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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 Lead XCENA, a four-year-old chip startup with offices in South Korea and the U.S., has raised $135 million in a Series B round at a valuation of $570 million. The company aims to solve the structural bottleneck in AI infrastructure by designing a chip that places compute capabilities closer to DRAM. Revolutionizing AI Infrastructure with Memory-Centric Architecture Every time you ask ChatGPT a question, your request triggers a data relay race. Information leaves memory, passes through a CPU for preprocessing, travels to a GPU for heavy computation, and then makes its way back — and that entire journey repeats for every single word the AI generates. XCENA's chip, the MX1, connects to the CPU through CXL (Compute Express Link), processing data before it ever needs to leave the memory module. The Data Analysis XCENA's successful funding round reflects investor enthusiasm around the company's potential to significantly reduce AI infrastructure costs. The startup has designed a chip that brings compute capabilities much closer to DRAM, allowing routine data operations to be handled near memory, without the costly round trips between CPUs, GPUs, and memory. This approach could lead to substantial savings for hyperscalers spending tens of billions a year on AI infrastructure. The Impact Analysis The recent rise in memory prices and related stocks points to a broader shift in AI infrastructure toward memory-centric architectures. XCENA's thesis is that "inference isn't just a compute problem; it's increasingly a memory scaling problem." The company's chip aims to handle tasks directly within the memory module itself, reducing the need for multiple servers and cutting costs. The Prediction With mass production chips scheduled to roll off Samsung's foundry lines by the end of 2026, XCENA expects to generate revenue starting in 2027. The company's ideal customers are hyperscalers, and it is in early-stage conversations with several global memory vendors. XCENA's innovative approach and vertical integration could give it a competitive edge in the market.

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.

The Launch of Sesame's Conversational AI On Thursday, the AI startup Sesame, co-founded by Oculus' founders and others from the VR company that sold to Meta, released a public preview of the conversational AI agents it's been developing for over a year. With its new iOS app, Sesame is rethinking the traditional AI chatbot experience popularized by apps like ChatGPT, creating one where conversation flows, even if the AI needs time to think. Reimagining AI Conversation Flow As the company explains in its launch announcement, "There's an inherent tension between replying quickly and taking the time to compose thoughtful responses. A slower response is usually more correct, but it can also feel unnatural if it takes too long." To address this challenge, Sesame claims to have built fast search and retrieval systems, so the AI can have up-to-date information, as well as technology that allows it to run multiple parallel searches while speaking, weaving those results into its responses as it talks. That means the AI will talk more like a human, even pivoting mid-sentence if need be, as it taps into newer information — as a human might when remembering another key fact or point they want to add. User Growth and Development Milestones The app offers four distinct AI agents called Maya, Miles, Simone, and Charlie, each of which have their own distinct voice, personality, point of view, and memory. Maya and Miles were previously available in Sesame's Research Preview of its technology, where they were soon accessed by over one million people within the first few weeks, said Sesame investor Sequoia at the time. (The company had then just raised its $250 million Series B from Sequoia and others and was opening up a beta.) During the beta, Sesame learned from user feedback and rolled out features such as search cards with image results for visualizing concepts, notes for capturing takeaways, a texting mode for those times when speaking aloud is not an option, and support for deep dives where you can get more in-depth results. There's also a new incognito mode for private conversations, which allows the agents access to prior context but saves nothing to memory. Transforming the AI Landscape The app, however, is only the first step toward Sesame's bigger plans for AI involving intelligent eyewear, which the team expects to launch in 2027. Before that, the agents will also learn to do more than just think with you, Sesame hints, suggesting they'll later be able to take action on your behalf — hence why they're called "agents" in the first place, instead of just chatbots. That is potentially even more interesting, as working with agentic tools or apps today requires being able to prompt for what you need and have a specific idea of what you want to happen, and sometimes, even how it should happen. A conversational agent that you could talk to naturally could help you take the next steps, without you having to perfect the command you're giving it. The Road to AI-Powered Eyewear The iOS app is out today in 39 countries, and the full experience is free for the time being. However, there still may be a short waitlist at sign-up. An Android preview is coming in the future, the company says.

