Another Big Consulting Firms Does Smart Software… Sort Of
September 3, 2024
This essay is the work of a dumb dinobaby. No smart software required.
Will programmers and developers become targets for prosecution when flaws cripple vital computer systems? That may be a good idea because pointing to the “algorithm” as the cause of a problem does not seem to reduce the number of bugs, glitches, and unintended consequences of software. A write up which itself may be a blend of human and smart software suggests change is afoot.
Thanks, MSFT Copilot. Good enough.
“Judge Rules $400 Million Algorithmic System Illegally Denied Thousands of People’s Medicaid Benefits” reports that software crafted by the services firm Deloitte did not work as the State of Tennessee assumed. Yep, assume. A very interesting word.
The article explains:
The TennCare Connect system—built by Deloitte and other contractors for more than $400 million—is supposed to analyze income and health information to automatically determine eligibility for benefits program applicants. But in practice, the system often doesn’t load the appropriate data, assigns beneficiaries to the wrong households, and makes incorrect eligibility determinations, according to the decision from Middle District of Tennessee Judge Waverly Crenshaw Jr.
At one time, Deloitte was an accounting firm. Then it became a consulting outfit a bit like McKinsey. Well, a lot like that firm and other blue-chip consulting outfits. In its current manifestation, Deloitte is into technology, programming, and smart software. Well, maybe the software is smart but the programmers and the quality control seem to be riding in a different school bus from some other firms’ technical professionals.
The write up points out:
Deloitte was a major beneficiary of the nationwide modernization effort, winning contracts to build automated eligibility systems in more than 20 states, including Tennessee and Texas. Advocacy groups have asked the Federal Trade Commission to investigate Deloitte’s practices in Texas, where they say thousands of residents are similarly being inappropriately denied life-saving benefits by the company’s faulty systems.
In 2016, Cathy O’Neil published Weapons of Math Destruction. Her book had a number of interesting examples of what goes wrong when careless people make assumptions about numerical recipes. If she does another book, she may include this Deloitte case.
Several observations:
- The management methods used to create these smart systems require scrutiny. The downstream consequences are harmful.
- The developers and programmers can be fired, but the failure to have remediating processes in place when something unexpected surfaces must be part of the work process.
- Less informed users and more smart software strikes me as a combustible mixture. When a system ignites, the impacts may reverberate in other smart systems. What entity is going to fix the problem and accept responsibility? The answer is, “No one” unless there are significant consequences.
The State of Tennessee’s experience makes clear that a “brand name”, slick talk, an air of confidence, and possibly ill-informed managers can do harm. The opioid misstep was bad. Now imagine that type of thinking in the form of a fast, indifferent, and flawed “system.” Firing a 25 year old is not the solution.
Stephen E Arnold, September 3, 2024
The Seattle Syndrome: Definitely Debilitating
August 30, 2024
This essay is the work of a dumb dinobaby. No smart software required.
I think the film “Sleepless in Seattle” included dialog like this:
What do they call it when everything intersects?
The Bermuda Triangle.”
Seattle has Boeing. The company is in the news not just for doors falling off its aircraft. The outfit has stranded two people in earth orbit and has to let Elon Musk bring them back to earth. And Seattle has Amazon, an outfit that stands behind the products it sells. And I have to include Intel Labs, not too far from the University of Washington, which is famous in its own right for many things.
Two job seekers discuss future opportunities in some of Seattle and environ’s most well-known enterprises. The image of the city seems a bit dark. Thanks, MSFT Copilot. Are you having some dark thoughts about the area, its management talent pool, and its commitment to ethical business activity? That’s a lot of burning cars, but whatever.
Is Seattle a Bermuda Triangle for large companies?
This question invites another; specifically, “Is Microsoft entering Seattle’s Bermuda Triangle?
The giant outfit has entered a deal with the interesting specialized software and consulting company Palantir Technologies Inc. This firm has a history of ups and downs since its founding 21 years ago. Microsoft has committed to smart software from OpenAI and other outfits. Artificial intelligence will be “in” everything from the Azure Cloud to Windows. Despite concerns about privacy, Microsoft wants each Windows user’s machine to keep screenshot of what the user “does” on that computer.
Microsoft seems to be navigating the Seattle Bermuda Triangle quite nicely. No hints of a flash disaster like the sinking of the sailing yacht Bayesian. Who could have predicted that? (That’s a reminder that fancy math does not deliver 1.000000 outputs on a consistent basis.
