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Case study: Build-A-Bear Workshop

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• Colin Shaw  

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Let me tell you about one of my favorite stores, Build-A-Bear Workshop, which we believe encapsulates a number of the aspects of the Customer Experience that are fundamental to all businesses.

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© 2005 Colin Shaw

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Shaw, C. (2005). Case study: Build-A-Bear Workshop. In: Revolutionize Your Customer Experience. Palgrave Macmillan, London. https://doi.org/10.1057/9780230513457_12

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Case Study Analysis: Build-a-Bear Workshop

A-Bear customers demonstrate, differentiating each of these three concepts.

Clark emphasizes on the need of entertainment, self esteem, and belongingness and fulfills children’s wants of experience by making, customizing, and personalizing bear through many stages: choose me; stuff me; hear me; stitch me; fluff me; dress me; name me by the children. In this way, the company has brought a lot of entertainment and experience to children.

2. In detail, describe all facets of Build-A-Bear’s product. What is being exchanged in a Build-A-Bear transaction? The specifics of the tangible item: including the various options for the basic stuffed animal-the clothing, voice box, name, and birth certificate. The experience: including the ability to customize and personalize each part of the product.

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It also includes being a part of the creative process and coming away with an Item that Is a piece of the customer. The store ambiance and even waiting In line are also part of the experience. Price of the bear as well as other cost factors given by the customer are being exchanged In a Build-A-Bear transaction.

4. Discuss in detail the value that Build-A-Bear creates for its customers.

Fulfilling needs and wants: It involves customers in all production actions, empowers them to choose design according to their wish, customize it and to create a toy. Self- experience: when a child buys a toy, he/ she has a need of entertainment and If he/ she buys a toy which Is made by him/ her, It Is a new experience, which gives him great self satisfaction and confidence.

5. Is Build-A-Bear likely to be successful in continuing to build customer relationship? Winy or Winy not:’ Yes, Decease AT Clacks personal Interaction Walt customers and utilization of both high and low tech communication. As long as she fulfills customer needs and wants, the company will continue to be successful.

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Build-A-Bear Workshop Case Study

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“Build-A-Bear: Build-A-Memory” Company’s Case Case Study

• To find inspiration for your paper and overcome writer’s block
• As a source of information (ensure proper referencing)
• As a template for you assignment

Give examples of needs, wants, and demands that Build-A-Bear customers demonstrate, differentiating each of these three concepts. What are the implications of each on Build-A-Bear’s actions?

Identification of customers’ needs, wants and demands is an important strategy to any marketing department. This is because a firm will be in a position to offer products that march the customers’ needs, wants, or demand leading to their satisfaction. Maxime Clark is aware of the above fact. Through interaction with the customers in her stores, she has collected ideas on their needs and demands.

This has enabled her to produce products that guarantee customer satisfaction leading to annual increases in the store’s profits. The store does not only offer children a variety of teddy bears to choose from, but also provides them with the experience to learn and create their own customized teddy bears. The store is also composed of child-friendly assembly lines made up of various stations i.e. “choose me”, “stuff me”, “stitch me”.

This provides children with a fantasy world where they interact and satisfy their social needs. In the end, they create a need for affection and belonging based on the memory and experience they have when they visit the store. Customers want to gain knowledge on how to customize their products and express their delight or concerns on the same. The store has created an interactive website (BuildABearVille.com) where customers can access information on their products and freely express themselves. Customers demand authority over the teddy bears they purchase and the store has given them the power of customization.

Describe all facets of Build-A-Bear’s product. What is being exchanged in a Build-A-Bear transaction?

BuildABear has concentrated on continuous improvement of the quality of its products rather than explore other business ventures. The firm’s initial product was making teddy bears but this changed over time. In line with the store’s objective, it has created a fantasy world where kids are treated to an experience of a kind. Therefore, in addition to the product, the store also sells experience to the kids. This facet has enabled kids to develop an attachment to the initial product of the company. Apart from making teddy bears, the store also gives the customer a unique opportunity to make the teddy bears themselves basing on their desires.

This has created a sense of control among the store’s customers which has resulted in loyalty to the store’s products. Moreover, the extensive involvement of their customers in the personalization process through their website ( BuildABearVille.com ) has created a platform for interaction with the customers. In addition to getting a teddy bear, a customer can play games and do other activities on this site. Having fun on the store’s website is considered a new product of the store.

Which of the five marketing management concepts best describes Build-A-Bear Workshop?

BuildABear has utilized the product marketing concept. The concept dwells on the idea that customers or consumers will purchase the products that offer quality, performance, and improved features. Maxime Clark collects ideas from kids about their needs and uses these ideas to improve the company’s product. She achieves this by putting herself in the customer’s shoes, interacting with them in her stores, and communicating with them frequently through emails and her website.

The store has now created a genuine fantasy world that is child friendly and is made up of several work stations that give kids an amazing experience. As a way of improving on their product, the store has also given a chance for customers to personalize their products. The customer can improve on the store’s product or he/she can create his/her product to match his/her requirements. Creating an interactive website for the store is also seen as a way of improving the quality and performance of the product. The website improves service delivery to customers by serving millions of customers at the same time. These improvements on the store’s initial product have contributed positively to the profits of the store.

Discuss in detail the value that Build-A-Bear creates for its customers

BuildABear offers a variety of added values to its customers. Unlike their competitors, the BuildABear store designs unique products that are very marketable. They provide customers with freedom of expression by allowing them to create their products in their ways. This has been viewed as a move to empower their customers. It has enabled their customers to get a more attractive, quality, and customized product.

Their products are also priced cheaply at $ 25 as compared to their competitor’s $ 50 to $ 100. The store has also added value to its products through exploiting technology. The website (BuildABearVille.com) provides extra services to the customers who want to have fun. They can play games and do other related tasks in the store. Lastly, the store has created a healthy and vibrant relationship with its customers by listening and providing superior products to them. These values have enabled the store to stand out among its competitors.

