Great Moments in Modern Management: The Mailchimp Move

September 28, 2021

I like the phrase “high school science club management methods.” No one else seems to care. I spotted a exemplary management maneuver. “Mailchimp Employees Are Furious After the Company’s Founders Promised to Never Sell, Withheld Equity, and Then Sold It for $12 Billion.” The “it” refers to the company, not “the equity,” but, hey, what does one expect from a mash up of Silicon Valley “real” news and German quality control. You will have to pay to read the original story. Money is needed for copy editors or a BMW lease.

I noted this passage:

The founders told anyone who would listen they would own Mailchimp until they died and bragged about turning down multiple offers. “It was part of the company lore that they would never sell,” said a former Mailchimp employee, who like others interviewed for this story were granted anonymity because they were unauthorized to discuss sensitive internal matters. “Employees were indoctrinated with this narrative.”
The two founders did sell.

Well, what do you know? A high flying online email marketing outfit said one thing and did another. Gee, that rarely happens.

I wish the HSSCMM would catch on. The methods are proliferating like snorts in the high school lunch room when someone mentions “the prom.” Oh, those mail monkeys all grown up!

Stephen E Arnold, September 28, 2021

Alphabet Spells Out YouTube Recommendations: Are Some Letters Omitted?

September 23, 2021

I have been taking a look at Snorkel (Stanford AI Labs, open source stuff, and the commercial Snorkel.ai variants). I am a dim wit. It seems to me that Google has found a diving partner and embracing some exotic equipment. The purpose of the Snorkel is to implement smart workflows. These apparently will allow better, faster, and cheaper operations; for example, classifying content for the purpose of training smart software. Are their applications of Snorkel-type thinking to content recommendation systems. Absolutely. Note that subject matter experts and knowledge bases are needed at the outset of setting up a Snorkelized system. Then, the “smarts” are componentized. Future interaction is by “engineers”, who may or may not be subject matter experts. The directed acyclic graphs are obviously “directed.” Sounds super efficient.

Now navigate to “On YouTube’s Recommendation System.” This is a lot of words for a Googler to string together: About 2,500.

Here’s the key passage:

These human evaluations then train our system to model their decisions, and we now scale their assessments to all videos across YouTube.

Now what letters are left out? Maybe the ones that spell built-in biases, stochastic drift, and Timnit Gebru? On the other hand, that could be a “Ré” of hope for cost reduction.

Stephen E Arnold, September 23, 2021

Big Tech Defines Material: What Does That Really Mean to Oligopolistic-Type Outfits?

September 16, 2021

I noted a US government study called “Non HSR Reported Acquisitions by Select Technology Platforms: 2010-2019: FTC Study.” The report, assuming it is spot on, suggests that large companies interpreted the word “material” differently from what some financial / accountant types think it means; for example, “Items are considered to be material when they have an excessive impact on reported profits, or on individual line items within the financial statements.” [Source: The Google, of course.] Some MBAs and accountants have remarkably flexible connotative skills. Is this a Deloitte Touche-type touch?

The report states:

image

My hunch is that standard deviation is not a hot topic at Zoom happy hours. The standard deviations in the table above suggest that the big tech outfits in the study pretty much redefined “material,” bought stuff and did not make a big deal about it, and chugged along in their cheerfully unregulated state during the period of the study.

The report states:

The five technology platform 6(b) respondents identified 616 non-HSR reportable transactions above $1 million, in addition to 101 Hiring Events and 91 Patent Acquisitions. The respondents reported an additional approximate 60 transactions below $1 million and 160 financial investments. Voting Security (Control) and Asset acquisitions comprise 65% of all of the above transactions. When excluding Hiring Events, Patent Acquisitions, and transactions below $1 million, Voting Security (Control) and Asset acquisitions comprise 85% of the transactions.

I interpret this to mean that the big tech outfits in the sample decided what to report and what to ignore; that is, the deals were not material. There’s that MBA word again.

