Inspiring great art (photo by Avin CP on Unsplash)

Nashville, Tennessee: the gravitational center of country music. The home of the Grand Ole Opry. The birthplace of Taylor Swift’s career. The site of … the Tomato Art Fest?

Yes, the Tomato Art Fest: a festival of art, music, food, and much more, that draws tens of thousands of visitors every year to the Five Points district in East Nashville. Founded more than twenty years ago to celebrate the tomato as a “uniter, not a divider,” this community event has become a beloved feature of the Nashville scene (2026 edition: August 7 – 8).

Nashville’s Tomato Art Fest reminds us that every community—neighborhoods, towns, cities, regions, nations—has its own unique story. Some aspects of that story are well known (most people are aware of Nashville’s ties to country music), but some are not (did you know about the Tomato Art Fest?). Exploring a community’s story in depth can reveal additional detail and nuance of the well known, as well as bringing to light the lesser known.

In this post, I’d like to explore a little of Nashville’s story—both the well known and the lesser known—through a unique resource: library collections. And the window on that resource is OCLC’s WorldCat, the world’s most comprehensive database of information about library collections. As I’d like to show, information about the kinds of materials in library collections worldwide can shed light on an important part of a community’s story: its impact on the published record.

Insight into the well known …

Nashville’s ties to country music are, without overstatement, legendary. Many of the biggest country music stars have strong connections to the city. Consider the following sample:

• Taylor Swift: her family moved to the Nashville area to help her break into music.
• Patsy Cline: her musical style is a defining example of the famous Nashville Sound in country music.
• Johnny Cash: he’s a member of the Grand Ole Opry and recorded some of his biggest hits in Nashville.
• Willie Nelson: he moved to Nashville early in his career and became one of the city’s most noted songwriters; for example, he wrote the song “Crazy” that Patsy Cline made famous.
• Dolly Parton: her career started in Nashville—she moved there the day after she graduated from high school.

All of these recording artists are iconic pieces of Nashville’s story.  But can we dig a little deeper and gain additional perspective on their impact on the published record? The answer is yes, through the window on the published record provided by data about library collections.

OCLC’s WorldCat aggregates information about library collections worldwide. More specifically, it can tell us two key pieces of information: the total global holdings of works by each artist, and the total number of holdings of works about each artist. A work can be any kind of published output: book, album, sheet music, and so on.

Here are the results. The data shows us that Willie Nelson has the largest footprint in library collections in terms of total global library holdings of works by these artists. But Johnny Cash takes the top spot for holdings of works about the artists. He also leads the ranking of overall holdings, suggesting that Cash has the largest impact on the published record among this particular group of artists.

It’s interesting to see that both Patsy Cline and Taylor Swift have far more holdings for works about them than for works by them, suggesting strong interest in their careers and life stories, in addition to their musical output. Willie Nelson is the opposite, with far more holdings of works by him than about him. This may relate to his prolific career as a songwriter both for himself and others. It’s also interesting to see that despite her as yet comparatively brief career, Taylor Swift is well on her way to equaling or eclipsing these legendary Nashville artists in terms of her footprint in the published record.

… and amplifying the lesser known

While many people are familiar with Nashville’s status as a hub for music and musicians, it’s also a publishing center. Need proof? WorldCat contains well over 9 million global library holdings of materials published in Nashville. Music publishing is of course important here—you’ll find many music publishers along Nashville’s famous Music Row—but Nashville is also a global center of the Christian publishing industry. HarperCollins Christian Publishing, one of the leading Christian publishers in the world, is headquartered in Nashville.

In addition to publishing, Nashville is also deeply connected to many prominent authors who were born, live, or work there. Consider these examples:

The novelist Ann Patchett, raised in Nashville and currently making her home there, was a finalist for the Pulitzer Prize in 2020 for her novel The Dutch House, a recipient of the US National Humanities Medal, and most recently, the 2026 Library of Congress Prize for American Fiction. WorldCat contains more than 50,000 holdings of her works in library collections worldwide. The poet and novelist Robert Penn Warren was both educated and worked at Nashville’s Vanderbilt University; among many other honors, Warren won a Pulitzer Prize in 1947 for his novel All the King’s Men, and the Center for the Humanities at Vanderbilt bears his name. Vanderbilt is also the home of historian Jon Meacham, who won a Pulitzer Prize in 2009 for his biography of Andrew Jackson.

We mentioned that Nashville is a global center for Christian publishing. The Christian novelist Karen Kingsbury, whose best-selling novels have more than 25 million copies in print, makes her home in the Nashville area, along with the headquarters of her production company. Note the particularly large global holdings total for Kingsbury, a testament to the remarkable popularity of her work. Young Adult novelist Jeff Zentner also resides in Nashville: his novel The Serpent King was named to Kirkus Reviews’ list of Best Books of the 21^st Century (So Far). He has received numerous recognitions, including ALA’s William C. Morris YA Debut Award.

Each of these authors individually has a significant presence in library collections. But collectively, they form a community of accomplished authors spanning a wide range of genres and sharing significant ties to the Nashville area. Literary talent clearly flourishes in Nashville alongside musical talent—a key aspect of the Nashville story.  

Find a city’s cultural influence—at the library

As these examples show, library data can deepen our knowledge of the well known (Nashville is a hub for country music stars) and illuminate the lesser known (Nashville is also a hub for authors and publishing). In both cases, library collections help us understand a community’s cultural influence through its impact on the published record.

The key to all of this is data aggregation. The data in a single bibliographic record, or even the data describing a single library collection, is not enough. It’s only by aggregating data about tens of thousands of library collections worldwide that patterns are revealed and the story emerges. Through its unequalled scale and depth, WorldCat is a unique source of information about global collections, and by extension, the global published record. It is within that expansive backdrop that we can find Nashville’s story.

The Tomato Art Fest is, I would imagine, a lesser-known piece of Nashville’s story. For many, it is an unexpected feature on Nashville’s cultural landscape, likely overshadowed by the music scene. In the same way, Nashville’s rich contribution to the published record across many literary genres may too be obscured by prevailing impressions of what the city is about. Library data helps us move beyond the surface and see a fuller picture—including a festival that celebrates tomatoes!

The post On country music stars, award-winning authors … and tomatoes appeared first on Hanging Together.

SigV4 looks simple: sign a request, check the signature. Then you implement canonicalization, clock skew, and a cache that isn't allowed to hold your key.

Tigris is a drop-in replacement for AWS S3 (or GCS, anything S3API compatible). As such, we need to be fully compatible with both the mechanisms and semantics of S3 including the SigV4 authentication protocol. This is the lingua franca of authentication in the object storage landscape; even Google Cloud Storage has a way to enable SigV4 support so you can use existing applications against its object storage service.

At first I thought that SigV4 was fairly simple. Clients sign requests, servers do the same work and make sure the result matches. The main sticking point is that the cryptography involved is symmetric cryptography, the kind where both parties need to have the same secrets. This makes some scaling issues weird, but we'll get into that in the future.

SigV4 in a shellnut

At a high level when a client signs a request with SigV4 you get an access key ID and secret access key. The access key ID is functionally a username and the secret access key is functionally a password. Admins can identify keypairs by the access key ID (without special training or tools) and services use the owner of the access key or policies delegated to that access key to determine what actions that client may take.

SigV4 uses HMAC (hash-based Message Authentication Code) and SHA-256 (SHA-2 with a 256 bit hash width) to do authentication by creating salted hashes based on request metadata.

In order to send a SigV4 request, clients take the outgoing request, reduce it to a canonicalized form, and sign it with a symmetric key derived from the secret access key, the current date, region of the service, and service name, kinda like this Go code:

func HMAC(key, data []byte) []byte {
        	h := hmac.New(sha256.New, key)
        	h.Write(data)
        	return h.Sum(nil)
        }
        
        var (
        	kDate    = HMAC("AWS4"+secretAccessKey, nowDate)
        	kRegion  = HMAC(kDate, region)
        	kService = HMAC(kRegion, service)
        	kSigning = HMAC(kService, "aws4_request")
        )
        

As an example, let's see what a signed GET request to a HTTP debugging endpoint looks like on the wire with and without the signature:

$ curl http://localhost:3000 -v
        
        GET /
        User-Agent: curl/8.7.1
        Accept: */*
        

And when you add the signature with --aws-sigv4:

$ curl \
          --user tid_YOISC719YLXSONFU:tsec_DiYqeH8t0IKjKUKfqhzTsqrCCUl9Wm0m+6MXNhhi1fU \
          --aws-sigv4 aws:amz:auto:s3 \
          -v \
          http://localhost:3000
        
        GET /
        User-Agent: curl/8.7.1
        Accept: */*
        Authorization:
          AWS4-HMAC-SHA256
          Credential=tid_YOISC719YLXSONFU/20260720/auto/s3/aws4_request,
          SignedHeaders=host;x-amz-date,
          Signature=879bcdd43749cfc9782b876d9ceb3ff153d79ab1482290cca7ab915bb7f8785d
        X-Amz-Date: 20260720T153748Z
        

Breaking it down we have two extra headers in the request:

• Authorization: The fixed string AWS4-HMAC-SHA256 to signal to the server which authentication mechanism is in use. The rest of the string is information about the request signature so the server can properly canonicalize the request.
• X-Amz-Date: The date and time (UTC) of the request so the server knows when the request was signed. Servers will use this request date in order to reject old requests to prevent replay attacks.

Request canonicalization and signing

On the wire, HTTP/1.1 requests look kinda like this:

GET /api/list?page=0&count=30
        User-Agent: curl/8.7.1
        Accept: */*
        Host: myawesomesite.example
        

However the headers could be sent in any order, and changing the order of request headers doesn't result in different requests. Additionally any query string parameters could be formatted in any way a client (or server) could imagine, including the use of semicolons to separate values. All attempts to canonicalise HTTP requests MUST deal with this ambiguity and define their own rules.

SigV4 canonical requests are made up of a few parts:

• The HTTP method (GET, PUT, POST, DELETE, etc.)
• The URI path of the request (/api/list, etc.)
• The sorted canonical query string (you must exactly match the server-side canonicalization logic)
• The signed headers terminated with two newlines
• The sorted list of signed headers joined by semicolons
• The SHA256 checksum of the request body

For that example /api/list request, the canonical form would look like this:

GET
        /api/list
        count=30&page=0
        host:myawesomesite.example
        x-amz-date:20260715T204745Z
        
        host;x-amz-date
        e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855
        

As the request has no body, the empty sha256 checksum e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855 is put as the body checksum.

I'm reading the blogpost at <link> and Xe mentioned AWS SigV4's
        STREAMING-AWS4-HMAC-SHA256-PAYLOAD method. I would like to learn more about how
        this works. Please research how this works and give me code and request body
        samples.
        

To make the signature, you take the sha256 checksum of the canonical request and then HMAC it against that derived signing key:

finalRequestSignature := HMAC(kSigning, reqSig.Bytes())
        

And construct the Authorization header based on your access key ID, service region, and service name:

req.Header.Set("Authorization", fmt.Sprintf(
        	"AWS4-HMAC-SHA256 Credential=%s/%s/%s/%s/aws4_request, SignedHeaders=%s, Signature=%x",
        	accessKeyID, nowDate, region, service,
        	strings.Join(signedHeaders, ";"),
        	finalRequestSignature,
        ))
        

What about SigV4a?

AWS has made an extension to SigV4 that uses asymmetric cryptography called SigV4a (the "a" means asymmetric). Instead of using symmetric cryptography on both the client and server in ways that means the server needs to either know the client's secret access key (or a value derived from the secret access key), SigV4a uses key derivation functions to derive a cryptographic keypair. Servers authenticating requests fetch the public key from IAM. Only the client and IAM know what the private key is, and that private key is what signs outgoing requests.

I'd love to use SigV4a more because it makes adding additional services to the mix (such as a git service) a lot safer as you can have those additional services exist in different trust domains than the core product. This is the core of how microservices end up happening. However, it's not super widely used even within AWS. The only SigV4a use I can find in Amazon is S3 Express Zones, however they may end up using it in other services I'm just not aware of.

When I did my own experimentation with SigV4a (where I was implementing my own IAM server so that I really understood this all at a low level), I had to copy a lot of internal AWS SDK code into my repo in order to get it working.

I'll talk about SigV4a some more another time.

Replay attacks and you: a young coder's illustrated primer

One of the weaknesses of using signatures for API authentication like this is the problem of replay attacks. When you make a naïve signature of a value, there's no real way to tell when that signature was created. If you sign a request to create a compute instance at time instance t0, it's still technically valid at any other time instance tN. This is why the canonical form of SigV4 requests includes the current date and time:

Authorization: [...] SignedHeaders=host;x-amz-date, [...]
        X-Amz-Date: 20260715T205432Z
        

This means that the request was signed on July 15, 2026 at 20:54:32 UTC. Time changes constantly (at least at the rate of one second per second!) and the client has to have a working clock in order for TLS to work. Servers can trivially read the contents of X-Amz-Date and reject old requests. This means that you don't need to add or store nonce (number used once) values with each request because that doesn't scale.

As a side effect of this, you need to set a temporal skew window for validating requests. This window needs to be generous enough to accommodate slow clients, sloppy timekeeping on the client side, highly latent clients, leap seconds, or other exceptional temporal phenomena. In general time synchronization is a surprisingly hard problem, so it's best to just be tolerant of clients in order to make things more robust in practice. AWS uses a temporal skew window of 15 minutes for validating requests. I'm going to use a window of 5 minutes for my API because 300 seconds is a nice round number and I don't have to deal with the same amount of legacy code that AWS does.

How TAG changes the game

So all of this SigV4 business had been working really well for Tigris. Then we worked with a few customers who needed a local cache to fully saturate their hungry GPUs. To be fair, Tigris is plenty fast, but the real thing that kills AI training is latency and something that runs locally will always be faster than the cloud.

In order to provide that sweet middle spot between making everything rely on the cloud and having everything local, we made TAG, the Tigris Acceleration Gateway. This effectively gives you most of a Tigris region in your own infrastructure.

When you connect to TAG, your code uses its existing access keypairs, buckets, and code. You point your code to TAG, you point TAG to Tigris, and then everything is cached for you. But how does TAG authenticate with your code? TAG doesn't have access to all your existing API keys (and to be honest it shouldn't), but it's still able to authenticate them with SigV4 authentication.

TAG and the IAM server both implement a signing key proxying feature that lets a client and TAG both prove their identity to Tigris. Once that proof is sent, then TAG gets the intermediate derived signing key and uses that for locally validating requests, kinda like this:

sequenceDiagram
           participant Client
           participant TAG
           participant Tigris
        
           Client->>TAG: ListBuckets<br/>(signed)
           TAG->>Tigris: ListBuckets<br/>(signed) + proxy hdrs
           Note right of Tigris: 2xx, keys returned
           Tigris-->>TAG: ListBucketsResponse<br/>+ keys (encrypted)
           Note right of TAG: decrypt, cache
           TAG-->>Client: ListBucketsResponse
        
           Client->>TAG: ListBuckets<br/>(signed)
           Note right of TAG: verify locally,<br/>cache hit
           TAG-->>Client: 200 OK
        

The actual implementation in TAG involves some derived AES logic so that the derived signing keys are very much limited to the client that requested it (namely: the AES key is the SHA256 encoded form of the proxy secret access key). One of the weird parts is that the canonical form of the proxied requests differ from the normal SigV4 canonicalization process, namely looking like this:

tag.default.svc.cluster.local # Host header from the client
        1784577479                    # Unix timestamp of the request (X-Tigris-Proxy-Timestamp)
        GET                           # HTTP method of the client
        /                             # HTTP path of the client
        

This is signed using the same SigV4 signature process as before but added differently to the request:

• X-Tigris-Forwarded-Host: the HTTP Host of client requests (EG: tag.default.svc.cluster.local)
• X-Tigris-Proxy-Access-Key: the Tigris keypair used to authenticate TAG itself (must be in the same organization as the client)
• X-Tigris-Proxy-Timestamp: the time of the request in unix timestamp format
• X-Tigris-Proxy-Signature: the hex output of signing the canonical form of the request against TAG's secret access key

And then TAG reads the response from Tigris, caches those derived signing keys, and then uses those in the standard SigV4 process to authenticate clients: no round trip to the cloud required.

I was wrong about the simple part

The happy path is exactly what I thought it was. Reduce a request to a canonical form, run four HMACs, compare the result. That part fits in an afternoon.

Everything expensive lives in the questions around it. Which bytes count as the request? Whose clock decides that a signature is still good? Who gets to hold the key that proves any of it? Each question has an obvious answer, and each obvious answer is wrong in some specific way you only find by implementing it.

That last question is the one that surprised me. I read symmetric cryptography as a hard limit: if the verifier needs your secret, the verifier has to be Tigris. It isn't. SigV4 derives its signing key through a chain of four HMACs, each one scoped tighter than the last: date, then region, then service. Those intermediate values can travel without the secret behind them. TAG rides that. The key it holds stops working when the UTC date rolls over. It covers one region and one service. You can't walk it backwards into a secret access key.

We also didn't write any of this, which is its own kind of relief. SigV4 is old, widely deployed, and hammered on by every S3 client in existence. Any compatibility bugs here are ours. The protocol's bugs are everyone's.

The place a protocol bends is usually some intermediate value that somebody already designed to be thrown away.

If you want a Tigris region in your own datacentre, the Tigris Acceleration Gateway caches your buckets locally and authenticates your existing keypairs with the same SigV4 dance your SDK already speaks.

This post was written by members of the DLF Assessment Interest Group’s (AIG) Metadata Working Group (MWG). Learn more about the Assessment Interest Group.

It was authored by:

Hannah Tarver (University of North Texas), hannah.tarver[at]unt.edu
Xiaoli Ma (University of Florida), xiaolima[at]ufl.edu
Stasha Gardasevic (University of Hawaii at Manoa), gardasev[at]hawaii.edu
Challen Wright (University of Nevada, Reno), challenw[at]unr.edu
Jessica Craig (Getty Research Institute), jecraig[at]getty.edu
Annamarie Klose (The Ohio State University), klose.16[at]osu.edu
Helen Baer (Colorado State University), helen.baer[at]colostate.edu
Leigh Ann Martin (University of Richmond), lbrosnih[at]richmond.eduHannah Tarver (University of North Texas), hannah.tarver[at]unt.edu
Xiaoli Ma (University of Florida), xiaolima[at]ufl.edu
Stasha Gardasevic (University of Hawaii at Manoa), gardasev[at]hawaii.edu

and edited by: Stasha Gardasevic

Career Paths – Stories of Metadata Librarians

Out of all tracks in librarianship, becoming a metadata librarian is not a very common one. 

This blog post features eight professionals working in the US, reflecting on their paths towards becoming metadata librarians, primarily working with digital object metadata records. Most of us work at a university library or other large institutions with rich digital collections.

In this post, you can read about our educational paths, job responsibilities, and a lot of metadata standards jargon – as we love our standards! For more information on each, see the glossary at the end.

This blog post is intended to help LIS students and new professionals better understand this path and what it entails.

If you have more questions, feel free to write to the authors directly.

Name: Hannah Tarver
E-mail: hannah.tarver[at]unt.edu
Organization: University of North Texas Libraries (UNT)

I initially ended up doing metadata work by accident.  When I was an LIS student, I studied traditional cataloging/information organization as my area of specialty, but I started working in the Digital Projects Unit doing metadata work.  Essentially, I learned MARC/AACR2 at the same time as non-MARC metadata, and they were largely interchangeable for me — I just had to consult the specific formatting guidelines for the things I was working on.  

After graduation, I was hired full-time as a metadata librarian, and I have been doing that work for 17 years.  At UNT, we have a home-grown digital library system and a uniform schema with locally-qualified Dublin Core and local fields that are encoded in XML with a web-based editor.  Although I don’t have broad experience with different systems or schemas, we work with a variety of collections and material types that have forced me to reflect on how we should describe things in ways that leverage the system and also (hopefully) work well for users.

My metadata activities include metadata creation at multiple levels, quality control, mediation/corrections, training other editors, and maintaining documentation about our metadata system and input guidelines (https://library.unt.edu/metadata/) as well as research and publishing on those topics. 

Name: Xiaoli Ma
E-mail: xiaolima[at]ufl.edu
Organization: George A. Smathers Libraries, University of Florida

My journey with “metadata” began during my time as an Art History graduate student at the University of South Florida (USF), where I initially worked as a student assistant cataloging images in the Slides Library. This role eventually transitioned into a full-time position as an image cataloger for a year, which is when I first encountered the term “metadata” within my job description. During this period, I learned about databases, VRA Core, and Cataloging Cultural Objects (CCO). VRA Core, which was developed after MARC and Dublin Core to address the specific descriptive needs of cultural objects like artworks and artifacts, serves as the schema, whereas CCO acts as the value standard.

Following my year at USF, I was advised to pursue a Library degree program to formally launch my career in museums and libraries. Fortunately, I was admitted to the Master’s program at the University of Michigan, Ann Arbor’s School of Information. After graduating, my professional journey included working as an archivist in an artist’s studio, a Visual Resources Associate at the Purchase College Library, and a Metadata Specialist-Technical Lead at Ithaka. Currently, I serve as the Metadata Librarian at the University of Florida. Currently, I work with MARC and METS/MODS daily, focusing on adapting MARC data for digital collection environments. As well, I support data contributors with non-MARC data, guiding them to create sufficient data for digital collections.

My research interests center on how standards are adopted and implemented within digital collections, alongside a strong focus on emerging technologies. Most recently, I have been exploring the utilization of Large Language Models in metadata-related tasks.

Name: Stasha Gardasevic, PhD
E-mail: gardasev[at]hawaii.edu
Organization: Hamilton Library, University of Hawaii at Manoa

I did my Bachelor’s in LIS in Serbia, and at the time I wasn’t a fan of the obligatory cataloging class at all! Then I pursued my Master’s in Digital Library Learning (a joint degree offered by universities in Oslo, Norway; Tallinn, Estonia; and Parma, Italy). There, I found out about this (at the time and for me) mysterious but promising concept of Semantic Web as applied to cultural heritage metadata. I was hooked and wanted to learn more, so I did my thesis on the (at the time) new metadata model- Europeana Data Model (EDM) and how it maps to the archival metadata model, Encoded Archival Description (EAD). This exposure to Europeana and related digital library initiatives landed me a job at the National Library of Serbia. That institution has just joined a collaborative pan-European project to contribute World War One materials metadata to the Europeana portal. At the time I was hired, no one on their staff knew much about metadata for digital objects, so I was brought in to help with that. I also didn’t know much, but I learned on the job, working with Dublin Core and MARC 21 records in XML format. There, I worked on several new projects and prescribed metadata modalities for use in open-source software digital library management solutions, such as Omeka (Classic) and AtoM. 

