81–90 of 92 posts
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.
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>> 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…
> 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.…
> It's definitely not.
> When they say O(n^2) startup they clearly mean a startup which achieves n^2 results in n time.
How did you manage to write this down without noticing what you were saying?
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…
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.
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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)…
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 gro…
Uber exploits drivers (car cost, maintenance, and depreciation) for illusionary freedom as a substitute for taxis. Not really groundbreaking except screwing people.
I’m assuming there are similar problems with Lyft.
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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.
Only in algorithmic analysis. Big-O generally is used to describe and classify any arbitrary function.
Uber exploits drivers (car cost, maintenance, and depreciation) for illusionary freedom as a substitute for taxis. Not really groundbreaking except screwing people.
Why doesn’t there exist another Uber like company that doesn’t do this? I’m assuming there are similar problems with Lyft.
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…
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 yo…
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.
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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 bankr…
You need to look for hungrier landlords.
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.