Creepy Robots? Absolutely
January 29, 2026
Did you know that robotics has advanced so much they’re now making robots smaller than a grain of salt? It’s something out of science-fiction, but Science Daily shares the scoop on these tiny machines: “Scientists Create Robots Smaller Than A Grain Of Salt That Can Think.” University of Pennsylvania assistant professor Marc Miskin is the senior author of a paper that describes these itty bitty wonders.
These teeny tiny robots are called microrobots. They measure 300x200x50 micrometers and that’s barely visible without magnification. They’re programmed to swim, think, and survive for long stretches of time. What’s even cooler is that they’re powered by light.
Each microrobot is equipped with a microscopic computer that contains programmed instructions. The computers can detect temperature changes and the microrobot adjusts their movements accordingly. They move through using their own propulsion:
"Instead of bending or flexing, the robots generate an electrical field that gently pushes charged particles in the surrounding liquid. As those ions move, they drag nearby water molecules with them, effectively creating motion in the fluid around the robot. ‘It’s as if the robot is in a moving river,’ says Miskin, ‘but the robot is also causing the river to move.’”
Miskin worked with David Blaauw’s team from the University of Michigan to design solar panels that would power the robots. The team also redesigned the robots’ programmed instructions so they would inside the computer’s limited memory.
There are limitless applications for these minuscule robots, from medical to weapons usage. Let’s hope they’re used for good and not evil.
Whitney Grace, January 29, 2026
Management Is the Problem, Not Technology
January 22, 2026
Inc. said at the end of 2025 that “The Tech Industry Is Dying” and offered its opinions about how to fix it up the ageing buggy. One of its editorials says the tech industry is being demoted to a commodity like insurance and other and a doc in the box in a strip mall. The reason? Top talent isn’t being nurtured and desirable products aren’t being designed and built.
Now this premise strikes me as a management challenge. A worker has to know what the job or task is. If a manager cannot explain it, how can the technology worker know what to do? The answer seems to me that the employee has to figure that out alone. No wonder outfits like Amazon can bring down half of the US Web sites or Verizon outages leave people without mobile access or law enforcement without communications.
But let’s look at what Inc. thinks:
The editorial’s author Joe Procopio suggests five ways to save the industry are as common sense. For example, don’t wait for others to innovate and always meet shortsightedness with facts. Groupthink is another hurdle to jump that is worse to maneuver than meerkats over a cliff:
“Consensus rule is dominating the tech industry more than it ever has. I’ll explain why this happens. My wife brought home a game where everyone takes turns being the first to guess an answer to a question by placing their marker on one of a few hundred options. It sucks having to go first, because then you don’t have the luxury of being able to put your marker closer to the consensus of markers. Then, when the answer is revealed, everyone can see just how wrong you were. If you want to lead, you can’t just be right. You have to convince everyone that their marker should be near your marker. Techies are terrible at getting consensus. Hopefully that analogy I just gave you helps you understand what you’re dealing with, so you can fight groupthink in a way that doesn’t get you ostracized and fired.”
Procopio says assume control over AI and remember to take risks (within reason of course). These suggestions sound like a self-help motivational course, not just ways to improve the tech industry.
Let’s think about this “consensus.” An employee without an effective manager or even a coherent job description may talk with a colleague. The colleague’s views become the input the employee needs. Calling this “consensus” misses the point. Organizations with managers who are not able to perform the employee’s job cannot provide guidance. Therefore, non-management allows the manager to say, “You work it out with your team.”
The team may be clueless. The results of this approach are visible in many firms. How many Copilot features are available? How many different interfaces does Google present to its users? How many products on eBay are as described?
In my opinion, Inc. dodges the core issue: Management methods deliver cleverness, an individual’s idea of what must be done, and an ultimately unstable technical house of cards. Is 2026 providing examples of positive change? I can’t think of one, but it is early in the year. I am not optimistic. My Internet just went down… again.
Whitney Grace, January 22, 2026
The Drivers for 2026
January 14, 2026
The new year is here. Decrypt.co runs down the high and lows in, “Emerge’s 2025 Story of the Year: How the AI Race Fractured the Global Tech Order.” The main events of 2025 revolve around China and the US battling for dominance over the AI market. With only $256,000, the young Chinese startup Deepseek claimed it trained an AI model that matched OpenAI. OpenAI spent over a hundred million to arrive at the same result.
