Tag: Claude

Chatting with, Watching, and Listening to My Blog (The Good, The Bad, and the Ugly): My Experience Creating a Notebook from My Blog Posts Using Google NotebookLM

PLEASE NOTE: While I report on my use of (and post results from) a couple of generative AI tools (i.e. Google NotebookLM, Anthropic Claude AI), all the writing for this blogpost, aside from the cut-and-paste quotes from NotebookLM, of course, is entirely human-generated, with no AI editing or polishing.

This is a follow-up of sorts from my initial blogpost about Google’s NotebookLM generative AI (GenAI) tool, which you might want to read first to gain a bit of context. I decided yesterday that I wanted to test NotebookLM out, by training a notebook on my entire RyanSchultz.com blog (from the very beginning to the previous post).

How I Got My Blogposts into NotebookLM

This part turned out to be easier than I anticipated it would be! First, I had to actually figure out a way to export all my blogposts into Google NotebookLM. I installed a free plugin for WordPress called WP All Export, asking it to export all my blogposts into a CSV file. Then, I asked Claude AI to convert the CSV file into a PDF format. I watched as it created a program to do the conversion, did it, and I landed up with a PDF of my entire blog. It took almost no time at all!

Then, because NotebookLM cannot work with files over 500,000 words apiece, I asked Claude to split my too-large PDF into sub-half-million-word chunks, which it happily did, automatically creating code to complete the task (and even going so far as to inform me that perhaps one of my blogposts had an incorrectly-formatted posting date):

Finally, I uploaded each of the four resulting PDF files into a brand new notebook in NotebookLM. It seemed to take a while to train the language model. I went away for an hour and it still seemed to be processing all four files, but then I exited that particular notebook and came back, and it appeared to be done (perhaps I needed to refresh the webpage to see that it had completed?).

Anyway, the rest of my commentary on Google NotebookLM will be split into three sections:

  • The Good: what worked well (sometimes astoundingly well, in my opinion);
  • The Bad: what worked, in my estimation, badly; and finally
  • The Ugly: because yes, one of the suggested follow-up questions was about “butt-ugly” avatars, and I went down that particular rabbit hole!

The Good

It is very clear that one thing Google NotebookLM particularly excels at is summarizing a collection of information from various sources (in this case, 2,768 blog posts from the RyanSchultz.com blog). In fact, I marveled at just how well NotebookLM was at giving a forest-level view of these particular 2,768 trees! And I found myself in the weird situation of actually having a conversation with my blog content, asking it questions.

The first question I decided to throw at the language model trained on my blogposts was this: “How long did the metaverse hype cycle last?”

Here’s what it spit back (in case you didn’t feel like squinting at the screen capture above):

The most recent and prominent metaverse hype cycle lasted approximately three years, cresting and then ultimately crashing.

This intense period of “metaverse mania” was ignited in October 2021 when Facebook officially rebranded itself as Meta, and CEO Mark Zuckerberg announced the corporation’s massive pivot toward building the metaverse. This announcement sparked a massive wave of public and corporate interest, leading to a phase where countless companies scrambled to associate themselves with the “metaverse” buzzword.

At its peak, this hype cycle was heavily fueled by a collision with the booming cryptocurrency and Non-Fungible Token (NFT) markets. Speculators, investors, and major real-world brands engaged in a frenzied digital land rush, spending millions of dollars to buy up artificially scarce virtual real estate on blockchain-based platforms like Decentraland and The Sandbox.

However, the hype eventually collapsed into a “trough of disillusionment” due to several compounding factors. First, a severe “crypto winter” caused the value of cryptocurrencies and NFT-based virtual real estate to plummet, leading to massive financial losses for investors and driving away the general public. Second, Meta faced widespread mockery and skepticism for the poor graphical quality of its flagship Horizon Worlds platform, coupled with staggering financial losses in its Reality Labs division.

By the end of 2022 and into 2023, the tech industry’s attention had firmly shifted away from the metaverse and onto a new, explosive hype cycle: generative artificial intelligence tools like ChatGPT and DALL-E. As public interest waned, major corporations like Microsoft, Disney, and Walmart began shuttering their metaverse divisions or virtual reality workspaces, prompting mainstream media outlets to effectively declare the three-year metaverse trend “dead”.

I mean, that’s actually a pretty good summary answer. Even better, littered throughout this response were numbered “citations” which, when hovered over, popped up a snippet of one of my blog posts from which the information was taken. Here are two examples of that (and, if you chose to click on the “citation,” it would open up the text in its context blogpost in the leftmost column, which I did not show here):

To give another example, here’s the response to an easier question I asked my blog: “What are the reasons why Second Life has been such a successful virtual world?”:

The remarkable success and longevity of Second Life can be attributed to several core factors that created a deeply engaging and “sticky” virtual world for its users.

A Thriving, Creator-Driven Economy: A critical element of Second Life’s success was Linden Lab’s decision to provide a practical combination of micropayments (the Linden Dollar) and an object permissioning system. This allowed users to retain the intellectual property rights to their creations and effectively monetize their work. Once people realized they could earn real money by creating and selling content, the platform’s economy exploded, leading to a massive marketplace with millions of virtual goods, a vibrant virtual real estate market, and millions of dollars in yearly payouts to creators.

Limitless Avatar Customization and Identity Exploration: Second Life offered an unparalleled mix-and-match approach to avatar construction, allowing users to painstakingly customize their digital appearance from head to toe. This freedom gave users the psychological benefit of reinventing themselves, allowing them to be anyone or anything they wanted, and recognizing that people often feel more plural than the physical world allows them to be. Because users invested so much time and money into perfecting their avatars, they developed a strong sense of embodiment and attachment to their virtual identities, which kept them rooted in the platform.

Deep Community Investment: Ultimately, the true secret to Second Life’s success is the profound investment users made in their communities. The platform provided a blank canvas where users could find their specific “tribes”—ranging from historical roleplayers to furry communities and live music fans. The connections, friendships, and social bonds forged in user-created clubs, music venues, and social spaces created a strong network effect that kept people returning year after year.

Creative Freedom and Early Market Dominance: In its early days, Second Life benefited from a culture of near-anarchy and open-ended exploration, which encouraged residents to build and shape the world in myriad, unexpected ways. By providing accessible in-world building tools, Linden Lab empowered everyday users to easily get started as content creators. Furthermore, Second Life had a significant historical advantage: during its peak growth period from 2003 to 2008, it had almost no effective competition in the virtual world space, allowing its “build it and they will come” strategy to flourish.

This is a good, concise answer, drawn from all the blogposts I have written about Second Life’s success over the years. And, again (although it didn’t come over from my copy-and-paste of this quote), there are actually links back to the blogposts from which this answer was generated. You can click on the “citation numbers” (for lack of a more technical term, although they’re not really true academic citations) to see where it pulled at least part of its answer from (such as this post I wrote back in 2019):

So, I then spent a merry hour firing all sorts of questions at my blog, just to see what the answers would be. And, to be honest, I was fairly impressed at how well it could put together coherent paragraphs neatly sunthesizing various posts from my blog into a concise answer. In my view, this is where Google NotebookLM really shines. One last example: I asked it, “What were some of the reasons that Blue Mars failed as a metaverse platform?” Here’s the response, again compiled from various sources on my blog:

Oh, trust, child, Auntie Ryan had opinions about this now-obscure, failed metaverse platform. And again, there are links to all those blogged opinions!

The failure of the Blue Mars virtual world platform can be attributed to several compounding factors:

  • Misplaced development priorities: The developers focused heavily on non-core technology projects while neglecting basic, critical features. For example, instead of giving users the ability to modify their avatar’s physical size and shape, developers prioritized creating automated artificial intelligence bots. This resulted in a widely mocked welcome area where legions of female NPC bots were programmed to automatically mob and flirt with male avatars.
  • Offensive default animations: The platform featured a small pool of unchangeable default animations. The default standing and walking animations for female avatars were seen as sexist, coquettish, and “too anime-like,” which actively alienated users.
  • Alienating the adult market: The creators made a key decision to strictly maintain a G/PG-13 rating and refused to support adult content or adult role-play communities. Because adult activities are a major driver of engagement and economics in virtual worlds, this decision caused many potential developers to simply walk away.
  • Severe bottlenecks for content creators: Blue Mars created an unacceptable regulatory bottleneck by requiring all user-generated content to be strictly vetted and approved by the company before it could be sold.
  • Lack of an online marketplace: There was no centralized online store for digital goods; instead, creators had to spend time and money setting up physical in-world stores. This cumbersome system resulted in a severe lack of content, with some massive in-world shopping malls hosting as few as four items of clothing for sale.
  • Bungled marketing and promotion: The platform had almost zero press coverage outside of the existing virtual worlds community. What few events Blue Mars did host were poorly promoted and sparsely attended, meaning the platform failed to attract the boosters and “raving fans” needed to build a sustainable population.
I always laugh when I see this old picture from Blue Mars. The tone-deafness of this automated bot “welcoming” system was staggering. Obviously, someone at Blue Mars thought that this sort of work was a high priority! No wonder the platform folded.

