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The Expanded Partnership Lovable and Google announced an expanded multi-year collaboration on Wednesday. Lovable, the fast-growing Stockholm vibe-coding startup, has long been a Google Cloud user. Under the new agreement, it will be a much bigger one. The Deal Details While the companies did not disclose the dollar figure, a person with knowledge of the deal tells TechCrunch it involves a fivefold increase in Lovable’s footprint on Google Cloud, including AI usage. As part of the deal, this individual tells us, Lovable will gain expanded access to both Anthropic’s Claude — the AI model widely used for coding tasks — and Google’s own Gemini models. The Financial Impact Google invested $10 billion in Anthropic in cash and compute credits in April, promising another $30 billion if Anthropic hits certain performance targets. Anthropic raised a staggering $65 billion round that valued the company at nearly $1 trillion. Lovable crossed $400 million in annualized revenue in February, having added $100 million in a single month with just 146 employees. The Strategic Implications The deal also plugs Lovable into several other parts of Google’s ecosystem. Lovable’s new agent will be available through Google Cloud’s enterprise agent marketplace, the Gemini Enterprise Agent Gallery — an arrangement the two companies first telegraphed at Google’s major U.S. cloud conference in April. And to help secure the code that both humans and agents write, Lovable will integrate with Wiz, Google’s biggest ever acquisition at $32 billion, which officially closed only in March. The Future Outlook By selling Lovable’s agents through Google’s marketplace, the cloud giant says enterprise procurement and billing will be simplified, making it easier for Lovable to land more enterprise customers. The calculus for Google is simple enough. If it can keep both Lovable and Anthropic growing by attracting deep-pocketed enterprises, the revenue helps fund the $180 billion to $190 billion in capital expenditures Google plans to spend this year.

Alphabet, the parent company of Google, has announced plans to raise up to $80 billion (£59 billion) in equity to finance its aggressive artificial intelligence infrastructure expansion. This monumental fundraising effort underscores the sheer scale of capital required to compete in the modern AI landscape and sets the stage for a transformative year in tech finance.Alphabet's Mega-Equity Raise and the Berkshire Hathaway BetThe fundraising initiative includes a notable $10 billion share sale to Berkshire Hathaway, the investment conglomerate long associated with the retired investment guru Warren Buffett. Historically, Berkshire has stepped in to provide crucial liquidity during pivotal market moments, such as the famous $5 billion investment in Goldman Sachs during the 2008 financial crisis. Alphabet stated the fresh capital will directly support its world-class AI compute infrastructure to meet unprecedented customer demand for its Gemini system and enterprise cloud services.Decoding the $80 Billion Capital DeploymentWhile the headline figure is staggering, the deployment strategy reveals a nuanced financial approach. The $80 billion package is structured to address both operational expansion and internal financial mechanics:$40 billion is explicitly dedicated to scaling AI infrastructure and global compute capacity.$40 billion is allocated to cover an administrative change regarding tax obligations for the vesting of employee equity awards.The raise features an initial $30 billion paired with the $10 billion from Berkshire, alongside a flexible $40 billion drip-feed mechanism to be used gradually over time.Although $80 billion represents one of the largest equity fundraisings globally, it amounts to less than 2% of Alphabet's massive $4.6 trillion market capitalization. This year alone, the company's total capital expenditure is expected to reach between $180 billion and $190 billion.The Shift from Capital-Light Tech to Infrastructure HeavyweightsThis move serves as a stark reminder to Wall Street that the era of tech giants operating as capital-light free cash flow machines is fading. Market strategists at Deutsche Bank note that funding the AI capital expenditure boom is becoming a central, pressing topic for global markets. However, analysts at Hargreaves Lansdown emphasize that Alphabet is spending from a position of strength rather than distress. With Google Cloud growth accelerating, search proving resilient, and AI compute demand vastly outstripping current supply, Alphabet's investment is backed by tangible business momentum.The Looming AI IPO Wave and Market ExpectationsAlphabet's aggressive capital raise precedes a highly anticipated wave of AI-driven public offerings. Anthropic, the creator of the Claude chatbot and currently the world's most valuable startup at a $965 billion valuation, has confidentially filed for an initial public offering. Furthermore, industry heavyweights like OpenAI and Elon Musk's SpaceX (which includes the xAI startup) are also preparing to go public. As these industry titans enter the public markets, investors will increasingly demand concrete proof that massive data center buildouts will translate into durable, long-term revenue growth.

