InfoWorld AI·
AI-generated code is riddled with security flaws, yet enterprises are shipping more of it than ever before. Why? Perhaps they’re over-confident, lack true visibility into security risks, or are simply choosing to ignore the problem and hope it goes away. It’s a dangerous game to play at the dawn of the agentic AI era, as underscored in a new report from app security company Checkmarx. The survey of thousands of security leaders exposes an underlying naivete about AI-built code and its vulnerabilities, even as tools like Anthropic’s Mythos are uncovering security flaws orders of magnitude faster than any human security team could ever hope to. “Mythos-class models collapse the window between a vulnerability existing and a working exploit being available from months to minutes,” the report notes. Enterprises relying on traditional security tools and methods, it says, “cannot survive this reality.” Security as an afterthought Checkmarx’s survey of 2,350 CISOs, AppSec managers, and develop
Read full articlePalantir CEO Alex Karp has said enterprise customers are unhappy with how frontier AI labs operate. He said companies question whether leading model developers understand their business needs. His remarks came as OpenAI and Anthropic move toward public listings. Palantir…
Artificial intelligence is evolving from chatbots and simple search tools into what’s being called Agentic AI.
The 2026 State of AI Coding report shows vibe coding is mainstream, but unverified trust is causing a production crisis SAN FRANCISCO, June 10, 2026 — New Relic today released its […] The post New Relic Report Reveals AI-Generated Code Grades Higher in Review, Yet Triggers Rise in Production Incidents appeared first on AIwire.
The phrase “agentic AI” has moved from the whitepapers of research labs into the boardrooms of the Fortune 500, the pitch decks of venture-backed startups, and the strategy documents of governments trying to make sense of what is happening to the global economy. Yet for all the noise, a surprisingly small number of the people […]
The expanded partnership accelerates enterprise AI integration, enhancing governance and operational efficiency, crucial for digital transformation. The post Microsoft and KPMG extend global tie-up to power agentic AI for enterprises appeared first on Crypto Briefing.
Anyone can build an app now. But nobody seems to care. Well, not nobody. VCs keep funding startups that add AI to, well, everything. But users aren’t buying the massive influx of new apps. In a chart shared by Jen Zhu Scott based on the new National Bureau of Economic Research’s working paper “Writing Code vs. Shipping Code,” iOS app releases have exploded since the advent of agentic AI. That would perhaps be cause for celebration had app reviews not declined during this same period, and apps with significant usage have stayed essentially flat. In other words, more apps but almost nobody new showing up to use them. For those of us that grew up in open source, it’s a familiar problem. The greater the abundance of code, the greater the need to help would-be customers navigate it through marketing (including branding), sales, etc. AI is creating so much noise, in terms of new code, new products, etc., that the real work has shifted to taste-making. Getting more but not using more I’ve bee
Every enterprise, from a seed-stage startup deploying its first automated workflow to a Fortune 50 firm rebuilding its entire labor model, now depends on agent software to plan, reason, execute, and iterate without constant human instruction. The CEOs building that software are, in a very real sense, deciding what autonomous work looks like in the […]
Agentic AI has moved from conference hype to a budget line item. This is where the conversation gets more interesting and more uncomfortable. Unlike traditional AI systems that respond to a single prompt, classify a document, recommend an action, or generate a summary, agentic AI systems are designed to pursue goals. They plan, call tools, inspect results, retry failed steps, consult memory, hand off tasks to other agents, and sometimes critique their own work before producing an answer or taking an action. That extra autonomy is the value proposition. It also introduces the cost problem. A single chatbot interaction may consume a few thousand tokens. A useful agentic workflow can consume hundreds of thousands or millions of tokens per day because it does more than answer a question. It decomposes the problem, retrieves context, reasons through options, invokes APIs, checks the output, and often runs multiple passes before reaching a result. Therefore, the economics need to be understo
