AI

In one corner, we have Demis Hassabis, CEO of DeepMind, telling The Guardian in 2024 that AI will be “10 times bigger than the Industrial Revolution.” Disruption is inevitable, he says, but humanity will adapt, as it always does. The challenge is simply to manage the turbulence. In the other corner, we have Joseph Weizenbaum, reflecting in the 1980s on his earlier work designing a banking system for processing physical cheques. It was an intricate, technically satisfying project. Only years later did it occur to him that no one had asked whether automating cheque processing at scale was socially desirable, or what knock-on effects it might have. “It never occurred to me to ask,” he admitted. ...

In 1991, The Tech at MIT published an interview with Joseph Weizenbaum, the computer scientist best known for creating ELIZA and later becoming one of the field’s sharpest internal critics. Speaking with Diana ben‑Aaron, he dissected the role of computers in education, their entanglement with the military, and the ethical evasions of scientists. Three decades later, his words are less a time capsule and more a mirror — the issues he named have not only persisted but mutated into modern forms, from AI hype cycles to tech‑military partnerships dressed up in start‑up chic. This post is a “then/now” rendering of that interview: his points in their original spirit, and how they look in the world of 2025. ...

Ten years ago, Nonprofit AF , in Weaponized data: How the obsession with data has been hurting marginalized communities, warned that nonprofits’ data obsession could dehumanise and harm marginalised communities—reducing lived experiences to reductive metrics, ignoring power dynamics, and prioritising funder dashboards. Now it’s 2025. Technology, AI, data regulation, and global politics have transformed—but many threats have only intensified. What changed? What has improved Stronger legal guardrails (sometimes) Since GDPR (2018), Europe and other jurisdictions have enacted data protections that at least nominally strengthen consent and individual’s rights. Data-sharing rules and transparency mandates force nonprofits to be somewhat more accountable. ...

As AI systems gallop into the mainstream, humanity is left staggering somewhere behind, still deciding whether it is riding a horse or being trampled by one. The outcomes are not fixed. But the trajectories are becoming harder to ignore. We explored some speculative futures within the box—Human-AI symbiosis, Turbulent coexistence, and Dystopian displacement—as thought experiments and cautionary tales. Each story reflects plausible developments rooted in today’s technological, political, and economic fault lines. None are entirely fiction. And none are inevitable. ...

It is fashionable to believe that technological progress is inevitable, and that artificial intelligence will, barring catastrophe, continue its relentless march forward. But there is a future—quietly lurking just beyond the smug grins of Silicon Valley keynote speeches—where AI does not progress much further at all. Not because of some singularity, nor because we all upload our brains into the cloud, but because we collectively decide: “That is quite enough, thank you.” ...

Elena remembered when the world still made some sort of sense. Not much, admittedly—it had always teetered somewhere between absurd and unbearable—but at least back then, she could lie to herself about having a job, a future, or a say in how things turned out. Now, her morning routine involved checking two things: whether the universal basic income had landed in her bank account (it had not), and whether the nearest AI surveillance drone was watching (it was). ...

Elijah never quite knew how to answer the question, “So, what do you do?” He could say AI liaison, but that sounded pompous and vaguely sinister. He could say digital compliance coordinator, but even his mother snorted at that one. In truth, he spent most of his days arguing with regulatory software about whether the hospital’s cancer diagnostics model violated EU data transparency directives or merely flirted with them. It was 2028, and Elijah worked at a hospital that could diagnose rare cancers with 99% accuracy. The machine—he refused to call it a colleague—could parse blood data, family history, and MRI scans in seconds. It was not always right, but it was close enough that human oversight had become more symbolic than necessary. ...

Pau was late, again, thanks to the AI-run tram that insisted on pausing for precisely 12.4 seconds at each station “for optimal urban harmony.” “Urban harmony my arse,” he muttered, stepping out into Barcelona’s midday sun, which was now neatly moderated by micro-reflective paint and smart algae rooftops. Somewhere, a city-wide AI had just nudged the temperature down a degree using a predictive cloud-seeding protocol. It was not magic. It just felt that way. ...

Humanity has always stared into the void and asked, “Why are we here?”—usually followed by, “And who’s responsible for all this?” Whether whispered to trees, inscribed in sacred texts, or processed through quantum servers, the search for meaning has been relentless, imaginative, and often contradictory. What follows is a sweeping tour through the shifting landscapes of belief—from stone age spirits to silicon soulcraft. Along the way, we explore not only what people believed, but how those beliefs evolved, adapted, and occasionally exploded in dramatic fashion. ...

In 2025, the financial sector continues its courtship with generative AI, hoping it will finally make compliance less of a bureaucratic slog. According to Global Relay’s State of AI in Surveillance Report 2025, attitudes are thawing: there’s been a 19% drop in firms reluctant to implement AI. Apparently, nothing eases doubts like the promise of automated paperwork and plausible deniability. Still, not all is rosy. Explainability remains elusive, regulators are breathing down necks, and integration often resembles a Frankensteinian patchwork. Innovation, it seems, comes with a compliance hangover. ...