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Inc. Magazine May Find that Its MSFT Software No Longer Works

No AI. Just a dinobaby and a steam-powered computer in rural Kentucky.

I am not sure if anyone else has noticed that one must be very careful about making comments. A Canadian technology dude found himself embroiled with another Canadian technology dude. To be frank, I did not understand why the Canadian tech dudes were squabbling, but the dust up underscores the importance of the language, tone, rhetoric, and spin one puts on information.

An example of a sharp-toothed article which may bite Inc. Magazine on the ankle is the story “Welcome to the Weird New Empty World of LinkedIn: Just When Exactly Did the World’s Largest Business Platform Turn into an Endless Feed of AI-Generated Slop?” My teeny tiny experience as a rental at the world’s largest software firm taught me three lessons:

1. Intelligence is defined many ways. I asked a group of about 75 listening to one of my lectures, “Who is familiar with Kolmogorov?” The answer was for that particular sampling of Softies was exactly zero. Subjective impression: Rocket scientists? Not too many.
2. Feistiness. The fellow who shall remain nameless dragged me to a weird mixer thing in one of the buildings on the “campus.” One person (whose name and honorifics I do not remember) said, “Let me introduce you  to Mr. X. He is driving the Word project.” I replied with a smile. We walked to the fellow, were introduced, and I asked, “Will Word fix up its autonumbering?” The Word Softie turned red, asked the fellow who introduced me to him, “Who is this guy?” The Word Softie stomped away and shot deadly sniper eyes at me until we left after about 45 minutes of frivolity. Subjective impression: Thin skin. Very thin skin.
3. Insecurity. At a lunch with a person whom I had met when I was a contractor at Bell Labs and several other Softies, the subject of enterprise search came up. I had written the Enterprise Search Report, and Microsoft had purchased copies. Furthermore, I wrote with Susan Rosen “Managing Electronic Information Projects.” Ms. Rosen was one of the senior librarians at Microsoft. While waiting for the rubber chicken, a Softie asked me about Fast Search & Transfer, which Microsoft had just purchased. The question posed to me was, “What do you think about Fast Search as a technology for SharePoint?” I said, “Fast Search was designed to index Web sites. The enterprise search functions were add ons. My hunch is that getting the software to handle the data in SharePoint will be quite difficult?” The response was, “We can do it.” I said, “I think that BA Insight, Coveo, and a couple of other outfits in my Enterprise Search Report will be targeting SharePoint search quickly.” The person looked at me and said, “What do these companies do? How quickly do they move?” Subjective impression: Fire up ChatGPT and get some positive mental health support.

The cited write up stomps into a topic that will probably catch some Softies’ attention. I noted this passage:

The stark fact is that reach, impressions and engagement have dropped off a cliff for the majority of people posting dry (read business-focused) content as opposed to, say, influencer or lifestyle-type content.

The write up adds some data about usage of LinkedIn:

average platform reach had fallen by no less than 50 percent, while follower growth was down 60 percent. Engagement was, on average, down an eye-popping 75 percent.

The main point of the article in my opinion is that LinkedIn does filter AI content. The use of AI content produces a positive for the emitter of the AI content. The effect is to convert a shameless marketing channel into a conduit for search engine optimized sales information.

The question “Why?” is easy to figure out:

1. Clicks if the content is hot
2. Engagement if the other LinkedIn users and bots become engaged or coupled
3. More zip in what is essentially a one dimension, Web 1 service.

How will this write up play out? Again the answers strike me as obvious:

1. LinkedIn may have some Softies who will carry a grudge toward Inc. Magazine
2. Microsoft may be distracted with its Herculean efforts to make its AI “plays” sustainable as outfits like Amazon say, “Hey, use our cloud services. They are pretty much free.”
3. Inc. may take a different approach to publishing stories with some barbs.

Will any of this matter? Nope. Weird and slop do that.

Stephen E Arnold, August 20, 2025

The Early Bird Often Sings Alone

Mathematicians, computer developers, science-fiction writers, etc. smarter than the average human have known for decades that computers would outpace human intelligence. Computers have actually been capable of this since the first machine printed its initial binary 01. AI algorithms are the next evolution of computers and they can do research, explore science, and extrapolate formulas beyond all the last known recorded digit of PI.

