‘All the police are going to get me for is running a funeral parlor without a license.’ — John Wayne Gacy
$350,000 […] connected to the home is a 2500 sq ft legitimate jail with 9 cells, booking room and 1/2 bath.
economics
If you’re not inside, you’re outside, okay?
Mergers-and-acquisitions bankers make lists of companies that should do mergers or acquisitions, equity capital markets bankers make lists of companies that should issue stock, debt capital markets bankers make lists of companies that should issue bonds, etc. Once you have made the lists you get on planes (in normal times) and meet with the companies on the lists to explain to them why they should do mergers or issue stock or whatever. Occasionally they say “hmm you are right we should do a merger” and hire you to do the merger; then you will spend some time actually doing the merger, and you’ll get paid lots of money. But the top of the funnel consists of making lists.
You have to call them and say “hi it would help a lot with your company if you would do a merger.” For that, you need some finance. You need to say “you have a division that is underperforming and if you sold it the rest of your company would be better, so let me sell it for you and take a commission.” Or “there’s this company out there whose CEO wants to retire so you could buy it cheap and it would integrate really well with your widgets business and be accretive to earnings.” Or whatever. The list-making exercise requires some financial analysis. Not a whole ton: This is the top of the funnel, and you do not necessarily need a deep and nuanced understanding of all aspects of the company’s business and competitive landscape in order to come up with some acquisitions and divestitures it could do, though that does help. But, some financial analysis.
This can be creative interesting work, or it can be kind of sterile tedious work; in any case it tends to be unrewarding work, in the sense that if you come up with 100 possible deals and end up executing one of them that’s a pretty good hit rate. A lot of targeting begins with junior bankers making spreadsheets of companies that might be plausible targets based on some crude financial criteria; the senior bankers who have actually met with the companies whittle the spreadsheet down to the realistic targets, and then try to set up meetings with those companies to pitch ideas that still have a low probability of leading to a deal.
What if you could outsource all that work to the companies themselves? What if you built a targeting app that identifies plausible deals based on some crude financial criteria, then sent it to all the companies and said “hey maybe you should do M&A, this app will tell you, if it does then definitely give us a call.” Goldman Sachs Group Inc. has an app now.
art { Christopher Wool, Untitled, 1992 | Christopher Wool, Hole in Your Fuckin Head, 1992 }
Business as usual. In. Out. Hello. Goodbye.
Profits from organized crime are typically passed through legitimate businesses, often exchanging hands several times and crossing borders, until there is no clear trail back to its source—a process known as money laundering.
But with many businesses closed, or seeing smaller revenue streams than usual, hiding money in plain sight by mimicking everyday financial activity became harder. “The money is still coming in but there’s nowhere to put it,” says Isabella Chase, who works on financial crime at RUSI, a UK-based defense and security think tank.
The pandemic has forced criminal gangs to come up with new ways to move money around. In turn, this has upped the stakes for anti-money laundering (AML) teams tasked with detecting suspicious financial transactions and following them back to their source. […]
According to the United Nations Office on Drugs and Crime, between 2% and 5% of global GDP—between $800 billion and $2 trillion at current figures—is laundered every year. Most goes undetected. Estimates suggest that only around 1% of profits earned by criminals is seized. […] The problem for criminals is that many of the best businesses for laundering money were also those hit hardest by the pandemic. Small shops, restaurants, bars, and clubs are favored because they are cash-heavy, which makes it easier to mix up ill-gotten gains with legal income. […]
Older systems rely on hand-crafted rules, such as that transactions over a certain amount should raise an alert. But these rules lead to many false flags and real criminal transactions get lost in the noise. More recently, machine-learning based approaches try to identify patterns of normal activity and raise flags only when outliers are detected. These are then assessed by humans, who reject or approve the alert.
This feedback can be used to tweak the AI model so that it adjusts itself over time. Some firms, including Featurespace, a firm based in the US and UK that uses machine learning to detect suspicious financial activity, and Napier, another firm that builds machine learning tools for AML, are developing hybrid approaches in which correct alerts generated by an AI can be turned into new rules that shape the overall model.
Originally created from chickens too old to lay eggs, McNuggets are now created from chickens with unusually large breasts. They are stripped to the bone, and ground up into a sort of chicken mash, which is then combined with all sorts of additives and preservatives, pressed into familiar shapes, breaded and deep-fryed, freeze-dried, and then shipped to a McDonald’s near you.
