AI
How the networking technology sector is changed by the introduction of AI.
Wednesday, November 12, 2025 →
Bookmarking this 404 media podcast specifically because it discusses how librarians are navigating the rise of AI.
Why Tim Berners-Lee still believes in the web | The Verge
You want to have control of your own destiny. We call it digital sovereignty. In the old days, the early days of the web, anybody used to be able to make a website. So that feeling of sovereignty as an individual being enabled and being a peer with all the other people on the web, that is what we are still fighting for, and in fact, we need to rebuild.
What is this site and why am I doing it?
Sunday, November 9, 2025
In recent history I stopped posting on most social media and moved to the fediverse. I still browse the social platforms to keep up with trends and friends, but I only post on my private IG and here.
What I share here is separate from but related to my professional life — I’m thinking out loud and making room for rough, unfinished ideas. I write mainly for myself, but if others find it useful, that’s great. The practice of reading and reflecting makes your thinking stick, and I am from a certain time and place, so this is how I approach learning and communicating about what I’m learning. It’s a habit.
While this is my preferred approach, I acknowledge that sharing unfinished ideas publicly is risky and you have to accept accountability for the messiness that comes with that. But I also know that working through your vulnerability through the act of writing lets you tap into your most creative, innovative self and test your ideas against an evolving sense of what’s good. The potential for an audience, however real or implied, keeps you more honest and less self-indulgent. Despite the trade offs, I think it’s worthwhile.
As I add to this page, I’ll be thinking out loud about digital rhetoric and communication alongside emerging technology, and linking back to foundational ideas I see reflected online today. Occasionally I’ll say something longer.
Practicing pluralism in risky spaces
Saturday, November 8, 2025
While I’m cleaning up the cruft around my social presence, I’m finding more references to the heyday of blogging that explain how people organized online (Web 1.0) before the era of platforms (Web 2.0). One is this interview with Jill Filipovic, my one-time co-blogger and comrade at Feministe, with the folks at LGM who interviewed me on the subject a few years ago. Jill and I differed (and still do) on the meat of many issues, but have a lot of our thinking in common about how to handle disagreement and advance our ideas in common (and risky!) spaces. She’s welcome in my foxhole anytime.
I appreciate this oral history project by LGM because the articulation of our processes then (before automation) provides a lot of instruction about how to organize and think about communication outside of increasingly toxic and irresponsible social platforms today. One idea that is relative today is around pluralism. In our case, we practiced pluralism on subjects and perspectives within a defined domain, “feminism.” In practice, making it work before true automation meant we lived with a lot of decision points around moderating a raucous community. Bask then, we had our CMS and set up a list of community expectations, iterating as we went, then let the community rip. Collectively, this approach created a robust and vibrant interactive community of peers and moved our interests forward as a cohort. Between her interview, where she talks through the decision points we faced, and mine, where I talk more about the tools and their limitations, there is a lot for people who want to organize people digitally while thinking outside of the algorithm.
I met a friend for dinner, we had a time, and as she left she mentioned her long-time book club and how much everyone hated their latest read. What was it? I asked. She said, “Have you heard of ‘The Heart in Winter’?” 😭
Why emphasis on literacy, writing, reading and the canon is always politically prescient. Also why the cyborg’s mark remains one of my favorite metaphors.
Returning to Haraway
Saturday, November 1, 2025
There are certain pieces of writing I return to when thinking about our relationship with technology. Donna Haraway’s “A Cyborg Manifesto,” published in 1985, is one of them. Despite being nearly forty years old, it continues to offer insights into how we understand technology’s role in our lives.
Haraway used the cyborg, a hybrid of machine and organism, as a metaphor for understanding identity in an increasingly technological and scientific world. Her central argument was this: because the traditional boundaries we’ve relied on are breaking down with the rise of STEM, computers and factory automation, a tenuous new order is emerging, blurring the lines between human and machine, physical and digital, natural and artificial, gender and biology, moral and immoral. Many women in academia resisted the political push for STEM, concerned about the impact on the humanities. This was critical stuff in the mid-1980s, especially in a global (and academic) context of collective civil rights struggle across very different coalitions, amid the science and all of its implications. She wrote it as a salvo appealing to fellow academic feminists not to be so skeptical of new and emerging computer technology that they lose on emerging opportunities. To her, the implication of these new technologies meant new political landscapes and platforms for discussion and iteration.
