The Biotech Venture Capital Math Problem

Posted March 15th, 2012 in Exits IPOs M&As, General Venture Capital, VC-backed Biotech Returns

Everyone has heard of the monster returns to some venture funds from high-flyng social media and technology companies: Facebook’s potential 800x for Accel, Google’s 350x for KPCB and Sequoia, Zynga’s 100x+ for Union Square, Foundry, and Avalon.  These single big tech wins returned (or will return) multiples of their entire funds.

The sad reality is that these types of “halo deal” wins just don’t happen in Life Science venture capital investing.  We have great success stories in the 5x-15x range, like Amira, Avila, Enobia, Plexxikon – but 100x+ returns aren’t in the genetics of our ecosystem.  The venture model in LS is about higher consistency and lower rates of failure, as discussed previously in this blog.

But this lack of massive outliers has important implications for what the optimal fund size should be in Life Science venture capital, and how to deliver returns to LPs.  This subject has been explored by others: Bijan Salehizadeh’s blog on the historic returns by fund size, and Kevin Lalande of Sante Ventures has a well written whitepaper on why venture doesn’t scale, especially in LS.

Fundamentally, returns at the fund level all come down to a relatively simple math problem: cash in, cash out.  VCs hope (aspirationally) to achieve 3x gross returns for their Limited Partners, which after fees and profit sharing ends up near a net return of 2.25x.  In a textbook venture fund cash flow J-curve, this equates to about ~25% net IRR over the 10-year life of the fund.  Most LPs would be very pleased with those outcomes.  In the 2000s, as noted before, the entire asset class under-delivered those expectations, but it’s still the goal of most early stage venture firms.

So what’s the math for what it takes to achieve a 3x gross return for a fund?  “Cash in” is obviously a function of fund size.  “Cash out” – or distributions paid out upon exits – is highly dependent upon two critical variables: exit sizes and ownership at exit. 

Here’s a table that captures the magnitude of the returns required to achieve a 3x fund as a function of fund size.  Assuming a range of possible average ownerships across the entire portfolio of 10-40%, the table highlights the implied total aggregate value of all the portfolio companies of the fund.

For example, for a 3x return with 20% average exit ownership, a $150M fund requires over $2.2B in total value to be created by its portfolio companies, and a $600M fund requires $9B.  Those are very big numbers, and the latter one is bigger than all the M&A exits of last year, the best in recent memory.  The numbers above are so large as to be amorphous and hard to digest.  When we consider what those values imply about the deals specifically, the challenge becomes clear.

To do that analysis requires a few assumptions: I’ve assumed smaller funds have fewer deals (a $150 fund has 15 deals) and larger funds have more (a $600 fund has 30 deals).  Fewer deals than this will increase the numbers below dramatically.  I also generously assumed that two-thirds of the deals are positive (>1x), and the remaining third goes to zero (performance well above industry-wide distributions).  With these in mind, the figure below captures the relationship between the average exit value across the portfolio of these “positive” deals, and the average ownership.  Each of the isoquant lines represent what’s required to reach a 3x gross return for each fund size.  The takeaway: the numbers required for the average deal are staggeringly large for the bigger funds.

For example, at an average 20% ownership, a $600M fund needs 20 of its 30 deals (all the ones that are positive) to be valued on average at $450M.  This is an average valuation for all the winners.  For reference, according to BioCentury data, there have only been 17 venture-backed M&A deals from 2007-2011 with disclosed upfront payments bigger than $400M, and only a small handful of IPOs to break that valuation in their offerings.  The top decile of all exits in LS are above $400M.  So to hit 3x on a $600M fund essentially means that fund has to own 20% of every great top decile deal over an entire investment cycle, and own only those winning deals, not the mediocre ones.  Similar math problems constrain $450M funds, as they need on average 20% ownership of 15+ deals worth over $400M.  This doesn’t seem feasible in biotech.

On the other hand, for smaller funds, the math might begin to work.  To hit 3x, a $150M fund needs to have 20% average ownership across 10 exits with an average value of $200M.  Since these are averages, even just one deal with 30% ownership exiting at a $400M valuation has a big impact on the average.  The distribution of returns to achieve this 3x outcome is certainly still challenging on a $150M fund, but I’d argue it’s at least in the realm of the possible.

