Practicality aside, this would be the end of "progress". No new discovery, no new exploration, no deeper understanding of how we got here, where we are or why we are here. The problem with 'bliss on tap' is assumption that there would be no problems left to solve... the mortgaging of our future potential for our present satisfaction.
The OP is stating a truism that I agree with (true with all truisms). Google is your default search engine for any query where (you believe) Google offers the highest probability of delivering the intended content/ answer.
So any product, in any vertical, that can respond to a query with greater utility than google - and condition it's userbase that that is the case - has the potential to supplant google for those queries. Great example is the drift of search traffic for flight travel on google to direct traffic to sites like kayak and expedia. (another example is the shift of people search).
The challenge of competing against kayak-like companies that offer great products in specific verticals, will only grow. So if you believe search traffic will not naturally decline over time you are effectively arguing that google will be able to outperform the pace at which new products will be built that have kayak (flight search) / linkedin (people search) / coverhound (auto insurance search) - like potential.
Travel search is one vertical in which Google has a serious interest. In 2011, they acquired ITA Software,† which developed QPX, a widely used, Lisp-based airfare search and pricing system. They have since rolled out Google Flights, which competes rather directly with Kayak and Expedia.
Those are profits in absolute terms. I suspect the author is arguing that relative to the size of the company, wage ratios are important. I would argue he is mistaking consequence for cause. Companies with egalitarian wage ratios can afford (read 'need') equitable wage ratios precisely because of the nature of their business (think software firms with barriers to entry, high margins that are dense with human capital vs a company) vs (a chemicals company, that produces a fungible good and can only compete on price - business is characterized by low margins and a greater proportion of unskilled labor - ie more wage equality would put you out of business)
Author states that Silicon Valley corporate structures are more egalitarian when measured against traditional firms. Author attributes this to a more progressive, 'stakeholder' culture in SV.
Question: human capital is disproportionately more important to building value in the tech sector than it is in the broader economy (think mines, manufacturing etc). Isn't it more likely that a more equitable equity split is a pre-requisite for success in SV as opposed to being a consequence of it?
I believe the stakeholder culture is because tech companies often have the potential for rapid growth. For example, a barbershop's capital is mostly human, but there isn't a big chance it will IPO. This means the best barbers don't have much incentive to choose their barbershop based on equity in the compensation package.
It's not quite apples to apples. Bankers get paid way more (so they sell off less of their stock) and they are also often paid in RSUs or other forms of stock comp that can't be sold for several years. Tech workers are generally paid with stock that vests each year, and if they're smart they'll sell that down immediately (as they still generally have lots of future stock they're still exposed on, from a portfolio perspective.)
With a deep understanding of markets and trading I fail to see why you see 'luck' as an explanatory variable is inversely correlated with the frequency of your trades (notwithstanding the effect of trading expenses)?
From what I have gleaned the following seems to be true:
1. Your algorithms worked (made money)
2. Then your algorithms did not work, but you could not figure out why
If you do not know why something stopped working it seems unlikely that you had a full understanding of why it was working in the first place. Without understanding the nature of the predictive value of the algorithm while it was working, its success seems to be good fortune.
Your algorithm could have shown a systematic correlation to any number of factors that could have created strong performance over several months. Performance would then be attributed to accidentally 'timing' a favorable market.
Very important to understand that making $500k speculatively is not evidence of an 'edge', nor is trading frequency evidence of the absence of luck. From March 2009 through much of 2010, the market was strongly bullish - if his algorithm showed a positive market bias then his returns would primarily be a function of timing (read luck: and there are a million variants on the nature of the bias that could be unwittingly responsible for his returns, despite the frequency of trades).
We cannot even tell if $500k is a good risk adjusted return - we have no information on volatility, nature of the exposure or most importantly how much money he started with?
Not exactly shocked Jim Simons didn't return his email. But completely shocking that he walked away from a successful automated trading strategy... the only thing rarer than a free lunch is a man willing to walk away from one. suspect.
1. I cannot get even a remote sense for the nature of his risk exposure from looking at his daily returns.
3. The point here is that a systematic bias in his algorithm will expose his trading strategy to the good graces of market fortune (luck) regardless of whether he trades a million, billion or once a day. The source of the bias is irrelevant.
4. did not see where he said that but that very much confirms 'timing' / which in this case I interpret as luck as being at least a contributing factor.
Ian, thanks for the feedback. We do a lot of user testing and the problems you are experiencing are new to us. We pay $30 for a user test session, can you email me at info at smartasset dot com, so we can get something lined up. Thank you.
On the change in your income. When you buy a property there is an opportunity cost associated with the cash that you use to purchase the property (down payment + closing costs). This money would have accrued interest income had you continued to rent, hence the larger value for income in the rent scenario.
We should have thought of this. We will get this incorporated, until then: without funding you simply need to multiply the exit value by your starting % ownership less dilution from employees (good approximation).
on the back-end we are:
> ensuring our users qualify for mortgages based on the zipcode and financial profile
> further seeing if they qualify for any government programs
> running their taxes on a federal, state and local level to understand if there are any tax benefits to home ownership (almost always overestimated for lower incomes and underestimated for higher incomes)
> drawing on local real estate tax data
> drawing on local transactional expenses (including any transactional taxes you need to pay, like mortgage recording tax in nyc)
> the above, including your financial inputs are then 'modeled'. We actually build several different models for you, and through the comparison of those models we can give advice on a level of accuracy that could not previously be approached.
The result is utility, accuracy and ease of use that we believe is dramatic on improvement on financial calculators. And we are really just scratching the surface on what the modeling engine can do for many different decisions.