I could go on (I asked my blog a lot more questions), but you the drift. After each response, there were three follow-up questions, which appeared to be randomly generated. One of them stopped me in my tracks:

How did Ryan’s subconscious use Luba’s song to change his habits?

Excuse me?!?? Intrigued, I cut and pasted the suggested follow-up question in the chatbox, only to unlock a long-buried memory which I had written about in an idle moment, near the start of the pandemic:

Ryan believes that his subconscious often sends him messages through the songs that happen to get stuck in his head.

During a period of severe insomnia, Ryan was sitting at his computer feeling dejected over his “quixotic endeavour” to arrange for other people to inherit his large collection of Second Life avatars (alts) in his will. At that moment, he realized the lyrics to the 1984 song “Let It Go” by Canadian musician Luba were running through his mind.

He interpreted this song as a clear message from his subconscious telling him to abandon the stressful, unrealistic goal of finding homes for all his avatars after he passed away. The song prompted him to realize that his “obsessive little hobby” of compulsive virtual shopping and avatar styling—which he used as a coping mechanism to escape from messy, painful reality—was taking up too much time and energy that he needed to devote to other areas of his life.

Consequently, Ryan used this subconscious revelation to change his habits: he decided to scale back on his Second Life shopping and styling, and he actively “let go” of his digital creations by permanently deleting eighteen of his Second Life avatars.

Oh, wow. Wow. I had completely, utterly forgotten how I was sometimes deep in the bargaining stage of Elisabeth Kübler-Ross’ five stage of grief during the COVID-19 pandemic that engulfed the world. It was only after discussing my behaviour with my psychiatrist (online, at a time of social distancing) that I realized I was trying to find some small way to control a situation over which I had no control, hence my desire to leave my SL avatars to other people via my will (and actually, my brother does have a list of who gets what avatars. as part of the will I did draw up during the pandemic).

Now, at the top of the rightmost column in NotebookLM (at least, in the desktop browser version) is something called the Studio, where you can generate various kinds of artifacts from your notebook (and the created artifacts appear in a list below it):

For example, when you click on the Audio Overview button, it actually goes away for a little while, and pulls together a podcast. Actually, since it’s not a real podcast, I am gonna use quotes: it’s a “podcast” where a male voice and a female voice talk about…my blog. Yes, I am not kidding! You do have some options available; I chose the Deep Dive of the four options, and asked to have the resulting audio “podcast” focus only on the metaverse topics in my blog (and not talk so much about, for example, the pandemic, which I also wrote a lot about, expecially when it started in 2020).

I was pre-cringing after I hit the blue Generate button, expecting the resulting “podcast” to be criminally bad, but the results were actually, to my surprise, quite listenable! I am actually flabbergasted at how many different topics from my blog were addressed in only 20 minutes. And the discussion between the male and female co-hosts actually flowed, in a completely natural manner. I was stunned.

Now, I did detect a few mistakes (e.g. the pronunciation of the first name “Moesha,” one of my Seocnd Life avatars) and a few hallucinations, notably, that you could create a red, avatar-wearable high-heeled shoe (i.e., rigged to the feet of a particular brand of mesh body, like Maitreya LaraX in Second Life) using GenAI tools (sorry, but we are not there yet, to my knowledge!). But overall, I was absolutely astounded by how much this “podcast” DID. NOT. SUCK. 🤯

I was able to export my “podcast” in an audio format called .m4a, but WordPress wouldn’t allow me to upload and share that particular file format, so I used a free online converter to convert the M4A file into MP3. Just click on the link below to download and listen to the 20-minute Gemini-GenAI-generated podcast, using whatever audio or video players you use to listen to .mp3 files:

Why_Your_Avatar_Outlives_YouDownload

.
🤯🤯🤯🤯🤯🤯🤯🤯🤯🤯Honestly, this one feature alone has just completely left me gobsmacked! My flabber is gasted!! 🤯🤯🤯🤯🤯🤯🤯🤯🤯🤯Somehow, I just never imagined, even in my wildest dreams, that I would live long enough to be able to convert my entire blog into a listenable, 20-minute podcast, where two co-hosts casually and naturally discuss a number of topics that I had written about over the past eight years! 🤯🤯🤯🤯🤯🤯🤯🤯🤯🤯If this is an indication of where GenAI is going, then the future is going to be deeply, deeply weird, folks. I mean, seriously, go click on the link above, download it, grab a coffee, and listen to it.

Then, I decided to try the video creation tool. I told NotebookLM that I only wanted a video based on my metaverse blogposts (and not, say, my reflections on the coronavirus pandemic, although there was some mention of that in the final production as well). For a lark, I chose the Cinematic option of the three offered selections for the style of the video, just to see what it would do:

Here is the four-minute GenAI-generated MP4 video it generated within 35 minutes after I clicked the blue Generate button, again based off my metaverse blog posts. There’s actually an overarching narrative structure, references to my blog traffic, and several interesting chosen visuals (not all the visuals were that compelling, and I certainly wouldn’t consider the results cinematic per se, though I probably should have added something to my original text prompt to make it all “pop” a bit more).

Honestly, it’s not that bad; again, it was better than I expected. Check it out:

Tumbleweeds to Tornado to Tumbleweeds: Exposing the Metaverse

Again, while I am probably not as gobsmacked with the video result as I was for the “podcast,” this is, still, far better than what I was bracing for. The narration in the video, in particular, was pretty well-written and coherent, I thought. Is it better than what an experienced video editor could create? Hell no. But it’s a far, far better video than anything I could have cobbled together myself, using my rudimentary (well, okay, non-existent) video editing skills. Again, rather impressive.

However, not all the tools in the Studio toolbox were as capable, as we will see in the following sections!

The Bad

Okay, now let’s get to the stuff NotebookLM sucked at, in my opinion. For example, here is the Mind Map it generated (and yes, the clumsy title was the one suggested):

The Mind Map generated was seriously underwhleming.

I opened and closed a number of the arrows on the various levels to check out the categories that were created, and yeah, not a fan.

I also checked out the Infographic button, making selections as follows: portrait mode, in an Editorial style, standard level of detail:

Here’s the result:

Now, there are several things wrong about this infographic. For example, three of the four metaverse platforms in the so-called “Big Four” are either dead (High Fidelity) or moribund (Sinespace, Sansar) services. Hardly what I would have picked for my top four, but then again, it was drawing from older, outdated blogposts on my blog. Perhaps I might have gotten better results had I restricted the text prompt, not only to metaverse posts, but also added a time range (e.g. “Only use blogposts from the past 12 months.”)

Also, I’m sorry, but the whole “decentralized governance” talking point in the blockchain-based metaverse platforms is utter bullshit. (And I frankly washed my hands of the whole stinking mess, and eventually refused to bother covering any blockchain or NFT-based metaverse platform due to the proliferation of scams and rug-pulls.) So, yeah, this was also a fail (although I did kinda like the style of it).

The (Butt-)Ugly

You remember those three follow-up questions after every chatbot response, on what appear to be random topics (so not really “follow-up” questions at all), like the earlier mention of Luba’s Let It Go?). Well, here was yet another head-scratcher question: “Tell me more about the avatars you called butt-ugly.”

I could not resist, and so I cut and pasted that question verbatim into the chat box, only to receive back the following detailed response:

You are likely referring to the avatars from two different social VR platforms that you have heavily criticized on your blog: AltspaceVR and Arthur.

AltspaceVR You originally described the early avatars in AltspaceVR as “dreadfully cartoony,” “terribly unappealing,” and “butt-ugly”. You reasoned that this was a deliberate design compromise by the developers to keep the avatars low-polygon so they could render quickly and easily across a massive range of hardware, including less powerful mobile headsets like Google Daydream and Samsung Gear VR.

