Outvoxed: The Perils of New Age Publishing in Time of Rona
July 17, 2020
CNBC which continues to delight with “real news” published “Vox Media Preparing Round of Layoffs As Business Fails to Improve Amid Coronavirus Pandemic.”
DarkCyber’s reaction was, “How can this be? So hip, so with it, so confident with its flagship podcast. So very Silicon Valley.”
The write up reports:
Vox was 40% off its revenue forecast for the second quarter and plans to miss its full-year target by 25%
Yikes.
CNBC continued:
Vox furloughed about 100 employees in April, or 9% of its staff, until July 31 as Covid-19 affected advertising budgets. Many of the furloughed workers who haven’t already taken buyouts will be laid off, according to a person familiar with the matter. These employees primarily work for parts of Vox that were especially hit hard by the Pandemic, such as SBNation, Curbed and the company’s events group. There are likely to be additional job cuts, two people said.
One possible bright spot is the over talkers program billed as Pivot. Maybe the Pivot for fee educational series will raise the Vox in exaltation.
The Rona Era may inflict further unpleasantness on informed individuals. DarkCyber particularly enjoys the management suggestions Vox experts articulate.
Well, CNBC is reporting news. Vox just makes the news.
Stephen E Arnold, July 17, 2020
Palantir Technologies and Semi Hard Numbers
July 10, 2020
Palantir Technologies is super secretive. The company plans to become publicly held. Does secret and public match up for you?
“Palantir Built Itself into a $20 Billion Success with a Secretive and Controversial business. Now It’s Prepping for Life As a Transparent Public Company” offers some numbers; for instance:
- The company is valued at $20 billion US
- The company’s technology is 17 years young
- The company has raised about $3 billion US in funding
- 200 employees sent a letter to top management complaining about Palantir’s work for US Customs.
None of these numbers indicate if the company is profitable.
Important? Probably not in today’s fraught economic environment.
Stephen E Arnold, July 10, 2020
How Many Ads Can a YouTube Video Hold? Answer: Never Enough
July 10, 2020
We spotted a HackerNews post wondering if the YouTube (free version) was getting more ad love from the merrie band of Googlers.
The answer is, “Absolutely.”
The Google bean counters are well aware of the cost of the “free” video service. Thus, the free video service has to generate cash and more cash so the system can produce infinite cash. That’s logical in a Googley way I think.
In the comments to the original question on HackerNews, an entity named Operyl wrote:
If I understand correctly from a friend, the problem is YouTubers (and YouTube/Google) are currently making _much less_ money per ad. It sounds like more are getting shoved per video to make up for it (iirc, it’s up to YouTube to determine this?).
I don’t know what iirc means, but the rest of the post is clear. More money is needed.
Observations:
- YouTube ads are more and more annoying. The fix obviously is to pay Google money. Most of the annoying ads go away. Google is discovering subscriptions. Undoubtedly Google will think subscription revenues for other services just like BMW and its heated steering wheel stroke of genius. German logic, of course. Ever read Kant? Congruent indeed.
- The YouTube ads are increasingly irrelevant when I check out some YouTube videos. I love the tours of the Incan ruins. Ads about all sorts of things unrelated to Peruvian stone work appear. Therefore, the famous smart algorithm is just spewing ads to burn up inventory is one thought which crossed my mind.
- The autoplay of post viewing content are interesting as well. How many of those ads are viewed BEFORE the YouTube user identifies which tab is playing the pitch to go Adobe? My hunch. Zero if these startled views are like me.
Net net: Those grousing about Google’s monetization quest have not seen anything yet. Why? The cost hole for the Google is probably close to infinite as long as there are former TikTok users looking for a home. Infinite costs can only be offset by infinite revenue. That too is logic worthy of a Google flashing logo pin.
Stephen E Arnold, July 9, 2020
Subscriptions: Spreadsheet Fever Fuels the Magazine Model
July 9, 2020
Nothing is easier. Plug in a series of four numbers, highlight the cells, and drag the little black box. Excel spits out the “projected next number.” Magic.