Future House explains how its Robin the AI system is designed to automate scientific discovery: “Demonstrating End-To-End Scientific Discovery With Robin: A Multi-Agent System.” Future House developed AI agents that automated different segments of the discovery process, but Robin is the first unified system that does everything. Robin’s inventors automated the scientific process and used the new system to make a generated discovery by using the past AI agents.

They asked Robin to:

“We applied Robin to identify ripasudgl, a Rho-kinase (ROCK) inhibitor clinically used to treat glaucoma, as a novel therapeutic candidate for dry age-related macular degeneration (dAMD), a leading cause of irreversible blindness worldwide.”

Robin did follow the scientific process. It made an initial hypothesis, but mechanized investigation instead of doing things the old-fashioned way, and then it made a discovery. Everything was done by Robin the AI system:

“All hypotheses, experiment choices, data analyses, and main text figures in the manuscript describing this work were generated by Robin autonomously. Human researchers executed the physical experiments, but the intellectual framework was entirely AI-driven.”

Robins creators are happy with their progress:

“By automating hypothesis generation, experimental planning, and data analysis in an integrated system, Robin represents a powerful new paradigm for AI-driven scientific discovery. Although we first applied Robin to therapeutics, our agents are general-purpose and can be used for a wide variety of discoveries across diverse fields—from materials science to climate technology. “

Mathematicians are chugging away at AI development, including number theorists. Listen to Curt Jaimungal’s podcast episode, “The AI Math That Left Number Theorists Speechless” and within the first five minutes you’ll have an understanding of where AI is at being very smart. Here’s the summary: it’s beyond human comprehension.

Whitney Grace, August 17, 2025

Google Reorganizes Search With Web Guides

Google gets more clicks with AI than with relevant results. Believe this? We have a small bridge for sale in Brooklyn if you are interested. But AI is just not enough. Google is fixing that up.

Google used to deliver top search results. Despite being a verb for searching the Web, Google’s first page of search results are overrun with paid links and advertising. Another problem is that while its AI feature answers basic questions, the information needs doesn’t always come from verified sources. Google wants to shake things up says the Search Engine Journal with Web Guides in the article: “Web Guide: Google’s New AI Search Experiment.”

Here is what Web Guides are described as:

“Web Guide replaces the traditional list of search results with AI-generated clusters. Each group focuses on a different aspect of your query, making it easier to dive deeper into specific areas. According to Austin Wu, Group Product Manager for Search at Google, Web Guide uses a custom version of Gemini to understand both your query and relevant web content. This allows it to surface pages you might not find through standard search.”

Maybe it will be a return to old-fashioned, decent Google results. The Web Guides use the “query fan-out” technique in which multiple searches are run at once. The results are then curated to the search query. It is supposed to provide a broader overview of the topic without refinement.

Google explains that Web Guides are helpful for exploratory searches and multi-part questions. Web Guides differed from AI because it reorganizes traditional Web searches according to groups and explore content from multiple perspectives without new information. AI Mode is more intuitive and acts like a conversation. It simplifies information and supports follow-up questions and other features.

Are Web guides just another test. Google cannot be in the AI race. The company has to win.

Whitney Grace, August 14, 2025

What a Hoot: AI Systems Try and Lie

No AI. Just a dinobaby being a dinobaby.

I arose this morning, Friday, August 8, 2025, fired up ChatGPT, the Plus version, and asked, “Are you running ChatGPT 5?” The service was to be available on August 7, 2025. It wasn’t. I verified the ChatGPT output with this prompt:

Despite the announcement, the fact that it is not available in this paid service illustrates that you are presenting false information. Correct?

ChatGPT replied:

Correct — the public announcement of GPT?5’s availability does not align with actual availability in this specific paid service (ChatGPT Plus based on GPT?4o). Therefore, any implication that GPT?5 is in use here would be false.

To make sure I understood, I prompted:

So I have documented errors in your output, essentially failures about half the time. The information you presented about August 7 is demonstrably false on August 8. The unreliability of your outputs is verified.