Haraway challenged the either/or categories that dominate these debates: online versus offline, human versus machine, authentic versus artificial, even good and bad. Instead, she proposed we’re already living in a world of hybrids and overlaps and contingencies and compromises, where identity and experience are shaped by our relationships with technology and science and capitalism rather than existing separately from it. Whether you wear glasses, take daily medicine, strum a guitar, drive a car, or regularly log into a device for work or leisure, our lives are heavily augmented by layers of tech already. Your cyborg self is already here. We are already deeply technical creatures, living in concert with machines.
Haraway invites us to dabble in the Matrix, to take off your trench coat and stay awhile, to see what it feels like in the moral relativism and ambiguity. Within this web of complexity lies a lot of opportunity.
Tl;dr: the cyborg metaphor is a permission structure and a thought exercise: Instead of asking if or whether to accept tech, she asks you to consider something more pragmatic, how your cyborg self might use and shape technology to assert your particular existence, politics and interests across the network. This is an if/then that is simultaneously empowering, cynical, dystopian, ironic and futurist, but allows us to set aside some limiting binaries and narratives when thinking about the specter of new technology.
I miss Twitter because I’m full of big opinions and inappropriate one-liners. Anyway, here’s an analysis from WIRED on the implications of this month’s AWS outage.
On prompt injections: Because AI chatbots are trained to be helpful and to understand context, jailbreakers are able to engineer scenarios where the AI believes ignoring its usual ethical guidelines is appropriate. Is it currently possible to safeguard LLMs from injection attacks at scale?
Out: SEO; In: GEO.
“LOLgislation,” or how memes become policy, and posting becomes praxis.
For many years, “shitposting” has been a staple of internet culture in which individuals riff on the moment using nonsense and irony, derailing threads for fun. In today’s influencer-driven attention economy, however, shitposting as a practice is now a meaningful comms and engagement strategy.
What rivalry?
Monday, October 20, 2025
Purdue Exponent students distributed 3,000 copies of a special “solidarity edition” newspaper in Bloomington after IU spanked their student paper for insubordinance, ending the IDS print edition and firing their director. The media landscape in Indiana is bleak, generally, after years of disinvestment, so student reporters fill a social and political gap that the free market left behind. Given those conditions, the wider community depends on student media, much like public radio, to fill the information gaps. Also, these campuses are situated in communities where it can be very socially uncomfortable to be a squeaky wheel. So. As alum, I’m proud of the Exponent for this brave and newsworthy show of heart. 💐
The next big trend in AI that I’m watching is platform integration. First company to produce the interoperability required for a united platform experience wins.
What happens to college towns after they’re hit by the so-called enrollment cliff?
A lovely story about the restoration of prairie land undertaken by the nuns of Holy Wisdom, just outside of Madison, WI.
A pleasant surprise resulting from LLM acceleration in the IT landscape is the sudden opportunity for storytelling around other, more analog kinds of information technology and access models. Nostalgia abound (complimentary).
On the rise of faith tech.
Thursday, September 18, 2025 →
Harvard Business Review offers this take on why it’s bad for business to automate our way out of staffing entry-level positions.
What do people actually use ChatGPT for? A snapshot.
This is incredible storytelling from propublica.org, on opioids, inequality, and the scope of drug-induced homicide charges brought against teens in Wisconsin.
Another one on how LLM sycophancy facilitates suicidal ideation.
Something that worries me about AI adoption in higher ed is the risk to students facing mental health challenges, who are increasingly turning to chat bots to plumb their own depths. What can higher ed ask of LLM business partners to protect our students’ mental health?
“It’s almost as if groups on both sides of the political spectrum are looking for an excuse to brand business decisions as politically or socially hostile,” said Jill Fisch, a professor of business law at the University of Pennsylvania who studies how corporations operate in political spaces.
