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Hi, I’m Brad Feld, a managing director at the Foundry Group who lives in Boulder, Colorado. I invest in software and Internet companies around the US, run marathons and read a lot.

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You Don’t Mean Average, You Mean Median

Comments (42)

Every quarter, without fail, a bunch of articles appear talking about the venture capital industries investment pace as a result of the PWC MoneyTree report.  I used to get calls from all of the Denver / Boulder area reporters about my thoughts on these – that eventually stopped when I started responding “who gives a fuck?”

A few days ago I got a note from Steve Murchie about his new blog titled Angels and Pinheads.  I’m glad Steve is blogging about this as he’s got plenty of experience and thoughts around the dynamics of angel investors – some that I agree with and some that I don’t.  Regardless, my view is that there more there is out there, the better, as long as people engage in the conversation.

In his post Mind the Gap he made an assertion that “the VC industry has effectively stopped investing in seed stage ($500K and less) and startup-stage ($2M and less) opportunities.”  As a VC who makes lots of investments between $250k and $2M, and who has plenty of good friends who happen to be VCs that also make investments in this range (such as Union Square Ventures, First Round Capital, True Ventures, SoftTech VC, FB Founders, Alsop Louis, O’Reilly Alpha Tech, and Highway 12), I thought Steve’s assertion was wrong and I told him so in the comments.  He countered with the PWC Moneytree data on Q3 VC investments.

Stage Total $M % of Total # Deals Avg / Deal $M
Later Stage 1611 33.49 168 9.6
Expansion 1610 33.48 185 8.7
Early Stage 1081 22.49 198 5.5
Startup/Seed 507 10.54 86 5.9

Steve’s response to the Startup/Seed “Average Deal Size” was “WTF??!”  While that is the correct reaction, his conclusion (that VCs aren’t investing between $250k and $2M) is incorrect for two simple reasons: (1) the data is the PWC MoneyTree Report is incorrect and incomplete and (2) the interesting number to look at, assuming the data is correct, is the Median, not the Average.  If you wonder why, Wikipedia’s explanation is pretty good: “The median can be used as a measure of location when a distribution is skewed, when end values are not known, or when one requires reduced importance to be attached to outliers, e.g. because they may be measurement errors.”

Let’s look at the underlying data in Silicon Valley (that results in the above table) to understand this better.  Going to the PWC Moneytree Startup/Seed investments in Silicon Valley for Q309, you get the following:

image

The first six “startup/seed” investments each raised $10M or more.  Now, I’ll accept that these might be classified as “startup rounds” (e.g. the first round of investment) but no rational person would categories these as seed investments.  But, for purposes of this example, let’s keep them in the mix.  The average is $6.4M and the median is $5.0M.  Now, let’s toss out only the ones $10M or great since these clearly aren’t “seed” investments.  Our average is now $3.4M and the median is now $2.0M.

I’m still feeling generous (e.g. I’ll waive reason #1 – that the data is incorrect / incomplete – for the time being).  Let’s look at the PWC Moneytree Startup/Seed investments in New England  for Q309.

image

The average is $8.4M and the median is $5M.  Now, toss out everything above $10M.  The average is now $3.9M and the median is $4M.

But it gets better.  Let’s take all of the PCW Moneytree Startup/Seed Investments in the US for Q309.  There are 86 of them and as we know from the first table the average is $5.9M.  But the median is $4M.  Now, toss out the ones above $10M.  The average is now $4M and the median is now $3M.  This exercise – again – assuming the data is correct – shows the difference between average and median, as well as how much the numbers are skewed upward by “startup/seed” investments $10m or more.

I’m not going to try very hard to show that that the data is incorrect, but I’ll give you two examples.  The first is FourSquare, a well known seed investment led by Union Square Ventures and O’Reilly AlphaTech.  It was a $1.35M financing, has three employees, and occurred in 9/09.  This is about as close to the definition of a seed investment as you can get.  Yet, PWC Classifies it as Early Stage (plus they got the investment amount wrong as they list it as $1.15M.)  For reference, Dow Jones VentureSource classifies this as a seed investment and gets the amount right.