The Rise of AI Financial MarketsThe most important market of the future could be in LLM tokens — and financial groups are rushing to build new infrastructure for them. China's Shanghai Futures Exchange is currently designing a derivatives market for AI tokens, while major derivatives exchanges CME Group and the Intercontinental Exchange (the owner of the NYSE) have separately announced they're working on launching futures contracts for renting GPUs.Building the AI Derivatives InfrastructureGPU markets are still maturing, but given the wide range of companies using, selling, and renting GPUs, there's already a robust market for spot prices on GPU rental, typically charged by the hour. This has prompted major financial players to develop futures contracts that would allow businesses to hedge against fluctuating compute costs.Enterprise plans for major AI companies are commonly denominated in tokens: OpenAI, for example, charges $5 per million input tokens, and $30 per million output tokens if you want to use the API for its latest GPT-5.5 model. Even cloud providers are increasingly offering the opportunity to charge per token, as in Amazon's Bedrock system.The Economics of GPU and Token PricingAccording to data from AI Mining Co., which tracks daily GPU rental pricing across 28 marketplaces and cloud providers, median prices for Nvidia H100 GPUs ranged from $1.40 to $4.27 per hour across 13 marketplaces, while the average price for H200 GPUs were between $2.34 and $5 per hour across 10 marketplaces.Just over the past seven days, average H100 prices ranged from $2.79 to $3.33, showing the volatility that makes futures contracts attractive for risk management.Transforming the AI Investment LandscapeThe effort comes amid an unprecedented buildout of AI infrastructure. Cloud service providers, private equity firms, and infrastructure players alike have poured hundreds of billions into building data centers, anticipating that demand for GPUs and compute will continue to rise.An emerging crop of global neocloud companies is also vying for a piece of this demand. Some of these new entrants are specializing, focusing on inference, while others are competing with cloud giants like Oracle, AWS, and Google Cloud to offer their services to AI companies.The Future of AI Financial InstrumentsBy targeting AI tokens, the Shanghai exchange's derivative product would be tied to how AI companies price their services, giving businesses, investors, and data center operators a way to hedge against the cost of compute. As AI becomes increasingly central to business operations, these financial instruments will likely become essential components of the technology investment ecosystem.

The AI Security Imperative At a recent event in Los Angeles, Google Cloud COO Francis deSouza stressed that security can't be an afterthought in AI adoption. He advocated for a platform approach to security, warning against 'shadow AI' where employees use consumer tools without organizational oversight. The Risks of 'Shadow AI' DeSouza highlighted the risks associated with employees using unauthorized AI tools, which can lead to security breaches and data exposure. He emphasized that companies need to demand security, governance, and auditability from their platforms from the start. The Challenge of Keeping Pace with AI Threats The threat landscape has changed fundamentally, with the average time between an initial breach and the next stage of an attack dropping from eight hours to 22 seconds. The attack surface has expanded beyond the traditional network perimeter, and companies need to adapt to this new reality. Google's Own AI Security Challenges Despite deSouza's sound advice, Google itself faces challenges with AI security. The company has refunded developers who incurred large bills due to unauthorized API calls to Gemini models. Google's automated systems had upgraded their billing tiers without explicit consent, leading to surprises for developers. The Future of AI-Native Defense DeSouza sees the emergence of AI-native, fully agentic defense as a solution to the challenges posed by AI threats. This approach involves using agents to drive defense, allowing humans to oversee and focus on high-level decision-making. The Skills Gap in AI Security The industry faces a shortage of people qualified to oversee AI security, and the vulnerabilities introduced by AI are multiplying faster than security teams can address them. According to LinkedIn's CISO Lea Kissner, it may take several years for the industry to understand AI security in a sustainable way.

