[HN Gopher] O(n) vs. O(n^2) Startups
___________________________________________________________________
O(n) vs. O(n^2) Startups
Author : ocean_moist
Score : 66 points
Date : 2025-05-15 04:54 UTC (3 days ago)
(HTM) web link (rohan.ga)
(TXT) w3m dump (rohan.ga)
| brudgers wrote:
| _An O(n) startup grows_
|
| If it grows at O(n) it is not a startup in the way "startup" is
| used in Silicon Valley. It is just an ordinary new business.
|
| It's worth noting that starting an ordinary new business is hard.
| Probably as hard as starting a startup for anyone who has not
| started a new business a few times before. And maybe harder
| because most new businesses are undercapitalized and therefore
| likely to suck up personal capital while startups get to use
| other people's money.
| ocean_moist wrote:
| I use business, startup, and company interchangeably. Generally
| I tried to use business for O(n), startup for O(n^2), but I
| guess I wasn't strict with my usage...
|
| Perhaps they are harder to start, but they are also vastly more
| likely to succeed to their O(n^2), and this is not only due to
| the increased barrier to entry.
|
| That's what I mean when I say, founders are more likely to
| succeed at O(n) companies.
| brudgers wrote:
| Most businesses are playing pinball...success this month/year
| means you can play again next month/year.
|
| Very few have successful exits where the owner walks away
| with life changing money and no obligation.
| kylecordes wrote:
| Unfortunately, many businesses are arguably a bit worse
| than that: success this year means you put even more at
| risk next year.
|
| (For example, you get successful enough that you need a bit
| of office space. Well, your little business is not going to
| persuade anyone with nice office space to lease to it
| alone... Landlords will instead demand the owners
| personally guarantee the lease, i.e. commit to paying it or
| go bankrupt trying, even if the business shrinks and no
| longer needs or can afford the space. So you thought,
| great, we had a couple years of strong growth. Now you get
| to commit personally to five years of a huge expense.
| Congratulations.)
| Aurornis wrote:
| Where are you seeing commercial office space require 5
| year personal guarantees from the founders? Terms like
| that would have everyone laughing as they hung up the
| phone around here.
| brudgers wrote:
| In the US, that sounds rather normal for normal triple-
| net commercial space for a normal new business run by
| people without established commercial relationships.
|
| Typically, new businesses don't get very much credit on
| favorable terms without established commercial
| relationships.
|
| Dealing with failing or failed businesses is just not
| worth the hassle for most established businesses working
| in the established business market segment.
|
| Silicon Valley is probably different because the business
| relationships are different. And of course month to month
| and short lease real estate are an entirely different
| market segment than triple-net.
| SoftTalker wrote:
| You need to look for hungrier landlords.
| chubot wrote:
| Related: _Black Swan Farming (2012)_
|
| https://news.ycombinator.com/item?id=4497461
|
| https://paulgraham.com/swan.html
|
| It is interesting that YC started as being more Founder friendly
| ... and I guess "Founder's Fund" did too
|
| But there is still some divergence in interests ... i.e. if you
| have to make a choice between a safer O(n) path and a riskier
| O(n^2) path, then the investor prefers the riskier path
|
| Or I'd be very interested in an argument that they don't
| mperham wrote:
| Don't forget about us O(1) solo entrepreneurs!
| smahs wrote:
| Still O(n).
| wavemode wrote:
| N is customers not employees
| mitthrowaway2 wrote:
| In the article it's apparently "time since launch".
|
| Kind of like how an O(n^2) sorting algorithm sorts n^2
| elements in time n. Right?
| wavemode wrote:
| No, it's the opposite. An O(n^2) algorithm sorts n items in
| n^2 time. So O(n^2) is worse than O(n).
|
| (should really be th rather than O but you get my point)
| danjl wrote:
| > O(n) companies can't afford to hire the absolute best talent.
