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While artificial intelligence has stormed into law firms and accounting practices with billion-dollar startups like Harvey leading the charge, the global consulting industry—a $250 billion behemoth—has remained stubbornly analog. A London-based startup founded by former McKinsey consultants is betting $2 million that it can crack open this resistant market, one Excel spreadsheet at a time.Ascentra Labs announced Monday that it has closed a $2 million seed round led by NAP, a Berlin-based venture capital firm formerly known as Cavalry Ventures. The funding comes with participation from notable founder-angels including Alan Chang, chief executive of Fuse and former chief revenue officer at Revolut, and Fredrik Hjelm, chief executive of European e-scooter company Voi.The investment is modest by the standards of enterprise AI — a sector that has seen funding rounds routinel

For much of 2025, the frontier of open-weight language models has been defined not in Silicon Valley or New York City, but in Beijing and Hangzhou.Chinese research labs including Alibaba's Qwen, DeepSeek, Moonshot and Baidu have rapidly set the pace in developing large-scale, open Mixture-of-Experts (MoE) models — often with permissive licenses and leading benchmark performance. While OpenAI fielded its own open source, general purpose LLM this summer as well —

Chinese artificial intelligence startup DeepSeek released two powerful new AI models on Sunday that the company claims match or exceed the capabilities of OpenAI's GPT-5 and Google's Gemini-3.0-Pro — a development that could reshape the competitive landscape between American tech giants and their Chinese challengers.The Hangzhou-based company launched DeepSeek-V3.2, designed as an everyday reasoning assistant, alongside DeepSeek-V3.2-Speciale, a high-powered variant that achieved gold-medal performance in four elite international competitions: the 2025 International Mathematical Olympiad, the International Olympiad in Informatics, the ICPC World Finals, and the China Mathematical Olympiad.The release c

When Liquid AI, a startup founded by MIT computer scientists back in 2023, introduced its Liquid Foundation Models series 2 (LFM2) in July 2025, the pitch was straightforward: deliver the fastest on-device foundation models on the market using the new "liquid" architecture, with training and inference efficiency that made small models a serious alternative to cloud-only large language models (LLMs) such as OpenAI's GPT series and Google's Gemini. The initial release shipped dense checkpoints at 350M, 700M, and 1.2B parameters, a hybrid architecture heavily weighted toward gated short convolutions, and benchmark numbers that placed LFM2 ahead of similarly sized competitors like Qwen3, Llama 3.2, and Gemma 3 on b

One malicious prompt gets blocked, while ten prompts get through. That gap defines the difference between passing benchmarks and withstanding real-world attacks — and it's a gap most enterprises don't know exists.When attackers send a single malicious request, open-weight AI models hold the line well, blocking attacks 87% of the time (on average). But when those same attackers send multiple prompts across a conversation via probing, reframing and escalating across numerous exchanges, the math inverts fast. Attack success rates climb from 13% to 92%.For CISOs evaluating open-weight models for enterprise deployment, the implications are immediate: The models powering your customer-facing chatbots, internal copilots and autonomous agents may pass single-turn safety benchmarks while failing catastrophically under sustained adversarial pressure."A lot of these models have started getting a little bit better," DJ Sampath, SVP of Cisco's AI s

A stealth artificial intelligence startup founded by an MIT researcher emerged this morning with an ambitious claim: its new AI model can control computers better than systems built by OpenAI and Anthropic — at a fraction of the cost.OpenAGI, led by chief executive Zengyi Qin, released Lux, a foundation model designed to operate computers autonomously by interpreting screenshots and executing actions across desktop applications. The San Francisco-based company says Lux achieves an 83.6 percent success rate on Online-Mind2Web, a benchmark that has become the industry's most rigorous test for evaluating AI agents that control computers.That score is a significant leap over the leading models fro

As AI, cloud, and other technology investments soar, organizations have to make investment decisions with increased speed and clarity. Practices like FinOps, IT financial management (ITFM), and strategic portfolio management (SPM) help stakeholders evaluate opportunities and trade-offs for maximum value. But they depend on unified, reliable data. And that’s often where the challenge begins.AI can surface insights from data within specific domains, but important decisions rarely rely on a single source of data. To account for operational and organizational factors as well as financial impact, finance and IT teams have to cut through disconnected systems, outdated data, and inconsistent definitions of value. Real control over technology spend comes from financial intelligence — turning fragmented inputs into actionable, context-rich insights.