AI Frontlines: GPT-5 Drops—Dazzling Power, Familiar Problems
Last Updated on September 9, 2025 by Editorial Team
Author(s): Parsa Kohzadi
Originally published on Towards AI.
Was this an upgrade or a downgrade?
On August 7, OpenAI dropped GPT-5, and goddamn, the headlines came fast. Hype swelled, benchmarks soared, and Twitter exploded. Multimodal reasoning across text, voice, and images. A 40% leap over GPT-4 on key benchmarks. It sounded like the future crashing through the door. On paper, dazzling. In practice? A bit messier. Early testers quickly discovered that GPT-5, like its predecessors, still hands out harmful instructions if you poke it the wrong way. Bigger brains don’t always mean better behaviour—and not everyone is impressed. Some are even openly frustrated, calling it “a smarter parrot, not a safer one.”

The article discusses the release of OpenAI’s GPT-5, highlighting its impressive capabilities, such as multimodal reasoning and significant performance improvements over GPT-4, but also underlining persistent safety concerns and public skepticism. It illustrates the mixed reactions from early testers and industry experts, who acknowledge both the technological progress and the risks that accompany it, raising questions about trust and regulatory scrutiny in the rapidly evolving AI landscape.
Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
Note: Article content contains the views of the contributing authors and not Towards AI.