Back to Seattle. I don’t think failure or extreme stress is due to the water. The weather, maybe? I don’t think it is the city government. It is probably not the multi-faceted start up community nor the distinctive vocal tones of its most high profile podcasters.
Why is Seattle emerging as a Bermuda Triangle for certain firms? What forces are intersecting? My observations are:
- Seattle’s business climate is a precursor of broader management issues. I think it is like the pigeons that Greeks examined for clues about their future.
- The individuals who works at Boeing-type outfits go along with business processes modified incrementally to ignore issues. The mental orientation of those employed is either malleable or indifferent to downstream issues. For example, Windows update killed printing or some other function. The response strikes me as “meh.”
- The management philosophy disconnects from users and focuses on delivering financial results. Those big houses come at a cost. The payoff is personal. The cultural impacts are not on the radar. Hey, those quantum Horse Ridge things make good PR. What about the new desktop processors? Just great.
Net net: I think Seattle is a city playing an important role in defining how businesses operate in 2024 and beyond. I wish I was kidding. But I am bedeviled by reminders of a space craft which issues one-way tickets, software glitches, and products which seem to vary from the online images and reviews. (Maybe it is the water? Bermuda Triangle water?)
Stephen E Arnold, August 30, 2024
Good Enough: The New Standard of Excellence
August 20, 2024
![green-dino_thumb_thumb_thumb_thumb_t[1]](https://arnoldit.com/wordpress/wp-content/uploads/2024/08/green-dino_thumb_thumb_thumb_thumb_t1.gif "green-dino_thumb_thumb_thumb_thumb_t[1]")
This essay is the work of a dumb dinobaby. No smart software required.
I read an interesting essay about software development. “[The] Biggest Productivity Killers in the Engineering Industry” presents three issues which add to the time and cost of a project. Let’s look at each of these factors and then one trivial downstream consequence of implementing these productivity touchpoints.
The three killers are:
- Working on a project until it meets one’s standards of “perfectionism.” Like “love” and “ethics”, perfectionism is often hard to define without a specific context. A designer might look at an interface and its colors and say, “It’s perfect.” The developer or, heaven forbid, the client looks and says, “That sucks.” Oh, oh.
- Stalling; that is, not jumping right into a project and making progress. I worked at an outfit which valued what it called “an immediate and direct response.” The idea is that action is better than reaction. Plus is demonstrates that one is not fooling around.
- Context switching; that is, dealing with other priorities or interruptions.
I want to highlight one of these “killers” — The need for “good enough.” The essay contains some useful illustrations. Here’s the one for the perfectionism-good enough trade off. The idea is pretty clear. As one chases getting the software or some other task “perfect” means that more time is required. The idea is that if something takes too long, then the value of chasing perfectionism hits a cost wall. Therefore, one should trade off time and value by turning in the work when it is good enough.
The logic is understandable. I do have one concern not addressed in the essay. I believe my concern applies to the other two productivity killers, stalling and interruptions (my term for context switching).
What is this concern?
How about doors falling off aircraft, stranded astronauts, cybersecurity which fails to protect Social Security Numbers, and city governments who cannot determine if compromised data were “good” or “corrupted.” We just know the data were compromised. There are other examples; for instance, the CrowdStrike misstep which affected only a few million people. How did CrowdStrike happen? My hunch is that “good enough” thinking was involved along with someone putting off making sure the internal controls were actually controlling and interruptions so the person responsible for software controls was pulled into a meeting instead of finishing and checking his or her work.
The difficulty is composed of several capabilities; specifically:
- Does the person doing the job know how to make it work in a good enough manner? In my experience, the boss may not and simply wants the fix implemented now or the product shipped immediately.
- Does the company have a culture of excellence or is it similar to big outfits which cannot deliver live streaming content, allow reviewers to write about a product without threatening them, or provide tactics which kill people because no one on the team understands the concept of ethical behavior? Frankly, today I am not sure any commercial enterprise cares about much other than revenue.
- Does anyone in a commercial organization have responsibility to determine the practical costs of shipping a product or delivering a service that does not deliver reliable outputs? Reaction to failed good enough products and services is, in my opinion, the management method applied to downstream problems.
Net net: Good enough, like it or not, is the new gold standard. Or, is that standard like the Olympic medals, an amalgam. The “real” gold is a veneer; the “good” is a coating on enough.
Stephen E Arnold, August 20, 2024
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DeepMind Explains Imagination, Not the Google Olympic Advertisement
August 8, 2024
This essay is the work of a dinobaby. Unlike some folks, no smart software improved my native ineptness.