{ In many states, especially in the South and Midwest, traffic at fast-food restaurants is now higher than it was before the restrictions | Washington Post | Continue reading }
C’est la vie, say the old folks
{ While the Great Recession in 2007–2009 reduced wealth in all age groups, the broader long-term trend has been that the wealth of older age groups (65-75+) has increased, while the wealth of successive cross-sections of younger age groups (25-54) has fallen. | Brookings Economic Studies | PDF }
All the leaves are brown, and the sky is grey
In Germany and China, they already reopened all the stores a month ago. You look at any survey, the restaurants are totally empty. Almost nobody’s buying anything. Everybody’s worried and cautious. And this is in Germany, where unemployment is up by only one percent. Forty percent of Americans have less than $400 in liquid cash saved for an emergency. You think they are going to spend? You’re going to start having food riots soon enough. Look at the luxury stores in New York. They’ve either boarded them up or emptied their shelves, because they’re worried people are going to steal the Chanel bags. The few stores that are open, like my Whole Foods, have security guards both inside and outside. We are one step away from food riots. There are lines three miles long at food banks. That’s what’s happening in America. You’re telling me everything’s going to become normal in three months? That’s lunacy. […]
They just decided Huawei isn’t going to have any access to U.S. semiconductors and technology. We’re imposing total restrictions on the transfer of technology from the U.S. to China and China to the U.S. And if the United States argues that 5G or Huawei is a backdoor to the Chinese government, the tech war will become a trade war. Because tomorrow, every piece of consumer electronics, even your lowly coffee machine or microwave or toaster, is going to have a 5G chip. That’s what the internet of things is about. If the Chinese can listen to you through your smartphone, they can listen to you through your toaster. Once we declare that 5G is going to allow China to listen to our communication, we will also have to ban all household electronics made in China. So, the decoupling is happening. We’re going to have a “splinternet.” It’s only a matter of how much and how fast. […]
I was recently in South Korea. I met the head of Hyundai, the third-largest automaker in the world. He told me that tomorrow, they could convert their factories to run with all robots and no workers. Why don’t they do it? Because they have unions that are powerful. In Korea, you cannot fire these workers, they have lifetime employment. But suppose you take production from a labor-intensive factory in China — in any industry — and move it into a brand-new factory in the United States. You don’t have any legacy workers, any entrenched union. You are going to design that factory to use as few workers as you can. […] But you’re not going to get many jobs. The factory of the future is going to be one person manning 1,000 robots and a second person cleaning the floor. And eventually the guy cleaning the floor is going to be replaced by a Roomba because a Roomba doesn’t ask for benefits or bathroom breaks or get sick and can work 24-7. […]
There’s a conflict between workers and capital. For a decade, workers have been screwed. Now, they’re going to be screwed more. […]
Millions of these small businesses are going to go bankrupt. Half of the restaurants in New York are never going to reopen. How can they survive? They have such tiny margins. Who’s going to survive? The big chains. Retailers. Fast food. The small businesses are going to disappear in the post-coronavirus economy. So there is a fundamental conflict between Wall Street (big banks and big firms) and Main Street (workers and small businesses). And Wall Street is going to win.
photo { Susan Meiselas, Soldiers search bus passengers along the Northern Highway, El Salvador, 1980 }
Rock, get up, get down, miuzi weighs a ton
Enter Spotify, a platform that is definitely not the answer. In fact, it only exacerbates such conundrums. Yet for now it has manipulated the vast majority of music industry “players” into regarding it as a saving grace. As the world’s largest streaming music company, its network of paying subscribers has risen sharply in recent years, from five million paid subscribers in 2012 to more than sixty million in 2017. Indeed, the platform has now convinced a critical mass that paying $9.99 per month for access to thirty million songs is a solid, even virtuous idea. Every song in the world for less than your shitty airport meal. What could go wrong? […]
Indeed, Spotify’s obsession with mood and activity-based playlists has contributed to all music becoming more like Muzak, a brand that created, programmed, and licensed songs for retail stores throughout the twentieth century. In the 1930s, the company prioritized workplace soundtracks that were meant to heighten productivity, using research to evaluate what listeners responded to most. […]
Spotify playlists work to attract brands and advertisers of all types to the platform. […] We should call this what it is: the automation of selling out. Only it subtracts the part where artists get paid.
{ The Baffler (2017) | Continue reading | Thanks Tim }
I want to grow my own food but I can’t find bacon seeds
ExxonMobil, Shell, and Saudi Aramco are ramping up output of plastic—which is made from oil and gas, and their byproducts—to hedge against the possibility that a serious global response to climate change might reduce demand for their fuels, analysts say. Petrochemicals, the category that includes plastic, now account for 14 percent of oil use and are expected to drive half of oil demand growth between now and 2050, the International Energy Agency (IEA) says. The World Economic Forum predicts plastic production will double in the next 20 years.
{ Wired | Continue reading | Thanks Tim }
previously { The missing 99%: why can’t we find the vast majority of ocean plastic? }
photo { Kate Ballis }
Is there ever a day that mattresses are not on sale?
