Egress Fees Outpace GPU Cost AWS charges $0.09 per GB for data transfer out to the internet. A single RAG pipeline processing 10,000 queries daily with 50 KB embedding payloads per request generates roughly 15 TB of egress per month — that is $1,350 before you factor in vector DB …
Editorial team 
Google I/O 2026: How AI Agents Replaced the Search Box
Google replaced its 25-year-old search box with an AI-powered interface at I/O 2026. The new “intelligent search box” accepts text, images, files, video, and Chrome tabs, powered by Gemini 3.5 Flash. Instead of blue links, users get interactive AI-generated experiences, custom visualizations, and “information agents” that monitor the web around …
LLM Gateways Cut 72% of Wasted API Spend in Production
Wasted LLM Spend: The Gateway Fix Enterprise LLM API spend crossed $8.4 billion in 2025, and the majority of teams hardcode a single frontier model for every request — including the 80% that could run on a model costing one-tenth the price. LLM gateways fix this systematically. A workload of …
Function Calling Accuracy Plummets in Production Workflows
Benchmarks Claim 95%. Production Disagrees. The Berkeley Function Calling Leaderboard (BFCL V4) reports that GPT-4o achieves over 90% accuracy on single-function tool calls. Add a second tool to the context, and accuracy drops by double digits. Add five, and you’re in a different regime entirely. The gap between benchmark function …
Agent Memory Is Just a Vector DB. That’s the Problem.
The Benchmark Numbers Full-context injection into an LLM prompt scores 72.9% accuracy on the LoCoMo benchmark at 17.12 seconds p95 latency. Flat vector retrieval drops to 66.9% accuracy — but cuts latency to 1.44 seconds. That is a 6-point accuracy gap buying a 91% speed gain and a 90% reduction …
Multi-Agent Reliability: 85% Per Step, 20% at Step 10
The Compound Failure Equation Here is the math that most teams deploying multi-agent AI systems have never computed: if each agent step succeeds 85% of the time — a rate most vendors would call impressive — a 10-step workflow completes successfully just 19.7% of the time. That is 0.8510 = …
Portugal Wants to Be Europe’s Data Brain — Here’s How
Portugal sits at the intersection of four continents via submarine cables, with green energy, 98% fibre coverage, and direct Atlantic connectivity through Sines. Samuel Carvalho, CEO of TelCables Europe, tells SAPO the country can become Europe’s data brain — but only if it acts now on regulation, investment incentives, and …
Google Search Profiles Launch: What Publishers Must Do Now
Google Search Profiles officially launched on June 4, 2026, giving publishers and creators a dedicated, shareable space inside Google Search to showcase articles, videos, and social posts. It is a direct E-E-A-T signal — publishers who claim and optimize their profile gain a knowledge panel upgrade, a followable presence on …
Agentic AI Workflows Cost 5x More Than You Budgeted
One Agent Call Becomes Fifteen Google’s TPU 8i dedicates 288 GB of HBM and a dedicated Collectives Acceleration Engine specifically because a single agentic request now triggers an average of 6-12 downstream model calls. The infrastructure bill for “let me ask the AI” has quietly multiplied, and most teams haven’t …
AI Cybersecurity Market Hits $133B — Breaches Still Rising
The AI cybersecurity market is projected to exceed US$ 133 billion by 2030, with global security spending reaching US$ 183.9 billion in 2026 alone — a 15% year-over-year increase. Yet the average cost of a data breach has climbed to US$ 4.88 million, organizations take 277 days to detect incidents, …
Anthropic Wants AI Pause, Sells Mythos to NSA for Offense
On June 4, 2026, Anthropic published a lengthy post calling for a global “temporary pause” on frontier AI development, warning that recursive self-improvement could arrive before institutions are prepared. Within 24 hours, the Financial Times revealed that the same company has embedded half a dozen engineers inside the US National …
Speculative Decoding Cuts MoE Inference Cost by 19%
19% Cheaper Inference, Zero Accuracy Loss Red Hat benchmarked gpt-oss-120B with Eagle3 speculative decoding on vLLM v0.13.0 and measured a 19.4% reduction in cost per 1M output tokens on H200 GPUs running SWE-bench workloads. Not a benchmark trick — production throughput at 200 concurrent requests, with output distributions mathematically identical …