|
| O(n) companies tend to have more experienced founders and
| engineers in my experience. This is partly why they have "nice
| deadlines, clear SoW" and "understand their customers" enough to
| have PMF. The strength of their talent, experience and job
| networks often greatly outweighs the cash incentives, allowing
| them to hire top candidates. They do not just hire "to fit a job
| description" because, since money was tighter, they are super
| conservative about hiring, have always done the job they are
| hiring for themselves for a long time, and know exactly what they
| need. It is the O(n^2) companies that hire for job descriptions
| that fit the positions the VCs tell them they need. I think your
| experiential datapoints may be too sparse.
| jbmsf wrote:
| And also, engineers who are sick of the stupidity that comes
| from having too much VC money and not enough wisdom to use it.
| Aurornis wrote:
| In my experience the heavily VC funded companies have a lot of
| very talented and experienced people, too.
|
| But they have so much money and pressure to hire that they
| start dipping deeper and deeper into their candidate pipeline.
| They start lowering their standards to keep the employee count
| growing. This results in a mix of talented people trying to get
| work done and a lot of people who are good at interviewing and
| stretching the truth about their experience.
|
| Every time I've been at a company like this, they tell
| themselves they'll hire fast and fire fast to compensate. Then
| they never fire fast or at all, because nobody wants their
| little empire to shrink.
| ryandrake wrote:
| The vast, vast majority of companies don't need "the absolute
| best talent." Their product is a JSON interface to someone
| else's service. You don't need John Carmack to write that.
| Companies comically overestimate the level of talent they
| actually need, and let positions stay open for months,
| sometimes years, looking for that unicorn programmer they don't
| actually need, and passing up hundreds of candidates who would
| work out fine.
| ocean_moist wrote:
| Experience != talent. Perhaps a better way to phrase it is that
| they hire the minimum needing to succeed in a well defined
| role. Startups aren't afforded this comfort as the roles are
| not well defined.
| bee_rider wrote:
| Huh.
|
| Not working in the field, I assumed startups went like sigmoids
| (everything is a sigmoid after all).
|
| Exponential at first as word of mouth spreads, then linear as
| your users start bumping into each other and word of mouth stops
| working, and then you eventually start leveling off near carrying
| capacity (you've hit your addressable market).
|
| I thought the game was to try to get bought by some massive
| company while you are in the linear phase (where you are big
| enough to be treated seriously but your growth rate still looks
| absurdly high).
| withinboredom wrote:
| This is actually a better model and one that more closely
| reflects reality. You can see it on revenue as well, since even
| if growth is exponential, churn is a percentage of your total
| paying users. Thus, it produces a sigmoid curve unless you can
| get churn to 0% (pro-tip: you can't).
|
| But, these are the two basic levers for a SaaS: growth and
| churn.
| jvanderbot wrote:
| The first thing I learned when I joined my first business was
| covered in orientation with leadership: every product or
| business unit is a sigmoid, and to maintain growth you must
| add new products or business units without spending too much
| to do so. Then the overall company profit can grow linearly
| or whatever by being a sum of sigmoid functions that spawn
| over time.
|
| Good leadership knows when the flattening will happen and
| pivots.
|
| This is called "innovation". That really stuck with me as a
| mental model.
| thaumasiotes wrote:
| > You can see it on revenue as well, since even if growth is
| exponential, churn is a percentage of your total paying
| users. Thus, it produces a sigmoid curve unless you can get
| churn to 0% (pro-tip: you can't).
|
| Exponential growth means that additional users are a
| percentage of your total users.
|
| It is trivial to see that adding a source of exponential
| decay will give you another exponential function. All churn
| (as you've defined it) does is lower the exponent. It will
| never take you from exponential to sigmoid.
| nis251413 wrote:
| Sigmoid (or the logistic function specifically rather) _is_
| exponential until you get close to the "turning point" (or
| rather, its growth bounded from below by an exponential).
| It's as you approach that point that it becomes linear, and
| after that its growth decays.
|
| However you are sort of right that "churn" does not
| necessarily have to do with it being sigmoid because it
| will be anyway. It may be bring it earlier if the churn
| rate surpasses the user growth, but that's probably not
| important here.