I admit it. I am suspicious of Google “announcements,” ArXiv papers, and revelations about the quantumly supreme outfit. I keep remembering the Google VP dead on a yacht with a special contract worker. I know about the Googler who tried to kill herself because a dalliance with a Big Time Google executive went off the rails. I know about the baby making among certain Googlers in the legal department. I know about the behaviors which the US Department of Justice described as “monopolistic.”
When I read “What Bosses Miss about AI,” I thought immediately about Google’s recent mass market televised advertisement about uses of Google artificial intelligence. The set up is that a father (obviously interested in his progeny) turned to Google’s generative AI to craft an electronic message to the humanoid. I know “quality time” is often tough to accommodate, but an email?
The Googler who allegedly wrote the cited essay has a different take on how to use smart software. First, most big-time thinkers are content with AI performing cost-reduction activities. AI is less expensive than a humanoid. These entities require health care, retirement, a shoulder upon which to cry (a key function for personnel in the human relations department), and time off.
Another type of big-time thinker grasps the idea that smart software can make processes more efficient. The write up describes this as the “do what we do, just do it better” approach to AI. The assumption is that the process is neutral, and it can be improved. Imagine the value of AI to Vlad the Impaler!
The third category of really Big Thinker is the leader who can use AI for imagination. I like the idea of breaking a chaotic mass of use cases into categories anchored to the Big Thinkers who use the technology.
However, I noted what I think is unintentional irony in the write up. This chart shows the non-AI approach to doing what leadership is supposed to do:
What happens when a really Big Thinker uses AI to zip through this type of process. The acceleration is delivered from AI. In this Googler’s universe, I think one can assume Google’s AI plays a modest role. Here’s the payoff paragraph:
Traditional product development processes are designed based on historical data about how many ideas typically enter the pipeline. If that rate is constant or varies by small amounts (20% or 50% a year), your processes hold. But the moment you 10x or 100x the front of that pipeline because of a new scientific tool like AlphaFold or a generative AI system, the rest of the process clogs up. Stage 1 to Stage 2 might be designed to review 100 items a quarter and pass 5% to Stage 2. But what if you have 100,000 ideas that arrive at Stage 1? Can you even evaluate all of them? Do the criteria used to pass items to Stage 2 even make sense now? Whether it is a product development process or something else, you need to rethink what you are doing and why you are doing it. That takes time, but crucially, it takes imagination.
Let’s think about this advice and consider the imagination component of the Google Olympics’ advertisement.
- Google implemented a process, spent money, did “testing,” ran the advert, and promptly withdrew it. Why? The ad was annoying to humanoids.
- Google’s “imagination” did not work. Perhaps this is a failure of the Google AI and the Google leadership? The advert succeeded in making Google the focal point of some good, old-fashioned, quite humanoid humor. Laughing at Google AI is certainly entertaining, but it appears to have been something that Google’s leadership could not “imagine.”
- The Google AI obviously reflects Google engineering choices. The parent who must turn to Google AI to demonstrate love, parental affection, and support to one’s child is, in my opinion, quite Googley. Whether the action is human or not might be an interesting topics for a coffee shop discussion. For non-Googlers, the idea of talking about what many perceived as stupid, insensitive, and inhumane is probably a non-started. Just post on social media and move on.
Viewed in a larger context, the cited essay makes it clear that Googlers embrace AI. Googlers see others’ reaction to AI as ranging from doltish to informed. Google liked the advertisement well enough to pay other companies to show the message.
I suggest the following: Google leadership should ask several AI systems if proposed advertising copy can be more economical. That’s a Stage 1 AI function. Then Google leadership should ask several AI systems how the process of creating the ideas for an advertisement can be improved. That’s a Stage 2 AI function. And, finally, Google leadership should ask, “What can we do to prevent bonkers problems resulting from trying to pretend we understand people who know nothing and care less about the three “stages” of AI understanding.
Will that help out the Google? I don’t need to ask an AI system. I will go with my instinct. The answer is, “No.”
That’s one of the challenges Google faces. The company seems unable to help itself do anything other than sell ads, promote its AI system, and cruise along in quantumly supremeness.
Stephen E Arnold, August 8, 2024
Train AI on Repetitive Data? Sure, Cheap, Good Enough, But, But, But
August 8, 2024
We already know that AI algorithms are only as smart as the data that trains them. If the data models are polluted with bias such as racism and sexism, the algorithms will deliver polluted results. We’ve also learned that while some of these models are biased because of innocent ignorance. Nature has revealed that AI algorithms have yet another weakness: “AI Models Collapse When Trained On Recursively Generated Data.”
