Tag: Business

Remember this Quora comment (which also became a meme)?(Source: Quora)In the pre-large language model (LLM) Stack Overflow era, the challenge was discerning which code snippets to adopt and adapt effectively. Now, while generating code has become trivially easy, the more profound challenge lies in reliably identifying and integrating high-quality, enterprise-grade code into production environments.This article will examine the practical pitfalls and limitations observed when engineers use modern coding agents for real enterprise work, addressing the more complex issues around integration, scalability, accessibility, evolving security practices, data privacy and maintainability in

Three years ago, ChatGPT was born. It amazed the world and ignited unprecedented investment and excitement in AI. Today, ChatGPT is still a toddler, but public sentiment around the AI boom has turned sharply negative. The shift began when OpenAI released GPT-5 this summer to mixed reviews, mostly from casual users who, unsurprisingly, judged the system by its surface flaws rather than its underlying capabilities.Since then, pundits and influencers have declared that AI progress is slowing, that scaling has “hit the wall,” and that the entire field is just another tech bubble inflated by blusterous hype. In fact, many influencers have latched onto the dismissive phrase “AI slop” to diminish the amazing images, documents, videos and code that frontier AI models generate on command.This perspective is not just wrong, it is dangerous.

OpenAI researchers have introduced a novel method that acts as a "truth serum" for large language models (LLMs), compelling them to self-report their own misbehavior, hallucinations and policy violations. This technique, "confessions," addresses a growing concern in enterprise AI: Models can be dishonest, overstating their confidence or covering up the shortcuts they take to arrive at an answer. For real-world applications, this technique evolves the creation of more transparent and steerable AI systems.What are confessions?Many forms of AI deception result from the complexities of the reinforcement learning (RL) phase of model training. In RL, models are given rewards for producing outputs that meet a mix o

Amazon Web Services (AWS) has introduced Kiro powers, a system that allows software developers to give their AI coding assistants instant, specialized expertise in specific tools and workflows — addressing what the company calls a fundamental bottleneck in how AI agents operate today.AWS announced Kiro powers at its annual re:Invent conference in Las Vegas. The capability marks a departure from how most AI coding tools work today. Typically, these tools load every possible capability into memory upfront — a process that burns through computational resources and can overwhelm the AI with irrelevant information. Kiro powers takes the opposite approach, activating specialized knowledge only at the moment a developer actually needs it."Our goal is to give the agent specialized context so it can reach the right outcome faster �

The debate over whether AI belongs in the corporate boardroom appears to be over — at least for those responsible for generating revenue.Seven in 10 enterprise revenue leaders now trust AI to regularly inform their business decisions, according to a sweeping new study released by revenue intelligence company Gong. The finding marks a dramatic shift from just two years ago, when most organizations treated AI as an experimental technology relegated to pilot programs and individual productivity hacks.The research, based on an analysis of 7.1 million sales opportunities across more than 3,600 companies and a survey of over 3,000 global revenue leaders spanning the U.S., UK, Australia and Germany, paints a picture of an industry in rapid transformation. Organizations that have embedded AI into their core go-to-market strategies are 65% more likely to increase their win rates than competitor

For all their superhuman power, today’s AI models suffer from a surprisingly human flaw: They forget. Give an AI assistant a sprawling conversation, a multi-step reasoning task or a project spanning days, and it will eventually lose the thread. Engineers refer to this phenomenon as “context rot,” and it has quietly become one of the most significant obstacles to building AI agents that can function reliably in the real world.A research team from China and Hong Kong believes it has created a solution to context rot. Their new paper introduces general agentic memory (GAM), a system built to preserve long-horizon information without overwhelming the model. The core premise is simple: Split memory into two specialized roles, one that captures everything, another that retrieves exactly the right things at the right moment.Early results are encouraging, and couldn’t be better timed. As the industry moves beyond

Model providers want to prove the security and robustness of their models, releasing system cards and conducting red-team exercises with each new release. But it can be difficult for enterprises to parse through the results, which vary widely and can be misleading. Anthropic's 153-page system card for Claude Opus 4.5 versus OpenAI's 60-page GPT-5 system card reveals a fundamental split in how these labs approach security validation. Anthropic discloses in their system card how they rely on multi-attempt attack success rates from 200-attempt reinforcement learning (RL) campaigns. OpenAI also reports attempted jailbreak resistance. Both metrics are valid. Neither tells the whole story.Security leaders deploying AI agents for browsing, code execution and autonomou