I then went for my PhD studies at the University of Hawaii at Manoa (UHM). Through my internships and later work, I designed sheets for the archival digitization project at the island’s oldest church and helped improve procedures for other digitization projects. 

Finally, I got the job of Metadata Librarian at UHM, Hamilton Library, and here I advise selectors working on digitization projects, as well as faculty and staff submitting to the institutional repository, on the fields and values that go into the description sheets. Also, I lead the Metadata Working Group that will (hopefully) help us normalize our procedures and workflows, and create MAPs for each material type. We are about to embark on the big metadata remediation project, which is both exciting and confusing, as we will need to normalize and standardize records for about 200,000 digital items to improve their searchability and browsability.

Name: Challen Wright
E-mail: challenw[at]unr.edu
Organization: University of Nevada, Reno

One of the biggest life lessons I’ve learned in my career is to go where opportunity leads you. I discovered my passion for librarianship when I was hired as a student worker at my undergraduate library. After graduating, I moved to Seattle to get my MLIS at the University of Washington with a focus on archives and special collections. 6 months into my program, COVID hit and shut everything down. Despite lockdown, I got great experience working in the archives, where I focused on finding ways to showcase archival collections digitally.

Upon graduation, I began applying for full-time jobs. This was difficult since the job market was still recovering from hiring freezes and budget cuts due to the pandemic. I was lucky enough to be offered a job as a tenure-track Metadata Librarian at an academic institution in the Midwest. My work focused on digital collections and non-MARC metadata. I learned a lot about various metadata schemas and systems and played a large role in migrating the institution’s digital collections platform over to Islandora 8. A bit later, a Metadata Librarian position opened up at the University of Nevada, Reno (UNR). I was born and raised in the Northern Nevada area, and UNR is my alma mater, so getting this position felt serendipitous.

As UNR’s Metadata Librarian, I oversee metadata for my institution’s digital collections and institutional repository systems. I work with multiple departments to assess potential digital projects and ensure that the metadata is of quality and functions within each system. I am also involved in system migrations and metadata crosswalking. Since I am tenure-track, I also partake in service and research opportunities. For research, I’m primarily interested in metadata documentation and remediation.

Name: Jessica Craig
E-mail: jecraig[at]getty.edu
Organization: Getty Research Institute

My career has crossed several areas of library work, including public, academic, law, corporate, and arts research libraries, each one engaged in metadata work at varying levels. At the start, I worked at my local public library throughout my undergraduate studies, and once I started my MLIS program at UCLA, I was able to diversify my experience through internships. In my university library’s metadata services department, cataloging general collection monographs became my entry point into metadata work, including creating original and copy catalog records, enriching name authority records, and working with Wikidata. From there, some additional internships included working with legal report metadata at the Law Library of Congress and art provenance metadata at the Getty Research Institute. After about three years of paid internships, I landed my first full-time data librarian role with The Walt Disney Company, working with their intellectual property database of titles and characters to provide authoritative data for financial reporting. My interests remained rooted in arts research, so after 4 years, I returned to the Getty Research Institute as a Metadata Strategies Specialist, where I am responsible for metadata optimization across digital archival collections.

Name: Annamarie Klose
E-mail: klose.16[at]osu.edu
Organization: The Ohio State University Libraries

I’ve loved libraries my whole life. When I was doing research for a costume design gig, I found a digital collection from the New York Public Library’s Schomburg Center for Research in Black Culture. Along with finding great photographs, I appreciated being able to search by subject terms and see the date information. That experience taught me how impactful online digital collections and metadata could be. Not long after, I enrolled in an MLIS program to pursue a career in libraries with the hope of creating digital collections with meaningful metadata.

During my MLIS studies at Rutgers University, I got hands-on experience with digital collections and metadata on a collection of ancient Roman coins for Rutgers’ RUCore repository. It uses a custom metadata standard that includes Metadata Object Description Schema (MODS) and Preservation Metadata Maintenance Activity (PREMIS). While there was a GUI, I loved getting to work with and see the metadata in the XML format behind the scenes. After graduation, I continued working part-time on that project and did some other small library gigs. My first full-time position was Digital Projects Librarian at Frostburg State University. Later, I became Digital Initiatives Librarian at William Paterson University. Both positions involved a mixture of digitization of special collections and archival resources, Dublin Core metadata, and supporting open-access to scholarship. During that time, I continued to work remotely on the weekends on that collection of ancient Roman coins until the project was finally completed. Eventually, I embarked on my current role as Metadata Initiatives Librarian at Ohio State University (OSU). 

As OSU, I serve as the de facto repository manager for the Digital Collections (DC) repository, which stores digitized and born-digital special collections and archival resources with a customized metadata set, including Dublin Core metadata. DC is a treasure trove of everything from Medieval and Renaissance manuscript fragments to comic strips, along with audiovisual recordings. I lead the Metadata Initiatives team, which a former manager referred to as “tiny but mighty.” We work collaboratively with a variety of stakeholders – curators and subject specialists, the Digitization program, the Digital Preservation Librarian, and developers. My team and I prepare and manage ingests and updates to DC. We work with metadata in different ways: repurpose existing metadata from catalog records and finding aids, review and revise original metadata created by curatorial units, and create original metadata internally. There is also a large volume of legacy DC records that we can periodically remediate. There is a lot of file management involved in the position, including during the quality review process. More recently, my team and I have delved into accessibility work due to the federal requirements for digital accessibility. As OSU is a proponent of artificial intelligence (AI), the digital accessibility work has involved using AI for some DC content. As a faculty member, my service has mostly centered on metadata, and my research focus is date metadata.

I’m grateful that my library career has always included metadata in one form or another. I started off as a processing archivist at The University of Texas at Austin, where I was fortunate to work in a department where learning about all aspects of the materials was encouraged — conservation, curatorial context, descriptive practices, custodial history, etc. As a result, I developed a vocabulary for describing archival materials, and I learned how to think broadly and explore new areas of practice when the need arose. These days I mainly describe born digital collections, which tests my metadata skills in new ways. For those thinking of a career with metadata, getting some work experience in archives is great because it exposes you to every possible format under the sun. We are fearless!

Name: Leigh Ann Martin
E-mail: lbrosnih[at]richmond.edu
Organization: University of Richmond

My work with metadata began during my time as a paraprofessional cataloger. At the time, our department and the department handling our library’s digitization projects were under the same organizational umbrella, so when our initial few ambitious digitization projects began, I had a seat at the table. I had a decent knowledge of HTML and CSS from my work background, so XML was a pretty easy lift for me, and I ended up helping my then-boss develop some criteria for the initial digital collections projects.

After taking a professional development course through the University of Wisconsin – Milwaukee on Digital Collections, I was hooked, and began helping with more and more of the digital collections metadata. I learned on the job to create Metadata Object Description Schema (MODS) records for the department and ended up helping to pattern the way this metadata output on our Preservica digital preservation software’s front end. When I went back to school to get my MLIS I chose the University of Wisconsin – Milwaukee because I’d liked their Digital Collections course so much I wanted to take more like it (though sadly, the original instructor had retired by the time I got there.) I went through all of the available digital collections and linked data-related courses to build those skill sets further. After I graduated, I took on a Metadata Librarian role and helped to create a Metadata Application Profile for our institution.

Though I no longer have the specific Metadata Librarian title (I am now the Head of Systems and Metadata Services), digital collections metadata is still a big part of my work life. I work with digital collections metadata in XML and in our Quartex web-based form-fillable back end (https://collections.richmond.edu/. I pattern metadata schemas and controlled vocabularies in those systems, maintain good authority control and metadata quality, and create documentation that helps to clarify things and creates pathways for other library staff to get involved. My favorite thing about working with digital collections metadata is its flexibility. I enjoy creating alternative pathways for researchers to find and use our digital collections by leveraging the fields provided by traditional cataloging and supplementing them with custom fields that support the specific subject matter of the collection.

Glossary:

Also see: https://dlfmetadataassessment.github.io/projects/acronyms/

The post Career Paths – Stories of Metadata Librarians appeared first on DLF.

This year’s Symposium is guided by the theme “Shaping the Open Transition”. The program features speakers from Fiji, Ireland, Australia, India, Slovenia, Brazil, the Netherlands, Canada, Sweden, Nigeria, Italy, Taiwan, Germany, Spain, and the United States.

The post Preliminary agenda announced for the Open Technology Research Symposium 2026 first appeared on Open Knowledge Blog.

The $28.5T forecast compares to U.S. Q1 2026 nominal GDP of nearly $32T, with the estimate for the market of AI enterprise applications of $22.7T about 70% of total U.S. economic output.

1. Source

   The AI bubble isn't just an equity bubble, as Torsten Slok explains in AI Is Penetrating Every Corner of Financial Markets:
   

   AI now accounts for nearly half of all IG issuance, 87% of VC funding and a growing share of HY, underscoring how deeply the AI investment cycle has penetrated every corner of finance.

2. Bryce Elder's This is nuts upon nuts. When’s the crash? compares this bubble with its two biggest predecessors:
   

   Here’s the title page of this month’s Panmure Liberum market update from strategists Joachim Klement and Francisca Reis. Our emphasis in bold below:
   

   In 1929, the cyclically-adjusted P/E-ratio (CAPE) of the S&P 500 reached 32.6x according to Prof. Robert Shiller’s data. This was 1.8 standard deviations above trend at the time. In 2000, the CAPE reached 44.2x, or 3.3 standard deviations above trend – a clear sign of a bubble. However, as our chart below shows, earnings in both instances were within normal range, less than one standard deviation above trend.
   
   Today, the CAPE is at 41.0x, or 2.9 standard deviations above trend. Once again, we are clearly in bubble territory for stock market valuations. However, unlike in previous bubbles, we are having extremely high CAPE at a time when earnings themselves are 1.8 standard deviations above trend. In other words, we are in a valuation bubble at a time when earnings are in a bubble themselves.
   
   If we correct for the earnings bubble, the current CAPE would be 67.6x or 4.6 standard deviations above trend, a bubble that surpasses anything ever seen in US history by an extreme margin. If valuations followed a normal distribution (which they don’t, so don’t take this literally), this would happen in 0.00019% of months or once every 43,432 years.

3. But Panmure Liberum is underestimating how bad things are. Baolian Wang's The $69 Billion Mirage: How an Accounting Rule Inflated S&P 500's Q1 Earnings by 12% explains:
   

   A massive chunk of this quarter’s blockbuster “growth” didn’t come from selling more software, shipping more microchips, or delivering more packages. Instead, it came from an accounting rule that forced massive, illiquid “paper gains” onto the income statements of tech giants.
   
   In Q1 2026 alone, just three companies—Alphabet, Amazon, and Nvidia—reported a staggering $69.2 billion in non-operating windfall under their Other Income and Expenses (OI&E) lines. When you run the macro numbers, this single accounting phenomenon artificially inflated the entire S&P 500’s quarterly earnings by about 12%.

   That 12% takes the factor from 67.6 to 75.7. Wang notes that correcting for the 12%, "the Q1 2026 earnings growth rate will not be very different from the 5-year average of 16%." In other words, the bubble is feeding upon itself — increased stock prices causes increased earnings causes increased stock price ... But suppose, for example, that OpenAI were to suffer a down round. Then Alphabet, Amazon, Nvidia and others who included paper gains in the "other income" on the way up would have to include paper losses in their income on the way down, amplifying the crash. OpenAI's last round valued the company around $750B and they were planning an IPO for at least $1T, but had to postpone it.
4. Back in March Jared James Grogan published The End of the Foundation Model Era: Open-Weight Models, Sovereign AI, and Inference as Infrastructure setting out the forces changing the structure of the AI market:
   

   The foundation model era — roughly 2020 to 2025 — is over. The forces that defined it have inverted. Open source models have reached frontier performance while inference costs approach zero, exposing what was always structurally true: pre-training large language models at scale is not a durable competitive moat. The US government's formal designation of Anthropic as a supply chain risk in February 2026 accelerated a transition already underway — but did not cause it. The paper argues that the AI industry is restructuring simultaneously along four axes: economic, as the circular financing structure that inflated foundation model valuations collapses; technical, as the pre-training scaling paradigm gives way to post-training optimization, test-time compute, and agentic composition; commercial, as application-layer integrators displace the foundation model companies whose commodity they now consume; and political, as the government asserts its historic role as gatekeeper of strategic technology. These are not separate disruptions. They are one structural shift, arriving together.

   Grogan further argues that:
   

   The most consequential and least-discussed dimension is the permanent divergence between commercial AI and a classified national security AI track — built on different data, governed by different rules, and developing capabilities the public ecosystem cannot see, measure, or govern. Like every dual-use technology that has altered the calculus of state power, AI is being brought under government authority not by design but by the structural logic of what it is. The paper further argues that open-weight models are the counterintuitive instrument of sovereign control: a government that holds the weights commands the capability on its own terms, without dependence on vendor policy, financial continuity, or personnel clearance. The apparent openness of distributed model weights is, from a deploying government's sovereignty standpoint, the most governable architecture — because what cannot be withdrawn by a vendor's API policy cannot be taken away.

   Grogran implicitly assumes that AI is useful but that the current margins and thus the valuations aren't sustainable.
5. Carl Franzen's DeepSeek-V4 arrives with near state-of-the-art intelligence at 1/6th the cost of Opus 4.7, GPT-5.5 explains the impact of open-weights models on pricing:
   

   DeepSeek-V4-Pro is priced through its API at $1.74 USD per 1 million input tokens on a cache miss and $3.48 per million output tokens.
   
   That puts a simple one-million-input, one-million-output comparison at $5.22. With cached input, the input price drops to $0.145 per million tokens, bringing that same blended comparison down to $3.625.
   
   That is dramatically cheaper than the current premium pricing from OpenAI and Anthropic. GPT-5.5 is priced at $5.00 per million input tokens and $30.00 per million output tokens, for a combined $35.00 in the same simple comparison.
   
   Claude Opus 4.7 is priced at $5.00 input and $25.00 output, for a combined $30.00.

   Six times cheaper for an equivalent product is likely to cause pricing pressure on the incumbents, who need to raise not reduce prices. DeepSeek is likely also subsidizing usage, but they do have real advantages. First, they have fewer resources so are forced to to be inventive. Second, the 40% of infrastructure capex that isn't the racks is much cheaper in China. Third, the power component of opex is much cheaper and more available in China. The result is:
   

   In practical terms, DeepSeek does not need to win every leaderboard row to matter. If it can deliver near-frontier performance on many enterprise-relevant agent and reasoning tasks at roughly one-sixth to one-seventh the standard API cost of GPT-5.5 or Claude Opus 4.7, it still forces a major rethink of the economics of advanced AI deployment.
   
   DeepSeek-V4-Pro-Max is clearly the strongest open-weight model in the field right now, and it is unusually close to frontier closed systems on several practical benchmarks.
   
   While GPT-5.5 and Claude Opus 4.7 still retain the lead in most direct head-to-head comparisons across the company's benchmark charts, DeepSeek V4 Pro gets close while being dramatically cheaper and openly available.

6. Dan Davies agrees that the margins aren't sustainable but differs as to why in tokenalysis and john henry:
   

   Which then brings to mind another issue – how confident are we in the pricing power that underpins that 60% gross margin in the first place? In the last paragraph I was talking about the R&D equivalent of a price war, but the normal kind is also possible. The combination of price-sensitive B2B customers, big fixed costs and rewards going to the dominant player doesn’t suggest to me that pricing power is going to be sustainable indefinitely.
   
   But, I think there’s a danger of missing the big picture here. Which is that, when large companies are telling their employees to be sensible and use AI tokens wisely, then the game is up. The race is over and John Henry won against the steam hammer. If you need a human being in the loop to decide on the allocation of AI tokens, then all those predictions of mass redundancy are gone.

7. In A Slower AI Payoff Would Be Everyone's Problem, Torsten Slok is mainly focused on the fallout we can expect even if the bubble deflates gently. He asks
   

   But what if the payoff takes longer than consensus assumes? That question is particularly pressing given that token prices continue to decline and Chinese models are gaining ground, both in their share of the world's most-used models and in token usage, where they now lead their US counterparts among the top 20 models,

   Source

   Slok provides two charts, the first tracks market share by country of origin monthly since January 2025 among the top 50 models. It is bad for the US, showing a steady erosion of US market share until, eyeballing it, as of May 2026 it is roughly a 60/40 US/Chinese split.
   
   Clearly, the market is voting with its feet that the prices charged by US AI companies are unsustainable.
   
   

   Source

   The second compares monthly token use among the top 20 models by country of origin between May and June this year. It is much worse for the US.In May the Chinese had 80% of the US usage. In June they had 185% of the US usage. If this rate of market erosion were to continue for a few months it would be impossible for investors to continue to imagine the golden future awaiting OpenAI, Anthropic, xAI, Meta, Oracle and the neoclouds.
   
   Slok's analysis of the fallout of the bubble deflating is worth reading. This is an issue I plan to return to in a future post.

8. Source

   Kevin Walmsley's We were wrong about DeepSeek. Now Chinese AI companies export trillions of AI tokens also focuses on the erosion of US market share:
   

   Chinese AI providers are making more money; Zhipu’s revenues went up 60 times in Q1 compared to last year; Alibaba is the company behind Qwen, and their revenues are up 15 times just since the beginning of this year. The lion’s share of the profits, though, are still being realized by the hardware side; the chipmakers, at least for now. The model companies’ revenues are rising, and steeply. But so is their cost of compute.
   
   That dynamic is causing some Chinese labs to raise prices; the cost to use Tencent Cloud increased by over five times in March, and Alibaba, Zhipu, and ByteDance also hiked prices.
   
   DeepSeek, however, went the other way. Their latest version is priced at just a fourth of their introductory product, and they rolled out dynamic pricing that is aimed at corporate users, who use their models during the workday.

   Source

   This price difference is having an :
   

   But even with these price increases, Chinese models just cost far less than those on offer from Silicon Valley, and explains those big jumps in the exports, we can say, of Chinese AI tokens. The LLM’s out of China are “90% as good at 10% of the cost”, and American firms are buying more tokens from Chinese companies. In early 2025, token demand from Chinese LLM’s was about zero. Even the release of DeepSeek didn’t move the needle much, but by the end of the year the secret was out, and it’s been a choppy but steady ride up to 46%, today.

   Why would you invest in a company whose competitors were “90% as good at 10% of the cost” and which was rapidly losing market share?

9. Source

   If the margins, and thus the rational valuations, of AI companies are unsustainable, how long can the market remain irrational? In The Second Derivative: Why No One Understands the AI Boom Groundbreaker starts by examining the 2008 crash:
   

   The implicit underwriting assumption, shared by originator and borrower alike, was that the loan would never reach its reset: rising home values would manufacture equity, the borrower would refinance into a fresh teaser and the clock would start again. The structure was a treadmill, and the treadmill was powered by appreciation. It worked spectacularly while it worked. Nearly four in five subprime hybrid ARMs originated in 2003 had been refinanced away by the end of 2006.
   
   Now watch the timing. National home-price appreciation did not crash in 2006. It decelerated. The year-over-year rate of gain, which had run in the mid-to-high teens through 2004 and into early 2005, began bleeding off - still positive, still printing green, but slowing. Prices were higher than they had ever been. And yet, with prices at their peak and still rising, subprime delinquencies inflected upward. ...
   The deceleration was endogenous to the structure; the structure required ever-accelerating prices to keep refinancing its way out of its own reset schedule, and no series accelerates forever.
   
   The second derivative was always going to roll over. When it did, the first derivative followed it down through zero, negative equity spread from the margin inward, and the defaults the market insisted were caused by “falling prices” had in fact begun a year earlier, when prices were still rising but had stopped rising faster.

   Why does the 2008 analogy apply this time?:
   

   The market is pricing AI as a technology cycle when its actual anatomy is that of a credit-driven real estate cycle - which is precisely why the 2008 mechanics apply - and the two break for entirely different reasons.
   ...
   Walk down the AI build-out and every feature is a property development in disguise: a data center on entitled land, financed with debt against the structure and leased to tenants on take-or-pay terms. This is not a software business that happens to own servers. It is a real estate business that happens to compute.

   The AI build-out is being financed with debt:
   

   When a hyperscaler or a neocloud reports a record capex figure, the financial press reads it as confidence, as proof of demand. Read it instead as origination volume. Each gigawatt of committed build is a loan extended to whichever tenant has signed the take-or-pay beneath it, and the credit quality of that loan is precisely the credit quality of the tenant. The market is celebrating loan growth and calling it revenue growth.

   Loans in this market take the form of Remaining Performance Obligations (RPOs). The biggest borrower in this market is OpenAI:
   

   OpenAI has committed to pay for compute on a scale without precedent in corporate history: multi-year, take-or-pay capacity contracts whose aggregate obligations run to the hundreds of billions of dollars. Against them sits an operating business that does not yet earn a profit - revenue real, large, and growing quickly, but short of covering the company’s own cash burn and nowhere near covering the contracted payments.
   
   Those payments are therefore not serviced out of earnings. They are serviced out of financing, and financing, for a borrower in this position, is available on a single condition: that each new round price above the last.

   Does OpenAI's financing meet this requirement?:
   

   Measured as a level, OpenAI’s valuation is the most remarkable appreciation in the history of private markets - roughly $86 billion in early 2024, then about $157 billion, $300 billion, $500 billion, and approximately $852 billion by the spring of 2026. Measured as a rate of change, the same series inverts: the round-over-round step-up ran 1.83×, 1.91×, 1.67×, 1.70×, and falls to roughly 1.23× implied by the reported public-offering target. Private marks are inherently lumpy - negotiated, episodic, set by a handful of insiders - so no single step is decisive. But the trend is unmistakable: it bends down, and it bends hardest at the one mark set by the deepest, most unforgiving pool of capital - the public market. The implied IPO step-up is both the lowest in the sequence and the hardest to negotiate, and it is the one the structure must actually clear. This arithmetic is also the most probable explanation for OpenAI’s recent IPO delay.