| thaumasiotes wrote:
| If the churn rate surpasses user growth, you won't go
| sigmoid, you'll stay exponential. You'll just be
| shrinking instead of growing. To be sigmoid, you need the
| growth rate to vary.
| withinboredom wrote:
| Growth is not a function of users, though, it is a function
| of something else that *may* be exponential, for a time.
| Users don't beget users (word-of-mouth doesn't last
| forever; ad money has diminishing returns), and eventually
| your market will be saturated, so you can't grow
| exponentially forever.
| ocean_moist wrote:
| Yeah sigmoid is good. I kind of hint at this when I mention
| "saturate their TAM at some rate".
|
| You can think about it like we are looking at the concave up
| portion of the sigmoid only. The early growth phase.
| mgraczyk wrote:
| They are sigmoids, but for some the plateau is 30 years in the
| future with a 5T market cap.
|
| For example Facebook's revenue is still increasing at an
| increasing rate, 21 years later
| alangibson wrote:
| This is an incredibly important thing to understand. Buffet
| himself said it's better to be an average business in a great
| market that to be a great business in an average market.
| ptero wrote:
| Interesting. Not doubting this, but one of his sayings I
| saw many times is that "it is better to buy a great company
| at a good price than a good company at a great price",
| implying that great companies are worth significant
| premiums.
| godelski wrote:
| I took the author's use of O(n) vs O(n^2) as a framing point
| rather than a literal model. It just seems to be missing the
| forest for the trees. Besides, we can approximate sigmoids with
| linear or quadratic functions when windowing them. Considering
| startup as context I think we know what part of the graph we're
| talking about... Do we see that exponential explosion or is the
| sigmoid much more flat. Replace the x in your sigmoid with (ax)
| and is a <1 or >=1?
| nis251413 wrote:
| Big-O notation is about asymptotics. You have to approach
| something, and typically there is some infinity involved
| because if it is not, then you can just compute things
| instead of giving asymptotic approximations, or else you have
| 10^10*n and 0.001*n^2-10^20*n and the big-O asymptotics at
| infinity are useless for smaller numbers. I understand what
| OP tries to say but that's not really a good framing point
| for many reasons. If you want to talk about a finite period
| of time, use a regression model, not asymptotics. But that's
| probably also a more personal preference around using
| mathematics colloquially but also in a manner that is not a
| good metaphor and does not correspond with what the
| mathematical theory is referring to. And I am not sure at all
| whether it is very well understood "what part of the graph
| we're talking about", in the sense that the modern
| organization of economy is far from acknowledging the fact
| that resources on earth are actually finite. Talking about
| O(n) and O(n^2) or O(exp(n)) as if growth can be indefinite
| comes with a specific kind of mindset, and the frame used
| reflects this type of mindset.
| godelski wrote:
| > Big-O notation is about asymptotics.
|
| Excuse me, but what made you believe I do not understand
| this? I'm not sure what comment you're responding to, but
| it sure isn't mine. >> I took the author's
| use of O(n) vs O(n^2) as a framing point rather than a
| literal model.
|
| Honestly, the reason I said this is because from the
| article | The reason I borrow the
| asymptotic notation is because it implies the growth rate
| is an upper bound (best case scenario) and generalizes away
| specific constant factors and sums. The analogy breaks down
| when you force n or n^2 imply something numerically
| specific about your growth rate, or introduce functions
| with different growth rates like logs or exponentials. For
| now we will (somewhat unprincipledly) stick with two sole
| classes: O(n) and O(n^2).
|
| Along with their emphasis on "vaguely". I can forgive the
| author for bad verbage. It is a personal blog where they're
| not trying to sell anyone on a fully fledged out idea and
| appear to be trying to spur conversations. Especially
| considering it looks like they are an undergrad. Frankly, I
| can understand them despite the wrong words. Given this, it
| would require me to operate in bad faith by rejecting the
| main thing they are attempting to communicate by focusing
| on the details that ultimately don't matter to their claim.