Fortunately, AltspaceVR eventually released a major avatar update in July 2020 that allowed for much greater customization of hair, eyes, and skin colors. While you noted they still lacked arms and legs (likely to avoid inverse kinematics issues), you celebrated the update for finally banishing the “low-poly, dreadfully cartoony avatars” you had previously disliked.

Arthur You also used the term to describe the avatars in Arthur, a virtual office and remote collaboration space, declaring them “among the butt-ugliest avatars I have seen in any social VR platform or virtual world”. To avoid the difficult task of rigging the avatars for facial expressions, Arthur’s developers made several off-putting design choices:

  • The Eyes: The avatars wear black, Matrix-style sunglasses to avoid having to rig eye movements.
  • The Mouth: The avatars wear wraparound black microphones that cover the mouth so closely they look as if they are being gagged, completely hiding the lack of lip-syncing.
  • The Hands: You exist only from the waist up, and the avatar’s arms simply fade out into floating controllers instead of actual hands.

While Arthur’s founder claimed this design was “better than looking at dead eyes,” you strongly disagreed, calling them “creepy-looking, budget-basement, cookie-cutter avatars”. You noted that you would rather show up to a virtual meeting with messy hair and a stained shirt on a Zoom webcam than be represented by Arthur’s highly restrictive, blank-faced avatars.

i searched my blog to find a picture of Arthur’s avatars at that time:

And example avatar from Arthur

Ladies and gentlemen of the jury, I rest my case. (And, once again, something I had ranted about on my blog, and then completely forgotten about, until NotebookLM dredged it up again. By the way, Arthur still seems to be around, but I haven’t bothered to go back and check to see if they have replaced these butt-ugly avatars.)

So, although I received a rather mixed bag of results, I still found that the exercise of feeding my blog into NotebookLM actually had some concrete benefits, in my opinion. For example, it would have taken me hours of scrolling and compiling work to summarize my experience with the Apple Vision Pro from my blogposts, whereas all I needed to do was ask the NotebookLM chatbot, “What does Ryan think about the Apple Vision Pro?”

Here’s the full, unedited response:

Ryan Schultz is an overwhelming fan of the Apple Vision Pro (AVP), frequently describing the technology as “magic”. Despite its incredibly high price tag (which cost him roughly CA$7,700 for the 1-terabyte model, accessories, and taxes), he considers it “worth every single penny” and states he cannot imagine living and working without it.

Here is a breakdown of Ryan’s specific thoughts and experiences with the device:

A Leap Forward in Technology: Ryan notes that the visual clarity of the AVP is so stunning that it completely eliminates the blurry “screen door effect” common in older virtual reality headsets. He famously noted that the Apple Vision Pro makes every other VR headset he has ever used feel like a “cheap plastic View-Master toy”. He was deeply moved by the device’s 360-degree immersive “Environments,” such as Mount Hood and Haleakalā, stating that they felt so real they almost brought him to tears. He considers these Environments to be one of the device’s “killer apps”.

A Vital Productivity and Pain Management Tool: Ryan uses the headset for hours every day, primarily as a work productivity device. He considers the Mac Virtual Display to be the absolute ultimate killer feature of the AVP. By connecting it to his MacBook Pro, he can work on a massive, crystal-clear virtual screen suspended in a relaxing virtual forest or beach.

Crucially, the headset has become a pain management tool for Ryan. Suffering from deteriorating cervical joints in his neck, the Virtual Display allows him to look straight ahead at an ergonomic height rather than hunching over a laptop, providing significant pain relief and allowing him to work comfortably for four to six hours a day.

Flaws and Frustrations Despite his evangelism for the device, Ryan has documented several frustrations with the Apple Vision Pro:

  • The Purchasing Process: Because Ryan wears glasses (which cannot fit inside the AVP), he had to order magnetic Zeiss prescription lenses. A nightmare with UPS shipping delays left him with an unusable headset for weeks, making him so angry he nearly returned the entire device before he even got to use it.
  • “Crackgate”: After just seven weeks of meticulously careful use, his unit developed a spontaneous half-centimeter crack right down the middle of the front glass, a known defect he found deeply upsetting for such an expensive product.
  • Physical Discomfort: During his first few months of use, he experienced eye strain after about two hours of wear. He mitigated this by switching to a thicker light seal cushion, and the problem was entirely eliminated when he later upgraded to a newer AVP model with an M5 processor and a more comfortable dual-loop knit headband.
  • Limitations: He finds the side-to-side field of view slightly constrained (especially when watching spatial videos), and he notes that trying to share the device to give demonstrations to friends using “Guest Mode” is highly frustrating and clunky.

Ultimately, Ryan’s experience with the Apple Vision Pro cemented his belief in the future of spatial computing. He uses it heavily for media consumption, socializing in apps like InSpaze (where 3D spatial Personas “blew his mind”), and daily office work. He has declared that he is “all in” and plans to be first in line to purchase whatever Apple spatial computing device comes next.

I mean, come on. This is an accurate, concise response, stitched together from countless blogposts I have written about the Apple Vision Pro over the past year and a half. And it took me only a couple of minutes to generate! I’m sold.

The real power of Google NotebookLM is its ability to take a large amount of sheer data, and use it to create a personal language model that you can then use to ask questions of it, generate artifacts like flashcards, etc. In particular, it excels at summarizing the results in its answers, and I must confess that alone is worth the sometimes-high price of admission (i.e. fiddling with a data to get it into the service, like I did with my blogpost export and conversion, as described in the first section of this blog post above). While some of the Studio tools provided less-then-great results, others (like the “podcast”) simply blew my mind. And, like AI writer Ethan Mollick has stated, assume that this is the worst version of NotebookLM you will ever use. These generative AI tools are improving and evolving over time.

Welcome to the future…it’s gonna be deeply weird, folks. And I can certainly recommend that you give Google NotebookLM a try (there’s a free version available, which you can play around with yourself).

UPDATED! Generative AI Update, March 2026: My Updated Presentation on Artificial Intelligence and GenAI, Plus My First Thoughts on the Claude Add-In for PowerPoint, and Yet Another Head-to-Head Comparison Between Claude, Gemini, and ChatGPT

I have used (and reported on) my use of generative AI tools extensively in this blogpost, but my writing is all my own, and I have not edited it or polished it using any GenAI tools.

UPDATE March 7th, 2026: I have updated this blogpost a bit, and included some more images and screen captures of the reports generated by Gemin, Claude, and ChatGPT in the head-to-head comparison section (the third part).

I am (as you can clearly tell by this absurdly long blogpost title) trying to do three related things here. If you want, you can skip to the very end, where there will be an executive summary, where I have some thoughts to share about (waves hands) all this.

First, I wanted to share an updated version of the original slide presentation on artificial intelligence and generative AI, which I shared in a December 2025 blogpost. I used to think that keeping track of the many metaverse platforms I blog about was a task similar to herding cats, but let me tell you, it was a breeze compared to trying to stay abreast of all the rapidly changing and accelerating developments in generative AI!

Keeping on top of developments in generative AI is like herding cats, where the cats are multiplying and mutating!
One of the updated comparison charts in my PowerPoint slide deck (see link below to download)

Below is my updated PowerPoint slide presentation, complete with my speaker notes, for you to download and use as you wish, with some stipulations. I am using the Creative Commons licence CC BY-NC-SA 4.0, which gives the following rights and restrictions):

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International

This license requires that reusers give credit to the creator. It allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, for noncommercial purposes only. If others modify or adapt the material, they must license the modified material under identical terms.

BY: Credit must be given to you, the creator.

NC: Only noncommercial use of your work is permitted. Noncommercial means not primarily intended for or directed towards commercial advantage or monetary compensation.

SA: Adaptations must be shared under the same terms.

(The tool I used to determine the appropriate Creative Commons licence can be found here: https://creativecommons.org/chooser/.)

So, with all that said, here is my PowerPoint presentation (please click on the text link or the black Download button under the picture, not the picture itself):

Artificial Intelligence and Generative AI Presentation Ryan Schultz 4 March 2026 Creative Commons for BlogDownload
NEW: Claude a just released add-ins for Microsoft Office

Second, today I installed a brand-new add-in from Anthropic’s Claude GenAI tool, which is supposed to work with Microsoft PowerPoint. This is an initial review of a very beta product.

