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OpenAI said that ChatGPT free and Go users are also getting a new think button for complex queries.

This generation of twentysomethings is so disillusioned with swipe-based dating apps that they'll try literally anything else — even an AI matchmaker.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Google’s AI empire is being reshaped. Here’s what’s changed. After a wave of painful losses in the tech talent wars, delays to its next flagship model, and murmurings of poor morale,…

arXiv:2608.04012v1 Announce Type: new Abstract: Artificial intelligence systems are increasingly expected to operate over repeated cycles of interaction, adaptation, and update rather than through isolated one-shot outputs. This raises a fundamental theoretical question: can an AI system persist indefinitely without incurring unbounded structural aging? This paper develops a long-run persistence framework for AI systems based on the redundancy-adjusted Artificial Age Score (AAS). The model extends AAS from a static evaluative measure into a cycle-level functional that generates an age sequence across repeated operation. At each cycle, structural age is defined through a weighted, redundancy-aware logarithmic penalty over component consistency levels. Within this framework, cycle-level age is shown to be well defined and uniformly bounded, thereby excluding explosive pointwise aging. On this basis, the paper defines a hierarchy of asymptotic regimes, including burdened persistence, zero-burden persistence, oscillatory persistence, and cumulative terminal burden. It also establishes comparative ordering, sensitivity bounds, convergence under componentwise stabilization, persistence u

arXiv:2608.04066v1 Announce Type: new Abstract: How do you verify a long-horizon agent when its own state and self-reports are exactly what you cannot trust? We present an agent instrument built so that verification is structural rather than post-hoc. A deterministic Executive owns all belief; a language model may only file typed proposals, and a claim is admitted only when a prediction pre-registered before acting is matched against observation by code. Two properties make the instrument a verifier of its own science, not just of the agent: every run invalidates itself when per-organ write-error, render-size, or salted-canary-echo floors are breached (four of the first eight architecture runs were invalidated, each localizing a real defect); and a render-invisible shadow reference compiles the plan the full system would have committed in every ablation cell, so drift metrics are defined even where the mechanism under test has been removed. Using this instrument we report a clean, single-variable result on a failure every long-horizon agent suffers: ablating the commitment mechanism flips goal-abandonment from 0.00 to 1.00 while binding error stays flat at 0.00 (three seeds per cel

Impression share ran the auction years. Citation share runs this one. A 90-day sprint for measuring and winning AI search visibility. The post I Helped Scale Google Ads To Billions – Here’s How I’d Build An AI Search Strategy Today appeared first on Search Engine Journal .

Set expectations before tactics: parametric standing moves on model generations, responds to being described, and sits in functions nobody measured against it. The post Why Part Of Your AI Authority Takes Years, Not Campaigns & Why It Comes From Other People appeared first on Search Engine Journal .

AI research habits are reshaping PPC accounts. Here is when to consolidate, when to segment, and how conversion values keep budget flowing to valuable buyers. The post How AI Search Trends Are Changing PPC Campaign Structures – Ask A PPC appeared first on Search Engine Journal .

AI sovereignty issue raised in new guidance.

Starts with existing agents and coding assistant traffic.

Under 'Look to 28' technology strategy.