AMD's latest financial results show a remarkable surge in its data center business, with revenue more than doubling to $6.7 billion, up 107% from the previous year. This growth is fueled by the increasing demand for AI capabilities. Meanwhile, gaming revenue has dropped 31% year-over-year to $779 million, impacted by price increases and shortages of key gaming components for products like Xbox Series X/S, PS5, and Steam Deck. Overall, the company's revenue rose 50% year-over-year to an all-time high of $11.5 billion, with data centers now accounting for 58% of total revenue.
SpaceX's revenue from its AI division surged to $2.6 billion, more than tripling year-over-year, primarily due to agreements providing computing power to other AI firms. Despite generating substantial revenue, this division reported a $1.5 billion loss this quarter, an improvement over last year's figures. Notable partnerships include deals with Anthropic and Google in mid-2023, positioning SpaceX against other cloud providers like CoreWeave. The expansion into AI is also leading to increased company expenditures.
The spotlight is on open-weight AI models as instability hits major US tech companies. French AI lab Mistral finds itself in an ideal position to capitalize on this change, benefiting from the recent market dynamics.
Airtable's valuation has sharply dropped from $11.7 billion in 2021, impacted by the surge of generative AI technologies. Bending Spoons has now acquired the automation platform for $1.28 billion, marking a significant shift after Airtable's IPO listing. For more details, visit Silicon Republic.
Reports from the media indicate that Germany’s The Exploration Company and Paris-based Mistral AI are among the initial recipients of backing from the EU's $5 billion Scaleup Europe Fund. This initiative aims to accelerate the growth of promising startups across Europe.
According to Andrew Torre from Visa, the recent $2.4 billion acquisition of BioCatch will enhance Visa's ability to detect fraud proactively, protecting consumers at the moment of payment.
The U.S. government has placed a ban on the import of humanoid robots equipped with AI, cameras, and sensors, highlighting worries that these devices could be exploited for surveillance or cyberattacks. This move aims to protect national security by preventing potential foreign espionage through advanced robotics technology.
At Kilo Code, engineers spend just about 1% of their time directly coding, with AI agents handling the rest, according to co-founder Emilie Schario. This shift raises new challenges: deciding which tasks can be safely delegated to AI, managing errors made by models, supporting multiple AI systems, and controlling rising token costs. For tech leads from Replit, Kilo Code, and Symbotic, integrating AI agents into workflows is a positive and natural evolution.
Jared Go of Symbotic emphasizes that AI excels at creating new code (greenfield) but struggles with maintaining or updating existing code (brownfield), requiring human involvement. Replit takes a cautious approach by scoring AI-generated pull requests for risk—low-risk ones auto-merge; others get human review. Their AI operates in secure cloud environments, autonomously debugging complex issues and delivering fixes.
Multi-model strategies are becoming essential. Kilo Code supports over 500 models, allowing companies to use costly top-tier models for planning before switching to cheaper ones for execution. Respecting data policies and model limitations is crucial for safe AI deployment. Replit actively chooses models to balance cost and performance on behalf of users.
Cost control is vital as AI usage grows. Kilo Code advises using expensive models only for planning and cheaper models for development. Internally, they monitor usage closely, tracking cost per pull request to measure true value. Symbotic sets monthly spending caps and monitors trends to keep budgets in check. Replit found non-engineering users sometimes cause unexpected spikes, like running expensive models for routine tasks.
Transparency, smart model routing, sensible defaults, and clear ROI are key to managing AI coding costs sustainably. Most tasks don’t require cutting-edge models, and careful oversight helps maximize productivity without breaking budgets.
Alibaba's Qwen team revealed Qwen3.8-Max, a powerful 2.4-trillion-parameter multimodal language model designed for autonomous software engineering and complex enterprise tasks. This new AI model reportedly outperforms leading competitors like GPT-5.6 Sol Max and Fable 5 in agentic computing, scoring highest on benchmarks such as OSWorld-Verified and PaperBench. Alibaba plans an open release of Qwen3.8-Max weights next week, potentially allowing enterprises to self-host this advanced technology under a yet-to-be-disclosed license. The model excels in long-duration project execution, computer use, research automation, and multimodal industrial workflows, all while offering competitive pricing that could reshape enterprise adoption. Qwen3.8-Max represents a strategic pivot for Alibaba, aiming to provide an autonomous coworker for extended horizon enterprise automation, contrasting with more conversational AI approaches.
Enterprise teams often struggle with AI chatbots that fail to retain context across users or track long-term effectiveness. At VB Transform 2026, Asana's CPO Arnab Bose revealed their solution: Agentic Work Management (AWM), an AI operating system designed to function as cooperative teammates integrated with human workflows. Built on Asana’s 18-year-old Work Graph—a dynamic database mapping tasks, projects, portfolios, and company goals—AWM allows AI agents to share memory and update project statuses collaboratively across the organization.
To protect sensitive information, Asana developed strict access controls ensuring confidential projects remain secure and their details inaccessible to unauthorized users. AWM also employs dynamic model routing that assigns AI tasks to appropriate models based on complexity, optimizing efficiency and cost predictability by charging a flat rate per task.
Unlike typical stateless chatbots, AWM records metadata from completed tasks to create persistent workflows that benefit multiple users. Early adopters like CoreWeave have streamlined product launches by automating task assignment and bottleneck detection, freeing humans to focus on strategic decisions.
Despite competition from AI model providers offering their own agent products, Asana leverages extensive workflow experience and industry-specific procedures to deliver a deeply integrated, scalable system suited for enterprise use.
