Because a sale for cash is a basic legal contract that predates modern society by millenia, whereas a LBO that PE uses to purchase companies is a weak spot in American Capitalism created at the intersection of:
1.Shareholder primacy. Under Delaware corporate law (which governs most large U.S. public companies), once a board decides to sell, directors have a fiduciary duty to maximize the price shareholders receive. A premium cash offer from a PE firm is hard to refuse without legal exposure.
2.Interest deductibility. The tax code lets companies deduct interest payments but not dividends, which makes debt-heavy capital structures more tax-efficient. LBOs exploit a feature of tax law that exists for many reasons unrelated to private equity.
3.Freedom of contract and limited liability. Sponsors can put a thin equity check into a holding company, have that company borrow on the target's assets, and walk away if it fails, because limited liability is the foundation of corporate law generally.
It is not analogous because if you sell your house and the sale money is not enough to cover your mortgage you are still on the hook for what's left of the principal.
A leveraged buyout is exclusively on the purchased company's books, so if the company goes to zero the PE parent company is not on the hook for a single penny.
You should interview for the skill that is required in the age of AI-assisted coding.
That is, looking at code that's been written by AI and see what's wrong, what's superfluous and what's missing.
For preparing the code for the interview, I would suggest prompting Claude Code using a requirements document that's purposefully a bit vague so that the AI will have to make choices when writing that code.
When you have the interviewee come over, show them the code and have them criticize those choices and edit the code manually (I know) so that they can demonstrate that they can intervene in the AI process at the correct inflection points.
Was the data scientist role only about building NLP models?
Are the LLms gonna build Churn prediction models?
Tell the PM why stopping the A/B test halfway through is a bad idea?
Push back on loony ideas of applying ML to predicting sales from user horoscopes?
Maybe the role is a bit tinier in scope than 10 years ago, but I see it as a good thing. If you looked at DS positions on job search sites the role descriptions would be all over the place, maybe now at least we'll see it consolidate.
The reason why people form two lines to board the Marunouchi line in Ikebukuro is not because they prioritize seating, or going as soon as possible.
The true reason is that the line forks into two lines later on, with the final station being either Honancho or Ogikubo. The second line that form is just for the people whose destination would not be served by the incoming train.
So a reporter used an AI detection tool that returned positive from the user provided Uber Eats internal document and that proves it’s fake?
Is this article entirely bought and paid for by Uber?