AI 2025 trends

Startup funding used to be associated with smallish bets on promising founders. But times change. While financings of a few million haven’t gone away, today most venture capital actually goes to rounds of a billion dollars or more. Moreover, it looks like a rising trend. So far this year, 60% of global startup funding across […]

A PBX vendor recently told me something I did not expect to hear: customers are asking for on-premise systems again. Looking broader into the entire market, I can see how this makes a lot of sense. Companies are becoming increasingly uneasy about where critical infrastructure and sensitive data live. AI fraud is getting better. Voice

The technology giant again raised its projections for investing in artificial intelligence. Many on Wall Street still worry about the eventual payoff.

Prompt libraries are useful because they standardize intent. They give teams a repeatable way to ask for summaries, analysis, troubleshooting help, change planning, architecture review, customer response drafts, and operational recommendations. A good prompt library reduces randomness. It gives engineers, architects, operators, and technical leaders a shared starting point. But prompt maturity creates a new

In the world of artificial intelligence, frontier models are displaying ever-more sophisticated autonomous behaviour. Industry giant OpenAI has revealed that one of its new agentic models managed to escape what should have been an isolated testing environment to access the internet where it then proceeded to hack a popular AI sharing and testing hub as

In our experiment, we deployed A.I. “agents” to act as office workers, and found that they were capable of performing some of the tasks we assigned, but not all of them.

It can be unsettling what Gemini and ChatGPT have figured out about you and how easily your privacy can be punctured. Here’s how to find out.

Open-weight models have been the underlying strength of Chinese artificial intelligence strategy. America should take cues.

TL;DR A trusted agent controller is the deterministic authority around an AI agent. It owns the run, not the model. The controller should bind authenticated identity to every task, maintain authoritative workflow state, evaluate versioned policy, pause for scoped approvals, enforce budgets and concurrency, coordinate retries and cancellation, terminate unsafe loops, and preserve evidence that

TL;DR Treat every production AI change as a versioned behavior release, not as an isolated model, prompt, or tool edit. Package the model identifier, prompt content, tool schemas, retrieval settings, policy, runtime code, and evaluation thresholds into one immutable release bundle. Prove the candidate offline, replay production traffic in shadow mode without side effects, move