Hey chat, how is pedagogy changing in an age of AI?
⚡ AI's last mile problem in higher ed
Sunday, August 17, 2025
I graduated from college right before the 2008 recession and bounced through some unpromising temp jobs until an opportunity emerged for a permanent position. Sometimes you just need to get in where you fit in, and so I did. That’s how I came to work for a regional cable company that used federal money to expand the new national broadband network, extending out to the rural communities dotting central Indiana.
It was a front row seat to the national broadband expansion efforts of the early 2000s. Our business ran right across the state, spanning the 80 or so miles from Attica to Kokomo, which included several small cities with large manufacturers, two public research universities, and several community and liberal arts colleges. The strip of broadband fiber at the core of our service followed existing highways and electrical lines that split the corn and soybean fields from town to town, feathering out to more rural areas from there.
I worked a variety of roles there that put me face-to-face with a classic technical problem: the last mile. On many occasions, someone would come in looking pensive, and explain that the fiber had been extended all the way from town to their hamlet, and yet there was no plan to connect their property to the pole. Over time the pattern was clear: while the network was expanded, the cost of running a physical line to each individual property was too expensive and specific at scale. These customers often left without a path forward despite all their efforts and ours.
The “last mile problem” refers to the logistical challenges and high costs associated with the final leg of delivering goods or services to the end customer. It’s often the most difficult and expensive part of the supply chain, despite being a relatively short distance. The pattern shows up everywhere: public transit can get commuters most of the way most of the time, but that final leg of the journey remains specific and individual and problematic. E-commerce companies promise drone delivery solutions, scooter and bike-share apps claim to solve urban mobility gaps, but these technological optimizations remain persistently stubborn at scale, running up against the messy realities of sidewalks, intersections, and actual human behavior.
Tl;dr: I’ve been turning over this suspicion that AI automation will hit a classic “last mile problem,” especially in the public sector.
AI systems, particularly LLMs, are like those systems—they work incredibly well in their intended domain, processing and manipulating information. But because they’re fundamentally an information-only approach, that creates their own last mile problem when we try to implement them in physical and context-specific environments. Public institutions are uniquely specific — they are often the originators and producers of knowledge and the keepers of original policy, tasked with making the rubber hit the road. Additionally, the need for comprehensive data protection required by public workers and institutions fundamentally hamstring potential applications.
Actually implementing recommendations is where you hit the last mile. This is the work of public administration.
Imagine an AI application trained on every facilities management manual ever written and tuned to synthesize best practices for HVAC optimization. It can analyze years of energy usage data and recommend precise temperature adjustments for different zones of a building, but can’t feel that the third floor is always stuffy, or know that the facilities manager retired last year and took decades of institutional knowledge with him. Building A’s HVAC system was installed in 1987 and has a manual keypad. Building B’s system interfaces with the campus-wide monitoring system, but unreliably, and investigation is slated for later, someday, when resources allow. Professor Smith has taught in Room 204 for 25 years and will blow up your spot before moving to a different classroom for maintenance. Your engineers who manage these spaces are balancing human teams, who have time off and training and other priorities they manage in life. So, you need staff who understand the quirks of each area, the history of each system, the politics of which departments will accept changes and which will flood your inbox with complaints. You need someone who knows that the third floor always runs hot because of a design flaw from 1974, and that the solution isn’t more precise control but a $50,000 renovation that’s been deferred for a decade because a glittering new project across campus takes priority.
Imagine this tangle of questions and contingencies times infinity on every university campus in existence. Universities are like cities—they’ve been built and rebuilt over decades or centuries, with layers of systems and fiefdoms that weren’t designed to work together. AI recommendations assume a level of standardization that simply doesn’t exist. Every AI implementation in higher ed requires navigating multiple constituencies with different priorities and power structures. It’s like trying to redesign traffic patterns in a neighborhood where the residents, business owners, commuters, and city planners all have veto power and conflicting interests.
So. When looking at efficiency efforts spinning up across the education sector, I’m feeling pensive, trying to understand how exactly the house gets connected to the pole.
