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The United States and India are at a pivotal moment in their economic partnership, as both nations weigh the benefits of deeper trade cooperation against the backdrop of a rising China. Recent diplomatic engagements suggest a willingness to reset the relationship, but lingering policy differences and geopolitical concerns pose significant challenges.US‑India Trade Relations at a CrossroadsNegotiations have focused on reducing tariffs, expanding market access for technology and agricultural products, and aligning regulatory standards. Both sides cite the need for a more resilient supply chain that can counterbalance Chinese dominance in key sectors.Economic Stakes and Recent Trade DataBilaterally, trade has shown steady growth over the past five years, with both countries seeking to double the value of exchanged goods by the end of the decade.U.S. firms are increasingly looking to India for manufacturing and software services, while Indian exporters aim to capture a larger share of the U.S. consumer market.Geopolitical Implications of a Renewed PartnershipThe prospect of a stronger US‑India trade bond is intertwined with strategic concerns about China’s expanding influence in the Indo‑Pacific. Both Washington and New Delhi view economic cooperation as a tool to reinforce shared security objectives and to present a united front in regional forums.Challenges Hindering Full ReconciliationDifferences over intellectual property protections and data localization requirements.Domestic political pressures in both countries that caution against rapid liberalization.Ongoing disputes related to market access for certain sectors, such as pharmaceuticals and renewable energy.Future Outlook: Paths to a Sustainable PartnershipAnalysts suggest that incremental agreements—starting with sector‑specific pacts—could pave the way for a broader trade framework. Continued high‑level dialogues and joint initiatives on technology standards are likely to shape the trajectory of US‑India economic ties in the coming years.

The Inevitable Integration of AI in DevelopmentIn 2026, artificial intelligence has become an inseparable tool for developers, yet this reliance may be masking a critical productivity crisis.Researchers at METR discovered that most developers will not participate in studies without AI assistance.This dependency suggests a psychological shift where AI is no longer viewed as an assistant but a requirement.The "Tokenmaxxing" Crisis and Budget BlowoutsThe trend of measuring productivity by token usage, known as "tokenmaxxing," has led to significant financial waste.Amazon shut down its internal leaderboard, Kirorank, after employees gamed the system to run up costs.Uber reportedly exhausted its 2026 AI budget in just four months without measurable project increases.Self-reported data shows a 2x increase in perceived value, but independent analysis suggests 44% of tokens are spent fixing bugs generated by AI.Code review tools indicate AI produces 1.7x more problems than human code.The Hidden Cost of Speed: Maintenance and QualityWhile AI generates code faster, it introduces long-term maintenance costs that developers are currently ignoring.Programmer James Shore warns that trading a temporary speed boost for permanent indenture is a dangerous strategy.Researchers from Singapore Management University have confirmed that AI-generated code can introduce significant long-term maintenance burdens.The Future of Human-AI CollaborationThe industry is moving toward a model where AI is a junior developer that requires constant oversight.Scott Wu (Cognition) admits his AI agent Devin is currently a junior-to-mid-level programmer.Experts recommend that humans must review AI work as carefully as they would a junior developer's code.Software architecture and security design must remain human-centric tasks.

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.”

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The Film’s Core Concept: Turning Internet Liminality into CinemaThe Guardian review details how *Backrooms* follows architect‑turned‑store‑owner Clark (played by Chiwetel Ejiofor) as he discovers a portal to an endless maze of fluorescent‑lit, drop‑ceiling rooms. The film expands the viral “backrooms” meme—originally a series of YouTube shorts made with Blender and After Effects—into a feature‑length narrative while retaining its minimalist visual language.Production Insight: A 20‑Year‑Old Director’s Low‑Budget MasteryDirector Kane Parsons, the youngest ever to helm an A24 feature, built the original series using free software, demonstrating how low‑cost tools can generate high‑impact horror aesthetics. The movie’s production emphasizes practical set design—repeating office‑style corridors, yellow lighting, and drop ceilings—to evoke the “junkspace” described by architects like Rem Koolhaas.Financial Snapshot: A24’s Continued Investment in Indie HorrorBudget details were not disclosed, but A24’s recent horror slate averages $5‑10 million per film.Box‑office expectations align with the studio’s strategy of modest budgets paired with strong niche appeal.Why It Matters: Architecture as a New Horror FrontierThe film taps into academic concepts such as Mark Augé’s “non‑places” and Juhani Pallasmaa’s idea of architecture as mental space, positioning the built environment itself as the antagonist. By visualising bureaucratic infinity, *Backrooms* expands horror beyond monsters to the sterile, endless corridors of modern capitalism.Looking Ahead: The Future of Liminal‑Space HorrorParsons’ success suggests a growing appetite for horror that interrogates everyday environments. Expect more studios to mine internet subcultures and architectural theory, blending low‑budget VFX with philosophical storytelling to attract both genre fans and critical audiences.