Think about this. Mail out 10 million snail mail pitches for a year’s subscription to a jazzy magazine, maybe Psychology Today or something similar. Fire up the spreadsheet, plug in the estimated number of sign ups, and project how much money will flow into the coffers of the magazine publisher or the third party handling the campaign from an office in Hoboken.
Subscriptions are the “next big thing” for many businesses. Here in rural Kentucky, our single car wash sells a “subscription.” The idea is that the car wash gets upfront money, and the lucky buyer can drive in one every two weeks and get the horse and buggy hosed down. Working good? Not so much.
BMW is selling subscriptions to features like heated steering wheels. Tesla, the auto company owned by Joe Rogan star, Elon Musk has subscriptions on its radar too.
Twitter, according to Bloomberg, the socially positive and continually uplifting information service, may be going to a subscription model. The DarkCyber research team has long considered Twitter a very useful tool for misinformation, disinformation, and reformation. Asking “fake personas” to pay for the service may work. On the other hand, industrious individuals may find the steady stream of innovations in encrypted messaging apps a possible complement. But look at those Excel projections. Imagine a 1,000,000 subscribers at $10 US a month. Wow, drag those tiny black squares. Count your bonus now.
The Quibi short form video service is subscription based. No one on the DarkCyber team has downloaded the app nor peered over someone’s shoulder while social distancing outside the general store in our small town. (It is near the vacant subscription car wash.)
According to a possibly specious, wildly incorrect, and statistically flawed report, Quibi’s subscription model is not selling like Rona N95 masks. The rock solid “real” news outfit Verge published “Quibi Reportedly Lost 90 Percent of Early Users after Their Free Trials Expired.”
The marketing technique implemented get six issues free and then pay only $10 US a month approach. How are magazines doing these days? Yep, stunning business.
The write up recycles data from a “research firm” named Sensor Tower and reports:
Streaming service Quibi only managed to convert a little under 10 percent of its early wave of users into paying subscribers, says mobile analytics firm Sensor Tower. According to the firm’s new report on Quibi’s early growth, the short-form video platform signed up about 910,000 users in its first few days back in April. Of those users, only about 72,000 stuck around after the three-month free trial, indicating the app had about an 8 percent conversion rate.
Short form video content is available mostly for free. Ever hear of Funimate?
Let’s step back. Advertising online is a monopoly game with two outstanding firms managing the dice, the money, and the cute little tokens. Direct mail is more expensive. With creative, list rentals, and fulfillment house fees, figure $5 to $7 per envelope delivered by snail mail. The promo can be cheaper if you go with a single “please, subscribe” flier in a ValPak envelope. Inserts in a daily newspaper. Okay, that’s a great idea. Door knob hanging? Nope. Banner ads on the Adf.ly network. Yeah, maybe?
Subscription plays are looking good when viewed through the blood shot eyes of someone with spreadsheet fever.
Reality may be different. Even National Geographic is a non profit. Hey, there’s an idea for BMW, Twitter, and Quibi. When this bout of spreadsheet fever winds down, consider the benefits of becoming a non governmental organization: Donations, fund raisers, merchandise, and more.
Stephen E Arnold, July 9, 2020
Will Insurance Companies Tie Rates to Rage?
July 7, 2020
The community-driven navigation app Waze, owned by Google, has refreshed its design. The company changed up the color scheme, logos, icons, and typeface—the sort of tweaks one would expect to keep users engaged. One particular change, however, is more intriguing. Engadget reveals, “Waze Lets Drivers Display their Moods in the App.” That could prove to be very useful information for some advertisers, individuals, and government entities. Writer Christine Fisher reports:
“Waze is also adding something called Moods, a feature that will ‘capture users’ emotions.’ ‘Celebrating the passion and authenticity of its users, Waze hopes that the update will harness the “humanness” that can often be lost within inhumane traffic conditions,’ the company wrote in a press release. It’s unclear if Moods will be shared with nearby Waze users. Letting other drivers know how you feel doesn’t necessarily sound like a great idea, but for the most part the Mood icons look too cute to induce serious road rage. ‘Hopefully our new look reminds users of the magic of our community and the way we work together for better,’ said Jake Shaw, head of creative at Waze.”
