GenX, GenY, and Probably GenAI: Hopeless Is Not a Positive
October 13, 2025
This essay is the work of a dumb dinobaby. No smart software required.
Generation Z is the first generation in a long time that is worse off than their predecessors. Millennials also have their own problems too, because they came of age in a giant recession that could have been avoided. Millennials might have been teased about their lack of work ethic, but Generation Z is much worse. The prior generations had some problem solving skills, this younger sect (not all of them) lack the ability to even attempt to solve their problems.
Fortune embodied the mantra of the current generation in the article: “Suzy Welch Says Gen Z and Millennials Are Burnt Out Because Older Generations Worked Just As Hard, But They ‘Had Hope.’” Suzy Welch holds a MBA, served as a management consultant, and is the editor in chief of the Harvard Business Review. She makes the acute observation that younger generations are working the same demanding schedules as prior generations, but they lack hope that hard work will lead to meaningful advancement. Young workers of today are burnt out:
The sense of powerlessness—to push back against climate change, to deal with grapple with effects of the political environment like diminished public health and gun violence, and most notably to make enough money to support lifestyles, family, housing, and a future—has led to an erosion of institutional trust. Unlike baby boomers who embraced existing institutions to get rich and live a comfortable life, the younger generations do not feel that institutions—which are perceived as cumbersome, hierarchical, and a source of inequality and discrimination—can improve their situation. When combined with the economic realities Welch identified, where hard work no longer guarantees advancement, this helps explain why more than 50% of young people fear they will be poorer than their parents during their lifetime, according to Leger’s annual Youth Study.”
Okay. The older generations had hope while the younger ones are hopeless. Maybe if there was a decrease in inflation and a rise in wages the younger people wouldn’t be so morbid. Fire up the mobile. Grab a coffee. Doomscroll. Life will work out.
Whitney Grace, October 13, 2025
Parenting 100: A Remedial Guide to Raising Children
October 13, 2025
This essay is the work of a dumb dinobaby. No smart software required.
I am not sure what’s up this week (October 6 to 10, 2025). I am seeing more articles about the impact of mobile devices, social media, doom scrolling, and related cheerful subjects in my newsfeed. A representative article is “Lazy Parents Are Giving Their Toddlers ChatGPT on Voice Mode to Keep Them Entertained for Hours.”
Let’s take a look at a couple of passages that I thought were interesting:
with the rise of human-like AI chatbots, a generation of “iPad babies” could seem almost quaint: some parents are now encouraging their kids to talk with AI models, sometimes for hours on end…
I get it. Parents are busy today. If they are lucky enough to have jobs, automatic meeting services keep them hopping. Then there is the administrivia of life. Children just add to the burden. Why not stick the kiddie in a playpen with an iPad. Tim Apple will be happy.
What’s the harm? How about this factoid (maybe an assertion from smart software?) from the write up:
AI chatbots have been implicated in the suicides of several teenagers, while a wave of reports detail how even grown adults have become so entranced by their interactions with sycophantic AI interlocutors that they develop severe delusions and suffer breaks with reality — sometimes with deadly consequences.
Okay, bummer. The write up includes a hint of risk for parents about these chat-sitters; to wit:
Andrew McStay, a professor of technology and society at Bangor University, isn’t against letting children use AI — with the right safeguards and supervision. But he was unequivocal about the major risks involved, and pointed to how AI instills a false impression of empathy.
Several observations seem warranted:
- Which is better? Mom and dad interacting with the kiddo. Maybe grandma could be a good stand in? Or, letting the kid tune in and drop out?
- Imagine sending a chat surfer to school. Human interaction is not going to be as smooth and stress free as having someone take the kiddo’s animal crackers and milk or pouting until kiddo can log on again.
- Visualize the future: Is this chat surfer going to be a great employee and colleague? Answer: No.