Let’s do another one.  This time look at what PWC MoneyTree has on First Round Capital

image

compared to what Crunchbase has on First Round Capital for Q309.

image

The differences that I think are incorrect on PWC’s part are that (1) GumGum is missing, (2) CoTweet is classified as Early Stage instead of Seed, (3) BigDeal is missing, (4) DNAnexus is missing (although it looks like it might have happened in Q2 even though it was widely reported in August), (5) Continuity Engine is classified as Early Stage instead of Seed, (6) ClickEquations is missing, (7) Sofa Labs shows up twice, and (8) Sofa Labs is classified as Early Stage instead of Seed.  Now Crunchbase is missing Project Fair Bid (even though they reported on it) so they aren’t perfect, but at the minimum the misclassification between Seed and Early Stage is dramatic.  Just for grins I looked these up in Dow Jones VentureSource and their data is closer to CrunchBase’s (especially the Round Type), but there are still differences.

Ever since I started investing in 1994 I’ve heard people spouting VC investment statistics to justify different viewpoints.  I’ve always felt this was a “garbage in / garbage out” phenomenon.  While there are some academics that do rigorous work around this (and understand the difference in importance between averages, medians, and er – statistically significant results), they are few and far between.  And – most of the data people actually use and discuss is stuff like the PWC Moneytree Report.

I keep fantasizing that this madness will stop, but I doubt it will.  In the mean time, I think I’ll go for an average run at a median pace.

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  • http://www.facebook.com/derekscruggs Derek Scruggs

    Median is a sorely underused metric. And I suspect the main reason is there is no native median() function in sql.

    • http://intensedebate.com/people/bfeld Brad Feld

      Yeah, but it’s not that hard (usually) to do.  Our friend Google helped me find a bunch of examples:

      http://scorreiait.wordpress.com/2008/10/28/how-to… />
      I particularly love the Sybase ASE one. 

      • http://twitter.com/derekscruggs @derekscruggs

        Next time you're asked, send the journalist that link. S/he will surely appreciate it. ;)

  • http://intensedebate.com/people/smurchie Steve Murchie

    Thanks for doing this – I was too lazy to dig into the data when we were trading mail (and rest assured I'm well versed in mean, median, mode, skewness and kurtosis). But now you've got me interested…

    • http://intensedebate.com/people/smurchie Steve Murchie

      Looking at this in some more detail (based on what is available to the unwashed masses), the classification of stage seems to be the most bogus part of the reporting. The definitions at https://www.pwcmoneytree.com/MTPublic/ns/nav.jsp?… not only eave room for interpretation, but also fall prey to a common survey error: if it's too hard to think about, pick a random response. Perhaps as a result, you see small investments made in all stages, and they don't map well to the amount raised. For example, BrightKite, which you know well, got a $40K infusion from DFJ in 3Q09 that was listed as "Later Stage". I'm guessing interns are populating their submissions to PWC…

      Anyone from PWC want to open up the dataset for some analysis?

      • http://intensedebate.com/people/bfeld Brad Feld

        Yup – they are lousy classifications.  Expansion and Later are fine, but Seed/Start-Up and Early is just wrong. 

  • http://twitter.com/terrycojones @terrycojones

    Hi Brad

    This is slightly OT, but I am (or was) a median problem junky. By that I mean algorithms for median finding, and lower bound proofs to show limits on all algorithms. Solving the median problem optimally is a really hard nut. It was a long time before it was realized that you could find the median of 5 things in just 6 comparisons (not 7, which is what it takes you to sort 5). There's a great history of algorithms and lower bounds slowly converging on 2n and 3n to find the median of n things. The constant is still not known (AFAIK), and there's even a nice conjecture which, if true, implies the existence of a 2.5n algorithm – without giving any hint about the algorithm. I did an M.Math on the median problem 20+ years ago, and even used to dream about algorithms for it! Then I got better :-) There's a lot of really nice fundamental CS in there.

