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Think It. The * It * Becomes Real. Think Again?

No AI. Just a dinobaby working the old-fashioned way.

Fortune Magazine — once the gem for a now spinning-in-his-grave publisher —- posted “MIT Report: 95% of Generative AI Pilots at Companies Are Failing.” I take a skeptical view of MIT. Why? The esteemed university found Jeffrey Epstein a swell person.

The thrust of the story is that people stick smart software into an organization, allow it time to steep, cook up a use case, and find the result unpalatable. Research is useful. When it evokes a “Duh!”, I don’t get too excited.

But there was a phrase in the write up which caught my attention: Learning gap. AI or smart software is a “belief.” The idea of the next big thing creates an opportunity to move money. Flow, churn, motion — These are positive values in some business circles.

AI fits the bill. The technology demonstrates interesting capabilities. Use cases exist. Companies like Microsoft have put money into the idea. Moving money is proof that “something” is happening. And today that something is smart software. AI is the “it” for the next big thing.

Learning gap, however, is the issue. The hurdle is not Sam Altman’s fears about the end of humanity or his casual observation that trillions of dollars are needed to make AI progress. We have a learning gap.

But the driving vision for Internet era innovation is do something big, change the world, reinvent society. I think this idea goes back to the sales-oriented philosophy of visualizing a goal and aligning one’s actions to achieve that goal. I a fellow or persona named Napoleon Hill pulled together some ideas and crafted “Think and Grow Rich.” Today one just promotes the “next big thing,” gets some cash moving, and an innovation like smart software will revolutionize, remake, or redo the world.

The “it” seems to be stuck in the learning gap. Here’s the proof, and I quote:

But for 95% of companies in the dataset, generative AI implementation is falling short. The core issue? Not the quality of the AI models, but the “learning gap” for both tools and organizations. While executives often blame regulation or model performance, MIT’s research points to flawed enterprise integration. Generic tools like ChatGPT excel for individuals because of their flexibility, but they stall in enterprise use since they don’t learn from or adapt to workflows, Challapally explained. The data also reveals a misalignment in resource allocation. More than half of generative AI budgets are devoted to sales and marketing tools, yet MIT found the biggest ROI in back-office automation—eliminating business process outsourcing, cutting external agency costs, and streamlining operations.

Consider this question: What if smart software mostly works but makes humans uncomfortable in ways difficult for the user to articulate? What if humans lack the mental equipment to conceptualize what a smart system does? What if the smart software cannot answer certain user questions?

I find information about costs, failed use cases, hallucinations, and benefits plentiful. I don’t see much information about the “learning gap.” What causes a learning gap? Spell check makes sense. A click that produces a complete report on a complex topic is different. But in what way? What is the impact on the user?

I think the “learning gap” is a key phrase. I think there is money to be made in addressing it. I am not confident that visualizing a better AI is going to solve the problem which is similar to a bonfire of cash. The learning gap might be tough to fill with burning dollar bills.

Stephen E Arnold, August 27, 2025

Apple and Meta: The After Market Route

No AI. Just a dinobaby working the old-fashioned way.

Two big outfits are emulating the creative motif for an American television series titled “Pimp My Ride.” The show was hosted by rapper Xzibit, who has a new album called “Kingmaker” in the works. He became the “meme” of the television program with his signature phrase, “Yo, dawg, I heard you like.”

A DVD of season one, is available for sale at www.bol.com.

Each episode a “lucky person” would be approached and told that his or her vehicle would be given a make over. Some of the make overs were memorable. Examples included the “Yellow Shag Disaster,” which featured yellow paint and yellow shag carpeting. The team removed a rat living in the 1976 Pacer. Another was the “Drive In Theater Car.” It included a pop up champagne dispenser and a TV screen installed under the hood for a viewing experience when people gathered outside the vehicle.

The idea was to take something that mostly worked and then add-on extras. Did the approach work? It made Xzibit even more famous and it contributed the phrase “Yo, dawg, I heard you like” to the US popular culture between 2004 and 2007.

