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Breaking Down the Complex Language of AI Artificial intelligence is changing the world, and simultaneously inventing a whole new language to describe how it’s doing it. Spend five minutes reading about AI and you’ll run into LLMs, RAG, RLHF, and a dozen other terms that can make even very smart people in the tech world feel insecure. This glossary is our attempt to fix that. We update it regularly as the field evolves, so consider it a living document, much like the AI systems it describes. Artificial General Intelligence (AGI) Artificial general intelligence, or AGI, is a nebulous term. But it generally refers to AI that’s more capable than the average human at many, if not most, tasks. OpenAI CEO Sam Altman once described AGI as the “equivalent of a median human that you could hire as a co-worker.” Meanwhile, OpenAI’s charter defines AGI as “highly autonomous systems that outperform humans at most economically valuable work.” Google DeepMind’s understanding differs slightly from these two definitions; the lab views AGI as “AI that’s at least as capable as humans at most cognitive tasks.” Confused? Not to worry — so are experts at the forefront of AI research. AI Agent An AI agent refers to a tool that uses AI technologies to perform a series of tasks on your behalf — beyond what a more basic AI chatbot could do — such as filing expenses, booking tickets or a table at a restaurant, or even writing and maintaining code. However, as we’ve explained before, there are lots of moving pieces in this emergent space, so “AI agent” might mean different things to different people. Infrastructure is also still being built out to deliver on its envisaged capabilities. But the basic concept implies an autonomous system that may draw on multiple AI systems to carry out multistep tasks. API Endpoints Think of API endpoints as “buttons” on the back of a piece of software that other programs can press to make it do things. Developers use these interfaces to build integrations — for example, allowing one application to pull data from another, or enabling an AI agent to control third-party services directly without a human manually operating each interface. Most smart home devices and connected platforms have these hidden buttons available, even if ordinary users never see or interact with them. As AI agents grow more capable, they are increasingly able to find and use these endpoints on their own, opening up powerful — and sometimes unexpected — possibilities for automation. Chain-of-Thought Reasoning Given a simple question, a human brain can answer without even thinking too much about it — things like “which animal is taller, a giraffe or a cat?” But in many cases, you often need a pen and paper to come up with the right answer because there are intermediary steps. For instance, if a farmer has chickens and cows, and together they have 40 heads and 120 legs, you might need to write down a simple equation to come up with the answer (20 chickens and 20 cows). Coding Agent This is a more specific concept that an “AI agent,” which means a program that can take actions on its own, step by step, to complete a goal. A coding agent is a specialized version applied to software development. Rather than simply suggesting code for a human to review and paste in, a coding agent can write, test, and debug code autonomously, handling the kind of iterative, trial-and-error work that typically consumes a developer’s day. Compute Although somewhat of a multivalent term, compute generally refers to the vital computational power that allows AI models to operate. This type of processing fuels the AI industry, giving it the ability to train and deploy its powerful models. The term is often a shorthand for the kinds of hardware that provides the computational power — things like GPUs, CPUs, TPUs, and other forms of infrastructure that form the bedrock of the modern AI industry. Deep Learning A subset of self-improving machine learning in which AI algorithms are designed with a multi-layered, artificial neural network (ANN) structure. This allows them to make more complex correlations compared to simpler machine learning-based systems, such as linear models or decision trees.

