Ellis AI has officially come out of stealth mode, securing $10 million in seed funding to develop AI solutions tailored for private credit managers. This new venture, led by repeat founder Ryan Williams, aims to innovate within the private credit space using advanced artificial intelligence.
While employees of OpenAI and Anthropic have become more reserved on social media, AI researchers from Chinese laboratories are increasingly active on X. They use the platform to share insights, attract skilled professionals, and influence the worldwide AI dialogue.
CoreMap secured $37 million in an oversubscribed Series C funding round, with Medtronic as a key investor. This capital injection will support the development of advanced, data-driven tools aimed at improving the precision of atrial fibrillation ablation procedures.
The observability landscape for AI agents is rapidly evolving, raising important questions for enterprises about the right tools and strategies to use. Observability startup Groundcover recently raised $100 million in funding, signaling growing momentum in this competitive market. The firm highlights a shift in how telemetry data from AI systems is managed — advocating that this data should reside within the customer's own cloud infrastructure rather than being processed by vendor-managed systems.
Traditional observability platforms, long dominated by incumbents like Datadog and Splunk, are challenged by the scale and complexity AI introduces. As autonomous AI agents generate increasing telemetry data, the old model of sampling or limiting data is no longer sufficient. Groundcover's approach is to offer a bring-your-own-cloud (BYOC) model, where telemetry storage and processing happen inside AWS, Azure, or Google Cloud environments owned by the customer, avoiding data ingestion pricing and granting full control over telemetry.
Central to Groundcover’s technology is eBPF, which enables deep monitoring of system behavior without requiring manual application instrumentation. This, combined with BYOC and a host-based pricing model, aims to provide predictable costs and extensive observability coverage.
Groundcover also foresees observability platforms serving AI agents directly, using data to inform autonomous software development and operational decision-making. While the market remains crowded and competitive, the company positions itself as an innovator designing observability for an AI-driven future, betting on a fundamental architectural change rather than incremental AI feature additions.
Summary: Groundcover is reshaping the observability market by keeping AI telemetry within enterprises' own cloud environments. Their BYOC model, paired with eBPF technology, addresses the growing telemetry demands of AI systems while offering cost predictability and full data control. As AI increasingly influences software operations, Groundcover’s approach signals a shift toward observability designed specifically for autonomous AI-driven workflows.
Just two weeks after releasing its first open source AI language model, Inkling, Thinking Machines, led by ex-OpenAI CTO Mira Murati, unveiled Inkling-Small. This new model delivers nearly the same performance as its larger predecessor while being about one-quarter the size. Inkling-Small is a 276-billion-parameter multimodal reasoning model with an Apache 2.0 license, supporting text, image, and audio inputs with a context window of up to one million tokens. Despite having fewer active parameters, it surpasses Inkling on several benchmarks, including coding and reasoning tasks. The model is designed to reduce compute requirements, inference costs, and deployment complexity, fitting enterprises with moderate GPU resources. Although it’s still too large for typical laptops or workstations, its smaller footprint enables easier operation on enterprise GPU servers or cloud clusters. The permissive Apache 2.0 license facilitates broad commercial use and customization. Thinking Machines also offers full weights on Hugging Face and fine-tuning support through its Tinker API. Inkling-Small exemplifies a more efficient, repeatable engineering pipeline, promising ongoing improvements and adaptability for diverse AI applications in coding, multi-modal workflows, and more.
Over the past two years, Uber has formed partnerships with nearly 30 companies in the autonomous vehicle sector, including direct investments in some. This strategic move is helping Uber build a comprehensive ecosystem to advance its autonomous vehicle ambitions.
Recent breakthroughs in China's AI, robotics, and chip technology have disrupted markets and stirred debate across the U.S. tech sector. In just a month, these advancements have unsettled financial markets and created rifts among American tech leaders, prompting urgent responses from the White House. While Silicon Valley once viewed China's rise as a competitive backdrop to fend off regulation, the swift progress now forces U.S. CEOs into open conflict on how to manage the influx of Chinese innovations reshaping their industry.
In the latest Equity podcast episode, Sam Altman emphasizes the need for the AI industry to slow down the speed of its advancements, encouraging a more measured approach to development.
In June, Menlo Ventures announced its largest capital raise ever, $3 billion across two funds to fuel AI startups at all stages. Menlo Ventures XVII focuses on seed and Series A companies, while Menlo Inflection IV backs growth-stage startups from Series B onward, spanning the AI ecosystem from foundational models to healthcare and consumer applications. With this funding, Menlo can support promising firms through every growth phase. Partner Matt Murphy, pivotal in shaping Menlo’s AI strategy since 2015, has led investments in AI innovators such as Anthropic, Lovable, Suno, and more. He emphasizes that AI startups require much larger capital than prior software waves, with the best moving quickly to dominate the market. Menlo is adopting a barbell strategy, making larger concentrated bets on standout companies, while keeping founder-focused relationships. Murphy notes AI is entering a new phase where scale, optimization, and multi-model approaches become key, and that infrastructure, software delivery, and developer tools are critical growth areas. He highlights bottlenecks around production readiness, security, and compute management that startups must solve to succeed. Menlo actively supports early-stage AI research teams and sees this as a pivotal "land-grab moment" in a rapidly evolving market rich with opportunity but high bar for winners.
AWS now supports the integration of the vibe coding platform Superblocks directly into AWS customers' private clouds. This integration marks a significant move towards separating application development from underlying models, enhancing flexibility and control for developers.
