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Can you explain the "float" observations and argument?


Sure, short answer is supply and demand.

Longer answer is that by only allowing a small float to trade, you have artificially limited the number of shares available(supply). All other things being equal, a small float leads to a higher share price as people will have to pay more to get shares if they want them.

Now, this will come to a head as eventually large holders and employees will be allowed to trade their shares, thus increasing the supply of shares.

Also given Lyft has no assets to use as collateral against a debt issuance and I'll work with the assumption that they'll need to raise money again given the losses Lyft will probably issue new shares again in the open market(secondary offering) within 3 years(this is my opinion) further increasing hte supply of shares.

This will obviously increase the supply of shares available and, as micro economics teaches us, lower the price per share.

Now you'll start to think something about efficient markets and shouldn't this be priced in. And the answer is yes, kindof.

But the truth is that looking at historical chats you can often identify the times when share came off restriction by the dip in the chart leading up to those days.

Think of it like the bitcoin price, most people aren't factoring in coins that we assume are lost or ones that belong to satoshi from his/her initial mining. If all of those btc hit the market, even in a slow and orderly way, the price of BTC, all other things being equal, would go down.


I'll be really surprised if they go 3 years before another offer.


Engineering: - Infrastructure (500M MAU) - Web Dev - iOS Dev - Android Dev - Design - UX - Localization

Data: - Ranking timelines - Ad performance - Recommendations - Business analytics - R&D

Product: - Coordinating across the above 2 and adding/measuring (new) features

Sales: - Ads

Finance/HR/Admin/Accounting/Office Manager(s)/Recruiting: - Running a company


What? This is a really interesting technical design post outlining how they use technology to solve real problems at scale.


[flagged]


That's important. But it does not stand in opposition to this.


Or - or, and just hear me out, there are occasional real world complications that cause unforeseen errors and people only talk about the tail end of poor predictions rather than the 95+% good experiences.


That seems unlikely, everybody in my area (that I speak to) loathes these delivery services because they consistently screw these things up. It’s not 1 in a 100, it’s more like 9 out of 10..


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