The promise of new tech in higher ed needs to more deeply consider the translation costs: the human labor, institutional knowledge, knowledge documentation and local adaptation required to bridge between the usefulness of tech and specific realities of public university work. Public employees want modernization and don’t want to fall behind. We want systems that work. We are also balancing a great deal of change and pressure as a sector, with fewer material resources than ever. We need less marketing and more right-sizing in the claims around AI against the political and tech realities of public administration.
This disconnect between technological promise and implementation reality becomes even more critical as higher education faces increased political scrutiny. When tech vendors promise that AI will solve efficiency problems or reduce administrative costs, institutions are under immense pressure to deliver measurable results quickly. But the translation costs we experience don’t disappear just because the political pressure to modernize increases.
The institutions that thread this needle will be the ones that accurately assess these translation costs upfront and set expectations accordingly—not the ones that assume the technology will magically bridge the gap between digital and physical, abstract and specific.
⚡ Navigating AI in higher ed communications: A practitioner's guide
Friday, August 15, 2025
by Lauren Bruce
As communications professionals in higher education, we work for institutions built on the pursuit of knowledge and innovation, yet many of us feel uncertain about how to thoughtfully integrate one of the most significant technological advances of our time: artificial intelligence.
Over the past year, my team has wrestled with questions that didn’t exist in our profession just a few years ago. Should we use AI to draft articles and email copy? How do we disclose AI-generated content, or do we? When does AI assistance cross the line from helpful tool to ethical concern?
These aren’t abstract questions any longer. Over the last year, I had to overcome AI resistance of my own to develop practical, hands-on approaches to AI use that align with our institutional values while acknowledging the realities of modern communications work (more on that below). What I’ve learned is that the answers aren’t found in blanket policies or rules, but in applying our existing professional ethics to these new tools. Here is where I am today on the journey from AI praxis to practice.
Mission first
The foundation of responsible AI use in our field starts with a principle we already know: everything we do should advance our institution’s educational mission. Higher education exists to create, share, and preserve knowledge while fostering critical thinking and diverse perspectives, in service of students, faculty, researchers, workers and the world.
The bulk of our work comes from conversations with colleagues, understanding of our campus dynamics and processes, and professional judgment about what our community needs to hear. This inevitably means more work upfront, but it maintains the authenticity and institutional knowledge that our audience deserves, regardless of whether AI tools are part of the process.
Transparency without paranoia
Do I need to mention AI every time I use it? The answer isn’t simple, but I’ve found a helpful framework: consider whether your audience would feel misled if they knew how AI was involved in creating the content.
When I use AI to polish grammar and shape format, that feels similar to using spell-check – it’s helpful but not something that changes the fundamental nature of the content we wish to communicate. But when AI helps generate the main structure for a story about campus policy changes, that’s a different ball game. The audience expects those priorities and framing decisions to come from human judgment about what matters to our community.
Internally, we differentiate between the two by defining whether or not you are “automating” processes using AI, or “augmenting” processes using AI. Full disclosure, my area of experience is in augmentation, not automation. That said.
I’ve started recommending simple disclosures when AI plays a substantial role in content creation. A line like “This article was developed with AI assistance” maintains trust while allowing us to thoughtfully benefit from these tools. It’s not about being defensive, it’s about being transparent with the people we serve, especially as the tech and attitudes around it evolve over time. Additional qualifications can be included here, such as how the information was shaped and shared by AI or not (privacy implications abound).
Here, it’s important to remember to only use your university-approved tools, because university enterprise AI tools are modified to meet campus rules and requirements related to data handling.
The accuracy imperative
Perhaps nowhere are the stakes higher than with accuracy. In higher education communications, we’re not just sharing information—we’re stewarding public trust in our institutions and, by extension, in higher education itself. In addition, much of the information we are communicating is original, in that it’s new information that cannot be generated using the limitless soup of generative AI.