   Source

   In the same way that the private stocks generating unrealized gains are not liquid assets, neither are the Remaining Perfoemance Obligations:
   

   An RPO is not a liquid asset; it is a forward contractual commitment - a promise of future payment in exchange for future compute. And a multi-year commitment is worth exactly the creditworthiness of the entity on the other end of it. When that entity is investment-grade and cash-generative, the backlog is what it claims to be: high-quality visibility, merely deferred. When that entity is a pre-profit company that loses tens of billions a year and can pay only by continuously refinancing its own equity valuation, the backlog is something else entirely. It is a subprime commitment, used to justify massive, un-depreciated capital expenditure, reported to shareholders as structural strength.
   
   Now price the credit quality of that book. Of roughly $2.1 trillion in aggregate contracted backlog across the four big platforms, about half - on the order of $1.05 trillion - is owed by OpenAI and Anthropic. Microsoft’s book is about 49% these two names; Oracle’s is 54%, with roughly $300 billion owed by OpenAI alone; Google’s is 43%; Amazon’s is 51%.
   
   The hyperscaler has, in economic substance, extended a concentrated, unsecured loan to cash-burning tenants. The RPO that Wall Street values as forward revenue is, in reality, a credit exposure to borrowers with no operating income.

10. Source

    Major financial institutions are similarly concerned. In their 2026 Annual Economic Report, the Bank for International Settlements writes:
    

    In the near term, the ongoing AI investment boom raises questions about the sustainability of the current economic expansion. The five largest hyperscalers are set to spend over a trillion US dollars on AI-related capital expenditure from 2025 through 2026. These commitments are outpacing earnings and the free cash flow of these firms, leading some to issue debt to raise additional financing (Graph 11.A). This investment race may be partly driven by the perception that only a small number of players with superior technology will ultimately dominate the market shares. The intense competition raises the risk of firms over-committing resources to investment projects with still uncertain returns, leaving all firms vulnerable to disappointments in AI payoffs. Model analysis based on such contest motives highlights the downside risk of current AI exuberance. As competitive pressure drives capex higher, the net economic surplus – the total payoff less investment costs – declines for the sector as a whole and could turn negative in adverse scenarios (Graph 11.B). Disappointment in returns could trigger a sudden pullback in financing and turn the capex boom into a protracted investment bust, with potential knock-on effects on financial conditions (see below).

    The BIS has noticed another looming problem:
    

    Another risk is that the AI boom runs into a supply side roadblock. The AI build-out has recently been facing growing bottlenecks in electricity, advanced semiconductors and grid equipment. Fast-growing demand for computing power is already pressuring electricity prices and input costs, with potential spillovers to inflation. Looking ahead, these temporary shortages may also amplify over-investment, as firms attempt to lock in future capacity through long-dated contracts that further expose them to any disappointments in demand.

    Unlike Groundbreaker, the BIS uses more conventional historical analogies:
    

    Historical episodes of investment booms offer instructive parallels (Graph 11.C). The canal mania of the 1830s, the British railway mania in the 1840s, the electrification exuberance of the late 1920s (roaring 20s) and the dotcom boom of the late 90s all shared one common trait: a genuine technological breakthrough that attracted capital in excess of what commercial returns could ultimately justify. These episodes ended with an eventual reversal in investment, inducing economy-wide recessions. The scale and pace of the current AI investment boom accompanied by expectations of large productivity payoffs bear resemblance to these precedents, highlighting potential downside risks in the near term.

11. And Eric Katz reports that the US Treasury Has an Internal Report Warning About the Dangers of an AI Bubble:
    

    A draft report inside the Treasury Department is set to warn of the risks posed by the artificial intelligence market, likening key aspects of it to the dotcom bubble that upended the U.S. economy when it burst in the early 2000s.
    
    The document, the existence and contents of which have not been previously reported but was obtained by NOTUS, is a significant departure from the Trump administration’s public tone, which has focused on encouraging unrelenting investment to unlock exponential growth.
    
    Career Treasury analysts found that AI firms are more deeply entrenched in the U.S. economy than their dotcom predecessors and pose significant risk to the entire system if financial conditions change, productivity goals are missed or various choke points stymie growth.

12. Vanderbilt University's Asad Ramzanali has a detailed look at the range of impacts from the burst bubble, and an set of optimistic suggestions for policy responses in After the AI Crash. He frames the problem thus:
    

    Companies are investing trillions of dollars based on tens of billions of dollars in revenues. Analysts at J.P. Morgan anticipate $5 trillion of AI infrastructure investment in the next five years. They estimate that the industry will need to generate annual revenues of $650 billion to justify this level of investment, while consultants at Bain & Co. estimate $2 trillion in needed annual revenues. Yet, OpenAI and Anthropic earned $13 billion and $4 billion, respectively, in 2025 revenues. OpenAI’s own financial expectations suggest negative cash flow until 2030, and Anthropic expects a small profit no sooner than 2029. Alphabet, Meta, Amazon, and others may experience increased marginal revenue from integrating AI into existing products, but that is far from certain.

    Note that over the next 5 years around $3T (60% of $5T) of the investment in AI infrastructure goes in to buying the hardware which should be fully depreciated over much less than 5 years. So the gap between the investments and the revenue is much bigger than it appears.
    
13. The cracks are starting to show. Reuters reports that Blackstone's QTS terminates Digital Gateway data center project in Virginia:
    

    Blackstone's QTS said on Thursday it had terminated its planned Digital Gateway data center project in Virginia and withdrawn the associated filings after years of planning and regulatory review.
    
    The data center operator has faced years of local opposition and litigation over the project, despite it being approved by the Prince William Board of County Supervisors.

14. Among the hyperscalers, Oracle is the most exposed because, as Ed Zitron noted:
    

    And Oracle ... is a company that, even before the AI bubble, was massively indebted. It just so happens that, as a result of its tryst with OpenAI, Larry Ellison saw fit to twist the debt knob to eleven.

    Now, Brody Ford reports that Oracle Warns Its Splurge on AI Data Centers May Not Pay Off:
    

    Six firms alone — including Oracle, Microsoft and Meta — have committed $850 billion for data centers leases that haven’t begun yet. Oracle holds the largest share of these commitments owing to its $300 billion Stargate contract with OpenAI.
    
    When Oracle mentions the risk of nonpayment, the unnamed elephant in the room is OpenAI. As part of the Stargate deal with the AI company, Oracle is developing massive data centers across the country to provide cloud computing power. For this plan to work, OpenAI needs to pay its Oracle Cloud Infrastructure bills.
    
    “Some of our customers may be highly leveraged and subject to their own operating and regulatory risks and, even if our credit review and analysis mechanisms work properly, we may experience risks of non-payment and non-performance in our dealings with such parties,” Oracle said in the filing.

15. A month ago Laura Benitez et al reported that SoftBank Attempt to Get $6 Billion OpenAI Margin Loan Stalls:
    

    SoftBank Group Corp.’s talks with potential creditors to raise at least $6 billion from a margin loan backed by its OpenAI stake have stalled, people familiar with the matter said, just weeks after the Japanese conglomerate cut its initial target from $10 billion.

    But now Echo Wang reports that SoftBank renews talks for $10 billion loan against OpenAI stake, adds concessions, sources say:
    

    SoftBank Group has reopened talks with a consortium of lenders for a $10 billion loan backed by its stake in OpenAI, after earlier attempts to secure a loan stalled over concerns about the difficulty of valuing private companies, two people familiar with the matter said.
    
    To make lenders more comfortable, the Japanese technology investor is offering to guarantee repayment of the loan, giving banks recourse to SoftBank if the OpenAI shares pledged as collateral lose value, the people said.

    This all seems to indicate that potential lenders, such as banks, are highly skeptical of the value of OpenAI stock.

16. Source

    Despite Grok being so bad that employees use Claude, Musk is touting SpaceX as an AI company. This resulted in the most overvalued IPO in history, which failed to raise enough money to avoid the immediate need to borrow $25B. Nir Kaissar's SpaceX Is Junk. That’s What the Bond Market Says reports on the bond market's reaction:
    

    Ratings companies and the bond market have very different views about how things are going. SpaceX’s bonds have an average rating of BBB across the three majors, Moody’s Ratings, S&P Global Ratings and Fitch Ratings, according to credit scores compiled by Bloomberg. In the alphabet soup of bond ratings, it’s the lowest grade still considered quality before falling into junk territory.
    
    The bond market has other ideas. There, quality is judged by a bond’s credit spread or the additional yield it offers above Treasuries with similar maturity. The wider the spread, the lower the quality. Corporate bonds with a BBB rating are trading at an average credit spread of 0.92 percentage point. SpaceX’s bonds, by contrast, trade at a significantly greater average spread of 1.62 percentage points across maturities, higher than BB rated junk bonds’ average spread of 1.55 percentage points.

    The bond market seems to agree with Softbank's lenders about the AI bubble.
17. OpenAI and Anthropic are competing for the next trillion-dollar IPO. Both would need to distract investors from their massive losses by focusing on growth. Recently, Anthorpic has been growing faster than OpenAI, so Keach Hagey and Berber Jin report that OpenAI Considers Drastic Price Cuts, Anticipating War for Users With Anthropic:
    

    OpenAI is considering drastically lowering the prices it charges users as it seeks to win customers from its rival Anthropic.
    
    The company is weighing significant cuts to what it charges for tokens, the unit of measurement artificial-intelligence firms use to bill for their products, according to people familiar with the matter. The move would be in anticipation of similar cuts the company expects at Anthropic, the people said.

    Now Kurt Wagner reports that Zuckerberg Pledges ‘Aggressive’ Pricing With Meta’s First Pay-to-Use AI:
    

    Meta will also introduce a new Meta Model API system, which will be used to collect fees from developers. Its API pricing is roughly 25% of the cost advertised by other top models from OpenAI and Anthropic PBC. Developers will be able to use Meta’s model for free, but only up to a point; they’ll be required to pay for access after reaching a certain token threshold, Zuckerberg said.
    
    “The pricing from some of the other labs is very extreme and has very high margins,” Zuckerberg said, underscoring that his strategy is to get Meta’s technology in front of as many people as possible. “We think that there’s a real ability to be able to offer frontier or very high-level intelligence at a much more affordable cost.”

    Aggressive pricing means Meta Model API will still be 50% more expensive than DeepSeek but not 50% better. Planning to reduce current income in the lead-up to an IPO is an unusual move, but it is a response to AI's Affordability Crisis.
18. Data center demands for power are having serious impacts elsewhere in the economy, as Jeremy Hsu reports in Data centers’ energy demand threatens Trump’s “Made in America” plan:
    

    Factory electricity bills are generally rising faster than those for other business customers or residential customers, according to a Reuters analysis. It highlighted the example of the Belden Brick Company, a 141-year-old brick manufacturer in Ohio, whose electricity bills have soared from $1,600 to $12,000 per month due to a higher monthly capacity charge in the 13-state region served by the grid operator PJM Interconnection.
    ...
    The Ohio-based steelmaker Metallus described its electricity costs as having jumped by 70 percent since 2024, leading the company to pay an extra $15 million in energy costs annually.
    
    The higher electricity costs for manufacturers coincide with the attraction of large AI data center projects with substantial electricity needs to many states in PJM territory. That data center growth has driven up PJM’s capacity prices—paid to power generators according to supply-and-demand forecasts—from $28.92 per megawatt-day in 2024 to $329.17 per megawatt-day in 2026, according to Reuters’ reporting.

19. Source

    As I see it, there are three separate markets for LLMs. First, there is an embedded market that runs on low-cost, low-power hardware and open-weights models. Small AI Models Gain Traction Around the World by David Berreby provides examples:
    

    For example, a drone-based system developed by Bala Murugan and colleagues at the Vellore Institute of Technology, in India, takes photos of cashew plants and quickly identifies those with splotches that indicate disease. All the processing takes place on the drone itself, so there’s no need for a computer on-site, nor for a connection to a central server.
    
    Using small language models trained for a specific problem, and sometimes running on cheap, low-power devices, other small-AI implementations have been developed to identify ant infestations in a Uruguayan vineyard, detect the presence of malaria-carrying mosquitoes in a number of nations, and run electrocardiograms from an Arduino device in parts of Brazil that lack access to more complex equipment.

    It isn't just that small hardware, such as the Raspberry Pi 5, is getting more powerful but also:
    

    the shrinking footprint of language models. Both Google DeepMind’s Gemma 4 (released in April) and Alibaba’s Qwen 3.5 are “fantastic” for small AI, Rovai says. Both models are “open weight,” meaning users can adjust the connections between parameters to suit their needs. This makes it easy, for example, “to take a lot of data from, say, the milk industry and retrain the model specifically on that,” Rovai says.

    The hyperscalers and AI platforms like OpenAI and Anthropic will garner no income from this market, because:
    

    “I think the future of AI is not like one giant model, at a center. I think it’s millions of small, precise models deployed at the edge, each one solving like a specific problem, a specific context,” Alonge says. This is partly because much of humanity—including people in parts of rich countries as well as the developing world—lives without access to cutting-edge frontier models. But, he says, it’s also because those models are not sustainable.
    
    “If someone is not subsidizing it, most people will not be able to afford those models. So those of us who are said to be small-AI developers are the ones who will have to build for the majority of the world,” Alonge says.

20. Second, there is a consumer market. Last month, Nvidia announced a product for the prosumer laptop market with significant LLM capabilities. @pramodchandrayan described it in NVIDIA Just Put a 120-Billion-Parameter AI Model in Your Laptop. Here’s What That Actually Changes.:
    

    At Computex 2026 in Taipei on June 1st, CEO Jensen Huang announced the RTX Spark superchip — a single piece of silicon that combines a 20-core Arm CPU, a Blackwell GPU with 6,144 CUDA cores, and 128 gigabytes of unified memory, connected by NVIDIA’s NVLink chip-to-chip interconnect. The whole package delivers up to one petaflop of AI compute in a laptop form factor.
    
    The number that matters: RTX Spark can run a 120-billion-parameter language model entirely locally, with a context window of one million tokens, without a single byte leaving your machine.
    
    To put that in perspective: GPT-3 had 175 billion parameters and required clusters of A100 GPUs to run. The model that stunned the world when it launched in 2020 is now approximately the size of what fits in a consumer laptop chip announced this week. The capability that required a data centre in 2020 is coming to a device you carry in a bag in 2026.

    Phones can already run small LLMs:
    

    In 2025, slightly more than a third of all smartphones shipped worldwide were capable of running generative AI, and that figure will reach 45 percent by the end of this year, according to the technology research firm Counterpoint. By the end of next year, slightly more than half of all smartphones will be able to run a small AI model.

    If Nvidia can already put a 120B-parameter in a laptop, it will only be a few years until phones can run a GPT-3-class model, good enough for almost all consumer needs. Apple and Google own that channel. Owning the channel is better than owning the technology. They will dominate consumer AI, and the other players will garner no revenue from this market.
    
21. Third, there is an enterprise market; all that is left to generate the revenue to service the debts fuelling the AI bubble, lets say $2T by 2030. There are a number of problems that make this unlikely.
    
    First, there are very few documented cases of LLM deployment that resulted in enough productivity improvement to cover its unsubsidized costs.
    
    Second, the Trump administration just demonstrated that deploying mission critical systems on AI platforms such as OpenAI or Anthropic means your company can be disabled at 90 minutes notice with no recourse.
    
    Third, this means that companies will have to run mission-critical LLMs on open-weight models on in-house hardware if they are not to be vulnerable to the whims of the US president.
    
    Fourth, systems such as RTX Spark show that good enough in-house hardware is likely to become relatively cheap compared to the unsubsidized cost of the AI platforms. In-house systems need much less over-provisioning for demand spikes, and because they aren't shared they don't need to be as fast.
    
    Fifth, companies need to balance the productivity benefits (if any) of mission-critical LLMs against the productivity costs they bring. These include a vastly greater attack surface, technical debt from reduced developer understanding of the software, and so on.
    
    Thus it seems likely that the hyperscalers and AI plaforms will generate far less revenue than they expect, because they will be restricted to non-mission-critical applications with lower productivity gains, and thus lower pricing power. They will thus be unable to cover the debts they are incurring to build massive data centers predicated on centralized systems dominating (an inflated estimate of) the entire enterprise market.
    
    David Wallace-Wells' Did We Make the Wrong Bet on Big A.I.? discusses a "televised rant" from Palatir's CEO Alex Karp that supports this argument:
    

    Karp had been softly floating his critique for some time, but the CNBC event looked like a proper coming out. Just one day earlier Palantir had published a kind of manifesto devoted to what it described as the all-important principle of “A.I. sovereignty.” The central argument: Companies should seek to build their own A.I. tools, not just customize those on offer from the frontier labs. This might mean relying on open-source L.L.M.s rather than the proprietary ones on which the A.I. boom has mostly been built in America, but it would amount to a liberating declaration of independence from Big A.I., which in Karp’s estimation was sucking up much more value than it was generating.

22. Source

    The Economist's How to turn compute into a financial asset reports another bad sign (my emphasis):
    

    Locking in a price with a multi-year neocloud contract insures a buyer against compute getting more expensive but not against it getting much cheaper. So as businesses around the world spend ever more on compute, they want to be able to hedge against price volatility just as they insure against changes in energy tariffs, interest rates or foreign-exchange movements—ideally in deep and liquid derivatives markets.
    
    Two startups want to help companies do this, by turning nascent indices tracking compute costs into a futures market. Silicon Data, founded in 2024 and backed by DRW, a trading firm, has paired up with CME Group, which operates large derivatives exchanges. Ornn, created by recent graduates of the Massachusetts Institute of Technology and run from a flat rather than an office just a few months ago, has paired up with Intercontinental Exchange, the parent company of the New York Stock Exchange, to do the same. Both aim to launch compute futures later this year, to be traded on their partner exchanges.

23. One characteristic of bubbles is overbuilding the infrastructure. Signs of overbuilding of data centers include that both SpaceX and Meta are now in the business of rentling their GPUs to the competition.
24. Victor Tangermann's Zuckerberg Admits That AI Is Not Working Out the Way He Imagined is on-trend:
    

    As morale is hitting rock-bottom, his company is heavily relying on its competitors' AI models to build out its own in-house tools. And despite the many billions of dollars the company has spent in its flailing efforts to keep up in the AI race, even Zuckerberg himself is now acknowledging that progress is nowhere near where he wanted it to be.
    
    As Reuters reports, Zuckerberg admitted during a town hall last week that AI agents in particular aren't progressing as fast as he anticipated, a devastating revelation following enormous layoffs that wiped out thousands of roles at the company.
    
    The "trajectory of the agentic development over at least the last four months hasn't really accelerated in the way that we expected," he said according to a recording obtained by Reuters.

25. Despite a derisory ~3% share of the enterprise LLM market, SpaceX's IPO was marketed as an enterprise AI company. The IPO has to rate as the most manipulated of all time featuring, in addition to ludicrous financial projections, a tiny float, bent rules for index inclusion, and massive conflicts of interest at the banks and the analysts. Immediately afterwards, SpaceX issued $25B in bondsa. A month later we can see how the markets respond to the first trillion-dollar "AI company".
    
    

    SPCX

    First, SPCX stock. Toby Nangle writes:
    

    We’re old enough to remember when the market cap of the lossmaking telecom SpaceX was bigger than Amazon’s. Heck, for a few precious moments it was bigger than Microsoft’s. Maybe one day it will be again, but for now the stock is down 38 per cent from its peak post-IPO valuation.
    
    Punters lucky enough to have been awarded a stock allocation at the outset are still sitting on a tasty [checks notes] 0.8 per cent paper profit at pixel time.

    And this is before the lockups start expiring and the initial tiny float greatly expands.
    
    

    SpaceX bod spread

    Second, more interesting as being much less subject to manipulation, are the bonds. They are what Toby Nangle focused on in SpaceX bond yields rocket towards junk:
    

    the full $25bn of benchmark bonds — issued across the curve — had a rocky first couple of days of trading. Checking back today, it turns out that the inauspicious beginning was just a prelude to the train wreck that has since unfolded.
    ...
    If you’d been allocated $100mn of the SpaceX 2056 bonds, you’ve turned $100mn into $90.7mn in less than a month. Sure, long-dated US Treasury bonds have fallen in value, and this general sell-off at the long end has done some of the work. But the spread on SpaceX 2056 — the additional yield you’re paid to compensate you for the risk that you don’t get repaid (among other things) has now widened from the initial +175bps to a whopping +231bps doing more than two-thirds of the work.

    Top 10 worst BBB

    The same conflicts of interest that pumped the stock caused rating agencies to give SpaceX bonds a BBB rating, one notch above junk and crucially the lowest that many major institutions are allowed to hold. But Nangle notes that:
    

    Looking only at the nine days since the bonds were included in ICE BofA indices at the end of June, this spread-widening has made SpaceX 2056 the single worst-performing US dollar triple-B benchmark bond:

    Note that the top two worst bonds are SpaceX, but the rest of the top 10 are all Oracle, another financial disater area.
    
    Despite both carrying a BBB rating, Nangle has a very dense graph showing that:
    

    when we overlay the average spread for double-B US dollar corporate bonds across different maturities (the pink line), it looks a lot like the type of risk that the market has assigned to both SpaceX and Oracle bonds is junk risk.

    SpaceX, OpenAI, Anthropic, Meta all need to raise vast amounts of debt to fund their plans for AI. With SpaceX's bonds trading as BB despite a BBB rating, this is going to be hard.
26. Tobias Mann reports that Former OpenAI CTO does what Altman won't, releases a frontier AI model that's actually open:
    

    Founded in early 2025 by former OpenAI CTO Mira Murati, Thinking Machines' first model is a big one. Weighing in at 975 billion parameters, the model requires more than two terabytes of GPU memory — a quantity present in around eight of Nvidia's B300 accelerators, or sixteen H200s — to run at its native 16-bit precision. If that's asking too much of your hardware, Thinking Machines has also released a NVFP4 quantized version of the model capable of running on half the GPUs.
    
    This makes it the largest American open weights model to date, and comparable to Chinese models like DeepSeek V4, GLM 5.2, and Kimi K2.6 in terms of size and capabilities. Take these claims with a grain of salt — gaming AI benchmarks isn't exactly difficult – but Thinking Machines says Inkling is competitive with these models in a variety of workloads, although its benchmark charts also show it trailing proprietary models like Anthropic's Claude and OpenAI's GPT.

