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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.

The Rise of Moonshot AI Chinese AI companies are making waves in the industry, despite not having the same level of funding as their Western counterparts. Moonshot AI, a Beijing-based AI lab, has raised about $2 billion at a valuation of $20 billion, according to a post by Huafeng Capital. Investor Interest and Funding Details The round was led by Chinese food delivery company Meituan's VC arm, Long-Z Investments, with participation from Tsinghua Capital, China Mobile, and CPE Yuanfeng. This recent funding brings Moonshot's total raised to $3.9 billion over the past six months. The Data Analysis Valuation: $20 billion Funding raised: $2 billion Annual recurring revenue: $200 million (as of April) Previous valuation: $4.3 billion (end of 2025), $10 billion (early 2026) The Impact Analysis The fundraising comes as investor appetite for open-weight AI models made by Chinese labs surges. Moonshot's Kimi models have gained significant traction, with the latest model, Kimi K2.6, being the second-most used LLM on distribution platform OpenRouter. The Prediction With demand for open source AI models on the rise, Moonshot AI and its competitors are poised for further growth. Other Chinese AI labs, such as DeepSeek, are reportedly in talks to raise outside capital, while some have even gone public on the back of demand for their AI models.

The Lead At the Milken Institute Global Conference, leaders from across the AI supply chain gathered to discuss the current state and future of artificial intelligence. They touched on various challenges, including chip shortages, energy constraints, and the potential for new AI architectures. The Bottlenecks in AI Development The discussion highlighted several bottlenecks in AI development. Christophe Fouquet, CEO of ASML, noted that despite efforts to accelerate chip manufacturing, the market will likely remain supply-limited for the next two to five years. Francis deSouza, COO of Google Cloud, pointed out the immense demand for AI infrastructure, with Google Cloud's revenue growing 63% and its backlog nearly doubling to $460 billion. The Data and Energy Constraints Qasar Younis, co-founder and CEO of Applied Intuition, emphasized that the bottleneck for his company is not silicon but data gathered from the real world, which is essential for training physical AI models. The energy required to power AI infrastructure is also a significant concern. deSouza mentioned that Google is exploring data centers in space to address energy constraints, although this comes with its own set of challenges. New AI Architectures and Their Implications Eve Bodnia, founder of Logical Intelligence, discussed a different approach to AI, focusing on energy-based models (EBMs) that aim to understand the underlying rules of data, similar to human brain function. This approach could be particularly useful for applications requiring an understanding of physical rules, such as chip design and robotics. The Future of AI: Agents, Guardrails, and Trust Dmitry Shevelenko, chief business officer of Perplexity, talked about the evolution of its search product into a 'digital worker' called Perplexity Computer. This tool is designed to act as a staff that a knowledge worker can direct, raising questions about control and security. Shevelenko emphasized the importance of granularity in permissions and actions to ensure trust and security. The Geopolitical and Generational Impact The discussion also touched on the geopolitical implications of physical AI and its impact on national sovereignty. Younis noted that physical AI manifests in the real world in ways that governments can't ignore, leading to questions about safety, data collection, and control. Regarding the impact on the next generation, the panelists were optimistic, highlighting the potential for AI to help address significant problems and unleash new levels of creativity and opportunity.

