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Deadline Approaches for Startup Battlefield 200 Founders have until June 8, 11:59 p.m. PT to submit their applications for Startup Battlefield 200, the flagship competition at TechCrunch Disrupt 2026 in October. With only three days left, the window to pitch on the Disrupt Stage at San Francisco’s Moscone West is rapidly closing. What the Competition Offers to Early‑Stage Startups Live pitch in front of top investors, media, and the global startup ecosystem. Potential to win $100,000 in equity‑free funding. Broad exposure that can accelerate customer acquisition and future fundraising. Eligibility: bootstrapped, pre‑seed, seed‑stage, and select Series A startups with a working MVP. Numbers That Highlight the Program’s Track Record Alumni have collectively raised more than $32 billion. Over 250 exits have been recorded among past participants. Notable alumni acquisitions include Microsoft, Google, Salesforce, Uber, and Amazon. Iconic companies launched from the battlefield: Dropbox, Discord, Mint, Fitbit, Trello. Why This Matters for Early‑Stage Founders In a competitive fundraising environment, visibility on a stage watched by venture capitalists and industry influencers can be a decisive advantage. The combination of cash prize, media coverage, and direct investor access creates a catalyst for rapid growth, especially for startups still shaping their market category. Looking Ahead to TechCrunch Disrupt 2026 The selected Battlefield cohort will present in October at Moscone West, positioning themselves for follow‑on funding rounds and strategic partnerships. As the tech ecosystem converges on San Francisco, participants can expect heightened networking opportunities and potential deals that extend well beyond the event itself.

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.

The Leadership Shift at Apple Apple is on the cusp of a new era as Tim Cook prepares to step down as CEO in September, handing over the reins to John Ternus, the current hardware chief. The Challenges Facing the New CEO Ternus will inherit a business with a strong foundation but also one that is undergoing significant changes. The App Store's 30% cut is under scrutiny, and the control Apple once had over developers is being challenged. The rise of vibe-coded apps is redefining what it means to build on Apple's platform. The Potential Acquisition of Cursor In a separate development, Elon Musk is reportedly considering a $60B bid for Cursor, a move that has significant implications for the tech industry. The Implications for Startups and the Tech Ecosystem This transition at Apple and the potential acquisition of Cursor are being closely watched by startups and investors. The hosts of TechCrunch's Equity podcast discuss these developments and their potential impact on the tech ecosystem. What's Next As the tech industry continues to evolve, all eyes will be on Ternus and his leadership of Apple, as well as the outcome of Musk's bid for Cursor. One thing is certain: these developments will have far-reaching consequences for the tech world.

The Lead This week at TechCrunch’s StrictlyVC event in Athens, I sat down with three top VCs to discuss the current state of venture investing, the wave of mega-IPOs, and where they see opportunities in AI. VC Insights on AI and Mega-IPOs The conversation featured Niko Bonatsos of Verdict Capital, Andreas Stavropoulos of Threshold Ventures, and Ben Blume of Atomico. They discussed the potential impact of SpaceX's reported $1.75 trillion valuation at IPO, as well as the opportunities and challenges in the AI space. The Data Analysis SpaceX's potential $1.75 trillion valuation at IPO OpenAI and Anthropic potentially not far behind in terms of valuation Three-quarters of all venture capital raised over the last year went into five companies $500 million fund looking at the same opportunities as people investing from a $10 billion or $15 billion fund The Impact Analysis The VCs discussed how the current flood of capital into AI may be justified by future earnings, but also acknowledged the risk of extreme FOMO (fear of missing out). They also touched on the challenges of pricing deals when things are moving fast and the importance of looking beyond age as a proxy for entrepreneurial potential. The Prediction The VCs see opportunities in areas such as consumer fintech, AI interacting with the physical world, and robotics. They predict that the next generation of companies will be able to go after much larger markets and that immigrant founders will continue to play a significant role in driving innovation.