As noted above, a fund’s returns or “cash out” is a function of both exit values and ownership stakes.  Both of those variables have real constraints on them that make the biotech venture fund math problem very challenging for large funds:

  • Exit sizes in LS have a natural ceiling.  Historically, the median exit value for venture-backed R&D-based biotech firms over the past five or ten years has been around $150-175M, based on CapIQ data, as well as that from Lalande’s analysis.  As noted above, the top decile threshold for deal values is roughly $400M, and has been for a decade.  The reasons for this ceiling are intrinsic to the early stage life science business: risky R&D projects face large discount rates, development burns lots of cash, and it takes a long time.  Very few R&D-led biotech companies have escaped gravity in the past decade and gotten above $1B valuations.  And when they do, they’ve typically consumed so much capital that the returns on those deals are often muted.  As noted in prior analysis, the amount of equity capital invested tends to inversely correlate with cash-on-cash returns.  This “natural ceiling” on biotech exit values is a constraint that can be managed but needs to be factored into how we raise funds and build companies; if a biotech venture investor chooses to ignore it, they’ll be fighting a natural law like gravity.
  • Biotech exits and value creation occur in a discontinuous, unpredictable way.  Lalande’s analysis (Figure 7) elegantly shows the lack of correlation between the amount of equity raised and the eventual exit values.  The historical odds of getting a top decile $400M+ exit value are roughly the same for a deal that has raised $30M vs one that has raised $150M.  While it’s nearly impossible to bootstrap the discovery and development of a clinical stage program on only a few million dollars, it’s clear that above a certain threshold of invested capital the downstream value creation is more stochastic than many would like to believe.
  • Ownership stakes diminish with capital intensity and syndication.  As the above analysis shows, average ownership across a portfolio is a big driver of fund returns.  But maintaining ownership in the face of increasing capital intensity in R&D is hard, especially as burn rates increase in clinical development and the company’s financing risk becomes greater.  This typically leads to over-syndication.  And big syndicates mean less ownership for everyone.  It’s very hard for one investor to go above their pro-rata in a good deal to maintain or grow ownership over time.  All these factors conspire to make it very hard to maintain high ownerships.  Avila had no investor larger than 25%, and Adnexus and Novexel had none larger than 20%.  Reviewing the S1s of some recent IPOs highlights this point further: after Clovis and Verastem IPOs, every investor was below 15%, and after AVEO and Ironwood IPOs none of the private investors were greater than 10%.  These were widely acclaimed successful recent IPOs, but due to capital intensity their lead venture investors don’t hold ownerships consistent with returning large funds.  History may prove me wrong: they could break out of the biotech valuation range and become $5B+ mid-cap biotechs.  I hope this comes to pass, but the odds are very long.

So with these constraints, and the simple math on what it takes to deliver returns, what’s the optimal fund size going forward?  Only time will tell, but the cards are clearly not stacked in favor of delivering great returns from large funds in the Life Sciences.  It’s fair to say that a $250M fund has math on its side – and on the LPs side.

The above analysis comes with a few caveats:

  • The 3x gross fund multiple aspiration may be too aggressive, because only 16% of VC funds between 1981-2003 vintages achieved this threshold (SVB). More modest projections will still likely generate IRRs that beat other asset classes over the coming decade: a fund with a 2.25x gross multiple typically leads to a ~1.8x net multiple, and net IRRs in the 15-19% range (following a textbook J-curve).  But even 2.25x gross return target only changes the exit value requirements by ~25% or so, still leaving extremely large average exit values/frequencies for the large funds to achieve.  The chart below captures the different impacts of lowering the gross multiple on a $150M and $600M fund.  While most of the curves are reasonable for the $150M fund, the $600M fund only becomes realistic around the 1.5x gross point, hardly something to celebrate.   

  • Shorter holding periods could provide attractive IRRs for a fund despite lower target multiples.   The late stage thesis is that the time-to-exit for their deals are shorter, and so a 2x gross fund may still have a good IRR if it returns capital over 3-5 years instead of 5-10 like an early stage venture fund.  This may very well be the case, but late stage LS investing is not as low risk as perceived, as I’ve written about previously.   
  • Higher ownerships could offset many of these challenges.  A number of venture firms active in the life sciences, including Atlas and Third Rock Ventures, do venture creation around new startups where we’ll often own more than 50% during the early days of the company.  If one were able to maintain super-sized ownerships through to an exit, that could have a profound impact on overall fund returns (as the figures above reflect).
  • Betting on large Black Swans in biotech may work in the future. The past isn’t predictive of the future, so maybe we’ll have a lot more Pharmasset-like exits.  Its worth noting that, at the exit, Pharmasset’s largest venture shareholder, Burrill & Company, owned 5.7% and returned north of $500M from the $10.8B acquisition, estimated to be a 30-40x return.  This is clearly a huge outcome in the Life Sciences.  I’d like to believe that Pharmasset-like, “100-year flood” biotech valuations are likely to happen more frequently in the future, but betting on the luck of a feeding frenzy is a tough investment strategy to get right with reliable frequency.  