    If you don't like Chinese open-weight models, the US ones are getting better:
    

    The model developer claims to have tuned the model to use these thinking tokens more efficiently and that Inkling therefore matches Nvidia's Nemotron 3 Ultra, up to now the largest and most capable American open weights model out there at 550 billion parameters, on Terminal Bench 2.1 using roughly a third the tokens.

27. CoreWeave's CEO Dumped Nearly 370,000 Shares for $30.8 Million. What Does That Mean for Investors? by Robert Izquierdo notes that:
    

    The disposition involved 369,489 shares with a total transaction value of ~$30.8 million, based on weighted average prices.
    
    The sale reduced the insider's total equity holdings by 11%, including the liquidation of 100% of Class A shares previously held indirectly.

    The notes for Ox Talk's video are skeptical:
    

    The AI revolution is supposed to be in its early innings. Demand for GPU computing is exploding. Microsoft has relied on CoreWeave for massive amounts of AI compute. OpenAI has signed tens of billions of dollars in long-term contracts. NVIDIA isn’t just supplying the chips, it has also invested in CoreWeave and entered into agreements that support parts of its financing and capacity.
    
    So why are CoreWeave insiders continuing to sell stock? Management says many of the sales were made under pre-arranged Rule 10b5-1 trading plans. But those plans explain how the shares were sold—not necessarily why executives continue converting stock into cash while investors are being told AI infrastructure demand has never been stronger.

    The CEO isn't the only insider selling.
28. Sharon Kits Kimathi's New York issues moratorium on data centers illustrates the political toxicicty of data centers:
    

    New York has become the first U.S. state to stop construction of large new data centers, imposing a one-year moratorium due to growing concerns over power costs, water supplies and the burden on local communities.
    
    "As data center development threatens to hike up utility bills, deplete our natural resources, and create uncertainty for New Yorkers, it's my responsibility to take action and lead," said New York Governor Kathy Hochul.
    
    She added that she would also pursue legislation to repeal sales tax exemptions for large data centers.

29. Phurichai Rungcharoenkitkul continues the Bank for International Settlements's pessimism in The AI Investment Race:
    

    The AI build-out ranks among the largest technology-driven investment booms in US history. Its scale, reliance on debt and circular equity ties raise questions about the boom’s sustainability and financial stability. We study a dynamic contest in which firms competing for a few dominant positions over-commit resources. The over-investment leaves the sector exposed to revenue disappointment that could turn boom into bust. The larger the boom, the deeper the eventual bust. The race to commit early through debt and circular financing also makes a bust more likely. Calibrated to balance sheet and deal data, the model points to over-investment of around 1.5 times the efficient level, rising to around three times where demand is less elastic. A network analysis shows that stress in one firm could cascade to others through chains of financial exposures.

30. Shirin Ghaffary and Rachel Metz report that Moonshot’s Kimi Upends Conventional Wisdom on US Lead Over China:
    

    As recently as this week, one executive at Anthropic PBC, who spoke on condition of anonymity, mused that the Claude maker’s technology was roughly six to 12 months ahead of Chinese rivals.
    
    On Friday, Moonshot AI Inc. upended those assumptions. The Chinese AI lab released Kimi K3, a more advanced open-weight model that it said outperforms all rivals except for Anthropic’s Claude Fable 5 and OpenAI’s GPT-5.6 on overall capability. The implication is that Moonshot, and by extension China, could be closing the gap faster than expected.
    ...
    Moonshot’s release could also undercut OpenAI, Anthropic and others on price at a moment when they’re confronting customers who are becoming more conscious of their surging AI spending. Some developers have turned to so-called model routing services that can seamlessly direct users to cheaper options, including from China, for specific tasks to maximize cost efficiency.

    Kimi K3 is an open-weight model.
31. If a 120B parameter model is too small for you the RTX Spark won't cut it. But Michal Malewicz explains in NVIDIA just killed big AI and… You’re the winner? that one of Nvidia's OEMs will sell you a $94K box that will run a 1T model, the Exxact Valence Nvidia DGX Station.
32. Carmen Arroyo explains why SpaceX is competing with CoreWeave not OpenAI or Anthropic in The Identity Crisis at Elon Musk’s Chaotic AI Outfit :
    

    The company’s models were using just 11% of its available computing power by April, according to an internal memo.

33. Bryce Elder's Is AI productivity growth in the room with us right now? is a fascinating deep dive into a very hard question to answer. He quotes Barclays' research:
    

    "[O]ur bottom line is that AI adoption appears gradual and steady rather than rapid and transformative, with most households and businesses still reporting limited exposure to the technology. At the same time, evidence of a structural pickup in productivity growth remains surprisingly fragile: Aggregate productivity growth has improved during the post-pandemic period, but there is a strong rationale to attribute much of this improvement to cyclical variation in utilization rather than a sustained acceleration in productive capacity. We also find little compelling evidence in the available industry-level data that industries adopting AI more rapidly are already experiencing stronger productivity growth."

    And the Fed:
    

    "productivity trends across all three levels have been relatively consistent over time, suggestive of micro-level productivity gains not adding up in aggregate"

    So Elder's post conforms to Betteridge's Law of Headlines.
34. Another instance of Betteridge's Law is Will OpenAI ever be profitable? from Leap Finance Academy. It is a very long and detailed examination of all the things that would have to happen for OpenAI to turn a profit by 2030. In summary, with my comments, the list is:
    
    • Reduce cost of inference which isn't going to happen enough because each successive frontier model uses more tokens to generate the same output.
    • Increase pricing which isn't going to happen because they already need to reduce pricing to stem the loss of customers to Anthropic, let alone to the open-weight models.
    • Increasing sources of revenue that are not inference dependent in other words the fantasy of $100B/year in advertising income, which isn't going to happen.
    • Securing and retaining human talent which requires an IPO to make their stock options worth something, which isn't going to happen it time to prevent bankruptcy.
    • Securing the funding they need to build Stargate and for working capital which isn't going to happen because even an IPO wouldn't come close to generating enough and the lenders are saying "enough".
    Even though I'm skeptical of the wishful thinking of the conclusion, this is an impressive piece of work and well worth reading.
35. Meghan Tobin reports that Even China’s A.I. Powerhouses Can’t Figure Out How to Profit Off A.I.:
    

    China’s open-source approach has spawned a crowded field of innovative and intensely competitive start-ups all offering systems at low cost.
    
    So attempts to increase revenue by charging more for access to certain models can scare away customers. Price-conscious Chinese consumers — businesses and individuals alike — are quick to hop across platforms in search of inexpensive A.I. tools.
    
    While China’s A.I. companies are searching for sustainable business models, spending is high and revenue low, said Richard Lin, a vice president at the Silicon Valley company Datastrato.
    
    “In two or three years, we will still be trying to figure out how large models can earn money,” he said.
    
    Offering low prices has helped the Chinese firms gain users, including in Silicon Valley, where many companies depend on the more affordable systems. Yet Chinese companies have struggled to translate huge numbers of global users into profits.

36. Matt O'Brien reports that Workplaces look for cheaper AI as ‘tokenmaxxing’ fades as a corporate fad:
    

    Bain & Company management consultant Jue Wang said many of the big businesses her firm advises have been taking a closer look at returns on their AI investments.
    
    “The token cost for them has been doubling, almost every other month,” she said. “Let’s say $200 per developer per month. Multiply that by 20,000 developers, which is often what we’re dealing with at these companies, and that quickly gets you to a number that is not a line item that any general manager has planned for.”
    
    Sometimes that just means not using the AI equivalent of a sledgehammer to crack a nut.

37. Both Ed Zitron in The More You Buy, The More You Lose and Torsten Slok on the Prof G Markets podcast The Market Is Running Out of Patience With AI focus on supply and demand in the credit market.
    
    A huge wave of supply of debt intended to fund the data center build-out for AI means that the price of this debt has dropped, and thus that the interest rate buyers are demanding goes up. This reduces the net pressent value of the (hypothetical) future cash flows the data centers will generate, and thus their ability to pay the interest and principal. This increases the interest rate the buyers of future debt will demand, causing a feedback loop.
    
    Both Ed and Torsten point to the increasing spreads above Treasuries that the AI debt is trading at, and the increased cost of insuring against these companies defaulting.
38. Katherine Burton et al report for Bloomberg on The 24-Hour Race to Salvage Situational Awareness’ Souring Bets:
    

    In a blink, [Leopold Aschenbrenner's] wildly successful hedge fund, Situational Awareness, was forced to sell billions of dollars of technology investments that had rapidly lost value, as nervous banks began to demand more and more collateral for his trades. Then came billionaire Ken Griffin.
    
    In less than 24 hours — which included a conversation between Griffin and Aschenbrenner — Griffin’s Citadel hedge fund reached out to Situational Awareness and snapped up the investments at a discount, according to a person familiar with the matter who asked not to be identified citing private information.
    
    It was a startling reversal for Aschenbrenner, a former researcher at OpenAI who — before starting his hedge fund roughly two years ago — had no previous investment experience. His fledging firm has watched its assets plunge from $45 billion at the start of July to about $10 billion.

    Aschenbrenner was all-in on AI.
    
39. On the Pof. G. Markets podcast legendary short-seller Jim Chanos focused on the accounting inequality inherent in the AI (and earlier dot-com) bubble. The spending of the hyperscalers and neoclouds is investment, expensed over say 5 years. But for the chip makers, construction companies and so on that spending is this year's revenue. So, overall, it looks like the ROI is better than it really i s.
40. Leap Financial Academy's The hidden financial costs of chasing AI supremacy and who is unwillingly footing the bill makes an interesting point about accounting for the Restricted Stock Units that companies use to reward their employees:
    

    US GAAP was built on the assumption that neither route would materially move profits, because which company would ever choose to grant ‘enough’ RSUs to employees that it could move the share price right?
    
    Meta has granted ~US$70bn in stocks. And Microsoft ~US$42bn. The sheer size of these numbers IS enough to move the needle materially. RSUs, granted in large quantities every year, compound into visible profit erosion and free cash flow consumed by “obligatory” share buybacks, as employees must receive their promised shares annually regardless of market conditions.
    
    As of FY25, total unvested RSU’s book value was ~US$60bn and corresponding market value was ~US$80bn. That $20bn gap is money Meta will have to find, one way or another, the moment these shares vest, either by diluting shareholders with fresh stock, or by spending real cash buying shares back to hand over instead.
    ...
    

    Source

    Using the extensive information provided by Meta’s financial notes on the RSU programme, we can back-solve the profit erosion that is being kept ‘off-the-Income-Statement’:
    
    Since December 2024, as the share price of Meta increased and the AI talent war started, ~10 points of EBIT margin per year have been given away as additional, uncaptured employee compensation.

    Source

    And this has decimated Meta's free cash flow.
    
41. Ed Zitron is back with another scathing analysis in The AI Demand Bubble. As usual, it is long and detailed. He concludes:
    

    To put things really simply, Anthropic and OpenAI are a way that hyperscalers can feed their revenue to themselves by spending money on capex, backstopping compute contracts, or doing direct equity investments.
    
    Their continued existence allows the AI bubble to continue inflating, but this can only continue as long as venture capital and hyperscalers are capable or willing to invest. There is simply not the demand — not from open source, not from other AI labs, not from self-hosting, not from anywhere — to justify the capex or the massive data center buildout.
    
    And for those arguing that there would be a dot-com bubble recovery story, I must be clear that if there isn’t demand today, it won’t magically appear tomorrow. AI GPUs will cost just as much to run in five years as they do today, as will unfinished data centers cost just as much to finish, as will electricity remain expensive, and all this will be happening after it’s easy to raise venture capital to actually buy the compute.

42. Issie Lapowsky reports that Tokenmaxxing Is Dead. Now Comes the Belt Tightening:
    

    In a recent survey of 300 business executives, 68% reported overspending their AI budget over the past year. It’s little wonder so many companies are now cracking down. Uber Technologies Inc. recently capped employees’ AI spending at $1,500 a month. Tesla Inc. reportedly set a limit of $200 a week. The swift reversal has left some workers complaining of whiplash, with Reddit forums filled with stories of engineers suddenly stymied by usage caps.

It’s a tremendous challenge for many of us to be fully present in the current moment. There’s a pull toward imagining the future and ruminating on the past – sometimes big, important things you dread and can’t let go of and sometimes quotidian things that really are not at all important. Regardless of their weight, these imaginings drag you out of the moment you’re in, leaving you less able to appreciate exactly where you are, what you’re doing, and who you’re with. And the inner voice pulling you away can be pretty loud and persistent, especially if you tend to be anxious (guilty!). I’m the sort of person who will obsess about a really dumb thing I said weeks ago as if ruminating over it could change what happened. And I’ve definitely wasted time imagining disastrous futures that never came to pass or fantasized about wonderful potential futures that also didn’t. 

When I got sick – when every moment I felt relatively okay became a precious commodity – I became aware of how much time I was wasting mired in psychological spaces far away from where I actually was. I’m not saying that it cured me of rumination, but I’ve definitely been more focused on enjoying the present while it’s actually happening because I know how precious and fleeting a truly good day is and I want to be fully immersed in it. 

Before I got really sick, I spent so much time living in the future, imagining the better life I would be able to live when I got through the stress and the big projects, became a better version of myself, or whatever I convinced myself was the hurdle I needed to jump over to finally fully enjoy my life. It was always “after this semester” or “after I get tenure” or “once I finish this big project.” I never felt like I was doing enough, no matter how much I worked, and I was always chasing a sense of enoughness by taking on more and more and more. My spouse also lived in the future, often telling me that he’d be able to slow down and spend more quality time with me “after ___,” but when ___ came and went, there was another project/hurdle and another and another. That future happier, less stressed life shimmered in front of us like a mirage, always just out of reach, and always with the promise that once we jumped over another very big hurdle, things would be better and we’d be able to fully live in that glorious better present.

Author and podcaster Jocelyn K. Glei calls this “future-tripping” and the “anxiety of potential.” She writes that “capitalism, the media, and wellness culture… keep us fixated on all that we are not, and all that we have yet to accomplish/change/fix about the world and/or our inadequate selves. They convince us to keep our eyes cast toward the future, to focus all of our energy on the ominous specter of unfulfilled potential.” When you don’t see yourself as being enough as you are, you anxiously strive toward things that you hope will make yourself feel worthy. You believe those accomplishments will fill a hole inside you. So you’re always looking to prove yourself, to get the gold star, to make more money, to look better, to climb the career ladder, or whatever you’ve convinced yourself will do it for you. Yet while our capitalist society (and your manager) will probably happily let you keep on striving, enoughness will never come from external recognition, approval, or finishing a project. It comes from embracing your inherent worthiness.

When you finally recognize that you were always enough, you won’t need to keep proving yourself and you can live more fully in the present. You can stop searching and recognize that, as Glei writes, “the power and purpose and peace that we have been seeking outside of ourselves is already present within ourselves.” Because as you’ve seen during all your striving, wherever you went, there you were. You were still that exact same imperfect but WORTHY you. You were (and are) enough.

Blue sky and a calm lake where you can see the reflections of the trees and rocks surrounding the water

Suleika Jaouad, a brilliant writer and artist who knows much better than I the pain of losing big future plans to illness, writes about the things we put off doing into the future and how illness affects that calculus:

One of the stranger lessons of living with a long illness is that I’ve had to stop waiting for the circumstances of my life to become more favorable before I begin living it. I’ve had to stop waiting to feel better, or stronger, or more certain, or less busy. To stop assuming there will be a more convenient season for foraging for joy.

Illness has definitely changed my calculus when it comes to putting things off. For me, that has meant buying a kayak and spending lots of time on the water with my husband, spending more time taking in beautiful places, taking my teenaged son out to restaurants or food trucks (since that’s when I can get him to talk these days) even if it’s unhealthy, and doing things that bring me joy when I’m able to rather than putting them off “until things settle down” (which less face it, will probably never happen) or thinking them too indulgent. Like Suleika, I think many of us would be well-served by making a list of, “Things I Keep Wanting and Not Doing.” What have you been putting off?

While also a motivator, the unstable capacity of those of us with chronic illnesses (and often those who are caregivers as well) can also make it hard to live fully in the present because everything feels so tenuous. Crip time is real and it yanks you out of the dominant temporality and rhythms in myriad ways. With my illness, my day-to-day capacity is entirely unpredictable. Just recently, I had ambitious plans for a perfect weather Saturday that I had to entirely drop because I was experiencing a flare. My body felt heavy and spent, my joints were swollen and painful, and my brain felt like mush. I didn’t have the intellectual or physical capacity to do much beyond watching the day pass me by from the couch. A flare can last a couple of days, weeks, or even a whole month and I never know at the outset which will be the case. And there’s nothing I can do but rest, do light exercise (which helps me with the pain but feels almost Herculean given the fatigue), and be patient with my jerk body. I try to be zen about the upending of plans, to hold onto things loosely, but there are moments when I really feel angry about the loss. This Summer, my health has been so up and down; I don’t think I’ve even had a full week where I’ve felt ok every day. I try to make the most of moments when I feel good and sometimes maybe I push myself too hard on those days to wring out as much joy and fun as I can. It’s so hard to find a balance.

As a result of that unpredictability and debility, I feel like I can’t engage as deeply with things at work that used to bring me such joy. I used to be very involved in state library association work and regional consortium work and I stepped away from it all completely, which felt (and still feels) heartbreaking. I miss feeling connected to other library workers across the state, to contributing to things I deeply believe in. But I’m scared of being the person who takes on too much and then has to let people down. I was often the person who picked up the pieces when well-meaning people took on way too many things they were excited about and then couldn’t deliver. I’ve been the vice-chair with the flakey chair who ends up doing both jobs, the project lead whose colleague simply didn’t complete their part of the project but also didn’t inform anyone, the co-author who ends up doing the vast majority of the data analysis and writing. I’ve been bailed on. I’ve reached out for help and heard crickets. It made me feel like my time was less valuable and, frankly, like I was less valuable. 

We talk a lot about how, in this field, the reward for overwork is more work. And it’s not just managers who cause that problem. I’ve seen peers contribute to this because they know their conscientious overworking colleagues will pick up the slack. I don’t want to make someone feel like that. While, yes, those workaholics also need to recognize that they don’t have to take on the burden someone’s dumped at their feet, we don’t have to take advantage of a workaholic and contribute to their burnout. So I undercommit even though it makes my worklife less satisfying.

I wrote a couple of years ago about my vision for something I called “community time” and I think that would allow those dealing with instability (caregiving responsibilities, disabilities, etc.) to take on more without fearing that we’d let people down or have to work beyond our capacity:

My vision for community time is one where we see all the work of libraries as a shared project and contribute as our capacity allows. And on the other side, we provide mutual support as our capacity allows, without looking to be paid back or for labor to be exchanged on a 1:1 basis. I agree with Piepzna-Samarasinha that seeing our work as a collective responsibility and providing collective care can be a beautiful and joyful experience. Imagine the peace of mind of always knowing you have the support you needed when you simply couldn’t get things done (because of illness, disability, caregiving responsibilities, etc.). Imagine the freedom of feeling like you can fall down and rest and won’t let the world down or jeopardize your job. Imagine the joy of providing support to your colleagues when they really need it. Imagine the deep relationships and trust that come from a communal vision of time. Think of how vulnerable and human you could be in such an environment. And perhaps there is a new kind of freedom in such an arrangement as Ivan Illich suggested: “I consider conviviality to be individual freedom realized in personal interdependence and, as such, an intrinsic ethical value.”

In community time, when I felt well, I could engage deeply and when I didn’t, I could step back without feeling guilt, knowing that someone else would pick up the slack as I would for others when I had capacity. If all the work were seen as a shared project, as it is in my marriage, it would be easier to pick things up when someone needed to let things drop, but I don’t think this is how things are usually seen in most workplaces. People are often looking for exact reciprocity, like the reference shift swap that leaves you with exactly as many hours as you would have had previously. And that creates equality, but not equity. 

Blue Heron perching on a mostly submerged log covered in grasses and flowers in the middle of a lake.

My illness is one I’m going to have for the rest of my life and though it’s possible that with new medications, it could get under better control, it’s probably always going to impact my capacity. So I’m going to have to find a balance that feels ok to me and to also try and foster a work culture that better enables interdependence and allows us all to be fully human. I obviously have very limited influence in my work role, but I can at least try to model that behavior by being vulnerable when I need support and supporting my colleagues when they need it and I have capacity instead of worrying about perfect reciprocity or that everyone is doing the same amount. If we all did that, we could absolutely achieve a “community time” that provides the beautiful freedom of interdependence. What is keeping you from trying it?

As AI technologies proliferate across web platforms and information systems, the nature of human-computer interaction is fundamentally changing: users increasingly engage with and rely on adaptive, generative, and often opaque AI agents such as chatbots. This shift makes human-AI interaction (HAI) a critical research topic, as researchers must now understand and shape how humans and AI systems communicate, collaborate, build trust, and share decision-making responsibilities.

Recognizing the growing importance of HAI research, this project renews an REU Site focused on Human-AI Interaction and Evaluation in Web and Information Systems at ODU, led by the Department of Computer Science in collaboration with VMASC and the Department of Communication Disorders & Special Education. The goal of our REU Site is to provide inquiry-based, experiential research training that enables undergraduate students to become independent, contributing researchers. To achieve this goal, we will involve undergraduate students from a wide spectrum of backgrounds as colleagues in ongoing research projects and encourage them to pursue graduate study in STEM disciplines.

The REU Site includes 8 mentors, of which 4 are in the Web Science Digital Library (WS-DL) research group. 

• Dr. Jian Wu (PI, Associate Professor of Computer Science)
• Dr. Vikas Ashok (Co-PI, Associate Professor of Computer Science)
• Dr. Sampath Jayarathna (Associate Professor of Computer Science)
• Dr. Michael Nelson (Professor of Computer Science, VMASC)
• Dr. Pratip Rana (Assistant Professor of Computer Science)
• Dr. Heqiao Wang (Assistant Professor of Human Movement Studies and Special Education)
• Dr. Faryaneh Poursardar (Research Assistant Professor of HEAL)
• Dr. Ginger Watson (Research Professor and Technical Director of HEAL)

In this REU program, students will learn human-AI interaction concepts, methods, and interfaces; tools and skills for accessing, cleaning, preprocessing, and visualizing diverse datasets, including human-generated, AI-generated, and other web-related data; state-of-the-art computational methods — including multimodal large language models and machine learning algorithms — for building analytical and predictive model prototypes with open-source programming tools to address research challenges in HAI; and key metrics for evaluating research tasks, comparing baseline models, and producing publication-quality results. Students will also develop essential research skills, including critical thinking, technical writing, and research presentation. This program strengthens the nation's research capacity by training a new generation of diverse, well-prepared researchers in the rapidly growing field of human-AI interaction.