The prospect of data-driven ads, linked to expressed preferences by identifiable people, proved in this past decade to be irresistible. From 2010 through 2019, revenue for Facebook has gone from just under $2 billion to $66.5 billion per year, almost all from advertising. Google’s revenue rose from just under $25 billion in 2010 to just over $155 billion in 2019. Neither company’s growth seems in danger of abating.
The damage to a healthy public sphere has been devastating. All that ad money now going to Facebook and Google once found its way to, say, Conde Nast, News Corporation, the Sydney Morning Herald, NBC, the Washington Post, El País, or the Buffalo Evening News. In 2019, more ad revenue flowed to targeted digital ads in the U.S. than radio, television, cable, magazine, and newspaper ads combined for the first time. It won’t be the last time. Not coincidentally, journalists are losing their jobs at a rate not seen since the Great Recession.
Meanwhile, there is growing concern that this sort of precise ad targeting might not work as well as advertisers have assumed. Right now my Facebook page has ads for some products I would not possibly ever desire.
{ Slate | Continue reading | Thanks Tim }
Three metamorphoses of the spirit do I designate to you: how the spirit becometh a camel, the camel a lion, and the lion at last a child.
Some examples of people quickly accomplishing ambitious things together.
Dee Hock was given 90 days to launch the BankAmericard card (which became the Visa card), starting from scratch. He did. In that period, he signed up more than 100,000 customers.
[…]
On August 9 1968, NASA decided that Apollo 8 should go to the moon. It launched on December 21 1968, 134 days later.
[…]
The Empire State Building. Construction was started and finished in 410 days.
Three quarks for Muster Mark!
In 2016, London-based DeepMind Technologies, a subsidiary of Alphabet (which is also the parent company of Google), startled industry watchers when it reported that the application of artificial intelligence had reduced the cooling bill at a Google data center by a whopping 40 percent. What’s more, we learned that year, DeepMind was starting to work with the National Grid in the United Kingdom to save energy throughout the country using deep learning to optimize the flow of electricity.
Could AI really slash energy usage so profoundly? In the three years that have passed, I’ve searched for articles on the application of AI to other data centers but find no evidence of important gains. What’s more, DeepMind’s talks with the National Grid about energy have broken down. And the financial results for DeepMind certainly don’t suggest that customers are lining up for its services: For 2018, the company reported losses of US $571 million on revenues of $125 million, up from losses of $366 million in 2017. Last April, The Economist characterized DeepMind’s 2016 announcement as a publicity stunt, quoting one inside source as saying, “[DeepMind just wants] to have some PR so they can claim some value added within Alphabet.” […]
Many of McKinsey’s estimates were made by extrapolating from claims made by various startups. For instance, its prediction of a 10 percent improvement in energy efficiency in the U.K. and elsewhere was based on the purported success of DeepMind and also of Nest Labs, which became part of Google’s hardware division in 2018. In 2017, Nest, which makes a smart thermostat and other intelligent products for the home, lost $621 million on revenues of $726 million. That fact doesn’t mesh with the notion that Nest and similar companies are contributing, or are poised to contribute, hugely to the world economy.
‘But as the power of Hellas grew, and the acquisition of wealth became more an objective, the revenues of the states increasing, tyrannies were established almost everywhere.’ –Thucydides
“Financial machine learning creates a number of challenges for the 6.14 million people employed in the finance and insurance industry, many of whom will lose their jobs — not necessarily because they are replaced by machines, but because they are not trained to work alongside algorithms,” said Marcos Lopez de Prado, a Cornell University professor. […]
Nasdaq runs more than 40 different algorithms, using about 35,000 parameters, to look for market abuse and manipulation in real time.
photo { Matthew Reamer }
We are advised the waxy is at the present in the Sweeps hospital and that he may never come out!
Ethos Capital, a new commercial investment firm founded in the past few months in Boston, has 2 staff and only one major investment: a deal to acquire the 501c3 non-profit [Public Interest Registry] that currently runs the .org domain (valued at a few $B), for an undisclosed sum.
This was initiated immediately after ICANN decided in May, over almost universal opposition, to remove the price cap on .org registrations with no meaningful price protections for existing or future registrants.
{ The Longest Now | Continue reading }
Internet Society (ISOC) has sold the .org registry Public Interest Registry (PIR) to private equity company Ethos Capital. […] PIR generated $101 million in revenue in 2018 and contributed nearly $50 million to Internet Society. […]
Ethos Capital is a new private equity firm lead by Erik Brooks. Brooks was at Abry Partners until earlier this year. Abry Partners acquired Donuts and installed former ICANN President of Global Domains Akram Atallah in the top spot there. […] The other person at Ethos is former ICANN Senior Vice President Abusitta-Ouri.
