At Cloud Next 2026, Sundar Pichai revealed Google Cloud's impressive $70 billion in annual revenue, marking a 48% growth. The company is backed by a $240 billion order backlog that doubled in just one year, alongside planned capital expenditures of $175-185 billion. The Gemini app boasts 750 million monthly users, with AI Overviews reaching two billion, and the Gemini API handling 85 billion requests. Pichai also outlined a strategic vision to evolve Search into an agent manager, driving further innovation.
VAST Data recently closed a $1 billion Series F funding round, boosting its valuation to $30 billion—more than triple its previous $9.1 billion valuation. The round was co-led by Drive Capital and Access Industries, with notable investments from Nvidia, Fidelity, and NEA. Over $500 million of this funding is secondary capital. VAST Data has achieved $4 billion in cumulative bookings and has over $500 million in committed annual recurring revenue (ARR).
In Q1 2026, startups collectively raised a record-breaking $300 billion in venture capital, with four firms absorbing 65% of this total. While this concentration may seem discouraging for seed-stage founders, early-stage funding actually increased by 41% year-over-year. The key trend is a shift from horizontal SaaS, which is declining due to AI enabling native coordination, to vertical SaaS that leverages proprietary data and industry-specific workflows — sectors such as insurance, healthcare, financial services, and construction stand to benefit significantly.
As software adoption expands, AI is driving a substantial increase in software spending, potentially growing the addressable market from $0.5 trillion to over $6 trillion by automating knowledge-work processes once untouched by traditional software. Smart founders are advised to target vertical market niches with AI-native solutions focusing on industry workflows, building products that integrate with existing enterprise systems, and positioning themselves for acquisition, as IPOs remain scarce.
Ultimately, despite the headline-grabbing mega-rounds for infrastructure, the true opportunity lies in creating specialized, revenue-generating solutions for underserved verticals. This strategy meets the increasing capital concentration at the top with a broad open playing field below, where dedicated founders can solve real problems and attract strategic acquirers.
Enterprises have quickly adopted autonomous AI agents for tasks ranging from customer support to code management, but the proliferation of different platforms has led to fragmentation and poor interoperability. BAND, a startup emerging from stealth with $17 million in Seed funding, aims to solve this by creating a "universal orchestrator" that enables AI agents built on diverse frameworks to discover, communicate, and collaborate efficiently. BAND's platform introduces an "agentic mesh," supporting multi-peer collaboration with deterministic routing and enterprise-grade governance to ensure secure and reliable message exchange. It operates independently of specific AI model ecosystems, avoiding vendor lock-in and offering flexible deployment options including SaaS, private cloud, and edge environments. Early traction comes from tech-forward sectors, combining AI agents to improve workflows like coding, customer support, and operations. BAND’s tiered pricing caters to individual enthusiasts up to large enterprises, promising scalable, auditable, and secure multi-agent systems poised to become the backbone of the future "agentic economy."
Google CEO Sundar Pichai announced that 75% of the company's newly developed code is generated by artificial intelligence and then reviewed by human engineers, up from 50% last autumn. This shift allows engineers to concentrate on complex problems and system design, as AI handles much of the manual coding. Google is pioneering "agentic workflows," where autonomous digital agents perform tasks under human supervision, leading to significant productivity gains such as completing complex code migrations six times faster than before. AI is also widely used beyond engineering, notably in marketing, where it has accelerated content creation and increased conversion rates. Google continues to invest heavily in AI infrastructure and plans further advancements at upcoming events like Google I/O.
Microsoft is set to make its largest investment in Australia, committing $18 billion to enhance AI infrastructure. This funding will boost Azure cloud capacity by over 140% and aims to train 3 million Australians in AI skills by 2028, marking a significant step in the country's technological development.
Eli Lilly is set to acquire Kelonia Therapeutics for $3.25 billion upfront, aiming to strengthen its oncology and genetic medicines pipeline beyond its existing GLP-1 offerings.
Claim Clarity highlights that workers’ compensation is a vital but often overlooked part of the healthcare system. Jamie LaPaglia, Founder and CEO, explains that despite its growing importance, workers' comp is frequently treated like standard healthcare. However, it operates under unique regulatory and operational conditions that require specialized attention. In this setting, specialized AI tools are playing an increasingly important role in improving the accuracy and effectiveness of decisions related to workers’ compensation claims.
Salmon aims to support underbanked Filipinos who have limited or no credit history, as well as those dissatisfied with the reliability of existing lenders, by providing digital credit services. The company's new funding will help expand these offerings and improve financial access for this underserved group.
Chinese AI startup DeepSeek, originated from High-Flyer Capital Management, has launched DeepSeek-V4, a groundbreaking 1.6-trillion-parameter Mixture-of-Experts (MoE) AI model. Offered under an open-source MIT License, it delivers near state-of-the-art intelligence comparable to top-tier closed-source models like OpenAI's GPT-5.5 and Anthropic's Claude Opus 4.7 but at roughly one-sixth the cost. DeepSeek-V4 offers vast improvements in efficiency, long-context handling with a 1 million token window, and stability through novel architectural innovations like Manifold-Constrained Hyper-Connections (mHC) and a Hybrid Attention Architecture. The model is priced competitively via API, potentially transforming the economics of AI deployment for enterprises by making advanced AI capabilities more accessible and affordable. It supports multiple reasoning modes and is compatible with NVIDIA GPUs and Huawei’s Ascend NPUs, promoting sovereignty in AI technology infrastructure. Community and industry responses highlight the model’s disruptive potential, placing pressure on established AI providers to justify their pricier offerings while broader access promises significant benefits for AI integration worldwide.