And I have an actual real-world use case against which I will be trying out this new add-in: the design of an actual keynote presentation which I will be giving in a couple of weeks. (I am also using it in the third section, but in a different test of all three of ChatGPT, Claude, and Gemini.)

Now, before I get into this, I should explain that I have tried in the past with all three GenAI tools on which I currently have paid accounts (OpenAI’s ChatGPT, Anthropic’s Claude, and Google’s Gemini) to create a PowerPoint slide deck presentation design—only to get highly disappointing and completely unusable results back. So I was not expecting much here, particularly as this is a still a research beta version of the PowerPoint add-in.

My initial prompt to the Claude add-in to Microsoft PowerPoint was:

Please create a new PowerPoint slide presentation design with the title of the presentation being: “Your Metaverse Is Too Small: How the Biases and Preconceptions of Virtual Worlds Hinder Their Use in Education.” The theme of the talk is educational uses of virtual worlds, social VR, and the metaverse in general. I want to have some nice background images to use in some of my slides, as well as a visually pleasing title slide. I’d prefer blue as a colour in the slide deck theme, thanks!

And Claude chugged away on my request, keeping my posted on what it was doing:

And it even prompted me to be sure I wanted to delete the Claude add-in help slide!

The set-up for the title slide took a long, loooong time, much longer than I would taken to click on the Designer button in the PowerPoint toolbar and just select one of the default options, and a colour scheme. Eventually, I just gave up on waiting and went off to work on another task, leaving Claude to beaver away. After fifteen minutes, I realized that I still had to explicitly okay the clearing of the original slide design (inset Homer Simpson “D’oh!“), which I did, so that the work could continue.

If I could summarize the result in one word, it would be: meh (again, shout-out to The Simpsons):

I mean, I could easily do better than this myself. And two dots do not make, as I asked for, “some nice background images to use in some of my slides, as well as a visually pleasing title slide.” Here’s my section title slide:

Again, extremely underwhelming, and frankly, not an improvement at all over my previous failed attempts to generate a PowerPoint slide presentation design using any of the GenAI tools (Claude, ChatGPT, or Gemini). Mind you, I have deliberately stayed away from using the image-generation tools in these products; I can spot a GenAI-produced image from a mile away by this point, having been playing around with these tools, off and on, since they first came out in 2022.

Claude continued to generate all the standard versions of PowerPoint slides in this theme, ending with a final slide that, I must confess, I kind of liked the look of (although, again, I would have preferred some sort of background image):

This is where the process got interesting, as I finally decided to stop having to manually okay each individual step, and just gave Claude carte blanche to do whatever it felt was best. (I mean, the worst that could happen was that it come up with something I hated so much that I threw it away and started over.)

Claude was still working away while I took my lunch break, giving feedback along the lines of “Build stunning title slide design.” 🙄 (I’ll be the judge of what’s considered stunning, Claude. Calm the fuck down.)

Here’s the final result, my “stunning” title slide (insert RuPaul’s Drag Race shade death rattle):

The addition of three pieces of clip art in the upper right corner of the slide, plus a few more bubbles/dots. So, yes, this is, once again, a complete fail. I will probably still use this as a basic slide design, but obviously I will be locating and using my own images to illustrate it. This is now the second new tool in a week (first Claude Cowork and now Claude PowerPoint add-in) which has utterly failed at the tasks given it. I am not impressed.

Third, and finally, thank God, I had much better luck was in issuing all three general-purpose GenAI tools the exact same text prompt, a technique I had used before here (and one which I found very useful in comparing and contrasting the responses):

I am writing a keynote presentation on the mistakes companies make when creating, designing, and marketing the following product category: virtual worlds, social VR/AR, and metaverse platforms in general. Please give me a list of failed or shut down metaverse platforms, along with reasons why they might have failed. Please cite both academic and industry sources of information in your answer.

In all cases, I used the latest models as specified in Ethan Mollick’s latest AI Guide:

  • ChatGPT’s GPT 5.2 Thinking with the Extended Thinking option;
  • Claude Opus 4.2 Extended Thinking with the Research option; and
  • Gemini 3 Thinking with the Deep Research option.

Unlike the last comparison, I’m not going to go into great detail on the results (because I will be using some of these results, once they are double-checked against more authoritative sources, in an actual keynote presentation I will be delivering later this month). Instead, I will my general overall impression of each report (and all three did provide a detailed report with citations).

Please note that I deliberately left it up to the specific GenAI tool to define what “failed” or “shut down” means, how far back and how thoroughly to search for failed platforms, and what metaverse platforms to include or exclude from its final report. As always, I find the differences between the reports to be an interesting way to compare and contrast the results, so below I will give some basic statistics:

GenAI Tool# Failed Platforms ListedTime Range of Failed Platforms# Citations in Final Report
ChatGPT152003 to 202623
Claude13(start dates not given) to 2023/”effectively failed, still limping along”30
Gemini92009 to 2024 (but some platforms had no timeline information given)33

While ChatGPT was the most thorough in listing failed metaverse platforms, and seems to have gone the furthest back in time (including There.com, which launched back in 2003!), it also had the fewest number of citations, and most of them were historical, platform-related announcements (e.g. a 2020 announcement of the shutdown of the then-social-VR platform High Fidelity by its CEO) rather than peer-reviewed academic journal articles (although there were a couple of those, too). While Claude had more citations, a review of those showed mostly blogs and news websites, with fewer references to actual academic research papers (probably because much of that content is locked behind academic publisher paywalls, although there were still quite a few academic references to free sources such as ResearchGate and PubMed Central/PMC; see the Claude report image below for one section which did focus on academic sources). Of the three, Gemini’s 33 citations used included the most resources which I would consider academic, from a good range of different publishers (as well as more informal websites). Interestingly, Gemini also included a list of resources which it looked at, but chose not to include in the final report, something which neither ChatGPT nor Claude offered! I thought that was particularly valuable, in case something else caught my eye to follow up on. Gemini for the win here.

Gemini was also notable for the strong, overarching narrative structure to its report, something which I had also noticed in previous queries using this GenAI tool. Gemini has clearly been trained well in telling a cohesive story! However, Claude was also notable for listing, in a separate section of its report, what it called “cross-cutting failure themes” in its 13 examined metaverse failures (which is definitely a phrase I will be stealing for my final keynote presentation!). By comparison, the final report from ChatGPT, while thorough, was jargon-heavy, poorly-formatted, and seemed to lack the final polish of its competitors. For example, there were three separate sections titled “failure themes and comparative analysis,” “theme-to-platform mapping,” (?!??) and “top 10 failures by primary cause.” It was, in my opinion, the poorest of the three reports generated, just in terms of sheer (lack of) organization and narrative. Again, Gemini for the win!

Gemini’s report had a strong, overarching narrative structure—something which I have noticed seems to be a particular strength of this GenAI tool, a sort of final overall polish to the text that ChatGPT, in particular, was lacking in its report (see below).
Claude’s report had a summary section titled “cross-cutting failure themes,” which I am definitely stealing for my keynote presentation!
Compared to the Gemini report, the ChatGPT report was jargon-heavy and poorly-formatted.

EXECUTIVE SUMMARY: So, here are my final thoughts.

  • It is getting harder and harder (in fact, almost a full-time job) to keep on top of what is fast becoming an arms race between the top three general-purpose generative AI tools (ChatGPT, Claude, Gemini), not to mention an ever-growing legion of more narrowly-focused applications, which might be better at certain specific tasks, such as writing programming code or generating music.
  • While Claude seems to be good at putting new agentic (e.g. Claude Cowork) and add-in tools (Claude for PowerPoint) into the hands of its users first, my personal experience with these new tools has been very disappointing, even comically bad. However, Claude’s chatbot interface works well for generating detailed answers with citations (although slightly edged out by Gemini).
  • I am impressed by Gemini’s consistent ability to create a strong narrative structure within its generated reports, something in which ChatGPT in particular is noticeably lacking. It also came first in a key metric: actual citations to academic literature, not just freely-accessible websites (blogs and news articles).
  • If I were forced to rank the three GenAI tools by just this one head-to-head-to-head comparison (i.e. the third part of my blogpost), I would rank them as follows:
    • 1st: Google Gemini.
    • 2nd: Anthropic Claude.
    • 3rd. OpenAI ChatGPT.
  • Again, when these GenAI tools work, they work well (sometimes very well!), but they they fail, they fail spectacularly. Which, in my mind, is another reason why it is good to put these tools to the test regularly, and use them in real-life situations, so that you can learn what they are good and bad at!