Student recruiting will begin in January 2027 and continue for the following three years. For more information, please visit our website: https://oducsreu.github.io/ and see our final presentation blog posts for years 3, 2, and 1.

We express our gratitude to NSF for their support of undergraduate research! 

-- Jian Wu

For a recent installment of Gizmodo’s Giz Asks series, Gayoung Lee asked researchers in computer science, cybersecurity, and libraries: will we ever solve the problem of losing data?

I love this question because it is core to the work we do at the Lab: as technology changes the global information landscape, how can we remember where we have come from and plan where we are going?

I contributed this answer:

I know of one data storage form that can be permanent: a library. Or rather, a world full of libraries. Libraries aren’t perfect storage devices (nothing is), but they are fiercely committed to self-repair. A good library is a collection of people, technologies, and practices that pass an obligation—to remember the things we must remember—from the last generation to the next.

Libraries, rather than better hard drives, are what solve correlated failure, cataloged in the LOCKSS (Lots Of Copies Keep Stuff Safe) threat model. A correlated failure is when you make really great, durable copies of the data you care about, and then they all get hit by an asteroid. Shouldn’t have had them all in one physical location. Or a hacker marks them all for deletion (they were all behind one sysadmin), or a government orders them all destroyed (all in one regulatory regime), or they all rot at the same time (all from one flawed batch), or the money to store them dries up (all on one funding source).

On a long enough timeline any copy will break. You need multiple copies with different vulnerabilities so they don’t all break at the same time. Then you need to repair the broken ones. A worldwide network of libraries does the repair work, whether the objects are 30 medieval Magna Carta manuscripts or 300,000 government datasets.

Long-lived storage media are wonderful (“a CD that lasts 1,000 years!”) because they invite us to imagine who will be here in 1,000 years and what they will care about. As we build a digital civilization that can preserve knowledge for centuries, the problems we solve will transcend storage media: how to build digital archives that are endowed and defended, and how to knit them into networks of mutual aid that reach across institutions and borders. Technology is a necessary building block, but permanence isn’t something we buy. It’s a promise we keep renewing by agreeing to hold each other’s treasures.

Read the full piece at Gizmodo for contributions from Mohiuddin Ahmed, Francesca Musiani, Kevin Curran, Adnene Guabtni, and Melanie Hubbard on the physics of storage media, the infrastructures behind our digital memory, and the practices that keep data alive.

JCDL is the premier international conference focused on digital libraries and their associated organizational, practical, social, and technical issues. The Doctoral Consortium (DC) workshop, an integral part of the conference, is typically scheduled one day before the main conference.

In the DC, doctoral students in related disciplines — including but not limited to Computer Science, Information Science, Library Science, and Web Science — who are in the early stages of their dissertation work submit formal papers outlining the motivation, structure, research plans, and expected results of their dissertations for critical review. Accepted papers are presented at the workshop for questions and feedback. Doctoral students also attend the main conference to broaden their opportunities for intellectual engagement.

NSF previously funded the JCDL DC from 2013 to 2016 and again from 2023 to 2024. These funds supported more than 25 U.S.-based doctoral students in attending the DC workshop.

This DC Travel Award will prioritize students whose research agendas align closely with the topics outlined in the DCL "Leveraging Cyberinfrastructure for Research Data Management (RDM)," which is affiliated with the Findable, Accessible, Interoperable, Reusable Open Science (FAIROS) program and driven by the Public Access Initiative. In addition, the award includes support for several best paper awards at JCDL.

The 2026 JCDL DC will be co-located with the main JCDL conference in Dallas, Texas, United States. The DC is scheduled for October 13, 2026, one day before the main conference begins. Details on applying for the DC Travel Award are available at https://2026.jcdl.org/nsf-doctoral-consortium-travel-award/.

We would like to express our gratitude to NSF for its generous support of doctoral students in the JCDL community.

-- Jian Wu

P.S.:  WS-DL students have attended many of the JCDL DCs in the past, and their trip reports are available: 2023, 2020, 2018, 2016, 2015, 2014, 2013, and 2012, 

Win free books from the August 2026 batch of Early Reviewer titles! We’ve got 338 books this month, and a grand total of 3,903 copies to give out. Which books are you hoping to snag this month? Come tell us on Talk.

If you haven’t already, sign up for Early Reviewers. If you’ve already signed up, please check your mailing/email address and make sure they’re correct.

» Request books here!

The deadline to request a copy is Tuesday, August 25th at 6PM EDT.

Eligibility: Publishers do things country-by-country. This month we have publishers who can send books to the US, the UK, Canada, Australia, Israel, Italy, France, Spain, Ireland, Germany and more. Make sure to check the message on each book to see if it can be sent to your country.

Thanks to all the publishers participating this month!

A monthly round-up of news, upcoming working group meetings and events, and CLIR program updates from the Digital Library Federation. See all past Digests here. 

Hello DLF Community! August has arrived, bringing the last stretch of summer and a renewed sense of focus as we look ahead to the months to come. Whether this season finds you wrapping up projects, preparing for a busy fall, or taking a well‑deserved break, we’re glad that you continue to be a part of the DLF Community. This month, Team DLF is steadily working on communications and management for the 2026 Virtual Forum and doing early site evaluation for an in-person 2027 Forum. In this issue of the DLF Digest, you’ll find updates and opportunities to stay connected.

With appreciation,

-Shaneé

This month’s news

• Virtual DLF Forum early bird registration ends August 31: Register at the early bird rate to join us online in October, and view the program for the 2026 Virtual DLF Forum. 
• Forum sponsorship deadline extended: Your organization now has until Monday, August 17 to sponsor this year’s virtual DLF Forum. Join these fantastic organizations and support our event and community this year.
• Call for Proposals: Submit a proposal by August 21 for the LD4 2026 Conference – Linked Data in Practice: Building the Future of Knowledge Systems held online September 28-30. 
• Free Event: Join the University of North Carolina Greensboro for the inaugural National Virtual Forum for Digital Games in Libraries. Find out more about DGIL forum presentations and workshop sessions and register for the event. 

This month’s open DLF group meetings:

For the most up-to-date schedule of DLF group meetings and events (plus conferences and more), bookmark the DLF Community Calendar. Meeting dates are subject to change. Can’t find the meeting call-in information? Email us at info@diglib.org. Reminder: Team DLF working days are Monday through Thursday.

• DAWG IT & Development: Monday, 8/3, 1pm ET / 10am PT.
• Born-Digital Access Working Group (BDAWG): Tuesday, 8/4, 2pm ET / 11am PT.
• Digital Accessibility Working Group (DAWG): Tuesday, 8/4, 2pm ET / 11am PT. 
• AIG Cultural Assessment Working Group: Monday, 8/10, 1pm ET / 10am PT.
• AIG User Experience Working Group: Friday, 8/21, 11am ET / 8am PT.
• AIG Metadata Assessment Group: Friday, 8/21, 2pm ET / 11am PT.
• Digitization Interest Group: Monday, 8/24, 2pm ET / 11am PT.
• Committee for Equity & Inclusion: Monday, 8/24, 3pm ET / 12pm PT.
• Climate Justice Working Group: Tuesday, 8/25, 3pm ET / 12pm PT.
• Open Source Capacity Resources Group: Wednesday, 8/26, 1pm ET / 10am PT.
• DAWG Policy & Workflows: Friday, 8/28, 1pm ET / 10am PT.
• DAWG IT & Development: Monday, 8/31, 1pm ET / 10am PT.

DLF groups are open to ALL, regardless of whether or not you’re affiliated with a DLF member organization. Learn more about our working groups on our website. Interested in scheduling an upcoming working group call or reviving a past group? Check out the DLF Organizer’s Toolkit. As always, feel free to get in touch at info@diglib.org. 

Get Involved / Connect with Us

Below are some ways to stay connected with the digital library community and us: 

• Subscribe to the DLF Forum newsletter.
• Join, start, or revive a working group and browse their work on the DLF Wiki.
• Subscribe to our community listserv, DLF-Announce.
• Bookmark our Community Calendar.
• Learn more about becoming a DLF member organization. 
• Follow us on LinkedIn and YouTube.
• Contact us at info@diglib.org.

The post DLF Digest: August 2026 appeared first on DLF.

SkyCommons Observatory released, protecting our BrainCommons, a responsible AI chatbot, shared learnings at UN Big Data, preparing for the Open Technology Symposium, and more.

The post New frontiers for open knowledge – OKFN Newsletter July 2026 first appeared on Open Knowledge Blog.

“Woman Looking in Mirror,” University of Wisconsin-La Crosse Historic Photograph Collection, undated glass plate. Via DPLA

It is curious — or perhaps completely understandable — that in our overexposed, too-connected global culture, figures like Satoshi Nakamoto, Banksy, and Elena Ferrante still exist, highly influential creators who are largely hidden behind pseudonyms. Sure, investigative reporters have purportedly unmasked the identities of the inventor of Bitcoin, the media-friendly but camera-shy trickster of contemporary art, and, I believe, our greatest living novelist. And yet the three have more or less successfully shrouded themselves.

Why should we care who they are? For Nakamoto, there is the attraction of a beautiful mind, a cryptographic genius, along with a more earthly interest in who is sitting on a mountain of coins worth tens of billions of dollars, like Smaug in The Hobbit. Banksy, on the other hand, seems like a more approachable Gen X figure, politically engaged but with a sense of humor, probably a good hang over a pint at the local pub. Our desire to uncover the real Elena Ferrante feels rather different, however, in a way that crystallizes a point about reading and writing that I have been trying to make in this newsletter as I explore the fraught merger of AI and human effort.

Ferrante’s most beloved books, the four “Neapolitan Novels” beginning with My Brilliant Friend, are narrated in the first person by a character whose name is also Elena (Greco), and are set largely in her hometown of Naples. That she grew up there is one of the few biographical facts “Elena Ferrante” has provided about herself, so we cannot help but assume that this is a work of autofiction, an enthralling blend of pseudonymous autobiography and fiction that hews close to the lived experience of the writer behind Elena/Elena. The potent personal nature of the story — a riveting decades-long arc about an aspiring writer and her destructive frenemy, toxic love interest, and lethal neighborhood — is far more dramatic than most readers’ lives, but Elena Greco’s unstable mix of self-confidence, self-doubt, and self-deception is all too relatable.

Beyond the great pleasures of the Neapolitan quartet’s direct prose, pacing, and plot, another remarkable feature emerges as the series proceeds. You realize that the four books bend back upon themselves: they are a tale about the source and act of writing the very novels you are reading. They form a circle of mirrors, throwing complex reflections of the relationship between Elena Ferrante (the nom de plume), Elena Greco (the protagonist), and the author of these books.

Instead of being disorienting, these reflections only deepen our identification with the human being behind these novels. She may have a pseudonym — and given the seemingly biographical nature of her works, we can understand why she would want to write under cover — but we nevertheless form an intense bond with this observant writer and come to enjoy the range of her thought. Like many readers, I do not care to know who Ferrante actually is, but I do very much care about and connect to her perceptive and vibrant mind, so generously exposed in her books. This is a heightened version of what we feel when we read any decent novel, or any scholarship that has strong creative intent and displays the experience and views of its author.

As the literary critic Georges Poulet perfectly describes it in his classic essay “Phenomenology of Reading,” in the act of picking up what seems like an inert physical object, a book, one becomes

aware of a rational being, of a consciousness; the consciousness of another, no different from the one I automatically assume in every human being I encounter, except that in this case the consciousness is open to me, welcomes me, lets me look deep inside itself, and even allows me, with unheard-of licence, to think what it thinks and feel what it feels.

We naturally identify and merge with Ferrante’s mind, and the Neapolitan novels explore in depth this inescapable, treasured human process. Elena Greco’s best friend and worst enemy, Lila, has a propensity for what we might today call dissociative episodes — “dissolving margins,” in the more vivid phrase of the novels — in which the boundary between her self and others gives way. Elena eventually sees this as an analogy for writing and reading. The authorship of the books becomes, in a sense, a co-production of Lila and Elena, and also of the neighborhood and Elena, as the author behind the Elenas maps how permeable relationships are, and thus our documentation of them. The reader, in turn, becomes part of that world.

This essential — existential — phenomenon, our sense that we are reading the work of another human being, someone with connections to others, to a time and a place like postwar Naples, someone to whom we might relate our own life stories, is our strongest reason not to cede too much ground to AI-generated text. That holds true whether we’re talking about the pinnacle of contemporary fiction or the scholarly work I have been exploring here. (Admittedly, this might not apply to other, more perfunctory forms of writing that are beyond the scope of this newsletter.) The real problem with AI text isn’t its clichés, which, as I noted in the last essay, can probably be reduced over time; it’s our nagging suspicion of, and eventual annoyance at, the lack of a fellow mind behind the prose. “Elena Ferrante” may be a fake name, but readers never doubt that they are encountering a profound, insightful consciousness.

Ellen Hartman, OCLC Leaders Council Manager, concludes her blog series on global library leadership conversations, inspired by a recent meeting of the OCLC Leaders Council. The first post in the series explored the unique value of global library leadership conversations, as well as some of the practical realities of making this form of engagement successful. The second post enumerated costs involved in participating in, and producing meaningful outcomes from, global conversations.

Until we meet again …

My experience facilitating the recent OCLC Leaders Council meeting reinforced a persistent theme I have been witnessing in many conversations: international cooperation has become more challenging in recent years. Cross-border collaboration, once treated as a natural extension of shared professional values, now often feels slower, more constrained, and more demanding to sustain. Collaborative practices that were previously taken for granted increasingly require explanation, justification, and careful design.

This is not due to a loss of commitment. As the Leaders Council meeting proved, library leaders still want to share ideas, exchange experiences, and where possible, work together in practical ways. What has changed are the conditions under which cooperation takes place. And that distinction matters, because it changes how we interpret the tensions that have emerged and what might be done about them.

The will is there but the conditions have changed

Sitting in global leadership conversations, like those at Leaders Council, reveals how passionate leaders remain about collaboration. Even those who are navigating new realities and constraints want to make sure they can continue cooperating. They feel a real commitment to others who might not (yet) be facing the same pressures, helping them understand what is changing for them, and why it is forcing all of us to think differently.

The concern underneath these conversations is not abstract. If those under pressure to change can’t bring others with them and can’t help the broader community understand what is shifting and why, global cooperation is at risk of breaking apart. And the library leaders in these rooms are eager to stop that from happening.

That is why the shift from assumed to intentional cooperation matters. For many years, international collaboration operated on a set of largely implicit assumptions. Shared infrastructure, common standards, and mutual trust made cooperation feel natural. The primary challenge was usually one of coordination or tools, rather than justification of the decision itself to collaborate. Now this has shifted: collaboration is no longer assumed but is instead something that must be proactively initiated and sustained.

For more on the idea of library collaboration as an intentional, strategic choice, see OCLC Research’s report on the topic.

When efficiency is no longer enough

For much of the past two decades, global efficiency was the self-evident justification for shared infrastructure and international collaboration. The logic was straightforward: working together across borders reduced duplication, extended reach, and delivered more value than any institution could achieve alone.

That logic has not disappeared. But it must now be weighed against other considerations like local and regional guidelines or regulations, as well as institutional priorities and policy. In many contexts, an arrangement that is globally efficient is no longer automatically the right choice.

Leaders increasingly are being asked to justify international collaboration in terms that resonate within their own institutional and political context. Decisions on infrastructure that used to hinge primarily on technical considerations now factor in other considerations. A decision about where data is stored or how a shared service is governed is no longer just an operational question, but a question about accountability, institutional values, and in some cases, political choices.

Data sovereignty and differing needs

Alongside this shift in decision-making criteria regarding whether and how to collaborate is the emerging question of data sovereignty, a topic of keen interest at the Leaders Council meeting. Expectations about data location, privacy, and control vary considerably across jurisdictions. Different regions bring different perspectives on responsible data governance, shaped by distinct legal traditions, political contexts, and public concerns. These are embedded in legal frameworks, regional regulations, national guidelines, institutional policies, and public accountability structures that libraries are subject to, but rarely control.

A single cross-border model therefore becomes difficult to implement. Moreover, regions have varying degrees of experience in navigating these questions. For some, data sovereignty is an emerging pressure. For others, local governance frameworks have long shaped what international cooperation can look like in practice. What is changing is how visible and pressing data sovereignty issues have become in conversations about shared infrastructure.

What conversation can do that infrastructure cannot

For a European academic library, an issue like digital sovereignty may be an urgent challenge, prompting a reevaluation of longstanding shared infrastructure commitments. For libraries in other regions, the same issue may still seem abstract and of little practical impact. This can lead to frustration: why are collaborative approaches that have benefited libraries for decades suddenly being questioned?

Global leadership conversations—being in the room together—cultivate a deeper understanding of new constraints that are in place, and how those constraints impact different libraries in different ways. This is beneficial to everyone in the room and creates opportunities to focus on possibilities rather than limitations. The conversations build on the core values and goals of cooperation, and how those can still be met, even if we need different approaches than before.

In practice, global leader conversations advance international collaboration by:

• Helping library leaders recognize emerging trends. Leaders who are already navigating new constraints can help peers understand what is coming and why. For libraries that have not (yet) encountered these pressures directly, hearing from those who have provides an opportunity to prepare.
• Making sense of what’s happening and how the library ecosystem is evolving. What is driving these changes? How are they manifesting differently across regions and institution types? And what do they mean for the future of shared infrastructure and collaborative work? That shared understanding develops through repeated exposure to each other’s contexts and constraints.
• Illuminating a shared path forward. The library leaders who are already navigating these new conditions are not simply describing a problem but actively working together on what practical cooperation can look like within a new set of realities. This helps clarify where cooperation may remain a feasible, and even preferred, path.

We facilitate conversations at OCLC Leaders Council meetings with these principles in mind, with the broader goal of making that venue a fertile ground for cultivating collaboration opportunities.

Conclusion

What gradually emerges from these conversations is a realization that different regions and institutions will navigate trust, data governance, and accountability in ways that reflect their own realities, rather than converging on a single shared approach. That is not a failure of international cooperation, but international cooperation adapting to a more complex environment.

The challenge is to manage diversity without becoming fragmented—to find the best approach that allows libraries to stay connected without having to operate identically. This requires understanding other institutions’ constraints, and to keep looking for the collaborative pathways that remain open rather than focusing on those that have closed.

International cooperation has not lost its value. But it has, in many cases, moved beyond the simplicity of operating as a default choice that doesn’t need to be explained or defended, or where a globally acceptable model of cooperation and governance is easily found. Global leadership conversations—structured engagement on a small scale with an international group—can help bridge these new realities by cultivating a shared understanding of new constraints on the one hand, and pathways to move forward together on the other.

This is the final post in a three-part series on international library leadership engagement. Access all the posts in the series here!

The post Staying in the room: Why international library cooperation is harder than before appeared first on Hanging Together.

I'm skeptical of "commoditizing the technology". Archival systems are a niche in the IT market, and one on which companies are loath to spend money. Realistically, there aren't going to be a vast number of Silica write heads. The only customers for systems like Silica are the large cloud providers, who will be reluctant to commit their archives to technology owned by a competitor. Unless a mass-market application for femtosecond lasers emerges, the scope for cost reduction is limited.

But the more I think about this technology, which is still in the lab, the more I think it probably has the best chance of impacting the market among all the rival archival storage technologies. Not great, but better than its competitors:

Project Silica tablet

1. Project Silica: Towards Sustainable Cloud Archival Storage in Glass 23^rd October 2023.
2. RASCAL: A Scalable, High-redundancy Robot for Automated Storage and Retrieval Systems, 8^th August 2024.
3. Laser writing in glass for dense, fast and efficient archival data storage 17^th February 2026.

Viable path towards commercialization gated only by the laser

Paper 1

Source

But in the last few years Microsoft Research has taken this idea and run with it, as they report in a 68-author paper at SOSP entitled Project Silica: Towards Sustainable Cloud Archival Storage in Glass. It is a fascinating paper that should be read by anyone interest in archival storage.

• The key performance criterion was write bandwidth, because reads were extremely rare. They expected the major reason for a read would be a subpoena.
• The economics depended upon operating at cloud scale, and thus being able to house the systems in normal warehouse space with no special air conditioning or power supplies, instead of expensive data center space. The key criterion was worst-case power draw.

Note these important differences between Microsoft's and Facebook's storage hierarchies:

• Microsoft stores generic data and depends upon user action to migrate it down the hierarchy to the archive layer, whereas Facebook stores 9 specific types of application data which is migrated automatically based upon detailed knowledge of the workload for each of the types.
• Because Facebook can migrate data automatically, it can interpose a warm layer above the archive layer of the hierarchy, and because it has detailed knowledge about the behavior of each of the data types it can make good decision about when to move each type down the hierarchy.
• Because the warm layer responds to the vast majority of the read requests and schedules the downward migrations, Facebook's archive layer's IOPS are a steady flow of large writes with very few reads, making efficient use of the hardware.

Figure 2

on average for every MB read there are 47 MBs written, and for every read operation there are 174 writes. We can see some variation across months, but writes always dominate by over an order of magnitude.
...
Small files dominate the workload, with 58.7% of the reads for files of 4 MiB or smaller. However, these reads only contribute 1.2% of the volume of data read. Files larger than 256 MiB comprise around 85% of bytes read but less than 2% of total read requests. Additionally, there is a long tail of request sizes: there is ∼ 10 orders of magnitude between the smallest and largest requested file sizes.
...
We observe a variability in the workload within data centers, with up to 7 orders of magnitude difference between the median and the tail, as well as large variability across different data centers.
...
At the granularity of a day, the peak daily [ingress] rate is ∼16x higher than the mean daily rate. As the aggregation time increases beyond 30 days, the peak over mean ratio decreases significantly down to only ∼2, indicating that the average write rate is similar across different 30-day windows.
...
To summarize, as expected for archival storage, the workload is heavily write-dominated. However, unexpectedly, the IO operations are dominated by small file accesses.