A couple final comments on the theme as well:

  • Tech VC isn’t insulated entirely from fund size challenges.  Gravity affects Tech as well, and while the constraints of exit size and ownership are much less rigid, they remain important variables for technology-focused venture capital.  The massive >$1B funds being raised today will require massive exits, and many of them.  But with the non-zero probability of “halo deals” delivering outsized returns, some large funds can still work in some frothy investment cycles for the Tech sector.  Groupon is likely to return nearly 1x of NEA’s $2B+ fund – a good example of this.
  • Fund allocations are a better term than fund size.  I’ve used the term “fund size” in the above analysis; the better definition is probably LS “fund allocation” since it applies to LS investing from both LS-only and diversified funds.  The “natural ceiling” and ownership considerations outlined above are more specific to LS than other sectors in a broader diversified venture portfolio.  The latter portfolio can combine the consistency of a $150M LS allocation (and 3x aspiration) with a similar sized exposure to the potential outlier alpha of Tech in a modestly sized diversified fund. 
  • GPs benefit financially from large funds.  I saved this one for last, but there’s an even simpler math at play here: in many cases, since management fees off of the large funds could make a GP rich on their own, who needs a 3x fund?  I’d like to believe this isn’t the driver of the large funds out there, but I’d be remiss if I didn’t mention it.  This undoubtedly contributes to why some GPs continue to push for bigger funds, despite the fact that those fund sizes have historically delivered less attractive returns than funds in the sub-$250M range (see SVB report).

At the end of the day, fund returns are all about the math of “Cash-in, Cash-out”.  And achieving what could be deemed attractive “Cash-out” metrics has a lot to do with the “Cash-in” part of the equation.

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  • Bruce,

    Thanks for your article. I’d like to pose a question about the life science entrepreneurship space, but context first:

    One of the things we have seen over the past 15 years in the tech (software) space is dramatic reduction in costs to get operations off the ground, which make outsized returns much more possible for tech space investors. In the tech space you used to have to spend $2.5 million buying servers, computers, etc in order to run a business in the internet-connected world. Nowadays, those costs are down by at least an order of magnitude largely due to hardware having migrated to the cloud.

    If such an innovation in infrastructure were possible for this space, then it’s feasible that investors in LS could see more “black swan” sort of returns on their investments. 

    Now for the question: In your mind, do you think LS is likely to see such infrastructure innovation in the next 10 years, and if you have any opinion on it, what form do you imagine it would come in?

    Thanks again for this article – it’s eye opening.

    Kevin Bebak

  • Bruce,

    Thank you very much for this post – it is very illuminating. 

    Seems that today’s risk aversion that manifests in fewer LS deals, more frequent syndicates and longer exit cycles would make getting to that 20% ownership more difficult. A catch-22 emerges – how do you raise a larger fund in order to assure that your lower ownership still produces enough winners in order to make the 3x mark that LPs require. 

    Makes you think twice about being a LS investor if you don’t know the space well enough and can’t immediately spot a winner. 


  • Kudos to Bruce on a great post.

    Kevin – What Bruce and some other VCs are doing with virtual, asset-light biotechs is the only hope in LS. But drug and device development will never be like tech where a product can now be launched for tens of thousands on the app store.  

  • Thanks for the feedback, Bijan (and for pointing out this post in the first place!).

    I appreciate the example you pose as well. Certainly there’s no way to bring a drug or medical device to market for anything close to tens of thousands of dollars. There’s several orders of magnitude difference there.

    The revenue difference between a wildly successful app and a blockbuster drug are also several orders of magnitude apart, though, $millions vs $billions. Having grown up alongside the internet I have trouble seeing why an order of magnitude shift in cost structure of devices/drugs is impossible.

    There’s still a ton I need to learn, and perhaps that’s still why I’m on the optimistic side!

    Thanks again for your feedback; it helps me learn

  • Igor R

    Others have said it already, but great post. Very interesting read. Thank you.