    Regards,
    Terry

    • http://twitter.com/entrep_thinking @entrep_thinking

      Most statistical packages do provide this now and as Terry just noted, there's a cheat to estimate it from the mean & SD. Unfortunately, it's damned hard to do statistical inference with median (terrifiyng math deleted), LOL) From an information theory perspective, median IS the right indicator, the mean is just easier to calculate in large data sets.

      My first job was at an investment bank & you had the same mean/median problem which get amplified when you scale up to larger portfolios over time. Investment returns data (and most naturally occurring economic data period) are not distributed normally – it's actually a Pareto distribution (closely related the Pareto's 80-20 rule). Typically, it's *almost* normal but skewed and fatter tails (as Taleb notes, more black swans). Anyway, the rule in econometrics & polimetrics.. when the median & mean diverge, use the median, even though it ruins many of the statistical inference tools. (Yes, that's why econometric projections often stink.. skewed data that they transform into oblivion, LOL) Thanks, Brad, for resurrecting my inner math nerd.

      • http://intensedebate.com/people/bfeld Brad Feld

        “Econometric projections often stink [because they are] skewed data that they transform into oblivion” – great phrase – I’m definitely using it.  They also often stink because they are really studying history and trying to predict the future from it.  Uh – yeah.

        • http://twitter.com/entrep_thinking @entrep_thinking

          In my career in forecasting, great explanations often yield very lousy forecasts (Correlation versus causation). "Simplistic" models often can predict well. Understanding possible causation requires, um, thinking about how things work & fit together … or you can just crunch numbers. Alas, we see this even in serious academic work too.

          Taking skewed, non-normal (& possibly kurtotic) data and torturing it until it fit a normal distribution destroys information & when done badly adds bias & skew. Worse… It's all too tempting to bend the data until it fits our preconceived notions.

          Anyway, thanks!

          • http://intensedebate.com/people/bfeld bfeld

            Well said – I completely agree.

  • http://twitter.com/reecepacheco @reecepacheco

    Reminds me of the average salary for Geography majors at UNC – 6 figures!

    Related topic: notable Geography majors – Michael Jordan

  • http://intensedebate.com/people/bfeld Brad Feld

    Awesome example.

  • DaveJ

    Surprised you didn't also mention the bio/IT gap. This generates a bimodal distribution because the financing needs are categorically different on the seed/early stage end. It seems to me that any funding statistics that do not separate these are suspect. This may also solve part of your outlier problem – for example, in the New England stats above the first seven are all bio/pharma deals.

    • http://intensedebate.com/people/bfeld Brad Feld

      Yup – I mentioned that it the comments with Murchie but ran out of gas in this post.  It’s an important point how IT vs. bio vs. cleantech is categorized, although I’d still suggest that a “$17m seed investment” is an oxymoron regardless of category.

  • Les Makepeace

    Wouldn't more meaninful metrics be
    a.) how much delployable capital is in funds
    b.) what is the thesis (size/stage/industry/etc.) of those funds
    Using past data isn't a great predictor of future activity.

    • http://intensedebate.com/people/bfeld Brad Feld

      I completely agree with your statement “Using past data isn't a great predictor of future activity.”  I think this is one of the problems with all of this analysis – it doesn’t really have any predictive ability, even if it was more accurate.

      Re: more meaningful metric – I’m not sure as – while those are useful to understand what is going on in a fund, they are probably not that useful in the aggregate.

  • http://www.moxellc.com Doug Wulff

    Hard to imagine PWC not taking your logic into consideration. Excellent case study for College stat classes.

    In regards to capital…I’m a startup raising a seed round. While the data can’t predict future behavior, it provides some solace that I don’t suck as bad as I think. Staying hungry and optimistic for 2010.

    Thanks for the great article!

  • http://intensedebate.com/people/bfeld Brad Feld

    Unfortunately I think most people forget their college stats class the millisecond they complete it, other than people who use stats for a living, and even then, most don’t have a clue what they are doing!  And – as you say – “stay hungry and optimistic” – good things happen to those that stay at it.