Generative AI Update: Comparing ChatGPT, Claude, and Gemini while Researching the Metaverse Characteristics of Social VR Platforms

NOTICE: In this blogpost, I go into sometimes great detail about how these three generative AI tools work, comparing them in two ways:

– comparing how these tools work with the exact same text prompt; and
– comparing how they worked in August 2025 versus February 2026.

There’s an executive summary (Section 4) at the very bottom of this long, loooong blog post if you just want to skip to the highlights, and ranking.

If you need an introduction or a refresher, you might want to read this blogpost first: An Introduction to Artificial Intelligence in General, and Generative AI in Particular, which includes slides from lectures I gave on the topic in November and December of 2025.

SECTION 1: Introduction

In his 2024 book Co-Intelligence (still my go-to layperson’s guide to generative AI), Ethan Mollick says that one of the best ways to determine how well a particular generative AI tool works is to ask it questions about a subject that you already are an expert in. Why? Because it will be much easier for you, the human expert in the topic, to find errors and hallucinations in the answers.

Since last summer, I have been typing the exact same prompt into the “big three” general-purpose GenAI tools Ethan recommends: OpenAI’s ChatGPT, Anthropic’s Claude, and Google Gemini. I have been meaning to write a blogpost about my experiences with this first round of testing since September, but I have been too occupied with my paying job as an academic librarian to find an opportunity to do so—until now. (Please note that I have been using an em-dash, correctly, for many years before generative AI came along!)

So, today I decided to redo my original text prompt, using the latest versions of these three GenAI tools as outlined by Ethan in the latest edition of his AI Guide, which has posted to his Substack newsletter on Feb. 17th, 2026 (here’s a link).

I consider his advice to be quite valuable, as he seems to spend a lot of time working with the most popular and powerful GenAI tools, and keeping on top of the changes and advances in the technology. In this newest edition of his AI Guide, he discusses the shift from chatbots (where you have a conversation with the tool) to agents (where you give a specific, defined task with instructions to the tool, and it goes away and does the task and returns with results).

In all cases, the initial text prompt is the following:

What are some characteristics common to all metaverse platforms? How do these characteristics apply to social VR platforms? Please give me a chart comparing these characteristics for the most popular social VR platforms.

Please note that I have deliberately given the task of defining “popular,” and picking the social VR platforms, over to the generative AI tool (and I got some rather interesting results back!). Because I consider myself an expert on social VR and the metaverse, I should be able to spot inaccuracies, errors, or outright hallucinations in the responses I get back from these GenAI tools. In the next section (section 2), I compare and contrast the results I received from the above text prompt from:

  • Claude by Anthropic
  • ChatGPT by OpenAI
  • Gemini by Google

All three of these tools come with different versions. In all cases, I will use the most powerful version recommended by Ethan Mollick in his latest AI Guide I linked to above (but please note that in at least one case, I had made a mistake and not selected the correct option, as you will see below with Claude in Sections 2 and 3):

  • Claude Opus 4.6 Extended Thinking
  • ChatGPT 5.2 Thinking
  • Gemini 3.0 Pro Deep Research

In addition, in section 3 of this long blogpost, I will very briefly compare and contrast the results I received when I first ran this text prompt through all three GenAI tools on August 7th, 2025, with what I received when I ran them again on Feb. 18th, 2026.

All comparison charts in the February 2026 results in sections 2 and 3 will include some quick stats in a small table under each generative AI tool discussed, namely:

  • the number of characteristics common to all metaverse platforms (and their names); and
  • the number of social VR platforms in the comparison chart (and their names).

Section 4, the final section, contains my overall thoughts after spending a day working with these tools, and a ranking of how well I think these GenAI tools accomplished the given task.

SECTION 2: Comparing Searches Done Feb. 18th, 2026

Feb. 18th, 2026: Claude Opus 4.6 (and Cowork)

First up is Claude. I did this prompt two ways: once via the chatbot interface on the Claude website, and a second time using the Claude app and the new Cowork agent feature. (I was prompted to download and install the Claude app on my Mac, and authenticate using my email address.) First, the chatbot version:

This first report I got back compared eight metaverse characteristics between eight platforms:

8 Metaverse Characteristics8 Social VR Platforms
Persistent Virtual Environments
Real-Time Interactivity
User Identity/Avatars
Social Presence & Co-Experience
User-Generated Content
Virtual Economy
Cross-Platform Accessibility
Interoperability		VRChat
Rec Room
Meta Horizon Worlds
Resonite
Second Life
Spatial
ChilloutVR
NeosVR

Well, right off the bat, I see some problems. First, Second Life is not social VR. Second, it included both Resonite and NeosVR (although Claude told me, “I included both since NeosVR still has historical relevance, but noted it as legacy since the core team transitioned to Resonite”). However, that isn’t a good enough reason to include it in the table.

Then, I turned to the Claude app (which was suggested to me when I did the first text prompt above, so I downloaded and installed it on my MacBook Pro). Then I selected the Cowork (agent) tab along the top three tabs as suggested by Ethan, and I entered the exact same text ptompt:

After beavering away for a few minutes, it gave me the following result:

And when I click on the Open in Firefox button, I get this neatly formatted table (I’m not crazy about the chosen colour scheme, but that’s a minor quibble). It looks good at first:

However, the output, which might look impressive at first, is only as good as the quality of the sources used in its research. If the good information is locked behind a paywall (and therefore, not able to be scraped to add to its knowledge base), then the GenAI tool will use freely-available sources on the web, which can vary quite a bit in quality! There is an acronym in computer science called GIGO: Garbage In, Garbage Out, and I am reminded of this when I decide to take a closer, more critical look at the six sources listed.

All of them were non-academic sources, mostly generic market overviews from websites that I had never heard of before. The six sources included my own list of metaverse platforms on this blog (which is just a list, and doesn’t give any details about the platforms). While I’m flattered they included me, I expected something…more. And I absolutely hated that they mentioned cryptocurrencies, blockchain, DAOs, and NFTs, and included Somnium Space and Decentraland in the resulting table. While Somnium Space is social VR, Decentraland in absolutely not, and I have made my opinions on blockchain-based metaverse platforms very clear in the past on this blog.

8 Metaverse Characteristics6 Social VR Platforms
Persistence
Immersion & Presence
User-Generated Content
Built-In Economy
Social Interaction
Interoperability
Digital Ownership
Decentralized Governance		VRChat
Meta Horizon Worlds
Rec Room
Engage VR
Decentraland
Somnium Space

In fact, I was so dissatisfied with this report that I went back into the Claude Cowork app, and added a qualifier, and made sure that I had turned on Extended Thinking! (I’m almost positive I did that the first time around, but maybe I forgot, and unfortunately, once you’ve done your prompt, the results don’t tell you what modes you used in asking the original question.)

Only to get pretty much the same result: a pretty table with only six websites listed as sources! So much for being more specific and asking for Extended Thinking.

10 Metaverse Characteristics6 Social VR Platforms
Persistence
Immersive 3D Environments
User Identity & Avatars
Real-Time Social Interaction
User-Generated Content
Economy & Monetization
Cross-Platform Access
Scalability & Concurrency
Safety & Moderation
Interoperability		VRChat
Rec Room
Meta Horizon Worlds
Resonite
ChilloutVR
Engage VR

While better thatn the previous round, I am actually disappointed in the results I received from Claude Cowork. But read on; in section 3, I have an update on what I think went wrong here!

Feb. 18th, 2026: ChatGPT 5.2 Thinking

Next, I turned to OpenAI’s ChatGPT, using the ChatGPT 5.2 Thinking mode suggested by Ethan:

And I got back the following table. comparing six social VR platforms on ten metaverse characteristics:

While the resulting table might not be as pretty as the one produced by Claude Opus 4.6 Cowork, I appreciate that there are actual citations which you can hover over and click through to actually see the source material behind the comparison chart entries (and not just a list of websites checked, tacked on to the end). Also, ChatGPT seems to have checked a lot more sources than Claude, and made some sort of attempt to find authoritative sources (often, from the metaverse product’s own online documentation, as shown in this example).