The Unexpected Partnership On Wednesday, xAI and Anthropic announced a surprise partnership that has the Claude-maker buying out "all of the compute capacity at [xAI's] Colossus 1 data center," roughly 300MW that allowed Anthropic to immediately raise its usage limits. It's a huge deal for xAI, likely worth billions of dollars. More importantly, it immediately monetized one of the company's most impressive accomplishments, turning xAI from a consumer to a provider of compute. The Strategic Implications It's tempting to see the arrangement as a shot at OpenAI amid the ongoing lawsuit. But Musk's explanation on X was that xAI had already moved training to a newer data center, Colossus 2, and xAI simply didn't need them both. In the short term, there's an obvious logic at work. xAI's existing products are mostly focused on Grok, which has seen plummeting usage since the image generation debacles earlier this year. The Financial Impact xAI's partnership with Anthropic is likely worth billions of dollars. xAI was valued at $230 billion in its January funding round. CoreWeave, which oversees a comparable quantity of computing power, is worth less than a third of that. The Industry Context But beyond the short-term benefit, the Anthropic partnership sends an unusual message about where Elon Musk's priorities really lie. It suggests the company's real business may be more about building data centers than training AI models. It's rare to see a major tech company treat compute resources this way when companies like Google and Meta, which are also training models, are building more data centers. The Future Outlook By focusing on data centers (earthbound and otherwise), xAI is positioning itself more like a neocloud business: buying GPUs from Nvidia and renting them out to model developers like Anthropic. It's a far more difficult business, squeezed by both chip suppliers and the shifting cycles of demand. Musk's version of a neocloud is more ambitious, as you might expect. Some of the data centers might be in space — at least by 2035, if things go according to plan.

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.

Meta's Strategic Move into Humanoid RoboticsMeta announced the acquisition of Assured Robot Intelligence (ARI), a startup focused on foundation models that enable humanoid robots to understand, predict, and adapt to human behavior. The deal, made for an undisclosed sum, brings ARI’s co‑founders and research team into Meta’s Superintelligence Labs research division.Acquisition Details and Team IntegrationThe integration will see ARI’s leadership—co‑founders Xiaolong Wang and Lerrel Pinto—join Meta’s AI unit. Wang, a former Nvidia researcher and UC San Diego associate professor, and Pinto, a former NYU professor and co‑founder of Fauna Robotics (acquired by Amazon), both hold multiple prestigious awards.Acquisition price: undisclosedPrevious funding: undisclosed seed round from AIX VenturesTeam focus: foundation models for whole‑body humanoid control and self‑learningFinancial Forecasts and Market Size ProjectionsIndustry analysts remain divided on the long‑term value of humanoid robotics:$38 billion market estimate by 2035 (Goldman Sachs)$5 trillion market estimate by 2050 (Morgan Stanley)These figures illustrate both the massive upside and the uncertainty surrounding a technology still in its early commercial phase.Implications for the AI and Robotics LandscapeBy absorbing ARI, Meta gains:Deep expertise in robot‑centric model training, a pathway many experts see as essential for achieving artificial general intelligence (AGI).Accelerated development of consumer‑grade humanoid platforms, complementing Meta’s existing research on AI models and hardware.A competitive edge over rivals such as Amazon, Google, and Tesla, all of which are racing to embed AI in physical agents.Even if Meta ultimately opts not to ship a consumer robot, the acquisition signals a firm commitment to the research frontier where AI learns through embodied interaction rather than static data.Future Outlook: From Lab Prototypes to Consumer HumanoidsAnalysts anticipate a multi‑year timeline before any Meta‑branded humanoid reaches the market. Short‑term milestones include:2026‑2027: Integration of ARI’s models into Meta’s internal simulation pipelines.2028‑2029: Prototype demonstrations of household‑task robots for internal testing.Early 2030s: Potential pilot programs with select partners or developers.Success will hinge on breakthroughs in whole‑body control, energy efficiency, and safe human‑robot interaction—areas where ARI’s award‑winning team is already positioned to lead.