Lead: Google Announces Antigravity 2.0 at I/O 2026Google revealed the next generation of its agentic coding platform, Antigravity 2.0, featuring an updated desktop application, a command‑line interface, and a developer SDK. The rollout leverages the new Gemini 3.5 Flash model and introduces revised AI Ultra subscription tiers. Feature‑Rich Desktop, CLI, and SDK RolloutDesktop app enables orchestration of multiple agents, simultaneous task execution, and background scheduling.Native voice‑command support extends the experience found in Gmail and Docs.New Antigravity CLI lets programmers create agents directly from the terminal; existing Gemini CLI users are encouraged to migrate.Antigravity SDK provides custom‑agent building blocks for Google Cloud customers and includes export tools for moving projects to local environments.Integration points with Google AI Studio, Android, and Firebase streamline end‑to‑end workflows. Pricing Shifts and AI Limits: New Ultra PlansIntroducing an $100 AI Ultra plan offering 5× higher AI limits than the Pro tier.Top‑tier Ultra plan price reduced from $250 to $200, delivering 20× higher limits.Pricing aligns with recent tiered offerings from competitors Anthropic and OpenAI. Implications for the Agentic Coding LandscapeThe expanded Antigravity suite positions Google as a direct challenger to emerging agentic coding tools such as Cursor. By bundling voice interaction, CLI access, and a robust SDK, Google aims to capture both enterprise developers (via AI Studio templates) and individual programmers seeking tighter integration with Google Cloud services. Future Trajectory of Google’s Agentic EcosystemWith the Gemini 3.5 Flash model co‑developed through Antigravity, Google is likely to embed agentic capabilities deeper into consumer products—evident in the upcoming real‑time UI generation for Search. Expect continued investment in custom agent templates, tighter Cloud‑Antigravity connectivity, and further price‑tier refinements to stay competitive in the rapidly evolving AI‑assisted development market.

Google unveiled Antigravity 2.0, the latest iteration of its agentic coding suite, adding a desktop application that can orchestrate multiple agents, a command‑line interface for developers, and an SDK for custom workflows. The enhancements are built on the newly released Gemini 3.5 Flash model and aim to deepen integration across Google’s AI ecosystem.Antigravity 2.0 Expands to Desktop, CLI, and SDKDesktop app enables simultaneous execution of multiple agents and scheduling of background tasks.Native voice‑command support mirrors functionality already in Gmail and Docs.New CLI tool replaces the older Gemini CLI, offering terminal‑based agent creation.SDK lets developers build custom agents and connect Antigravity to Google Cloud projects.Export tool in AI Studio allows projects to be downloaded for local development.Pricing Shifts and New AI Ultra TierIntroduces an AI Ultra plan at $100 per month with 5× higher limits than the Pro tier.Reduces top‑tier price from $250 to $200, delivering 20× higher limits.Pricing aligns with recent tiered offerings from competitors such as Anthropic and OpenAI.Strategic Implications for the Developer EcosystemThe integration of Antigravity with AI Studio, Android, and Firebase creates a seamless pipeline from prototype to production, encouraging enterprise adoption. By exposing a CLI and SDK, Google lowers the barrier for developers to embed agentic coding into existing workflows, potentially accelerating the shift toward AI‑augmented software development.Future Outlook: Wider Adoption and Competitive PositioningWith the multi‑agent desktop experience and expanded pricing options, Antigravity 2.0 positions Google to capture a larger share of the emerging agentic‑coding market. Expect increased usage in consumer products like Search, where real‑time UI generation will showcase the platform’s capabilities, and a growing ecosystem of third‑party templates in AI Studio.

Google Unveils Gemini Spark: A 24/7 Agentic Assistant Integrated with GmailAt the I/O developer conference on 2026-05-19, Google introduced Gemini Spark, a personal AI agent that runs continuously on Google Cloud and can act on behalf of users across email, documents, and the web.Gemini Spark Architecture and Core CapabilitiesBuilt on the latest Gemini base models combined with the Antigravity agentic harness.Operates on dedicated virtual machines, eliminating the need for a constantly‑on laptop.Out‑of‑the‑box integrations with Gmail, Google Docs, Sheets, Slides, and other Workspace apps.Users can email Spark via a dedicated Gmail address; the agent can browse the web through Chrome.Mobile tracking via the new Android Halo system.Availability, Pricing Model, and Early Adoption MetricsCurrently in internal testing; slated for release to Google AI Ultra subscribers next week.Pricing has not been disclosed; Google has indicated a subscription‑based model aligned with its AI Ultra tier.Early pilots show small businesses using Spark to monitor inboxes and draft responses, reducing missed customer queries.Strategic Impact on Google Workspace and Competitive AI LandscapeDeep integration gives Google a unique data advantage, leveraging users' email histories to deliver context‑aware assistance.Positions Google directly against Anthropic’s Claude Cowork and OpenAI’s ChatGPT Agent, but with native Workspace connectivity.Potential to increase stickiness of Google Workspace subscriptions and drive higher adoption of the AI Ultra tier.Future Roadmap: Expansion, Ecosystem Integration, and Market OutlookGoogle plans to add more third‑party connections via its MCP ecosystem over the coming months.Continuous updates to the agentic harness aim to broaden long‑horizon task handling.Analysts expect Gemini Spark to accelerate Google’s AI revenue growth and intensify competition in the enterprise assistant market.