I think the “Pimp My Ride” concept has returned for Apple and Meta. Let me share my thoughts with you.

First, I noted that Bloomberg is exploring the use of Google Gemini AI to Power the long suffering Siri. You can read the paywalled story at this link. Apple knows that Google’s payments are worth real money. The idea of adding more Google and getting paid for the decision probably makes sense to the estimable Apple. Will the elephants mate and produce more money or will the grass get trampled. I don’t know. It will be interesting to see what the creative wizards at both companies produce. There is no date for the release of the first episode. I will be watching.

Second, the story presented in fragments on X.com appears at this X.com page. The key item of information is the alleged tie up between Meta and MidJourney:

Today we’re proud to announce a partnership with @midjourney , to license their aesthetic technology for our future models and products, bringing beauty to billions.

Meta, like Apple, is partnering with an AI success in the arts and crafts sector of smart software. The idea seems to focus on “aesthetic excellence.” How will these outfits enhance Meta. Here’s what the X.com comment offers:

To ensure Meta is able to deliver the best possible products for people it will require taking an all-of-the-above approach. This means world-class talent, ambitious compute roadmap, and working with the best players across the industry.

Will these add-one approaches to AI deliver something useful to millions or will the respective organizations produce the equivalent of the “Pimp My Ride” Hot Tub Limousine. This after-market confection added a hot tub filled with water to a limousine. The owner of the vehicle could relax in the hot tub while the driver ferried the proud owner to the bank.

I assume the creations of the Apple, Google, Meta, and MidJourney teams will be captured on video and distributed on TikTok-type services as well as billions of computing devices. My hope is that Xzibit is asked to host the roll outs for the newly redone services. I would buy a hat, a T shirt, and a poster for the “winner” of this new AI enhanced effort.

Yo, dawg, I heard you like AI, right?

Stephen E Arnold, August 26, 2025

And the Problem for Enterprise AI Is … Essentially Unsolved

No AI. Just a dinobaby working the old-fashioned way.

I try not to let my blood pressure go up when I read “our system processes all your organization’s information.” Not only is this statement wildly incorrect it is probably some combination of [a] illegal, [b] too expensive, and [c] too time consuming.

Nevertheless, vendors either repeat the mantra or imply it. When I talk with representatives of these firms, over time, fewer and fewer recognize the craziness of the assertion. Apparently the reality of trying to process documents related to a legal matter, medical information, salary data, government-mandated secrecy cloaks, data on a work-from-home contractor’s laptop which contains information about payoffs in a certain country to win a contract, and similar information is not part of this Fantasyland.

I read “Immature Data Strategies Threaten Enterprise AI Plans.” The write up is a hoot. The information is presented in a way to avoid describing certain ideas as insane or impossible. Let’s take a look at a couple of examples. I will in italics offer my interpretation of what the online publication is trying to coat with sugar and stick inside a Godiva chocolate.

Here’s the first snippet:

Even as senior decision-makers hold their data strategies in high regard, enterprises face a multitude of challenges. Nearly 90% of data pros reported difficulty with scaling and complexity, and more than 4 in 5 pointed to governance and compliance issues. Organizations also grapple with access and security risks, as well as data quality, trust and skills gaps.

My interpretation: Executives (particularly leadership types) perceive their organizations as more buttoned up than they are in reality. Ask another employee, and you will probably hear something like “overall we do very well.” The fact of the matter is that leadership and satisfied employees have zero clue about what is required to address a problem. Looking too closely is not a popular way to get that promotion or to keep the Board of Directors and stakeholders happy. When you have to identify an error use a word like “governance” or “regulations.”

Here’s the second snippet:

To address the litany of obstacles, organizations are prioritizing data governance. More than half of those surveyed expect strengthened governance to significantly improve AI implementation, data quality and trust in business decisions.