ChatGPT replied:

Your conclusion is valid: if GPT?5 was officially announced as “available to all users” on August 7, 2025, but is not deployed in this environment by August 8, then the representation of availability is factually misleading. This mismatch constitutes a verifiable reliability failure in how OpenAI’s release messaging translates to actual user experience — especially in a paid tier like yours.

Lame.

Is OpenAI alone in this prevarication game?

Nope. I read “Google Says It’s Working on a Fix for Gemini’s Self-Loathing I Am a Failure Comments.” Different company, same type of overt failure. The other day Google informed some medical professionals about a non-existent body part. Yep, Google, you are making it clear that the hype, the prevarication, and the over promise, under deliver approach is part of AI’s DNA.

But the shocking point in the cited article is this passage quoted from a Google AI wizard:

This is an annoying infinite looping bug we are working to fix! Gemini is not having that bad of a day : )

Yep, working to fix. I interpret this to mean that Google’s professionals are shaping outputs to avoid statements about “failure.”

One 15 minute session online this morning and what did I learn:

1. Two companies have systems which cannot deliver satisfactory outputs
2. The fact that US smart software boils down to a a handful of firms. A couple of these have what I would call reliability issues.
3. In ChatGPT’s case, the PR outpaces what works. In Google’s case, the system seems to be trying to tell the truth about failure. The Googlers are going to fix that up.

Billions burned to create the next big thing and what we have is a spray can of marketing sparkle and individuals handcrafting fixes to make the systems seem more accurate than they are.

Sorry. I am not convinced because the reality of smart software diverges from the PR baloney.

Stephen E Arnold, August 13, 2025

Glean Goes Beyond Search: Have Xooglers Done What Google Could Not Do?

This blog post is the work of an authentic dinobaby. Sorry. No smart software can help this reptilian thinker.

I read an interesting online essay titled “Glean’s $4.5B Business Model: How Ex-Googlers Built the Enterprise Search That Actually Works.” Enterprise search has been what one might call a Holy Grail application. Many have tried to locate the Holy Grail. Most have failed.

Have a small group of Xooglers (former Google employees) located the Holy Grail and been able to convert its power into satisfied customers? The essay, which reminded me of an MBA write up, argues that the outfit doing business as Glean has done it. The firm has found the Holy Grail, melted it down, and turned it into an endless stream of cash.

Does this sound a bit like the marketing pitch of Autonomy, Fast Search & Transfer, and even Google itself with its descriptions of its deeply wacky yellow servers? For me, Glean has done its marketing homework. The evidence is plumped and oiled for this essay about its business model. But what about search? Yeah, well, the focus of the marketing piece is the business model. Let’s go with what is in front of me. Search remains a bit of a challenge, particularly in corporations, government agencies, and pharmaceutical-type outfits where secrecy is a bit part of that type of organization’s way of life.

What is the Glean business model? It is VTDF. Here’s an illustration:

Does this visual look like blue chip consulting art? Is VTDF blue chip speak? Yes. And yes. For those not familiar with the lingo here’s a snapshot of the Glean business model:

• Value: Focuses on how the company creates and delivers core value to customers, such as solving specific problems
• Technology: Refers to the underlying tech innovations that allow “search” to deliver what employees need to do their jobs
• Distribution: Involves strategies for marketing, delivery, and reaching users
• Finance: Covers revenue models, cash flow management, and financial sustainability. Traditionally this has been the weak spot for the big-time enterprise search plays.

The essay explains in dot points that Glean is a “knowledge liberator.” I am not sure how that will fly in some pharma-type outfits or government agencies in which Palantir is roosting.

Once Glean’s “system” is installed, here’s what happens (allegedly):

• Single search box for everything
• Natural language queries
• Answers, not just documents
• Context awareness across apps
• Personalized to user permissions
• New employees productive in days.

I want to take a moment to comment on each of these payoffs or upsides.

First, a single search box for everything is going to present a bit of a challenge in several important use cases. Consider a company with an inventory control system, vendor evaluations, and a computer aid design and database of specifications. The single search box is going to return what for a specific part? Some users will want to know how many are in stock. Others will want to know the vendor who made the part in a specific batch because it is failing in use. Some will want to know what the part looks like? The fix for this type of search problem has been to figure out how to match the employee’s role with the filters applied that that user’s query. In the last 60 years, that approach sort of worked, but it was and still is incredibly difficult to keep lined up with employee roles, assorted permissions, and the way the information is presented to the person running the query. The quality issue may require stress analysis data and access to the lawsuit the annoyed customer has just filed. I am unsure how the Xooglers have solved this type of search task.