  • http://www.tarsnap.com/blog/ Colin Percival

    I'm not convinced that it's wrong to use the mean — as long as you're using the RIGHT mean. Most things financial are best examined via their logarithms; whereas most things physical are described by Normal distributions, most things financial are described by LogNormal distributions.

    Rather than examining the arithmetic mean or the median, I'd suggest looking at the geometric mean (aka. exp-mean-log). From the data above, this gives a SV mean of $3.8M (after fudging $0 investments to $0.5M) and a NE mean of $5.3M — which eliminates the excessive skew in the arithmetic mean numbers of $6.4M/$8.4M, but still reflects the fact that NE investments tend to be somewhat larger than SV investments… a fact which is entirely lost when you look at the median ($5M in both cases).

  • http://intensedebate.com/people/MattEmmi MattEmmi

    excellent post Brad, How can we evolve the landscape if the basics keep getting messed up.

    I missed a question regarding median, mean and mode on my MCAT. It's basic stuff every college attendee should grasp.

  • http://twitter.com/bradfordcross @bradfordcross

    @bfeld You might enjoy this classic essay by Stephen Jay Gould, "The Median isn't the Message." http://www.stat.berkeley.edu/users/rice/Stat2/Gou

    • http://intensedebate.com/people/bfeld Brad Feld

      Brilliant essay.  And completely consistent with my “who gives a fuck” message to reporters (and others) whenever I am asked about the meaning of the data and averages (or medians) for a period of time.  I love Gould’s reasoning as well as his story telling – he is so good.  Thanks for pointing this one out.

      • http://twitter.com/bradfordcross @bradfordcross

        Indeed. Several years ago I was working on an essay called "The Psychology of Mathematical Misjudgment" – a more in depth treatment of these topics with a dual inspiration from Gould's essay and the terrific talk by Charlie Menger "The Psychology of Human Misjudgment." http://vinvesting.com/docs/munger/human_misjudgem

        Maybe I'll try to find it, dust it off, and publish it this year. It is a really fun topic.

        • http://twitter.com/bradfordcross @bradfordcross

          It seems that I forgot to mention that I enjoyed your post. :-)

          I think another big issue here is the data is not a very good sample. The results will naturally be skewed because the data is skewed to larger deals. For example, I know there are a lot of sub-million-dollar deals and I'd imagine that it is difficult to get a broad database of these.

          Services like Crunchbase at least try to pick up the publicly announced deals, but many are still missing, and many more are never announced.

          I'd hazard a guess that another bias is that the % of deals included in these databases within a given range of amounts is proportional to the amount – the smaller the deal, the less likely it is to be represented in the databases (and/or represented correctly.)

          • http://intensedebate.com/people/bfeld Brad Feld

            Correct – I tried to point that out near the end but didn’t state it as clearly as you just did.  Simply – the data is incomplete and – as a result – the conclusions are crap.

    • http://intensedebate.com/people/PhilipHotchkiss PhilipHotchkiss

      Thank you for posting a link to this essay. And, it was wonderful to learn Mr. Gould lived until May 20, 2002. That's approximately 10 years from when he was diagnosed in 1982.

  • http://intensedebate.com/people/bfeld Brad Feld

    Hah!  Yeah – they don’t call me any more – they know better.

  • http://Www.oracle.com/crystalball Jim Franklin

    http://flawofaverages.com/ has lots of great examples of this fallacy and what to do about them.

  • http://intensedebate.com/people/basil_pete44271 basil_pete44271

    Great job of illuminating the BS, as usual. But in this case, I am afraid that Steve Murchie is ‘more right’ than you are on the question of VCs having “effectively stopped investing in seed stage ($500K and less) and startup-stage ($2M and less) opportunities.”

    Sure, I agree with your list of “good friends who happen to be VCs that also make investments in this range.” You and your friends are in a small minority of VCs. Most traditional VCs make VERY few startup and seed investments.

    I have believed for a while now that you have an identity crisis. You call yourself a VC but you act and think like an angel investor. Yeah, I know you have written about your angel deals, but you still think of yourself primarily as a VC.