Nvidia has committed over $40 billion to equity investments in AI companies during the first months of 2026, a mix of a massive $30 billion stake in OpenAI and several multi‑billion‑dollar deals with firms such as Corning and IREN. The spending underscores the chipmaker’s strategy to embed itself deeper into the AI ecosystem, even as critics label the moves “circular investments.”Strategic Stakes: From a $30 B OpenAI Bet to Multi‑Billion Deals with Corning and IRENAccording to CNBC, the bulk of the $40 billion total stems from a single $30 billion investment in OpenAI. In addition, Nvidia announced seven multi‑billion‑dollar equity placements, most recently up to $3.2 billion in glassmaker Corning and up to $2.1 billion in data‑center operator IREN. The chipmaker has also participated in roughly two dozen private‑startup rounds in 2026, adding to the 67 venture deals recorded in 2025.Numbers on the Table: Investment Breakdown and Deal VolumeTotal AI equity commitments in 2026 (first months): $40 billionFlagship OpenAI investment: $30 billionCorning deal size: up to $3.2 billionIREN deal size: up to $2.1 billionPublic‑company equity deals announced: 7Private‑startup rounds participated in 2026: ~24Industry Ripple Effects: Circular Investments and Competitive MoatsCritics argue the investments create “circular deals,” shuffling capital between Nvidia and its customers. Matthew Bryson of Wedbush Securities notes the pattern fits a “circular investment theme,” but adds that successful outcomes could reinforce Nvidia’s “competitive moat” by securing key AI workloads and data pipelines.What’s Next: Potential Outcomes for Nvidia’s AI EcosystemIf the funded companies deliver strong AI products, Nvidia could lock in long‑term demand for its GPUs and related hardware, strengthening its market dominance. Conversely, regulatory scrutiny over anticompetitive financing could arise. Analysts expect Nvidia to continue leveraging its balance sheet to shape the AI value chain throughout 2026 and beyond.

The Rise of Pit AI Swedish startup Pit, led by Voi co-founders Fredrik Hjelm and Adam Jafer, has gained attention for its innovative approach to enterprise AI. With a $16 million seed round led by a16z, Pit is poised to make a significant impact in the industry. Founders' Background and Vision Founded by Voi co-founders Fredrik Hjelm and Adam Jafer Jafer left Voi last summer after a seven-year tenure Hjelm is still Voi's CEO, but will play a less hands-on role in Pit Pit's vision is to create custom software to automate business processes, positioning itself as an 'AI product team as a service.' The company has developed two key products: Pit Studio, which lets enterprise employees guide it through processes that could be handled by AI-generated software, and Pit Cloud, which provides that software in a way that meets enterprise requirements on governance, certifications, and auditability. The Market Opportunity Pit is entering a crowded market, but hopes to differentiate itself through its unique approach and European DNA. The startup is targeting industrials and plans to benefit from the current tailwinds for sovereign tech, especially in critical sectors. Financial Backing and Growth Plans $16 million seed round led by a16z Backed by Pit's founders, Lakestar, executives from American tech companies, and wealthy families from the Nordics Pit is preparing to scale up commercially and is hiring solution engineers to drive enterprise adoption With its innovative approach and strong financial backing, Pit AI is one to watch in the European tech scene.

The Power of Anthropic's Mythos Model When Anthropic unveiled its new Mythos model in April, it also delivered a stern warning to anyone developing software. The model was so powerful at sniffing out software vulnerabilities, the lab claimed, that it had discovered thousands of high-severity bugs that would need to be fixed before it could be made public. Improving Software Security with AI Now, security researchers for Mozilla's Firefox browser are providing a closer look at what that process has looked like in practice, and what Mythos' powers mean for software security at large. In a post published on Thursday, Mozilla said Mythos has unearthed a wealth of high-severity bugs, including some that had lain dormant in the code for more than a decade. The Data Behind the Discovery In April 2026, Firefox shipped 423 bug fixes, compared to just 31 exactly a year earlier. The researchers have also published details on 12 of the bugs, which range from a pair of unusual sandbox vulnerabilities, to a 15-year-old error in how the browser parses an HTML element. The Impact on Cybersecurity The fact that the system helped reveal vulnerabilities in Firefox's 'sandbox' system is particularly impressive, given how intricate an attack that exploits it needs to be. To find sandbox vulnerabilities, the model must write a compromised patch for the browser, then attack the most secure part of the software with the new code implemented. Finding and demonstrating the bug is a delicate, multi-step process, requiring both creativity and close attention. The Future of AI in Cybersecurity It's still not clear how AI's emerging capabilities will change the broader balance of power in cybersecurity. One month since Mythos was previewed, most of the bugs discovered likely haven't been patched, which makes it hard to capture the full scope of their impact. Anthropic has been scrupulous about following responsible disclosure norms, but it's likely bad actors are using similar techniques behind the scenes, even if the models they're using aren't quite as good. The Prediction Speaking at a recent event, Anthropic CEO Dario Amodei was optimistic that the new tools would ultimately favor defenders. 'If we handle this right, we could be in a better position than we started, because we fixed all these bugs. There are only so many bugs to find,' Amodei said. 'So I think there's a better world on the other side of this.'