The icons are indeed very cute, we’ll give them that, and touting the “magic of community” sounds delightful. But giving away even more personal data seems like a bad idea to those of us who understand how various entities can use seemingly benign personal details. Founded in 2007, Waze is based in the San Francisco Bay area. Google bought the company for $966 million in 2013.
Cynthia Murrell, July 7, 2020
The Cost of Training Smart Software: Is It Rising or Falling?
July 6, 2020
I read “The Cost of AI Training is Improving at 50x the Speed of Moore’s Law: Why It’s Still Early Days for AI.” The article’s main point is that “training” — that is, the cost of making machine learning smart — is declining.
That seems to make sense. First, there are cloud services. Some of these are cheaper than others, but, in general, relying on cloud compute eliminates the capital costs and the “ramp up” costs for creating one’s own infrastructure to train machine learning systems.
Second, use of a machine learning “utility” like Amazon AWS Sagemaker or the similar services available from IBM and Google provides two economic benefits:
- Tools are available to reduce engineering lift off and launch time
- Components like Sagemaker’s off-the-shelf data bundles eliminate the often-tedious process of finding additional data to use for training.
Third, assumptions about smart software’s efficacy appear to support generalizations about the training, use, and deployment of smart software.
I want to =note that there are some research groups who believe that software can learn by itself. If my memory is working this morning, I think the jazzy way to state is “sui generis.” Turn the system on, let it operate, and it learns by processing. For smart software, the crude parallel is learning the way humans learn: What’s in the environment becomes the raw material for learning.
The article correctly points out that the number of training models has increased. That is indeed accurate. A model is a numerical recipe set up to produce an output that meets the modeler’s goal. Thus, training a model involves providing data to the numerical recipe, observing the outputs, and then making adjustments. These “tweaks” can be simple and easy; for example, changing a threshold governing a decision. More complex fixes include, but are not limited to, selecting a different sequence for the individual processes, concatenating models so that multiple outputs inform a decision, and substituting one mathematical component for another. To get a sense of the range of components available to a modeler, a quick look at Algorithms. This collection is what I would call “ready to run.”
The article includes a number of charts. Each of these presents data supporting the argument that it is getting less costly to training smart software.
I am not certain I agree, although the charts seem to support the argument.
I want to point out that there are some additional costs to consider. A few of these can be “deal breakers” for financial and technical reasons.
Here’s my list of smart software costs. As far as I know, none of these has been the subject of an analyst’s examination and some may be unquantified because those in the business of smart software are not set up to capture them:
- Retraining. Anyone with experience with models knows that retraining is required. There are numerous reasons, but retraining is often more expensive than the first set of training activities.
- Gathering current or more on point training data. The assumption about training data is that it is useful. We live in the era of so called big data. Unfortunately on point data relevant to the retraining task is a time consuming and can be a complicated task involving subject matter experts.
- Data normalization. There is a perception that if data are digital, those data can be provided “as is” to a content processing system. That is not entirely accurate. The normalization processes can easily consume as much as 60 percent of available subject matter expert and data analysts’ time.
- Data validation. The era of big data makes possible this generalization, “The volume of data will smooth out any anomalies.” Maybe, but in my experience, the “anomalies” — if not addressed — can easily skew one of the ingredients in the numerical recipe so that the outputs are not reliable. The output may “look” like it is accurate. In real life, the output is not what’s desired. I would refer the reader to the stories about Detroit’s facial recognition system which is incorrect 96 percent of the time. For reference, see this Ars Technica article.
- Downstream costs. Let’s use the Detroit police facial recognition system to illustrate this cost. Answer this question, please, “What are the fully loaded costs for the consequences of the misidentification of a US citizen?”
In my view, taking a narrow look at the costs of training smart software is not in the interests of the analyst who benefits from handling investors’ money. Nor are the companies involved in smart software eager to monitor the direct and indirect costs associated with training the models. Finally, it is in no one’s interest to consider the downstream costs of a system which may generate inaccurate outputs.
Net net: In today’s economic environment, ignoring the broader cost picture is a distortion of what it takes to train and retrain smart software.
