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IBM Technology Atlas: A Buzzword Blow Up

Written by an unteachable dinobaby. Live with it.

Do you need some handy buzzwords, jargon, or magnetic phrases for your marketing outputs? IBM has created a very useful tool. It is called the “IBM Technology Atlas.” Now an atlas (maybe alas?), according to the estimable Google, is “a book of maps or charts.” Now I know what you are thinking. Has IBM published a dead tree book of maps like the trucker’s road map sold at Buc-ees?

No. IBM is too high tech forward leaning for that.

Navigate to “IBM Technology Atlas.” Here’s what your browser will display:

I assume you will be asking, “What does this graphic say?” or “I can’t read it.” Spot on. This Technology Atlas is not designed for legibility like those trucker road maps. Those professionals have to know where to turn in order to deliver a load of auto parts. Driving a technology sports car is more subtle.

The idea with this IBM atlas is to use your cursor to click on one of the six areas of focus for IBM’s sales machine to deliver to customers between 2024 and 2030. I know 2024 was last year, but that’s useful if one wants to know where Lewis and Clark were in Missouri in 1804. And 2030? Projecting five years into the future is strategically bold.

Pick a topic from the option arrayed around the circle:

• AI
• Automation
• Data
• Hybrid Cloud
• Quantum
• Security.

Click on, for instance, AI and the years at which the checkpoint for targets appears. Note you will need a high resolution monitor because no scroll bar is available to move from year to year. But no problem. Here’s what I see after clicking on AI:

Alternatively, you can use the radar chart and click on the radar chart. For the year 2030 targets in AI, put your cursor under AI and place it between AI and Automation. Click and you will see the exploded detail for AI at IBM in 2030:

Now you are ready to begin your exploration of buzzwords. Let’s stick to AI because the future of that suite of technologies is of interest to those who are shoveling cash into the Next Big Thing Furnace as I write this news item with editorial color.

Here are some of the AI words from the 2030 section of the Atlas:

Adaptable AI
Biological intelligence
Cognitive abilities
Generalist AI
Human-machine collaboration
Machine-machine collaboration
Mutual theory of mind
Neuron heterogeneity
Sensory perceptions
Unified neural architecture
WatsonX (yep, Watson).

One can work from 2024 to 2029 and build a comprehensive list of AI jargon. If this seems like real busy work, it is not. You are experiencing how a forward leaning outfit like IBM presents its strategic road map. You—a mere human— must point and click your way through a somewhat unusual presentation of dot points and a time line.

Imagine how easy absorbing this information would be if one just copied the url, pasted it into Perplexity, and asked, “Give me a 25 word summary of this information.” I did that, and here’s what Perplexity replied:

IBM’s Technology Atlas outlines six roadmaps — AI, Automation, Data, Hybrid Cloud, Quantum, Security — for advancing performance, efficiency, and future IT/business evolution through 2030.

Well, that was easy. What about the clicking through the hot links on the radar chart?

That is harder and more time consuming.

Perplexity did not understand how to navigate the IBM Technology Alas.  (Ooops. I mean “atlas.” My bad.) And — truth be told — I did not either when I first encountered this new age and undoubtedly expensive combination of design, jargon collection, and code. Would direct statements and dot points worked? Yes, but that is not cutting edge.

I would recommend this IBM Alas to a student looking for some verbiage for a résumé, a start up trying to jazz up a slide deck, or a person crafting a LinkedIn blurb.

Remember! Neuron heterogeneity is on the road map for 2030. I still like the graphic approach of those trucker road maps available where I can buy a Buc-ee’s T shirt:

Is there a comparable T shirt for quantum at IBM in 2030? No? Alas.

Stephen E Arnold, September 17, 2025

China Smart, US Dumb: The Baidu AI Service

It seems smart software is good for something. CNBC reports, “AI Avatars in China Just Proved They Are Ace Influencers: It Only Took a Duo 7 Hours to Rake in More than $7 Million.” Chinese tech firm Baidu collaborated with two human influencers on the project. Reporter Evelyn Cheng tells us:

“Luo Yonghao, one of China’s earliest and most popular live streamers, and his co-host Xiao Mu both used digital versions of themselves to interact with viewers in real time for well over six hours on Sunday on Baidu’s e-commerce livestreaming platform ‘Youxuan’, the Chinese tech company said. The session raked in 55 million yuan ($7.65 million). In comparison, Luo’s first livestream attempt on Youxuan last month, which lasted just over four hours, saw fewer orders for consumer electronics, food and other key products, Baidu said.”

The experiment highlights Baidu’s avatar technology, which can save marketing departments a lot of money. We learn:

“Luo’s and his co-host’s avatars were built using Baidu’s generative AI model, which learned from five years’ worth of videos to mimic their jokes and style, Wu Jialu, head of research at Luo’s other company, Be Friends Holding, told CNBC on Wednesday. … AI avatars can sharply reduce costs since companies don’t need to hire a large production team or a studio to livestream. The digital avatars can also stream nonstop without needing breaks. … [Wu] said that Baidu now offers the best digital human product currently available, compared to the early days of livestreaming e-commerce five or six years ago.”