Generative text AI aka large language models (LLMs) are already changing the global landscape. While generative AI is still in its infancy, AI developers are already designing the next generation. There’s one big problem: LLMs. The first versions of Chat GPT were trained on data models that scrapped content from the Internet. GPT continues to train on models using the same scrapping methods, but it’s creating a problem:
“If the training data of most future models are also scraped from the web, then they will inevitably train on data produced by their predecessors. In this paper, we investigate what happens when text produced by, for example, a version of GPT forms most of the training dataset of following models. What happens to GPT generations GPT-{n} as n increases? We discover that indiscriminately learning from data produced by other models causes ‘model collapse’—a degenerative process whereby, over time, models forget the true underlying data distribution, even in the absence of a shift in the distribution over time.”
The generative AI algorithms are learning from copies of copies. Over time the integrity of the information fails. The research team behind the Nature paper discovered that model collapse is inevitable when with the most ideal conditions. The team did discover two possibilities to explain model collapse: intentional data poisoning and task-free continual learning. Those don’t explain recursive data collapse with models free of those events.
The team concluded that the best way for generative text AI algorithms to learn was continual interaction learning from humans. In other words, the LLMs need constant, new information created by humans to replicate their behavior. It’s simple logic when you think about it.
Whitney Grace, August 8, 2024
Publishers Perplexed with Perplexity
August 7, 2024
In an about-face, reports Engadget, “Perplexity Will Put Ads in it’s AI Search Engine and Share Revenue with Publishers.” The ads part we learned about in April, but this revenue sharing bit is new. Is it a response to recent accusations of unauthorized scraping and plagiarism? Nah, the firm insists, the timing is just a coincidence. While Perplexity won’t reveal how much of the pie they will share with publishers, the company’s chief business officer Dmitry Shevelenko described it as a “meaningful double-digit percentage.” Engadget Senior Editor Pranav Dixit writes:
“‘[Our revenue share] is certainly a lot more than Google’s revenue share with publishers, which is zero,’ Shevelenko said. ‘The idea here is that we’re making a long-term commitment. If we’re successful, publishers will also be able to generate this ancillary revenue stream.’ Perplexity, he pointed out, was the first AI-powered search engine to include citations to sources when it launched in August 2022.”
Defensive much? Dixit reminds us Perplexity redesigned that interface to feature citations more prominently after Forbes criticized it in June.
Several AI companies now have deals to pay major publishers for permission to scrape their data and feed it to their AI models. But Perplexity does not train its own models, so it is taking a piece-work approach. It will also connect advertisements to searches. We learn:
“‘Perplexity’s revenue-sharing program, however, is different: instead of writing publishers large checks, Perplexity plans to share revenue each time the search engine uses their content in one of its AI-generated answers. The search engine has a ‘Related’ section at the bottom of each answer that currently shows follow-up questions that users can ask the engine. When the program rolls out, Perplexity plans to let brands pay to show specific follow-up questions in this section. Shevelenko told Engadget that the company is also exploring more ad formats such as showing a video unit at the top of the page. ‘The core idea is that we run ads for brands that are targeted to certain categories of query,’ he said.”
The write-up points out the firm may have a tough time breaking into an online ad business dominated by Google and Meta. Will publishers hand over their content in the hope Perplexity is on the right track? Launched in 2022, the company is based in San Francisco.
Cynthia Murrell, August 7, 2024
Judgment Before? No. Backing Off After? Yes.
August 5, 2024
I wanted to capture two moves from two technology giants. The first item is the report that Google pulled the oh-so-Googley ad about a father using Gemini to write personal note to his daughter. If you are not familiar with the burst of creative marketing, you can glean a few details from “Google Pulls Gemini AI Ad from Olympics after Backlash.” The second item is the report that according to Bloomberg, “Apple Pulls Commercial After Thai Backlash, Calls for Boycott.”
I reacted to these two separate announcements by thinking about what these do it-reverse it decisions suggest about the management controls at two technology giants.
Some management processes operated to think up the ad ideas. Then the project had to be given the green light from “leadership” at the two outfits. Next third party providers had to be enlisted to do some of the “knowledge work”. Finally, I assume there were meetings to review the “creative.” Finally, one ad from several candidates was selected by each firm. The money paid. And then the ads appeared. That’s a lot of steps and probably more than two or three people working in a cube next to a Foosball tables.