Stephen E Arnold, July 6, 2020
Smart Software and an Intentional Method to Increase Revenue
July 6, 2020
The excellent write up titled “How Researchers Analyzed Allstate’s Car Insurance Algorithm.” My suggestion? Read it.
The “how to” information is detailed and instructive. The article reveals the thought process and logical thinking that allows a giant company with “good hands” to manipulate its revenues.
Here’s the most important statement in the article:
In other words, it appears that Allstate’s algorithm built a “suckers list” that would simply charge the big spenders even higher rates.
The information in the article illustrates how difficult it may be for outsiders to figure out how some smart numerical procedures are assembled into “intentional machines.”
The idea is that data allow the implementation of quite simple big ideas in a slick, automated, obfuscated way.
As my cranky grandfather observed, “It all comes down to money.”
Stephen E Arnold, July 6, 2020
Algolia Pricing
July 3, 2020
Years ago I listened to a wizard from Verity explain that a query should cost the user per cell. Now that struck me as a really stupid idea. Data sets were getting larger. The larger the data set, even extremely well crafted narrow queries would “touch” more cells. In a world of real time queries and stream processing, the result of the per cell model would be more than just interesting, it would be a deal breaker.
Pricing digital anything has been difficult. In the good old days of the late 1970s and early 1980s, one paid in many different ways — within the same system. The best example of this was the AT&T/British Telecom approach to online data.
Here’s what was involved. I am 77 and working from memory:
- Installation, set up, or preparation fee. This was dependent of factors such as location, distance from a node, etc.
- Base rate; that is, what one paid simply to be connected. This could be an upfront fee or calculated on some measurement which was intentionally almost impossible to audit or verify.
- Service required. Today this would be called bandwidth or connect time. The definition was slippery, but it was a way for the telcos of that era to add a fee.
If a connection went to a data center housing data, then other fees would kick in; for example:
- Hourly fee billed fractionally for the connect time to the database
- Per item fee when extracting data from the database
- A “print” or “type” fee which applied to the format of the data extracted
- A “report” fee because reports required cost recovery for the pre-coded template, query time, formatting, and outputting.
There were other fees, but the most fascinating one was the “threshold fee.” The idea is that paid for 60 minutes of connect time. When the 61st minute was required, the threshold was crossed, and the billing could go up, often by factors of 2X or more. No warning, of course. And the mechanism for calculating threshold fees were not disclosed to the normal customer. (After I became a contractor to Bell Communications Research, I learned that the threshold fees were determined based on “outside” or exogenous factors. In Bell Head speak this seemed to mean, “This is where we make even more money.”
To sum up, online pricing was a remarkable swamp. Little wonder that outsiders would be baffled at the online invoices generated by the online providers. Exciting, yes. Happy customers, nah. No one at the AT&T/British Telecom type outfits cared about non Bell Heads. No Young Pioneer T shirt? Ho, ho, ho. Pay your bill or we kill your account. Ho ho ho.
Algolia announced a new pricing plan. You can read about it here. The idea is to reduce confusion and be more “customer friendly.” What’s interesting to me is the string of comments on the Hacker News site. You can read these comments at this link.
There’s some back and forth with Algolia participating.
Some of the comments underscore the type of “surprise” that certain types of pricing models spark; for example, from alooPotato:
We (Streak) are in the same boat. Looks like we’d be paying approx half a million dollars a month on their new pricing which would be ~100x more than we are paying now. Haven’t heard from our enterprise rep but starting to get nervous… Sounds like the new pricing is for their ecommerce customers given how much value they provide them, doesn’t seem to make sense anymore for SaaS use cases.
ysavir takes a balanced view; that is, some good, some bad:
Not the GP, but I figure their point is as follows: If I’m running an e-commerce website, I don’t mind pay-per-search since those searches may turn into sales, so the cost is justified. My income scales with search count, and the Algolia price is part of user acquisition costs. If I’m running a SaaS business, the search is a feature for customers who have already paid, so I don’t see any further returns from the search being used. The more a client uses search, the less I’m profiting from having them as a client. They could potentially even cost me money to service them!