Here’s another passage I circled:

Most of the transactions that were classified into technology categories were concentrated in the categories of Mobility (mobile devices and device-based software and content, which comprised more than 10% of the acquired firms), Application Software (front-end applications such as CRM, ERP, SCM, BI, commerce and vertical business software, which comprised more than 9% of the acquired firms), and Internet Content & Commerce (internet destination and internet-enabled services, which comprised more than 6% of the acquired firms). In the Mobility and Application Software categories, the number of transactions peaked in 2015; in the Internet Content & Commerce category, the number of transactions peaked in 2011.

Observations:

  1. Fancy dancing is popular among the companies in the sample; notably, Alphabet/Google, Amazon, Apple, Facebook, and Microsoft
  2. Regulators, probably with MBAs, looked the other way
  3. The power of unregulated commercial enterprises makes clear who is in charge of many important technical and social activities.

Interesting stuff, and I am confident that a lawyer with an MBA can explain this misalignment about the meaning of “material.” I wonder if the hints about the behavior of the companies in the sample suggest that we now live in a digital banana republic with the centers of power concentrated among a few corporate entities in their plantation houses.

Stephen E Arnold, September 16, 2021

Lucky India. Google Wants to Help

September 16, 2021

Google seeks to clear up a misunderstanding. Odisha’s OrissaPost reports, “Google Says Firmly Sees Itself as Partner to India’s Financial Ecosystem.” At issue is Google Pay and its Spot platform. It sounds like some reports about its partnerships with banks may have given the impression Google is trying to supplant or undermine existing financial institutions in India. We learn:

“The company emphasized that in every geography where Google Pay is present, its stance is consistently one of partnering with the existing financial services and banking systems to help scale and enable frictionless delivery of financial products and services and contribute to the goal of financial inclusion. In a blogpost, Google India said there have been a few instances where these offerings have been reported as ‘Google Pay’s offerings’, which fuels misinterpretation. ‘To be clear, we have always looked at our role firmly as a partner to the existing financial ecosystem that brings unique skill sets and offerings to drive further adoption of digital payments in the country,’ it said. … The internet major also noted that its Spot platform works as an additional discovery channel for many businesses to build and offer new experiences to users to drive adoption of their services. The use cases span across ticket purchase, food ordering, paying for essential services like utility bills, shopping and getting access to various financial products.”

See the write-up or Google India’s blog post for more specific details. The company emphasizes bringing partners onto the Google Pay platform connects them to customers around India who would otherwise be unable to access their services, helping to “level social inequalities.” Aw Google, always looking out for the little guy aren’t you?

Cynthia Murrell, September 16, 2021

Silicon Valley: Fraud or Fake Is an Incorrect Characterization

September 10, 2021

I read “Elizabeth Holmes: Has the Theranos Scandal Changed Silicon Valley?” The write up contains a passage I found interesting; to wit:

In Silicon Valley, hyping up your product – over-promising – isn’t unusual…

Marketing is more important than the technology sold by the cash hype artists. Notice that I don’t use the word “entrepreneur,” “innovator,” “programmer,” or the new moniker “AIOps” (that’s artificial intelligence operations).

The Theranos story went wrong because there was not a “good enough” method provided. The fact that Theranos could not cook up a marginally better way of testing blood is less interesting than the fact about the money. She had plenty of money, and her failure is what I call the transition from PowerPoint to “good enough.”

Why not pull a me-too and change the packaging? Why not license a method from Eastern Europe or Thailand and rebrand it? Why not white label a system known to work, offer a discount, and convince the almost clueless Walgreen’s-type operation that the  Zirconia was dug out of a hole in a far-off country.

Each of these methods has been used to allow an exit strategy with honor and not a career-ending Tesla-like electric battery fire which burns for days.

The write up explains:

Particularly at an early stage, when a start-up is in its infancy, investors are often looking at people and ideas rather than substantive technology anyway. General wisdom holds that the technology will come with the right concept – and the right people to make it work. Ms Holmes was brilliant at selling that dream, exercising a very Silicon Valley practice: ‘fake it until you make it’. Her problem was she couldn’t make it work.

The transgression, in my opinion, was a failure to use a me-too model. That points to what I call a denial of reality.