| > If you want to talk about a finite period of time, use a
| regression model, not asymptotics
|
| They did. They said "startup". The whole time they bound
| the conversation to early businesses. I even directly
| stated this >> Considering startup as
| context I think we know what part of the graph we're
| talking about...
|
| Maybe you're referring to the preceding line
| >> Besides, we can approximate sigmoids with linear or
| quadratic functions when windowing them.
|
| Which again, same thing.
|
| My point is: you're derailing the conversation
|
| You are technically right, but you're derailing the
| conversation in an effort to prove your intellectual
| capabilities to a person who were not questioning them in
| the first place. You're flexing to the wrong group. You
| just responded to something my comment was never about.
| > I understand what OP tries to say but that's not really
| a good framing point for many reasons.
|
| So address what the OP tries to say, * _and while doing
| so*_ you can add additional technical correctness. * _This
| does not derail the conversation.*_ It _continues_ the
| conversation _and_ enhances it! You can do both! But as
| your comment stands (and bee_rider 's), you just are moving
| the conversation away from what OP wanted to discuss and
| instead hyper-fixating on what they themselves said is not
| the best language.
| danjl wrote:
| If only it was exponential in the beginning, as word-of-mouth
| spreads. Sigh. The reality is that you need to claw and scrape
| your way to your first customers. The numbers vary depending on
| whether your product is b2b, consumer, or more niche, but the
| first customers are the hardest. You rarely get word-of-mouth
| in the beginning. Instead, it comes much later, typically after
| a long period of slow growth as you learn more about your
| customer's workflows and problems and adjust the product to get
| closer to PMF.
| sshine wrote:
| Since O(n^2) is used as a proxy for "something superlinear, but
| don't get hung up on how much", you might also choose O(n^(1+e)),
| an upper bound characterised by some arbitrarily superlinear
| function.
| brap wrote:
| Why not O(nlog*(n)) startups
| sfpotter wrote:
| Not that it matters, but O(n log n) is often referred to as
| "quasilinear", but O(n^(1+eps)) is regarded as "superlinear"
| (and in fact grows faster than O(n log n) for any eps > 0).
| sshine wrote:
| Because, as the article states,
|
| > _The analogy [between asymptotic growth and company
| economic growth] breaks down when you force or imply
| something numerically specific about your growth rate, or
| introduce functions with different growth rates like logs or
| exponentials. For now we will (somewhat unprincipledly) stick
| with two sole classes: O(n) and O(n^2). Perhaps choosing a
| better two functions could more closely explain the growth
| dynamics of network effects, which could be more exponential.
| I think the analogy diminishes in value if you try to
| directly numerically match it to some growth metric._
|
| So the article specifically tries to be unspecific about
| _what_ superlinearity we 're talking about, and also calls it
| _vaguely superlinear_. Since O(n^(1+e)) is _arbitrarily
| superlinear_ (O(n^1) = O(n), and e is some arbitrary small
| amount, making it superlinear by definition, and practically
| nothing else), it is a good choice when that is all you wish
| to say.
|
| If you went with O(n log n), you'd get the same questions as
| with O(n^2): Why not O(...something else...): That's not the
| point! :-D
| ocean_moist wrote:
| Sure makes sense.
| SoftTalker wrote:
| Story applies to most people in regular life: the average return
| of working a regular job is far higher than playing the lottery.
| But for a rare few, playing the lottery works out.
| wavemode wrote:
| > An O(n) startup grows its key metric (revenue, users, etc.)
| roughly linearly with time--double the time, double the metric.
| An O(n^2) startup accelerates, with growth compounding super-
| linearly over time.
|
| Kind of a strange formulation to have n represent the key metric.
| In algorithm analysis, we would typically have n represent time
| (or some other cost). So we would say that the startup whose key
| metrics accelerate exponentially with time is actually an O(log
| n) startup - they only have to spend (log n) time to get n
| results.
| thaumasiotes wrote:
| >> An O(n) startup grows its key metric (revenue, users, etc.)
| roughly linearly with time--double the time, double the metric.