After Deepseek hit the Apple app store, Nvidia lost $600 billion in revenue as the largest drop in market history. Nvidia’s China market share fell from 95% to zero. The Chinese government banned all foreign AI chips from its datacenters, then the US Pentagon signed $10 billion in AI defense contracts.
China and the US are now warring a cold technology war. Deepseek exposed the US’s belief that controlling advanced chips would hinder China. Here’s how the US responded:
“The AI market entered panic mode. Stocks tanked, politicians started polishing their patriotic speeches, analysis exposed the intricacies of what could end up in a bubble, and enthusiasts mocked American models that cost orders of magnitude more than the Chinese counterparts, which were free, cheap and required a fraction of the money and resources to train.
Washington’s response was swift and punishing. The Trump administration expanded export controls throughout the year, banning even downgraded chips designed specifically for the Chinese market. By April, Trump restricted Nvidia from shipping its H20 chips.”
Meanwhile China retaliated:
“The tit-for-tat escalated into full decoupling. A new China’s directive issued in September banned Nvidia, AMD, and Intel chips from any data center receiving government money—a market worth over $100 billion since 2021. Jensen Huang revealed the company’s market share in China had hit "zero, compared to 95% in 2022.”
The US lost a big market for chips and China’s chip manufacturers increased domestic production by 40%. The US then implemented tariffs, then China responded by exerting its control over the physical elements needed to make technology in the strictest rare earth export controls ever. China wants to hit US defenses hard.
The Pentagon then invested in MP Materials with a cool $400 million. Trump also signed the Genesis Mission executive order, a Department of Energy-led AI initiative that the Trump administration compared to the Manhattan Project. Then China did…etc, etc.
Net net: Hype and hostility are the fuels for the months ahead. Hey, that’s positive Decrypt.
Whitney Grace, January 14, 2026
A Revised AI Glossary for 2026
January 5, 2026
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Another dinobaby post. No AI unless it is an image. This dinobaby is not Grandma Moses, just Grandpa Arnold.
I have a lot to do. I spotted this article: “ChatGPT Glossary: 61 AI Terms Everyone Should Know.” I read the list and the definitions. I have decided to invest a bit of time to de-obfuscate this selection of verbal gymnastics. My hunch is that few will be amused. I, however, find this type of exercise very entertaining. On the other hand, my reframing of this “everyone should know” stuff reflects on my role as an addled dinobaby limping around rural Kentucky.
Herewith my recasting of the “everyone should know” list. Remember. Everyone means everyone. That’s a categorical affirmative, and these assertions trigger me.
Artificial general intelligence. Sci-fi craziness from “we can rule the world” wizards
Agentive. You, human, don’t do this stuff anymore.
AI ethics. Anything goes, bros.
AI psychosis. It’s software and it’s driving you nuts.
AI safety. Sure, only if something does not make money or abrogate our control
Algorithms. Numerical recipes explained in classes normal people do not take
Alignment. Weaponizing models and information outputs
Anthropomorphism. Sure, fall in love with outputs. We don’t really care. Just click on the sponsored content.
Artificial intelligence. Code words for baloney and raising money
Autonomous agents. Stay home and make stuff to sell on Etsy
Bias. Our way is the only way
Chatbot. Talk to our model, pal
Claude. An example of tech bro-loney
Cognitive computing. A librarian consultant’s contribution to gibberish
Data augmentation. Indexing
Dataset. Anything an AI outfit can grab and process
Deep learning. Pretending to be smart
Diffusion. Moral dissipation and hot gas
Emergent behavior. Shameless rip off of the Santa Fe Institute and Walter Kaufman
End-to-end learning. Update models instead of retraining them
Ethical considerations. Pontifical statements or “"Those are my ethical principles, and if you don’t like them… well, I have others."
