You don't get to opt out of commodity AI. That's what "commodity" means: not "cheap" or "boring" but "compulsory." Ivan Illich saw this pattern with electricity, automobiles, schools. The moment something becomes a utility, non-participation becomes deviance. Prasad Prabhakaran's recent Wardley map of enterprise AI capabilities plots where different technologies sit on the evolution axis. The map is useful. But its most important insight is implicit: everything in the Commodity column is no longer a choice.
What follows is an expanded inventory: the original categories, what's missing from each, and the harder question of what the categories themselves fail to capture. The act of mapping shapes what gets mapped. The categories we use determine the investments we make. And some capabilities don't fit the Genesis-to-Commodity axis at all.
Jonathan Boymal, writing about education in the AI era, argued that deep reading, historically treated as foundational to intellectual development, requires reassessment. The humanist tradition from Simone Weil through Maryanne Wolf emerged "under conditions of relative informational scarcity." Those conditions no longer hold. Students now encounter algorithmic language that "asks less to be interpreted than to be accepted." The response, Boymal suggests, is lateral reading: moving across contexts rather than diving into single texts, asking where claims come from and how meaning differs elsewhere.
The counterpoint came from Johanna Winant in Boston Review, defending close reading's ongoing power. Close reading, she argues, "grounds and extends an argument, reasoning from what we all know to be the case to what the close reader claims is the case." Her students at West Virginia University learned to build arguments from the ground up, noticing details small enough to fit under a finger. One became a nurse who writes notes for doctors using argumentative techniques learned from literature. Another used the method to write a police report about an assault "so she would be understood and believed." Close reading, in this telling, isn't literary technique—it's transferable attention to detail that works in courtrooms and hospitals.
Look at what close and lateral reading share. Both assume an autonomous reader navigating information. Both treat texts as discrete objects to be approached with the right technique. Close reading says go deep; lateral reading says don't be naive. But both preserve the modernist figure of the individual reader making choices about what to trust and how to engage.
This is a family quarrel. The participants disagree on tactics while sharing deeper assumptions: the reader as subject, the text as object, reading as something the subject does to the object. The debate generates heat because both sides sense something is shifting, but neither quite names it. They're arguing about which room to occupy while the building's foundation moves.
The question isn't close versus lateral. It's what happens to reading when the reader—the individual, autonomous, choosing reader—starts to dissolve.
Dan Lorenc's multiclaude takes a counterintuitive position on multi-agent orchestration: the best way to coordinate AI agents working on the same codebase is to barely coordinate them at all. Instead of building sophisticated protocols to prevent conflicts and duplicate work, multiclaude embraces chaos and lets CI serve as the filter. The result is a system that ships more code precisely because it doesn't try to manage what each agent is doing.
This isn't accidental. The project calls its philosophy "The Brownian Ratchet," borrowing from physics: random motion in one direction, a mechanism that prevents backward movement, and net forward progress despite apparent disorder. The metaphor isn't decoration; it's the architectural blueprint.
Mark Carney said it plainly: nostalgia is not a strategy. He was talking about geopolitics, about Canada's relationship with an America that no longer plays by the old rules. But the line lands harder than he intended. It cuts through the entire discourse about AI and work, the endless back-and-forth between doomers and boosters, the think pieces and policy papers and LinkedIn manifestos. Nostalgia is not a strategy. You cannot wish your way back to a world that isn't coming back. Accept the fracture. Move forward.
He's right. And almost everyone responding to him is proving his point while thinking they're refuting it.
Peter Steinberger recently described his workflow with GPT-5.2's codex: he no longer reads code line by line but "watches the stream," trusting the model's output more than any previous generation. The striking phrase wasn't about capability. It was about practice. He called certain established workflows "charades"—rituals necessary for older models that become vestigial as the technology improves. Plan mode, he suggested, is "a hack."
This provokes a question that's been nagging at me as the primitives for AI collaboration proliferate: plan mode, terminals, memory, canvas, artifacts, statuslines, chat. We keep adding surfaces. We keep building more scaffolding. The implicit assumption is that more visibility and more control equals better collaboration. But what if the trajectory runs the other way? What if the primitives that define skilled collaboration are precisely the ones that disappear?
There is a drawer in my mind where the passports accumulate.
I do not mean this only as metaphor. Reading widely produces a particular sensation, one that rarely gets named. You finish a week in which you have moved from W.G. Sebald's melancholy wanderings to a paper on protein folding to Fernando Pessoa's heteronyms to something dense on market microstructure. And you notice that you were not quite the same person in each encounter. The reader of Sebald occupied a tempo, a quality of attention, that the reader of the protein paper could not sustain. Pessoa demanded a willingness to dissolve that the market microstructure paper would have found absurd.