Why a Living AI Glossary Matters NowArtificial intelligence is reshaping every industry, but its rapid evolution has spawned a parallel explosion of terminology that can leave even seasoned technologists feeling insecure. TechCrunch’s new glossary aims to provide a single, regularly‑updated reference that translates the most common AI buzzwords into plain language.Key Definitions from AGI to RLHFThe article walks readers through a spectrum of concepts, including:Artificial General Intelligence (AGI) – AI that outperforms humans on most economically valuable tasks, as defined by OpenAI and Google DeepMind.AI Agent – An autonomous tool that can perform multi‑step tasks such as expense filing, ticket booking, or code maintenance.API Endpoints – “Buttons” that let software components interact, enabling agents to automate third‑party services.Chain‑of‑Thought Reasoning – A technique that breaks problems into intermediate steps to improve accuracy.Compute – The hardware (GPUs, CPUs, TPUs) that powers AI model training and inference.Deep Learning – Multi‑layered neural networks that learn features directly from data.Diffusion – The process behind many generative AI models that learns to reverse noise‑added data.Distillation – A teacher‑student method for creating smaller, faster models like GPT‑4 Turbo.Fine‑Tuning – Adding task‑specific data to a pre‑trained model to improve performance.GAN – Generative Adversarial Networks that pit a generator against a discriminator to produce realistic outputs.Hallucination – When models generate inaccurate or fabricated information.Inference – Running a trained model to make predictions, often accelerated by specialized hardware.LLM – Large Language Models that power assistants such as ChatGPT, Claude, Gemini, and Llama.Memory Cache (KV Caching) – An optimization that stores intermediate calculations to speed up inference.Open Source vs. Closed Source – The debate over publicly available model code (e.g., Meta’s Llama) versus proprietary systems (e.g., OpenAI’s GPT).Parallelization – Executing many calculations simultaneously, a cornerstone of modern AI hardware.RAMageddon – The current shortage of memory chips driven by AI data‑center demand.Recursive Self‑Improvement (RSI) – Models that can redesign themselves, a potential step toward singularity.Reinforcement Learning from Human Feedback (RLHF) – Training models with reward signals to improve helpfulness and safety.Tokens & Throughput – The basic units of text processing that determine cost and performance.Quantifying the AI Vocabulary ExplosionThe glossary covers more than 30 distinct terms, each accompanied by concise explanations and links to deeper resources. By cataloguing this breadth, the piece highlights how quickly the AI lexicon has expanded within just a few years of mainstream adoption.Implications for Developers, Investors, and the PublicUnderstanding this terminology is no longer optional. For developers, clear definitions accelerate product building and reduce miscommunication when integrating APIs or deploying agents. Investors gain a sharper lens for evaluating startup pitches that hinge on concepts like fine‑tuning or distillation. Meanwhile, the broader public can better assess claims about “AGI” or “hallucinations,” mitigating hype‑driven misinformation.Future of AI Terminology and Industry AdoptionTechCrunch positions the glossary as a “living document,” promising regular updates as new techniques (e.g., emerging diffusion variants or next‑gen RLHF methods) appear. As AI systems become more autonomous and specialized, the vocabulary will continue to evolve, making ongoing education essential for anyone interacting with the technology.

The Rise of AI Psychosis in Corporate Decision MakingBox founder Aaron Levie has identified a troubling trend in corporate America: what he calls "AI psychosis," where executives and decision-makers become so enamored with artificial intelligence that they believe it can replace human jobs without understanding what those roles truly entail. This overenthusiasm for AI is leading to significant workforce reductions and a growing backlash from both employees and users.Workforce Reductions Fueled by AI AmbitionThe consequences of this AI psychosis are already becoming apparent in the tech industry. Productivity software company ClickUp recently cut 22% of its workforce, citing a shift toward AI agents. This move is part of a larger trend where tech layoffs in 2026 are already nearly matching the total number of layoffs seen throughout all of 2025. These cuts suggest that companies are prioritizing AI implementation over human talent, often without fully understanding the implications.User Backlash Against Forced AI IntegrationWhile companies push AI solutions, users are increasingly resisting. DuckDuckGo has seen a surge in installations from users who want Google to stop forcing AI into search results and simply provide traditional links. This user backlash highlights a disconnect between corporate AI strategies and actual consumer preferences, suggesting that not all AI implementations are welcome or beneficial.The Duality of AI AdoptionAs TechCrunch's Equity podcast hosts discuss, both the AI-pilled (those enthusiastically embracing AI) and the AI-skeptical (those questioning its implementation) may have valid points. The challenge lies in finding a balance where AI augments human capabilities rather than replacing them entirely, and where technology serves actual needs rather than being implemented for its own sake.Future of Work in an AI-Driven EconomyAs AI continues to evolve, companies must develop more nuanced approaches to workforce planning and technology implementation. The current trend of replacing human workers with AI agents may prove shortsighted if it leads to decreased product quality, poor user experience, and loss of institutional knowledge. The future likely lies in hybrid models where AI and humans collaborate, each bringing their unique strengths to the workplace.