Is Build-A-Bear likely to be successful in continuing to build customer relationships? Why or why not?

I think BuildABear will be successful by continuing to build its relationship with the customer. This is because Maxime Clark (CEO) is aware of the fact that understanding customers is important for the success of any firm. She thus devotes her energy to understanding their needs and improving on the products to satisfy the consumer’s needs.

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IvyPanda. (2021, March 23). "Build-A-Bear: Build-A-Memory" Company's Case. https://ivypanda.com/essays/build-a-bear-build-a-memory-companys-case/

""Build-A-Bear: Build-A-Memory" Company's Case." IvyPanda , 23 Mar. 2021, ivypanda.com/essays/build-a-bear-build-a-memory-companys-case/.

IvyPanda . (2021) '"Build-A-Bear: Build-A-Memory" Company's Case'. 23 March.

IvyPanda . 2021. ""Build-A-Bear: Build-A-Memory" Company's Case." March 23, 2021. https://ivypanda.com/essays/build-a-bear-build-a-memory-companys-case/.

1. IvyPanda . ""Build-A-Bear: Build-A-Memory" Company's Case." March 23, 2021. https://ivypanda.com/essays/build-a-bear-build-a-memory-companys-case/.

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IvyPanda . ""Build-A-Bear: Build-A-Memory" Company's Case." March 23, 2021. https://ivypanda.com/essays/build-a-bear-build-a-memory-companys-case/.

BUILD-A-BEAR

A WONDERFUL WORLD

Spreading joy, one bear at a time.

Build-A-Bear is truly a one-of-a-kind brand. Over the past 20 years, it has created over 160 million furry friends – and countless more smiles – in its quest to add a little more happiness and love to the world.

THE WORKSHOP EXPERIENCE

Putting heart into everything.

The Build-A-Bear Workshop experience is core to the company, serving as a whimsical world where families have fun creating furry friends and magical memories. Build-A-Bear wanted to bring that same engaging experience online.

SPREADING THE LOVE

The digital world of build-a-bear.

LYONSCG worked with Build-A-Bear to create a site as iconic and enjoyable as the brand itself. Joyous imagery, smart design, and custom-built Bear Builder functionality bring the magical nature of the Workshop online. The digital experience immerses shoppers in the positivity and love that makes the brand so special.

LET’S DISCUSS THE NEXT STEP IN YOUR COMMERCE JOURNEY.

DungeonMasterGPT Case Study

In honor of GPT-4o I’m releasing a CustomGPT super early, but it’s fun so far!

DungeonMasterGPT

I’d like it to do a lot of things, like manage large groups of NPCs and Battles; but meanwhile it’s just an open source project. With 4o it could be an NPC at the table. I’m geeking out.

Please give it a whirl and give me your feedback and suggestions!

Hey Everyone,

Thanks for checking out DungeonMasterGPT!

Just been continuing to work out a slew of updates and possibilities which others may find helpful in their own applications:

Character Creation

We’ve started to standardize how to present a character to a User vs storing it in a table for later use.

• DnDGPT helps to create a Player Character of any level, either automatically or step by step.
• It does the same for NPCs and UNPCs (Unique Non-Player Characters).
• It presents this information to the User as a simple two-column table.
• Then, if asked for a download, DnDGPT “pivots the table” (a prompt it understood perfectly and the most efficient way to achieve the desired result without an example) and exports the columns as rows instead.
• With a few prompts it’s possible to generate entire parties and groups, stories surrounding them, and fun art in a variety of styles, including “DND Classic Illustrations.”
• We are going to develop pre-made NPC templates and encounters, such as the population of a small village, castle, or a battle.

Map making is a lot of fun, and an unexpected challenge.

The goal is to make a top-down map to either a (10x10), (25x25), (50x25) scale where each unit is a standard Dungeons and Dragons base movement of 5ft x 5ft.

Initial Maps

Initial results get the general idea right out of the box, but we’ve had a lot of trouble getting “the grid” right:

The request was for a 10x10 jungle dungeon. You can see it’s got the right idea. There is clearly a grid… but it lacks the “chess board feel” we’re looking for.

Rewording the prompt, such as more emphasis on the grid overlay, only yielded similar results.

This result isn’t surprising as Dalle and AdobeAI universally seem to struggle with adding very structured aspects to images, such as cohesive language and grids. Not bad, but not quite.

A Separate Overlay

We used Adobe Illustrator to make some of the desired grid. Then started to work with different prompts that tried to get the model to start with the grid, or overlay it as a separate step using python.

Back to the drawing board, right? If you want to overlay something, you should specify the dimensions of the image and that of whatever you’re adding on top.

(We went with 1000 x 1000 px, a common dimension.)

Still, not bad… But not exactly. Plus, there are a few not-so-obvious steps, like working with opacity, which can be distracting.

Not bad! Probably at least good enough to keep the average gameday from degrading into a tavern brawl.

Added to Knowledge Base

After some experimentation, some fine-tuning files were added. The model definitely understands the request, mostly. (Here was a 10x10 request):

It’s lovely! But not exactly scaled 50ft x 50ft; the grid overlay (below) was supposed to be automatic, but it was still a separate step… And the model’s first try is better without it (visually).

Try it yourself:

• Ask the model to make a 10x10 map.
• Give specifics or tell it to make up the whole map.
• If you want a grid, ask it to overlay the map with dndgpt_map_overlay_10x10.png with 50% opacity using python. (lol)
• If you get something cool, please post it here!

Next Steps The whole goal with this is to work with the model using the Grid as a Strategic Map Interface so you can interact and work with the model at high-level strategy.