Here are some examples of how a not-so-good solution has delivered to users a disappointing product or service yet flourished. How many of these have entered your personal ionosphere?

  1. Proprietary app stores which offer mobile software which is malware? The purpose of the proprietary app store is to prevent malfeasance, right?
  2. Operating systems which cannot provide security? My newsfeed is stuffed full of breaches, intrusions, phishing scams, and cloud vulnerabilities. How about that Microsoft Exchange and Azure security or the booming business of NSO Group-types of surveillance functionality?
  3. Self-driving vehicles anyone? Sorry, not for me.
  4. Smart software which is tuned to deliver irrelevant advertising despite a service’s access to browser history, user location, and email mail? If I see one more ad for Grammarly or Ke Chava when I watch a Thomas Gast French Foreign Legion video in German, I may have a stroke. (Smart software is great, isn’t it? Just like ad-supported Web search results!)
  5. Palantir-type systems are the business intelligence solutions for everyone with a question and deep pockets.

The article is interesting, but it sidesteps the principal reason why Theranos has become a touchstone for some people. The primum movens from my vantage point is:

There are no meaningful consequences: For the funders. For the educational institutions. For the “innovators.”

The people who get hurt are not part of the technology club. Maybe Ms. Holmes, the “face” of Theranos will go to jail, be slapped with a digital scarlet A, and end up begging in Berkeley?

I can’t predict the future, but I can visualize a Michael Milkin-type or Kevin Mitnick-type of phoenixing after walking out of jail.

Theranos is a consequence of the have and have not technology social construct. Technology is a tool. Ms. Holmes cut off her finger in woodworking class. That’s sort of embarrassing. Repurposing is so darned obvious and easy.

More adept pioneers have done the marketing thing and made a me-too approach to innovation work. But it does not matter. This year has been a good one for start ups. Get your digital currency. Embrace AIOps. Lease a self driving vehicle. Use TikTok. No problem.

Stephen E Arnold, September 10. 2021

Techno-Psych: Perception, Remembering a First Date, and Money

September 9, 2021

Navigate to “Investor Memory of Past Performance Is Positively Biased and Predicts Overconfidence.” Download the PDF of the complete technical paper at this link. What will you find? Scientific verification of a truism; specifically, people remember good times and embellish those memory with sprinkles.

The write up explains:

First, we find that investors’ memories for past performance are positively biased. They tend to recall returns as better than achieved and are more likely to recall winners than losers. No published paper has shown these effects with investors. Second, we find that these positive memory biases are associated with overconfidence and trading frequency. Third, we validated a new methodology for reducing overconfidence and trading frequency by exposing investors to their past returns.

The issue at hand is investors who know they are financial poobahs. Mix this distortion of reality with technology and what does one get? My answer to this question is, “NFTs for burned Banksy art.”

The best line in the academic study, in my view, is:

Overconfidence is hazardous to your wealth.

Who knew? My answer is the 2004 paper called “Overconfidence and the Big Five.” I also think my 89-year-old great grandmother who told me when I was 13, “Don’t be over confident.”

I wonder if the Facebook artificial intelligence wizards were a bit too overconfident in the company’s smart software. There was, if I recall, a question about metatagging a human as a gorilla.

Stephen E Arnold, September 9, 2021

Change Is Coming But What about Un-Change?

September 8, 2021

My research team is working on a short DarkCyber video about automating work processes related to smart software. The idea is that one smart software system can generate an output to update another smart output system. The trend was evident more than a decade ago in the work of Dr. Zbigniew Michalewicz, his son, and collaborators. He is the author of How to Solve It: Modern Heuristics. There were predecessors and today many others following smart approaches to operations for artificial intelligence or what is called by thumbtypers AIOps. The DarkCyber video will become available on October 5, 2021. We’ll try to keep the video peppy because smart software methods are definitely exciting and mostly invisible. And like other embedded components, some of these “modules” will become components, commoditized, and just used “as is.” That’s important because who worries about a component in a larger system? Do you wonder if the microwave is operating at peak efficiency with every component chugging along up to spec? Nope and nope.