Yes, the “early” days of five or six years ago, when the pandemic forced companies and workers to explore their online options. Both landed on livestreaming to generate sales and commissions. Now, it seems, companies can cut the human talent out of the equation. How efficient.

Cynthia Murrell, September 12, 2025

Deadbots. Many Use Cases, Including Advertising

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

I like the idea of deadbots, a concept explained by the ever-authoritative NPR in “AI Deadbots Are Persuasive — and Researchers Say, They’re Primed for Monetization.” The write up reports in what I imagine as a resonant, somewhat breathy voice:

AI avatars of deceased people – or “deadbots” – are showing up in new and unexpected contexts, including ones where they have the power to persuade.

Here’s a passage I thought was interesting:

Researchers are now warning that commercial use is the next frontier for deadbots. “Of course it will be monetized,” said Lindenwood University AI researcher James Hutson. Hutson co-authored several studies about deadbots, including one exploring the ethics of using AI to reanimate the dead. Hutson’s work, along with other recent studies such as one from Cambridge University, which explores the likelihood of companies using deadbots to advertise products to users, point to the potential harms of such uses. “The problem is if it is perceived as exploitative, right?” Hutson said.

Not surprisingly, some sticks in the mud see a downside to deadbots:

Quinn [a wizard a Authetic Interactions Inc.] said companies are going to try to make as much money out of AI avatars of both the dead and the living as possible, and he acknowledges there could be some bad actors. “Companies are already testing things out internally for these use cases,” Quinn said, with reference to such uses cases as endorsements featuring living celebrities created with generative AI that people can interactive with. “We just haven’t seen a lot of the implementations yet.”

I wonder if any philosophical types will consider how an interaction with a dead person’s avatar can be an “authetic interaction.”

I started thinking of deadbots I would enjoy coming to life on my digital devices; for example:

• My first boss at a blue chip consulting firm who encouraged rumors that his previous wives accidently met with boating accidents
• My high school English teacher who took me to the assistant principal’s office for writing a poem about the spirit of nature who looked to me like a Playboy bunny
• The union steward who told me that I was working too fast and making other workers look like they were not working hard
• The airline professional who told me our flight would be delayed when a passenger died during push back from the gate. (The fellow was sitting next to me. Airport food did it I think.)
• The owner of an enterprise search company who insisted, “Our enterprise information retrieval puts all your company’s information at an employee’s fingertips.”

You may have other ideas for deadbots. How would you monetize a deadbot, Google- and Meta-type companies? Will Hollywood do deadbot motion pictures? (I know the answer to that question.)

Stephen E Arnold, September 2, 2025

AI Will Not Have a Negative Impact on Jobs. Knock Off the Negativity Now

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

The word from Goldman Sachs is parental and well it should be. After all, Goldman Sachs is the big dog. PC Week’s story “Goldman Sachs: AI’s Job Hit Will Be Brief as Productivity Rises” makes this crystal clear or almost. In an era of PR and smart software, I am never sure who is creating what.

The write up says:

AI will cause significant, but ultimately temporary, disruption. The headline figure from the report is that widespread adoption of AI could displace 6-7% of the US workforce. While that number sounds alarming, the firm’s economists, Joseph Briggs and Sarah Dong, argue against the narrative of a permanent “jobpocalypse.” They remain “skeptical that AI will lead to large employment reductions over the next decade.”

Knock of the complaining already. College graduates with zero job offers. Just do the van life thing for a decade or become an influencer.

The write up explains history just like the good old days:

“Predictions that technology will reduce the need for human labor have a long history but a poor track record,” they write. The report highlights a stunning fact: Approximately 60% of US workers today are employed in occupations that didn’t even exist in 1940. This suggests that over 85% of all employment growth in the last 80 years has been fueled by the creation of new jobs driven by new technologies. From the steam engine to the internet, innovation has consistently eliminated some roles while creating entirely new industries and professions.

Technology and brilliant management like that at Goldman Sachs makes the economy hum along. And the write up proves it, and I quote:

Goldman Sachs expects AI to follow this pattern.

For those TikTok- and YouTube-type videos revealing that jobs are hard to obtain or the fathers whining about sending 200 job applications each month for six months, knock it off. The sun will come up tomorrow. The financial engines will churn and charge a service fee, of course. The flowers will bloom because that baloney about global warming is dead wrong. The birds will sing (well, maybe not in Manhattan) but elsewhere because windmills creating power are going to be shut down so the birds won’t be decapitated any more.

Everything is great. Goldman Sachs says this. In Goldman we trust or is it Goldman wants your trust… fund that is.

Stephen E Arnold, September 2, 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

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

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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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