“My mage is at A1 and moves to G10 via Misty Step,” or “my horde of undead minions marches from A9 to B9.”

Ultimately, we want there to be more of a connection between the details of the map—“there’s a pyramid from (D5, F3) x (F6, G4) and a horde of mummies between you and [probably cursed] pharaoh gold.”—And a way of quickly identifying simple terrain types via coordinates. (If anyone has any thoughts?)

Also, File Limit? Also, does anyone know about a CustomGPT file size limit?

When we tried to load the 25x25 grid into the knowledge base the GPT became unsaveable… which MIGHT be because there are like 12 medium sized .pdfs. Which means they’ll need to be compressed.

Making Logos with ChatGPT

We’re just continuing to go coo-coo bananas with this thing and have decided to make a project as fully automated as possible.

Starting, with a logo, of course, which is the first thing everyone wants to do in small business. So, here’s how you do it. (Hint: It’s still not “fully automated.”)

Logo Conceptualization

This is the easy part. After a long-ish conversation with DungeonMasterGPT (dndgpt), we came up with a concept that had the feel of the classic DND with some modern what-not.

Okay, so right out of the box that is AWESOME. So AWESOME. It’s not a “Logo” yet, but man! What a concept.

You can also see there are several small hallucinations and inconsistencies, like the attempt at the “œ” in the logo type. A+ for effort, though.

Add Some 4o Magic

In between the above image, and this one below, the CustomGPTs clearly got an upgrade to 4o. I don’t remember what I was asking about in the above image, “what font did you use?”

Well, the model took it upon itself to redraw the image:

Oh my stars and garters—quintuple wow! Holy smokes, this is PERFECT. It’s STILL not a “logo,” but wow! Without being asked, the model corrected the image with incredible clarity. This saved an entire step and several hours.

Making a Modern Logo

To be a modern logo, we still need this in a variety of vector formats with other aspects, such as font, made consistent. To do this, it’s still necessary to pull into Adobe Illustrator.

Otherwise, truly, the only thing that needed to happen with the actual graphic was that it be converted to vectors and simplified.

But to be more widely useable, the font still had to be identified and the logo type had to be rebuilt:

In the above image, we use native Adobe tools to identify the font ChatGPT had in mind. We chose Shackleton Narrow and Open Sans Light .

In the second step, we make that look like the original graphic. Yes, it “looks” almost identical, but the key difference is that now the process is exactly repeatable.

Conclusion and What Features Would Be Helpful

So, as you can see, we can get ChatGPT to do a lot of the work, not all. Dalle still persistently creates weird chat hallucinations, and it’s still not possible output images as vectors.

Based on this experience and the first attempts at image overlays in the above post, we think ChatGPT would benefit from an “assembly layer,” when working with images.

It can already call python and add image layers (just like you would with Adobe), but we envision something more robust. Perhaps this assembly layer can call fonts, too.

So, instead of imagining an image with text, it imagines an image, creates a space for text, then adds the text layer using a specified font (or a font that can be specified) typing the font on the image… also making the visual experience more consistent.

Otherwise, all of this is enormously fun, and we’ve got a lot more to share.

Thanks for reading and trying out the cGPT… make sure to check out the new instagram… (lawl).

Hasbro owns trademark on Dungeon Master. Be careful!

Thanks for the update, though!

You are correct, but to the best of my knowledge, this is covered under the open license agreement which allows use of, and alteration of, such terms and images.

This would not be possible without that license.

In fact, I should note that every step of this has become a study in the ethical use of AI—both in image creation and logo design, and the wider copyright concerns you’ve highlighted.

Patent and trademark rights are not licensed under this Public License.

Dungeon Master is a trademark not just copyrighted. But if you’ve checked, you should be good.

I just know Hasbro has a history of cracking down. It’s why many use Game Master instead. I’m surprised OpenAI let it through…

It’s in the same genre/industry too, which is why I would be concerned…

fantasy role-playing games and board games, and game accessories; namely, booklets containing role-playing game scenarios, and computer game programs

I guess we’ll find out! This is a gray area to me.

To my knowledge, since the GPT and everything else I produce clearly point to the license—which is stated explicitly in the “about” section of the cGPT and in other places—and follows the criteria for referencing the license—I think bases are covered.

Also, I think, adding “GPT” to the title makes it different enough from the trade marked phrase to be considered “different enough.”

Either way, I’m not attached to the name of the thing in the slightest. It’s an open source project in the spirit of being open source.

Hopefully Hasbro doesn’t come out with their own Dungeon Master with AI… It’ll likely be on D&D Beyond…

I run a lot of D&D stories and D&D random tables on my blogs … maybe we should do an email interview with you?

Oh well. I’d be working on it already if I were them!

…but I’d also be building it as an Assistant and just White Label whatever their “ai” turns out to be. Given the close contact with NVidia during Baldur’s Gate 3 I’m certain that they’re doing cool things.

Meanwhile, I’m learning a ton from this project so I’m happy.

Sure! I’d love that, please give me a ring.

Okay, so we’re working on condensing parts of the game manual into a table, and using ChatGPT’s new data features to do it. (I’m going to make a full post about it later.)

After some tedium, I was like, draw me a picture of two dragons fighting (for said instagram), and also a map of the fight.

Since it was a pretty awesome picture, it became suddenly important to know who won the fight… like any true nerd would want to know.

We’re currently working on a thrilling tale of how a Necromancer gets the Bone Dragon he always wanted.

Looks to me like you have ‘Dungeon GPT Ultimate’. Great project!

Hey Everyone!

Hope y’all are well.

So, my first clever idea was to take the Dungeons and Dragons SRD and break it into smaller parts, shown above, unable to save with 21 files.

It is a well organized, 400 page document that was simple to pull into Adobe Acrobat and break into smaller chapters. The sections are well referenced in the Instructions and this works unexpectedly well.