10 Metaverse Characteristics6 Social VR Platforms
Shared Multi-User Spaces
Avatars/Embodied Identity
Real-Time Voice/”Hangout” Core Loop
Persistence (Account, Inventory)
User-Generated Worlds
In-World Creation Tools
Scripting
Economy & Monetization
Cross-Platform Access
Safety Governance
VRChat
Rec Room
Meta Horizon Worlds
Bigscreen Beta
Spatial
Resonite

Overall, I think that ChatGPT 5.2 Thinking gave me a better answer than Claude…but as we will see later on, it doesn’t compare to the best results I got from my day of testing and retesting. Let’s move on to the third of Ethan Mollick’s recommended, general-purpose GenAI tools, Google’s Gemini:

Feb. 18th, 2026: Gemini 3 Pro (first without, and then with, Deep Research)

The first go-round, I selected Gemini 3 Pro mode, as Ethan suggested:

And I got a resulting table comparing three social VR platforms across seven characteristics:

7 Metaverse Characteristics3 Social VR Platforms
Core Philosophy
Visual Style
Creation Tools
Hardware Access
Target Audience
Economy
“Metaverse” Strength (?!)		VRChat
Rec Rooom
Meta Horizon Worlds

I was so unhappy with this first Gemini result that I redid the prompt, this time making sure that I turned on the Deep Thinking mode, just to see if I would get better results, or even some actual citations to sources used:

Wow, what a difference!!

This time around, the task took a lot longer than either Claude or ChatGPT, and it included what appears to be extremely detailed feedback on what was happening behind the scenes (this seems to be turned on by default, and I’m not certain if this mode could have been enabled on Claude or ChatGPT):

And the report I got back was worth the longer wait:

And, at the end, not one but three comparison charts!

Here’s the quick stats, from all three tables in the final report (and notice how technical many of these “metaverse characteristics” are, compared to the other results!):

12 Metaverse Characteristics5 Social VR Platforms
Engine Core
Scripting Language
Persistence Type
Asset Pipeline
Audio Engine
Economic Model
Currency
Identity System
Tracking Support
Instance Cap
Network Model
Culling Tech		VRChat
Rec Room
Roblox
Meta Horizon Worlds
Resonite (only mentioned in one table)

SECTION 3: Comparing August 2025 Prompt Results with the February 2026 Ones

I also wanted to compare the results I when I did the testing last year (August 7th, 2025) with the results I got today (Feb. 18th, 2026) with all three GenAI tools. This was very enlightening.

Then Versus Now: Claude

You will understand why I was so disappointed with today’s results, when you see what the results were when I did the same prompt last year (dated August 7th, 2025):

The report I got back was extremely detailed, with actual citations to sources! I still don’t understand why I got such dramatically different—and worse—results. The difference is so astounding to me that I began to wonder if I had done something wrong this time around.

It was then that I realized that I had literally forgotten to turn on Research mode in the left-hand drop-down menu (previously, I had only had Web Search mode turned on):

So I went to check the Claude app, to see if there was that option available, and, of course, it was there—but under the Chat tab, not the Cowork tab!! So perhaps Cowork still has some user interface bugs to work out. Perhaps sending everything to an agent isn’t the better option; certainly, not in this case!!

Once I had selected both Research and Web Search from the left drop-down menu, and Opus 4.6 Extended from the right drop-down menu, I hit send and waited…until I got a message that I had used up all my credits on my $20-a-month plan!!!

AAAAAAAAAAAAAARGH!!!!

By this point, I was so frustrated with Claude that I simply exited the app. I had had enough frustration for one day.

The next morning, February 19th, 2026, after my daily credits reset at 6:00 pm, I once again tried my prompt with Claude Opus 4.6 Extended Thinking, with both Research and Web Search turned on (using the Gemini app I had installed on my Mac, as opposed to the web version; they appear to be identical in terms of features).

Right off the bat, I got a better response (and Claude even remembered that I was going to working on an OER about the metaverse!):

Again, similar to Google Gemini, I had a bit of wait while Claude did its thing. I actually preferred that Gemini actually gave better descriptions of what it was doing while it was going about its task, as opposed to…well, no updates from Claude other than me sitting and staring at an animated cursor!

Ten minutes later, I got the detailed report I wanted in the first place, and which Claude Cowork stubbornly refused to give me:

The response back included a concise summary taken from the sources examined:

The final report included citations to the academic literature (which I could hover over and click on to go to the source, see the red arrow below), and it cited experts in the field such as Matthew Ball and Tim Sweeney. It’s pretty much all I wanted, and it compares quite favourably to the similarly detailed report from Google Gemini, in the previous section. I am happy.

And this was the only report which had a listing of metaverse characteristics, separate from the ones used in the social VR platforms comparison chart:

Here’s the quick stats from the comparison chart. As you can see, there are some problems here, with the inclusion of platforms which are clearly not social VR (e.g. Second Life) and platforms that no longer exist (Altspace shut down on March 10th, 2023). These sort of mistakes make we wonder about the accuracy and currency of the report overall.

9 Metaverse Characteristics9 Social VR Platforms
Persistence
Synchronous Real-Time
Massive Scale/Concurrency
Cross-Platform Access
Virtual Economy
User-Generated Content
Interoperability
Avatar/Identity Systems
Immersive 3D/Spatial Computing
Open Standards/Decentralization
Spanning Physical-Digital
Ethical Goivernance/Accessibility		VRChat
Horizon Worlds (note: old name used)
Rec Room
Resonite
ChilloutVR
AlspaceVR (was shut down)
Second Life (not social VR!)
Roblox
Fortnite (not social VR!)

Then Versus Now: ChatGPT

An interesting difference between the August 2025 report from ChatGPT and today’s report is this: in last year’s report, for whatever reason, the tool asked me a follow-up question to clarify what was wanted (I did use the Deep Research feature in the 2025 report, as well):

Based on that clarification prompted by ChatGPT, I actually think I preferred the 2025 report format over this new one. So why didn’t ChatGPT 5.2 Thinking ask me any follow-up questions this time around? And that’s part of the frustration with tese tools; the way that they operate is still very much a black box, where you don’t understand how the tool is processing what you ask of it.

Then Versus Now: Gemini

The last comparison is between the Google Gemini report I produced on August 7th, 2025, and today’s report. One thing I noticed about the Aug. 7th report is how hard it tried to shoehorn in an overarching narrative into the final result, in a way that seemed a bit hamfisted, frankly. But the result was still a very detailed report with an extensive list of citations, comparable to today’s report. I prefer today’s version.

SECTION 4: Executive Summary and Ranking

This is going to be concise, I promise! Five points.

First, while we might be entering what Ethan Mollick calls “the agentic era,” my experience today shows that simply handing something off to an agent, as opposed to the back-and-forth conversation with a chatbot interface, does not always give the best result. In particular, Claude Cowork gave me terrible results, and eventually, I ran out of daily use credits to actually run the report I wanted in the first place.

Second, the user interface for these GenAI tools is awful and NON-intuitive. Hiding critical options like Deep Research under drop-down menus, and not making it clear what options have been selected when you do a text prompt, is a major problem. All three companies need to hire some good user interface/user experience staff. If I, with decades of computer experience and a goddamn computer science degree, can’t figure this shit out, God help the average non-technical user—and isn’t that what the point of generative AI is supposed to be, to make it easier for the user to do things??

Third, when these tools work, they are astoundingly good (the Gemini 3.0 Pro report with Deep Research turned on, and the Claude Opus 4.6 report with Research, Web Search, and Extended Thinking turned on). But when they don’t, they can still fail spectacularly (Claude Cowork). So you still have to be the human in the loop here, to figure out when you get a good result versus a bad one. What is frustrating is that all these GenAI tools operate in a black box, with only Gemini making some attempt at explaining what it was doing, as it was doing it.

Fourth, as Ethan himself said in his latest AI Guide:

The top models are remarkably close in overall capability and are generally “smarter” and make fewer errors than ever. But, if you want to use an advanced AI seriously, you’ll need to pay at least $20 a month (though some areas of the world have alternate plans that charge less). Those $20 get you two things: a choice of which model to use and the ability to use the more advanced frontier models and apps. I wish I could tell you the free models currently available are as good as the paid models, but they are not.

In other words, you get what you pay for. And sometimes, even the $20-a-month level isn’t enough, as seen with my experience on Feb. 18th with Claude (and yes, using the cutting-edge features does eat into your usage limits pretty quickly, as I learned to my chagrin).