Plus, the about faces by the two companies did not take much time. Google caved after a few days. Apple also hopped on its havester and chopped the India advertisement quickly as well. Decisiveness. Actually decisiveness after the fact.
Why not less obvious processes like using better judgment before releasing the advertisements? Why not focus on working with people who are more in tune with audience reactions than being clever, smooth talking, and desperate-eager for big company money?
Several observations:
- Might I hypothesize that both companies lack a fabric of common sense?
- If online ads “work,” why use what I would call old-school advertising methods? Perhaps the online angle is not correct for such important messaging from two companies that seem to do whatever they want most of the time?
- The consequences of these do-then-undo actions are likely to be close to zero. Is that what operating in a no-consequences environment fosters?
I wonder if the back away mentality is now standard operating procedure. We have Intel and nVidia with some back-away actions. We have a nation state agreeing to a plea bargain and the un-agreeing the next day. We have a net neutraility rule, then don’t, then we do, and now we don’t. Now that I think about it, perhaps because there are no significant consequences, decision quality has taken a nose dive?
Some believe that great complexity sets the stage for bad decisions which regress to worse decisions.
Stephen E Arnold, August 5, 2024
Fancy Cyber Methods Are Useless Against Insider Threats
August 2, 2024
This essay is the work of a dinobaby. Unlike some folks, no smart software improved my native ineptness.
In my lectures to law enforcement and intelligence professionals, I end the talks with one statement: “Do not assume. Do not reduce costs by firing experienced professionals. Do not ignore human analyses of available information. Do not take short cuts.” Cyber security companies are often like the mythical kids of the village shoemaker. Those who can afford to hire the shoemaker have nifty kicks and slides. Those without resources have almost useless footware.
Companies in the security business often have an exceptionally high opinion of their capabilities and expertise. I think of this as the Google Syndrome or what some have called by less salubrious names. The idea is that one is just so smart, nothing bad can happen here. Yeah, right.
An executive answers questions about a slight security misstep. Thanks, Microsoft Copilot. You have been there and done that I assume.
I read “North Korean Hacker Got Hired by US Security Vendor, Immediately Loaded Malware.” The article is a reminder that outfits in the OSINT, investigative, and intelligence business can make incredibly interesting decisions. Some of these lead to quite significant consequences. This particular case example illustrates how a hiring process using humans who are really smart and dedicated can be fooled, duped, and bamboozled.
The write up explains:
KnowBe4, a US-based security vendor, revealed that it unwittingly hired a North Korean hacker who attempted to load malware into the company’s network. KnowBe4 CEO and founder Stu Sjouwerman described the incident in a blog post yesterday, calling it a cautionary tale that was fortunately detected before causing any major problems.
I am a dinobaby, and I translated the passage to mean: “We hired a bad actor but, by the grace of the Big Guy, we avoided disaster.”
Sure, sure, you did.
I would suggest you know you trapped an instance of the person’s behavior. You may not know and may never know what that individual told a colleague in North Korea or another country what the bad actor said or emailed from a coffee shop using a contact’s computer. You may never know what business processes the person absorbed, converted to an encrypted message, and forwarded via a burner phone to a pal in a nation-state whose interests are not aligned with America’s.
In short, the cyber security company dropped the ball. It need not feel too bad. One of the companies I worked for early in my 60 year working career hired a person who dumped top secrets into journalists’ laps. Last week a person I knew was complaining about Delta Airlines which was shown to be quite addled in the wake of the CrowdStrike misstep.
What’s the fix? Go back to how I end my lectures. Those in the cyber security business need to be extra vigilant. The idea that “we are so smart, we have the answer” is an example of a mental short cut. The fact is that the company KnowBe4 did not. It is lucky it KnewAtAll. Some tips:
- Seek and hire vetted experts
- Question procedures and processes in “before action” and “after action” incidents
- Do not rely on assumptions
- Do not believe the outputs of smart software systems
- Invest in security instead of fancy automobiles and vacations.
Do these suggestions run counter to your business goals and your image of yourself? Too bad. Life is tough. Cyber crime is the growth business. Step up.