However, .pdfs are gigantic and then there was that whole file limit, thing… which I only discovered when I wanted to add more of those grid overlays.

So my next clever idea was to break the lists contained, like the monster manual, down to a spreadsheet. The goal was to use the new native 4o features to do it.

Turn a PDF into a Spreadsheet Using ChatGPT 4o

This is a screen shot of the first page of the “A” section of the D&D SRD.

You can tell a few things just by looking:

• This is a well organized document.
• It lends itself to tabular format… we’re probably doing what the original writers of the document did in reverse.
• There is A LOT of information available for each monster.

First Attempt With A Simple Prompt

Because of all the text in each monster, you know you’re going to run into issues with the Context Window (the size of the prompt ChatGPT can work with).

The section we were working with is 68 pages long.

So, first we just tried to start with a few A’s.

0 Aboleth Large aberration lawful evil 17 135 (18d10 + 36) 1 Animated Armor Medium construct unaligned 18 33 (6d8 + 6) 2 Ankheg Large monstrosity unaligned 14 39 (6d10 + 6) 3 Azer Medium elemental lawful neutral 17 39 (6d8 + 12)

It did really good! I was impressed. It missed some information, made up information or got it from a completely different section of the manual, but otherwise created a table exactly as asked. The errors it had were reasonable, like the Monster Appendix doesn’t exactly define a whole type of Action, and ChatGPT didn’t know what to do with that information.

Long story short:

• I manually identified the missing columns, and we had to make several up to describe the information.
• “Parent Monster,” “Unique Ability,” and “Legendary Action,” though not all monsters have all of these abilities, they can have up to 5 each. (I went through and counted.) It was pretty cool that the model understood the Unique Ability column so readily. (See the full prompt below.)
• Adding these columns to the spreadsheet and instructing the model how to fill them worked immediately.

Prompt Too Long and Hallucinations

Given all of that information getting put into the spreadsheet, there was some wrestling with the proper amount to work with.

• Experimentation showed we could usually grab 3-4 monsters at a time without causing errors.

There was also a fair amount of wrestling with the model to have it just pull the monsters from the specified section without making any up… though, I admit, at the Dinosaur section we might have filled in a few gaps. (No stegosaurus? Really?)

• Experimentation with having the model identify the next four line items yielded inconsistent results.
• In the end, I just listed the next 3-4 monsters in the list based on a quick look and whether “I thought it might be too long for the Window.”
• The model wanted to jump ahead, often trying to combine steps in a single prompt, which caused errors. We ended up working out a (tedious) step-by-step system that reduced errors significantly. (The full prompt is below.)
• It had to be instructed a few times to not skip steps, but apparently repetition is effective.
• I got through 105 entries (up to the Elementals) in about 2 hours one wild Saturday night. ChatGPT estimated I saved 8 hours manually copying everything back and forth. Not bad.

Lessons and Next Steps

• This was tedious, but much faster than it could have been after we got into a rhythm.
• I think the Assistants API might be better suited for this type of task if anyone has any thoughts about mapping PDFs to Spreadsheets.
• The model is unexpectedly AWESOME at making up D&D monsters. It was truly difficult to stop it from making more up. (Later, my precious.)

Step-by-Step Instructions

• Identify the Monsters Section :
• Determine the section of the monsters to work on (e.g., “Monsters (B)”, “Monsters (C)”) in the dndgpt_srd_cc_v5.1_monsters_atok.pdf
• Confirm the list of monsters within that section with the user.
• Chunk the Work :
• Break down the list into manageable chunks (e.g., 3-4 monsters at a time).
• Confirm the specific monsters to be processed in each chunk with the user.
• Extract Information :
• For each chunk, locate the details of each monster from the source PDF.
• Extract relevant information such as Name, Type, Alignment, Armor Class, Hit Points, Speed, Ability Scores, Senses, Languages, Challenge Rating, Unique Abilities, Actions, and Legendary Actions.
• Structure the Data :
• Use the existing table structure and columns to fill in the extracted data.
• Ensure the information is mapped to the correct columns, including Challenge, Unique Ability fields, Action fields, and Legendary Action fields.
• If a monster has multiple abilities or actions, use the respective numbered fields (e.g., Unique Ability 1, Action 1).
• Input Data into Spreadsheet :
• Enter the extracted and structured data into the spreadsheet.
• Add new rows for each monster and ensure that all columns are filled appropriately.
• Review and Confirm :
• After entering the data, review the spreadsheet for any errors or missing information.
• Confirm with the user that the data has been correctly entered.
• Proceed to the Next Chunk :
• Repeat the process for the next chunk of monsters, ensuring each step is followed.
• Continue until the entire section of monsters has been processed.
• Handle Special Cases :
• For Parent Monsters (e.g., “Dragons, Metallic”), include the Parent Monster name in the designated column.
• Ensure consistency in naming and formatting throughout the table.
• Save and Provide Updated File :
• Save the updated spreadsheet after completing each chunk.
• Provide a download link for the updated file to the user for review.
• Feedback and Adjustments :
• Incorporate any feedback or adjustments provided by the user.
• Ensure the final spreadsheet is accurate and complete.

Custom Fields Details

• Parent Monster :
• Purpose : To group related monsters under a common category.
• Usage : This field is particularly useful for monsters that belong to a specific type or family, such as “Dragons” or “Demons.”
• Source Correspondence : This field is not explicitly mentioned in the source document. It is inferred based on the grouping of similar monsters. For example, all dragon types (e.g., Black Dragon, Blue Dragon) would have “Dragon” as their Parent Monster.
• Unique Ability :
• Purpose : To capture special traits or abilities that a monster has beyond its basic stats and actions.
• Usage : Each unique ability of a monster is listed under “Unique Ability 1,” “Unique Ability 2,” etc., up to “Unique Ability 5” to ensure all abilities are captured.
• Source Correspondence : This information is usually found in the descriptive text following the monster’s main stats. For instance, a dragon’s “Fire Immunity” or a drider’s “Fey Ancestry” and “Spellcasting” abilities.