| An O(n^2) startup accelerates, with growth compounding super-
| linearly over time.
|
| > Kind of a strange formulation to have n represent the key
| metric. In algorithm analysis, we would typically have n
| represent time
|
| In the quote you pulled, n _is_ time. If n were the key metric,
| everything would be [?](n).
|
| > So we would say that the startup whose key metrics accelerate
| exponentially with time is actually an O(log n) startup - they
| only have to spend (log n) time to get n results.
|
| No, you don't know how the notation is used.
| wavemode wrote:
| > In the quote you pulled, n _is_ time.
|
| It's definitely not. If their usage of O(n) has n as time,
| then they wouldn't say an O(n^2) startup has accelerated
| growth of the key metric. You'd be squaring the time, which
| means _slowing down_ growth of the key metric.
|
| When they say O(n^2) startup they clearly mean a startup
| which achieves n^2 results in n time. Which is the opposite
| of how the notation would typically be used.
|
| > No, you don't know how the notation is used.
|
| No, you're confidently wrong.
| danjl wrote:
| Normally, with big-O notation, the goal is to _reduce_
| complexity. The author 's wording kinda reverses that
| assumption only to "surprise" you in the end? A somewhat forced
| irony.
| Maxatar wrote:
| Big-O notation does not have a goal, it's a description not a
| strategy.
| nis251413 wrote:
| Well, you may want to increase complexity in some contexts,
| eg in cryptography.
| ocean_moist wrote:
| Only in algorithmic analysis. Big-O generally is used to
| describe and classify any arbitrary function.
| Maxatar wrote:
| > In algorithm analysis, we would typically have n represent
| time (or some other cost).
|
| No, n is never time in any kind of algorithmic analysis. n is a
| function of the size of the input and the output is some
| measure of the cost related to the input.
|
| In O(n^2), the size of the input is n and the amount of time,
| or space, or some measure of the cost has an upper bound that
| is proportional to n^2.
| wavemode wrote:
| > In O(n^2), the size of the input is n and the amount of
| time, or space, or some measure of the cost has an upper
| bound that is proportional to n^2.
|
| Yes, this is my point. In the article, they classify an
| O(n^2) startup as one which achieves n^2 results in n time,
| which is the opposite of how the notation is typically used.
| ridiculous_leke wrote:
| I wonder what's optimal for me as an employee. I am working in a
| _O(n)_ startup where colleagues are nice, work is streamlined yet
| challenging, and I do see growth potential in the long term.
| Several _O(n^2)_ founders have reached out recently and the pay
| is attractive(even after accounting for a move to an HCOL area).
| danjl wrote:
| Or, really, to say the unsaid bit out loud: there are lots of
| important considerations when taking a job. The author seems to
| assume that money is the only driver, when, for many top
| candidates, money is not their primary motivation. The ability
| to plan well and thereby reduce stress is a good measure of the
| management experience. Other non-cash incentives tend to be
| given out more readily at well-run non-enterprise companies,
| including remote work, longer vacations, and more strategic
| control, to name just a few.
| robocat wrote:
| Modern society tends to severely overemphasize money as the
| optimisation goal. This is an emergent behaviour of our good
| capitalist system.
|
| Your time is precious. You spend it once and you can't
| predictably get any more of it.
|
| I suggest you choose your optimisation goal function very very
| carefully to suit the outcomes you want (money is only an
| intermediate step). It's hard to decide what we really want.
| Money is the default game that we see our peers playing (and
| it's easy to gain moderate success at the money game). It
| requires more attention to find and learn from people that have
| had success playing less common games.
|
| Cynically (or even conspiratorially) investigate the suggested
| life defaults for you by your society as though they were dark
| patterns designed to mislead you.
|
| I like what Naval wrote about status games (money is only one
| aspect of status). Paraphrased: Status is a
| zero-sum game, not a positive-sum game. There's always a subtle
| competition going on between status and wealth. For example,
| when journalists attack rich people or the tech industry,
| they're really bidding for status. The problem is, to win at a
| status game you have to put somebody else down. That's why you
| should avoid status games in your life - because they make you
| into an angry combative person. You're always fighting to put
| other people down and elevate yourself and the people you like.