I wish I was little bit taller I wish I was a baller
A Japanese hotel offers a room that costs only $1 per night, but there’s a catch — the guest’s entire stay is livestreamed on YouTube.
Are we someone else when we lie?
[Google CEO] Eric Schmidt continued: “Our business is highly measurable. We know that if you spend X dollars on ads, you’ll get Y dollars in revenues.” At Google, Schmidt maintained, you pay only for what works.
Karmazin was horrified. He was an old fashioned advertising man, and where he came from, a Super Bowl ad cost three million dollars. Why? Because that’s how much it cost. What does it yield? Who knows. […]
In 2018, more than $273bn dollars was spent on digital ads globally, according to research firm eMarketer. Most of those ads were purchased from two companies: Google ($116bn in 2018) and Facebook ($54.5bn in 2018). […]
Picture this. Luigi’s Pizzeria hires three teenagers to hand out coupons to passersby. After a few weeks of flyering, one of the three turns out to be a marketing genius. Customers keep showing up with coupons distributed by this particular kid. The other two can’t make any sense of it: how does he do it? When they ask him, he explains: “I stand in the waiting area of the pizzeria.” […] Economists refer to this as a “selection effect.” It is crucial for advertisers to distinguish such a selection effect (people see your ad, but were already going to click, buy, register, or download) from the advertising effect (people see your ad, and that’s why they start clicking, buying, registering, downloading). […]
The online marketing world has the same strategy as Luigi’s Pizzeria and the flyer-handling teens. The benchmarks that advertising companies use – intended to measure the number of clicks, sales and downloads that occur after an ad is viewed – are fundamentally misleading. None of these benchmarks distinguish between the selection effect (clicks, purchases and downloads that are happening anyway) and the advertising effect (clicks, purchases and downloads that would not have happened without ads).
It gets worse: the brightest minds of this generation are creating algorithms which only increase the effects of selection. Consider the following: if Amazon buys clicks from Facebook and Google, the advertising platforms’ algorithms will seek out Amazon clickers. And who is most likely to click on Amazon? Presumably Amazon’s regular customers. In that case the algorithms are generating clicks, but not necessarily extra clicks.
Nature does not work with an end in view
[A]verage quality-adjusted single-family house prices, corrected for overall inflation, have risen a paltry 1.1% at a compound annual rate since 1972. […] Since 1972, 30-year fixed-rate mortgage rates in real terms have averaged 4.1%, meaning it has cost the homeowner 3% per year to own a house before taxes, maintenance, utilities and insurance. That’s a real negative return.
photo { Frank Lloyd Wright at the Guggenheim Museum during construction, photographed by Sam Falk, 1957 | NY Times }
How do we make use of this life that we still have?
Several weeks ago, I met up with a friend in New York who suggested we grab a bite at a Scottish bar in the West Village. He had booked the table through something called Seated, a restaurant app that pays users who make reservations on the platform. We ordered two cocktails each, along with some food. And in exchange for the hard labor of drinking whiskey, the app awarded us $30 in credits redeemable at a variety of retailers. […]
To throw cash at people every time they walk into a restaurant does not sound like a business. It sounds like a plot to lose money as fast as possible. […]
If you wake up on a Casper mattress, work out with a Peloton before breakfast, Uber to your desk at a WeWork, order DoorDash for lunch, take a Lyft home, and get dinner through Postmates, you’ve interacted with seven companies that will collectively lose nearly $14 billion this year. […]
The meal-kit company Blue Apron revealed before its public offering that the company was spending about $460 to recruit each new member, despite making less than $400 per customer. […] since Blue Apron went public, the firm’s valuation has crashed by more than 95 percent. […]
photo { Detroit Science Center, 1979 }
unrelated { Apple announces $2.5 billion plan to ease California housing crisis }
Why, why, tell ‘em that it’s human nature
Neumann created a company that destroyed value at a blistering pace and nonetheless extracted a billion dollars for himself. He lit $10 billion of SoftBank’s money on fire and then went back to them and demanded a 10% commission. What an absolute legend.
Dave, although you took very thorough precautions in the pod against my hearing you, I could see your lips move
An artificial intelligence hiring system has become a powerful gatekeeper for some of America’s most prominent employers […]
Designed by the recruiting-technology firm HireVue, the system uses candidates’ computer or cellphone cameras to analyze their facial movements, word choice and speaking voice before ranking them against other applicants based on an automatically generated “employability” score. HireVue’s “AI-driven assessments” have become so pervasive in some industries, including hospitality and finance, that universities make special efforts to train students on how to look and speak for best results. More than 100 employers now use the system, including Hilton, Unilever and Goldman Sachs, and more than a million job seekers have been analyzed.
But some AI researchers argue the system is digital snake oil — an unfounded blend of superficial measurements and arbitrary number-crunching, unrooted in scientific fact.