Glean's Rapid Growth in Enterprise AI Search Glean, a company often described as the Google for enterprise, has reached $300 million in annual recurring revenue (ARR), a three-fold increase from the $100 million milestone it reached just 15 months ago. This growth is particularly remarkable given the increasing competition in the enterprise AI search market from tech giants like Google, Microsoft, and OpenAI. The Competitive Landscape and Glean's Unique Value Proposition According to Glean CEO Arvind Jain, the company's early mover advantage and deep understanding of customers' business needs set it apart from competitors. Glean's AI tools achieve this understanding by connecting to and learning from enterprises' internal software systems, creating a "context graph" that helps reduce AI computing costs. The Cost-Cutting Advantage of Glean's AI Tools Glean's context graph helps enterprises cut AI computing costs by reducing the number of tokens consumed. This results in significant cost savings for customers, making it a major selling point in a market where many companies are struggling with AI budget overruns. Business Model and Pricing Structures Glean offers various pricing structures, including a consumption-based model and a hybrid model that combines a fixed monthly fee with separate usage fees. The company's customers include Databricks, Reddit, Pinterest, and Samsung. The Future Outlook for Glean and Enterprise AI Search As the enterprise AI search market continues to grow, Glean's focus on cost-cutting and its unique value proposition position it well for future success. With a valuation of $7.2 billion and a strong customer base, Glean is poised to remain a leader in the space.

Asana's Strategic AI AcquisitionAsana has acquired the workflow automation company StackAI for $75 million, marking a significant step in the company's broader AI pivot. The acquisition aims to position Asana as an "AI-native workplace platform" and integrate StackAI's agent-building capabilities into Asana's existing work management system. The announcement was made Thursday afternoon to coincide with Asana's earnings and investor call.StackAI's Workflow Automation CapabilitiesStackAI, built as an AI workflow-automation system, designs agents to operate within existing business systems, pulling in data from platforms like Salesforce, Slack, and Gsuite. The company, founded by Tony Rosinol and Bernard Aceituno, will join Asana as part of the acquisition. StackAI has faced competition from automation tools like Zapier as well as AI labs like OpenAI and Anthropic in the rapidly evolving AI automation space.Financial Terms and Funding BackgroundThe acquisition comes as StackAI had raised just under $20 million, according to PitchBook data, with most of it coming in a recent $16 million Series A round. That round included funding from Gradient, Epakon Capital, Lobby VC, LifeX Ventures, and Vercel CEO Guillermo Rauch. While the $75 million acquisition price represents a significant premium over StackAI's funding, it reflects Asana's commitment to accelerating its AI capabilities.Asana's AI-Native TransformationWhile users are most familiar with Asana's work management system, the company has been releasing AI-oriented products in recent years, including the AI Studio agent builder and AI Teammates series of pre-built automations. Asana believes its deep integration into existing corporate workflows provides a key advantage, allowing it to distill context and training data that would otherwise be unavailable. This acquisition specifically aims to "agentify the most complex business processes end-to-end," according to CEO Dan Rogers.Future of Human-Agent Work in EnterpriseAsana has struggled on public markets during the AI era, losing more than half its market cap value since the introduction of ChatGPT. However, revenue has continued to grow steadily, and the new leadership is confident that human-agent products will enable a rebound. With this acquisition, Asana aims to accelerate its roadmap into "the next phase of human-agent work," potentially differentiating itself from both traditional work management platforms and standalone AI automation tools in the competitive enterprise software landscape.