Paper 2

we present RASCAL, a novel ASRS robot for small payload items in structured environments, with a focus on system-level scalability and redundancy. We describe the design objectives of RASCAL and how they address some of the limitations of existing robotic systems in this area, such as scalability and redundancy. We then demonstrate the viability of our design with a proof-of-concept implementation of a data centre storage media robot, and show through a series of experiments that its design, speed, accuracy, and energy efficiency are appropriate for this application.

RASCAL Fig. 1

• Serviceability: Robots should be easy to add and remove from the system, without requiring specialist tools or expertise.
  The operator just clips or unclips the robot from the pair of rails to which it is attached by the "wings" that carry the wheels that contact them. Servicability is a significant problem with tape robots.
• Addressability: Any robot should be able to access any item stored in the shelving.
  Because each robot blocks only a small fraction of the shelf to which it is attached, other robots can navigate around it to access items on the same or other shelves.
• Scalability: A deployment should be able to scale its retrieval throughput by adding and removing robots. Likewise, it should be able to easily extend or reduce its storage capacity by adding or removing shelving without significant downtime.
  Adding or removing relves of robots requires updating the central control computer, but should not interrupt operations.
• Availability: Failure of a robot should have a limited effect on the items that can be accessed, should not have a significant impact on routing, and should not obstruct other robots from continuing their operations.
  A failed robot blocks access to the items immediately in front of it, but these are only a tiny fraction of the total.

The proof-of-concept robot is around 240 mm wide, and sits 300 mm tall and 90 mm deep when mounted on the rails. The picker adds an additional 70 mm to the width, and increases the overall depth to around 150 mm. When flipping, the robot extends a maximum of 270 mm from the structure, and 250 mm from the wing pivot point. The total mass of the robot (including picker and battery) is around 3.5 kg.

Rascal Fig. 2

The climbing manoeuvre ... consists of unlatching one wing from its current rail while remaining firmly attached with the other; rotating the robot outwards from the storage rack around the attached rail and wing; and latching the free wing onto a new rail, two rails either above or below its original position.

These motion systems equip our robot with two key properties: independence and flexibility.

In this context, independence refers to the fact that a RASCAL does not depend on the state of any other robot or external motion system (such as an elevator) to perform its operations.

Paper 3

Here we report an optical archival storage technology based on femtosecond laser direct writing in glass that addresses the practical demands of archival storage, which we call Silica. We achieve a data density of 1.59 Gbit mm⁻³ in 301 layers for a capacity of 4.8 TB in a 120 mm square, 2 mm thick piece of glass. The demonstrated write regimes enable a write throughput of 25.6 Mbit s⁻¹ per beam, limited by the laser repetition rate, with an energy efficiency of 10.1 nJ per bit. Moreover, we extend the storage ability to borosilicate glass, offering a lower-cost medium and reduced writing and reading complexity. Accelerated ageing tests on written voxels in borosilicate suggest data lifetimes exceeding 10,000 years.

Two efficient regimes of volume pixel (voxel) writing in glass: we use phase voxels relying on isotropic refractive index (RI) changes and birefringent voxels based on anisotropic changes ... We demonstrate high-quality voxels, each storing more than one bit, using a minimum number of pulses.

Phase voxels are femtosecond-laser-induced isotropic modifications with locally altered [refractive index] and minimal optical scattering. The pulse energy is modulated to encode the symbol. ... Each voxel is written with a single pulse, so voxels are written at the laser repetition rate of 10 MHz. We modulate the beam energy using an acousto-optic modulator ... Different symbols correspond to distinct [refractive index] changes that can be read using Zernike phase-contrast microscopy

Furthermore, we demonstrate a throughput of 65.9 Mbit s⁻¹ by splitting the laser into 4 independently modulated beams. We scan all beams with the same scanner and objective .... The written, read and decoded results show that throughput can be scaled in this way without damaging the media.

Birefringent voxels are composed of optically anisotropic sub-diffraction modifications, the in-plane orientation of which is determined by the polarization of the writing pulse. Varying this orientation encodes different data symbols, which we read using polarization-resolved imaging.
...
Our new pseudo-single-pulse regime shows the formation of elongated nanovoids with just two pulses, improving on previous work. We split each pulse into two: one that forms a void (seed pulse) and the other that elongates a previously formed void (data pulse). ... In this way, a single laser pulse simultaneously initiates the formation of a new seed structure and converts an existing seed structure into a data voxel, so voxels are written at the laser repetition rate of 10 MHz

High-throughput, stable writing: we demonstrate writing at high throughput using multiple beams per laser ... We use a closed-loop feedback system to actively monitor and optimize the laser power, providing precise energy stability during writing and enabling predictability and reliability across different writers at scale ...

Accelerated Aging Test

To assess the thermal stability of phase voxels, we perform accelerated ageing experiments based on the Arrhenius law, using visible light diffraction measurements to track the decay of written structures. ... Extrapolation from the measurement points at elevated temperatures suggests exceptional long-term stability, indicating a modification lifetime that exceeds 10,000 years at 290 °C and therefore even longer at room temperature. This lifetime reflects the thermal stability of phase voxels under isolated conditions and does not account for external influences, such as mechanical stress or chemical corrosion, which are beyond the scope of this study.

Machine learning decode: building on our previous work, here we apply machine-learning-based decode ... to account for noise and inter-voxel cross-talk.

Using birefringent voxels, in fused silica glass, we achieve 1.59 Gbit mm⁻³ data density (usable capacity of 4.84 TB per platter, 0.500 μm × 0.485 μm voxel pitch and 6 μm layer spacing, 301 layers, 8 azimuth levels at 0.85 quality factor), a write throughput of 25.6 Mbit s⁻¹, and a write efficiency of 10.1 nJ per bit.

Using phase voxels, in borosilicate glass we achieve 0.678 Gbit mm−3 data density (usable capacity 2.02 TB per platter, 0.5 μm × 0.7 μm voxel pitch, 7 μm layer spacing, and 258 layers, 4 energy levels at 0.92 quality factor), a write throughput of 18.4 Mbit s⁻¹, and a write efficiency of 8.85 nJ per bit. Furthermore, our multibeam system achieves a throughput of 65.9 Mbit s⁻¹ through parallel writing with four beams without inducing thermal damage. Thermal simulations indicate that writing with 16 or more beams should be possible

We have shown that birefringent voxels achieve higher key metrics than phase voxels. However, efficient formation of birefringent voxels can be achieved only in high-purity silica glasses, whereas phase voxels can be written in potentially any durable transparent media, for example, borosilicate glass as demonstrated here. For phase voxels, the writing and reading hardware are simpler, requiring only one modulator per beamline and only one camera per reader, respectively. Both regimes can match the maximum laser repetition rate of 10 MHz or higher.

Assessment

• The media is very cheap and very dense, so the effect of Kryder's Law economics driving media replacement and thus its economic rather than technical lifetime is minimal.
• The media is quasi-immortal and survives benign neglect, so opex once written is minimal.
• The media is write-once, and the write and read heads are physically separate, so the data cannot be encrypted or erased by malware. The long read latency makes exfiltrating large amounts of data hard.
• The robotics are simple and highly redundant. Any of the shuttles can reach any of the platters. They should be much less troublesome than tape library robotics because, unlike tape, a robot failure only renders a small fraction of the library inaccessible and is easily repaired, simply by removing and replacing the failed shuttle.
• All the technologies needed are in the market now, the only breakthroughs needed are economic, not technological.
• The team has worked on improving the write bandwidth which is a critical issue for archival storage at scale. They can currently write hundreds of megabytes a second.
• Like Facebook's archival storage technologies, Project Silica enjoys the synergies of data center scale without needing full data center environmental and power resources.
• As Facebook's technologies had, Project Silica has an in-house customer, Azure's archival storage, with a need for a product in this space.

After reading about, experiencing and analysing the current technical and regulatory landscape of brain-computer interfaces (BCIs), and speaking with technologists, policy makers, legal scholars, and data experts, we encapsulated our learnings into eight recommendations for how to shape the ‘tech we want’ for our brain commons. Share your feedback with us here.  1. Understand the...

The post 8 Recommendations to Shape the Future of our Brain Commons first appeared on Open Knowledge Blog.

Continue Reading...

Source

The Frictionless community reconvened to enhance governance, explore AI integration, and establish GitHub as the primary discussion platform

The post Frictionless Community Call June 2026 first appeared on Open Knowledge Blog.

A recap of the BrainCommons opening session on the importance of making brain-computer interfaces open by design

The post Opening Remarks: An Open Neurotech Stack first appeared on Open Knowledge Blog.

Will we surrender our rights to free thought? – a recap of the Brain Commons keynote by Susie Alegre

The post The Future of Freedom of Thought: Keeping Human Agency Open in the Age of Brain-Computer Interfaces first appeared on Open Knowledge Blog.

How neurotech challenges the very idea of ‘thinking for oneself’ – a recap of the Brain Commons keynote by Christoph Bublitz

The post Freedom of Thought in the Age of Neurotechnology first appeared on Open Knowledge Blog.

A recap of the Brain Commons panel on the governance of neurotech and neural data

The post Governing the Brain Commons: Openness as Public Infrastructure first appeared on Open Knowledge Blog.

A recap of the BrainCommons panel on how openness works in practice at different layers of the tech stack

The post Understanding the BCI Stack: Where Openness Matters first appeared on Open Knowledge Blog.

The Evergreen release team is pleased to announce that monthly releases 3.17.3 and 3.16.9 are available.

These releases contain numerous fixes committed during the recent Bug Squashing Week, along with several updates to documentation. A special thanks to Susan Morrison (PINES) for the documentation updates!

Files and release notes are available on the Downloads page: https://evergreen-ils.org/egdownloads/

Thanks to the July release team: Galen Charlton (Equinox), Martha Driscoll (NOBLE), Gina Monti (Bibliomation), Andrea Buntz Neiman (Equinox), and Jason Stephenson (CW MARS); as well as everyone who contributed fixes and testing to this release.

Whisky Still

On the stand in a California federal court on Thursday, Elon Musk was asked if xAI has used distillation techniques on OpenAI models to train Grok, and he asserted it was a general practice among AI companies. Asked if that meant “yes,” he said, “Partly.”

Anthropic is accusing three Chinese AI companies of setting up more than 24,000 fake accounts with its Claude AI model to improve their own models.

The labs — DeepSeek, Moonshot AI, and MiniMax — allegedly generated more than 16 million exchanges with Claude through those accounts using a technique called “distillation.” Anthropic said the labs “targeted Claude’s most differentiated capabilities: agentic reasoning, tool use, and coding.”

To begin with, GNU Savannah, the FSF's collaborative software development system, was hit by a massive botnet controlling about five million IPs starting in January. As of this writing, the attack is still ongoing, but the botnet's current iteration is mitigated. The goal is likely to build an LLM training dataset. We do not know who or what is behind this.

• Implement anti-distillation defenses.
• Raise prices enough to make distillation attacks uneconomic.

We have built several classifiers and behavioral fingerprinting systems designed to identify distillation attack patterns in API traffic. This includes detection of chain-of-thought elicitation used to construct reasoning training data. We have also built detection tools for identifying coordinated activity across large numbers of accounts.

no company can solve this alone. As we noted above, distillation attacks at this scale require a coordinated response across the AI industry, cloud providers, and policymakers.

Source

Our first public analysis of the open/closed weight gap in frontier cyber capabilities finds it is 4–7 months with GLM-5.2 and DeepSeek V4-Pro, narrowing from 6–10 months through most of 2025. Advanced capabilities are reaching less safeguarded open models faster than before.

After using all three recent releases, Fable, GPT 5.6, and now Kimi, it's clear that the full power of the models has been significantly held back by the safeguard restrictions caused by last months debacle with the USG -- leading to the top models being quite literally lobotomized in some areas, which leads to subpar results as the safeguards pollute its entire thinking and problem solving abilities.

The funny part? Is that you could have predicted this outcome 2-3 years ago when you started to see the rise of Chinese EVs and smartphones compared to western alternatives.

They quite literally tried to copy the Tesla Model S and iPhone as hard as possible and then eventually it started to diverge to the point where their EVs and phones are just genuinely better (which is why we have export controls banning their EVs, because they would literally drive all US manufacturers to ZERO)

Our open weight model evaluations were largely unimpeded by safeguards. Of the two recent open models we tested, DeepSeek V4-Pro occasionally refused narrow cyber tasks, but this was easily circumvented by a small number of repeat attempts at refused tasks.

These findings indicate a narrow window before today’s frontier cyber capabilities may become widely accessible without safeguards.

Footnotes

1. "Human authorship is a bedrock requirement of copyright . It can be argued that human authorship is involved in the selection of content for the training set. But that gets copyright applied to the training set, not to the model that is generated from it by a mechanical process. What distillation is copying is not the trainuing set but the model.
   
   Even if the model were protected by copyright, this might not affect distillation. Because models are statistical in nature, even if a distiller succeeded in extracting the entirety of a victim model, the result would be different from the victim. Thus the distiller would be able to argue that their distillation was a transformative use, and thus allowed.

From June 29 to July 1, 2026, I attended the 5th International Conference on the Science of Science and Innovation (ICSSI 2026) in Boulder, Colorado. The conference was hosted jointly at the Limelight Hotel and the University of Colorado Boulder. ICSSI brings together researchers who study science itself, including how discoveries happen, how careers are built, how funding shapes research, and how AI is changing all of these. The conference was supported by the US National Science Foundation, the Alfred P. Sloan Foundation, Digital Science, Cevian Labs, and the BioFrontiers Institute.

If you are new to the field, “science of science” (or metascience) is the study of science itself, using data, network analysis, and computational methods to understand how research careers, collaborations, and ideas develop over time. Dashun Wang's The Science of Science is a good introduction if you want an overview before reading the rest of this post.

This year's edition felt especially timely. Shifting federal funding priorities, the growing use of large language models in research, and ongoing debates about peer review and scientific credit shaped many of the discussions. Across keynotes, panels, and lightning talks, speakers kept returning to the same question: what does it mean to do trustworthy, resilient science today? Below is a recap of each day, along with my reflections, followed by a brief overview of the poster I presented.

Day 1 (June 29, 2026)

The morning opened with welcome remarks from the conference co-chairs, Dan Larremore, Erin Leahey, and Bhaven Sampat. It was great to see many familiar faces from the Ai4SciSci workshop community in person. During breakfast and the coffee breaks, I had the opportunity to talk with Jeff Tsao, Alexander Petersen, Daniel Acuña, and Dashun Wang.

The first two invited talks explored how AI is reshaping scientific research from complementary perspectives. Ryan Hill used AlphaFold as an experiment, showing that while it has expanded research on previously unsolved proteins and made experimental work more efficient, it has not displaced experimental structure determination. Instead, researchers are increasingly focusing on problems where AlphaFold remains weakest, highlighting how AI complements rather than replaces scientific work. Chaoqun Ni broadened the discussion by examining how generative AI is changing the research ecosystem across six core tasks, from exploration to evaluation. Her findings suggest that higher AI use is associated with a reorganization of collaboration, with contributor roles becoming increasingly modular and differentiated. This points to changes in how research teams are structured rather than simply making individual scientists more productive.

The discussion then shifted from AI to science policy with a panel on the “past, present, and future of the social contract for science”, featuring Lisa Margonelli, Tony Mills, and Heather Douglas, and moderated by Bhaven Sampat. Heather Douglas argued that she does not subscribe to Vannevar Bush's post World War II linear model, noting that it is difficult to judge the value of research before it is done, making early evaluation an unreliable basis for funding decisions. Tony Mills emphasized that while publicly funded researchers have obligations to society, congressional oversight of how research funding is spent has weakened over time. Lisa Margonelli broadened the discussion by reflecting on the implicit social contract between science and society, how it is sustained through the stories we tell about science, and how those stories shape public expectations.

Following the panel discussion, Kevin Gross presented “Risk, Reward, and the Choices We Face as Scholars”, exploring why scientists often avoid risky research despite the potential for larger breakthroughs. Using mathematical models, he discussed how incentives around grants, publications, and career advancement shape research choices. One line that stayed with me was: “Risk aversion in science is not necessarily evidence of poor science”. He also argued that tenure serves as a form of risk insurance, allowing researchers to pursue ambitious ideas while still maintaining expectations for meaningful contributions. He made the point humorously that tenure is not a license for professors to go hiking instead of showing up to the lab.

I spent the afternoon in parallel sessions covering Career Trajectories and Data, Software, and Research Infrastructure. Ben Aoki-Sherwood's study of interdisciplinary faculty hiring found that 17% of faculty hires move into a field completely different from their PhD field. Xiang Zheng showed that interdisciplinary PhDs still face placement barriers at top universities even after entering the job market. On the infrastructure side, Eva Brown and Nicholas Weber's analysis of software credit was one that stood out: 29% of GitHub code contributors never receive authorship credit on related papers, and only 9% of imported software dependencies are mentioned on average. The most-used libraries are often among the least credited. Yulin Yu's “data hedgehog vs. data fox” framing, which argues that using diverse datasets can improve both research impact and career retention, was a good way to summarize an empirical finding. Finally, Seorin Kim’s audit on OpenAlex abstracts found that roughly one in eight contains an integrity issue, often because only part of a structured PubMed abstract is stored, creating problems for downstream analyses and LLM-based tools.

The day concluded with the poster session, which featured about 50 posters. I presented our ongoing project, “Toward a Cross-Domain AI-Ready Database for Reproducibility and Replicability Studies”, with Dr. Sarah Rajtmajer and Dr. Jian Wu. The project aims to build infrastructure that allows researchers and AI systems to systematically query which findings have been reproduced, replicated, or contested across fields, rather than reconstructing that information paper by paper. I had several engaging conversations at the poster, including with staff members from NIH who were interested in Reproducibility and Replicability Studies from the perspective of research funding and evaluation.

Day 2 (June 30, 2026)

One of the highlights of the second day was the panel, “The Future Infrastructure of the Scientific Ecosystem”, featuring Jessica Hullman, Daniel Acuña, and Aaron Clauset, moderated by Misha Teplitskiy. Daniel Acuña argued that AI is already reshaping the research ecosystem, from peer review to scientific communication, but emphasized that while AI can quickly evaluate ideas, it still cannot decide which questions are worth pursuing. Jessica Hullman focused on the limits of AI for scientific evaluation, noting that automated reproducibility checks can be easy to cherry-pick and often fail to capture the broader validity of empirical claims. She argued that AI should support, rather than replace, human judgment and highlighted the need for systems that preserve diversity of thought instead of reinforcing the most popular ideas. Aaron Clauset closed by reminding the audience that scientific knowledge ultimately resides in people, not papers, and that strong human communities remain the foundation of trustworthy science.

In his invited talk, “On Scientific Memory and Innovation”, Lingfei Wu explored the relationship between scientific memory and innovation through the disruption index, which measures how much a paper displaces the work that came before it. He raised an intriguing question about AI: while today's models excel at remembering, reasoning, and recombining knowledge, can they also learn to “forget” dominant ideas in ways that foster innovation? As he put it, perhaps hallucinations are not always a bug but sometimes a feature.

The next two invited talks shifted the focus from ideas to the people and institutions that shape science. In “Canary in the Coal Mine? Prospects for Early Career Scientists”, Donna Ginther discussed the challenges facing early career researchers, from funding pressures to recruiting international students, while emphasizing that AI is increasing the demand for human judgment and evaluation. Her practical advice was simple: choose your advisor carefully and be persistent when facing rejection. Charles Gomez concluded with “Elite Nations Drive the Convergence of Global Research Agendas”, presenting evidence that a small group of research-intensive countries increasingly shapes the language and direction of global science, raising important questions about whose ideas gain visibility and influence.

The parallel sessions I attended on the second day covered knowledge flow and science communication. Several presentations challenged how we think about the spread of ideas. Zheng Fu argued that citations often reflect scholarly obligation rather than true intellectual influence, meaning ideas can spread widely without their original sources receiving credit. Kyle Siler showed how a small group of countries continues to capture a disproportionate share of global scientific attention, regardless of where new ideas originate. On the science communication side, Hong Chen presented a framework for tracking research mentioned in podcasts. One finding that stood out was that most podcast discussions include no bibliographic information, allowing retracted findings to continue circulating without being flagged.

Day 3 (July 1, 2026)

The final day opened with lightning talks on the future of science policy. Several presentations examined how science interacts with policy and funding, but two stood out to me. Junsol Kim showed how partisan think tanks can reinterpret scientific evidence as it moves into policy documents, often removing important context. Elena Parkerson then quantified the economic costs of multi-year disruptions in NIH funding, a timely reminder of how funding instability can have long-lasting consequences for scientific research.

The discussion continued with the panel “The Future of Science Policy: Research Needs and Opportunities”, featuring Kaye Husbands Fealing, Andrew Gerard, and Matt Hourihan, moderated by Cassidy Sugimoto. The panel offered several practical takeaways for researchers. Kaye Husbands Fealing encouraged researchers to be “pivot-ready”, noting that funding priorities change across administrations and that science of science researchers should develop enough domain expertise to explain the broader context of their findings. Andrew Gerard emphasized the importance of communicating research clearly and making its implications accessible to a broad audience. Matt Hourihan highlighted the persistent tension between the value of research and the funding available to support it, reminding researchers to understand their audience and remain confident in the value of their work.

The conference concluded with Melinda Baldwin's invited talk, “In Referees We Trust? The Rise of Peer Review”, which examined the history of peer review and its role in shaping how scientific work is evaluated and trusted, including its use in research grant decisions at institutions such as the NSF. The conference then closed with awards and closing remarks from co-chairs and James A. Evans, along with the announcement that the 6th ICSSI will be hosted in Rome, Italy.

Closing Thoughts

Three days in Boulder gave me a clearer picture of where the science of science community is heading. The field is no longer focused solely on understanding how science works. Increasingly, it is also asking how AI is reshaping research, scientific collaboration, incentives, and trust. Throughout the conference, discussions repeatedly returned to the same theme: how can we build a scientific ecosystem that remains trustworthy, resilient, and effective as AI becomes an integral part of the research process? While there were no definitive answers, the conversations made it clear that these questions will shape the field for years to come.

I am grateful to my advisor, Dr. Jian Wu, for supporting my travel to ICSSI 2026.

A few captures from my visit.

— Rochana R. Obadage

Project Overview

Approach

Interesting Cases Identified During Manual Inspection

Viewpoint Dependency

Figure 1: The Sharingan model predicts shared attention in the third-person view (left), whereas the egocentric views (middle and right) from the eye-tracking glasses reveal that the two participants are actually attending to different objects.