| Status games are always going to exist; there's no way around
| it. Realize when you're getting attacked by someone else and
| they're trying to look like a goody-two shoes. They're trying
| to up their own status at your expense. They're playing a
| different game. And it's a worse game.
|
| Disclaimer: I've had moderate success at chasing money. I've
| had less success at optimizing for other goals (work in
| progress in my 50s).
|
| Money has no maximum so it's a weird goal to try and reach. I
| wonder why Warren Buffett waited until 95 to decide to retire?
| He would easily be the richest man in the world if he hadn't
| charitably given so much away.
|
| Another relevant paraphrased snippet from an interview about
| better lives for the elite: https://archive.ph/kF0YR
| There's this study called the American Freshman Survey
| [edit:snip] In the 1960s, 50% of students said making as much
| money as possible was a really important goal. Today, that's
| 80% to 90%. That change shows that this is not human nature. It
| is culture.
| curiousgibbon wrote:
| This is one of the myriad situations where Omega should have been
| be used, not O. What are they teaching in schools these days?
| ccppurcell wrote:
| Should be O(n) vs Omega(n^2)
| curiousgibbon wrote:
| Yep
| TypingOutBugs wrote:
| The author is an early CS bachelors student so... they might
| still learn this in school
| ocean_moist wrote:
| I actually passed my discrete math class and final a few days
| ago and got the big O vs Theta vs Omega question right.
|
| The reality is that companies often _underperform_ their best
| case possible growth rate. O(n) and O(n^2) are meant to
| represent the _best possible growth rate_ which may be
| practically be underperformed.
|
| You may be thinking about algorithmic analysis where the term
| "worst case" is used for the upper bound, but here, the upper
| bound represents the _best case_. Sort of counter-intuitive but
| the underlying mathematical notation is properly defined.
| curiousgibbon wrote:
| It's entirely nonsensical to use O as a lower bound though.
| You could have two companies no growth whatsoever in value
| and correctly state that one has O(n) growth rate and the
| other has O(n^2) because a constant is both O(n) and O(n^2)
| (and O(n!) and O(exp(n^n)) ...). The author is trying to
| argue that there's some separation between two hypothetical
| startups' growth rates and as such an upper bound on one, say
| O(n), and a lower bound on the other, say Omega(n^2), is
| warranted. It sounds like you're not entirely an expert
| despite your recently-passed final. Strange concept, eh?
| ocean_moist wrote:
| I am actually the author.
|
| You're right that mathematically, a function with constant
| (or no) growth _is_ O(n)and also O(n^2), and
| O(anything_that_grows_faster).
|
| My use of "O(n) startup" and "O(n^2) startup" is intended
| to classify the _type_ of business based on its *inherent
| best-case growth potential or ceiling*.
|
| An O(n) startup in my framework is one whose fundamental
| business model, market, or structure means its growth,
| _even in its best-case scenario_ , is capped at roughly
| linear. It _cannot_ achieve sustained super-linear growth;
| its upper bound _is_ linear.
|
| An O(n^2) startup is one whose model (e.g., strong network
| effects) has the _potential_ for super-linear (which I 've
| simplified to n^2) growth as its best-case scenario. It
| might be underperforming (even flat, and thus also
| technically O(n) in that moment), but its _design_ allows
| for a fundamentally different, higher growth ceiling. The
| whole point is illustrate potential withholding
| implications or conclusions from its current growth rate,
| which is necessary at a companies inception.
|
| So, yes, a flat-lining "O(n^2) type" startup would
| currently show growth that is O(c) (and thus also O(n)).
| But the point of my labels is to say that an "O(n) type"
| startup, by its very nature, _cannot_ achieve the n^2 best-
| case that the other type _can_ , even if both are
| struggling.
|
| The labels describe the _class_ they have, dictating their
| _asymptotic best-case limit_ , not just any loose upper
| bound on current, possibly sub-optimal, performance. The
| separation I'm arguing for is based on that fundamental
| difference in their _potential trajectory's ceiling_.
