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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.

Groq's New Funding Round Groq is looking to raise $650 million in new funding from existing investors, sources tell Axios, as it leans into its inference neocloud business that relies on its homegrown AI chip and systems. The Nvidia Deal and Its Impact In December, Groq struck one of those not-an-acquisition agreements with Nvidia for a reported $20 billion, which involved the departure of some top-level senior Groq employees to the chip giant and the licensing of Groq’s hardware technology to Nvidia. The Focus on Inference Cloud Business The new direction is led right now by Groq’s interim CEO and CFO, Adam Winter and Matt Eng, respectively. The company's inference cloud business lets developers and enterprises host their inference-hungry apps. Inference is the processing that happens after an AI prompt and is currently a much bigger need in the AI world than model training. The Funding Commitment Groq's backers Disruptive and Infinitium have agreed to fill the round should other existing investors not want their pro-rata shares. The $650 million in funding is essentially guaranteed.

Groq's Ambitious Funding Round Groq, an AI chip startup, is looking to raise $650 million in new funding from existing investors, sources tell Axios, as it leans into its inference neocloud business that relies on its homegrown AI chip and systems. The Nvidia Deal and Its Implications In December, Groq struck a not-an-acquisition agreement with Nvidia for a reported $20 billion, which involved the departure of some top-level senior Groq employees to the chip giant and the licensing of Groq's hardware technology to Nvidia. The Focus on Inference Cloud Business The new direction is led by Groq's interim CEO and CFO, Adam Winter and Matt Eng, respectively. The company's inference cloud business lets developers and enterprises host their inference-hungry apps. Inference is the processing that happens after an AI prompt and is currently a much bigger need in the AI world than model training. The Funding Dynamics Groq's backers Disruptive and Infinitium have agreed to fill the round should other existing investors not want their pro-rata shares. The $650 million in funding is essentially guaranteed. The funding round highlights the ongoing investments in AI chip startups and the growing demand for inference capabilities in the AI ecosystem.

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 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.

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The LeadThe Kiwibit Bird Feeder Pro 4K AI Camera has emerged as a revolutionary backyard accessory that combines traditional bird feeding with cutting-edge artificial intelligence. This smart device not only attracts birds but also identifies and tracks their visits, creating an engaging experience for nature enthusiasts.The Event DetailsSetting up the Kiwibit Bird Feeder Pro is straightforward with multiple mounting options for poles, window ledges, or trees. The device features dual seed compartments for easy refills and cleaning, and a solar panel to eliminate battery concerns. Key specifications include 2.4 GHz Wi-Fi support, cloud storage, built-in two-way audio, and a 130-degree wide-angle lens.Once installed, the feeder connects to the Kiwibit app, which notifies users when birds visit, allows them to watch recordings, and tracks all visits. The app uses Kiwibit's proprietary bird-identification algorithm to recognize over 10,000 bird species, providing detailed information about each species through integration with Wikipedia.The Data AnalysisPriced between $179.99 and $249.99 depending on the model, the Kiwibit Bird Feeder Pro offers significant value for wildlife enthusiasts. During testing, the device successfully recorded visits from six different bird species, with users receiving notifications for each visitor. The Activity tab tracks metrics such as visit count, videos recorded, and species observed, providing quantifiable data about backyard biodiversity.However, the system has a notable limitation in accurately counting visits. When a bird remains in front of the camera for extended periods, the AI may register multiple visits even if the bird hasn't moved significantly, affecting the accuracy of the visitation data.The Impact AnalysisThe Kiwibit Bird Feeder represents a significant advancement in smart wildlife observation, merging technology with nature appreciation. This device democratizes ornithology by making bird identification accessible to casual observers without requiring specialized knowledge. The integration of AI technology transforms passive bird feeding into an interactive, educational experience that encourages users to learn about local wildlife.Additionally, the device's ability to capture and share moments creates new opportunities for community engagement among nature enthusiasts, similar to how Pokémon GO transformed casual exploration into a social gaming experience.The PredictionAs AI technology continues to advance, we can expect future iterations of smart bird feeders to improve in accuracy and functionality. The current limitations in visit counting will likely be addressed through enhanced computer vision algorithms. Furthermore, we anticipate the integration of additional features such as automated food dispensing based on species recognition, weather-adaptive feeding schedules, and expanded social sharing capabilities.The success of devices like the Kiwibit Bird Feeder suggests a growing market for smart wildlife observation technology, potentially leading to more sophisticated backyard ecosystems that monitor and interact with various species beyond birds, creating comprehensive backyard biodiversity monitoring systems.

The UN's Call for Digital Child ProtectionThe United Nations High Commissioner for Human Rights has called for urgent action to protect children online, demanding it be made a "priority."In a statement released on Friday, Volker Turk called for stronger action by governments and tech companies to make online platforms safer."Enhancing protection of children online is an urgent priority," he insisted.New Guidelines for Safer Digital EnvironmentsThe call comes amid a global push for greater accountability and oversight of social media platforms, with countries testing age-based bans and stricter regulations, and pressure growing on technology companies.Alongside the statement, the UN human rights office released a set of guidelines aimed at improving children's safety online and protecting their rights through stronger regulation.The measures include safeguards around age verification processes, mandatory child rights impact assessments, and involving children in shaping regulatory responses.Balancing Safety and Privacy Concerns"We need much wider action – by governments and companies – to ensure that the platforms themselves are made safer by design, that data is protected, that those responsible for harm can be held to account, and that children's rights and needs are fully respected throughout," Turk said."Whatever regulations are adopted, it is essential to avoid inadvertently causing further harms. For example, age verification done wrong can both fail at its goal and endanger the privacy of both kids and adults," he added.Turk added that regulations focused only on the age of users risk leaving unchanged the design choices and algorithmic practices that make platforms unsafe in the first place.Global Response: Age-Based RestrictionsRestrictions on children's access to social media have proliferated globally following Australia's adoption of legislation restricting access for children under 16 in December 2025.Indonesia and Malaysia have also introduced age-based restrictions, while across Europe, many countries are also considering limiting children's access to social media.Austria said in late March that it plans to ban social media for children under the age of 14, with draft legislation expected to be finalised by June. Denmark and France are also set to ban social media platforms for children under 15.Spain's prime minister announced in early February that the country plans to ban social media for children under 16, while the United Kingdom is weighing similar restrictions.Beyond Bans: The Need for Comprehensive RegulationHowever, some child safety experts say bans alone do not go far enough, calling instead for tighter regulation requiring technology companies to better moderate harmful content and platform design.Chris Sherwood, chief executive of Britain's National Society for the Prevention of Cruelty to Children, has previously called on governments to "ensure harmful content is blocked at the source" and for "platforms no longer using design tricks that keep teens hooked".