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In March, I put my iPhone into a yellow cardboard box with MO stamped on top'the M looked like a riff on the Motorola logo; the O looked like a flower. Over the next several weeks, I left my phone there for roughly 23.5 hours out of every day. I did so as a participant in 'Month Offline,' which started last year in Washington, D.C., as a kind of Dry January challenge, but for smartphones. Now it is a fledgling business with a footprint in New York City. Members of each monthlong 'cohort' pay $75 for the experience, during which they swap their iPhones for a lower-tech device and participate in weekly meetups. I joined the cohort that began on March 2 and received an email just before the first meeting: 'Excited 2 see u soon,' it said. My month offline began with the MO pledge'a document with curious capitalization that declared us all 'Free and Independent Human Beings' who were 'Absolved from all dependence on big tech and their attention-grabbing algorithms.' By signing at the bottom, I agreed to 'forego' the use of my smartphone for 30 days and thereby 'trade dopamine for daylight, doomscrolls for detours, pixels for paper maps.'...

' Hey, Linas here! Every day, I break down 3 stories shaping the future of FinTech & Artificial Intelligence - plus the money movements and trends worth tracking. First time here' 370k+ FinTech and AI leaders get this daily. Join them: Shortly after announcing their phenomenal 2025 financial results, Revolut has published PRAGMA (arXiv: 2604.08649), a family of encoder-style Transformer foundation models pre-trained on banking event sequences from roughly 25 million users across 111 countries. The training corpus spans approximately 40 billion events and 207 billion tokens. What's interesting here is that the architecture replaces the old paradigm of siloed, task-specific models with hand-crafted features: a single shared backbone transfers learned representations across credit scoring, fraud detection, lifetime value prediction, communication engagement, and more. PRAGMA is a structural bet on a specific theory of value creation: that rich, longitudinal behavioral data from a Super App generates embeddings universally better than anything hand-crafted per task, and that the competitive moat in AI is no longer algorithms but proprietary event scale....

As AI systems take on more organizational decision-making, traditional models of accountability ' focused on identifying a single culprit when something goes wrong ' are breaking down. Drawing on recent research, the authors introduce narrative responsibility, a framework that maps the real story behind failures, distributes ownership across teams, and embeds ongoing reflection into everyday practice. This approach is essential for organizations navigating the complexity of AI-enabled decisions. Early one morning in 2018, a self-driving Uber vehicle fatally struck a pedestrian in Tempe, Arizona. The world had questions: Who was responsible' Was it the safety driver behind the wheel' The engineers who designed the algorithms' Uber's leadership' Or the regulators who had allowed autonomous-vehicle testing' The inability to name a single culprit signaled a profound shift in how responsibility must be understood and attributed in the age of intelligent technologies. As organizations deploy increasingly autonomous systems such as drones, trading bots, or algorithmic decision makers (like automated resume screeners or credit assessment tools), agency becomes distributed, emerging from the complex interplay of human and machine actions. Decisions, once linear and traceable, now unfold across networks of people and artificial intelligence systems, introducing new forms of influence and unpredictability....