|
| If I used Omega this would imply _the actual_ growth rate
| of the startup would have to strictly be better n or n^2.
| neilv wrote:
| > _O(n) companies can 't afford to hire the absolute best talent.
| [...]_
|
| > _O(n^2) companies hire high agency people. [...] People are
| generally given a lot of equity to join and as a reward._
|
| O(n^2) is often a matter of ZIRP-VC-powered artificial-growth
| (e.g., their example of Uber). That also includes hiring a large
| _quantity_ of people.
|
| For factors in _genuine_ O(n^2) growth, you might be onto
| something: with structuring culture to leverage employee agency,
| and for using meaningful equity to help align employees with
| business success.
| danjl wrote:
| Or, O(n^2) companies are founded and run by people motivated
| solely by making lots of money, while O(n) companies are there
| for multiple reasons: passion for the topic, making employee's
| lives better, and helping their customers solve problems.
| Though those motivations exist at O(n^2) companies too, they
| are in the shadow of making money. While, at O(n) companies,
| money is also essential, but it exists to support the other
| goals. A slight shift in priorities can make all the
| difference.
| avereveard wrote:
| Counter hypothesis: fast but linearly growing early stage startup
| acquire good early funding and enter into a growth loop dominated
| by the ability to invest these fund in marketing, which increase
| valuation and allow for further funding, fueling more growth etc.
|
| After all cost of customer acquisition is largely dominated by
| external factors and cost per user mostly linear until close to
| market saturation.
|
| Now there might be economy of scale intervening at some point
| increasing the margin per user, which feed back into growth, but
| on average fast growing startup are cash negative until much
| later in life.
|
| TLDR I think the implications in the article is inverting cause
| and effect
| kristopolous wrote:
| When I saw the headline I thought it was referring to complexity,
| legacy and stagnation of tech debt
| vessenes wrote:
| To quote a private equity investor friend: "I've known startup
| CEOs of billion-dollar companies that are flat broke. Meanwhile
| people with $50mm/ARR dating sites from Europe live like kings."
|
| A good reminder that it's worth deeply understanding venture
| portfolio economics before you get on the ride. Not that it's a
| bad ride. But it's a ride.
| xyst wrote:
| What ever happened to providing a good service? Why does
| everything have to be a "unicorn"? You greedy capitalists, and
| billionaires have sucked out the life of everything -- tech,
| food, airlines.
|
| VC culture, private equity, and "hyper-growth" mentality has
| screwed over many good companies that once provided good services
| to the community. Good paying jobs with excellent benefits and
| providing upward mobility.
|
| Now the labor benefits are shrinking, company loyalty is gone,
| customers screwed over, labor exploited with minimal in return,
| rising cost of living, increasing wage disparity, abuse of
| powers.
|
| One can argue the neoclassical/neoliberal economic theory and
| "Reagan-omics" that birthed PE/VC culture gave power to the
| idiocracy we see today.
| Snawoot wrote:
| > What ever happened to providing a good service?
|
| I'm getting an impression it's just not profitable enough. For
| many years I get a feeling that business is considered sound
| only if it is superprofitable (not exactly the right term, but
| still) in order to cover all losses.
|
| Probably it's because of market competition required to be at
| least noticed. Some companies' spendings for marketing are
| greater than for R&D, production and operations combined. Maybe
| we got ourselves into a situation where everywhere competing
| for low-hanging fruits or trying to make customer believe it's
| the service they need while all of it doesn't really overlap
| with real society needs.
| ark296 wrote:
| VC-land is a strange place with strange laws. If you stay in it
| for too long, you forget that most of the world doesn't follow
| the power law, and that most of the VC-reasoning just does not
| help.
| dvt wrote:
| > I think many prospective founders, if their goal is money,
| should optimize for O(n) businesses from day 1.
|
| Honestly, I don't think anyone "picks" the kind of business they
| want to run. You just kind of go with the flow. If you raise VC
| money, you follow their lead, if you're running a small bakery,
| you'll do whatever makes sense there.