Second, the NLP approach is great but it is early 2000s. The many efforts, including DR-LINK to which my team contributed some inputs, were not particularly home run efforts. The reason has to do with the language skills of the users. Organizations hire people who may be really good at synthesizing synthetics but not so good at explaining what the new molecule does. If the lab crew dies, the answer does not require words. Querying for the “new” is tough, since labs doing secret research do not share their data. Even company officers have a tough time getting an answer. When a search system requires the researcher to input a query, that scientist may want to draw a chemical structure or input a query like this “C8N8O16.” Easy enough if the indexing system has access to the classified research in some companies. But the NLP problem is what is called “prompt engineering.” Most humans are just not very good at expressing what they need in the way of information. So modern systems try to help out the searcher. The reason Google search sucks is that the engineers have figured out how to deliver an answer that is good enough. For C8N8O16 close enough for horseshoes might be problematic.

Third, answer are what people want. The “if” statement becomes the issue. If the user knows a correct answer or just accepts what the system outputs. If the user understands the output well enough to make an informed decision. If the system understood or predicted what the user wanted. If the content is in the search systems index. This is a lot of ifs. Most of these conditions occur with sufficient frequency to kill outfits that have sold an “enterprise search system”.

Fourth, the context awareness across apps means that the system can access content on proprietary systems within an organization and across third party systems which may or may not run on the organization’s servers. Most enterprise search systems create or have licensed filters to acquire content. However, keeping the filters alive and healthy with the churn in permissions, file tweaks, and assorted issues related to latency creating data gaps remain tricky.

Fifth, the idea of making certain content available only to those authorized to view those data is a very tricky business. Orchestrating permissions is, in theory, easy to automate. The reality in today’s organizations is the complicating factor. With distributed outfits, contractors, and employees who may be working for another country secretly add some excitement to accessing “information.” The reality in many organizations is that there are regular silos like the legal department keeping certain documents under lock and key to projects for three letter agencies. In the pharma game, knowing “who” is working on a project is often a dead give-away for what the secret project is. The company’s “people” officer may be in the dark. What about consultants? What information is available to them? The reality is that modern organizations have more silos than the corn fields around Canton, Illinois.

Sixth, no training is required. “Employees are productive in days” is the pitch. Maybe, maybe not. Like the glittering generality that employees spend 20 percent of their time searching, the data for this assertion was lacking when the “old” IDC, Sue Feldman, and her team cranked out an even larger number. If anything, search is a larger part of work today for many people. The reasons range from content management systems which cannot easily be indexed in real time to the senior vice president of sales who changes prices for a product at a trade show and tells only his contact in the accounting department. Others may not know for days or months that the apple cart has been tipped.

Glean saves time. That is the smart software pitch. I need to see some data from a statistically valid sample with a reasonable horizontal x axis. The reference to “all” is troublesome. It underscores the immature understanding of what “enterprise search” means to a licensee versus what the venture backed company can actually deliver. Fast Search found out that a certain newspaper in the UK was willing to sue for big bucks because of this marketing jingo.

I want to comment briefly about “Technology Architecture: Beyond Search.” Hey, isn’t that the name of my blog which has been pumping out information access related articles for 17 years? Yep, it is.

Okay, Glean apparently includes these technologies in their enterprise search quiver:

