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DeepSeek’s recent decision to drastically cut pricing on its V4-Pro model by 75% should have been unequivocally good news for enterprise AI vendors and developers. Instead, many are discovering that cheaper models don’t automatically translate into healthier margins. The reason is simple: While inference costs plummet, agent systems are voraciously consuming tokens faster than prices are …

DeepSeek cut prices 75%. The 100x problem remains Read More »

Slopsquatting represents an emerging supply chain threat made possible by AI hallucinations. As developers increasingly rely on AI coding assistants, they unknowingly grant cybercriminals access to their software from day one.  Understanding what slopsquatting is Slopsquatting is a new type of supply chain attack that uses large language model (LLM) hallucinations to inject malicious code …

Forget typosquatting; slopsquatting is the software supply chain threat created by AI coding tools Read More »

An enterprise AI agent answers with total confidence, but the number is wrong. Nobody catches it until someone traces it back to a stale metric definition or a document the retrieval system never pulled. The model did not fail. The context it was given did. In the past six months, 57% of enterprises traced a …

57% of enterprises have watched AI agents be confidently wrong. The fix is an agentic context layer, but who has one? Read More »

OpenAI on Thursday launched ChatGPT Work, a new AI agent embedded inside its flagship chatbot that aims to transform ChatGPT from a question-and-answer tool into an autonomous work platform capable of executing complex, multi-step tasks across users’ email, calendars, code repositories, and messaging apps. The product is powered by OpenAI’s latest flagship model, GPT-5.6, and …

OpenAI introduces ChatGPT Work, a cloud-based AI agent that manages tasks across email, Slack and calendars Read More »

Enterprise companies are running AI agents ahead of the controls needed to manage them — and they deployed that way knowingly. That is the central finding from VentureBeat Research’s June survey of 573 technical leaders at companies with 100 or more employees, fielded across five parallel surveys of the agentic stack.  Enterprises are now retrofitting …

Wall Street is debating the AI buildout. Enterprises just answered: 86% say their GPUs run at half capacity or less Read More »

Half of enterprises have deployed an AI agent or LLM feature that passed internal evaluations and yet still caused a customer-facing failure — one in four more than once — according to the June 2026 VB Pulse survey of 157 qualified enterprise respondents at companies with 100 or more employees. The sample is self-selected rather …

Enterprise AI is entering an evaluation gap: Agents are gaining autonomy faster than companies can verify them Read More »

The vast majority of business data is tabular — living in data warehouses, CRMs, and financial ledgers — yet building a reliable model from it still means training a new one from scratch for every dataset, then maintaining hyperparameter tuning loops, feature engineering, and retraining pipelines to fight data drift. Google Research is proposing a …

Google’s TabFM skips per-dataset training and still predicts on tables it’s never seen Read More »

Share one API key across five AI agents, and a single compromised agent inherits the reach of all five. The attacker immediately benefits from the accumulated permissions of every workflow that the key touches. The forensic trail goes cold at the credential level because five agents on one account leave no record of which agent …

Shared API keys expose AI agents at 69% of enterprises, new VentureBeat research finds Read More »

A team routing queries across a coding specialist, a logic specialist, and a generalist model assumes each will cover the others’ blind spots. A new study evaluating 67 frontier models from 21 providers shows that assumption is mathematically flawed — and the flaw has a name: the co-failure ceiling. The assumption works like this: as …

Enterprises using multiple AI models are underestimating failure rates by 2.25x Read More »

Presented by SAP Generating code with AI is fast, but getting that code to run reliably inside a large enterprise, integrated with live systems, governed for compliance, and maintainable over years requires foundational work that most organizations underestimate. While 81% of all organizations have a detailed strategy, only 12–16% reach AI‑driven execution, says SAP’s Michael …

The enterprise AI challenge nobody solves with code generation alone Read More »