Foom. GenZ’s spelling of the Road Runner’s cartoon beep beep
Generative adversarial network. Jargon fog for inputs along the way to an output
Generative AI. Reason to fire writers and PR people
Google Gemini. An example of tech bro-loney from an ad sales outfit
Guardrails. Stuff to minimize suicides, law suits, and the proliferation of chemical weapons
Hallucination. Errors
Inference. Guesses
LLM. Today’s version of LSMFT
Machine learning. Math from half century ago
Microsoft Bing. Beats the heck out of me
Multimodal AI. A fancy way to say words, sound, pix, and video to help un-employ humans or did this type of work
Natural language processing. Software that understands William Carlos Williams’ poetry
Neural network. Lots of probability and human-fiddled thresholds
Open weights. You can put your finger on the scale too
Overfitting. Baloney about hallucinations, being wrong, and helping kids commit de-living
Paperclips. Less sexy than The Terminator but loved by tech bros who like the 1999 film Office Space
Parameters. Where you put your finger on the scale to fiddle outputs
Perplexity. Another example of tech bro-loney
Prompt. A query
Prompt chaining. Related queries fed into the baloney machine
Prompt engineering. Hunting for words and phrases to output pornography, instructions for making poison gas, and ways to defraud elders online
Prompt injection. Pressing enter after prompt engineering
Quantization. Jargon to say, “We won’t need so much money now, Mr. Venture Bankman”
Slop. Outputs from smart software
Sora. Lights, camera, you’re fired. Cut.
Stochastic parrot. A bound phrase that allowed Google to give Timnit Gebru a chance to find her future elsewhere
Style transfer. You too can generate a sketch in the style of Max Ernst and a Batman comic book
Sycophancy. AI models emulate new hires at McKinsey & Company
Synthetic data. Hey, we just fabricate data. No copyright problems, right
Temperature. A fancy way to explain twiddling with parameters
Text-to-image generation. Artists. Who needs them?
Tokens. n-grams but to crypto dudes it’s value
Training data. Copyright protected information, personally identifiable information, and confidential inputs in any format, even the synthetic made up stuff
Transformer model. A reason for Google leadership to ask, “Why did we release this to open source?”
Turing test. Do you love me? Of course, the system does. Now read the sponsored content
Unsupervised learning. Automated theft of training data
Weak AI (narrow AI). A model trained on a bounded data set, not whatever the AI company can suck down
Zero-shot learning. A stepping stone to artificial intelligence able to do more than any miserable human
I love smart software.
Oh, the cited source leaves out OpenAI’s ChatGPT. This means “Titanic” after the iceberg.
Stephen E Arnold, January 5, 2025
Tech Whiz Wants to Go Fishing (No, Not Phishing), Hook, Link, Sinker Stuff
December 17, 2025
Another dinobaby post. No AI unless it is an image. This dinobaby is not Grandma Moses, just Grandpa Arnold.
My deeply flawed service that feeds me links produced a rare gem. “I Work in Tech But I Hate Everything Big Tech Has Become” is interesting because it states clearly what I have heard from other Silicon Valley types recently. I urge you to read the essay because the discomfort the author feels jumps off the screen or printed page if you are a dinobaby. If the essay has a rhetorical weakness, it is no resolution. My hunch is that the author has found himself in a digital construct with No Exit signs on the door.
Thanks, Venice.ai. Good enough.
The essay states:
We try to build products that help people. We try to solve mostly problems we ourselves face using tech. We are nerds, misfits, borderline insane people driven by a passion to build. we could probably get a job in big tech if we tried as hard as we try building our own startup. but we don’t want to. in fact we can’t. we’d have to kill a little (actually a lot) of ourselves to do that.
This is an interesting comment. I interpreted it to mean that the tech workers and leadership who build “products that help people” are have probably “killed” some of their inner selves. I never thought of the luminaries who head the outfits pushing AI or deploying systems that governments have to ban for users under a certain age as being dead inside. Is it true? I am not sure. Thought provoking notion? Yes.
The essay states:
I hate everything big tech stands for today. Facebook openly admitting they earn millions from scam ads. VCs funding straight up brain rot or gambling. Big tech is not even pretending to be good these days.
The word “hate” provides a glimpse of how the author is responding to the current business set up in certain sectors of the technology industry. Instead of focusing on what might be called by some dinobaby like me as “ethical behavior” is viewed as abnormal by many people. My personal view is that this idea of doing whatever to reach a goal operates across many demographics. Is this a-ethical behavior now the norm.