My interpretation: Let’s talk about governance, not how poorly procurement is handled and the weird system problems that just persist. What is “governance”? Organizations are unsure how they continue to operate. The purpose of many organizations is — believe it or not — lost. Make money is the yardstick. Do what’s necessary to keep going. That’s why in certain organizations an employee from 30 years ago could return and go to a meeting. Why? No change. Same procedures, same thought processes, just different people. Incrementalism and momentum power the organization.

So what? Organizations are deciding to give AI a whirl or third parties are telling them to do AI. Guess what? Major change is difficult. Systems-related activities repeat the same cycle. Here’s one example: “We want to use Vendor X to create an enterprise knowledge base.” Then the time, cost, and risks are slowly explained. The project gets scaled back because there is neither time, money, employee cooperation, or totally addled attorneys to make organization spanning knowledge available to smart software.

The pitch sounds great. It has for more than 60 years. It is still a difficult deliverable, but it is much easier to market today. Data strategies are one thing; reality is anther.

Stephen E Arnold, August 26, 2025

Deal Breakers in Medical AI

No AI. Just a dinobaby working the old-fashioned way.

My newsfeed thing spit out a link to “Why Radiology AI Didn’t Work and What Comes Next.” I have zero interest in radiology. I don’t get too excited about smart software. So what did I do? Answer: I read the article. I was delighted to uncover a couple of points that, in my opinion, warrant capturing in my digital notebook.

The set up is that a wizard worked at a start up trying to get AI to make sense of the consistently fuzzy, murky, and baffling images cranked out by radiology gizmos. Tip: Follow the instructions and don’t wear certain items of jewelry. The start up fizzled. AI was part of the problem, but the Jaws-type sharp lurking in the murky image explains this type of AI implosion.

Let’s run though the points that struck me.

First, let’s look at this passage:

Unlike coding or mathematics, medicine rarely deals in absolutes. Clinical documentation, especially in radiology, is filled with hedge language — phrases like “cannot rule out,” “may represent,” or “follow-up recommended for correlation.” These aren’t careless ambiguities; they’re defensive signals, shaped by decades of legal precedent and diagnostic uncertainty.

Okay, lawyers play a significant role in establishing thought processes and normalizing ideas that appear to be purpose-built to vaporize like one of those nifty tattoo removing gadgets the smart system. I would have pegged insurance companies, then lawyers, but the write up directed my attention of the legal eagles’ role: Hedge language. Do I have disease X? The doctor responds, “Maybe, maybe not. Let’s wait 30 days and run more tests.” Fuzzy lingo, fuzzy images, perfect.

Second, the write up asks two questions:

• How do we improve model coverage at the tail without incurring prohibitive annotation costs?
• Can we combine automated systems with human-in-the-loop supervision to address the rare but dangerous edge cases?

The answers seem to be: You cannot afford to have humans do indexing and annotation. That’s why certain legal online services charge a lot for annotations. And, the second question, no, you cannot pull off automation with humans for events rarely covered in the training data. Why? Cost and finding enough humans who will do this work in a consistent way in a timely manner.

Here’s the third snippet:

Without direct billing mechanisms or CPT reimbursement codes, it was difficult to monetize the outcomes these tools enabled. Selling software alone meant capturing only a fraction of the value AI actually created. Ultimately, we were offering tools, not outcomes. And hospitals, rightly, were unwilling to pay for potential unless it came bundled with performance.

Finally, insurance procedures. Hospitals aren’t buying AI; they are buying ways to deliver “service” and “bill.” AI at this time does not sell what hospitals want to buy: A way to keep high rates and slash costs wherever possible.

Unlikely but perhaps some savvy AI outfit will create a system that can crack the issues the article identifies. Until then, no money, no AI.

Stephen E Arnold, August 26, 2025

Leave No Data Unslurped: A New Google T Shirt Slogan?

No AI. Just a dinobaby working the old-fashioned way.

That mobile phone is the A Number One surveillance device ever developed. Not surprisingly, companies have figured out how to monetize the data flowing through the device. Try explaining the machinations of those “Accept Defaults” to a clutch of 70-something bridge players. Then try explaining the same thing to the GenAI type of humanoid. One group looks at you with a baffled work on their faces. The other group stares into the distance and says, “Whatever.”

