Bigger Isn't Necessarily Better

Crunchbase has a story up today explaining that Series A and Series B rounds make up between 25% and 35% of all $100mm+ “supergiant” rounds every year.

That’s interesting but what would be more interesting is to compare the cohort of companies raising Series A and Series B supergiant rounds to the rest of the companies in a given year that raised Series A and Series B rounds.

What would interest me are success rates between the two cohorts. One could measure how many of each cohort are alive five years later. Or one could compare the stock price appreciation over the five year period between the two cohorts.

I have found, and written here, that performance of VC backed companies is inversely correlated to how much money they raise.

There are all sorts of reasons for that, but mostly it is that money is a burden, and anchor, it weighs you down and slows you down.

So I’d like to see the data on these supergiant A and B rounds. I suspect it will be pretty poor.


Comments (Archived):

  1. jason wright

    You’re a VC and not a journalist. Spot the difference. ‘The medium is the message’.Sloshy dynamics. Too much money equals too many options. There’s no rigour in that.

    1. Matt A. Myers

      That’s what I see with Elon Musk’s ventures as well. It’s a lot of money injected at first – but it isn’t much in terms of full scope.

    2. DeeJai23

      The rigour doesn’t come from the money – it comes from the management of the money. Knowing that whether $1 or $100M, that which is spent with data driven hypothesis on ROI is money well spent. For most, too much money simply begets laziness. For the ones who know how to use it and use it well, success is a gimme.

  2. Tom Labus

    And the health of the funds too!!!!

  3. JLM

    .The inverse correlation may be explained in part by the fact that companies that make a profit may not require additional VC funds.By definition companies that are still on the sugar tit are not yet able to internally fund their operations.Making a profit is an indicator of success, so these companies would fall within that subset of companies that are “successful.”JLMwww.themusingsofthebigredca…

    1. Matt A. Myers

      Sugar tit. Thank you for expanding my vocabulary – great imagery.

      1. JLM

        .Babies will sometimes not get with the whole nursing program — heck, they’re inexperienced — at first. This can be a real problem as once born, that is the whole feeding mechanism.In the South, the Mom puts a little sugar on the appropriate bit of anatomy, and — voila — everybody is happy.Glad I could help.JLMwww.themusingsofthebigredca…

        1. jason wright

          …and in the North?

          1. JLM

            .I don’t technically know about Northern babies. All of mine were born in Texas or Georgia.I suppose they put some molasses or vinegar on their appropriate bit of anatomy, no?JLMwww.themusingsofthebigredca…

          2. jason wright

            Neither do i, but I’m guessing Northern babies gurgle and poo too. Marmite?

  4. Mike

    Capital efficiency. I definitely agree with the premise, but would be good to back up with hard data as suggested.There is the theory around first mover advantage where heavy investment to rapidly achieve a market leading position can optimize company performance and investment returns in the long run. This might be an exception to the general observation, but probably only applies to a relatively small percentage of companies and markets?Also, it’s probably not the amount of cash that’s an issue, but the expectations that come with it. If you only have $2M in investable projects and someone gives you $4M with the same return expectations that could be a problem.

    1. fredwilson

      That was the Uber theory and it was wrong

      1. JLM

        .Uber had so many things wrong in retrospect. It’s a cab company with an app.JLMwww.themusingsofthebigredca…

        1. Tom Labus

          Working on a flying meatless something

        2. jason wright

          “It’s a cab company with an app.”Not according to Uber, which is the problem with Uber.Take some tech, pen a narrative, sell it to the world, and cash out first.

          1. JLM

            .Typo — “. . . cash out FAST.”JLMwww.themusingsofthebigredca…

          2. jason wright

            Don’t tell me, tell Masa.

      2. Mike

        That is only one case? But then again, the title of the post is Bigger Isn’t Necessarily Better, not Bigger is Never Better.

      3. pointsnfigures

        FWIW, the Uber and Lyft apps are not a joy to use. Lots of clicks, and their GPS in cities leaves a lot to be desired.