    The data is that angels invest in about 27x more startup and seed deals than traditional VCs: http://www.angelblog.net/Angels_Finance_27_Times_

    You are absolutely correct that the data on the VC industry is bogus – dangerously so: http://www.angelblog.net/VC_Return_Numbers_Are_Bo

    Keep up the great blogging – it’s important.

  • http://intensedebate.com/people/bfeld bfeld

    Hah!  I read that twice, then chuckled.  Nicely played.

  • http://intensedebate.com/people/bfeld bfeld

    Basil – as always – good, provocative stuff!

    Re: The data is that angels invest in about 27x more startup and seed deals than traditional VCs: http://www.angelblog.net/Angels_Finance_27_Times_… But, isn’t overall VC investment less than 10% of the total investment in private companies?  If so, this data would simply be confirming the ratio of activity.  I don’t have the data (and don’t feel like hunting around for data that shows this), but I’ve heard many times – from many different people (academics, angels, angel group leadership, VCs, entrepreneurs, other) that VCs invest in “less than 10% of the private companies funded each year.”  If this is true, isn’t your assertion about VCs only doing 4% of the angel investments in the measurement period simply directionally confirmatory data? 

    Re: the assertion that is going around that VCs have “effectively stopped investing in seed stage ($500k and less) and startup-stage ($2m and less), I think it’s important to look at longitudinal data – probably going back to the 1980s.  One would need to normalize this data by something for the period from 1998 to 2001 (the Internet bubble) when there was a huge spike in investment in the VC category.  I’ve never seen data that I would consider “good” on this so I only have my own experience, but the tempo (# of these investments / year) doesn’t seem to be declining precipitously from my perspective.  Yes – it bounced around from year to year, but the overall trend doesn’t feel like an curve that is asymptotically approaching zero.

    Also, in both cases, don’t forget to try to correct for survivorship bias.  Over the last 15 years there have been a lot of “visitors” in the VC category – people (or firms) who are VCs for less than a decade.  They entered in 1998 – 2001, were unsuccessful as VCs (or funds), and started exiting around 2005.  There’s a second wave of this going on right now with funds raised in the 2000 – 2003 time frame, that have not been successful (and hence not able to raise a second fund), and are now winding down their funds.  Many of these both of these categories made many seed / startup-stage investments early in their fund life (especially since they were smaller funds so they positioned themselves as seed investors), aggressively invested for 3 to 5 years, and then abruptly stopped investing because their five year investment period (where they could make new investments) was over.  In these cases, they haven’t made a new investment since 2006!  However, by 2011, they won’t be counted in any numbers any more since they’ll be out of the industry.

    Re: Identity Crisis: I’m very comfortable with my identity!  Having been an entrepreneur, and angel investor, and a VC, I’m most definitely a VC.  I just happen to be an “early stage VC”.

  • http://intensedebate.com/people/sigmawaite sigmawaite

    For a good essay on the situation and data, you get a A. There were lots of places you could have gotten off track but did not. Good.

    Generally to the readers, I would advise: When using data and statistics, be clear about what you are trying to conclude. Instead, too often people just toss out the mean, median, other percentiles, confidence intervals, etc. as if the point were to rise from sea, fall from the sky, etc. which it rarely does.

    In particular, yes, if have a random variable, then necessarily it has a distribution. My advice: Mostly f'get about the distribution. Certainly don't ask if it's Gaussian or log-normal. In particular, just talking about the distribution will make no useful truth rise from the sea.

    Yes, as in S. Gould, what you are more likely interested in is the conditional distribution given what additional, relevant data you have, and that can be much different. E.g., what is the conditional distribution of first round funding amounts given that the project was in IT instead of biotech? The distribution, expectation, percentiles are not something 'immutable' and typically change a lot given more information. E.g., in 'Wall Street', the conditional expectation of what the steel company was worth changed a lot given that Sir Raider was flying his Gulfstream to PA.

