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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.

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 Disrupt 2026 Stage: Two Paths to Influence The call for speakers offers two distinct formats designed to maximize engagement and knowledge transfer: Breakout Sessions: A 30-minute talk (up to 4 speakers) featuring a 20-minute audience Q&A;, limited to 100 attendees for high-impact interaction. Roundtables: A 30-minute speaker-led discussion without slides or AV, designed for intimate dialogue among up to 40 participants. Scaling the Narrative: The Scale of Disrupt 2026 With over 10,000 startup and VC leaders expected at Moscone West from October 13–15, the event serves as a critical nexus for discussing the next wave of innovation. The focus areas—AI, scaling, fintech, infrastructure, and robotics—highlight the industry's pivot toward complex, high-growth sectors. Shaping the Future of Tech Discourse This call for speakers is not merely a recruitment drive; it is a mechanism for curating the industry's future narrative. By inviting founders, investors, and operators to present, TechCrunch ensures the stage reflects real-world challenges and actionable insights rather than theoretical concepts. The Future of Industry Influence As the deadline approaches, the selection process—combining editorial review with an Audience Choice vote—signals a shift toward democratized content creation. The most influential voices of 2026 will be those who can engage directly with the community and demonstrate high-impact expertise before the cutoff.

The Final Countdown for TechCrunch Disrupt 2026 Savings This is it. The countdown is almost over. You now have until tonight at 11:59 p.m. PT to lock in Early Bird savings of up to $410 for TechCrunch Disrupt 2026 before prices increase. Event Overview: A Gathering of Tech's Elite If Disrupt has been on your must-attend list, this is your final chance to secure the lowest available rates before the next price jump hits. Once the deadline passes, so do the savings. Join 10,000+ founders, investors, operators, and innovators at Moscone West in San Francisco from October 13–15 for three days packed with networking, startup discovery, and conversations shaping the future of tech. Group Benefits: Bring Your Team at Reduced Rates Bring a plus-one at 50%, or bring a group to get an up to 30% discount. These options make it more affordable to attend with colleagues or team members. Why TechCrunch Disrupt Matters for the Industry TechCrunch Disrupt is where startup momentum accelerates. The event brings together the people actively building, funding, and scaling what's next across AI, fintech, SaaS, climate, cybersecurity, consumer tech, and beyond. What to Expect at the Conference With 300+ exhibiting startups, Startup Battlefield 200, curated networking experiences, and multiple stages of programming, Disrupt is built to help attendees make meaningful connections and real business progress. Who Should Attend Disrupt 2026 Disrupt is designed for founders raising capital, investors sourcing opportunities, operators scaling companies, and innovators looking for an edge. Whether you're launching your next startup, growing your network, or tracking the future of technology, Disrupt puts you in the room with the people driving the industry forward. High-Caliber Speakers and Sessions Every year, Disrupt brings together hundreds of influential voices across startups and venture capital. Past speakers have included leaders from the companies and firms shaping the future of AI, enterprise software, fintech, consumer tech, and more. This year will deliver the same high-caliber experience, with 200+ sessions across six industry-focused stages, plus roundtables and breakouts covering scaling, AI, fintech, infrastructure, robotics, and emerging technologies. Don't Miss the Early Bird Deadline Early Bird savings of up to $410 end tonight at 11:59 p.m. PT. After that, ticket prices increase. Register now to secure your TechCrunch Disrupt 2026 pass at a low rate before the deadline expires. Bringing more than just you? Save 50% on a second ticket, or up to 30% on community passes.

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.