The Vision for AI Coding Agents Cognition CEO Scott Wu made headlines again this week when his two-year-old AI coding agent startup raised $1 billion at a $26 billion valuation. Cognition is the maker of Devin, one of the first and, arguably, most successful AI coding agents. Devin, the CEO says, “naturally owns tasks end to end.” The Future of Software Development In fact, in the blog post announcing that raise, Cognition laid out a vision where “we are shifting to a world of self-driving software development.” So, could Devin replace, say, a mid-level L4 programmer? Yes, and no, Wu told TechCrunch. “We’ve never thought about it as replacing humans. I know it’s like a scenario, folks have said these things. It has never been our view.” Preserving the Joy of Programming Wu emphasizes that the goal is not to make human programmers obsolete. “We are all programmers ourselves,” he explained. “I started coding when I was nine.” He views agents as another layer of abstraction between envisioning a software product and producing it, similar to how visual development environments abstracted software creation away from machine instructions. The Role of Devin in Cognition Cognition says that Devin’s role in its own company is to ship nearly all the software. The company says that 89% of code committed by its engineers was committed by Devin, and the rest by local agents. Wu explains that his agent’s role is largely to do the kinds of long-tail maintenance tasks that many programmers don’t like to do anyway: bringing old software up to date; moving applications off one platform and onto another. The Future of AI Agents Wu predicts that agents will enter other fields where they will learn tasks, from customer service to medicine, but hopes the goal will be to augment human workers in those areas, too. “Code and software has been the first to move, but we’ll see this happen in all these other industries,” he predicts. “One thing that’s been clear to us since the beginning is, it should always be up to the human what to do … you really see this in software engineering, but I think it’s true in all these other professions too.”

The Final Deadline for TechCrunch Disrupt 2026 Speaker Applications Today is the last day to submit an application to speak at TechCrunch Disrupt 2026, which will return to Moscone West in San Francisco from October 13 to 15. The event is a premier gathering for startup and VC leaders, with over 10,000 attendees expected to explore the latest trends in AI, scaling, fintech, infrastructure, robotics, and the future of innovation. Speaker Opportunities at Disrupt 2026 TechCrunch is inviting founders, investors, operators, and technology experts to apply for a chance to take the stage. There are two session types available: Breakout Sessions: A 30-minute talk with up to 4 speakers and a 20-minute audience Q&A;, with a capacity for 100 attendees. Roundtables: A 30-minute speaker-led group discussion for up to 40 participants, focusing on insight and conversation without slides or AV. The Application and Selection Process Applications will be carefully reviewed by the TechCrunch editorial team. Finalists will be selected for the Audience Choice vote, where TechCrunch readers choose which sessions make it to the Disrupt Stage. To apply, interested individuals must submit their application before tonight’s deadline of 11:59 p.m. PT. Why Apply to Speak? TechCrunch is looking for high-impact speakers with actionable insights and real-world experience who can contribute meaningfully to the tech ecosystem. If you have expertise to share and want to help shape the conversations defining the tech industry, now is the time to apply.

The AI Psychosis Phenomenon Box founder Aaron Levie has coined the term 'AI psychosis' to describe a growing trend among CEOs: the belief that AI can seamlessly replace human jobs without understanding the intricacies of those roles. This phenomenon highlights a disconnect between the decision-makers and the realities of the workforce. The Disconnect Between AI Hype and Job Realities Recent layoffs: ClickUp cut 22% of its workforce for AI agents, and tech layoffs in 2026 are nearly matching all of 2025. Growing concerns: DuckDuckGo installs are climbing as users seek alternatives to Google's AI-driven search. The Impact on the Tech Industry The situation raises questions about the future of work and the role of AI. As the AI-pilled and AI-skeptical perspectives collide, the industry is left to ponder the implications. Key Takeaways and Future Outlook The discussion on TechCrunch's Equity podcast, featuring Kirsten Korosec, Anthony Ha, and Sean O'Kane, delves into the complexities of AI's impact on the workforce. With Waymo's new robotaxi hitting the road and significant deals on the horizon, the future of tech and AI is more uncertain than ever.