I came here to basically say this about running a company- but your comment was a better launching point.
As someone who has run several companies over the last 25 years and has read and or tried nearly ever "method" mentioned... Im now running a company where Im abandoning everything and just going at with skill and fearlessness... and no funding. It feels freeing, and we are growing.
Either you fight over a saturated market. (Zero sum)
Or you try to grow the market. (Non zero sum)
Or you try to diversify into new markets. (Mixed, non zero sum if its greenfield research into new technologies, zero sum if old).
In contrast, for a company that can't be started by a single app developer - getting out of the building won't help. Nobody in the space worth talking to will talk to you, for starters.
What do I know, I don't run a billion dollar startup. But there's a valuable "necessary but not sufficient" insight to all good advice. The lean startup IS good advice. The best I can do with your argument is "getting out of the building is no longer sufficient".
Sure. But it doesn't make the entire arch of how we got here "wrong". And yes, all companies were started with a few people, a few customers. So that's why there's nothing much here to see for me, other than defeatist sentiment.
Seriously, how do you even realistically approach taking on ASML. They spent decades and billions of (investment) dollars to do insane moonshot research and it paid off. But it also closed off the door behind them.
Entire countries (Russia, China, ...) have been trying to reproduce it. They have not succeeded yet.
There’s a skill issue there. I know a founder who’s able to get people to talk to him. As a result, his startup had F500 customers almost from the beginning.
But that’s the kind of thing that no amount of documented strategy and tactics is ever doing to be able to teach. I’ve watched it happening, but I can’t do it.
Taleb does the math as well IIRC, assuming there are x hundred thousand extreme risk takers, and outlier “correct bets” are y% chance, then you will have a surprisingly high number of people with a long series of “correct” bets behind them looking like business geniuses, from pure chance & basic statistics.
For me, this is where it breaks. There are two assumptions that the author must be challenged on.
1. Enough people know about these methods
2. Of those people enough use the methods properly
Judging from my own experience I can’t confirm neither of these. Even those people that know the approach rarely have the rigor to treat startups as a series of experiments. Ego plays a large part.
I don't think this article is very good, at all.
The most important thing I got from Feyerabend, Graeber and Sylvia Ashton-Warner was the invitation to colour outside the lines (though few but my mother would suspect Ashton-Warner of anarchic thought...)
I just finished reading Lowey's autobiography "Never Leave Well Enough Alone." A ripping yarn if you're hip to mid-century descriptions of design, martinis and trains. Lowey's most famous slogan (repeated in design schools everywhere) is MAYA : Most Advanced Yet Accessible. In other words... as a designer, you have to make something the client can recognize as a solution to their problems but advanced enough to justify the expense of upgrading.
I bring it up because I fear we have confused the two... being accessible and being advanced. While I'm happy to point out some of the advantages of AI, I should also mention we're letting the tail wag the dog. We've spent fourty-fifty years with a model of technology growth requiring increasingly greater returns on falling marginal real returns.
The difference between the benefit of technology between 1940 and 1950 was immense. Similar for the increased benefit brought about by the increase from 1950 through 1960. But the benefits between 2016 and 2026 are less about productivity improvements and more about finding more people to borrow money from.
What if we have eaten all the low-hanging worker-productivity fruit?
What if every increase in worker productivity requires increasingly greater capital investment and that investment yields increasingly smaller margins?
Is it time to reconsider Schumacher's argument in "Small is Beautiful" ? Is it time to work smarter rather than invest bigger?
All these thoughts are offered without evidence, but also without a foregone conclusion of their outcome.
I will be at the library, BLS website and local startup office collecting data.
I went a little nuts with modern anthropology and absolutely adore Giddens. He was actually the person I was visiting in London when I bumped into Graber. "The Consequences of Modernity" is a bit dense and filled with jargon, but I love it.
There was another post I commented on and mentioned "Everything Was Forever Until It Was No More," and while it's not a general-purpose discussion of how we model social systems, it is a great read. It's where the word "HyperNormalization" comes from and tells the story of the last generation of soviet citizens who noticed things weren't working too well, but grew up in a world where there were no alternatives to state bureaucracy and politics.
I also read a fair amount about pedagogy since I'm interested in structured systems of developing denotational systems for modeling human knowledge with the objective of communicating said knowledge. I love Sylvia Ashton-Warner. She wrote an autobiography called "Teacher" that's worth a read. I hung out with Seymour Papert and Cindi Solomon when I was a kid, so I have to mention Mindstorms. And Freire's "Pedagogy of the Oppressed" has some interesting bits if you can handle the 70s communista jargon. It's not full-on marxist, but if you're sensitive to such things, it can be distracting. But... what they all have in common is a framework for how to communicate (potentially) complex models and knowledge maps.
Read everything you can find by Jung, McLuhan and Claude Levi-Strauss.
"Eros and Magic in the Reniassance" by Ioan Cuilianu. Then read it again because it's hard to figure out what you just read when you only read it once.