  • Igor

    I believe Bruce had a recent post addressing the topic of how to decrease the early development and operational costs and which seems relevant to your question.

  • John

    It appears that the problem presented has two aspects:
    1. the assumed investment is $10M for the small fund, but $20M for the large fund. So there is a built-in steeper hill for the large fund.
    2. There are more opportunities to fail in the larger fund. But then it still comes down to the quality of the VC analysis and management.
    RE amount needed for investment, use of a mostly virtual model, doing research through CROs can lower costs by 5-20x, so this is obviously part of the optimal future approach, as Bruce and others have pointed out. In addition, there are hopeful signs in the LS world, signs that some big pharma will acquire programs with pre-clinical data (eg Roche-Marcadia, BI-Zealand Pharma; both in diabetes). Then the amount invested comes to the low million range for >>10x return.
    So smaller amounts invested with more care, earlier, and with a more rapid exit offers an attractive route…

  • Killu

    Interesting question. The most obvious cost lowering that we have seen in life sciences over the last 15 plus years comes from, and seemingly continues to come from, the advances in genomic and other -omic analyses (including but not limited to sequencing). For that to lead to business success, one has to assume that such genomic analyses will at some point lead to practically productive healthcare (sick care, wellness?) interventions that follow simple business rules of supply and demand of the offerings, and availability of buyers and their capital in the price range that makes the businesses feasible. Such is not the case today, with few exceptions, but the possibility remains. Then, outsized returns for life science investors that are based on lower technical and development costs, can become possible. – For now, the bigger problem remains: few interventions in life sciences from any modality lead to clear, strong, unquestionably valuable therapeutic effects (with very few exceptions…). As for prognostics and diagnostics, until there is something to do that has a clear, strong, unquestionably valuable effect after getting the prognosis or diagnosis, there is little incentive to pay for the latter.

  • Hi Igor, thanks for the note – any chance you remember the title of the post or can post a link to it?

  • John

    My interpretation of your post, Killu, leads me to disagree with several aspects.
    1. I am not aware of any cost lowering from any omics, to date.
    2. Most interventions in the life sciences lead to clear, strong valuable therapeutic effects or the FDA will not approve them.
    3. Diagnostics, especially theranostics, may lead to a better stratification of clinical trials and treated population (since many drugs only work well in a subset of patients) and have tremendous value thereby.

  • John, Killu, thanks both for weighing in. John, what is your hope that technologies like the one being developed at Columbia will have a dramatic impact on cost structure in drug development? I were to imagine a situation where therapeutics could be tested in an animal model that was artificially boosted in terms of it’s representativeness of the human model, I could see the cost of testing for safety and efficacy going down quite a lot.Let me know your thoughts?

  • ” use of a mostly virtual model, doing research through CROs can lower costs by 5-20x”

    This I did not know, and seems like the kind of shift needed to increase value to investors. It seems intuitive if you think about how tech has changed due to cloud-based hosting, virtual computing, outsource-ability of basic coding and data collection/entry.

  • John

    Your bit link is non functional due to the extra If at the end.
    The work is similar to the >20 yr old technology of reconstitution of human immune system in mice, but here is for a “specific” individual, using hematopoetic stem cells from specific donors. So maybe one could deduce differences in a Type 1 diabetic immune response, is the hypothesis.  However each individual’s immune system is individually derived from what is there genetically, but also individual exposure to antigens in life (some clones stimulated; some lost to apoptosis). So I do not find it particularly relevant to most drug discovery or to treating patients (who will pay for mouse experiments for an individual? Not my insurer). This is early academic basic research.

  • Killu

    John, apologies for not being more clear – I meant cost reductions based on technology development progress, akin to what has been happening in the tech space. In the -omics, especially in sequencing, the cost reductions from technology development have been similar to or greater than cost reductions in tech/IT space (if you take the long view, from the start of the Human Genome Project…remember Sanger or Maxam-Gilbert sequencing being the predominant sequencing technology, and fully loaded sequencing cost per base….?). However, as you point out, that has not translated (yet?) to cost reductions in drug development or healthcare cost reductions in general, which was also my point in the post above. For that to happen, we need to identify outcomes from -omic analyses that clearly point towards a preferable drug discovery/development path (or therapeutic/other medical intervention path) over another, that would result in significant cost reduction in terms of overall drug development or healthcare dollars spent. Plus, the cost of integrating the -omic analyses to the care or development pathway has to make business sense, which is often not the case today.