How Custom Fields Correspond to the Source Document

• In the source document, monsters are often grouped by type (e.g., “Dragons”). While the document itself may not label these groups explicitly, we use the Parent Monster field to ensure that related monsters are easily identifiable and categorized in the spreadsheet.
• Dragon Turtle : Amphibious trait, allowing it to breathe both air and water.
• Drider : Includes abilities like “Fey Ancestry” and “Spellcasting,” which describe innate magical abilities and resistances.
• Dryad : Features “Innate Spellcasting” and “Magic Resistance.”
• Duergar : Lists abilities such as “Duergar Resilience” and “Sunlight Sensitivity.”
• The “Actions” field captures the various combat actions a monster can take. Each action described in the source document is mapped to an appropriate action field (e.g., “Action 1,” “Action 2”).
• Multiattack routines and individual attacks (e.g., bite, claw, tail) are included here.
• This field captures special actions that can be taken outside the monster’s turn. These actions are described towards the end of the monster’s entry in the source document.

We went and summoned a Bone Golem then made him a tad OP.

Hey, @PaulBellow ! You seem kinda like the do-gooder type. As you can see, we’re raising a massive army of the undead over here… How do you feel about raising an army to try to stop me…? As you can see, there’s some sort of city off to the North West that might be interested in defending itself from the evil seeping from that old crater to the south.

Oh, and also work out some open source army mechanics for D&D while we’re at it and get the GPT to run the game board and ref the match.

That golem picture is literally the coolest thing Dalle3 has made so far.

Use an Assistant or the ChatGPT UI to Transfer PDF Information into a Spreadsheet?

In a simple experiment, (and a first try with the API) we tried to transfer a single monster to the master spreadsheet.

The monsters have a TON of tactical data that accomanies them, like you’ve seen above… But I also didn’t spend any time trying to make the process more efficient.

Estimating Costs of the API vs ChatGPT

Using 4o, and guesstimating that each monster is about the same length of tokens, give or take, at $0.10 per monster, that means we’re at $1.00 per 10 monsters.

Now, I don’t know how many monsters there are, but including them and times and other tabular information in the DND SRD, let’s say we’re at 200 items.

OpenAInomics

If you’re rocking a commercial budget, working with the API quickly becomes the desired method if you can stabilize the process and keep the system message concise.

…But! Transferring visual tabular information like this might always be verbally intensive, requiring oversight and constant attention, and so working line by line might continue to be a required option. In which case the verbal UI would be the better option.

Building NPC Templates

But going line by line is tedious. When you don’t you get interesting hallucinations in the data.

Based on the experience building the Monsters tables, I started using DungeonMasterGPT to start to build “NPC templates.”

The idea is to have various pre-made NPCs as a person might find in a “Small Village.”

In this case, it’s 15 NPCs which we made up on the spot.

NPC information is a Monster file with more story information, such as Traits, Flaws, and other role-playing options.

Loooots of Errors

It’s hard to work not knowing what a cGPT’s limits are and having to discover them through trial and error.

Making up characters in bulk was cool, but had some hallucinations, like abilities that were inappropriate for the level constraints… we got a level 6 cleric walkin around with Divine Intervention. Watch out, forces of evil.

Working with data in the Analyzer is cumbersome. It’s easy to select a cell or a range, but it’s difficult to move information around within the table compared to working with the information in Excel or Sheets.

You have to explicitly instruct the model to not output the table in all of the various formats it can in.

This is still moving super fast, there’s just need for strong oversight and data integrity. I haven’t seen this many errors in a spreadsheet since high school.

Calling the Spreadsheet in ChatGPT

Actually calling for the NPC template works immediately, BUT that’s only obvious to me through a lot of use.

I had to explicitly instruct the model to explicitly tell anyone asking about the NPC templates how to load the data.

“Please load the Small Village NPC Template into the Data Analyzer.”

But, it’s otherwise all up and running and pretty neat. Hopefully fills the frequent need of DMs to tell backstories of random NPCs in small hamlets.

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The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

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Why the Pandemic Probably Started in a Lab, in 5 Key Points

By Alina Chan

Dr. Chan is a molecular biologist at the Broad Institute of M.I.T. and Harvard, and a co-author of “Viral: The Search for the Origin of Covid-19.”

This article has been updated to reflect news developments.

On Monday, Dr. Anthony Fauci returned to the halls of Congress and testified before the House subcommittee investigating the Covid-19 pandemic. He was questioned about several topics related to the government’s handling of Covid-19, including how the National Institute of Allergy and Infectious Diseases, which he directed until retiring in 2022, supported risky virus work at a Chinese institute whose research may have caused the pandemic.

For more than four years, reflexive partisan politics have derailed the search for the truth about a catastrophe that has touched us all. It has been estimated that at least 25 million people around the world have died because of Covid-19, with over a million of those deaths in the United States.

Although how the pandemic started has been hotly debated, a growing volume of evidence — gleaned from public records released under the Freedom of Information Act, digital sleuthing through online databases, scientific papers analyzing the virus and its spread, and leaks from within the U.S. government — suggests that the pandemic most likely occurred because a virus escaped from a research lab in Wuhan, China. If so, it would be the most costly accident in the history of science.

Here’s what we now know:

1 The SARS-like virus that caused the pandemic emerged in Wuhan, the city where the world’s foremost research lab for SARS-like viruses is located.

