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O(n) vs. O(n^2) Startups

rohan.ga

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Re: O(n) vs. O(n^2) Startups

#31

Post by danjl »

ridiculous_leke wrote: post #23

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.

Re: O(n) vs. O(n^2) Startups

#32

Post by avereveard »

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

Re: O(n) vs. O(n^2) Startups

#34

Post by Maxatar »

wavemode wrote: post #21

> 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…

> 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.

Re: O(n) vs. O(n^2) Startups

#35

Post by Maxatar »

danjl wrote: post #26
wavemode wrote: post #21

> 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…

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.

Big-O notation does not have a goal, it's a description not a strategy.

Re: O(n) vs. O(n^2) Startups

#36

Post by danjl »

neilv wrote: post #30

> 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 c…

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.

Re: O(n) vs. O(n^2) Startups

#38

Post by vessenes »

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.

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