The Final Private Push: Anthropic Secures $65 BillionAnthropic has closed a monumental Series H funding round, raising $65 billion at a $965 billion post-money valuation. This capital injection represents the startup's largest private fundraising effort to date and signals that the company is likely in its final pre-IPO stage. The round brings the company's total capital raised to a staggering level, positioning it as a heavyweight contender in the generative AI sector just as public markets begin to open up to high-growth technology companies.The Infrastructure and Investor EcosystemThe funding round was co-led by a consortium of elite institutional investors, including Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital. Notably, the round saw participation from major infrastructure partners such as Samsung, SK Hynix, and Micron, highlighting the critical role hardware manufacturers are playing in the AI supply chain.Strategic Backing: Hyperscalers committed $15 billion, including a significant $5 billion from Amazon.Investor Demand: The round was highly competitive, with one institutional investor reportedly pledging up to $5 billion just to secure a meeting with the CFO.Use of Funds: Proceeds will be directed toward advancing safety research, expanding compute infrastructure, and scaling enterprise products.Valuation Wars and Revenue TrajectoryThis funding round places Anthropic at the epicenter of a fierce valuation war in the AI industry. The company's massive valuation comes as it reports a $47 billion revenue run rate and expects a 130% revenue surge to achieve its first operating profit. This financial performance contrasts sharply with the broader tech sector, illustrating the intense demand for high-performance AI models.Competitive Landscape: Anthropic's valuation rivals OpenAI, which raised $122 billion in March at an $852 billion valuation.Market Positioning: The company is reportedly preparing to launch models comparable to its powerful cybersecurity model, Mythos, which has been limited due to safety concerns.The Strategic Shift Toward Enterprise SafetyThe inclusion of infrastructure partners like Samsung and SK Hynix suggests a strategic pivot toward vertical integration. By securing hardware support, Anthropic ensures a stable supply chain for the compute-intensive models it is developing, such as the newly released Claude Opus 4.8. This model emphasizes agentic tasks, advanced coding, and self-correction capabilities, addressing a critical need for enterprises seeking reliable and safe AI solutions.The IPO Countdown and Market DominanceWith this massive capital raise and the release of advanced models, Anthropic is poised to lead the next phase of AI innovation. The company's ability to attract top-tier institutional investors and secure hardware partnerships positions it uniquely ahead of its IPO. As the race for AI dominance heats up, Anthropic's valuation and growth trajectory suggest it will be a key player in shaping the future of the public AI market.