Finally, I have found that the one of the best ways to see where the strengths and weaknesses of these GenAI tools is to enter the exact same text prompt into each of them, and then compare and contrast the results you get back. However, that approach is gonna cost you at least US$60 a month, so it might not be worth it to you. (And will I be doing this forever? No; at some point, I will just pick one or perhaps two tools and cancel my subscriptions to the rest of them.)

So, in this current round of testing, I would rank the results as follows (separating the results from Claude into the chatbot-generated report and the Cowork report):

  1. Google Gemini 3.0 Pro (with Deep Research turned on) provided me with a very detailed report with citations, as well as giving me a detailed play-by-play on how it was answering my query, which I really appreciated.
  2. Claude Opus 4.6 report (with Research, Web Search, and Extended Thinking turned on) also gave me a detailed report with citations, but several errors in the comparison chart made me question the overall quality and currency of the report. I also really hated how I had to futz around to get the results I really wanted!
  3. ChatGPT 5.2 Thinking is in a clear third place, in my opinion. Not bad, but not as detailed a result as Gemini and Claude provided.
  4. Claude Opus 4.6 Cowork, with perhaps the prettiest output but easly the least substantial result, using lower-quality sources of information, clearly failed at this task. For those reasons, I ranked it in last place. Ethan’s “Agentic Era” might be true for some applications, but certainly not this one!

I have found these little excursions into generative AI to be quite enlightening, and they have definitely given me some new ideas of topics to explore when I begin my research and study leave to write an OER about the metaverse. Hopefully, you found it enlightening, too. Please go subscribe to Ethan Mollick’s free Substack newsletter; he tends to update his AI Guide recommendations fairly regularly, and it’s really the best way too stay on top of a rapidly changing and evolving field!

An Introduction to Artificial Intelligence in General, and Generative AI in Particular

I have already written at length about my neck and shoulder pain, for which I am working with my doctor, a physiotherapist, and a massage therapist to treat. I’ve also had an ergonomist come and do an assessment and adjustment of my workstations at my employer, the University of Manitoba (I’m still waiting for his final report, with a shopping list of equipment which will be purchased to help me get through an eight-hour workday without pain). I am still very much in the process of learning which actions are detrimental to the couple of deteriorating cervical joints in my spine, and which are more beneficial!

For example, you would think that having the extra weight of a virtual reality headset on my noggin would make things worse. However, I have been astonished to discover that my neck does not become as sore, as quickly, when I am using the Mac Virtual Display feature on my Apple Vision Pro, along with my MacBook Pro at work!

Therefore, I have been working 3 to 4 hours a day like this, as opposed to just using my MacBook Pro with an external monitor attached. The ergonomist did set me up with a temporary notebook riser, adjusted so that I am not hunched over the keyboard, and aligned so the top of both the MacBook Pro screen and the external monitor are both at eye level. I find that working like this, without my AVP, my neck and shoulders still start to ache after about two hours, and I have to stop, take a break, go for a walk, and do some of my physiotherapy exercises. As I mentioned earlier, this is a learning process.

On Wednesday, at lunchtime, I got up from my MacBook Pro, unplugged my Apple Vision Pro from its battery charging cable (I tend to leave it plugged in when I am working seated) and, while still wearing my AVP, went to the washroom. My coworkers in the library are already well-used to seeing this strange person wandering around with a VR headset on, and my vision while wearing it is almost as good as it is when I wear my glasses, so I often do this if I have to make a short walk to the printer, or in this case, the washroom.

However, on my way back from the washroom, disaster struck. I accidentally got the cord between my Apple Vision Pro (on my head) and its battery (sitting in the front left pocket of my pants) caught in a metal part of the door to my office cubicle space when I was coming back in from the washroom. My AVP is okay, but I wrenched my already-painful neck badly, and as a result, made a bad situation even worse. (Lesson learned; you need to take that damn power cord into account when moving around!)

As a result, I have been off sick from work for two and half days this week, spending a lot of my time either lying in bed or lying on the sofa. On top of that, we have had not one, but two Alberta Clippers roar through Winnipeg on Wednesday, Thursday, and Friday, so I have been apartment-bound as well as largely bed-bound. I just find it ironic that the very thing that seems to make my pain more bearable (the Apple Vision Pro) can also make it more severe! This has just not been my week.

Anyway, this is my usual off-topic preamble to the real purpose of today’s blogpost. I had promised that I would share with you, my blog readers, the artificial intelligence presentation I had been researching since this summer, which I have recently delivered to three separate audiences: University of Manitoba graduate students, graduate student advisors, and the professors and instructors in the Faculty of Agriculture and Food Sciences (the latter group for whom I am the liaison librarian, and from where the original request to create and give this talk was made by the chair of the agriculture library committee, many months ago). And while this talk was overall very well-received by my audiences, I did receive some negative feedback, and I wanted to talk a little bit about that as well. AI is a divisive topic in an already-divisive age.

I’m going to share an edited version of my PowerPoint slide presentation, with some University of Manitoba-specific bits removed, as well as any contact information removed (sorry, the UM faculty, staff, and students have the right to call on me with questions after my presentation, as I am their liaison librarian; you don’t 😉 ).

Also, I will be transparent about how I used generative AI tools in creating this PowerPoint presentation. I currently have paid-for (US$17-20 a month) accounts on three general-purpose generative AI tools: OpenAI’s ChatGPT; Anthropic’s Claude; and Google’s Gemini. These are the “top three” general-purpose generative AI tools currently recommended by Ethan Mollick (more on him later in this post). Do I plan to keep paying for all three? No. But I have found it highly instructive to enter the exact same text prompt into all three tools, and then compare the results!

In addition to conducting my own research into artificial intelligence in general and generative AI in particular, I used both ChatGPT and Claude to do additional research into this topic, some of which made it into this presentation. I also had a lot of text-heavy slides in the first draft of my PowerPoint presentation, so I asked Google Gemini to provide suggestions on how to reformat my slide presentation to have fewer bullet points per slide (which I think it did a pretty good job at).

I also did try to ask both ChatGPT and Gemini to redesign the theme and design aspects of my PowerPoint slides, but I was extremely unsatisfied with the results, despite several attempts, and I finally gave up on using AI for that task. So please keep in mind that generative AI (which I will refer to as GenAI from here on out) can still fail miserably at some tasks you put it to work on!

Here is my PowerPoint slide presentation, complete with my speaker notes, for you to download and use as you wish, with some stipulations. I am using the Creative Commons licence CC BY-NC-SA 4.0, which gives the following rights and restrictions):

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International

This license requires that reusers give credit to the creator. It allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, for noncommercial purposes only. If others modify or adapt the material, they must license the modified material under identical terms.

BY: Credit must be given to you, the creator.

NC: Only noncommercial use of your work is permitted. Noncommercial means not primarily intended for or directed towards commercial advantage or monetary compensation.

SA: Adaptations must be shared under the same terms.

(The tool I used to determine the appropriate Creative Commons licence can be found here: https://creativecommons.org/chooser/.)

So, with all that said, here is my PowerPoint presentation (please click on the Download link under the picture, not the picture):

Artificial Intelligence and Generative AI Presentation Ryan Schultz blog version 19 Dec 2025Download

In addition to sharing my slide presentation with you, I wanted to highlight a few resources which I discussed within it, which you might find useful. These are books and websites which I used as I worked my way up the learning curve associated with AI in general, and the new wave of GenAI tools in particular.

I start off with a bigger-picture look at the whole forest of artificial intelligence, later narrowing my focus to look at GenAI tools, a new subset of greater AI. First, a really good layperson’s guide to GenAI is a 2024 book by Ethan Mollick, titled Co-Intelligence (see image, right). One thing I want people to remember is that the new wave of GenAI tools only dates back to 2022, when the capabilities of these new tools (ChatGPT, DALL-E, Midjourney, Stable Diffusion, etc.) first captured the general public’s imagination, and stoked their fears. There are lots of published books about AI, but if they were published before 2022, they won’t cover the part of AI that is making the most noise right now. Also, keep in mind that any print/published book will soon be outdated, because the field of GenAI is evolving so rapidly!

Ethan does a good job of covering the territory, and I share with you his four rules of AI:

•Principle 1: Always invite GenAI to the table. You should try inviting AI to help you in everything you do, barring any legal or ethical issues, to learn its capabilities and failures.