I find it amazing that decades after these tools became available that people do not understand the damage flowing bits do to thinking, self esteem, and social conventions. Empathy? Sure, just like those luminaries at Silicon Valley type AI companies. Warm, caring, trustworthy.
Stephen E Arnold, October 13, 2025
Weaponization of LLMs Is a Thing. Will Users Care? Nope
October 10, 2025
This essay is the work of a dumb dinobaby. No smart software required.
A European country’s intelligence agency learned about my research into automatic indexing. We did a series of lectures to a group of officers. Our research method, the results, and some examples preceded a hands on activity. Everyone was polite. I delivered versions of the lecture to some public audiences. At one event, I did a live demo with a couple of people in the audience. Each followed a procedure, and I showed the speed with which the method turned up in the Google index. These presentations took place in the early 2000s. I assumed that the behavior we discovered would be disseminated and then it would diffuse. It was obvious that:
- Weaponized content would be “noted” by daemons looking for new and changed information
- The systems were sensitive to what I called “pulses” of data. We showed how widely used algorithms react to sequences of content
- The systems would alter what they would output based on these “augmented content objects.”
In short, online systems could be manipulated or weaponized with specific actions. Most of these actions could be orchestrated and tuned to have maximum impact. One example in my talks was taking a particular word string and making it turn up in queries where one would not expect that behavior. Our research showed that a few as four weaponized content objects orchestrated in a specific time interval would do the trick. Yep, four. How many weaponized write ups can my local installation of LLMs produce in 15 minutes? Answer: Hundreds. How long does it take to push those content objects into information streams used for “training.” Seconds.
Fish live in an environment. Do fish know about the outside world? Thanks, Midjourney. Not a ringer but close enough in horseshoes.
I was surprised when I read “A Small Number of Samples Can Poison LLMs of Any Size.” You can read the paper and work through the prose. The basic idea is that selecting or shaping training data or new inputs to recalibrate training data can alter what the target system does. I quite like the phrase “weaponize information.” Not only does the method work, it can be automated.
What’s this mean?
The intentional selection of information or the use of a sample of information from a domain can generate biases in what the smart software knows, thinks, decides, and outputs. Dr. Timnit Gebru and her parrot colleagues were nibbling around the Google cafeteria. Their research caused the Google to put up a barrier to this line of thinking. My hunch is that she and her fellow travelers found that content that is representative will reflect the biases of the authors. This means that careful selection of content for training or updating training sets can be steered. That’s what the Anthropic write up make clear.
Several observations are warranted:
- Whoever selects training data or the information used to update and recalibrate training data can control what is displayed, recommended, or included in outputs like recommendations
- Users of online systems and smart software are like fish in a fish bowl. The LLM and smart software crowd are the people who fill the bowl and feed the fish. Fish have a tough time understanding what’s outside their bowl. I don’t like the word “bubble” because these pop. An information fish bowl is tough to escape and break.
- As smart software companies converge into essentially an oligopoly using the types of systems I described in the early 2000s with some added sizzle from the Transformer thinking, a new type of information industrial complex is being assembled on a very large scale. There’s a reason why Sam AI-Man can maintain his enthusiasm for ChatGPT. He sees the potential of seemingly innocuous functions like apps within ChatGPT.
There are some interesting knock on effects from this intentional or inadvertent weaponization of online systems. One is that the escalating violent incidents are an output of these online systems. Inject some René Girard-type content into training data sets. Watch what those systems output. “Real” journalists are explaining how they use smart software for background research. Student uses online systems without checking to see if the outputs line up with what other experts say. What about investment firms allowing smart software to make certain financial decisions.
Weaponize what the fish live in and consume. The fish are controlled and shaped by weaponized information. How long has this quirk of online been known? A couple of decades, maybe more. Why hasn’t “anything” been done to address this problem? Fish just ask, “What problem?”
Stephen E Arnold, October x, 2025
I spotted
At Google Innovation Never Stops or Gee a G
October 10, 2025
This essay is the work of a dumb dinobaby. No smart software required.