  5. sigmaalgebra

    ForI have found, and written here, that performance of VC backed companies is inversely correlated to how much money they raise.There are all sorts of reasons for that, but mostly it is that money is a burden, and anchor, it weighs you down and slows you down. Contacting VCs really slowed me “down” until I had accumulated overwhelming evidence of how VCs actually worked. I concluded that their ideas about (i) business, (ii) technology, and (iii) information technology (IT) are NOTHING like mine, and in (ii) and (iii) my qualifications are way out in the upper tail — with a good shot at being the best of all — for the people the Silicon Valley VCs work with. So, I gave up on Silicon Valley VCs! I don’t need an iron anchor and 20 ton anchor chain hanging off my little boat! Good riddance!For (i) business, heck I was a B-school prof! Right, that doesn’t count for much in business! I saw a startup, one of the best, at FedEx — was Director of Operations Research with my office next to founder Smith’s. I saved the company twice. Some things the company did well, some poorly. I saw some business, some good, some bad, at GE and IBM — before GE started its decline and when IBM had been voted the most admired company in the world for 5 years in a row.But, I’ve read carefully and kept lots of lessons on business from JLM — they look to me as the most credible and useful I’ve seen.Ballpark, I believe I know more about business than Page, Brin, Zuck, or Gates when they started out.I should know enough about business to get started, and if I run into trouble I have JLM’s phone number!Since my checkbook and my work to date should be sufficient for me, as a sole, solo founder, to reach nice profitability, with more pre-tax earnings in a year than nearly any Series A would provide, I should “raise” $0.00 which from the “correlation” means the best possible!Gee, I got some nice code written yesterday!!!!! It was a LOT of fun! I’m getting my recent move behind me! I understood the input data better — it is not perfectly clean and my code needs to handle that, and I found a nice way, somewhat general I may be able to use again! And I doubled the functionality! The code has 4 parts; I found a way to have all 4 essentially the same with just a few lines added specific to the parts. Today will finish part 4 — just a simple copy and a few lines of code. FUN stuff!Yup, for my startup, my high technology is just some pure/applied math derivations, and for IT that should be the very best kind! That nearly no IT startups are of this kind should mean a great opportunity in a green field of essentially no competition!Uh, the math I’m using is fully within some of the best math there is, and that is MUCH better IT than ANY of the artificial intelligence (AI) trivia. That VCs would rather fund electronic toys than some math IT that should uniquely well thrill billions of people means that I won’t have VC funded competition until after I’m already quite successful even assuming that others interested in business could duplicate or equal, which is unlikely, the power of my math.Gee, guys, math to make money? Who’d thunk? I mean, what successful investors, e.g., Buffett, made crucial use of math? Gee, let me think …! What was that number I heard, $100 B or some such? Naw, not Omaha; how ’bout Long Island?Ah, there’s a saying, IIRC,Nearly all math in the end when applied boils down to at least some use of linear algebra. Yup, there’s some linear algebra in my work; not a lot; it’s not the key stuff; but there’s some there. At first I wrote my own linear algebra code; nice code; worked fine; rock solid; but in the end, not to take merely a Cadillac approach or a BMW or MB approach but an approach something like what Cadillac built for Trump, proportionally somewhat wasteful of computer cycles, like maybe a 16 ton car instead a three ton car, I used the famous, rock solid, world-class, pillar of world technology, main benchmark in super computer performance Linpack. Works fine! Linpack is a fantastic Cat D-9 when I need only a shovel, overkill for what I need, but it works fine!So, there’s some linear algebra. Hmm, let me go to Amazon; be back soon: Okay, I’m back now. At Amazon at…isRoger A. Horn, Matrix Analysis: Second Edition.Right, linear algebra! Horn knows VERY well, in overwhelmingly precise terms, JUST what the heck he is writing about. His selection of topics is from the perspective of a mountain top — e.g., one of his topics is the interlacing eigenvalues theorem used in some curious new work recently reported at the Simons site. His proofs are all rock solid and sometimes nicely novel.As at…he was a Charles Loewner student at Stanford.In linear algebra, Horn is a world class guy.Naw, I don’t have a copy of his book. Instead I took his course and took careful notes! It was an advanced course in linear algebra. I looked at the course and told the faculty that I didn’t need it, that it was a waste of time for me, that I already knew nearly all of that material quite well. Yup, I’d never had a course in linear algebra, not even a first course, and Horn’s course was a second course. So, the faculty smiled at me, thinking I was about to get a lesson, a comeuppance, a challenge.Nope! I was fully correct. I already knew nearly all the material. I’d taught it to myself, from a quite good ugrad pure math background. And I’d learned from some of the world’s best sources, (i) Halmos, Finite Dimensional Vector Spaces written when Halmos was an assistant to von Neumann at the Institute for Advanced Study and essentially a finite dimensional introduction to Hilbert space of Hilbert and von Neumann, (ii) Nering’s book, where Nering was an Artin student at Princeton, (iii) Forsythe and Moler on numerical linear algebra, a world class source, (iv) M. Newman — I worked for him for a while — and his work in number theoretic methods for fast numerically exact linear algebra, and much, MUCH more. I’d already written very careful notes that were close to a textbook in the subject. I’d made applications to business and US national security.One day in the course, Horn got to the polar decomposition, and I got excited and blurted out:That’s my favorite theorem! Poor other students in the class: They were good students but by then were just hanging on by their fingernails and just hoping to stay up, and I blurted out that of course I already knew that result well and knew all the material well enough to know that this was a central result! And, yes, I knew a good proof.Once the homework grader made a mistake on my paper; I corrected him; and he made no more mistakes!I led the class by wide margins in the graded homework, the tests, and the exams. Horn’s evaluation was “Knows this material cold. Best performance in the class by a wide margin.”Yup, I know some linear algebra!Not many people in the world know linear algebra well at the level of Horn’s course. There are still fewer such people in IT in Silicon Valley!Linear algebra is far too simple, weak, and elementary for the important math for my startup, but there is some linear algebra in there!

    1. JLM

      .I thoroughly enjoy the breadth and depth of your comments.JLMwww.themusingsofthebigredca…

  6. Adam Pelavin

    It’d also be interesting to see a breakdown by company *age* when a supergiant round is raised. I’m generally skeptical of startups that raise a ton of money right out of the gate, but occasionally one will grow quite a bit before raising outside capital, in which case a larger first round might make more sense…

    1. Matt A. Myers

      Perhaps how long the founder(s) have been working on the problem is a better metric? Might take years before a company is formed?

      1. Adam Pelavin

        Could well be, particularly with experienced founders, though there’s also the matter of creating a team/product/culture and putting it into contact with the problem. I’d guess that starting with a supergiant round makes it harder to adjust those things as you learn, all else equal. But it’s another breakdown I’d be curious to see!

  7. Laura Yecies

    I think that the way to test this theory is to look at the companies that have successfully exited – IPO, high value M&A and look backwards at their series A & B round size. This may vary by industry. I believe that in healthcare the round size of those successful companies is large with the exception of Veeva. B2B SaaS may be lower (and why the Veeva exception).

  8. Pointsandfigures

    Yup. I am thinking that when these firms IPO we will get the liquid market value that might being some discipline down the chain. However VCs have raised bigger and bigger funds. Competition to get into best deals causes bloat

  9. Pointsandfigures

    BTW got the new iPhone 11 Max. Like it so far

  10. ALiu

    PitchBook just came out with the following article:…I remember reading this post and although the PitchBook article does not hit all of the methodology you suggested, it has other useful data.