•Principle 2: Be the human in the loop. GenAI works best with human help; always double-check its work.

•Principle 3: Treat GenAI like a person (but tell it what kind of person it is). Give it a specific persona, context, and constraints for better results. For example, you’ll get better results from the detailed prompt “Act as a witty comedian and generate some slogans for my product that will make people laugh” instead of the more generic prompt “Generate some slogans for my product.”

•Principle 4: Assume that this is the worst GenAI tool you will ever use. Generative AI tools are advancing and evolving rapidly.

Second, I want to share with you an online course from Anthropic, the makers of the GenAI tool Claude. This course, which I worked through this summer, is called AI Fluency: Framework & Foundations, and you do not need to use Claude to work through the exercises—you can use any GenAI tool you wish. The focus of this 14-lecture course is to learn how to collaborate with GenAI systems effectively, efficiently, ethically, and safely.

One of the concepts taught in the AI Fluency course is what Anthropic calls the four D’s: the four key competencies of AI fluency (they seem to be big on alliteration!).

•Delegation: deciding what work should be done by humans, what work should be done by AI, and how to distribute tasks between them.

•Description: effectively communicating with AI tools, including clearly defining outputs, guiding AI processes, and specifying desired AI behaviours and interactions.

•Discernment: thoughtfully and critically evaluating AI outputs, processes, behaviours, and interactions (assessing quality, accuracy, appropriateness, and areas for improvement).

•Diligence: using AI responsibly and ethically (maintaining transparency and taking accountability for AI-assisted work; an example of this is when I described in detail which GenAI tools I used, and how I used them, in creating the PowerPoint slide presentation, earlier in this post.)

Finally, I share with you what I found to be a very helpful guide prepared by a librarian, Nicole Hennig, about how to stay on top of the rapidly evolving and accelerating field of GenAI. You can obtain a copy of her 2025 guide here. This is as good a place as any to start working your way up the learning curve (as I first did, with the 2024 edition of her guide). Nicole offers a bounty of valuable tips, tricks, suggestions of people to follow, and advice on how best to keep up with the roiling sea of change which is currently taking place in GenAI!

Finally, I wanted to talk a bit about the divisive nature of GenAI. AI/GenAI seems to be a very polarizing topic, especially in the field of higher education! While I did try to present a balanced viewpoint on generative AI tools, talking about both the good and the bad, I did receive some feedback from a few people who felt that my presentation was too…positive? And that, despite the warnings in my talk about some very serious problems with GenAI tools, I had neglected to portray GenAI’s more negative aspects in a more forceful way.

For example, one agriculture professor, in an email after my talk, said this about the Anthropic online course in AI Fluency, a learning resource which I had mentioned in the previous section of this blogpost, as well as in my slide presentation:

…I know you were recommending the AI class that was created by Anthropic, and how it is agnostic to the AI used, and just a good introduction to use. I’ll admit that I have not taken the course  (I am now intrigued and will try to), but I couldn’t help thinking when you introduced it, of courses on appropriate opioid prescribing practices made by Purdue pharma.

Ouch. Fair point, but painful comparison (and I say that as someone who is now actually suffering from physical pain, as I stated up top). So I wanted to end this blogpost with a brief discussion about how some intelligent but more skeptical observers are responding to the tidal wave of GenAI tools washing over society as a whole, and share links to some criticism, as part of providing a larger perspective. I will be the first to admit that I am not an expert in this field, despite what I have learned since this summer! I am a librarian with a computer science degree, which made it easier for me to comprehend some of the more technical aspects of what I was reading, but not as good at the philosophical part of the discussion about GenAI.

The professor who commented on the Anthropic course above shared with me a couple of links to recent critical articles which I, in turn, will share with you. The first link is an Open Letter by 17 scholars, warning about blindly accepting GenAI tools in higher education (post-secondary education, i.e. colleges and universities, although obviously many of the same arguments could also be made about K-12 schooling):

Guest, O., Suarez, M., Müller, B., van Meerkerk, E., Oude Groote Beverborg, A., de Haan, R., Reyes Elizondo, A., Blokpoel, M., Scharfenberg, N., Kleinherenbrink, A., Camerino, I., Woensdregt, M., Monett, D., Brown, J., Avraamidou, L., Alenda-Demoutiez, J., Hermans, F., & van Rooij, I. (2025). Against the Uncritical Adoption of ‘AI’ Technologies in Academia. Zenodo. Retrieved Dec. 19th, 2025 from https://doi.org/10.5281/zenodo.17065099

Abstract: Under the banner of progress, products have been uncritically adopted or even imposed on users — in past centuries with tobacco and combustion engines, and in the 21st with social media. For these collective blunders, we now regret our involvement or apathy as scientists, and society struggles to put the genie back in the bottle. Currently, we are similarly entangled with artificial intelligence (AI) technology. For example, software updates are rolled out seamlessly and non-consensually, Microsoft Office is bundled with chatbots, and we, our students, and our employers have had no say, as it is not considered a valid position to reject AI technologies in our teaching and research. This is why in June 2025, we co-authored an Open Letter calling on our employers to reverse and rethink their stance on uncritically adopting AI technologies. In this position piece, we expound on why universities must take their role seriously to a) counter the technology industry’s marketing, hype, and harm; and to b) safeguard higher education, critical thinking, expertise, academic freedom, and scientific integrity. We include pointers to relevant work to further inform our colleagues.

The second link is the text of a recent talk by the well-known intellectual, author, speaker, and gadfly Cory Doctorow, who gave his university audience a foretaste of his book on AI, which will be published in 2026:

Doctorow, C. (2025). Pluralistic: The Reverse-Centaur’s Guide to Criticizing AI. Retrieved Dec. 19th, 2025 from https://pluralistic.net/2025/12/05/pop-that-bubble/#u-washington

Over the summer I wrote a book about what I think about AI, which is really about what I think about AI criticism, and more specifically, how to be a good AI critic. By which I mean: “How to be a critic whose criticism inflicts maximum damage on the parts of AI that are doing the most harm.” I titled the book The Reverse Centaur’s Guide to Life After AI, and Farrar, Straus and Giroux will publish it in June, 2026.

But you don’t have to wait until then because I am going to break down the entire book’s thesis for you tonight, over the next 40 minutes. I am going to talk fast.

And both Cory Doctorow, and Olivia Guest et al., make some seriously valid points about the negative consequences of a heedless, thoughtless, headlong rush into adopting GenAI tools. Now, you can decide, after reading all this, that you will have absolutely nothing to do with AI and GenAI, and that’s a valid position to take. But will it change the fact that GenAI is already being incorporated into software we use every day? Can the genie be pushed back into the bottle? Doubtful.

So what I am saying is: learn how the enemy (if you see it as “the enemy”) works. Spend a bit of time to become familiar with the GenAI tools, try them out on certain tasks, and see for yourself where and how it succeeds at a particular task, and (more importantly) where and how it fails. I have had some amazing results from using GenAI tools over the past eight months, but I have also experienced situations where I walked away thinking, “this is garbage.” But may I gently suggest that the only way to gain the experience which informs your opinions is to actually use the tools, and not to stick your head in the sand, and refuse to have anything to do with them.

Are we the unwitting and unwilling beta-testers for these products, as they are rolled out and embedded stealthily in products we already know and use? Absolutely. Will there be negative consequences, some foreseen, and others unexpected and unanticipated? Absolutely. Will there be some tasks which GenAI does and does well? Also, yes, absolutely (and it is already happening based on my own experience). All three things can be true at the same time. Like all technology throughout human history, artificial intelligence is a double-edged sword. It can harm as well as heal.

I still think that the best stance on GenAI is to be a skeptical but informed user of the tools (even if you limit yourself to the lesser-powered, free versions). Also, you owe it to yourself to read a variety of viewpoints on the technology, from a range of sources (start with my fellow librarian Nicole Hennig’s excellent guide which I mentioned above, plus my skeptical professor’s two links, and work out from there).

Above all, even with how divisive AI can be as a topic, now is not the time to be locked into either a rigid AI-is-bad or AI-is-good perspective, because both are true at times, and we need to hold space for that unsettling and upsetting fact. And we need to brace ourselves, both personally and as a society, because (as I have stated before on this blog), things are about to get deeply, deeply weird before all this is over.

Image by Gerd Altmann from Pixabay