• At the Wuhan Institute of Virology, a team of scientists had been hunting for SARS-like viruses for over a decade, led by Shi Zhengli.
• Their research showed that the viruses most similar to SARS‑CoV‑2, the virus that caused the pandemic, circulate in bats that live r oughly 1,000 miles away from Wuhan. Scientists from Dr. Shi’s team traveled repeatedly to Yunnan province to collect these viruses and had expanded their search to Southeast Asia. Bats in other parts of China have not been found to carry viruses that are as closely related to SARS-CoV-2.

The closest known relatives to SARS-CoV-2 were found in southwestern China and in Laos.

Large cities

Mine in Yunnan province

Cave in Laos

South China Sea

The closest known relatives to SARS-CoV-2

were found in southwestern China and in Laos.

philippines

The closest known relatives to SARS-CoV-2 were found

in southwestern China and Laos.

Sources: Sarah Temmam et al., Nature; SimpleMaps

Note: Cities shown have a population of at least 200,000.

There are hundreds of large cities in China and Southeast Asia.

There are hundreds of large cities in China

and Southeast Asia.

The pandemic started roughly 1,000 miles away, in Wuhan, home to the world’s foremost SARS-like virus research lab.

The pandemic started roughly 1,000 miles away,

in Wuhan, home to the world’s foremost SARS-like virus research lab.

The pandemic started roughly 1,000 miles away, in Wuhan,

home to the world’s foremost SARS-like virus research lab.

• Even at hot spots where these viruses exist naturally near the cave bats of southwestern China and Southeast Asia, the scientists argued, as recently as 2019 , that bat coronavirus spillover into humans is rare .
• When the Covid-19 outbreak was detected, Dr. Shi initially wondered if the novel coronavirus had come from her laboratory , saying she had never expected such an outbreak to occur in Wuhan.
• The SARS‑CoV‑2 virus is exceptionally contagious and can jump from species to species like wildfire . Yet it left no known trace of infection at its source or anywhere along what would have been a thousand-mile journey before emerging in Wuhan.

2 The year before the outbreak, the Wuhan institute, working with U.S. partners, had proposed creating viruses with SARS‑CoV‑2’s defining feature.

• Dr. Shi’s group was fascinated by how coronaviruses jump from species to species. To find viruses, they took samples from bats and other animals , as well as from sick people living near animals carrying these viruses or associated with the wildlife trade. Much of this work was conducted in partnership with the EcoHealth Alliance, a U.S.-based scientific organization that, since 2002, has been awarded over $80 million in federal funding to research the risks of emerging infectious diseases.
• The laboratory pursued risky research that resulted in viruses becoming more infectious : Coronaviruses were grown from samples from infected animals and genetically reconstructed and recombined to create new viruses unknown in nature. These new viruses were passed through cells from bats, pigs, primates and humans and were used to infect civets and humanized mice (mice modified with human genes). In essence, this process forced these viruses to adapt to new host species, and the viruses with mutations that allowed them to thrive emerged as victors.
• By 2019, Dr. Shi’s group had published a database describing more than 22,000 collected wildlife samples. But external access was shut off in the fall of 2019, and the database was not shared with American collaborators even after the pandemic started , when such a rich virus collection would have been most useful in tracking the origin of SARS‑CoV‑2. It remains unclear whether the Wuhan institute possessed a precursor of the pandemic virus.
• In 2021, The Intercept published a leaked 2018 grant proposal for a research project named Defuse , which had been written as a collaboration between EcoHealth, the Wuhan institute and Ralph Baric at the University of North Carolina, who had been on the cutting edge of coronavirus research for years. The proposal described plans to create viruses strikingly similar to SARS‑CoV‑2.
• Coronaviruses bear their name because their surface is studded with protein spikes, like a spiky crown, which they use to enter animal cells. T he Defuse project proposed to search for and create SARS-like viruses carrying spikes with a unique feature: a furin cleavage site — the same feature that enhances SARS‑CoV‑2’s infectiousness in humans, making it capable of causing a pandemic. Defuse was never funded by the United States . However, in his testimony on Monday, Dr. Fauci explained that the Wuhan institute would not need to rely on U.S. funding to pursue research independently.

The Wuhan lab ran risky experiments to learn about how SARS-like viruses might infect humans.

1. Collect SARS-like viruses from bats and other wild animals, as well as from people exposed to them.

2. Identify high-risk viruses by screening for spike proteins that facilitate infection of human cells.

2. Identify high-risk viruses by screening for spike proteins that facilitate infection of

human cells.

In Defuse, the scientists proposed to add a furin cleavage site to the spike protein.

3. Create new coronaviruses by inserting spike proteins or other features that could make the viruses more infectious in humans.