The essay states:
If tech loses people like us, all it’ll have left are psychopaths. Look I’m not trying to take a holier-than-thou stance here. I’m just saying objectively it seems insane what’s happening in mainstream tech these days.
I noted a number of highly charged words. These make sense in the context of the author’s personal situation. I noted “psychopaths” and “insane.” When many instances of a-ethical behavior bubble up from technical, financial, and political sectors, a-ethics mean one cannot trust, rely, or believe words. Actions alone must be scrutinized.
The author wants to “keep fighting,” but against who or what system? Deception, trickery, double dealing, criminal activity can be identified in most business interactions.
The author mentions going fishing. The caution I would offer is to make sure you are not charged a dynamic price based on your purchasing profile. Shop around if any fishing stores are open. If not, Amazon will deliver what you need.
Stephen E Arnold, December 17, 2025
Google Presents an Innovative Way to Say, “Generate Revenue”
December 9, 2025
Another dinobaby post. No AI unless it is an image. This dinobaby is not Grandma Moses, just Grandpa Arnold.
One of my contacts sent me a link to an interesting document. Its title is “A Pragmatic Vision for Interpretability.” I am not sure about the provenance of the write up, but it strikes me as an output from legal, corporate, and wizards. First impression: Very lengthy. I estimate that it requires about 11,000 words to say, “Generate revenue.” My second impression: A weird blend of consulting speak and nervousness.
A group of Googlers involved in advanced smart software ideation get a phone call clarifying they have to hit revenue targets. No one looks too happy. The esteemed leader is on the conference room wall. He provides a North Star to the wandering wizards. Thanks, Venice.ai. Good enough, just like so much AI system output these days.
The write up is too long to meander through its numerous sections, arguments, and arm waving. I want to highlight three facets of the write up and leave it up to you to print this puppy out, read it on a delayed flight, and consider how different this document is from the no output approach Google used when it was absolutely dead solid confident that its search-ad business strategy would rule the world forever. Well, forever seems to have arrived for Googzilla. Hence, be pragmatic. This, in my experience, is McKinsey speak for hit your financial targets or hit the road.
First, consider this selected set of jargon:
Comparative advantage (maybe keep up with the other guys?)
Load-bearing beliefs
Mech Interp” / “mechanistic interpretability” (as opposed to “classic” interp)
Method minimalism
North Star (is it the person on the wall in the cartoon or just revenue?)
Proxy task
SAE (maybe sparse autoencoders?)
Steering against evaluation awareness (maybe avoiding real world feedback?)
Suppression of eval-awareness (maybe real-world feedback?)
Time-box for advanced research
The document tries to hard to avoid saying, “Focus on stuff that makes money.” I think that, however, is what the word choice is trying to present in very fancy, quasi-baloney jingoism.
Second, take a look at the three sets of fingerprints in what strikes me as a committee-written document.
- Researchers want to just follow their ideas about smart software just as we have done at Google for many years
- Lawyers and art history majors who want to cover their tailfeathers when Gemini goes off the rails
- Google leadership who want money or at the very least research that leads to products.
I can see a group meeting virtually, in person, and in the trenches of a collaborative Google Doc until this masterpiece of management weirdness is given the green light for release. Google has become artful in make work, wordsmithing, and pretend reconciliation of the battles among the different factions, city states, and empires within Google. One can almost anticipate how the head of ad sales reacts to money pumped into data centers and research groups who speak a language familiar to Klingons.
Third, consider why Google felt compelled to crank out a tortured document to nail on the doors of an AI conference. When I interacted with Google over a number of years, I did not meet anyone reminding me of Martin Luther. Today, if I were to return to Shoreline Drive, I might encounter a number of deep fakes armed with digital hammers and fervid eyes. I think the Google wants to make sure that no more Loons and Waymos become the butt of stand up comedians on late night TV or (heaven forbid, TikTok). The dead cat in the Mission and the dead puppy in what’s called (I think) the Western Addition. (I used to live in Berkeley, and I never paid much attention to the idiosyncratic names slapped on undifferentiable areas of the City by the Bay.)