I read a wonderful example of Silicon Valley MBA thinking called “It’s Time to Say “Ok, Boomer!” to Old School Change Management.” At first glance, the ideas about efficiency and keeping pace with technical updates make sense. The write up states:

There are a variety of dated methods when it comes to change management. Tl;dr it’s lots of paper and lots of meetings. These practices are widely regarded as effective across the industry, but research shows this is a common delusion and change management itself needs to change.

Hasta la vista Messrs. Drucker and the McKinsey framework.

The write up points out that a solution is at hand:

DevOps teams push lots of changes and this is creating a bottleneck as manual change management processes struggle to keep up. But, the great thing about DevOps is that it solves the problem it creates. One of the key aspects where DevOps can be of great help in change management is in the implementation of compliance. If the old school ways of managing change are too slow why not automate them like everything else? We already do this for building, testing and qualifying, so why not change? We can use the same automation to record change events in real time and implement release controls in the pipelines instead of gluing them on at the end.

Does this seem like circular reasoning?

I want to point out that if one of the automation components operates using probability and the thresholds are incorrect, the data poisoned (corrupted by intent or chance) or the “averaging” which is a feature of some systems triggers a butterfly effect, excitement may ensue. The idea is that a small change may have a large impact downstream; for example, a wing flap in Biloxi could create a flood in the 28th Street Flatiron stop.

Several observations:

  • AIOps are already in operation at outfits like the Google and will be componentized in an AWS-style package
  • Embedded stuff, like popular libraries, are just used and not thought about. The practice brings joy to bad actors who corrupt some library offerings
  • Once a component is up and running and assumed to be okay, those modules themselves resist change. When 20 somethings encounter mainframe code, their surprise is consistent. Are we gonna change this puppy or slap on a wrapper? What’s your answer, gentle reader?

Net net: AIOps sets the stage for more Timnit Gebru shoot outs about bias and discrimination as well as the type of cautions produced by Cathy O’Neil in Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy.

Okay, thumbtyper.

Stephen E Arnold, September 8, 2021

Algolia: Now the Need for Sustainable, Robust Revenue Comes

August 27, 2021

We long ago decided Algolia was an outfit worth keeping an eye on. We were right. Now Pulse 2.0 reports, “Algolia: $150 Million Funding and $2.25 Billion Valuation.” The company closed recently on the Series D funding, bringing its total funding to $315 million. Putting that sum to shame is the hefty valuation touted in the headline. Can the firm live up to expectations? Reporter Annie Baker writes:

“This latest funding round reflects Algolia’s hyper growth fueled by demand for ‘building block’ API software that increases developer productivity, the growth in e-commerce, and digital transformation. And this additional investment enables Algolia to scale and serve the increased demand for the company’s Search and Recommendations products as well as fuel the company’s continued product expansion into adjacent markets and use-cases. … This new funding round caps a landmark year that saw significant growth and product innovation. And Algolia launched with the goal of creating fast, instant, and relevant search and discovery experiences that surfaced the desired information quickly. Earlier this year, the company had announced its new vision for dynamic experiences, advancing beyond search to empower businesses to quickly predict a visitor’s intent on their digital property in real time, in the session, and in the moment. And the business, armed with this visitor intent, can surface dynamic content in the form of search results, recommendations, offers, in-app notifications, and more — all while respecting privacy laws and regulations.”

Baker notes Algolia’s approach is a departure from opaque SaaS solutions and monolithic platforms. Instead, the company works with developers to build dynamic, personalized applications using its API platform. Over the last year and a half, Algolia also added seven new executives to its roster. Headquartered in San Francisco, the company was founded in 2012.

Cynthia Murrell, August 27, 2021

Silicon Valley Neologisms: The Palantir Edition

August 19, 2021

Do you remember the Zuckerland metaverse? (Yes, I know he borrowed the word, but when you are president of a digital country, does anyone dare challenge Zuck the First, Le Roi Numérique?)