Now the Google wants more data, fresh information, easily updated. Because why not? “Google Expands AI-Based Age Verification System for Search Platform.” The write up says:

Google has begun implementing an artificial intelligence-based age verification system not only on YouTube but also on Google Search … Users in the US are reporting pop-ups on Google Search saying, “We’ve changed some of your settings because we couldn’t verify that you’re of legal age.” This is a sign of new rules in Google’s Terms of Service.

Why the scope creep from YouTube to “search” with its AI wonderfulness? The write up says:

The new restrictions could be another step in re-examining the balance between usability and privacy.

Wrong. The need for more data to stuff into the assorted AI “learning” services provide a reasonable rationale. Tossing in the “prevent harm” angle is just cover.

My view of the matter is:

1. Mobile is a real time service. Capturing more information of a highly-specific nature is something that is an obvious benefit to the Google.
2. Users have zero awareness of how the data interactions work and most don’t want to know to try to understand cross correlation.
3. Google’s goals are not particularized. This type of “fingerprint” just makes sense.

The motto could be “Leave no data unslurped.” What’s this mean? Every Google service will require verification. The more one verifies, the fresher the identify information and the items that tag along and can be extracted. I think of this as similar to the process of rendering slaughtered livestock. The animal is dead, so what’s the harm.

None, of course. Google is busy explaining how little its data centers use to provide those helpful AI overview things.

Stephen E Arnold, August x, 2025

Stephen E Arnold, August 25, 2025

Copilot, Can You Crash That Financial Analysis?

No AI. Just a dinobaby working the old-fashioned way.

The ever-insouciant online service The Verge published a story about Microsoft, smart software, and Excel. “Microsoft Excel Adds Copilot AI to Help Fill in Spreadsheet Cells” reports:

Microsoft Excel is testing a new AI-powered function that can automatically fill cells in your spreadsheets, which is similar to the feature that Google Sheets rolled out in June.

Okay, quite specific intentionality: Fill in cells. And a dash of me-too. I like it.

However, the key statement in my opinion is:

The COPILOT function comes with a couple of limitations, as it can’t access information outside your spreadsheet, and you can only use it to calculate 100 functions every 10 minutes. Microsoft also warns against using the AI function for numerical calculations or in “high-stakes scenarios” with legal, regulatory, and compliance implications, as COPILOT “can give incorrect responses.”

I don’t want to make a big deal out of this passage, but I will do it anyway. First, Microsoft makes clear that the outputs can be incorrect. Second, don’t use it too much because I assume one will have to pay to use a system that “can give incorrect results.” In short, MSFT is throttling Excel’s Copilot. Doesn’t everyone want to explore numbers with an addled Copilot known to flub numbers in a jet aircraft at 0.8 Mach?

I want to quote from “It Took Many Years And Billions Of Dollars, But Microsoft Finally Invented A Calculator That Is Wrong Sometimes”:

Think of it. Forty-five hundred years ago, if you were a Sumerian scribe, while your calculations on the world’s first abacus might have been laborious, you could be assured they’d be correct. Four hundred years ago, if you were palling around with William Oughtred, his new slide rule may have been a bit intimidating at first, but you could know its output was correct. In the 1980s, you could have bought the cheapest, shittiest Casio-knockoff calculator you could find, and used it exclusively, for every day of the rest of your life, and never once would it give anything but a correct answer. You could use it today! But now we have Microsoft apparently determining that “unpredictability” was something that some number of its customers wanted in their calculators.

I know that I sure do. I want to use a tool that is likely to convert “high-stakes scenarios” into an embarrassing failure. I mean who does not want this type of digital Copilot?

Why do I find this Excel with Copilot software interesting?