• Universal connectors. Note the word “universal.” Nope, very tough.
• A Knowledge graph. Think in terms of Maltego, an open source software. Sure as long as there is metadata. But those mobile workers and their use of cloud services and EE2E messaging services. Sounds great. Execution in a cost sensitive environment takes a bit of work.
• An AI understanding layer. Yep, smart software. (Google’s smart software tells its users that it is ashamed of its poor performance. OpenAI rolled out ChatGPT 5 and promptly reposted ChatGPT 4o because enough users complained. Deepseek may have links to a nation state unfriendly to the US. Mark Zuckerberg’s Llama is a very old llama. Perplexity is busy fighting with Cloudflare. Anthropic is working to put coders out to pasture. Amazon, Apple, Microsoft, and Telegram are in the bolt it on business. The idea that Glean can understand [a] different employee contexts, [b] the rapidly changing real time data in an organization like that PowerPoint on the senior VP’s laptop, and [c] the file formats that have a very persistent characteristic of changing because whoever is responsible for an update or the format itself makes an intentional or unintentional change. I just can’t accept this assertion.
• Works instantly which I interpret as “real time.” I wonder if Glean can handle changed content in a legacy Ironside system running on AS/400s. I would sure like to see that and work up the costs for that cute real time trick. By the way, years ago, I got paid by a non US government agency to identify and define the types of “real time” data it had to process. I think my team identified six types. Only one could be processed without massive resource investments to make the other four semi real. The final one was to gain access to the high-speed data about financial instrument pricing in Wall Street big dogs. That simply was not possible without resources and cartwheels. The reason? The government wanted to search for who was making real time trades in certain financial instruments. Yeah, good luck with that in a world where milliseconds require truly big money for gizmos to capture the data and the software to slap metadata on what is little more than a jet engine exhaust of zeros and ones, often encrypted in a way that would baffle some at certain three letter agencies. Remember: These are banks, not some home brew messaging service.

There are some other wild assertions in the write up. I am losing interest is addressing this first year business school “analysis.” The idea is that a company with 500 to 50,000 employees can use this ready-to-roll service is interesting. I don’t know of a single enterprise search company I have encountered since I wrestled with IBM STAIRS and the dorky IBM CICS system that has what seems to be a “one size fits all” service. The Google Search Appliance failed with its “one size fits all.” The dead bodies on the enterprise search trail is larger than the death toll on the Oregon Trail. I know from my lectures that few if any know what DELPHES’ system did. What about InQuire? And there is IBM WebFountain and Clever. What about Perfect Search? What about Surfray? What about Arikus, Convera, Dieselpoint, or Entopia?

The good news is that a free trial is available. The cost is about $30 per month per user. For an organization like the local outfit that sells hard hats and uses Ironside and AS/400s, that works out to 150 times $360 or $54,000. I know this company won’t buy. Why? The system in place is good enough. Spreadsheet fever is not the same as identifying prospects and making a solid benefit based argument.

That’s why free and open source solutions get some love. Then built in “good enough” solutions from Microsoft are darned popular. Finally, some eager beaver in the information technology department will say, “Let me put together a system using Hugging Face.”

Many companies and a number of quite intelligent people (including former Googlers) have tried to wrestle enterprise search to the ground. Good luck. Just make sure you have verifiable data and not the wild assertions about how much time spend searching or how much time an employee will save. Don’t believe anything about enterprise search that uses the words “all” or universal.”

Google said it was “universal search.” Yeah, why after decades of selling ads does the company provide so so search for the Web, Gmail, YouTube, and images. Just ask, “Why?” Search is a difficult challenge.

Glean this from my personal opinion essay: Search is difficult, and it has yet to be solved except for precisely defined use cases. Google experience or not, the task is out of reach at this time.

Stephen E Arnold, August 12, 2025

Explaining Meta: The 21st Century “Paul” Writes a Letter to Us

No AI. Just a dinobaby being a dinobaby.

I read an interesting essay called “Decoding Zuck’s Superintelligence Memo.” The write up is similar to the assignments one of my instructors dumped on hapless graduate students at Duquesne University, a Jesuit university located in lovely Pittsburgh.

The idea is to take a text in Latin and sometimes in English and explain it, tease out its meaning, and try to explain what the author was trying to communicate. (Tortured sentences, odd ball vocabulary, and references only the mother of an ancient author could appreciate were part of the deciphering fun.)

The “Decoding Zuck” is this type of write up. This statement automatically elevates Mr. Zuckerberg to the historical significance of the Biblical Paul or possibly to a high priest of the Aten in ancient Egypt. I mean who knew?

Several points warrant highlighting.

