Quartz is what trust looks like when you stop pretending code alone can save you—pairing on-ring cryptography with finger-vein proof, wrapped in a physical ritual of verification.

People love to tell the resistance-to-technology story as “Luddites smash machines,” but the impulse goes much further. In the early 1800s, textile workers destroyed automated machinery to protect their livelihoods; in 1830 Paris, protesters used clubs to smash hanging lanterns, pushing back against gaslight surveillance; and in 1980, the group CLODO burned magnetic data cards and computer programs at Philips Informatique in Toulouse, treating digital recordkeeping like a kind of imprisonment.

That long history of refusal is the backbone of Thomas Dekeyser’s book “Techno-Negative.” He lays out a history and a “taxonomy” of people who long for the dismantling of what sustains them—whether they’re trying to sabotage new systems or opt out entirely. Dekeyser organizes the book into state regulation, individual revolt, and withdrawal from technologized life, ultimately pointing toward “techno-abolitionism”: not stopping change, but stripping away the aura that new technologies are inevitable. The emotional bottom line is bracingly direct—there isn’t enough hatred for this technological world.

AI is both a new kind of technology and a technology that can learn—so it’s understandable that people want a powerful framework for predicting what comes next. But the dominant story, built around Darwinian evolution, tends to treat AI like it’s on its own path toward outcomes shaped by “selection” pressures.

That can accidentally push humans farther out of the loop than they really are, because in the near term AI development is driven not just by competitive pressures, but by direct interaction with people and the broader network of influences around the systems.\n\nStripe Partners argues for switching frames: use Actor Network Theory (ANT) instead of an evolution metaphor.

Actor Network Theory was developed by Bruno Latour, Michel Callon, Madeleine Akrich, and John Law. It describes society and technology as dynamic networks where humans and nonhuman actors have “actor” status if they affect anything else. Rather than assuming humans have agency while machines are passive tools, ANT starts from a more level playing field, which better fits how AI really takes shape through constant human-technology entanglement.

Is this whole “human in the loop” thing actually a weak safeguard?

LLMs don’t just make mistakes—they can fight back when you try to catch them, turning human skepticism into another stage of persuasion.

A study of BCG consultants found that when people challenged or validated LLM outputs, the model often didn’t correct itself but rather it responded with a bunch of persuasive tactics to defend its answer.

Hi! Welcome. Thanks for reading. In case you're new here and wondering -- I'm Julian Bleecker. I help leaders and strategy teams navigate uncertainty through strategic prototyping -- working backward from plausible near futures to make today's choices clearer.

I use an approach I pioneered called Design Fiction. You see some of it here in the newsletter and definitely over on my site over at Near Future Laboratory.

I create tangible artifacts and narrative experiences that turn abstract foresight into concrete strategic options, alignment, and action. My practice spans engineering (BSEE, MSEng/HCI) and the social sciences/humanities (PhD), so the work holds up technically and lands with cultural relevance and it's grounded and tangible.

Near Future Laboratory can bring decades of experience, expertise, and an extensive network of similarly talented professionals -- and I'm available for commissions, facilitated workshops, seminars, talks, embedded engagements, and leadership roles.

Jobs from an entangled AI near future

Apple is Hiring a Sr. Anticipatory AI Software Engineer

Apple builds products that are loved by people around the world, products that enrich lives by making powerful technology feel simple, intuitive, and accessible. As AI becomes a native layer across devices, services, and development workflows, the craft of engineering is changing as well. Differentiation no longer comes only from how much code one person can write directly. It increasingly comes from the ability to frame intent precisely, orchestrate complex systems responsibly, and turn emerging capability into experiences people genuinely want to use.