiments done with “early hit” molecules, designed in the spirit of learning, need to be done as rigorously as possible. I’ve seen programs get derailed by early negative signals caused by “Hail Mary pass” in vivo experiments done with suboptimal hit series; hope and prayer studies are occasionally ok to do over time, but full transparency around the decisions, criteria, and outcomes needs to be in place.

Lastly, just because a biotech doesn’t have formal committees to designate “stage gate transitions” doesn’t mean this lack of “process” or “structure” should lack discipline.  I’m very confident that we have Development Candidate criteria in our startups that look as robust as Big Pharma’s, maybe without Lipinsky Rule biases – we just deploy them without cumbersome governance committees, associated box-checking exercises, or a strict “cover-your-ass” mentality to every one of them.

Fundamentally, scientific discipline has to be intrinsic to a biotech startup’s culture or they will inevitably drink their own Koolaid and lose their way.

Psychological safety requires comfort with brutal candor.

Organizational “safety” is hugely important: a culture where individuals can discuss the truth without fear of vengeful consequences. Almost every biotech has values up on the wall that reflect transparency, openness, and challenge; but the big question is do companies and their executives “walk the talk” and live these values with both safety and candor.

What if the Emperor has no clothes? Can scientists challenge the CSO’s conclusions, or the CSO’s proposed experimental next steps? Can researchers critically (respectfully) evaluate other team members’ data? Can the CEO’s positioning of the status of a lead program be questioned by the team? These are the types of situations that reveal a lot about the culture in practice inside a biotech startup.

If the CSO or CEO is hand-waving around the scientific strengths of the story, and no one feels able to challenge them, what happens?  Real innovative thinking suffers. And Boards are often fed this pablum rather than something of substance.

Having multiple mediums for communicating can create the right cultural outlets for this type of safe “champion and challenge” culture, everything from town hall style company discussions, open dialogues within working teams, cross-functional employee focus groups, discussions with the HR lead, or even anonymous suggestion boxes. Yelling a lot in the big company meeting about a concern isn’t always the right forum, so folks should be encouraged to find the right one. But good companies don’t let intellectual dissension fester quietly without an outlet – it needs to be shared in some format or another. As Pisano writes, “Unvarnished candor is critical to innovation because it is the means by which ideas evolve and improve.”

Our own investment model at Atlas is aimed to fostering this “champion and challenge” culture. We’re a flat, equal set of investing partners. There is no emperor (with or without clothes). There is no one boss that squelches discussion. We openly and respectfully challenge each other, and base a lot of our decisions on mutual trust in the partnership.

Collaboration but with individual accountability.

This fourth observation from Pisano is exactly in line with what I described previously as: “There is no ‘I’ in team, but know who has the “D” (#6 in this post).

Team work, across different disciplines, is critical to success in biotech, probably more so than some other innovative sectors; integrating biology, chemistry, tox, pharmacology, clinical, manufacturing and many other functions over the course of many years. Collaboration is the lifeblood of the drug R&D endeavor.

But, as Pisano notes, “too often, collaboration gets confused with consensus.”  This is spot-on.  Consensus in a world of biological ambiguity is hard. Further, consensus can be a huge bottleneck on the timelines, as energy is spent trying to get all the stakeholders on the same page. Eventually, an individual just has to make the decision. I called this the “D” in my post on culture. Someone needs to integrate the inputs but make a decision. And then be held accountable to that decision.

Biopharma R&D timelines often make real accountability difficult, however. Take decision like selecting one path or another, such as nominating a specific Development Candidate vs waiting for another to emerge, or picking an initial clinical setting vs another. These decisions take years to manifest into outcomes, positive or negative, to be able to reflect on whether they were the “right” decision. Sometimes one never really knows whether it was the best decision or not as science doesn’t always reveal itself that way. With timelines this long, ensuring the proper information, discussion, and challenge were integrated into key decisions is a big part of the accountability equation.

Endless, often headless, committee meetings in big organizations can crush accountability by de facto forcing consensus decisions. The flip side of accountability in these larger organizations is the “CYA” culture (“cover your a—“): if I have to put my name on this decision, I’m going to make sure 99.9% of the ambiguity and uncertainty is wrung out before making it. Innovative cultures can’t be run with 99%+ confidence intervals around decisions. Risk is essential, and working under 60-75% confidence is much more in line with nimble, smart risk-taking. Decisions need to be informed better than coin toss, but often the risks and unknowns remain significant.  Understanding how to make innovation-enhancing decisions as individuals and teams in this environment is crucial.

Lastly, consensus frequently kills the outliers. Within venture firms, there are countless examples of firms passing on seed-stage deals because they were crazy ideas and consensus didn’t exist, only then to watch them grow into unicorns. Nearly a decade ago, Atlas changed its approach to seed investing in that any individual partner could back a “seed deal” (size delimited) without consensus support. This empowered partners to take some risk. As those investments mature, we generally move towards a team consensus as capital intensity increases. But at the earliest phases of the decision process, when uncertainties are high and getting to consensus could prevent leaning forward into a “risky” seed, we think this approach works. For Atlas, mutual trust and respect often gets you to implicit consensus in the end.

Flat but strong leadership.

Organizational charts are fine to capture the structured layers of a company, which is important.  Who is reporting to who, and who is responsible for what functions.  How big are certain functions.  And using them to forecast how the company will look in 1-3 years is a useful tool. All this is valuable.

But the org chart only tells you about structure.  It does not reveal “cultural flatness”, as Pisano calls it, that reflects the level of empowerment around ownership. Culturally flat organizations often push decision-making closer to those with first-hand knowledge of the critical information.

It also means that C-level executives can’t sit in their corner office and make decisions in a vacuum.  To quote Pisano, “For senior leaders, it requires the capacity to articulate compelling visions and strategies (big-picture stuff) while simultaneously being adept and competent with technical and operational issues.”  This is the essence of a great biotech science leader in my mind: able to frame the big vision of a 60K foot view while also being able to engage at the 6 inches view of a critical experiment or study design when appropriate.

In short, cultural flatness goes both ways: C-level executives being versatile with the details of the programs and platform, with researchers being able to engage their leadership with contributions beyond the hierarchy.


Innovative cultures don’t just emanate from cool Patagonia vests.

Building and leading this kind of culture in a startup biotech isn’t easy. It requires a mix of seemingly contradictory behaviors, like the tolerance for failure but not incompetence. And the pieces of culture outlined by Pisano aren’t really an a la carte menu – they all need to be integrated into the DNA of a startup in order to encode for a high performing culture.

Summed up, and repeating the phrase from the beginning of the blog, Pisano nails it: “These cultures are not all fun and games.”