4. Infect human cells, civets and humanized mice with the new coronaviruses, to determine how dangerous they might be.

• While it’s possible that the furin cleavage site could have evolved naturally (as seen in some distantly related coronaviruses), out of the hundreds of SARS-like viruses cataloged by scientists, SARS‑CoV‑2 is the only one known to possess a furin cleavage site in its spike. And the genetic data suggest that the virus had only recently gained the furin cleavage site before it started the pandemic.
• Ultimately, a never-before-seen SARS-like virus with a newly introduced furin cleavage site, matching the description in the Wuhan institute’s Defuse proposal, caused an outbreak in Wuhan less than two years after the proposal was drafted.
• When the Wuhan scientists published their seminal paper about Covid-19 as the pandemic roared to life in 2020, they did not mention the virus’s furin cleavage site — a feature they should have been on the lookout for, according to their own grant proposal, and a feature quickly recognized by other scientists.
• Worse still, as the pandemic raged, their American collaborators failed to publicly reveal the existence of the Defuse proposal. The president of EcoHealth, Peter Daszak, recently admitted to Congress that he doesn’t know about virus samples collected by the Wuhan institute after 2015 and never asked the lab’s scientists if they had started the work described in Defuse. In May, citing failures in EcoHealth’s monitoring of risky experiments conducted at the Wuhan lab, the Biden administration suspended all federal funding for the organization and Dr. Daszak, and initiated proceedings to bar them from receiving future grants. In his testimony on Monday, Dr. Fauci said that he supported the decision to suspend and bar EcoHealth.
• Separately, Dr. Baric described the competitive dynamic between his research group and the institute when he told Congress that the Wuhan scientists would probably not have shared their most interesting newly discovered viruses with him . Documents and email correspondence between the institute and Dr. Baric are still being withheld from the public while their release is fiercely contested in litigation.
• In the end, American partners very likely knew of only a fraction of the research done in Wuhan. According to U.S. intelligence sources, some of the institute’s virus research was classified or conducted with or on behalf of the Chinese military . In the congressional hearing on Monday, Dr. Fauci repeatedly acknowledged the lack of visibility into experiments conducted at the Wuhan institute, saying, “None of us can know everything that’s going on in China, or in Wuhan, or what have you. And that’s the reason why — I say today, and I’ve said at the T.I.,” referring to his transcribed interview with the subcommittee, “I keep an open mind as to what the origin is.”

3 The Wuhan lab pursued this type of work under low biosafety conditions that could not have contained an airborne virus as infectious as SARS‑CoV‑2.

• Labs working with live viruses generally operate at one of four biosafety levels (known in ascending order of stringency as BSL-1, 2, 3 and 4) that describe the work practices that are considered sufficiently safe depending on the characteristics of each pathogen. The Wuhan institute’s scientists worked with SARS-like viruses under inappropriately low biosafety conditions .

In the United States, virologists generally use stricter Biosafety Level 3 protocols when working with SARS-like viruses.

Biosafety cabinets prevent

viral particles from escaping.

Viral particles

Personal respirators provide

a second layer of defense against breathing in the virus.

DIRECT CONTACT

Gloves prevent skin contact.

Disposable wraparound

gowns cover much of the rest of the body.

Personal respirators provide a second layer of defense against breathing in the virus.

Disposable wraparound gowns

cover much of the rest of the body.

Note: ​​Biosafety levels are not internationally standardized, and some countries use more permissive protocols than others.

The Wuhan lab had been regularly working with SARS-like viruses under Biosafety Level 2 conditions, which could not prevent a highly infectious virus like SARS-CoV-2 from escaping.

Some work is done in the open air, and masks are not required.

Less protective equipment provides more opportunities

for contamination.

Some work is done in the open air,

and masks are not required.

Less protective equipment provides more opportunities for contamination.

• In one experiment, Dr. Shi’s group genetically engineered an unexpectedly deadly SARS-like virus (not closely related to SARS‑CoV‑2) that exhibited a 10,000-fold increase in the quantity of virus in the lungs and brains of humanized mice . Wuhan institute scientists handled these live viruses at low biosafet y levels , including BSL-2.
• Even the much more stringent containment at BSL-3 cannot fully prevent SARS‑CoV‑2 from escaping . Two years into the pandemic, the virus infected a scientist in a BSL-3 laboratory in Taiwan, which was, at the time, a zero-Covid country. The scientist had been vaccinated and was tested only after losing the sense of smell. By then, more than 100 close contacts had been exposed. Human error is a source of exposure even at the highest biosafety levels , and the risks are much greater for scientists working with infectious pathogens at low biosafety.
• An early draft of the Defuse proposal stated that the Wuhan lab would do their virus work at BSL-2 to make it “highly cost-effective.” Dr. Baric added a note to the draft highlighting the importance of using BSL-3 to contain SARS-like viruses that could infect human cells, writing that “U.S. researchers will likely freak out.” Years later, after SARS‑CoV‑2 had killed millions, Dr. Baric wrote to Dr. Daszak : “I have no doubt that they followed state determined rules and did the work under BSL-2. Yes China has the right to set their own policy. You believe this was appropriate containment if you want but don’t expect me to believe it. Moreover, don’t insult my intelligence by trying to feed me this load of BS.”
• SARS‑CoV‑2 is a stealthy virus that transmits effectively through the air, causes a range of symptoms similar to those of other common respiratory diseases and can be spread by infected people before symptoms even appear. If the virus had escaped from a BSL-2 laboratory in 2019, the leak most likely would have gone undetected until too late.
• One alarming detail — leaked to The Wall Street Journal and confirmed by current and former U.S. government officials — is that scientists on Dr. Shi’s team fell ill with Covid-like symptoms in the fall of 2019 . One of the scientists had been named in the Defuse proposal as the person in charge of virus discovery work. The scientists denied having been sick .

4 The hypothesis that Covid-19 came from an animal at the Huanan Seafood Market in Wuhan is not supported by strong evidence.

• In December 2019, Chinese investigators assumed the outbreak had started at a centrally located market frequented by thousands of visitors daily. This bias in their search for early cases meant that cases unlinked to or located far away from the market would very likely have been missed. To make things worse, the Chinese authorities blocked the reporting of early cases not linked to the market and, claiming biosafety precautions, ordered the destruction of patient samples on January 3, 2020, making it nearly impossible to see the complete picture of the earliest Covid-19 cases. Information about dozens of early cases from November and December 2019 remains inaccessible.
• A pair of papers published in Science in 2022 made the best case for SARS‑CoV‑2 having emerged naturally from human-animal contact at the Wuhan market by focusing on a map of the early cases and asserting that the virus had jumped from animals into humans twice at the market in 2019. More recently, the two papers have been countered by other virologists and scientists who convincingly demonstrate that the available market evidence does not distinguish between a human superspreader event and a natural spillover at the market.
• Furthermore, the existing genetic and early case data show that all known Covid-19 cases probably stem from a single introduction of SARS‑CoV‑2 into people, and the outbreak at the Wuhan market probably happened after the virus had already been circulating in humans.