I think that Google leadership seeks in this document:
- To tell everyone it is focusing on stuff that sort of works. The crazy software that is just like Sundar is not on the to do list
- To remind everyone at the Google that we have to pay for the big, crazy data centers in space, our own nuclear power plants, and the cost of the home brew AI chips. Ads alone are no longer going to be 24×7 money printing machines because of OpenAI
- To try to reduce the tension among the groups, cliques, and digital street gangs in the offices and the virtual spaces in which Googlers cogitate, nap, and use AI to be more efficient.
Net net: Save this document. It may become a historical artefact.
Stephen E Arnold, December 9, 2025
The Web? She Be Dead
December 9, 2025
Journalists, Internet experts, and everyone with a bit of knowledge has declared the World Wide Web dead for thirty years. The term “World Wide Web” officially died with the new millennium, but what about the Internet itself? Ernie Smith at Tedium wrote about the demise of the Web: “The Sky Is Falling, The Web Is Dead.” Smith noticed that experts stated the Web is dead many times and he decided to investigate.
He turned to another expert: George Colony, the founder of Forrester Research. Forrester Research is a premier tech and business advisory firms in the world. Smith wrote this about Colony and his company:
“But there’s one area where the company—particularly Colony—gets it wrong. And it has to do with the World Wide Web, which Colony declared “dead” or dying on numerous occasions over a 30-year period. In each case, Colony was trying to make a bigger point about where online technology was going, without giving the Web enough credit for actually being able to get there.”
Smith strolls through instances of Colony declaring the Web is dead. The first was in 1995 followed by many other declarations of the dead Web. Smith made another smart observation:
“Can you see the underlying fault with his commentary? He basically assumed that Web technology would never improve and would be replaced with something else—when what actually happened is that the Web eventually integrated everything he wanted, plus more.
Which is funny, because Forrester’s main rival, International Data Corp., essentially said this right in the piece. ‘The web is the dirt road, the basic structure,’ IDC analyst Michael Sullivan-Trainor said. ‘The concept that you can kill the Web and start from square one is ridiculous. We are talking about using the Web, evolving it.’”
The Web and Internet evolve. Technology evolves. Smith has an optimistic view that is true about the Web: “I, for one, think the Web will do what it always does: Democratize knowledge.”
Whitney Grace, December 9, 2025
AI-Yai-Yai: Two Wizards Unload on What VCs and Consultants Ignore
December 2, 2025
Another dinobaby original. If there is what passes for art, you bet your bippy, that I used smart software. I am a grandpa but not a Grandma Moses.
I read “Ilya Sutskever, Yann LeCun and the End of Just Add GPUs.” The write up is unlikely to find too many accelerationists printing out the write up and handing it out to their pals at Philz Coffee. What does this indigestion maker way? Let’s take a quick look.
The write up says:
Ilya Sutskever – co-founder of OpenAI and now head of Safe Superintelligence Inc. – argued that the industry is moving from an “age of scaling” to an “age of research”. At the same time, Yann LeCun, VP & Chief AI Scientist at Meta, has been loudly insisting that LLMs are not the future of AI at all and that we need a completely different path based on “world models” and architectures like JEPA. [Beyond Search note because the author of the article was apparently making assumptions about what readers know. JEPA is short hand for Joint Embedding Predictive Architecture. The idea is to find a recipe to all machines learn about the world in a way a human does.]
I like to try to make things simple. Simple things are easier for me to remember. This passage means: Dead end. New approaches needed. Your interpretation may be different. I want to point out that my experience with LLMs in the past few months have left me with a sense that a “No Outlet” sign is ahead.
Thanks, Venice.ai. The signs are pointing in weird directions, but close enough for horse shoes.
Let’s take a look at another passage in the cited article.
“The real bottleneck [is] generalization. For Sutskever, the biggest unsolved problem is generalization. Humans can:
learn a new concept from a handful of examples
transfer knowledge between domains
keep learning continuously without forgetting everything
Models, by comparison, still need:
huge amounts of data
careful evals (sic) to avoid weird corner-case failures
extensive guardrails and fine-tuning
Even the best systems today generalize much worse than people. Fixing that is not a matter of another 10,000 GPUs; it needs new theory and new training methods.”