The Startup Market’s Most Urgent Risk: Reacting Too LateFounders and investors are now facing a bigger danger than moving slowly – they risk reacting after the market has already shifted. TechCrunch Disrupt 2026 is designed to help them act faster.Six Specialized Stages Tailored to Today’s Volatile MarketsFrom October 13–15 at Moscone West in San Francisco, Disrupt will host 10,000+ founders, investors and operators across 250+ sessions. The conference is organized into six distinct stages:Disrupt Stage – headline founders, tech leaders and top‑tier investors discuss broad market shifts.Builders Stage – fundraising, hiring, product‑market fit and go‑to‑market execution.Smart Money Stage – evolution of financial infrastructure and durable fintech models.Smart Systems Stage – physical‑world constraints such as data‑center capacity, energy and climate tech.AI in the Real World Stage – reliability of AI systems beyond demos.AI Stage (presented by Google Cloud) – impact of generative AI on SaaS and software businesses.Numbers That Show Disrupt’s Scale and SavingsEvent dates: October 13–15, 2026Attendees: 10,000+ founders, investors, operatorsSessions: 250+ across six stages, plus 200+ sessions highlighted in promotionSpeakers include Nina Achadjian (Index Ventures), Rajeev Dham (Sapphire Ventures), Josh Reeves (Gusto), Grant Lee (Gamma), Robby Stein (Google), Mo Jomaa (CapitalG), Jack Zhang (Airwallex), Lotti Siniscalco (Emergence Capital), Jeff Lawson (Inertia), David Kirtley (Helion).Early‑bird discount: save up to $410 on a pass and get 50% off a second ticket.Group discount: up to 30% off tickets for community registrations.Startup Battlefield 200 nominations close May 29.How the New Stages May Shift Founder‑Investor Decision‑MakingThe focused content aims to surface “signals shaping opportunity” – where attention is concentrating, which categories are accelerating, and how successful companies are positioning themselves. By separating AI‑native competition, fintech infrastructure, and physical‑world constraints, participants can prioritize capital allocation and product strategy with fewer guess‑work cycles.What’s Next for Disrupt and the Broader Startup EcosystemWith the six‑stage format, Disrupt positions itself as a real‑time market intelligence hub. If founders leverage the early‑bird pricing and apply for Battlefield 200, the conference could become a primary pipeline for capital in 2026‑27, especially as AI and infrastructure pressures intensify. Observers should watch post‑event reports for emerging investment trends and the adoption rate of “real‑world AI” solutions.

The Lead At the Milken Institute Global Conference, leaders from across the AI supply chain gathered to discuss the current state and future of artificial intelligence. They touched on various challenges, including chip shortages, energy constraints, and the potential for new AI architectures. The Bottlenecks in AI Development The discussion highlighted several bottlenecks in AI development. Christophe Fouquet, CEO of ASML, noted that despite efforts to accelerate chip manufacturing, the market will likely remain supply-limited for the next two to five years. Francis deSouza, COO of Google Cloud, pointed out the immense demand for AI infrastructure, with Google Cloud's revenue growing 63% and its backlog nearly doubling to $460 billion. The Data and Energy Constraints Qasar Younis, co-founder and CEO of Applied Intuition, emphasized that the bottleneck for his company is not silicon but data gathered from the real world, which is essential for training physical AI models. The energy required to power AI infrastructure is also a significant concern. deSouza mentioned that Google is exploring data centers in space to address energy constraints, although this comes with its own set of challenges. New AI Architectures and Their Implications Eve Bodnia, founder of Logical Intelligence, discussed a different approach to AI, focusing on energy-based models (EBMs) that aim to understand the underlying rules of data, similar to human brain function. This approach could be particularly useful for applications requiring an understanding of physical rules, such as chip design and robotics. The Future of AI: Agents, Guardrails, and Trust Dmitry Shevelenko, chief business officer of Perplexity, talked about the evolution of its search product into a 'digital worker' called Perplexity Computer. This tool is designed to act as a staff that a knowledge worker can direct, raising questions about control and security. Shevelenko emphasized the importance of granularity in permissions and actions to ensure trust and security. The Geopolitical and Generational Impact The discussion also touched on the geopolitical implications of physical AI and its impact on national sovereignty. Younis noted that physical AI manifests in the real world in ways that governments can't ignore, leading to questions about safety, data collection, and control. Regarding the impact on the next generation, the panelists were optimistic, highlighting the potential for AI to help address significant problems and unleash new levels of creativity and opportunity.