An analysis of SARS-CoV-2’s evolutionary tree shows how the virus evolved as it started to spread through humans.

SARS-COV-2 Viruses closest

to bat coronaviruses

more mutations

Source: Lv et al., Virus Evolution (2024) , as reproduced by Jesse Bloom

The viruses that infected people linked to the market were most likely not the earliest form of the virus that started the pandemic.

• Not a single infected animal has ever been confirmed at the market or in its supply chain. Without good evidence that the pandemic started at the Huanan Seafood Market, the fact that the virus emerged in Wuhan points squarely at its unique SARS-like virus laboratory.

5 Key evidence that would be expected if the virus had emerged from the wildlife trade is still missing.

In previous outbreaks of coronaviruses, scientists were able to demonstrate natural origin by collecting multiple pieces of evidence linking infected humans to infected animals.

Infected animals

Earliest known

cases exposed to

live animals

Antibody evidence

of animals and

animal traders having

been infected

Ancestral variants

of the virus found in

Documented trade

of host animals

between the area

where bats carry

closely related viruses

and the outbreak site

Infected animals found

Earliest known cases exposed to live animals

Antibody evidence of animals and animal

traders having been infected

Ancestral variants of the virus found in animals

Documented trade of host animals

between the area where bats carry closely

related viruses and the outbreak site

For SARS-CoV-2, these same key pieces of evidence are still missing , more than four years after the virus emerged.

For SARS-CoV-2, these same key pieces of evidence are still missing ,

more than four years after the virus emerged.

• Despite the intense search trained on the animal trade and people linked to the market, investigators have not reported finding any animals infected with SARS‑CoV‑2 that had not been infected by humans. Yet, infected animal sources and other connective pieces of evidence were found for the earlier SARS and MERS outbreaks as quickly as within a few days, despite the less advanced viral forensic technologies of two decades ago.
• Even though Wuhan is the home base of virus hunters with world-leading expertise in tracking novel SARS-like viruses, investigators have either failed to collect or report key evidence that would be expected if Covid-19 emerged from the wildlife trade . For example, investigators have not determined that the earliest known cases had exposure to intermediate host animals before falling ill. No antibody evidence shows that animal traders in Wuhan are regularly exposed to SARS-like viruses, as would be expected in such situations.
• With today’s technology, scientists can detect how respiratory viruses — including SARS, MERS and the flu — circulate in animals while making repeated attempts to jump across species . Thankfully, these variants usually fail to transmit well after crossing over to a new species and tend to die off after a small number of infections. In contrast, virologists and other scientists agree that SARS‑CoV‑2 required little to no adaptation to spread rapidly in humans and other animals . The virus appears to have succeeded in causing a pandemic upon its only detected jump into humans.

The pandemic could have been caused by any of hundreds of virus species, at any of tens of thousands of wildlife markets, in any of thousands of cities, and in any year. But it was a SARS-like coronavirus with a unique furin cleavage site that emerged in Wuhan, less than two years after scientists, sometimes working under inadequate biosafety conditions, proposed collecting and creating viruses of that same design.

While several natural spillover scenarios remain plausible, and we still don’t know enough about the full extent of virus research conducted at the Wuhan institute by Dr. Shi’s team and other researchers, a laboratory accident is the most parsimonious explanation of how the pandemic began.

Given what we now know, investigators should follow their strongest leads and subpoena all exchanges between the Wuhan scientists and their international partners, including unpublished research proposals, manuscripts, data and commercial orders. In particular, exchanges from 2018 and 2019 — the critical two years before the emergence of Covid-19 — are very likely to be illuminating (and require no cooperation from the Chinese government to acquire), yet they remain beyond the public’s view more than four years after the pandemic began.

Whether the pandemic started on a lab bench or in a market stall, it is undeniable that U.S. federal funding helped to build an unprecedented collection of SARS-like viruses at the Wuhan institute, as well as contributing to research that enhanced them . Advocates and funders of the institute’s research, including Dr. Fauci, should cooperate with the investigation to help identify and close the loopholes that allowed such dangerous work to occur. The world must not continue to bear the intolerable risks of research with the potential to cause pandemics .

A successful investigation of the pandemic’s root cause would have the power to break a decades-long scientific impasse on pathogen research safety, determining how governments will spend billions of dollars to prevent future pandemics. A credible investigation would also deter future acts of negligence and deceit by demonstrating that it is indeed possible to be held accountable for causing a viral pandemic. Last but not least, people of all nations need to see their leaders — and especially, their scientists — heading the charge to find out what caused this world-shaking event. Restoring public trust in science and government leadership requires it.

A thorough investigation by the U.S. government could unearth more evidence while spurring whistleblowers to find their courage and seek their moment of opportunity. It would also show the world that U.S. leaders and scientists are not afraid of what the truth behind the pandemic may be.

More on how the pandemic may have started

Where Did the Coronavirus Come From? What We Already Know Is Troubling.

Even if the coronavirus did not emerge from a lab, the groundwork for a potential disaster had been laid for years, and learning its lessons is essential to preventing others.

By Zeynep Tufekci

Why Does Bad Science on Covid’s Origin Get Hyped?

If the raccoon dog was a smoking gun, it fired blanks.

By David Wallace-Wells

A Plea for Making Virus Research Safer

A way forward for lab safety.

By Jesse Bloom

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Alina Chan ( @ayjchan ) is a molecular biologist at the Broad Institute of M.I.T. and Harvard, and a co-author of “ Viral : The Search for the Origin of Covid-19.” She was a member of the Pathogens Project , which the Bulletin of the Atomic Scientists organized to generate new thinking on responsible, high-risk pathogen research.

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