    I agree with you that most FDA approved products have strong and valuable enough effects, as they would not have been approved otherwise. However, it is also true that most of the currently funded innovative new programs aiming to create tomorrow’s therapeutics do not result in FDA approvals – most of such programs fail somewhere along the way. Only a small minority of currently funded innovative approaches in drug, device or diagnostic space lead to clear enough, strong enough, unquestionably valuable enough effects (and tolerable enough risk profiles) and thus result in regulatory approval.

    Let’s for a moment leave aside the well warranted criticisms of the FDA in terms of being too biased against risk – let’s pretend that getting to regulatory approval is not as big of a problem as it is today, and is back to the “more reasonable levels it used to be at”. Even so, it seems that we as an industry still need to figure out how to fix the following problem: most of what we do as innovators, does not work – at least not well enough, clearly enough, with strong and obvious enough positive effects on human health, when measured in larger populations as one needs to do for gaining regulatory approval. If it did, we would have many more drugs/devices/Dx getting approved and getting used. It is a baffling phenomenon, when you consider the billions spent on research and development, and the mind-boggling array of amazing innovation that such programs are based on.

    To me, this is not an opportunity to complain about things being as they are – instead, it is an opportunity to ask questions about, what is it about human health, biological systems, mixed populations, (and other factors?…) that we do not know and understand well enough yet, that leads to such a situation? What is it that we do not know well enough yet that creates a powerful limitation, or set of limitations, that lead to this kind of an unintended outcome from the multiplicity of our innovation approaches? What do we not know, and what do we not know that we do not know? 

    Perhaps the answer is that creating drugs, devices and Dx that would have strong, clear, obviously valuable effects on human health is a complex affair, and the low success rate is here to stay. That is possible, and would be sad if it were the full and final truth. Or – and I wish for this to be true! – it could be that there is a set of as of yet poorly understood limitations that, when better understood and addressed, can be removed –  and the hope is that by so doing, the productivity, success rate and capital efficiency of translating innovation to health interventions will be dramatically increased. So my hope is that the combination of asking questions and analyzing the “what is” from a perspective that frames the issues in a way that illustrates a general problem, could be valuable in terms of providing a set of high impact, high value hypotheses that could be collectively discussed, improved on, and tested, ultimately leading to high impact outcomes of great benefit. Including, but not limited to the cost benefit…

    As for specific cost reduction measures such as virtual development, they are useful tools in improving the capital efficiency per project, but in itself, they have little hope of improving the odds of creating products with stronger, clearer, more valuable health effects than average. Turning the argument upside down, if you had products with strong, clear, obviously valuable health effects, it would cost less to develop them (think of the benefits of seeing strong effects from R&D assays on to preclinical and clinical studies, and how that translates to cost efficiencies). So, back to the “big issue”…

  • Thanks for the note about the broken link, John. Should be fixed now. 

    I see your point about there not being payers for mouse cloning for particular individuals. “Personalized medicine” still seems like it has a lot of ground to cover in terms of proving its worth before anything like that would even be considered.

    I would be interested in learning more about the work from ~20 years back of reconstituting the general human immune system in the mouse… so if you know of any articles about that work please do share.

    Many thanks, k

  • Krassen

    Highlights a strong quantitative case for owning bigger chunks of winners. Yet, you so often see LS VCs seek “safety in numbers” comfort feeling,  where they seek multiple syndicate partners. I guess it makes a good “cover my a$S” strategy with respect to the LPs, but I wonder if posts like yours, bringing attention to the faulty economics of large syndicates would change things…

  • John

    I see how the second line of your post took my interpretation away from your meaning, re lowering costs within sequencing, not actual cost reductions in healthcare. (you should have said “in” rather than “from.”)

    Re your elaboration, it is too extensive to approach here (offline?) but I would simply say that my interpretation re the waste of billions in unsuccessful life science investment has related to the VC herd mentality (present blog excepted) that moves from one fad to another without the experienced/ knowledgable/wise guidance that would be appropriate (from building grand edifices to repurposing old junk, what is the latest one? New formulations for approved products).

    In the Big Pharma mode, there has been a major drive to address new, unproven therapeutic targets (omics driven?)  either in house or through biotech acquisitions and this has added great risk, with poor results on average, but at least with new knowledge. Now they double down by very large investments in academic labs to find new drugs (this one is easy to bet against!). Their high cost basis and slow decision-making are a separate issue.