1. It illustrates that accuracy has given way to close enough for horseshoes. Impressive for a company that can issue an update that could kill one’s storage devices.
2. Microsoft no longer dances around hallucinations. The company just says, “The outputs can be wrong.” But I wonder, “Does Microsoft really mean it?” What about Red Bull-fueled MBAs handling one’s retirement accounts? Yeah, those people will be really careful.
3. The article does not come and and say, “Looks like the AI rocket ship is losing altitude.”
4. I cannot imagine sitting in a meeting and observing the rationalizations offered to justify releasing a product known to make NUMERICAL errors.

Net net: We are learning about the quality of [a] managerial processes at Microsoft, [b] the judgment of employees, and [c] the sheer craziness that an attorney said, “Sure, release the product just include an upfront statement that it will make mistakes.” Nothing builds trust more than a company anchored in customer-centric values.

Stephen E Arnold, August 22, 2025

News Flash: Google Does Not Care about Publishers

No AI. Just a dinobaby working the old-fashioned way.

I read another Google is bad story. This one is titled “Google Might Not Believe It, But Its AI Summaries Are Bad News for Publishers.” The “news” service reports that a publishing industry group spokesperson said:

“We must ensure that the same AI ‘answers’ users see at the top of Google Search don’t become a free substitute for the original work they’re based on.”

When this sentence was spoken was the industry representative’s voice trembling? Were there tears in his or her eyes? Did the person sniff to avoid the embarrassment of a runny nose?

No idea.

The issue is that Google looks at its metrics, fiddles with its knobs and dials on its ad sales system, and launches AI summaries. Those clicks that used to go to individual sites now provide the “summary space” which is a great place for more expensive, big advertising accounts to slap their message. Yep, it is the return to the go-go days of television. Google is the only channel and one of the few places to offer a deal.

What does Google say? Here’s a snip from the “news” story:

"Overall, total organic click volume from Google Search to websites has been relatively stable year-over-year," Liz Reid, VP and Head of Google Search, said earlier this month. "Additionally, average click quality has increased, and we’re actually sending slightly more quality clicks to websites than a year ago (by quality clicks, we mean those where users don’t quickly click back — typically a signal that a user is interested in the website). Reid suggested that reports like the ones from Pew and DCN are "often based on flawed methodologies, isolated examples, or traffic changes that occurred prior to the rollout of AI features in Search."

Translation: Haven’t you yokels figured out after 20 years of responding to us, we are in control now. We don’t care about you. If we need content, we can [a] pay people to create it, [b] use our smart software to write it, and [c] offer inducements to non profits, government agencies, and outfits with lots of writers desperate for recognition a deal. TikTok has changed video, but TikTok just inspired us to do our own TikTok. Now publishers can either get with the program or get out.

PC News apparently does not know how to translate Googlese.

It’s been 20 plus years and Google has not changed. It is doing more of the game plan. Adapt or end up prowling LinkedIn for work.

Stephen E Arnold, August 21, 2025

The Risks of Add-On AI: Apple, Telegram, Are You Paying Attention?

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

Name three companies trying to glue AI onto existing online services? Here’s my answer:

• Amazon
• Apple
• Telegram.

There are others, but each of these has a big “tech rep” and command respect from other wizards. We know that Tim Apple suggested that the giant firm had AI pinned to the mat and whimpering, “Let me be Siri.” Telegram mumbled about Nikolai working on AI. And Amazon? That company has flirted with smart software with its Sagemaker announcements years ago. Now it has upgraded Alexa, the device most used as a kitchen timer.

“Amazon’s Rocky Alexa+ Launch Might Justify Apple’s Slow Pace with Next-Gen Siri” ignores Telegram (of course. Who really cares?) and uses Amazon’s misstep to apologize for Apple’s goofs. The write up says:

Apple has faced a similar technical challenge in its own next-generation Siri project. The company once aimed to merge Siri’s existing deterministic systems with a new generative AI layer but reportedly had to scrap the initial attempt and start over. … Apple’s decision to delay shipping may be frustrating for those of us eager for a more AI-powered Siri, but Amazon’s rocky launch is a reminder of the risks of rushing a replacement before it’s actually ready.

Why does this matter?