The Strategic Shift in iOS 27Just ahead of Apple’s Worldwide Developers Conference (WWDC) in June, leaked renders reveal a significant overhaul of the iPhone's interface, driven by a new generation of AI capabilities. The most visible change is the integration of Apple’s AI upgrade directly into the user experience, moving beyond simple voice commands to a comprehensive, card-style interface.The Dynamic Island as the AI Command CenterThe iconic black pill-shaped area at the top of the screen, known as the Dynamic Island, is set to become the central hub for AI interactions. While users can still trigger Siri via a button press, the primary mode of interaction will shift to the Dynamic Island. This allows for quick voice queries and searches, mimicking current usage patterns while offering a richer visual output.Furthermore, Apple is capitalizing on muscle memory by integrating AI-powered search into the swipe-down gesture. This feature, powered by a rebuilt AI model using Google's Gemini technology, allows users to search, launch apps, send messages, and manage calendar events directly from the search card.Scale as Apple's Competitive AdvantageApple’s primary weapon in this AI race is its sheer scale. With a total install base of 2.5 billion devices, Apple has an unmatched runway to introduce AI to users who have not yet adopted standalone tools like ChatGPT. While ChatGPT boasts 900 million weekly active users, Apple’s ecosystem offers a frictionless entry point for millions of new users.A Hybrid Approach to AI DevelopmentApple’s strategy mirrors its successful partnership with Google for search: leveraging external technology to meet immediate user demand while simultaneously developing proprietary solutions. By utilizing Google's Gemini under the hood for cloud-based intelligence and investing in local AI models for on-device processing, Apple aims to maintain its privacy-first brand without the prohibitive costs of building a massive AI infrastructure from scratch.The Standalone Chatbot ChallengerIn addition to system-wide integration, Apple is developing a dedicated Siri app designed to compete directly with market leaders like ChatGPT and Claude. This standalone application will feature past chat history, document uploads, and photo analysis, providing a robust alternative for users seeking advanced AI assistance.

The Misguided 'Green Datacentres' Policy A Scottish government policy aimed at attracting datacentres to build in Scotland has been criticized for ignoring the emissions impact of AI developments. The policy, which encourages 'green datacentres', lacks a clear definition of what constitutes a 'green datacentre', potentially allowing developers to claim their projects are environmentally friendly despite significant emissions. The Problem with Unclear Definitions The Scottish charity Action to Protect Rural Scotland (APRS) has raised concerns that the policy's lack of clarity could lead to developers receiving favourable treatment from local authorities, even if their projects have substantial emissions. APRS found that a datacentre in Edinburgh was able to argue it was a 'green datacentre' despite including 200 diesel backup generators, equivalent to 100,000 idling cars. The Data Analysis More than a dozen datacentres in Scotland are in the process of getting planning permission, including an AI growth zone in Lanarkshire, near Glasgow, which claims to be backed by £8.2bn in private investment. Collectively, they stand to use roughly 6.2GW of power – one-and-a-half times more than the peak power use of all of Scotland in the winter. The Impact Analysis The APRS has criticized the Scottish government's approach, saying that the underlying analysis used to support the policy was done in 2022, before the release of ChatGPT, and has not been updated since. This analysis concluded that any increase in emissions caused by datacentre use would be counterbalanced by a decrease in emissions as people travelled less, but it does not take into account the development of AI or its potentially massive energy consumption. The Prediction As the demand for datacentres continues to grow, driven in part by the development of AI, Scotland's policy on 'green datacentres' is likely to face increasing scrutiny. With more than 100 datacentre projects requesting gas connections, indicating they plan to burn gas to power themselves, the UK's climate goals may be at risk. The Scottish government will need to revisit its policy and provide clearer definitions and guidelines for what constitutes a 'green datacentre' to ensure that its ambitions for economic growth align with its net zero ambitions.

