The model, dubbed ERNIE-4.5-VL-28B-A3B-Thinking, is the latest salvo in an escalating competition among technology companies to build AI systems that can understand and reason about images, videos, and documents alongside traditional text — capabilities increasingly critical for enterprise applications ranging from automated document processing to industrial quality control.
Deep insights for enterprise AI, data, and security leaders
As software systems grow more complex and AI tools generate code faster than ever, a fundamental problem is getting worse: Engineers are drowning in debugging work, spending up to half their time hunting down the causes of software failures instead of building new products. The challenge has become so acute that it's creating a new category of tooling — AI agents that can diagnose production failures in minutes instead of hours.
As cloud project tracking software monday.com’s engineering organization scaled past 500 developers, the team began to feel the strain of its own success. Product lines were multiplying, microservices proliferating, and code was flowing faster than human reviewers could keep up. The company needed a way to review thousands of pull requests each month without drowning developers in tedium — or letting quality slip.
The San Francisco-based company announced Thursday the general availability of Baseten Training, an infrastructure platform designed to help companies fine-tune open-source AI models without the operational headaches of managing GPU clusters, multi-node orchestration, or cloud capacity planning. The move is a calculated expansion beyond Baseten's core inference business, driven by what CTO Amir Haghighat describes as relentless customer demand and a strategic imperative to capture the full lifecycle of AI deployment.
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As software systems grow more complex and AI tools generate code faster than ever, a fundamental problem is getting worse: Engineers are drowning in debugging work, spending up to half their time hunting down the causes of software failures instead of building new products. The challenge has become so acute that it's creating a new category of tooling — AI agents that can diagnose production failures in minutes instead of hours.
The San Francisco-based company announced Thursday the general availability of Baseten Training, an infrastructure platform designed to help companies fine-tune open-source AI models without the operational headaches of managing GPU clusters, multi-node orchestration, or cloud capacity planning. The move is a calculated expansion beyond Baseten's core inference business, driven by what CTO Amir Haghighat describes as relentless customer demand and a strategic imperative to capture the full lifecycle of AI deployment.
Across industries, rising compute expenses are often cited as a barrier to AI adoption — but leading companies are finding that cost is no longer the real constraint. The tougher challenges (and the ones top of mind for many tech leaders)? Latency, flexibility and capacity. At Wonder, for instance, AI adds a mere few cents per order; the food delivery and takeout company is much more concerned with cloud capacity with skyrocketing demands. Recursion, for its part, has been focused on balancing small and larger-scale training and deployment via on-premises clusters and the cloud; this has afforded the biotech company flexibility for rapid experimentation. The companies’ true in-the-wild experiences highlight a broader industry trend: For enterprises operating AI at scale, economics aren't the key decisive factor — the conversation has shifted from how to pay for AI to how fast it can be deployed and sustained. AI leaders from the two companies recently sat down with Venturebeat’s CEO and editor-in-chief Matt Marshall as part of VB’s traveling AI Impact Series. Here’s what they shared.
The announcement, made Thursday, centers on Ironwood, Google's latest custom AI accelerator chip, which will become generally available in the coming weeks. In a striking validation of the technology, Anthropic, the AI safety company behind the Claude family of models, disclosed plans to access up to one million of these TPU chips — a commitment worth tens of billions of dollars and among the largest known AI infrastructure deals to date.
Integrating AI models directly into extended detection and response (XDR) platforms is delivering breakthrough improvements in SOC investigation speed and accuracy.
Every SOC leader knows the feeling: drowning in alerts, blind to the real threat, stuck playing defense in a war waged at the speed of AI.
OpenAI has introduced Aardvark, a GPT-5-powered autonomous security researcher agent now available in private beta.
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Companies hate to admit it, but the road to production-level AI deployment is littered with proof of concepts (PoCs) that go nowhere, or failed projects that never deliver on their goals. In certain domains, there’s little tolerance for iteration, especially in something like life sciences, when the AI application is facilitating new treatments to markets or diagnosing diseases. Even slightly inaccurate analyses and assumptions early on can create sizable downstream drift in ways that can be concerning.
AI coding, vibe coding and agentic swarm have made a dramatic and astonishing recent market entrance, with the AI Code Tools market valued at $4.8 billion and expected to grow at a 23% annual rate. Enterprises are grappling with AI coding agents and what do about expensive human coders.
The developers of Terminal-Bench, a benchmark suite for evaluating the performance of autonomous AI agents on real-world terminal-based tasks, have released version 2.0 alongside Harbor, a new framework for testing, improving and optimizing AI agents in containerized environments.
Researchers at New York University have developed a new architecture for diffusion models that improves the semantic representation of the images they generate. “Diffusion Transformer with Representation Autoencoders” (RAE) challenges some of the accepted norms of building diffusion models. The NYU researcher's model is more efficient and accurate than standard diffusion models, takes advantage of the latest research in representation learning and could pave the way for new applications that were previously too difficult or expensive.
Even as concern and skepticism grows over U.S. AI startup OpenAI's buildout strategy and high spending commitments, Chinese open source AI providers are escalating their competition and one has even caught up to OpenAI's flagship, paid proprietary model GPT-5 in key third-party performance benchmarks with a new, free model.
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By now, enterprises understand that retrieval augmented generation (RAG) allows applications and agents to find the best, most grounded information for queries. However, typical RAG setups could be an engineering challenge and also exhibit undesirable traits.