Palantir Technologies (the Seeing Stone outfit with the warm up jacket fashion bug) introduced a tasty bit of jargon-market speak in its Q2 2021 earnings call:

Palantir’s meta-constellation software harnesses the power of growing satellite constellations, deploying AI into space to provide insights to decision-makers here on Earth. Our meta-constellation integrates with existing satellites, optimizing hundreds of orbital sensors and AI models and allowing users to ask time-sensitive questions across the entire planet. Important questions like, where are the indicators of wildfires or how are climate changes affecting crop productivity? And when and where are naval fleets conducting operations? Meta-constellation pushes Palantir’s Edge AI technology to a new frontier.

I think meta-constellation is a positive contribution to the American Silicon Valley-Denver lingo.

One of the interesting factoids in the write up is that the average customer “invests” lots of money in the firm’s software and services. The average customer yields $7.9 million. Let’s assume there was a touch of spreadsheet fever whipping the accountants. Chop that down to a couple of million, and the cowboy outfit is doing okay. Now the job is to corral those customers so there is sustainable, recurring revenue and generous profits going forward like little doggies heading to the meat processing facility.

Also, deploying the Palantirians’ system is as easy as cooking some of Cowboy Ken’s beans in an iron pot over a wood fire. The transcript faithfully reports:

In just two days, we were able to deploy an entire solution for this customer, leveraging our out-of-the-box functionality built in foundry, a time line previously unthinkable in the eyes of the customer. And frankly, it would have been unthinkable to us even three years ago, where an equivalent project might have taken three months. This is only possible because of our product. Innovations from software-defined data integration are driving the marginal cost of data integration to 0, archetypes and our no-code technologies that are driving the marginal cost of application development to zero.

Those data cowboys are moving faster than a branded calf on a crisp April morning.

The most interesting factoid is contained in this statement:

Given our strong cash flow position, we repaid our outstanding $200 million term loan facility and are currently debt-free. After paying off the debt, we ended the quarter with $2.3 billion in cash and cash equivalents.

I don’t want to raise a touchy subject, but this chart caught my attention:

image

That yellow line means that the company is losing money if I am interpreting the Google Finance graph correctly.

It may be helpful to consider that Palantir has never turned a profit. Let’s hope those Colorado transplants can covert expensive cows into hard cash after more than a decade grazing on the range. No digital cows, please. Leave those for the Facebook metaverse which is less than a meta-constellation in JRR Tolkien fantasy space.

Stephen E Arnold, August 19, 2021

Autonomy: An Interesting Legal Document

August 4, 2021

Years ago I did some work for Autonomy. I have followed the dispute between Hewlett Packard and Autonomy. Enterprise search has long been an interest of mine, and Autonomy had emerged as one of the most visible and widely known vendors of search and retrieval systems.

Today (August 3, 2021) I read “Hard Drives at Autonomy Offices Were Destroyed the Same Month CEO Lynch Quit, Extradition Trial Was Told.” The write up contains information with which I was not familiar.

In the write up is a link to “In the City of Westminster Magistrates’ Court The Government of the United States of America V Michael Richard Lynch Findings of Fact and Reasons.” That 62 page document contains a useful summary of the HP – Autonomy deal.

Several observations:

  1. Generating sustainable revenue for an enterprise search system and ancillary technology is difficult. This is an important fact for anyone engaged in search and retrieval.
  2. The actions summarized in the document provide a road map of what Autonomy did to maintain its story of success in what has been for decades a quite treacherous market niche. Search is particularly difficult, and vendors have found marketing a heck of a lot easier than delivering a system that meets users’ expectations.
  3. The information in the document suggests that the American judicial system may find this case a “bridge” between how corporate entities respond to the Wall Street demands for revenue and growth.

Like Fast Search & Transfer, executives found themselves making decisions which make search and retrieval a swamp. Flash forward to the present: Google search is shot through with adaptations to online advertising.

Perhaps the problem is that people expect software to deliver immediate, relevant results. Well, it is clear that most of the search and retrieval systems seeking sustainable revenues have learned that search can deliver good enough results. Good enough is not good enough, however.

Stephen E Arnold, August 4, 2021

Next Page »

  • Archives

  • Recent Posts

  • Meta