Tagging this plain language definition for later: “platforms… are any space or any institution that brings together buyers and sellers, speakers and listeners.”
The article argues that Silicon Valley’s shift from long-term employment to talent migration has created a model where workers maximize their individual compensation through frequent job changes, fundamentally altering tech industry culture and economics for the worse.
🍿Watched: Toni Morrison: The Pieces I Am
This PBS doc recently came to Netflix, and I tucked in expecting a nice but predictably boring documentary about one of my favorite authors. To the contrary, it really plumbs Morrison’s writing craft, not only as an author but as an editor who brought a generation of incredible thinkers into the limelight. She talks in depth about her writing process, her approach to authorship and editing, and how she kept these roles separate at the peak of her midcentury literary career in NYC’s publishing industry.
There’s something so powerful about hearing her discuss the craft, her deliberate choices, the refusal to center whiteness, and the insistence that Black readers were her intended audience. She saw her critics and wrote around their critiques with authority and confidence. This was a breath of fresh air since I’m so immersed in the AI era, which threatens to change our perceptions around value of writing, the choices and experiences behind the author, and how we consider the influence and responsibility of authorship.
The universe is telling me to read Neuromancer already.
Closer to the machine
There is something about the AI moment that reminds me a lot of when the internet was new. A lot of what was imagined and promised about the internet was never realized. But much was.
I’ve been reading Ellen Ullman’s memoirs - “Life in Code” and “Close to the Machine” - and her observations about proximity to technology feel relevant here. Being close to the machine means understanding its actual capabilities and limitations apart from the prevailing sales narratives. It also means a kind of loneliness, because you are working in a space that others don’t yet see clearly or fully understand.
I suspect people thinking seriously about AI right now will experience something similar: a stretch of hostility and discomfort while the rest of the world catches up and the consumer market level-sets on the promises being made. In the interim, the hype will not match the reality, and the reality will sometimes exceed the hype in ways no one predicted. And for a while, how it works and why it matters won’t be legible to everyone at once.
“Evidence from a study about workplace writers who use AI suggests that writers are outsourcing some of their research, editing, or drafting to AI, but that they retain responsibility for their writing.”
Crunching for clarity
In 1999, academic and theorist Judith Butler famously won an award for the worst academic sentence, raising good questions about how we read difficult texts, who gets to access academic ideas, and the role of academic and plain language in and around the academy:
The move from a structuralist account in which capital is understood to structure social relations in relatively homologous ways to a view of hegemony in which power relations are subject to repetition, convergence, and rearticulation brought the question of temporality into the thinking of structure, and marked a shift from a form of Althusserian theory that takes structural totalities as theoretical objects to one in which the insights into the contingent possibility of structure inaugurate a renewed conception of hegemony as bound up with the contingent sites and strategies of the rearticulation of power.
This was my era in college, and I remember feeling challenged and overwhelmed as an undergrad trying to read theory for the first time. So today, I asked Claude.ai to put it in plain language, just to see what would happen. Here’s the after:
Scholars used to think that capitalism shaped society in consistent, predictable patterns. But newer thinking focuses on how power works—how it repeats, overlaps, and gets reshaped over time. This brought time into the picture and moved away from older theories that treated social structures as fixed. Instead, recognizing that structures can change opened up a new way of understanding power: it's tied to specific moments and strategies, and it's always being renegotiated.
A lot of readers are fascinated with the “black box” of AI writing, and trying to reverse engineer what it does and why. John Gallagher goes down the rabbit hole and articulates some credible theories about why LLMs use lists and listing to create meaning, and why it matters.
French overlooks how smartphones and social media raised the stakes on debate and discussion, transforming campus discourse. Today’s students worry that one viral misstep (in countless directions) may define them forever.
Connected Places uses ICE as a case study to explore trust, safety, and community dynamics on decentralized social networks, examining how federation changes community moderation expectations we’ve developed from centralized platforms.
A new paper in Science Magazine explains how AI now allows propaganda campaigns to reach previously unprecedented scale and precision. This gets into the implications for organizations, institutions and nations.
The real problem is that it's not our quagmire
Tiktok is not much better or worse than other major social platforms, I say. The primary arguments against TikTok, including data collection, algorithmic manipulation, potential foreign government access, addiction and influence on public opinion, apply with equal or greater force to American platforms. Meta has faced billions in fines for allowing privacy violations, enabled documented election interference, and its algorithms have been linked to mental health harms and the amplification of extremist content globally, including perpetuating a genocide in Myanmar. Google and other domestic platforms vacuum up vastly more user data with fewer restrictions.
The distinguishing factor isn’t the behavior but the ownership: TikTok’s parent company ByteDance is subject to Chinese law and intelligence relationships, while Meta and Google are subject to U.S. law and intelligence relationships. That’s a legitimate policy distinction, but rarely articulated honestly. Instead, the debate has been framed around purportedly unacceptable harms that American tech companies perpetrate routinely, creating a kind of security theater that lets domestic platforms escape equivalent scrutiny while positioning a foreign competitor for a forced sale or ban.
The TikTok deal means American users will see a US-only algorithm. Brands and creators will likely see smaller audiences and higher costs for domestic reach. ByteDance faces split algorithms, divided workforces and parallel governance, complicating product delivery across global markets.
The promise of AI is that it makes work more productive, but the reality is proving more complex and less rosy.
I’m generally skeptical of anyone selling a solution to a social problem that relies on individual abstinence, so I tend to be annoyed with many arguments about the attention economy. I more or less land here on the question of AI, which I know many of my contemporaries will find similarly annoying.
Searching for Suzy Thunder: In the ’80s, Susan Headley ran with the best of them—phone phreakers, social engineers, and the most notorious computer hackers of the era. Then she disappeared.
While conspiring with a friend about life and work in these trying times, both of us confessed that we believe, at the root, that reading and writing are ultimately the cure for everything that ails us: collectively, individually, epistemically, existentially. Maybe that’s naive, but I’ll take it.
Make Canadian TV weird again (sponsored by The Red Green Show, probably).
Rules without lessons
If you spend time around cycling and pedestrian advocates, the debate between bans and regulations is familiar territory. When I got deep into road biking, where I learned to ride long distance through a red state with almost no bike infrastructure outside tight urban and exurban areas, one of the best things I did was take road classes through the League of American Bicyclists. You learn the rules of the road from a cyclist’s perspective and practice skills like riding with car traffic under expert guidance, including how to change a flat on the side of the road in the height of summer, gritty with sweat and road grime.
The challenge is that bike education isn’t standardized, so most cyclists never learn the fundamentals anyway. Many of us learned as kids and haven’t had a refresh since. I get stomach pain when I see people riding at night without a light, going too fast on a dedicated path, and adults riding their bike on a pedestrian sidewalk. But when I think about e-bike bans and pedestrian right-of-way debates, it strikes me that outside of getting a driver’s permit for car drivers, there’s essentially no infrastructure for learning how to share roads and paths safely. We’re trying to regulate behavior most of us didn’t learn in earnest.