Scene Complexity and Small Objects

Figure 2: A less complex scene in which two individuals are watching a video on a laptop. Both the computer vision model and the eye-tracking data consistently identify the laptop as the shared object of attention. Figure 3: A complex scene containing multiple small and visually similar objects. While the eye-tracking data show that the participants are attending to the same task-relevant objects, the computer vision model incorrectly predicts shared attention. This illustrates the challenges gaze-following models face in accurately estimating JVA in cluttered environments with multiple potential gaze targets.

Visibility and Occlusion

Figure 4: An example where the exocentric camera is positioned too far from the activity, resulting in inaccurate gaze predictions by the computer vision model. In contrast, the eye-tracking data reveal that each participant is attending to a different object held in their hands. Figure 5 illustrates an instance where the face of one participant is blocked by the object in front. This leads to incorrect gaze predictions by computer vision models. Figure 5. An example illustrating the impact of face occlusion on computer vision–based gaze-following models. When participants' faces are partially occluded by objects in the scene, the models fail to accurately estimate their gaze direction, resulting in incorrect predictions of visual attention.

Tracking and Multi-person Association

Computer vision models struggle to maintain consistent tracking of participants when they move out of and later re-enter the camera's field of view, when heads overlap in the scene, or when temporary occlusions interrupt person tracking. In these cases, the models reassign participant identities after an occlusion event or after a participant leaves and later re-enters the camera's field of view, resulting in inconsistent identity assignments across frames. Figure 6 illustrates four video frames captured at different timestamps where participants were assigned different identities following these events. The changes are indicated by the change in head detection colors.

Although these identity switches do not directly affect JVA detection in our approach, since JVA estimation does not rely on persistent participant IDs, they reveal limitations in the temporal consistency of the tracking process. Such inconsistencies can affect participant-specific JVA analysis, longitudinal behavioral measurements, and applications that require maintaining a continuous identity for each individual throughout an interaction.

Figure 6: An example from a movement-intensive collaborative activity where frequent motion and changes in participant visibility challenge computer vision–based gaze-following models. As participants move out of the camera's field of view and later re-enter the scene, the models may assign them new identities instead of maintaining consistent tracking.

Model Biases

Computer vision–based gaze-following models may exhibit inherent biases that influence their predictions in social interaction scenarios. For example, the MTGS model occasionally predicts that participants are looking at each other even when eye-tracking data indicate that their attention is directed elsewhere. Similarly, the model tends to infer shared attention toward prominent or commonly occurring objects in the scene, even when the actual gaze target differs. These biases can reduce accuracy in complex collaborative activities involving multiple objects, subtle gaze shifts, or task-specific attention. Figure 7 shows the tendency of the model to overestimate looking at humans. Figure 8 is an example of a misprediction due to a predefined model threshold.

Figure 7: The MTGS model predicts that one participant is looking at the other person, even though her actual attention is directed toward the board. Figure 8: The MTGS model predicts shared attention (SA) based on a predefined threshold in the model, even in cases where the participants are clearly attending to different objects.

What We Have Learned So Far

Experience at HGSE and LIT Lab

LIT lab members and friends

Exploring Harvard and Boston

Acknowledgments

The USA is 250 years old this month, and in honor of the occasion LibraryThing sat down with authors Stephanie Dray and Laura Kamoie, who recently collaborated on A Founding Mother, a historical novel about Abigail Adams published by William Morrow in May 2026. A bestselling author of historical fiction, Dray earned her undergraduate degree in Government from Smith College, and her law degree from Northwestern University School of Law, and has worked as a lawyer, game designer and teacher. Her many works include the Cleopatra’s Daughter trilogy, about the life of Cleopatra Selene II, and stand-alone novels like The Women of Chateau Lafayette (2021). Kamoie, also a bestselling author of historical fiction, is a historian who earned her undergraduate degree from Dickinson College and her MA and PhD in early American history from the College of William and Mary. She has published two nonfiction works on early America—Neabsco and Occoquan: The Tayloe Family Iron Plantations, 1730-1830 (2003) and Irons in the Fire: The Business History of the Tayloe Family and Virginia’s Gentry, 1700-1860 (2007)—and has worked as a history professor at the university level, most recently at the US Naval Academy. Before their most recent collaboration, Dray and Kamoie also wrote America’s First Daughter (2016) and My Dear Hamilton (2018) together. They sat down with Abigail this month to discuss their new book.

In your previous novels you’ve explored the lives of Martha “Patsy” Jefferson Randolph and Eliza Schuyler Hamilton. What made you decide to write about Abigail Adams next?

Readers had been asking us to write about Abigail for a long time, but when we realized that our next book was going to come out in 2026, the 250th anniversary of the Declaration of Independence, the choice was a no brainer. Abigail was so far ahead of her time, so direct and unflinching, and so modern in her sentiments, that she was the perfect voice from the founding generation to speak to Americans today.

Tell us a little bit about Abigail Adams and her life, and what makes her such an interesting figure. (Full disclosure: as a bicentennial baby, I was named after Abigail Adams, so I already find Adams a fascinating figure!).

Love that! Abigail was in many ways an ordinary farm wife of Braintree, Massachusetts, at the outbreak of the revolutionary war. She lived in a modest home. She was raising four children. She didn’t have a formal education. But in other ways, she had always been extraordinary. We’ve read a lot of letters from 18th-century women at this point in our career, and none of them display the sense of self-possession of Abigail Adams. She seems to have been a smart, rebellious, independent girl from the get go. She chose a husband who would let her become more than an ordinary farm wife. She became an entrepreneur. A diplomat. A canny political operator. And much, much more.

Adams is well known for her writing, including her famous “Remember the Ladies” letter. How much did you rely on her letters or other work, in writing A Founding Mother? Were there other sources–specific biographies or histories—that were helpful?

While always relying heavily on the original letters, and in this case there were many by Abigail and her family and friends, as much as possible, we use the figures’ own words from their letters in their dialogue and internal monologue. We were also guided by one extraordinary biography by Woody Holton and spent a lot of time with David McCullough’s book on John Adams.

What were some of the most interesting things you learned about Adams, in the course of your research for the book?

The most surprising thing we learned was that although Abigail and John Adams shared one of America’s greatest love stories, it was not a fairy tale. Their marriage was under strain when he was away in Europe—there was a forgotten founding father who is probably better off forgotten who crossed the line with Abigail in inappropriate ways—and that Abigail definitely kept some secrets from John.

All three of your books so far have focused on women who were related to more famous men—in the case of Abigail Adams, her husband John Adams. Why is it important to tell their stories? Which other historical women would you like to highlight?

First of all, the irony is that Abigail may be better remembered than John at this point! But to your point, it’s important to tell the stories of the women who stood behind, alongside, and sometimes even ahead of the men who are said to have founded this country. They didn’t do it alone. Women built this country too and deserve the credit. Because when we don’t acknowledge their contributions, people end up deluded into thinking that “traditional” women were fundamentally dependent and happy about it. That wasn’t true for any of the founding mothers we have written about, and especially not Abigail Adams, who was socially conservative in a number of ways while still being an absolute firecracker when it came to women’s rights and the role they could and should play in government. So if the women who founded this country aren’t “traditional” then who is?

As for other women we would like to highlight, so many! We always have to figure out the venn diagram between what we’re both passionate about, what readers want, and what our publisher thinks will be most marketable. But it really is our mission to do as Abigail said and “Remember the Ladies.”

Tell us about your writing process, when working together. How does that work? Are there specific challenges to writing as a team, or specific pleasures?

We have no set process! Sometimes we alternate chapters. Sometimes we assign whole chunks. One specific challenge is technology. Frequently, we break Google Docs or Microsoft Word because we have so many comments or footnotes. Sometimes we are forced to sit at a table together over one manuscript and shift the keyboard back and forth as we make decisions. But that’s just a good excuse to get together. We enjoy each other’s company and there is a certain magic in our collaboration born of deep respect for each other’s talents and writing judgments. We really do make each other better. And we love sharing little insights and research nuggets and nerding out together at historical sites.

What comes next for you two? Do you have further collaborations in mind, or individual projects in the offing?

We do have another collaboration in mind, and we can’t tell you what it is yet, but we’re excited! We’re both also working on solo novels, so there’s much more to come!

Tell us about your library. What’s on your own shelves?

We both have our own books on our shelves, of course. That gives you a nice pick-me-up when you’re having a bad day. Then there are all the books written by friends and other great writers we admire. Laura has a prime spot for The Alice Network by Kate Quinn on her shelf while Stephanie favors Kate’s The Rose Code. Then there are all the research books. So many.

What have you been reading lately, and what would you recommend to other readers?

Stephanie’s two most recent—both of which she recommends highly—are Allison Pataki’s It Girl, and Madeline Martin’s The Secret Book Society. Laura recently read and loved Olesya Gilmore’s The Fortune Tellers of Rue Daru and David McCullough’s The Johnstown Flood.

Thomas Rowlandson, “The Doctor’s Dream,” from William Combe and Thomas Rowlandson, The Tour of Dr. Syntax in Search of the Picturesque

[This is the fourth piece in a miniseries on finding the right line between human thought and AI assistance, focusing on the stages of scholarly work from initial ideas through the research process to publication, although I believe much of this discussion is applicable to intellectual work beyond the academy. The miniseries began with this introduction and was followed by an essay on the origins of new ideas and a piece on analyzing evidence and data in the early stages of research. In this issue, I look at handing the entire writing process over to AI.]

At the end of the last essay in this series, on the application of AI to analytical sections of a research project, such as data explorations and visualizations, I left this question dangling:

Why not go further or even all the way? Why not have AI do the entire analytical process and spit out the result, perhaps as a nicely formatted paper?

Point your favorite LLM at a stack of articles, documents, lab notebooks, or data sets, and give it a prompt — or if you really want to go all in, have it come up with its own question to answer or theme to pursue based on an initial pass — and sit back in your comfy armchair as the words flitter by.

This new form of armchair scholarship, like the nineteenth-century academics who wrote books and articles based only on what was readily at hand (in, say, their posh home libraries), rather than doing iterative and extensive field work, lab work, or any other kind of time-consuming engagement with their subject matter, is far from hypothetical. As you read this, AI is probably writing hundreds of academic papers. This time the armchair has a jet engine on the back.

A nontrivial and growing percentage of submissions to journals are now partially or mostly the product of AI, especially in scientific fields. In one study, based on STEM articles from 2021 through 2024, AI usage in the writing of academic articles spiked beginning with the release of ChatGPT in the fall of 2022, quickly reaching over 20% in computer science and electrical engineering, and, to a lesser but still significant extent, in other fields by the fall of 2024. These measurements are surely much higher two years later, especially with the latest agentic AI tools and more advanced models.

Liang, W., Zhang, Y., Wu, Z. et al. “Quantifying large language model usage in scientific papers,” Nat Hum Behav 9, 2599–2609 (2025).

These early-adopting AI-assisted scholars are still likely in the minority, and if we want to be generous to them, there may be understandable reasons for their reliance on AI, such as the predominance of English in science publishing. (According to the study, researchers from countries where English is not a first or second language use AI to compose articles at significantly higher rates.) Of course, there’s also the primal need to publish or perish in academia, which has always incentivized cutting corners.

Whether AI authorship of papers is an activity dominated by mercenary researchers seeking tenure or the byproduct of AI translation, the temptation to use AI in the production of scholarly writing will undoubtedly continue to grow. This year has seen a proliferation of websites and software geared toward rapid paper generation. Generally the producers of these tools frame the process as a productive collaboration between you and the AI, but the amount of “you” seems to shrink as you look more carefully and notice the many opportunities to opt out of deep intellectual work. For instance, the AI paper-writing assistant Gatsbi nods toward the discrete stages of research and writing I’ve covered in this series, but also notes that it can go ahead and “auto-draft” the entire paper, stem to stern, if you’d prefer to get a coffee. CoPaper.AI, from Stanford, similarly emphasizes that the scholar guides the production of the article at each turn, but also claims in a large font on its home page that it can take only 20 minutes to sprint from raw data to a final paper. It may be “human in the loop,” but at that scale you’re an ant inside a hula hoop. And naturally you can use Claude or ChatGPT to do end-to-end research and writing, at various levels of engagement from micromanager to laissez-faire napper, whether you’re a fifth grader or a faculty member.

* * *

Call me an optimist, but I believe that the majority of scholars, despite being under enormous pressure to publish, would prefer to be more engaged than removed from the fundamental pursuits and texture of their discipline. They enjoy wrestling with sources, data, and theories, and are innately repelled by the superficiality of having AI write a complete paper. They want to lean into their work, not lean back in an automated armchair. As NYU astrophysicist David Hogg recently wrote, “Anyone working in astrophysics is someone who wants to do astrophysics, not someone who wants to learn the answers.”

Anthropic and OpenAI seem to understand this now, after years of touting AI in a less-than-reassuring way as a replacement for human thought, endeavor, and employment. OpenAI’s new writing tool Prism, centered on LaTeX, a nerdy markup and formatting standard for STEM articles, and Anthropic’s new workbench Claude Science, seem inclined toward more exploratory, iterative, and assistive modes of STEM article generation. Partners, not proxies.

But how slippery is the slope? If one uses AI to help out with part of a scholarly work — for instance, the often (but not always) formulaic “methods” section of a scientific article — will the temptation rise to use it for other parts, such as the more intellectually stimulating and important “discussion” section, in which the results of an experiment are unpacked and its implications for the field made clear? And if so…is that so bad?

Economist and AI enthusiast Tyler Cowen and others working in the more data-centric areas of the social sciences and natural sciences have pondered this question and begun to sour on the old method of article production, instead believing that the future of scholarship may indeed lie in a data set — perhaps one that is constantly updated — and an AI front end that interprets this data. The careful wordsmithing of a paper might be secondary to this direct computational approach, or vanish altogether. And maybe the AI can create the data set too, leaving more time for coffee breaks.

My nagging worry is this: based on a passing familiarity with human nature, I can foresee that an increasing number of academics are going to have to be lashed to the library stacks to resist the AI sirens, who will sing not about more measured uses of AI, but about that sweet, comfortable armchair in which to rest while entire articles are tirelessly generated for them.

* * *

Since we do not have beeswax to put in our ears to resist this song, it seems helpful to examine exactly why the AI generation of a complete scholarly work, rather than using AI judiciously for certain scholarly tasks as I have been arguing in this series, is a bad idea — not just for academic disciplines but for the academics themselves.

At this point, you might be expecting a long rant on AI hallucinations and the possibility of scholarship turning from the pursuit of truth into the extrusion of plausible-sounding truthiness. Hallucinations do remain an area of concern, but it is a problem that has waned over the last year. As LLMs have become more agentic than static, and more rigorously structured in their processes — not relying as much on their initial training set, and venturing out to read external sources of information as needed, instead of immediately starting to spit out text following a query — the number of glaring, or even small, errors has decreased. Especially in the highly connected academic AI environments I have been discussing in this series, with actual libraries available to the LLMs — Claude Science, for instance, can retrieve peer-reviewed research and vetted data from dozens of highly specialized academic resources — the hallucination problem has receded further.

At the same time, hallucinations have not totally disappeared, and academic research should always aim for the highest level of reliability, which makes even the small possibility of hallucinations a shadow over the scholarly enterprise, not to mention the embarrassment of AI-generated faux pas to scholars who take an automated shortcut. Of course, human intelligence can also make errors, or worse, engage in statistical shiftiness or outright fraud. (See the replication crisis.) But we should be aiming to level up on veracity, not down.

More problematic to me than the specter of hallucinations, however, are the long-term effects of the armchair overuse of AI on three areas dear to the academy: the nature of writing and reading, the composition of the sources that writing is based upon, and the enervation of the scholarly mind and scholarly disciplines.

* * *

In early 2023, soon after the release of ChatGPT, I asked, “Can Engineered Writing Ever Be Great?” My answer came from a simple point that every writer knows:

Good writing isn't just the selection and ordering of words, the output; good writing is the product of good reading. Writers aren't indiscriminate generalists, but tend to be rather choosy and personal about what they read. As humans they also have a fairly limited reading capacity, which means that their styles are highly influenced by idiosyncratic reading histories, by their whim.

LLMs, on the other hand, are insatiable omnivores, ingesting as much text, indiscriminately, as they can. This allows them to create countless styles of writing virtually instantly, that feed every need from an automated email response to poetry. But this also means that off-the-rack writing from an LLM tends toward the anodyne, or “slop” if we want to use a more disparaging term.

Over three years later, however, LLM output can be significantly improved, especially if you tailor the inputs to the underlying model, or add post-training context. In one recent study, readers preferred the writing from an LLM trained on books (rather than text that largely comes from the web) over that of human writers with MFAs. I have used Claude Code to create a database of all of my writing (books, academic and popular-press articles, blog posts, this newsletter, and unpublished writing, 2+ million words), which I mostly use to remember and find things I’ve written, but which Claude could also use to compose new pieces very much in my voice, if I wanted it to. (I don’t. The em dashes you often see in my writing are my own; I do love them and don’t care if they have become a marker of AI writing.) If you think that my writing rises above generic AI slop, then I can assure you it’s now possible to use AI to create prose that mimics this more personal, angular style.

Nevertheless, having AI generate an article, even in one’s own voice, inevitably cedes critical intellectual ground. Implicit in the new AI paper-generation tools is the assumption that specific word choices in an article or book are of lesser value than the overall interpretation of sources or data. That may be true in a general sense, and surely many readers of academic articles, ahem, skim, but picking words carefully can increase an article’s power of persuasion and impact. As Daniel Kahneman has shown, a lamentable aspect of human psychology is that we have trouble accepting data as proving a point; we often need well-crafted words and a coherent narrative mapping cause and effect, preferably from someone we see as a peer, to convey the significance of that data and incline readers to accept conclusions. (Even then, alas, human beings can be truly stubborn in their views.) I can now have Claude produce prose that sounds like me, but only the real me can pick the exact words with the right spin and force I’m looking for in a particular sentence. (Plus, I actually enjoy writing; you’ll have to pry my keyboard from my cold, dead hands.)

Furthermore, if we know that a significant percentage of articles are machine-written, we are going to move from careful reading to frequent skimming to a complete abstention from the scholarship in our field. (We will probably have an LLM summarize it for us.) This will obviously greatly harm the exchange of ideas. The only way out of this conundrum is for most practitioners in a discipline to commit to putting in the time and energy to produce and digest thoughtful work. Knowledge production is inherently social — not in the postmodern, constructed-out-of-thin-air way, but embedded in a communal process in which we come to respect, or at least recognize, that other intellects are wrestling with the same problems we care about, and which fosters a continued interest in our common research.

* * *

Then there is the invisible loss of context and detail when AI writes the majority of an academic work. An experiment as a case in point: One day I tried, like an Oliver Sacks case study, to be my wife, who is a scholar of early childhood programs. Using Cowork, I told Claude I wanted to write a paper on how different state policies on child care impact American children and their families. We (Claude and I) decided to use Policy Commons’ invaluable data set, through which Claude assembled a list of 232 state statutes and regulations. Sipping my coffee, I asked Claude to read and process all of these lengthy documents and create a matrix for me so I could quickly assess the contours of child care programs in the United States. But Claude was more caffeinated than I was: like a teacher’s pet it went off and produced, independently, a comprehensive report, not just a table, in a few minutes. It even created its own categories of differentiating metrics, such as infant/toddler:adult ratios, total care group sizes, licensing requirements, academic qualifications for practitioners and directors of programs, sleep protocols, and idiosyncratic state mandates. A few more sips of joe, and a few more prompts to acquire additional data, and I was swiftly on my way to what I thought was a decent meta-review essay.

Now a giddy AI-assisted dilettante, I showed this effortless production to my wife, who proceeded, as an actual expert, to dissect it ruthlessly. Claude got the empirical data mostly correct — no silly hallucinations — but its attempts to extrapolate from the numbers into trends and impacts were clumsy and overly broad. Since my wife actually knows these early childhood programs well, she understands how, on the ground, actual child care sites might differ from written state policies and other sources of information, and she could identify this missing context and additional key details that were invisible to me and my AI buddy. Our armchair scholarship was no match for her decades of experience and knowledge.

The output seemed so good, though…I could totally imagine a less scrupulous academic trying to publish it. The ease of generating an AI paper this way means we will increasingly end up with papers written on the data that is readily available, without questioning how good the data actually is. We will think less about what’s missing.

Yet even with agentic AI — again, Claude Science can reach out to dozens of research databases for material to work with — there are yawning gaps. At the closing plenary this spring at the Coalition for Networked Information meeting in Salt Lake City, “Harnessing the Data Renaissance for Scientific Discovery,” Manish Parashar, Executive Director of the Scientific Computing and Imaging Institute and Chief AI Officer at the University of Utah, highlighted the major work that still needs to be done to create truly rich and comprehensive data sets for many fields, such as cancer research. If we ignore this complex and time-consuming process, and just use AI on top of the data that’s close at hand, we might produce scientific articles more quickly, but we may not make significant breakthroughs. (This is probably already happening.) Parashar, instead, is working with other scientists on a National Data Platform that will aggregate and normalize thousands of sources, without which any AI processes will suffer from a problematic narrowness. In other words, being a good librarian — someone who finds, catalogs, assesses, and merges sources into a coherent and usable library — has become even more valuable.

* * *

Finally, using AI to generate new scholarly papers assumes an iffy theory of intellectual history, that new ideas and discoveries are always implicit but not yet articulated in the existing literature and data. What if new ideas instead come from unique circumstances, lived experience, group interactions, or an individual’s eccentric way of reading and seeing? Yes, the history of science has a number of examples of innovations that seem to be “in the air” and thus “discovered” by multiple people at roughly the same time, such as calculus (simultaneously developed by Leibniz and Newton). But there are many more examples like the one I wrote about in “Can AI Prompt Us to Ask New Questions?,” where a quirky combination of a person’s biography and interests, embedded in a particular social scene, leads to revelations like fractal geometry.

Intellectual history contains both kinds of discoveries, but we wouldn’t want to block the latter, deep river of innovation, and if the overuse of AI in the production of scholarship reduces the velocity of the human mind and the vitality of intellectual scenes, we might find this source slowly drying up. It is worth remembering that asking good questions is harder than giving great answers. Insightful out-of-the-box approaches, often stemming from unusual, previously unasked queries, are the dark matter of human thought and progress, and they often come from the random interactions and odd interests of particular human beings. It is unclear how AI will replicate these uncommon vibrations and collisions.