First, the write up includes “The Zuckerberg Manifesto Pattern.” I have to admit that I have not directed much attention to Mr. Zuckerberg or his manifestos. I view outputs from Silicon Valley type outfits a particular form of delusional marketing for the purpose of doing whatever the visionary wants to do. Apparently they have a pattern and a rhetorical structure. The pattern warrants this observation from “Decoding Zuck”:

Compared to all founders and CEOs, Zuck does seem to have a great understanding of when he needs to bet the farm on an idea and a behavioral shift. Each time he does that, it is because he sees very clearly Facebook is at the end of the product life and the only real value in the company is the attention of his audience. If that attention declines, it takes away the ability to really extend the company’s life into the next cycle.

Yes, a prescient visionary.

Second, the “decoded” message means, according to “Decoding Zuck”:

More than anything, this is a positioning document in the AI arms race. By using “super intelligence” as a marketing phrase, Zuck is making his efforts feel superior to the mere “Artificial Intelligence” of OpenAI, Anthropic, and Google.

I had no idea that documents like Paul’s letter to the Romans and Mr. Zuckerberg’s manifesto were marketing collateral. I wonder if those engaged in studying ancient Egyptian glyphs will discover that the writings about Aten are assertions about the bread sold by Ramose, the thumb on the scale baker.

Third, the context for the modern manifesto of Zuck is puffery. The exegesis says:

So what do I think about this memo, and all the efforts of Meta? I remain skeptical of his ability to invent a new future for his company. In the past, he has been able to buy, snoop, or steal other people’s ideas. It has been hard for him and his company to actually develop a new market opportunity. Zuckerberg also tends to overpromise on timelines and underestimate execution challenges.

I think this analysis of the Zuckerberg Manifesto of 2025 reveals several things about how Meta (formerly Facebook) positions itself and it provides some insight into the author of “Decoding Zuck” as well:

1. The outputs are baloney packaged as serious thought
2. The AI race has to produce a winner, and it is not clear if Facebook (sorry Meta) will be viewed as a contender
3. AI is not yet a slam dunk winner, bigger than the Internet as another Silicon Valley sage suggested.

Net net: The AI push reveals that some distance exists between delivering hefty profits for those who have burned billions to reach the point that a social media executive feels compelled to issue a marketing blurb.

Remarkable. Marketing by manifesto.

Stephen E Arnold, August 12, 2025

The Human Mind in Software. It Is Alive!

Has this team of researchers found LLM’s holy grail? Science magazine reports, “Researchers Claim their AI Model Simulates the Human Mind. Others are Skeptical.” The team’s paper, published in Nature, claims the model can both predict and simulate human behavior. Predict is believable. Simulate? That is a much higher bar.

The team started by carefully assembling data from 160 previously published psychology experiments. Writer Cathleen O’Grady tells us:

“The researchers then trained Llama, an LLM produced by Meta, by feeding it the information about the decisions participants faced in each experiment, and the choices they made. They called the resulting model ‘Centaur’—the closest mythical beast they could find to something half-llama, half-human, [researcher Marcel] Binz says.”

Cute. The data collection represents a total of over 60,000 participants who made over 10 million choices. That sounds like a lot. But, as computational cognitive scientist Federico Adolfi notes, 160 experiments is but “a grain of sand in the infinite pool of cognition.” See the write-up for the study’s methodology. The paper claims Centaur’s choices closely aligned with those of human subjects. This means, researchers assert, Centaur could be used to develop experiments before involving human subjects. Hmm, this sounds vaguely familiar.

Other cognitive scientists remain unconvinced. For example:

“Jeffrey Bowers, a cognitive scientist at the University of Bristol, thinks the model is ‘absurd.’ He and his colleagues tested Centaur … and found decidedly un-humanlike behavior. In tests of short-term memory, it could recall up to 256 digits, whereas humans can commonly remember approximately seven. In a test of reaction time, the model could be prompted to respond in ‘superhuman’ times of 1 millisecond, Bowers says. This means the model can’t be trusted to generalize beyond its training data, he concludes.

More important, Bowers says, is that Centaur can’t explain anything about human cognition. Much like an analog and digital clock can agree on the time but have vastly different internal processes, Centaur can give humanlike outputs but relies on mechanisms that are nothing like those of a human mind, he says.”