For fun I've read some of the articles and books by Rory Sutherland.
Marketing is especially the key element here, and there is and never will be a permanent science of effective marketing. Culture is always changing and what gets attention today is blasé tomorrow.
Seems like all the other 9 that died insist on telling the one that survived that they were somehow wrong.
For sure, I do get that one can "do everything right" and still fail, I get that point, I get that there is no formula. But it seems like people want the reverse to be true: that everyone successful is only a lucky buffoon.
Here is the key principle.
Suppose that your odds of startup success are dominated by competition with other would-be startup founders. For example you compete for funding, good ideas, competent employees, and markets. If so, then the odds of success are set by the dynamics of that competition. In which case widespread access to effective advice on running startups does not improve the odds for a random founder succeeding. They just raise the quality of competition.
Think of it as being like a boxing tournament. If you learn how to box better, your odds of winning the tournament go up. If others learn to box better, your odds of winning the tournament go down. And even if everybody learns how to box better, we see the exact same number of winners.
Whether or not startups actually work this way is an empirical question. Based on a bunch of different data points, he argues that startups really do seem to work this way. And so the spread of good advice on running startups can't improve the odds of a random startup succeeding.
Most businesses fail because they solve for the easier bit (product) and then have no idea about the rest.
The crux of the issue for me is what Dr Iain McGilchrist highlighted — we attend to the world in two very different ways. One mode of attention is a broad, open awareness to what's 'out there' and the other mode is a much more narrow focus on the parts and pieces.
For startups, when you look at the actual cases, many successful founders, almost by definition, had to stumble across their insight in some emergent fashion. They either experience some pain and set about solving it (Dropbox); see some opportunity on the horizon (OpenAI); or stumble onto some idea while working on something else (Slack).
If you want to do a startup, or your current idea isn't working, and you don't have that vision of emergent opportunity, then what do you do? "Just look for some emergent opportunity" isn't very compelling advice (even if it's probably the most accurate).
This is where the punditry emerges. You have to use your other mode of attention in an attempt to brute force some insight through narrow-focused analysis, and that analysis is inherently constrained to your (by definition) barren environment. That gives you the Lean Startup, customer development, etc etc. This far more analytical approach requires (a) intense discipline; (b) a lot of luck because you're starting from a point of no opportunity; (c) enough volume to actually do the interrogation of reality.
And it may not work because it's simply using the wrong mode of attention, anyway!
Nevertheless, frameworks that exist in this realm all sound reasonable because, on one level, they are: what else can you do but interrogate reality in some methodical way? But the question TFA raises (in my mind) is whether shaking the tree like this — IF you even can with appropriate discipline — reveals emergent opportunity for startups at a scale that's reflected in the broad outcome data, and the answer appears to be no.
Interestingly, the book The Heart of Innovation[1] tries to tackle this by going to the extreme. It's not about finding some clues in fast iteration or mapping out a canvas with a nice value prop, it's about finding 'authentic' demand that's so compelling it's something users can't not do. (The 'not not' concept is hard to explain but creates a much more rigorous bar for innovation IMO.)
That's their backward-looking observation for innovations that stick (and reflects most of the cases in the book), but they're still faced with the same dilemma of what to do if you aren't blessed with emergent opportunity.
In that case, their solution is to ramp up the analysis even harder, with 150-200 "Documented Primary Interactions" observations. I.e., brute force observations even harder. Some of the authors are part of a startup accelerator with an (apparently) high hit rate, so it's not just speculation.
All told, it's amazing that billions and billions of dollars are allocated to startups and so little is invested in studying innovation itself, especially given how slight the predominant frameworks are. Yet new ways of thinking exist (like McGilchrist, or the Heart of Innovation approach), so I wonder if frameworks for innovation are still in their absolute infancy, really, where the ones that succeed suffer the memetic curse: simple enough to travel; too simple to be effective.
[1] Excellent overview here: https://commoncog.com/the-heart-of-innovation-why-startups-f...
Because entrepreneurship and markets overall are at the center of so many disparate human contexts, I just don’t think the scientific method is particularly applicable. I also think the minute you try to generalize between startups the fidelity of understanding the factors of success fall off exponentially. The most common failure mode—almost by definition—is failing to recognize that some seemingly good idea or pattern that worked in many other businesses just does not work in this particular context for whatever reason. This is why entrepreneurs that are too focused on theory and not enough on the details of their particular space tend to fail.
To me, The Lean Startup is useful food for thought, and can be useful in surprising ways (even in bigger companies), but the generalized ideas and statements are of very little value without a keen sense of applicability in context. Any “science” of entrepreneurship would basically be combining the systemic chaos of macroeconomics less the precision of standard financial metrics plus all the human factors of psychology. Fascinating to think about, but I doubt the best pundits and theorists would themselves make good entrepreneurs.
> no change in survival rates
> less series A
would this not imply that companies got more efficient at using their seed funding?
(But then again: The real dip in series A funding starts in 2018; so we might still see a dip in 10y survivability starting 2028)