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 Rise of Recursive Self-Improvement in AIThe word "recursion" is the latest buzzword in AI circles. Two separate startups have taken on the name, and many more have started referencing recursive self-improvement (RSI) in their roadmaps. Like AGI before it, RSI has become a three-letter byword for a cataclysmic AI takeoff – even if there's still a little disagreement about what it exactly means.In basic terms, RSI refers to an AI system that can continuously upgrade itself. Once AI systems can manage the upgrade cycle better than humans, the process can become a closed loop, limited only by the compute power they can access, and humans are no longer necessary or even helpful.Scary or not, that's a vision that a lot of AI labs are eager to chase.Key Players Pursuing Recursive SystemsEarlier this month, well-known AI researcher Richard Socher launched the aptly named Recursive Superintelligence with RSI as an explicit goal. "Our main focus is to build truly recursive, self-improving superintelligence at scale," Socher told TechCrunch at launch, "which means that the entire process of ideation, implementation, and validation of research ideas would be automatic."A number of other prominent researchers are already chasing that same goal, hoping for a breakthrough that will make recursive self-improvement possible.One of the most prominent is Andrej Karpathy, a legendary figure from Tesla and OpenAI, who is using agent swarms to train LLMs on simple tasks for a project he calls Auto-Research. Karpathy has been unusually open about the project, tweeting about milestones regularly and making the building blocks available through a public GitHub repo. So far, the work has mostly been confined to making minor improvements on a GPT-2 scale model — as Karpathy noted in March, "It's not novel, ground-breaking 'research' (yet)" — but it's been enough to convince lots of other researchers to follow the RSI dream. And with Karpathy now working on pre-training at Anthropic, he will have plenty of opportunity to apply the idea at a larger scale.Adaption — founded by Cohere and Google alum Sara Hooker — recently launched a similar tool called AutoScientist in an effort to automate frontier training. Like Karpathy's auto-researchers, the system trains agents to make incremental improvements — but for Adaption, the goal is to make it easier to train a full-scale frontier model. If those same researchers start to push the frontier forward, the system could quickly spiral into something very much like RSI.Disarray founder Doris Xin drew more specific RSI interest when her self-trained machine learning agent took home 28 medals in a recent Kaggle competition, beating out many human-trained agents. As she sees it, the major challenge is reliability."I would argue, given infinite compute and infinite time horizon, we are already there," Xin told me. "I want to make an argument that this is not a creative endeavor, really. It's just a lot of meat-and-potatoes engineering."The Current State of Self-Improving AIThere's also plenty of evidence that the AI industry isn't very close to recursive systems in any meaningful way — and is still grappling with talking to a wary public about its progress. So Google CEO Sundar Pichai basically admitted in a recent podcast interview."It's a continuum, and we are all definitely making progress," Pichai said. "But in the way people describe RSI, that would represent a next level of acceleration and would have a lot of implications, but we aren't quite there yet."But the continuum includes an awful lot of self-improving AI systems.In January, one of Anthropic's lead programmers for Claude Code estimated that "close to 100%" of his team's code was written by the tool — a frank admission that Claude Code was literally writing itself.Just because engineers are using an AI tool doesn't mean the tool can replace them — but Anthropic seems to be getting close to replacing engineers too. In a recent survey tied to the Mythos preview, five out of 18 Anthropic engineers believed that, with harness improvements, this version of Mythos could soon substitute for an L4 engineer — a midlevel programmer who can take on involved projects without supervision.Still, there were some of the same weaknesses you might expect."Some of Claude's major reported weaknesses compared to an L4 include: self-managing week-long ambiguous tasks, understanding org priorities, taste, verification, instruction-following, and epistemics," the report reads.In other words, its weaknesses are everything involved with self-direction, which is the cornerstone for RSI. But sure, for everything else, Claude is ready to step right in.Expert Perspectives on RSI TimelinesJust like the AGI term before it, the AI industry also can't tell us how far away it is from showcasing a meaningful recursive system. When Georgetown's Center for Security and Emerging Technology assembled a group of experts to study RSI last year, the group found a major split in assessments — some expecting an imminent "superintelligence" style explosion while others expected slower progress and an eventual plateau. But all agreed that recursion made the future especially difficult to predict.Helen Toner, director of CSET and a former board member at OpenAI, told TechCrunch that simply using AI tools to do AI research isn't enough to qualify as RSI. "They're just using AI for as much as they can," Toner told TechCrunch. "And I think that is different from the classic definition of RSI, which is really that there are no humans needed."Toner pointed to a recent post by METR's Ajeya Cotra, which distinguishes different milestones on the path to the AI research takeover. One step, which Cotra calls "adequacy," would come when the system can still perform research after all humans are removed — even if the resulting research isn't as valuable or efficient. "Parity" comes when an AI-only system is as good at research as a human-only system. "Supremacy," the final stage, comes when an AI-only system outperforms a collaborative system between humans and AI.Ultimately, Cotra concludes that AI is very close to the adequacy threshold of being able to produce some work on its own — similar to the incremental changes made by Karpathy's Auto-Research system. "I wouldn't be totally shocked if you told me this milestone had already passed, and I expect it to happen in the next couple years," Cotra wrote.She was less clear on when parity will come, but once it does, she thinks it would "massively accelerate the pace of AI progress, leading to AI research supremacy within another year."The Challenges Ahead for Recursive AIWith so much of AI built on scaling laws, there's a strong tendency to think RSI will follow the same curve. Toner thinks that many of those pursuing AI research and development via RSI "think of it as a pretty smooth ladder, where you can just keep scaling up."But even if AI researchers are able to make incremental improvements like Karpathy's auto-researchers, there will be larger challenges in handing off the whole process of research. Toner put it in terms of the history of computing, which has seen human beings handing off more and more of the process while still directing things from the top."We went from machine languages to assembly language and compiled languages; you're getting further and further from the guts of the computer," Toner said. "But the human is still, in some intuitive sense, running the show."Moving beyond that paradigm will take significant challenges, both in engineering and alignment. But even with the massive investments happening, there's no infinite compute available — and the basic trade-off between human labor and machine intelligence will be hard to overcome.The Future of Recursive Self-ImprovementAs for a total recursive AI system of apocalyptic visions? The only thing researchers essentially agree on is that, like AGI, it's not here yet.