The promise of personal AI assistants rests on a dangerous assumption: that we can trust systems we haven’t made trustworthy. We can’t. And today’s versions are failing us in predictable ways: pushing us to do things against our own best interests, gaslighting us with doubt about things we are or that we know, and being unable to distinguish between who we are and who we have been. They struggle with incomplete, inaccurate, and partial context: with no standard way to move toward accuracy, no mechanism to correct sources of error, and no accountability when wrong information leads to bad decisions...

Think of the Web as a digital territory with its own social contract. In 2014, Tim Berners-Lee called for a “Magna Carta for the Web” to restore the balance of power between individuals and institutions. This mirrors the original charter’s purpose: ensuring that those who occupy a territory have a meaningful stake in its governance. Web 3.0—the distributed, decentralized Web of tomorrow—is finally poised to change the Internet’s dynamic by returning ownership to data creators. This will change many things about what’s often described as the “CIA triad” of ...

American democracy runs on trust, and that trust is cracking. Nearly half of Americans, both Democrats and Republicans, question whether elections are conducted fairly. Some voters accept election results only when their side wins. The problem isn’t just political polarization—it’s a creeping erosion of trust in the machinery of democracy itself. Commentators blame ideological tribalism, misinformation campaigns and partisan echo chambers for this crisis of trust. But these explanations miss a critical piece of the puzzle: a growing unease with the digital infrastructure that now underpins nearly every aspect of how Americans vote...

This is a truly fascinating paper: “Trusted Machine Learning Models Unlock Private Inference for Problems Currently Infeasible with Cryptography.” The basic idea is that AIs can act as trusted third parties: > Abstract: We often interact with untrusted parties. Prioritization of privacy > can limit the effectiveness of these interactions, as achieving certain goals > necessitates sharing private data. Traditionally, addressing this challenge > has involved either seeking trusted intermediaries or constructing > cryptographic protocols that restrict how much data is revealed, such as > multi-party computations or zero-knowledge proofs. While significant advances > have been made in scaling cryptographic approaches, they remain limited in > terms of the size and complexity of applications they can be used for. In this > paper, we argue that capable machine learning models can fulfill the role of a > trusted third party, thus enabling secure computations for applications that > were previously infeasible. In particular, we describe Trusted Capable Model > Environments (TCMEs) as an alternative approach for scaling secure > computation, where capable machine learning model(s) interact under > input/output constraints, with explicit information flow control and explicit > statelessness. This approach aims to achieve a balance between privacy and > computational efficiency, enabling private inference where classical > cryptographic solutions are currently infeasible. We describe a number of use > cases that are enabled by TCME, and show that even some simple classic > cryptographic problems can already be solved with TCME. Finally, we outline > current limitations and discuss the path forward in implementing them...