    Here are some cases where can talk about a distribution:

    (1) An 'arrival process' with 'stationary, independent' increments will be a Poisson process with independent, identically distributed (iid) times between arrivals with exponential distribution. Nice. Qualitative assumptions, often can check just intuitively, with precise quantitative consequences. See Erhan Cinlar's 'Introduction'.

    Possible application: What is the probability a small package cargo airplane will be too heavy? Assume number of packages does not affect the distribution of the package weights; use Poisson for the number and historical data for the weights.

    (2) Under mild assumptions, the average of n iid random variables converges to the mean with probability 1 — strong law of large numbers. E.g., in betting, in the long run what you get is the mean. Nicest proof from the Martingale convergence theorem (Leo Breiman, 'Probability'). Yes, should realize that the rate of convergence does have to do with the population of 'black swans'.

    (3) Under mild assumptions, the distribution of the sum of n iid random variables divided by the square root of n converges to a Gaussian distribution. Central limit theorem (check Lindeberg-Feller). Black swans are again relevant but notice the Berry-Essen bound.

    For answering some questions, (1)-(3) can be useful.

    For the VC data, some candidate questions:

    (1) Is the whole world of finance drying up with growth stopping and all of us going to hell? Not yet.

    (2) If an entrepreneur has a good project, can it still get funded? Likely.

  • http://intensedebate.com/people/sigmawaite sigmawaite

    Brad,

    For a good essay on the situation and data, you get a A. There were lots of places you could have gotten off track but did not. Good.

    Generally to the readers, I would advise: When using data and statistics, be clear about what you are trying to conclude. Instead, too often people just toss out the mean, median, other percentiles, confidence intervals, etc. as if the point were to rise from sea, fall from the sky, etc. which it rarely does.

    In particular, yes, if have a random variable, then necessarily it has a distribution. My advice: Mostly f'get about the distribution. Certainly don't ask if it's Gaussian or log-normal. In particular, just talking about the distribution will make no useful truth rise from the sea.

    Yes, as in S. Gould, what you are more likely interested in is the conditional distribution given what additional, relevant data you have, and that can be much different. E.g., what is the conditional distribution of first round funding amounts given that the project was in IT instead of biotech? The distribution, expectation, percentiles are not something 'immutable' and typically change a lot given more information. E.g., in 'Wall Street', the conditional expectation of what the steel company was worth changed a lot given that Sir Raider was flying his Gulfstream to PA.

    Here are some cases where can talk about a distribution:

    (1) An 'arrival process' with 'stationary, independent' increments will be a Poisson process with independent, identically distributed (iid) times between arrivals with exponential distribution. Nice. Qualitative assumptions, often can check just intuitively, with precise quantitative consequences. See Erhan Cinlar's 'Introduction'.

    Possible application: What is the probability a small package cargo airplane will be too heavy? Assume number of packages does not affect the distribution of the package weights; use Poisson for the number and historical data for the weights.

    (2) Under mild assumptions, the average of n iid random variables converges to the mean with probability 1 — strong law of large numbers. E.g., in betting, in the long run what you get per play is the mean. Nicest proof from the martingale convergence theorem (Leo Breiman, 'Probability'). Yes, should realize that the rate of convergence does have to do with the population of 'black swans'.

    (3) Under mild assumptions, the distribution of the sum of n iid random variables divided by the square root of n converges to a Gaussian distribution. Central limit theorem (check Lindeberg-Feller). Black swans are again relevant but notice the Berry-Essen bound.

    For answering some questions, (1)-(3) can be useful.

    For the VC data, some candidate questions:

    (1) Is the whole world of finance drying up with growth stopping and all of us going to hell? Not yet.

    (2) If an entrepreneur has a good project, can it still get funded? Likely.

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  • Phil Sugar

    I would agree that you are a VC.

    Actually I would argue that if your median :-) investment size is over $5M you aren't really a VC.

    Traditionally (i.e. before the late 90's) these type of investments were not even called VC deals. They were called Mezzanine Rounds and could be syndicated by some of the independent high tech investment banks which were swallowed up.