The Lead When the founders of a mental health app for men called Mental discovered that one feature — AI interactive audio — was resonating strongly with users, they recognized a significant opportunity. This insight led to the creation of The Path, a new AI therapy application co-founded by renowned motivational speaker Tony Robbins and former Calm employees, which has now secured $14.3 million in seed funding. The Birth of a Safer AI Therapy Platform The Path emerged from observations made by co-founder and CEO Anson Whitmer and co-founder Tyler Sheaffer, who previously worked together at meditation app Calm. Whitmer's personal experiences with suicide in his family inspired him to pursue mental health technology. After working at Calm until 2021, he felt he could make a greater impact by addressing the unique, personal nature of people's mental health challenges. Whitmer sees large language models (LLMs) and AI as the bridge to providing personalized mental health care to everyone, especially given the shortage of therapists worldwide. "What's exciting and game-changing is that, for the first time in my career, I've seen that there's actually this possibility for every single person to have the personalized sort of access and care that they need to really get the help," he said. Funding and Celebrity Endorsement The Path has successfully raised $14.3 million in seed funding, led by Prime Movers Lab where Tony Robbins is a partner. Other notable investors include Olympic speed skater Apolo Anton Ohno, boxer Deontay Wilder, and Designer Fund. After Prime Movers invested, Robbins initially consulted on branding but his enthusiasm grew, leading to him becoming a co-founder. The author has since helped shape The Path into a therapy-plus-coaching app that incorporates his popular self-improvement methods. The app currently offers 11 virtual AI therapists that users can customize based on their preferences for directness and other details. While it's currently free to gain users, The Path plans to eventually charge $40 per month for the service. Superior Safety Benchmarks A key differentiator for The Path is its specially trained AI model, which has scored a 95 on the Vera-MH mental health safety AI benchmark. This significantly outperforms consumer chatbots, which top out at 65 on the same benchmark. According to Whitmer, consumer chatbots are "optimized for engagement," which is counterproductive to effective therapy and coaching that should focus on deep understanding rather than quick solutions. "It's meant to challenge you. It's not just meant to agree with you," Whitmer explains. The Path's AI is designed to help users dig out their assumptions and discover their own solutions rather than simply reinforcing ideas to keep users engaged. The startup's model is post-trained from open source models and doesn't use major consumer LLMs, positioning it as a specialized therapeutic tool rather than a generic chatbot wrapper. Market Potential and Future Outlook The mental health tech market is experiencing significant growth, with OpenAI reporting that at least 900 people use ChatGPT for mental health-related queries every week. This demonstrates the clear demand for AI-powered mental health solutions. However, The Path aims to capture a specific segment of this market by focusing on therapeutic rigor and safety. As mental health awareness continues to grow and technology becomes more sophisticated, AI therapy platforms like The Path could play an increasingly important role in addressing global mental health challenges. The combination of Tony Robbins' brand recognition, the technical expertise of the Calm alumni team, and the specialized focus on therapeutic safety positions The Path as a notable contender in the emerging field of AI-powered mental health care.

The Rise of AI Misinformation in Elections The Electoral Commission has called for new legal controls over misinformation from AI chatbots, after a thinktank found they had made serious mistakes during the recent Scottish election. The Study's Findings The thinktank Demos said its investigation had found that AI services gave voters misinformation to 34% of the questions it posed, which it said raised worrying questions about the lack of regulation of AI platforms in the UK. ChatGPT gave wrong information in 46% of its answers, including making up an expenses scandal. Replika had errors in 56% of its answers, inventing a date for a made-up expenses scandal and accusations of nepotism by a candidate. Google Gemini was wrong in 22% of cases, including saying a candidate had not taken a position on assisted dying when they were a supporter. The Impact of AI Misinformation Vijay Rangarajan, the Electoral Commission’s chief executive, said voters want accurate information to help them engage with democracy and it is concerning that AI tools have made the spread of false or misleading information dramatically faster and more accessible than ever. The Call for Regulation The Electoral Commission is pressing ministers to introduce legislation to make AI companies more accountable, including clearer duties on AI platforms to protect voters against misinformation and ensure algorithms do not mislead voters. The Future of AI Regulation Azzurra Moores, an associate director at Demos, said ministers could quickly introduce legal requirements to make AI companies liable under UK defamation and electoral law, introduce mandatory safeguards on accuracy, and force AI firms to allow researchers to independently test how their internal data and training sets worked.

AI search startups are attracting unprecedented investment as Google announces a shift to an AI‑powered search experience. The funding surge underscores a broader industry race to redefine discoverability with generative AI.Exa Labs Secures $250 Million to Challenge Google’s AI SearchBloomberg reports that Andreessen Horowitz‑backed Exa Labs closed a $250 million Series B round, valuing the company at $2.5 billion. The capital will be used to build a next‑generation search engine that rivals Google’s upcoming AI offering.Funding Landscape and Valuations Across the AI Search WaveExa Labs: $250 M raised, $2.5 B valuation.Parallel Web Systems (led by former Twitter CEO Parag Agrawal): $100 M raised, $2 B valuation, Sequoia Capital lead.Other notable entrants: Tavily, TinyFish, and Parallel Web Systems are also courting venture capital.Implications for Big Tech and the Future of SearchTraditional platforms such as Amazon, LinkedIn and Reddit are already experimenting with AI‑enhanced discoverability, creating a pool of potential acquirers for these startups. While ChatGPT currently dominates the AI search interface layer, OpenAI’s focus lies elsewhere, leaving space for niche players.Potential Paths for AI Search Startups and Market ConsolidationWith Google’s ad‑driven model protecting its core business, smaller labs may carve out specialized niches or become attractive acquisition targets for larger tech firms seeking AI search capabilities. The next 12‑18 months will likely see strategic partnerships, further fundraising rounds, and possible exits.