I read “Google’s Gradient G Icon Design Is Going Company Wide.” Usually Deepseek, the YouTube leadership, or a rando in advertising announces a quantumly supreme achievement. The stunning Google news for September 29, 2025, is presented this way:
Google used “brighter hues and gradient design” to “symbolize the surge of AI-driven innovation and creative energy across our products and technology.” The aim was to stay “true to Google’s iconic four colors,” with the last design refresh taking place 10 years ago.
The article includes the old G and the new forward leaning, innovative, quantumly supreme G. Here’s what I saw in the cited write up:
This is the old, backward leaning, non-innovative, un-quantumly supreme G.
Now here’s is the new forward leaning, innovative, quantumly supreme G:
That is revolutionary, boundary stretching, Leonardo DaVinci grade art.
I am impressed. Imagine the achievement amidst some staff concern about layoffs, and the financial headaches resulting from those data center initiatives, crypto services, and advertising sales efforts.
What’s next from the Google? Gee, this new G will be difficult to galvanize more grandiose game changers.
Stephen E Arnold, October 10, 2025
ChatGPT Finds Humans Useful
October 10, 2025
OpenAI is chasing consumers during primetime football games, we learn from 9to5Mac’s piece, “Pressure Mounts on Siri as ChatGPT Ads Start Airing on Primetime TV.” The first of these ads premiered during NFL Primetime. We are told the campaign focuses on ways people are using ChatGPT in their everyday lives, like creating recipes or fitness plans. So wholesome! (We assume they are leaving out the many downsides of overreliance on the tech.) Does this mean firm’s second Super Bowl ad will be more down to earth than its first one?
Writer Ben Lovejoy asserts this campaign highlights how embarrassingly far Apple’s Siri is behind ChatGPT. iPhone users have the option to get an answer from ChatGPT when Siri fails them. But, as Lovejoy notes, the permission prompt serves as a spotlight on Siri’s inadequacies.
The ad campaign comes with an interesting caveat. We learn:
“With growing concern in the creative sector around the use of AI, the company has gone out of its way to ensure that no artificial intelligence was used for the actual creative work. Creative Review reports: Crucially, the campaign was created largely through human endeavour, with the team at OpenAI noting that: ‘Human craft was central to the campaign’s creation. Every frame was shot on film, shaped by directors, photographers, producers and many more masters of craft.’ That ‘largely’ rider reflects that ChatGPT was used for some background work, with ‘streamlining shot lists and organising schedules’ given as examples.”
Will this acknowledgement that real life is better than AI fakery backfire on the premier AI company? And no Sora?
Cynthia Murrell, October 10, 2025
AI Has a Secret: Humans Do the Work
October 10, 2025
A key component of artificial intelligence output is not artificial at all. The Guardian reveals “How Thousands of ‘Overworked, Underpaid’ Humans Train Google’s AI to Seem Smart.” From accuracy to content moderation, Google Gemini and other AI models rely on a host of humans employed by third-party contractors. Humans whose jobs get harder and harder as they are pressured to churn through the work faster and faster. Gee, what could go wrong?
Reporter Varsha Bansal relates:
“Each new model release comes with the promise of higher accuracy, which means that for each version, these AI raters are working hard to check if the model responses are safe for the user. Thousands of humans lend their intelligence to teach chatbots the right responses across domains as varied as medicine, architecture and astrophysics, correcting mistakes and steering away from harmful outputs.”
Very important work—which is why companies treat these folks as valued assets. Just kidding. We learn:
“Despite their significant contributions to these AI models, which would perhaps hallucinate if not for these quality control editors, these workers feel hidden. ‘AI isn’t magic; it’s a pyramid scheme of human labor,’ said Adio Dinika, a researcher at the Distributed AI Research Institute based in Bremen, Germany. ‘These raters are the middle rung: invisible, essential and expendable.’”