Indeed. Still, even if the central assertion turns out to be malarky, there may be value in this research. Both vision scientist Rachel Heaton and computational visual neuroscientist Katherine Storrs are enthusiastic about the dataset itself. Heaton is also eager to learn how, exactly, Centaur derives its answers. Storrs emphasizes a lot of work has gone into the dataset and the model, and is optimistic that work will prove valuable in the end. Even if Centaur turns out to be less human and more Llama.

Cynthia Murrell, August 11, 2025

The China Smart, US Dumb Push Is Working

This blog post is the work of an authentic dinobaby. Sorry. No smart software can help this reptilian thinker.

I read “The US Should Run Faster on AI Instead of Trying to Trip Up China.” In a casual way, I am keeping an eye open for variations on the “China smart, US dumb” information I spot. The idea is that China is not just keeping pace with US innovation, the Middle Kingdom is either even or leading. The context is that the star burning bright for the American era has begun collapsing into a black hole or maybe to a brown dwarf. Avoidance of the US may be the best policy. As one of Brazil’s leaders noted: “America is not bullying our country [Brazil]. America is bullying the world.”

Right or wrong? I have zero idea.

The cited essay suggests that certain technology and economic policies have given China an advantage. The idea is that the disruptive kid in high school sits in the back of the room and thinks up a better Facebook-type system and then implements it.

The write up states:

The ostensible reason for the [technology and economic] controls was to cripple China’s AI progress. If that was the goal, it has been a failure.

As I zipped through the essay, I noted that the premise of the write up is that the US has goofed. The proof of this is no farther than data about China’s capabilities in smart software. I think that any large language model will evidence bias. Bias is encapsulated in many human-created utterances. I, for example, have written critically about search and retrieval for decades. Am I biased toward enterprise search? Absolutely. I know from experience that software that attempts to index content in an organization inevitably disappoints a user of that system. Why? No system to which I have been exposed has access to the totality of “information” generated by an organization. Maybe someday? But for the last 40 years, systems simply could not deliver what the marketers promised. Therefore, I am biased against claims that an enterprise search system can answer employees’ questions.

China is a slippery fish. I had a brief and somewhat weird encounter with a person deeply steeped in China’s somewhat nefarious effort to gain access to US pharma-related data. I have encountered a similar effort afoot in the technical disciplines related to nuclear power. These initiatives illustrate that China wants to be a serious contender for the title of world leader in bio-science and nuclear. Awareness of this type of information access is low even today.

I am, as a dinobaby, concerned that the lack of awareness issue creates more opportunities for information exfiltration from a proprietary source to an “open source” concept. To be frank, I am in favor of a closed approach to technology.

The reason I am making sure I have this source document and my comments is that it is a very good example of how the China good, America dumb information is migrating into what might be termed a more objective looking channel.

Net net: China’s weaponizing of information is working reasonably well. We are no longer in TikTok territory.

Stephen E Arnold, August 6, 2025

Can Clarity Confuse? No, It Is Just Zeitgeist

This blog post is the work of an authentic dinobaby. Sorry. No smart software can help this reptilian thinker.

In my newsreader this morning, popped this article “Why Navigating Ongoing Uncertainty Requires Living in the Now, Near, and Next.” I was not familiar with Clarity Global. I think it is a public relations firm. The CEO of the firm is a former actress. I have minimal knowledge of PR and even less about acting.

I plunged into the essay. The purpose of the write up, in my opinion, was to present some key points from a conference called “TNW2025.”  Conference often touch upon many subjects. One event at which I spoke this year had a program listing on six pages the speakers. I think 90 percent of the people attending the conference were speakers.

The first ideas in the write up touch upon innovation, technology adoption, funding, and the zeitgeist. Yep, zeitgeist.

As if these topics were not of sufficient scope, the write up identifies three themes. These are:

1. “Regulation is a core business competency”
2. “Partnership is the engine of progress”
3. “Culture is critical”.

Notably absent was making money and generating a profit.

What about the near, now, and next?

The near means having enough cash on hand to pay the bills at the end of the month. The now means having enough credit or money to cover the costs of being in business. Recently a former CIA operative invited me to lunch. When the bill arrived, he said, “Oh, I left my billfold at home.” I paid the bill and decided to delete him from my memory bank. He stiffed me for $11, and he told me quite a bit about his “now.” And the next means that without funding there is a greatly reduced chance of having a meaningful future. I wondered, “Was this ‘professional’ careless, dumb, or unprofessional?” (Maybe all three?)