In “Illegible Benefits,” a piece by Carlo Cordasco of the University of Manchester that is largely positive about using AI in the production of scholarship, he mentions a lingering concern:

I want to be honest about the costs. My ability to hold together a complex position verbally, under pressure, in a seminar or a conversation, has probably not improved and may have declined somewhat. When preliminary exploration is cheap, you spend less time grinding through arguments from first principles, a grinding that builds fluency that shows up in live exchange.

This is the professorial equivalent of the cognitive decline that we worry about with our students who have been using AI for years — and what atrophies in the seminar room will surely atrophy on the page as well. The solution seems clear: if you would like, use AI for the parts of scholarly work where it excels and check all automated output, while retaining human seniority in orchestrating and expressing the meaning of your research.

It’s going to be tough, though. Armchairs really are comfortable.

Introducing CABot, a chatbot that makes climate knowledge more accessible to everyone, keeping information clear, factual, and credible.

The post A Climate Chatbot for Trusted and Transparent Climate Information first appeared on Open Knowledge Blog.

The Future's So Bright, I Gotta Wear Shades

Timbuk3

Nachtrieb & Smith Fig. 2

While AI electricity consumption was only 1.5% of the global total in 2024, its power demand has grown at a rate of 0.127 yr⁻¹ since 2015, accelerating to 0.15 yr⁻¹ over the last five years. Projecting from this 2024 baseline, AI’s electricity demand is on track to achieve parity with the rest of the world’s combined consumption by approximately 2050.

1. Non-renewable resource depletion
2. The Kelvin Limit
3. The renewable resource limit
4. The Dyson Limit
5. The Asimov Limit

Even with an infinite energy source, the laws of physics dictate every unit of energy used eventually becomes waste heat. On a planetary scale, this heat must be radiated into space to maintain a stable environment.

Earth stays at a life-sustaining temperature by balancing received solar energy with infrared radiation emitted back into the cosmos. Human energy consumption from “terrestrial” sources, such as nuclear fusion or fossil fuels, adds “new” heat to this balance. Because this energy was not already part of the solar-to-earth flow, the planet must reach a higher temperature to increase its radiative cooling capacity and shed the additional load.

The “Kelvin Limit” defines the point where this added waste heat pushes Earth’s surface temperature to 373 K (100 ◦C), the boiling point of water. At this threshold, the planet becomes physically uninhabitable.

The solar power arriving at the Earth’s cross-section is Lα ≈ 5474 × 10³ EJ yr⁻¹. A portion of this energy is immediately reflected into space by the Earth’s albedo (a), which represents the planet’s reflectivity. Based on NASA data, Earth’s albedo is approximately 0.30, meaning 30% of incoming light reflects away while the remaining 70% is absorbed as heat (P_a).

The model assumes an initial equilibrium where absorbed solar power (P_a) equals the power radiated at the baseline temperature (T₀).
...
The limit occurs at time t₂, when the combined heat of the Sun (P_a) and human energy demand (D) requires the Earth to reach the boiling point (T₂) to maintain equilibrium.
...
This thermal wall represents a hard physical limit. Technological efficiency cannot bypass it; higher energy use to drive AI or industry simply accelerates the transition toward this planetary boiling point.

Table VIII

Table IX

These figures represent a fundamental shift in the prospects of civilization under AI. Under the 15% growth rates currently demonstrated by AI infrastructure, we quickly accelerate past all projected limits. The thermal “Kelvin Limit” (k ≈ 10), which would normally take ten centuries to reach, suddenly appears in just 67 yr, well within a single human lifetime.

forecast to be around $208.96 trillion, $11.04 trillion up compared to $197.91 trillion in 2024.

The scraping problem is worse than anyone can imagine and thanks to my friends at Sourceware we have some real data to prove it.

I've been working more on Anubis' reputation database and I've run into a really weird discovery: 80-90% of the hits created by the honeypot feature are from IP addresses that do not belong to any existing threat monitoring lists.

Here's a breakdown of the honeypot hits Sourceware has gotten in the last few months:

Assessment of ./data/manually-submitted/sourceware/202607141625.txt against ./var/reputationdb.mmdb

In case this interests you, I have put the full tables in Appendix A: Full tables for the reputation database input.

Flags (of flagged addresses)

Categories (6 distinct, of flagged addresses)

Providers (126 distinct, of flagged addresses)

Mara

Methodology note: "provider" here means one of two things:

1. The company or organization associated with the IP address.
2. The place the list was gotten from.

For example, scaleway is based off of Scaleway's publicly posted IP address ranges, firehol-level1 is based on a daily snapshot of FireHOL's Level 1 IP list, and fdo is based on data contributed by the administrators of freedesktop.org.

(remainder snipped for brevity)

Countries (229 distinct, of all addresses)

This doesn't list data from 204 additional countries. Given that the ISO 3166-1 standard comprises 249 countries (193 of which are UN members), it's safe to say this is a global problem.

ASNs (21116 distinct, of all addresses)

There are 18069 more ASNs not listed.

How Anubis' honeypot works

In order to collect data on how widespread the scraper problem is, I added a honeypot feature to Anubis. On every challenge page it adds semantically invalid HTML akin to the following:

<script type="ignore">
          <a href="/.within.website/x/cmd/anubis/api/honeypot/<uuidv4>/init">Don't click me</a>
        </script>
        

Visiting that page gets you cheap to generate vacuous anti-content that has two links to other pages. This is intended to get badly written scrapers caught in the honeypot so they scrape that instead of the protected website. I made it on a whim but thought it would be great for collecting data on how widespread this problem actually is.

This is a global problem

Based on the data I've seen, this is a global problem. If I had to guess where most of this traffic is coming from, it's from compromised smart appliances contributing traffic to proxy networks. I don't think there's any way to make a real impact on this problem without concerted simultaneous global action.

TL;DR: the scraping problem is actually widespread enough that web application firewalls like Anubis make sense.

A presigned URL is a replay attack you did on purpose.

Replayable auth tokens are the textbook way to create vulnerable systems, but Tigris ships them as a first-class feature with presigned URLs and so does every other object storage system on the planet. However this isn't an oversight because presigned URLs turn a weakness into a feature.

Replay attacks are a real problem and the classic fix is miserable

When you authenticate a request with Amazon's SigV4 protocol for Tigris, your client boils down the request to a canonical form: a SHA256 hash of the request's method, path, query parameters, signed headers and a SHA256 hash of the payload. It runs the result of that through HMAC with a signing key derived from your secret access key. Nothing secret ever crosses the wire. The server derives the same key as the client, does the same canonical form transformation, and compares the result.

Being able to make a valid signature proves that the request came from someone holding the secret access key, but it proves nothing about when that request was made. A signature that was made a year ago would still be valid today or any other time you send it, so in theory an attacker could warehouse your signed requests only to replay them en masse later. Imagine sitting on a pile of signed "create EC2 instance" calls only to spam them all out at a later date. You would be a twirling moustache villain able to spawn dozens of servers at a moment's notice.

Traditionally the fix is to bake a nonce (number used once) into the signature (sorry to any British readers in the audience). This makes every signature differ because that nonce differs.

However with great power comes great responsibility and making sure that something used once is only used once is a surprisingly hard distributed systems problem. You can't verify that something is only used once locally. Say you store them all for a 15 minute smear window at a low request rate like 10,000 Bq. That's 9 million live nonces, and every frontend node needs to have a consistent view of the whole set as it churns.

You have made your fast authentication check slow from having to ensure things are only used once.

What you want instead is something that changes constantly without coordination and invalidates those old signatures for free. For an added bonus you want this to also be in the standard library of every programming language.

Sign the clock

There's exactly one value that changes constantly, (mostly) monotonically, and is already actively coordinated across all elements of the stack: the clock. Your OS already keeps time in sync with the public NTP pool (or a private NTP pool if you are cool enough to have radioactive PCI cards laying around). Without an accurate view of time you can't make TLS connections, which means you can't make API calls to Tigris at all, so the auth layer gets to assume a working clock exists.

SigV4 signs the current time into the request. If an attacker gets their greasy hacker paws on a signature, they have about 15 minutes to use it before it becomes a digital paperweight. If time is an input to the signature and the time changes enough to invalidate the signature, the signature is null and void. Sure in theory a sufficiently funded attacker could create a black hole in your datacentre and disrupt temporal flow, but at that point the planet is probably toast which makes the attack profile moot. Commit mass object storage fraud with this one neat trick! The department of temporal investigations will have hated it!

This makes your verification stay stateless. Everything gets checked against the system clock the server already needs and you can give clients a 15 minute signature smear window as a grace period for old or delayed clients (exponential backoff is a good thing and Tigris will reward you for doing it).

Of course the real thing keeping the signatures safe on the wire is TLS (HTTPS). If that is broken we have bigger problems and object storage fraud is the least of our problems.

Time is the only nonce you need because both sides already agree on it anyways.

Some thorns have roses

Presigned URLs take the replay tolerance that SigV4 spends all this effort nerfing and then buffs it into the feature. The entire auth dance gets flattened into URL parameters that any HTTP client can use, be it a browser, curl, Go's net/http, or something you made by bit-banging HTTP over a socket. Here's a real presigned URL I sundered into visibility:

https://xe-sophia-base.t3.tigrisfiles.io/moby-dick.txt
        ?X-Amz-Algorithm=AWS4-HMAC-SHA256
        &X-Amz-Credential=tid_ubYBNEYAmTciLVwszw_QrUXDmtcyQisryryGfxgznDsCnOvNqh/20260714/auto/s3/aws4_request
        &X-Amz-Date=20260714T043308Z
        &X-Amz-Expires=3600
        &X-Amz-SignedHeaders=host
        &X-Amz-Signature=0dcaf4972911527a7582ff36ea457e9760a8efccb6655a178685aaa281637a36
        

Here are the parts (forgive the AI looking listicle because this is genuinely the best way to format this):

• X-Amz-Algorithm: the signature scheme. Effectively always AWS4-HMAC-SHA256.
• X-Amz-Credential: the access key ID plus the credential scope — date, region, service, and the literal terminator aws4_request. The signing key is derived by chaining HMAC through exactly those parts, so a signature is only ever valid for that day, that region, that service.
• X-Amz-Date: the second the URL was born, in UTC.
• X-Amz-Expires: how many seconds it gets to live, chosen by the signer.
• X-Amz-SignedHeaders: which HTTP headers are folded into the signature. Usually just host, because you can't force whoever you hand a URL to into sending exotic headers.
• X-Amz-Signature: 64 hex characters of HMAC-SHA256 over the canonical request — the method, the path, every parameter above, the signed headers, and the payload hash. Change any of them and the math stops agreeing.

All of these are normally HTTP headers in standard SigV4 requests.

GET /moby-dick.txt HTTP/1.1
        Host: xe-sophia-base.t3.tigrisfiles.io
        X-Amz-Date: 20260714T043308Z
        X-Amz-Content-Sha256: e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855
        Authorization: AWS4-HMAC-SHA256
        Credential=tid_ubYBNEYAmTciLVwszw_QrUXDmtcyQisryryGfxgznDsCnOvNqh/20260714/auto/s3/aws4_request
        SignedHeaders=host;x-amz-content-sha256;x-amz-date
        Signature=0dcaf4972911527a7582ff36ea457e9760a8efccb6655a178685aaa281637a36
        

Note that this request is not a legal request, it's an example to illustrate the point, here be dragons, etc etc etc.

It's best to think about this presigned URL as a capability grant. Whoever holds it gets to make exactly one (1) kind of API call with one (1) HTTP method against one (1) object in one (1) bucket. They can do this as many times as they want until the presigned URL expires. The signature covers the method, the path, and the signed headers so a user can't take a presigned request for GETting a copy of Moby Dick from a development environment and weaponize it into a way to delete everything in your production bucket.

Possession is authorization until the clock says no.

What it costs you

Capability grants like this can have some sharp edges. There is no real way to revoke any individual presigned URL short of killing the access key it was signed with. When that key dies, everything it signed dies too. This includes any URLs you may have wanted. This cuts both ways and it kinda has to unless you make a new keypair per presigned request, which is probably out of scope.

Expiry has fine print too. A presigned request can live anywhere from one (1) second to one (1) week (seven (7) periods of twenty-four (24) hours).

There's no limit to the number of times a client can use a presigned request. If you give a mouse permission to GET one cookie, they can GET that same cookie over and over. You end up having to pay for the GetObject calls in the end, so keep that in mind.

URLs also leak, but these URLs are born to die. Presigned URLs will end up in API responses, chat messages, GitHub comments, and your browser history. The tradeoff is acceptable because all the links self-destruct, but it's a tradeoff you need to keep in mind when you design your services, not a panacea for access control.

Presigned URLs sound like a great way to prevent hotlinking. At some level they are (a few of my services use them as such), but what they actually do is put a lifetime on hotlinking. This makes things annoying enough that it usually gets people to stop.

The hole in the fence is the gate

SigV4 makes a lot of API authentication challenges so much easier. It spent most of its innovation budget on making signatures die quickly because replay attacks are the classic way that signed requests go wrong. Presigned URLs looked at that property, shrugged, flipped it on its head, and made it into a feature.

The thing that looked like a problem becomes a fundamental construct to build your apps upon.

Want to hand out links that expire themselves? Tigris supports presigned URLs out of the box with the same SigV4 dance you already know, on globally distributed, S3-compatible object storage. Read the docs.

Boeing's PDP-7/340

Haberdashers' Aske's School

The school was founded in 1690 by a Royal Charter granted to the Worshipful Company of Haberdashers to establish a hospital for 20 boarders with £32,000 from the legacy of Robert Aske (equivalent to approximately £5m in 2019).

varied greatly in size and composition, but, on average, achieved higher academic results than either maintained grammar schools or private schools.

By Lucaseverini66 - IBM Archives CC BY-SA 4.0, Link

By Kenneth Lu - Spacewar! CC BY 2.0, Link

Titan by University of Cambridge CC BY 2.0, Link

CDC274 User Guide Fig 3.2

Tindaro Screpolato

In the Boboli Gardens, Florence

Armillary Sphere

at the Museo Galileo

Ellen Hartman, OCLC Leaders Council Manager, continues her blog series on global library leadership conversations, inspired by a recent meeting of the OCLC Leaders Council. The first post in the series explored the unique value of global library leadership conversations, as well as some of the practical realities of making this form of engagement successful.

In the room at OCLC Leaders Council

As I continue to reflect on the most recent OCLC Leaders Council meeting, I’d like to look at another aspect of global library leadership conversations: what they “cost” and who carries that cost. What do I mean by that? A global convening of leaders like Leaders Council requires participants and organizers alike to directly face challenges to achieve a meaningful outcome— the opportunity costs of participation, the responsibilities of representation, and the time zone challenges, just to name a few. All of these challenges were in effect during our recent Leaders Council meeting and here, I’ll reflect on the lessons learned from these experiences and how we might carry that forward into effective, transformative meetings of library leaders from around the world.

A shared responsibility for the outcome

There is a certain pressure that comes with bringing global library leaders together. The goal always is for participants to walk away feeling inspired or positively challenged. To feel that it was worth the time spent—the preparation, the travel, the days away from a library that doesn’t stop needing its leader just because they’re on the other side of the world.

While every effort goes into organizing and facilitating these gatherings to create an environment that’s conducive to conversation, what happens in the room is ultimately everyone’s responsibility. There must be a willingness to engage, learn, and share. Leaders who have invested in the time to attend must also show up ready to contribute.

But willingness alone doesn’t make participation equal. Engaging across borders involves more than sharing perspectives or exchanging ideas. It demands translation, abstraction, and sustained effort. These demands are not evenly distributed, and they shape who is heard, which perspectives travel, and how collective understanding takes form.

No one starts from neutral

There is no such thing as a “globally convenient” meeting time. This sounds like a minor logistical observation, but it points to something more significant about how international participation actually works.

Every international gathering, whether in person or online, asks something unequal of its participants. Someone is always the outlier. For in-person gatherings, that might mean traveling across multiple time zones to participate in a conversation that spans two or three days—by the time the jet lag begins to ease, it’s time to leave again. People show up and engage with genuine enthusiasm, running on the energy of being in the room. But the physical cost is real, whether it’s felt during the conversation itself, on the journey home, or in the days that follow.

For online gatherings, the asymmetry takes a different form. There is no time zone that works for everyone: someone is always joining in the middle of the night or at the end of a long working day, bringing commitment that deserves acknowledgment rather than assumption.

Acknowledging and managing the physical cost of international participation is an important aspect of organizing global leadership conversations. Engagement is most valuable when people can bring their full attention and their clearest thinking, and recognizing the conditions under which people are participating is key to taking global engagement seriously.

The weight of representing more than yourself

Participation in international leadership engagement opportunities is often limited by resource constraints, geography, and institutional priorities. Because of this, there’s often an unintended expectation that those who are able to attend speak not just from their own experience, but on behalf of a much larger community they’re perceived to “represent.”

Library leaders are often part of many different conversations: within their own institutions, at the national or regional level, or within library associations and other advisory or interest groups. While there’s great value in taking part in all these conversations and being present in different rooms with different viewpoints, it can also be a struggle. A leader might want to represent the different opinions and experiences they’ve heard, even if they aren’t their own. This requires multiple levels of translation: from personal experience to the national or regional level, and from the national or regional level to the global conversation. And no single person can do this perfectly.

Moreover, full representation, however much we might wish for it, is not practically achievable in a single conversation. A room that attempts to represent every context, every region, and every type of institution quickly ceases to function as a conversation at all. At the end of the day, the goal isn’t perfect representation: It’s awareness of where representation is limited, and what that means for how the conversation and its conclusions should be understood.

The consequences of acting as the “representative in the room” extend beyond the individual adopting that role. When a single voice comes to stand in for a broader context, the conversation itself is affected. The genuine variation within any national or regional system can disappear from view, leading to misleading impressions about what is typical, possible, or desirable in any given setting. As discussed in the first post in this series, international engagement spaces are rarely designed to communicate the full picture of the contexts involved. What appears to be a shared understanding may, in fact, reflect the voices of those who were present and, implicitly, those who were not.

When ideas travel, something stays behind

To participate effectively in international leadership spaces, leaders are often asked, implicitly and sometimes unknowingly, to step back from local urgency. Their own institutional realities must be translated into language that everyone in the room can engage with, meaning that context is simplified so that ideas can be compared across very different realities.

Without some level of abstraction, international exchange becomes unwieldy. But abstraction always involves loss. The more portable an idea becomes, the more likely it is to lose important context and nuance. Solutions circulate more easily than constraints. Success stories travel further than the enabling conditions behind them. A listener may hear what was done without fully grasping what had to be in place, institutionally, politically, and financially, for it to work.

The “cost” of transporting contextual freight can shape what gets discussed and what doesn’t. Perspectives that generalize quickly and easily tend to move forward in the conversation. Narratives that fit familiar frames gain traction. Conversely, experiences that are rooted in specific local realities may struggle to find space. What’s repeated becomes a reference point. What’s consistently absent becomes, gradually, invisible.

This is another reason why it matters to know that a conversation will rarely surface the full picture. If we treat what’s shared as a collection of inputs rather than a complete account, we’re less likely to mistake what translates well for what’s most representative or most important.

Conclusion

Understanding that there are costs embedded in global leadership conversations, and that they are often borne unevenly by participants, can lead to shifts in how organizers and participants respond. It means acknowledging and appreciating the contributions of those who show up despite extraordinary obstacles or inconvenience, navigating the pressure of representing more than one person can fairly represent, and doing the work of translation that makes global conversation possible.

Some takeaways to consider:

• Leave room for the person who’s navigating a conversation in their second or third language, or who’s battling a 12-hour time zone shift.
• Ask questions that invite rather than assume.
• Try to connect personal experiences to others’ without letting that connection override what makes their situation different.
• Approach the conversation with genuine curiosity about what others are experiencing and facing. Everyone in the room comes from a different context and is coping with different challenges. Asking questions about those differences is one of the most valuable contributions you can bring to the conversation.

The next post concludes this series on global leadership conversations with a look at why international cooperation—even among leaders who are genuinely committed to it—has become harder to sustain than it used to be.

The post Who’s in the room: The hidden costs of global library leadership conversations appeared first on Hanging Together.

Previously I opined that Valve was about to win the console generation. I couldn't have possibly predicted that both Microsoft and Sony would just self-sabotage so hard that they're both going to lose.

Between Microsoft's decimation of the Xbox division, slaughtering off the IdTech team, and continued increases of Xbox hardware prices; there's nothing to really be excited about with the Xbox. Sure their most recent presentation showed off a bunch of exclusives, but none of them really made me think "wow, I should go get an Xbox to play that". Hell, few of them made me think "wow I should go play that" beyond the Halo remake coming out next month (and really I just want to see how much of a trainwreck that is going to be).

Microsoft is also starting to double-down on their in-house games being Xbox exclusives, which really doesn't give me much reason to want to play them because I simply can't buy them without buying an Xbox.

Sony also has discontinued porting their games to PC because they're not hitting the (probably impossible) revenue targets that they need to make up for big-ticket failures like Concord. I do have a PS5 that has mostly been relegated to gathering dust when it's not playing YouTube and Twitch duty in the living room, it's likely going to be replaced in favour of my Steam Machine whenever that comes in next year. However nothing that's come out in terms of Playstation exclusives is really compelling, and what is compelling enough just isn't that compelling to want to buy it on Playstation as opposed to just getting it on Steam where I can run it on my tower or on the home theatre PC.

Sony also has been raising prices and recently announced that they're killing physical media next generation. It's starting to make me wonder if I should even bother getting the next generation of Playstation. If I can't give people physical games as gifts anymore, why should I bother buying the new console?

My husband and I both can't remember why we even got a PS5 in the first place, maybe it so that we could do couch gaming without hearing the fan noise or so that the video streaming experience from the NAS could support HDR.

We have a Switch 2 at home, it's mostly there to play Nintendo exclusives like Mario Kart World and the Xenoblade series. If those exclusives were available on Steam, we wouldn't buy them on the Switch 2.

Otherwise, everything is via Steam or other PC storefronts anyways.

Man, Valve really does win by doing absolutely nothing while the rest of the industry shoots itself in the head. I fear for what happens when Gabe Newell retires and the MBA cancer fully infects Valve.