Climbing the wrong hill (2009)(cdixon.org)
cdixon.org
Climbing the wrong hill (2009)
http://cdixon.org/2009/09/19/climbing-the-wrong-hill/
11 comments
How can smart, ambitious people stay working in an area where they have no long term ambitions?
The answer, of course, is money.
But Dixon prefers to camp out on algorithms and behavioural science rather than providing the simple answer, and if you read between the lines (and you're familiar with the startup scene, VC, and suppressed developer wages by collusion + H-1B visas + "talent shortage") it's not hard to figure out why.
The answer, of course, is money.
But Dixon prefers to camp out on algorithms and behavioural science rather than providing the simple answer, and if you read between the lines (and you're familiar with the startup scene, VC, and suppressed developer wages by collusion + H-1B visas + "talent shortage") it's not hard to figure out why.
Yes, its not as clear cut because being in finance vs. joining or launching a startup has a very different risk profile.
If you stay in finance for some time, you have probably made a lot of money by the time you get out. The plan is to work yourself to death as an investment banker and hope you made enough to retire (or become a VC, like him) at 35.
Work years for a startup, and you might not have made anything beyond living expenses. The upside is that the working culture will probably suck less and winning the startup lottery will make even more money. Also, scamming people and being a sociopath is optional (but see Uber).
If you stay in finance for some time, you have probably made a lot of money by the time you get out. The plan is to work yourself to death as an investment banker and hope you made enough to retire (or become a VC, like him) at 35.
Work years for a startup, and you might not have made anything beyond living expenses. The upside is that the working culture will probably suck less and winning the startup lottery will make even more money. Also, scamming people and being a sociopath is optional (but see Uber).
I wonder if the author also includes bootstrapped businesses when he says "start a startup". It's an option far less talked about, but far easier to succeed at and rather satisfying for those that do.
It's also a case where I think starting young has significant advantages. Life gets pretty good once you have bootstrapped revenues and a business somewhat on autopilot. Especially for the young, as they're relatively free and can enjoy location and time freedom more fully.
It's also a case where I think starting young has significant advantages. Life gets pretty good once you have bootstrapped revenues and a business somewhat on autopilot. Especially for the young, as they're relatively free and can enjoy location and time freedom more fully.
> Over the years, I’ve run into many prospective employees in similar situations. When I ask them a very obvious question: “What do you want to be doing in 10 years?” The answer is invariably “working at or founding a tech startup”...
It's great to ask what questions, but they should almost always be followed by why questions. Why do you want to work at or found a tech startup?
Startups have been glamorized by the media for well over a decade, and the hype today is at a fever pitch, so it's really no surprise that startups are so appealing. That doesn't mean that most people have a realistic understanding of what it's like working at or starting one.
Frankly, "I want to do a tech startup" is as much a red flag as "I want to make a lot of money." It's so non-specific as to be effectively meaningless. A startup is just a vehicle, and if you don't have an interest in driving somewhere specific, you're driving for the sake of driving.
> There is a natural human tendency to make the next step an upward one.
There's also a natural human tendency to believe that the grass is always greener somewhere else.
The prototypical Silicon Valley startup can be as demanding as an investment bank, and a good number are chaotic and dysfunctional to boot. And you don't have folks like Sam Altman talking about "founder depression" because running a startup is the next best thing to heaven.
It's great to ask what questions, but they should almost always be followed by why questions. Why do you want to work at or found a tech startup?
Startups have been glamorized by the media for well over a decade, and the hype today is at a fever pitch, so it's really no surprise that startups are so appealing. That doesn't mean that most people have a realistic understanding of what it's like working at or starting one.
Frankly, "I want to do a tech startup" is as much a red flag as "I want to make a lot of money." It's so non-specific as to be effectively meaningless. A startup is just a vehicle, and if you don't have an interest in driving somewhere specific, you're driving for the sake of driving.
> There is a natural human tendency to make the next step an upward one.
There's also a natural human tendency to believe that the grass is always greener somewhere else.
The prototypical Silicon Valley startup can be as demanding as an investment bank, and a good number are chaotic and dysfunctional to boot. And you don't have folks like Sam Altman talking about "founder depression" because running a startup is the next best thing to heaven.
There's a good reason near-term rewards are overvalued relative to long-term ones. Long term rewards might never happen.
This is not a good analogy. Money is the obvious reason one stays at the "lower hill". Thus it is not a question of whether the person can stoop down from the "lower hill" and get to the "higher" one. It's a question of which hill is really higher (by that person's standards).
That is difficult to have the resources banked sufficient to leap off the little hill and find yourself in the desert of despair, to then randomly leap back to the base of what turns out to be the big mountain, but not have the clout to climb it. I appreciate the comparison to an optimization search algorithm, but life is really complicated for most to have the stomach for that level of risk.
There is an important difference between the algorithm and personal career: climbing the other hill has nothing to do with climbing the highest hill, but that's not the case regarding career development.
Startups are creating new things and jobs, so how do you know that current jobs would not provide valuable assets in the future?
Speaking from experience this process does have demerits. For example - how long before you decide that the hill you just climbed is not the highest. It's easier to figure out that a banking career is not for you but it's not that easy to figure out that a startup job is not for you.
It makes me wonder - is there a good hill-climbing algorithm that takes account of uncertainty - where the hills can go up and down over time as you're climbing them? I suspect the naive algo might perform better in those conditions - maxima that are closer to you would be favored.
Simulated annealing was already mentioned. Also parallel tempering is very effective at finding the highest (or lowest) areas even faster.
I actually liked the analogy but applying algorithm analogies to one's life of course only goes so far. First, even if one decides to look at life as an optimisation problem, it is much more difficult than the hardest multiple objective optimisation problems. One of the reasons is that I'd equate the optimisation landscape not with static hills, but more similar to waves or sand dunes that keep shifting with time in a way that can at most give you short term predictions. We don't have the life span or processing power to use a computer algorithm to optimise a static "life" landscape, let alone a dynamic one.
I actually liked the analogy but applying algorithm analogies to one's life of course only goes so far. First, even if one decides to look at life as an optimisation problem, it is much more difficult than the hardest multiple objective optimisation problems. One of the reasons is that I'd equate the optimisation landscape not with static hills, but more similar to waves or sand dunes that keep shifting with time in a way that can at most give you short term predictions. We don't have the life span or processing power to use a computer algorithm to optimise a static "life" landscape, let alone a dynamic one.
Don't forget to count the number of steps! Reaching the top of a small hill might take 2 steps, and algorithms like simulated annealing (unlike actual annealing) keep track of the entropy change.
there is probably a change of hills with the time.
> Hills are dynamic
> Hills are interconnected
> Hills climber is not constant over time
tldr: Premature optimization is the root of all evil
That way the financial system in the form of VCs can extract maximum benefit for minimum expenditure.