I assume “generalization” to AI wizards has this freight of meaning. For me, this is a big word way of saying, “Current AI models don’t work or perform like humans.” I do like the clarity of “needs new theory and training methods.” The “old” way of training has not made too many pals among those who hold copyright in my opinion. The article calls this “new recipes.”
Yann LeCun points out:
LLMs, as we know them, are not the path to real intelligence.
Yann LeCun likes world models. These have these attributes:
- “learn by watching the world (especially video)
- build an internal representation of objects, space and time
- can predict what will happen next in that world, not just what word comes next”
What’s the fix? You can navigate to the cited article and read the punch line to the experts’ views of today’s AI.
Several observations are warranted:
- Lots of money is now committed to what strikes these experts as dead ends
- The move fast and break things believers are in a spot where they may be going too fast to stop when the “Dead End” sign comes into view
- The likelihood of AI companies demonstrating that they can wish, think, and believe they have the next big thing and are operating with a willing suspension of disbelief.
I wonder if they positions presented in this article provide some insight into Google’s building dedicated AI data centers for big buck, security conscious clients like NATO and Pavel Durov’s decision to build the SETI-type of system he has announced.
Stephen E Arnold, December 2, 2025
IBM on the Path to Dyson Spheres But Quantum Networks Come First
November 28, 2025
This essay is the work of a dumb dinobaby. No smart software required.
How does one of the former innovators in Fear, Uncertainty, and Doubt respond to the rare atmosphere of smart software? The answer, in my opinion, appears in “IBM, Cisco Outline Plans for Networks of Quantum Computers by Early 2030s.” My prediction was wrong about IBM. I thought that with a platform like Watson, IBM would aim directly at Freeman Dyson’s sphere. The idea is to build a sphere in space to gather energy and power advanced computing systems. Well, one can’t get to the Dyson sphere without a network of quantum computers. And the sooner the better.
A big thinker conceptualizes inventions anticipated by science fiction writers. The expert believes that if he thinks it, that “it” will become real. Sure, but usually more than a couple of years are needed for really big projects like affordable quantum computers linked via quantum networks. Thanks, Venice.ai. Good enough.
The write up from the “trust” outfit Thomson Reuters says:
IBM and Cisco Systems … said they plan to link quantum computers over long distances, with the goal of demonstrating the concept is workable by the end of 2030. The move could pave the way for a quantum internet, though executives at the two companies cautioned that the networks would require technologies that do not currently exist and will have to be developed with the help of universities and federal laboratories.
Imagine artificial general intelligence is like to arrive about the same time. IBM has Watson. Does this mean that Watson can run on quantum computers. Those can solve the engineering challenges of the Dyson sphere. IBM can then solve the world’s energy requirements. This sequence seems like a reasonable tactical plan.
The write up points out that building a quantum network poses a few engineering problems. I noted this statement in the news report:
The challenge begins with a problem: Quantum computers like IBM’s sit in massive cryogenic tanks that get so cold that atoms barely move. To get information out of them, IBM has to figure out how to transform information in stationary “qubits” – the fundamental unit of information in a quantum computer – into what Jay Gambetta, director of IBM Research and an IBM fellow, told Reuters are “flying” qubits that travel as microwaves. But those flying microwave qubits will have to be turned into optical signals that can travel between Cisco switches on fiber-optic cables. The technology for that transformation – called a microwave-optical transducer – will have to be developed with the help of groups like the Superconducting Quantum Materials and Systems Center, led by the Fermi National Accelerator Laboratory near Chicago, among others.
Trivial compared to the Dyson sphere confection. It is now sundown for year 2025. IBM and its partner target being operational in 2029. That works out to 24 months. Call it 36 just to add a margin of error.
Several observations:
- IBM and its partner Cisco Systems are staking out their claims to the future of computing
- Compared to the Dyson sphere idea, quantum computers networked together to provide the plumbing for an Internet that makes Jack Dorsey’s Web 5 vision seem like something from a Paleolithic sketch on the wall of the Lescaux Caves.
- Watson and IBM’s other advanced AI technologies probably assisted the IBM marketing professionals with publicizing Big Blue’s latest idea for moving beyond the fog of smart software.