Stephen E Arnold, August 2, 2024
A Reliability Test for General-Purpose AI
August 1, 2024
A team of researchers has developed a valuable technique: “How to Assess a General-Purpose AI Model’s Reliability Before It’s Deployed.” The ScienceDaily article begins by defining foundation models—the huge, generalized deep-learning models that underpin generative AI like ChatGPT and DALL-E. We are reminded these tools often make mistakes, and that sometimes these mistakes can have serious consequences. (Think self-driving cars.) We learn:
“To help prevent such mistakes, researchers from MIT and the MIT-IBM Watson AI Lab developed a technique to estimate the reliability of foundation models before they are deployed to a specific task. They do this by considering a set of foundation models that are slightly different from one another. Then they use their algorithm to assess the consistency of the representations each model learns about the same test data point. If the representations are consistent, it means the model is reliable. When they compared their technique to state-of-the-art baseline methods, it was better at capturing the reliability of foundation models on a variety of downstream classification tasks. Someone could use this technique to decide if a model should be applied in a certain setting, without the need to test it on a real-world dataset. This could be especially useful when datasets may not be accessible due to privacy concerns, like in health care settings. In addition, the technique could be used to rank models based on reliability scores, enabling a user to select the best one for their task.”
Great! See the write-up for the technical details behind the technique. This breakthrough can help companies avoid mistakes before they launch their products. That is, if they elect to use it. Will organizations looking to use AI for cost cutting go through these processes? Sadly, we suspect that, if costs go down and lawsuits are few and far between, the AI is deemed good enough. But thanks for the suggestion, MIT.
Cynthia Murrell, August 1, 2024
Crowd What? Strike Who?
July 24, 2024
This essay is the work of a dumb dinobaby. No smart software required.
How are those Delta cancellations going? Yeah, summer, families, harried business executives, and lots of hand waving. I read a semi-essay about the minor update misstep which caused blue to become a color associated with failure. I love the quirky sad face and the explanations from the assorted executives, experts, and poohbahs about how so many systems could fail in such a short time on a global scale.
In “Surely Microsoft Isn’t Blaming EU for Its Problems?” I noted six reasons the CrowdStrike issue became news instead of a system administrator annoyance. In a nutshell, the reasons identified harken back to Microsoft’s decision to use an “open design.” I like the phrase because it beckons a wide range of people to dig into the plumbing. Microsoft also allegedly wants to support its customers with older computers. I am not sure older anything is supported by anyone. As a dinobaby, I have first-hand experience with this “we care about legacy stuff.” Baloney. The essay mentions “kernel-level access.” How’s that working out? Based on CrowdStrike’s remarkable ability to generate PR from exceptions which appear to have allowed the super special security software to do its thing, that access sure does deliver. (Why does the nationality of CrowdStrike’s founder not get mentioned? Dmitri Alperovitch, a Russian who became a US citizen and a couple of other people set up the firm in 2012. Is there any possibility that the incident was a test play or part of a Russian long game?)
Satan congratulates one of his technical professionals for an update well done. Thanks, MSFT Copilot. How’re things today? Oh, that’s too bad.
The essay mentions that the world today is complex. Yeah, complexity goes with nifty technology, and everyone loves complexity when it becomes like an appliance until it doesn’t work. Then fixes are difficult because few know what went wrong. The article tosses in a reference to Microsoft’s “market size.” But centralization is what an appliance does, right? Who wants a tube radio when the radio can be software defined and embedded in another gizmo like those FM radios in some mobile devices. Who knew? And then there is a reference to “security.” We are presented with a tidy list.
The one hitch in the git along is that the issue emerges from a business culture which has zero to do with technology. The objective of a commercial enterprise is to generate profits. Companies generate profits by selling high, subtracting costs, and keeping the rest for themselves and stakeholders.
Hiring and training professionals to do jobs like quality checks, environmental impact statements, and ensuring ethical business behavior in work processes is overhead. One can hire a blue chip consulting firm and spark an opioid crisis or deprecate funding for pre-release checks and quality assurance work.
Engineering excellence takes time and money. What’s valued is maximizing the payoff. The other baloney is marketing and PR to keep regulators, competitors, and lawyers away.
The write up encapsulates the reason that change will be difficult and probably impossible for a company whether in the US or Ukraine to deliver what the customer expects. Regulators have failed to protect citizens from the behaviors of commercial enterprises. The customers assume that a big company cares about excellence.
I am not pessimistic. I have simply learned to survive in what is a quite error-prone environment. Pundits call the world fragile or brittle. Those words are okay. The more accurate term is reality. Get used to it and knock off the jargon about failure, corner cutting, and profit maximization. The reality is that Delta, blue screens, and yip yap about software chock full of issues define the world.
Fancy talk, lists, and entitled assurances won’t do the job. Reality is here. Accept it and blame.
Stephen E Arnold, July 24, 2024
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