|
| So while this is a fun intellectual exercise, it's an exercise in
| hindsight. In the moment, you're really just trying to survive
| the day-to-day and not really "optimizing" for a specific growth
| pattern.
| ocean_moist wrote:
| I think any prospecting founder should be able to answer the
| question "will it always take a fixed amount of work to get
| each new customer?".
|
| Generally if you have some sort of idea of what you want to do,
| you'll be more successful at it.
| scarface_74 wrote:
| Most founders - especially vc backed founders - only care
| about whether the optics look good enough for an acquisition
| or an IPO. They could care less if it fails after that.
|
| YC backed companies are no exception
|
| https://medium.com/@kazeemibrahim18/the-post-ipo-
| performance...
| fooker wrote:
| There's an interesting misunderstanding in this article.
|
| The argument for O(n) is well formed here.
|
| O(n^2) is not, the core argument is that these grow faster
| because of compounding. Compounding is fundamentally an
| exponential process, far larger asymptotically than a quadratic.
| nine_k wrote:
| True exponential growth is possible, but I suspect is rare,
| because the expenses can also compound, so a polynomial growth
| may be an acceptable approximation. It's also important to
| remember that "every exponential growth curve is a sigmoid in
| real life" (can't remember the source of the quotation).
| jxjnskkzxxhx wrote:
| > True exponential growth is possible, but I suspect is rare,
| because the expenses can also compound
|
| If you have an exponential (revenues) and another exponential
| of smaller rate (expenses; assume you have profit) then the
| difference is still an exponential.
| robocat wrote:
| The article clearly isn't meant to be mathematically correct:
| you are being over-rigorous in your criticism. From the
| article: Businesses generally grow following a
| few patterns. They generally have some TAM to saturate and
| saturate the TAM at some rate. This rate can be vaguely linear,
| what I call O(n), or vaguely superlinear, what I call O(n2).
| The reason I borrow the asymptotic notation is because it
| implies the growth rate is an upper bound (best case scenario)
| and generalizes away specific constant factors and sums. The
| analogy breaks down when you force n or n2 imply something
| numerically specific about your growth rate, or introduce
| functions with different growth rates like logs or
| exponentials. For now we will (somewhat unprincipledly) stick
| with two sole classes.
|
| Article would be better using something like O(linear) and
| O([?]linear). The big O notation is a useful and memorable
| metaphor, but the n squared is really confusing. The article
| also doesn't use Unicode for the notation - which fucks
| usability (e.g. I used screenshot OCR and reedited).
| ocean_moist wrote:
| Addressed in the footnotes:
|
| > [2] Perhaps choosing a better two functions could more
| closely explain the growth dynamics of network effects, which
| could be more exponential. I think the analogy diminishes in
| value if you try to directly numerically match it to some
| growth metric.
| nine_k wrote:
| > _[The conclusion is that] O(n) companies are higher EV than
| O(n^2) companies. I mean that, on average, a founder will make
| more money pursuing an O(n) company than an O(n^2) company. And
| not an insignificant amount, the amount of liquidity and networth
| a 20m ARR O(n) company is extremely hard to match by a
| traditional VC backed O(n^2) company._
|
| It's a bit like getting a regular job vs playing a lottery: the
| former gives you better financial results on average, while the
| latter gives you a chance to make it really big.
|
| (I also wish it were "linear companies" and "quadratic /
| exponential companies", or maybe "snooker-cue companies" vs
| "hockey-stick companies".)
| gmuslera wrote:
| I wonder about the cultural effects at large getting the O(n2)
| most of the funding. That expansive, aggressive and not always
| safe behavior is promoted, while the slow but solid approach is
| not. That shapes the ecosystem and the people in it, in and out
| those companies.
|
| Maybe it is a good way for short term profits, but that is just
| one metric. That kind of dynamic may be harmful in the long term,
| and in a really big scale.
___________________________________________________________________
(page generated 2025-05-18 23:01 UTC)