Net net: The spirit of avid science fiction devotees is effervescing. Does the idea of a network of quantum computers tickle your nose or your fancy? I have marked my calendar.
Stephen E Arnold, November 28, 2025
Turkey Time: IT Projects Fail Like Pies and Cakes from Crazed Aunties
November 27, 2025
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Another dinobaby original. If there is what passes for art, you bet your bippy, that I used smart software. I am a grandpa but not a Grandma Moses.
Today is Thanksgiving, and it is appropriate to consider the turkey approach to technology. The source of this idea comes from the IEEE.org online publication. The article explaining what I call “turkeyism” is “How IT Managers Fail Software Projects.” Because the write up is almost 4,000 words and far too long for reading during an American football game’s halftime break, I shall focus on a handful of points in the write up. I encourage you to read the entire article and, of course, sign up and subscribe. If you don’t, the begging for dollars pop up may motivate you to click away and lose the full wisdom of the IEEE write up. I want to point out that many IT managers are trained as electrical engineers or computer scientists who have had to endure the veritable wonderland of imaginary numbers for a semester or two. But increasingly IT managers can be MBAs or in some frisky Silicon Valley type companies, recent high school graduates with a native ability to solve complex problems and manage those older than they. Hey, that works, right?
Auntie knows how to manage the baking process. She practices excellent hygiene, but with age comes forgetfulness. Those cookies look yummy. Thanks, Venice.a. No mom. But good enough with Auntie pawing the bird.
Answer: Actually no.
The cited IEEE article states:
Global IT spending has more than tripled in constant 2025 dollars since 2005, from US $1.7 trillion to $5.6 trillion, and continues to rise. Despite additional spending, software success rates have not markedly improved in the past two decades. The result is that the business and societal costs of failure continue to grow as software proliferates, permeating and interconnecting every aspect of our lives.
Yep, and lots of those managers are members of IEEE or similar organizations. How about that jump from solving mathy problems to making software that works? It doesn’t seem to be working. Is it the universities, the on the job training, or the failure of continuing education? Not surprisingly, the write up doesn’t offer a solution.
What we have is a global, expensive problem. With more of everyday life dependent on “technology,” a failure can have some interesting consequences. Not only is it tough to get that new sweater delivered by Amazon, but downtime can kill a kid in a hospital when a system keels over. Dead is dead, isn’t it?
The write up says:
A report fromthe Consortium for Information & Software Quality (CISQ) estimated the annual cost of operational software failures in the United States in 2022 alone was $1.81 trillion, with another $260 billion spent on software-development failures. It is larger than the total U.S. defense budget for that year, $778 billion.
Chatter about the “cost” of AI tosses around even bigger numbers. Perhaps some of the AI pundits should consider the impact of AI failure in the context of IT failure. Frankly I am not confident about AI because of IT failure. The money is one thing, but given the evidence about the prevalence of failure, I am not ready to sing the JP Morgan tune about the sunny side of the street.
The write up adds:
Next to electrical infrastructure, with which IT is increasingly merging into a mutually codependent relationship, the failure of our computing systems is an existential threat to modern society. Frustratingly, the IT community stubbornly fails to learn from prior failures.
And what role does a professional organization play in this little but expensive drama? Are the arrows of accountability pointing at the social context in which the managers work? What about the education of these managers? What about the drive to efficiency? You know. Design the simplest possible solution. Yeah, these contextual components have created a high probability of failure. Will Auntie’s dessert give everyone food poisoning? Probably. Auntie thinks she has washed her hands and baked with sanitation in mind. Yep, great assumption because Auntie is old. Auntie Compute is going on 85 now. Have another cookie.
But here’s the killer statement in the write up:
Not much has worked with any consistency over the past 20 years.
This is like a line in a Jack Benny Show skit.
Several observations:
- The article identifies a global, systemic problem
- The existing mechanisms for training people to manage don’t work
- There is no solution.
Have a great Thanksgiving. Have another one of Auntie’s cookies. The two people who got food poisoning last year just had upset tummies. It will just get better. At least that’s what mom says.
Stephen E Arnold, November 27, 2025
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