And, increasingly, rushed. The write-up continues:
“[One rater’s] timer of 30 minutes for each task shrank to 15 – which meant reading, fact-checking and rating approximately 500 words per response, sometimes more. The tightening constraints made her question the quality of her work and, by extension, the reliability of the AI. In May 2023, a contract worker for Appen submitted a letter to the US Congress that the pace imposed on him and others would make Google Bard, Gemini’s predecessor, a ‘faulty’ and ‘dangerous’ product.”
And that is how we get AI advice like using glue on pizza or adding rocks to one’s diet. After those actual suggestions went out, Google focused on quality over quantity. Briefly. But, according to workers, it was not long before they were again told to emphasize speed over accuracy. For example, last December, Google announced raters could no longer skip prompts on topics they knew little about. Think workers with no medical expertise reviewing health advice. Not great. Furthermore, guardrails around harmful content were perforated with new loopholes. Bansal quotes Rachael Sawyer, a rater employed by Gemini contractor GlobalLogic:
“It used to be that the model could not say racial slurs whatsoever. In February, that changed, and now, as long as the user uses a racial slur, the model can repeat it, but it can’t generate it. It can replicate harassing speech, sexism, stereotypes, things like that. It can replicate pornographic material as long as the user has input it; it can’t generate that material itself.”
Lovely. It is policies like this that leave many workers very uncomfortable with the software they are helping to produce. In fact, most say they avoid using LLMs and actively discourage friends and family from doing so.
On top of the disillusionment, pressure to perform full tilt, and low pay, raters also face job insecurity. We learn GlobalLogic has been rolling out layoffs since the beginning of the year. The article concludes with this quote from Sawyer:
‘I just want people to know that AI is being sold as this tech magic – that’s why there’s a little sparkle symbol next to an AI response,’ said Sawyer. ‘But it’s not. It’s built on the backs of overworked, underpaid human beings.’
We wish we could say we are surprised.
Cynthia Murrell, October 10, 2025
AI Embraces the Ethos of Enterprise Search
October 9, 2025
This essay is the work of a dumb dinobaby. No smart software required.
In my files, I have examples of the marketing collateral generated by enterprise search vendors. I have some clippings from trade publications and other odds and ends dumped into my enterprise search folder. One of these reports is “Fastgründer John Markus Lervik dømt til fengsel.” The article is no longer online, but you can read my 2014 summary at this Beyond Search link. The write up documents an enterprise search vendor who used some alleged accounting methods to put a shine on the company. In 2008, Microsoft purchased Fast Search & Transfer putting an end to this interesting company.
A young CPA MBA BA (with honors) is jockeying a spreadsheet. His father worked for an enterprise search vendor based in the UK. His son is using his father’s template but cannot get the numbers to show positive cash flows across six quarters. Thanks, Venice.ai. Good enough.
Why am I mentioning Fast Search & Transfer? The information in Fortune Magazine’s “‘There’s So Much Pressure to Be the Company That Went from Zero to $100 Million in X Days’: Inside the Sketchy World of ARR and Inflated AI Startup Accounting” jogged my memory about Fast Search and a couple of other interesting companies in the enterprise search sector.
Enterprise search was the alleged technology to put an organization’s information at the fingertips of employees. Enterprise search would unify silos of information. Enterprise search would unlock the value of an organization’s “hidden” or “dark” data. Enterprise search would put those hours wasted looking for information to better use. (IDC was the cheerleader for the efficiency payoff from enterprise search.)
Does this sound familiar? It should every vendor applying AI to an organization’s information challenges is either recycling old chestnuts from the Golden Age of Enterprise Search or wandering in the data orchard discovering these glittering generalities amidst nuggets of high value jargon.