DeepSeek’s Funding Surge: From $20B to $45B in Weeks DeepSeek, the Chinese AI lab known for a cost‑efficient large language model, is in talks to raise its first venture‑capital round that could push its valuation to $45 billion, up from $20 billion just weeks earlier. First Venture Capital Round Targets Chinese AI Champion The round will be led by the state investment vehicle China Integrated Circuit Industry Investment Fund. Potential co‑investors include cloud giants Tencent and Alibaba. Founder Liang Wenfeng, who owns nearly 90% of the company, is seeking capital to retain talent amid competitor poaching. Valuation Leap and Investor Line‑up: Numbers at a Glance Previous valuation: $20 billion Target valuation: $45 billion Founder ownership: ~90% Key investors: China Integrated Circuit Industry Investment Fund, Tencent, Alibaba Model advantage: runs on Huawei chips, lower compute cost Strategic Implications for China’s AI Independence The funding aligns with Beijing’s goal to develop home‑grown AI hardware and software, reducing reliance on U.S. chips. By optimizing models for Huawei silicon, DeepSeek offers a domestic alternative to OpenAI and Anthropic, potentially accelerating China’s AI ecosystem. What the Next Funding Milestone Could Mean for Global AI Competition If the round closes at the projected valuation, DeepSeek could attract further private and state capital, scale its model offerings, and challenge Western AI leaders on both performance and cost. Analysts expect increased pressure on U.S. firms to secure supply chains and consider strategic partnerships in Asia.

QuTwo's Quantum Leap in Valuation QuTwo, the Finnish AI lab founded by former AMD Silo AI CEO Peter Sarlin, is now valued at €325 million (approximately $380 million) after raising a €25 million ($29 million) angel round. It’s a sign of enduring tailwinds for AI, quantum computing, and sovereign tech, especially for Europe-made companies. The Company's Product Strategy QuTwo’s name is a nod to quantum computing, but it hasn’t gone all in on quantum. Its core product, QuTwo OS, is an orchestration layer that directs tasks to classical, quantum, or hybrid architectures — with the idea that enterprise use cases are often best served by “quantum-inspired” computing, which uses classical chips to simulate quantum behavior on more reliable hardware. Enterprise AI Focus Enterprise AI will be QuTwo’s bread and butter. The company already secured some $23 million in committed revenue thanks to design partnerships with the likes of retail giant Zalando, for which it helped develop AI assistants. “AI is the north star that we will continue to aim for. Quantum is just a new type of compute,” said Sarlin, who is adamant that QuTwo is an AI company. Market Momentum and Funding Momentum has been building around Europe-based AI labs, and several of them have become overnight unicorns. Just last week, former DeepMind researcher David Silver secured $1.1 billion for his new endeavor, Ineffable Intelligence. QuTwo’s valuation and round size are somewhat modest in comparison but will let it pursue its roadmap under less pressure. Strategic Growth and Partnerships According to Sarlin, who serves as QuTwo’s executive chairman, this was a decision he also made for his previous company, Silo AI, which AMD acquired for $665 million in 2024. The main difference is that QuTwo wants the freedom to think long term, with a five- to 10-year horizon. The angel investors, including Yuri Milner, Xavier Niel, Nico Rosberg, Dieter Schwarz, and Niklas Zennström, could open doors across Europe.

Apple's AI Strategy Shift Apple is set to revolutionize its iOS experience with the upcoming release of iOS 27, later this year. The new operating system will introduce a feature called 'Extensions,' allowing iPhone users to choose from a variety of third-party large language models to power different functions within the iPhone's operating system. The 'Extensions' Feature The 'Extensions' feature will enable users to access generative AI capabilities from installed apps on demand, through Apple Intelligence features such as Siri, Writing Tools, Image Playground, and more. This move is expected to be available not only for iOS 27 but also for iPadOS 27 and macOS 27. AI Model Options Models from Google and Anthropic are currently being tested. The status of ChatGPT, currently available to users, remains unclear but may continue as an option. The Impact of AI on Apple's Strategy Apple's approach to AI is centered around integrating AI capabilities into its existing hardware rather than investing heavily in building out AI infrastructure and services. This strategy comes as the company is perceived to be behind in the AI space compared to its peers. The Future Outlook With Tim Cook stepping down and John Ternus taking over, Apple is poised to make significant changes in its AI strategy. The company's ability to generate substantial AI-based revenue suggests that its focus on user-centric AI experiences could pay off in the long run.