Now what about these themes. First, regulation means following the rules. I am not sure this is a competency. To me, it is what one does. Second, partnership is a nice word, not as slick as zeitgeist but good. The idea of doing something alone seems untoward. Partnerships have a legal meaning. I am not sure that a pharmaceutical company with a new drug is going to partner up. The company is going to keep a low profile, file paperwork, and get the product out. Paying people and companies to help is not a partnership. It is a fee-for-service relationship. These are good. Partnerships can be “interesting.” And culture is critical. In a market, one has to identify a market. Each market has a profile. It is common sense to match the product or service to each market’s profile. Apple cannot sell an iPhone to a person who cannot afford to pay for connectivity, buy apps or music, or plug the gizmo in. (I am aware that some iPhone users steal them and just pretend, but those are potential customers, not “real” customers.)

Where does technology fit into this conference? It is the problem organizations face. It is also the 10th word in the essay. I learned “… the technology landscape continues to evolve at an accelerating page.” Where’s smart software? Where’s non-democratic innovation? Where’s informed resolution of conflict?

What about smart software, AI, or artificial intelligence? Two mentions: One expert at the conference invests in AI and in this sentence:

As AI, regulation and societal expectations evolve, the winners will be those who anticipate change and act with conviction.

I am not sure regulation,  partnership, and coping with culture can do the job. As for AI, I think funding and pushing out products and services capture the zeitgeist.

Stephen E Arnold, August 1, 2025

AI, Math, and Cognitive Dissonance

This blog post is the work of an authentic dinobaby. Sorry. No smart software can help this reptilian thinker.

AI marketers will have to spend some time positioning their smart software as great tools for solving mathematical problems. “Not Even Bronze: Evaluating LLMs on 2025 International Math Olympiad” reports that words about prowess are disconnected from performance. The write up says:

The best-performing model is Gemini 2.5 Pro, achieving a score of 31% (13 points), which is well below the 19/42 score necessary for a bronze medal. Other models lagged significantly behind, with Grok-4 and Deepseek-R1 in particular underperforming relative to their earlier results on other MathArena benchmarks.

The write up points out, possibly to call attention to the slight disconnect between the marketing of Google AI and its performance in this contest:

As mentioned above, Gemini 2.5 Pro achieved the highest score with an average of 31% (13 points). While this may seem low, especially considering the $400 spent on generating just 24 answers, it nonetheless represents a strong performance given the extreme difficulty of the IMO. However, these 13 points are not enough for a bronze medal (19/42). In contrast, other models trail significantly behind and we can already safely say that none of them will achieve the bronze medal. Full results are available on our leaderboard, where everyone can explore and analyze individual responses and judge feedback in detail.

This is one “competition”, the lousy performance of the high-profile models, and the complex process required to assess performance make it easy to ignore this result.

Let’s just assume that it is close enough for horse shoes and good enough. With that  assumption in mind, do you want smart software making decisions about what information you can access, the medical prognosis for your nine-year-old child, or decisions about your driver’s license renewal?

Now, let’s consider this write up fragmented across Tweets: [Thread] An OpenAI researcher says the company’s latest experimental reasoning LLM achieved gold medal-level performance on the 2025 International Math Olympiad. The little posts are perfect for a person familliar with TikTok-type and Twitter-like content. Not me. The main idea is that in the same competition, OpenAI earned “gold medal-level performance.”

The $64 dollar question is, “Who is correct?” The answer is, “It depends.”

Is this an example of what I learned in 1962 in my freshman year at a so-so university? I think the term was cognitive dissonance.

Stephen E Arnold, July 28, 2025

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  Stephen E. Arnold monitors search, content processing, text mining and related topics from his high-tech nerve center in rural Kentucky. He tries to winnow the goose feathers from the giblets. He works with colleagues worldwide to make this Web log useful to those who want to go "beyond search". Contact him at sa [at] arnoldit.com. His Web site with additional information about search is arnoldit.com.

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