    Virtual approaches, most importantly in preclinical research, offer the opportunity to check out new ideas at the lowest possible cost and thereby prevent more Phase IIa failures (or market failures for repurposed products).

  • one60

    From the perspective of an entrepreneur seeking capital, this entry was quite illuminating.

    Would be interested to understand whether the  ‘math problem’ faced by large funds would  suggest raising money from smaller funds since they have ‘math on their side’ ? 

  • Jacksonuc

    A great read combining with Bij’s recent post. The data are very insightful

    I do have some reservation with the assumptions. taking a high level view on the size argument, there are three important assumptions at work: 

    1. large VC can’t increase their active deal number proportionally, therefore they need to achieve better return on each deal. sounds reasonable as the deal number is linked to partner number and large VC probably don’t have proportionally more GPs. 

    2. large VC don’t have a better access to good deals. This reminds me a quote from some tech VC (forgot the source and I’m paraphrasing): every year there is a few superstart deals that really matter, and a VC’s job is to find those deals and get in. It makes sense in tech VC (just take a look at FB), but given the fact that the value of a biotech startup is inherently defined by the value of the underlying science and the low ceiling of return potential, maybe it’s a non-issue for biotech VC? 

    3. large VC can’t get a higher equity percentage in good deals. now this doesn’t make sense to me, as the natural conclusion of one GP controlling more money in large funds would be they’ll be willing to invest more into each portfolio company.

    Theoretically the result would be determined by the balance between 1 and 3?

  • LifeSciVC

    All good points.

    1. Big funds do typically do more deals but not proportionately more… as you say, its defined by the number of partners and maintaining a manageable board load. 2. Unlike social media, where paying up to get in a great deal like Facebook has worked recently, valuations tend to be reasonable and deal competition isn’t as intense. 3. A large VC could certainly own a higher percentage but that tends not to be the case in practice. First, most big VCs don’t put $30-50M into single, brand new startups where they could be the only VC involved. Second, by the time a good early stage deal matures to a Series B or C, other VCs are typically involved and so disproportionately high ownerships are hard to acquire by any funds, including larger ones. Third, VCs tend to be social creatures and syndication with at least 2-3 other VCs just tends to be the status quo. Worth challenging that norm, but its still the norm.

    Bruce L. Booth, D.Phil.

    ATLAS VENTURE | 25 First Street, Suite 303 | Cambridge, MA 02141 | P: +1 (617) 588-2636 | C: +1 (917) 302 8571
    @LifeSciVC | Blog: | EA: Ann –

  • Jacksonuc

    Thanks a lot for the insight!

  • Joshua Anderson

    Here’s the problem with trying to equate drug deveolopment with creation of a new silicon-based consumer technology: cost of regulation. Apple doesn’t need to get new products approved by the FDA, which means that they don’t have to conduct very carefully controlled and extensive human testing before releasing a product. Waiting around for the day when drug companies won’t have to test drugs in human clinical trials to bring the cost of drug discovery down, is a pipe dream that I doubt will ever be seen in our lifetimes. Cost of sequencing and early preclinical screening has definitely come down dramatically in the last two decades, and this has helped the small start-up companies, but the FDA has gotten tougher in response to some already-approved blockbuster drugs causing dangerous side-effects in patients (think Vioxx). Human testing is here to stay for drug development, the best we can hope to do is choose candidates more wisely, spend less in development, and shorten time horizons to maximize value. The one advantage that biotech has is that if you do create a drug that addresses an unmet need, you know what the market is and you will have a monopoly, at least until you lose your patent protection, as opposed to say Apple which may find that customers don’t really need an iTV. Tech’s risk is in the marketplace, they face more competition and fickle consumers. Biotech’s risk is in development, they face innumerable unknown variables of human physiology and safety, but a more predictable consumer base if the product works.

  • Joshua, thanks for the reply and apt comparison of Tech vs Biotech companies. I think you nail it in terms of the risks and rewards of the different sectors.

    Which business are you in?

  • Joshua Anderson

    I’m a scientist with a Ph.D. in Biomedical Sciences. I’m thinking about trying to make the move over to Venture Capital, so I’m trying to learn as much as I can about the VC world, especially the part of the VC world that invests in biotech, as you might imagine.
    What’s your specialty?

  • I’m in sales of life science research equipment & reagents. Good luck in the migration 🙂 this is a good blog to follow. Bruce and the people he follows on twitter might be good for you to look at as well.