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).
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?
I think it is clear that O(n) vs O(n^2) is really just an analogy so we shouldn’t over-formalize it. But it is interesting to note that a sigmoid could be thought of as looking like either one, depending on when you look at it.
It makes me wonder if there’s some sampling bias that is accidentally being applied. Because, another way of looking at it could be: assume I observe companies only after they hit a particular size. The companies that are still in the exponential growth phase when I notice them; due to the shape of a sigmoid, those are likely heading much a higher carrying capacity. Those that have hit the linear phase before they are large enough for me to see them are not on as good a trajectory.
Sure sampling bias is probably a factor. But I'm sure it's also a factor in nearly every one of these discussions that isn't a serious and meticulous study.
For wondering, I believe this might help. If we parametrize sigmoid to be 1/(1+e^{-ax}) then our a factor is at play. We're interested in x<=0. When a < 1 then the function becomes flat and much more linear like. The larger a becomes the more quadratic it looks at near the origin (but still x<=0)
I still think the model is naive. Even just a sigmoid is. You need the composition of them and further parameterization. They are good for approximating as you originally suggested but this is similar to using fourier series. It's the fact that you have the three classes of second derivatives allowing you to strongly control the composite function through minimal parameterization and complexity. But that's also why I immediately understood this to be being using in an illustrative way rather than a serious one. I mean realistically so are you.
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.
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.
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.
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.
> 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.
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.
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.
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.
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.
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.
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.
> 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.
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.
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.
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.
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.
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).
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.
I can say that for me, at some point, you just really want a team with good vibes and low BS.
I took a 20% pay cut 3 years back and moved from one startup to another because the vibes were better with an older founding team. It was fantastic and we built some amazing stuff at an incredible pace.
That startup recently folded and the original startup reached out and brought me back at 150% comp of the other startup. But day to day I don't enjoy it as much and the vibes just aren't the same with this crew.
My lesson: team vibes are severely underrated and sometimes even more money doesn't make it better.
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.
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.
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?
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.
I see. But now you're in a weird state where you're saying (something a bit like) "at most n^2" to mean "not at most n". Which isn't particularly precise.
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.
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).
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).
> 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.
> [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.
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+ε)), an upper bound characterised by some arbitrarily superlinear function.
> 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+ε)) is arbitrarily superlinear (O(n^1) = O(n), and ε 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
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).
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
> 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.
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.
You learn about it in real analysis, but it's worth noting that in analysis you pretty much always use little-O, which is the one that makes the guarantee you need in that context.
> 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.
> 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.
>> 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 ϴ(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.
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.
> [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".)
> 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.
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.
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.
> 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.
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.
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.
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.
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.
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.)
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.
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.
Unless you want working plumbing and leaky roofs taken care of.
A hungry landlord is a landlord who is in dire financial straights.
Good landlords can afford to wait because they are using real-estate to park wealth, not as a means of putting food on the table. Think insurance company reserves and House of Windsor. It is a long play.
Uber exploits drivers (car cost, maintenance, and depreciation) for illusionary freedom as a substitute for taxis. Not really groundbreaking except screwing people.
Absolutely. The majority of gig economy and sharing economy startups are grifts cheating someone. I'd happy to be wrong if there were some legit startups making sellers/contractors much richer without exploiting any particular stakeholders unfairly.
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.
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
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.
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.
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).
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