These are not "perspectives" you have acquired. They are closer to visas stamped in a document you did not know you carried. Each grants temporary residence in a country with its own customs, its own texture of thought, its own way of standing in relation to time. And here is what nobody tells you: many of these countries no longer exist.
There's a particular kind of lie that introspection tells. You look back at your own thinking and see patterns, methods, a cognitive architecture. The retrospective gaze imposes structure. What was groping becomes strategy. What was accident becomes approach.
I've been writing essays for a few months now, and people occasionally ask how I think through these pieces. The honest answer is that I don't know. Not in the way the question implies. The question assumes a vantage point above the process, a control room where I select frameworks and deploy techniques. But that's not how it feels from inside. From inside, it feels more like following a scent.
Tim Ingold, the anthropologist, draws a useful distinction between two ways of moving through the world. The architect works from a plan, executing a design that exists complete before the first stone is laid. The hunter follows trails, reading signs, adjusting course, never knowing exactly where the path leads until arriving. Most descriptions of thinking sound architectural. Here's my method. Here's my framework. Here are the steps. But thinking, at least the kind I recognise in myself, is more like hunting. The trails are worn by passage, not drawn in advance.
The first industrial revolution made goods. The resistance of raw material to finished product—spinning cotton into thread, forging iron into rails—was the friction that defined an era. Then something shifted. The resistance moved up a level. We stopped just making goods and started making the machines that make goods. Resistance became: how do you build a factory? How do you systematize production itself?
This pattern recurs. Each time we solve the friction at one level, we create the conditions for the next level to become the bottleneck. And then we industrialize that. The resistance keeps moving up, and we keep following it, building machines to solve the problems created by the previous generation of machines.
Simon Wardley's evolution model—Genesis, Custom-Built, Product, Commodity—was supposed to describe how technologies naturally mature. Something new emerges (genesis), gets built bespoke for early adopters (custom), standardizes into products that compete on features (product), and eventually becomes undifferentiated infrastructure everyone assumes exists (commodity). The phases had a certain stateliness to them. You could watch a technology work through them over years, sometimes decades. The journey from ARPANET to commodity cloud computing took roughly forty years.
That stateliness is gone. What changed isn't just the pace but the structure. The Wardley phases have been industrialized. We've built factories for moving technologies through the evolution cycle, and those factories are getting more efficient with each iteration. The cycle that once took decades now completes in years; the cycle after that will complete in months. And each completion makes the next one faster, because the output of each cycle becomes the input for accelerating the next.
In 1980, Deng Xiaoping designated a fishing village of 30,000 workers as one of China's first Special Economic Zones. Shenzhen was an experiment: a 330 square kilometer sandbox where the central government could test policies too risky for the broader economy. Foreign ownership, contract labor, stock exchanges, land auctions—all were trialed there first. If they worked, they'd graduate to the mainland. If they failed, the damage would be contained.
The results exceeded anyone's projections. Against a national average of 10% annual GDP growth, Shenzhen grew at 58% from 1980 to 1984. By 1988, the central government had implemented many of Shenzhen's reforms across nearly 300 regions covering 20% of China's population. Today Shenzhen's GDP exceeds Hong Kong's. It's home to Tencent, Huawei, and DJI. The fishing village became the factory of the world.
This story usually gets told as a tale of economic liberalization, or of China's pragmatic approach to reform. But there's a different lesson buried in it, one that has nothing to do with economics and everything to do with how complex systems absorb change. The SEZ model is an architecture for experimentation at scale—bounded risk, clear graduation criteria, systematic diffusion. And that architecture is exactly what software development needs now that AI has made code cheap to produce.
Diarra Bousso runs an AI-first fashion house. She uses generative tools to prototype designs, tests them with Instagram polls before production, and operates an on-demand supply chain that lets her sell garments that don't exist yet—customers pay for AI renders of wool capes, and artisans manufacture them after the order comes in. She's flipped the fashion industry's cash flow equation: instead of spending fourteen months on prototypes, trade shows, and inventory before seeing revenue, she gets paid first.
When asked whether AI will replace designers, her answer is unequivocal: no. The tools amplify human creativity; they don't substitute for it. "You could use all the AI tools in the world," she says, "you will never get these images I just showed you because there's a lot of work behind it that comes from taste, that comes from being a designer, that comes from being an artist, that comes from culture, that comes from my upbringing."
This is the optimistic case for human irreplaceability in creative work, and it deserves to be taken seriously before we complicate it. The argument has three parts, and each contains real insight.