The Fortune article states:
There’s now a massive amount of pressure on AI-focused founders, at earlier stages than ever before: If you’re not generating revenue immediately, what are you even doing? Founders—in an effort to keep up with the Joneses—are counting all sorts of things as “long-term revenue” that are, to be blunt, nothing your Accounting 101 professor would recognize as legitimate. Exacerbating the pressure is the fact that more VCs than ever are trying to funnel capital into possible winners, at a time where there’s no certainty about what evaluating success or traction even looks like.
Would AI start ups fudge numbers? Of course not. Someone at the start up or investment firm took a class in business ethics. (The pizza in those study groups was good. Great if it could be charged to another group member’s Visa without her knowledge. Ho ho ho.)
The write up purses the idea that ARR or annual recurring revenue is a metric that may not reflect the health of an AI business. No kidding? When an outfit has zero revenue resulting from dumping investor case into a burning dumpster fire, it is difficult for me to understand how people see a payoff from AI. The “payoff” comes from moving money around, not from getting cash from people or organizations on a consistent basis. Subscription-like business models are great until churn becomes a factor.
The real point of the write up for me is that financial tricks, not customers paying for the product or service, are the name of the game. One big enterprise search outfit used “circular” deals to boost revenue. I did some small work for this outfit, so I cannot identify it. The same method is now part of the AI revolution involving Nvidia, OpenAI, and a number of other outfits. Whose money is moving? Who gets it? What’s the payoff? These are questions not addressed in depth in the information to which I have access?
I think financial intermediaries are the folks taking home the money. Some vendors may get paid like masters of black art accounting. But investor payoff? I am not so sure. For me the good old days of enterprise search are back again, just with bigger numbers and more impactful financial consequences.
As an aside, the Fortune article uses the word “shit” twice. Freudian slip or a change in editorial standards at Fortune? That word was applied by one of my team when asked to describe the companies I profiled in the Enterprise Search Report I wrote many years ago. “Are you talking about my book or enterprise search?” I asked. My team member replied, “The enterprise search thing.”
Stephen E Arnold, October 2025
AI Security: Big Plus or Big Minus?
October 9, 2025
Agentic AI presents a new security crisis. But one firm stands ready to help you survive the threat. Cybersecurity firm Palo Alto Networks describes “Agentic AI and the Looming Board-Level Security Crisis.” Writer and CSO Haider Pasha sounds the alarm:
“In the past year, my team and I have spoken to over 3,000 of Europe’s top business leaders, and these conversations have led me to a stark conclusion: Three out of four current agentic AI projects are on track to experience significant security challenges. The hype, and resulting FOMO, around AI and agentic AI has led many organisations to run before they’ve learned to walk in this emerging space. It’s no surprise how Gartner expects agentic AI cancellations to rise through 2027 or that an MIT report shows most enterprise GenAI pilots already failing. The situation is even worse from a cybersecurity perspective, with only 6% of organizations leveraging an advanced security framework for AI, according to Stanford.
But the root issue isn’t bad code, it’s bad governance. Unless boards instill a security mindset from the outset and urgently step in to enforce governance while setting clear outcomes and embedding guardrails in agentic AI rollouts, failure is inevitable.”
The post suggests several ways to implement this security mindset from the start. For example, companies should create a council that oversees AI agents across the organization. They should also center initiatives on business goals and risks, not shiny new tech for its own sake. Finally, enforce least-privilege access policies as if the AI agent were a young intern. See the write-up for more details on these measures.
If one is overwhelmed by the thought of implementing these best practices, never fear. Palo Alto Networks just happens to have the platform to help. So go ahead and fear the future, just license the fix now.
Cynthia Murrell, October 9, 2025
Antitrust: Can Google Dodge Guilt Again?
October 9, 2025
The US Department of Justice brought an antitrust case against Google and Alphabet Inc. got away with a slap on the wrist. John Polonis via Medium shared the details and his opinion in, “Google’s Antitrust Escape And Tech’s Uncertain Future.” The Department of Justice can’t claim a victory in this case, because none of the suggestions to curtail Google’s power will be implemented.