The point is that any pricing model — whether the AT&T/British Telecom type pricing “simplification” or a made-up, wacko approach like the IBM J1, J2, J3, etc. approach — is not going to meet the requirements of every customer.
The modern approach to pricing is to obfuscate and generate opaque variable prices. You can see this model in action by navigating to Amazon and running a query for “mens golf shirt and then zipping over to AWS and check out the prices for Sagemaker models to drive Athena. Got the difference, gentle reader?
The nifty world of enterprise search has been a wonderland of pricing methods. I flipped through the pricing data files for the three editions of the Enterprise Search Report which I began writing in 2002. Here are some highlights:
- Base fee plus engineering services. Upgrades priced individually.
- Base fee plus fixed price over a period of time.
- Variable elements like the crazy “per cell” idea from the guy who is now the head of Google Search (Oh, yeah!)
- Free if the customer (the US government) licensed other software
- One time charge. Upgrades are easy. Buy another license.
- Free. The vendor is in the business of selling engineering support, training, and custom widgets to make the search system sort of work.
- Whatever can be billed. This is extremely popular because the negotiation process reveals the allocated funds and the search system vendor angles to get as much of the allocated cash as humanly possible.
- Free for the first budget cycle. Then when funds become available, prices are negotiated.
- Custom quote only. NDA required.
Today, life is easier. One can download a free and open source search system, hit the local university for some “interns”, and let ‘er rip. Another alternative is to look for a hosted search service. Blossom.com maybe?
Net net: Pricing has one goal: Generate revenue and lock in for the vendor. That’s one reason why vendors of what I can search centric services are so darned lovable.
Stephen E Arnold, July 3, 2020
Alphabet Spells Cable Model
July 1, 2020
Cable company business models work. Alphabet Google faces some competitive pressure, looming regulatory handcuffs, and softness in its 20 year old “black box”magic ad matching machine.
The fix is to push aggressively and as quickly as possible to lock down clever ways to make money. The most recent example is to charge more than $700 per year to watch YouTube’s millennial cable programming.
You can get details in “YouTube TV Jumps 30% in Price Effective Immediately.” I found one passage interesting:
The news came at the end of a lengthy announcement of various new channels, which users cannot opt out of, all coming from the CBS/Viacom family of cable TV networks.
Does this bold, aggressive move mark the limit of Alphabet’s land grabbing?
No, it is one step on the path of locking down revenues in order to weather the approaching storm.
There are some flaws in Alphabet’s approach. For some YouTube quasi cable consumers, the other options have price tags too. Whatever the competitive environment offers, Alphabet will find inspiration.
What about the “cannot opt out.” That’s the new Google. Like it or leave it. Leaving may make perfect sense to the employees whose bonuses were gutted to pay for Google’s diversity aspirations.
Stephen E Arnold, July 1, 2020
A Moment of Irony: Microsoft and Facebook Ads
June 30, 2020
I recall reading a story about Microsoft’s purchasing a chunk of Facebook. Recode wrote about the deal in “It’s Been 10 Years Since Microsoft Invested in Facebook. Now Facebook Is Worth Almost As Much as Microsoft.”
I thought about this investment when I read “Microsoft Has Been Pausing Spending on Facebook, Instagram.”
The way I understand this is that Microsoft owns some Facebook shares. Facebook holds meetings for those who own stock. The meetings permit submission of questions from shareholders.
Some questions:
- Has Microsoft asked questions about Facebook’s ad practices at these meetings?
- Has Microsoft contacted Facebook management about its ad-related concerns?
- Has Microsoft management determined that selling its Facebook shares is a good or bad idea?
- Is the “pausing” virtue signaling or something more significant?
Hopefully one of the “real” news outfits will provide some information to help me answer these questions. If I were not so disinterested in Facebook, I could have one of the DarkCyber team jump in. And what about Microsoft’s financial thinking? Did Enron executives actually think about “energy”?
I do like the idea of a company which owns part of another company not liking the company’s policies. The action? Pausing. Yeah, maybe just another word for virtue signaling?
Stephen E Arnold, June 30, 2020
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