My view is that Apple’s and Amazon’s missteps make clear that bolting on, fitting in, and snapping on smart software is more difficult than it seemed. I also believe that the two firms over-estimated their technical professionals’ ability to just “do” AI. Plus, both US companies appear to be falling behind in the “AI race.”

But what about Telegram? That company is in the same boat. Its AI innovations are coming from its third party developers who have been using Telegram’s platform as a platform. Telegram itself has missed opportunities to reduce the coding challenge for its developers with it focus on old-school programming languages, not AI assisted coding.

I think that it is possible that these three firms will get their AI acts together. The problem is that AI native solutions for the iPhone, the Telegram “community,” and Amazon’s own hardware products. The fumbles illustrate a certain weakness in each firm. Left unaddressed, these can be debilitating in an uncertain economic environment.

But the mantra go fast or the jargon accelerate is not in line with the actions of these three companies.

Stephen E Arnold, August 20, 2025

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 Bubbling Pot of Toxic Mediocrity? Microsoft LinkedIn. Who Knew?

No AI. Just a dinobaby working the old-fashioned way.

Microsoft has a magic touch. The company gets into Open Source; the founder “gits” out. Microsoft hires a person from Intel. Microsoft hires garners an engineer, asks some questions, and the new hire is whipped with a $34,000 fine and two years of mom looking in his drawers.

Now i read “Sunny Days Are Warm: Why LinkedIn Rewards Mediocrity.” The write up includes an outstanding metaphor in my opinion: Toxic Mediocrity. The write up says:

The vast majority of it falls into a category I would describe as Toxic Mediocrity. It’s soft, warm and hard to publicly call out but if you’re not deep in the bubble it reads like nonsense. Unlike it’s cousins ‘Toxic Positivity’ and ‘Toxic Masculinity’ it isn’t as immediately obvious. It’s content that spins itself as meaningful and insightful while providing very little of either. Underneath the one hundred and fifty words is, well, nothing. It’s a post that lets you know that sunny days are warm or its better not to be a total psychopath. What is anyone supposed to learn from that?

When I read a LinkedIn post it is usually referenced in an article I am reading. I like to follow these modern slippery footnotes. (If you want slippery, try finding interesting items about Pavel Durov in certain Russian sources.)

Here’s what I learn:

1. A “member” makes clear that he or she has information of value. I must admit. Once in a while a useful post will turn up. Not often, but it has happened. I do know the person believes something about himself or herself. Try asking a GenAI about their personal “beliefs.” Let me know how that works.
2. Members in a specific group with an active moderator often post items of interest. Instead of writing my unread blog, these individuals identify an item and use LinkedIn as a “digital bulletin board” for people who shop at the same sporting goods store in rural Kentucky. (One sells breakfast items and weapons.)
3. I get a sense of the jargon people use to explain their expertise. I work alone. I am writing a book. I don’t travel to conferences or client locations now. I rely on LinkedIn as the equivalent of going to a conference mixer and listening to the conversations.

That useful. I have a person who interacts on LinkedIn for me. I suppose my “experience” is therefore different from someone who visits the site, posts, and follows the antics of LinkedIn’s marketers as they try to get the surrogate me to pay to do what I do. (Guess what? I don’t pay.)

I noted this statement in the essay:

Honestly, the best approach is to remember that LinkedIn is a website owned by Microsoft, trying to make money for Microsoft, based on time spent on the site. Nothing you post there is going to change your career. Doing work that matters might. Drawing attention to that might. Go for depth over frequency.

I know that many people rely on LinkedIn to boost their self confidence. One of the people who worked for me moved to another city. I suggested that she give LinkedIn a whirl. She wrote interesting short items about her interests. She got good feedback. Her self confidence ticked up, and she landed a successful job. So there’s a use case for you.

You should be able to find a short item that a new post appears on my blog. Write me and my surrogate will write you back and give you instructions about how to contact me. Why don’t I conduct conversations on LinkedIn? Have you checked out the telemetry functions in Microsoft software?

Stephen E Arnold, August 19, 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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