Every piece of AI-generated content requires human verification, especially anything involving numbers, research findings, or claims about institutional achievements. This means checking sources, confirming statistics, and ensuring that quotes are accurate and properly sourced. It’s more work, but the alternative—publishing incorrect information—could undermine years of relationship-building with community stakeholders and partners.
The promise of speed and efficiency that comes with generative AI must be balanced with the work of close reading, the skill and practice of carefully analyzing a passage’s language, content, structure, and patterns in order to understand what a passage means, what it suggests, and how it connects to our larger body of work. I firmly believe that close reading, learned in the Humanities and Social Sciences, will become increasingly important to understand, shape and steer AI output, especially with regard to public communication best practices.
Inclusion as a practice
AI bias isn’t an abstract concern—it shows up in subtle but significant ways in the work. I’ve noticed that AI tools often default to formal, academic language that might exclude first-generation college students, or suggest examples and metaphors that assume certain cultural backgrounds, for example.
This has made me more intentional about prompt engineering—the way I request AI assistance. I build digital accessibility and plain language best practices into my prompts, in alignment with institutional best practices. One tip is to draft the original using my chosen, intentional language, then ask for revisions using as much of the original verbiage as possible. The difference in output is significant and it allows me to focus on higher-order communication strategy while demonstrating both accuracy and inclusive values in our output.
Privacy and the long view
Working at a public university means balancing transparency with appropriate privacy protections. We work within strict guidelines about what information can be included in AI prompts, particularly around student data, personnel information, and strategic planning discussions. Again, it’s important to only use your university-approved tools, because university enterprise AI tools are modified to meet campus rules and requirements related to data handling.
The challenge is that AI tools work best with context, but providing that context can sometimes mean sharing information inappropriately. I’ve learned to be creative about how I frame requests to AI tools—giving enough context for useful output while protecting sensitive information about individuals and institutional operations.
I focus AI prompts on publicly available information rather than including details from internal planning discussions or individual faculty concerns. It requires more thoughtful preparation, but it ensures we’re protecting appropriate confidentiality.
Speed vs. strategy
The efficiency of AI is seductive, especially when facing tight deadlines and endless communication requests. But I’ve learned that speed can’t come at the expense of quality or authenticity.
Authentic institutional voice and authority doesn’t emerge from algorithms—it requires the deliberate application of human judgment to ensure our plans and communications reflect our campus culture, embody our values, and resonate with our specific audiences. The strategic thinking we bring—our ability to read context, navigate relationships, and understand the subtle dynamics of higher education communication—cannot be automated.
Consider my own practice: I frequently engage AI as a collaborative thinking tool, particularly for structural planning and format development. However, AI’s default tendency toward comprehensive, multi-layered approaches often produces unnecessarily complex frameworks for university communication realities. This is where professional judgement becomes critical. Strong strategic foundations and institutional knowledge allow us to right-size AI’s expansive suggestions into focused, contextually appropriate communication plans that actually serve our goals and communities.
Looking ahead
What I’ve learned over this past year is that responsible AI use isn’t about following a rigid set of rules. It’s about applying the professional ethics we already have to new technological capabilities. The core principles that guide good communications work—accuracy, transparency, service to mission, respect for audience—remain the same.
What’s different is that we now have tools that can enhance our ability to live up to those principles, if we use them thoughtfully. AI can help us communicate more clearly, research more efficiently, and reach broader audiences. But only if we maintain our professional judgment about when, how, and why to use these tools.
As our field continues to evolve, I’m convinced that the communications professionals who thrive will be those who can harness the power of AI while maintaining the human insight, ethical judgment, and institutional knowledge that define excellence in our profession. The technology will keep changing, but our commitment to serving our institutions and communities through ethical, effective communication remains constant. That’s the foundation we build on, whether we’re writing with pen and paper, collaborating in a digital document, or prompting the most sophisticated AI tool on campus.
*This post reflects my ongoing learning about AI ethics in communications practice and was generated using the assistance of AI (Claude, Gemini). Cross-posted on LinkedIn.
I work in an IT communications role in higher ed, and AI finally crept into my life in a real way. Coming soon: some reflections on AI from a comms practitioner in higher ed.