    I've looked at this data before and agree its total crap….you can't even try to analyze it because its plain wrong. Small deals are totally unreported…..and this would be expected. The people who like this info/place stock in it are service providers. They care because the more deals/money gets raised the more revenue they can generate.

    Just like "VC's" many of these guys have drank their own kool-aid and have gotten too big. Meaning you only like to do six figure engagements and that just doesn't work for a traditional startup.

    Frankly if you're an entrepreneur the situation is binary: you get funded or not and I would agree as you list out your group if your idea is a right fit for VC funding there are resources out there.

  • http://intensedebate.com/people/basil_pete44271 basil_pete44271

    Excellent response, Brad. Thanks.

    10% feels about right to me and I agree that definitive data is frustrating difficult to find. Scott Shane, in his book "Fools’ Gold", says “Estimates using data from the "Entrepreneurship in the United States Assessment" indicate that the friends and family capital market is about $139 billion annually.” The best numbers I can find indicate that traditional VCs and angels each invest about $20 to 25 billion per year.

    So the total seems to be somewhere in the $200 billion range, giving each of VCs and angels about 10% of the total. (I am still surprised by how big the Friends and Family component is.)

    In the interest of keeping the debate going, I would like to respectively disagree with your point about using “longitudinal data – probably going back to the 1980s”. The VC industry has changed SO much since then. Back in the 1980s, the average VC principle was responsible for investing about $3 million – not per year, but total. Many of my angel friends manage portfolios bigger than that.

    Back in the 1980s, the median VC investment in companies with M&A exits was around $5 million. Now it’s over $30 million.

    Here are a couple of graphs to illustrate those metrics: http://www.angelblog.net/Venture_Capital_Firms_Ar

    The size of today’s traditional VC funds is the single biggest reason that the old VC model is so badly broken.

    I like your term ‘visitors’ to describe a lot of the guys who built, and then failed to effectively manage, those huge VC funds created in the 1990s.

    The economy desperately needs more early stage investors like you and Fred Wilson who will actually invest in start-ups. And more greybeard VCs like Alan Patricof who will say publicly that to produce a good return, today’s VC funds should be around $75 million in size. And of course, a lot more angel investors.

    On the identity crisis, my comments may be more about my own ‘damage’. I co-founded a traditional VC fund in the early 2000s. About five years in, I realized that the ‘traditional VC model’ was broken – irreparably. It was painful to see all that work go into building something on a defective foundation. I don't want to identify with being a “VC” anymore because most people now associate that term with something that doesn’t work and needs to change.

    I wish we could find a new term that followed in the size sequence of:
    1.angel investor,
    2.angel fund
    3.<new name for a right-sized fund that works beneficially with entrepreneurs and produces an outstanding return for its stakeholders>

  • Phil Sugar

    I would agree with Brad that it feels like there are about the same "tempo" of seed and startup rounds getting done.

    There is a ton of other "noise" as we all agree was caused by "visitors" to the VC world…but the data is really irrelevant to everybody but service providers.

    The question is given that you have a company that is right for VC funding are there enough funds to provide funding and I think the answer to that is yes.

    People are always going to say its hard to raise VC money and that's because it is…..however, I would say that most companies are better off not raising it and that's not a knock on VC's that's the way it should be.

    I think Brad has a great example with Occipital and Josh Kopleman has written about it as well. I think frankly the hard part is having a company that is better off not raising VC have to compete with a company that raised VC money and now is spending it in a way that skews the market. (if you can steal their heat: i.e. leverage off of all the money they spend trying to artificially grow the market and outlast them it can work out well)

    But back to the topic as I think Brad puts it well is "who gives a fuck?" and that would be the lawyers/accountants that publish/read/take stock in this crap.

    If you are a VC or a entrepreneur why do you care? Are you going to stop investing or stop looking because of the data? Does it really affect you? No. It really only affects you if you suck a percentage of the money off in service fees.

  • http://intensedebate.com/people/bfeld bfeld

    Well said Phil!

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