The Funding Boost Sierra, an AI startup founded by Bret Taylor, has secured a $950 million funding round led by Tiger Global and GV. This investment pushes the company's post-money valuation above $15 billion, giving Sierra over $1 billion to further develop its AI-powered customer experience platform. Rapid Growth and Adoption The company has experienced rapid growth, expanding from four design partners a couple of years ago to now claiming over 40% of the Fortune 50 as customers. Sierra's platform handles billions of interactions across various sectors, including mortgage refinancing, insurance claims, and nonprofit fundraising. Revenue Milestones Sierra's revenue growth has been remarkable, achieving $100 million in annual recurring revenue (ARR) in November and reaching $150 million in ARR by February. This growth reflects the urgency enterprises feel about deploying AI and the costs associated with it. The Future of Enterprise AI The funding and growth of Sierra underscore the competitive race to own enterprise AI. Bret Taylor, who also serves as chairman of OpenAI and was formerly co-CEO of Salesforce, believes that AI can lead to lower costs and higher revenue for clients. Sierra's expansion into new areas, such as its 'agent as a service' tool called Ghostwriter, aims to automate complex tasks and make enterprise software more user-friendly. The Path Ahead With this significant investment, Sierra is poised to further develop its platform and potentially become a leader in the enterprise AI market. The company's success will depend on its ability to continue innovating and meeting the evolving needs of its customers in a rapidly changing AI landscape.

The Lead: Why Union Documentaries Matter NowFrom meat‑packers in Minnesota to Amazon warehouses on Staten Island, documentary filmmakers have spent 50 years chronicling the highs and lows of American labor. The latest restorations and releases show that these films are more than cinema‑verité; they are barometers of union strength and cultural attitudes toward collective action.From “Harlan County, USA” to “Union”: A 50‑Year Documentary Timeline1976 – Harlan County, USA (Barbara Kopple) captures a 1973 coal‑miners strike and sets the visual template for labor cinema.1990 – American Dream revisits the 1985‑86 Hormel strike, framing it as an “alternative State of the Union” for organized labor.2000 – American Standoff follows the Teamsters’ battle with Overnite Transportation, illustrating the turn‑of‑century logistics wars.2024 – Union documents the historic Amazon Labor Union drive on Staten Island, highlighting modern anti‑union consulting tactics.2026 – Who Moves America surveys UPS drivers ahead of a potential strike, juxtaposing the 1997 UPS walkout with today’s gig‑economy reality.Membership Numbers and Strike Frequency: The Data Behind the StoriesFrom 1980‑84, U.S. union membership fell by 2.7 million (≈10 %).The Hormel strike (1985‑86) saw 1,500 workers replaced, a turning point for corporate union‑busting.UPS’s 1997 strike involved 185,000 workers; the 2023 negotiations involve a workforce that is 30 % part‑time or contract.Amazon’s 2024 union drive marked the first successful unionization of a major U.S. fulfillment center since 2004.Corporate Narrative Evolution: From Armed Guard to PowerPoint PersuasionEarly films show miners confronting armed security, while later documentaries reveal a shift to polished C‑suite messaging. In Who Moves America, UPS CEO Carol Tomé likens negotiations to “arguing with her husband about a puppy,” a stark contrast to the gun‑toting enforcers in Harlan County, USA. By the 2020s, anti‑union consultants wield slide decks and “culture‑change” workshops, turning the battlefield from picket lines to conference rooms.Future Outlook: New Voices, New Platforms, and the Next Chapter for Labor FilmsStreaming services and independent crowdfunding are giving voice to immigrant and undocumented workers whose stories were previously marginalised. As gig‑economy contracts proliferate, documentary makers are poised to capture a new wave of “micro‑strikes” and digital organising. The genre’s dual role—as an archival record and a practical manual—suggests it will remain a vital tool for both activists and audiences seeking to understand the evolving landscape of American labor.