Some restrictions were passed that ban exclusivity deals and require data sharing, but that’s all. It’s also nothing like the antitrust outcome of the Microsoft case in the 2000s. The judge behind the decision was Amit Mehta and he did want to deliver a dose of humility to Google:
“Judge Mehta also exercised humility when forcing Google to share data. Google will need to share parts of its search index with competitors, but it isn’t required to share other data related to those results (e.g., the quality of web pages). The reason for so much humility? Artificial intelligence. The judge emphasized Google’s new reality; how much harder it must fight to keep up with competitors who are seizing search queries that Google previously monopolized across smartphones and browsers.
Google can no longer use its financial clout like it did when it was the 900 pound gorilla of search. It’s amazing how much can change between the filing of an antitrust case and adjudication (generative AI didn’t even exist!).”
Google is now free to go hog wild with its AI projects without regulation. Google hasn’t lost any competitive edge, unlike Microsoft in its antitrust litigation. They’re now free to do whatever they want as well.
Polonis makes a very accurate point:
“The message is clear. Unless the government uncovers smoking gun evidence of deliberate anticompetitive intent — the kind of internal emails and memos that doomed Microsoft in the late 1990s (“cut off Netscape’s air supply”) — judges are reluctant to impose the most extreme remedies. Courts want narrow, targeted fixes that minimize unnecessary disruption. And the remedies should be directly tied to the anticompetitive conduct (which is why Judge Mehta focused so heavily on exclusivity agreements).”
Big Tech has a barrier free sandbox to experiment and conduct AI business deals. Judge Mehta’s decision has shaped society in ways we can’t predict, even AI doesn’t know the future yet. What will the US judicial process deliver in Google’s advertising legal dust up? We know Google can write checks to make problems go away. Will this work again for this estimable firm?
Whitney Grace, October 8, 2025
Google Bricks Up Its Walled Garden
October 8, 2025
Google is adding bricks to its garden wall, insisting Android-app developers must pay up or stay out. Neowin declares, “Google’s Shocking Developer Decree Struggles to Justify the Urgent Threat to F-Droid.” The new edict requires anyone developing an app for Android to register with Google, whether or not they sell through its Play Store. Registration requires paying a fee, uploading personal IDs, and agreeing to Google’s fine print.
The measure will have a large impact on alternative app stores like F-Droid. That open-source publisher, with its focus on privacy, is particularly concerned about the requirements. In fact, it would rather shutter its project than force developers to register with Google. That would mean thousands of verified apps will vanish from the Web, never to be downloaded or updated again. F-Droid suspects Google’s motives are far from pure. Writer Paul Hill tells us:
“F-Droid has questioned whether forced registration will really solve anything because lots of malware apps have been found in the Google Play Store over the years, demonstrating that corporate gatekeeping doesn’t mean users are protected. F-Droid also points out that Google already defends users against malicious third-party apps with the Play Protect services which scan and disable malware apps, regardless of their origin. While not true for all alternative app stores, F-Droid already has strong security because the apps it includes are all open source that anyone can audit, the build logs are public, and builds are reproducible. When you submit an app to F-Droid, the maintainers help set up your repository properly so that when you publish an update to your code, F-Droid’s servers manually build the executable, this prevents the addition of any malware not in the source code.”
Sounds at least as secure as the Play Store to us. So what is really going on? The write-up states:
“The F-Droid project has said that it doesn’t believe that the developer registration is motivated by security. Instead, it thinks that Google is trying to consolidate power by tightening control over a formerly open ecosystem. It said that by tying application identifiers to personal ID checks and fees, it creates a choke point that restricts competition and limits user freedom.”
F-Droid is responding with a call for regulators to scrutinize this and other Googley moves for monopolistic tendencies. It also wants safeguards for app stores that wish to protect developers’ privacy. Who will win this struggle between independent app stores and the tech giant?
Cynthia Murrell, October 8, 2025
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