The Unexpected Surge in Mac Sales Apple's recent quarter saw iPhone sales and Services revenue take center stage, but the Mac segment quietly outperformed expectations. The tech giant reported $8.4 billion in Mac revenue for Q2, ended March 28, beating Wall Street's estimate of $8 billion. AI-Driven Demand Mac sales were up 6% year-over-year, defying expectations of flat growth. CEO Tim Cook attributed the growth to customers using Macs for local AI models, such as OpenClaw. The Mac mini and Mac Studio devices sold out in recent weeks, contributing to the surprise demand. The Role of New Product Launches Apple's recent product launches, including the MacBook Neo, played a significant role in the Mac sales growth. Cook described customer demand for the Neo as "off the charts" and higher than expected. Enterprise Demand and Market Trends Enterprise demand for Macs was a contributing factor, with companies like Perplexity turning to Macs for building AI assistants. The Mac mini was the top-selling desktop in China, a market experiencing high demand for AI-related products. School systems, such as Kansas City Public Schools, are also adopting Macs, with some dropping Chromebooks for the MacBook Neo. The Future Outlook Despite the strong demand, Mac revenue was flat on a quarter-over-quarter basis. Cook warned that it may take Apple "several months" to reach supply-demand balance on the Mac mini and Studio models.

In a Thursday post on X, Sam Altman confirmed that OpenAI will begin a controlled release of its GPT‑5.5‑powered cybersecurity suite, Cyber, to “critical cyber defenders” after publicly criticizing Anthropic for limiting access to its own tool, Mythos. OpenAI Mirrors Anthropic’s Gatekeeping with Cyber The announcement marks a clear shift from OpenAI’s earlier open‑access stance on its AI models. By restricting Cyber, the company aligns itself with Anthropic’s approach, positioning the limitation as a responsible safeguard against misuse. Application Process and Core Capabilities Prospective users must submit a detailed application outlining credentials, organizational role, and intended use cases. Cyber is designed for penetration testing, vulnerability identification (including exploitation), and malware reverse engineering. The toolkit aims to help enterprises discover security gaps and validate defenses before adversaries can exploit them. Security Community Reactions and Market Implications Industry observers see the move as both a protective measure and a competitive signal. While some praise the caution, others worry that limiting access could slow broader adoption of AI‑enhanced security solutions and give rivals a strategic edge. What’s Next for AI‑Powered Cyber Tools? OpenAI has indicated plans to broaden Cyber’s availability after consulting with U.S. government agencies and verifying user legitimacy. The trajectory suggests a phased expansion, with potential policy frameworks shaping how AI security tools are deployed across the sector.

Lead: Musk’s courtroom confession on AI distillationElon Musk told a federal judge that xAI had used distillation techniques on OpenAI models to help train its new chatbot Grok. The partial "yes" came during a high‑stakes lawsuit accusing OpenAI founders of betraying the nonprofit mission that originally guided the company.Musk’s courtroom admission on AI distillation practicesDuring Thursday's testimony, the judge asked whether xAI had employed systematic querying of OpenAI’s publicly available APIs to extract model behavior. Musk answered that such "distillation" is a "general practice among AI companies" and qualified his response with "Partly." The exchange underscores that the once‑rumored practice is now openly acknowledged in a legal setting.Distillation: prompting a model repeatedly to infer its internal weights and replicate its capabilities.Legal context: Musk is suing OpenAI, CEO Sam Altman, and co‑founder Greg Brockman for allegedly abandoning the nonprofit charter.Scale and rankings of AI playersWhile xAI remains a relatively small outfit—"just a few hundred employees"—Musk positioned it among the world’s top AI providers:1️⃣ Anthropic (ranked top by Musk)2️⃣ OpenAI3️⃣ Google4️⃣ Chinese open‑source modelsFounded in 2023, xAI’s rapid ascent to a contender in the market illustrates how distillation can accelerate capability development without the massive compute investments of larger rivals.Distillation’s threat to incumbents and industry responseThe practice erodes the advantage built by firms that have poured billions into custom silicon and data pipelines. By extracting knowledge from existing models, smaller labs can produce near‑equivalent performance at a fraction of the cost. In response, leading labs—including OpenAI, Anthropic, and Google—have launched a collaborative effort through the Frontier Model Forum to share defensive tactics, such as rate‑limiting suspicious query patterns and tightening terms of service.Future outlook: legal battles and the evolution of model trainingWith Musk’s admission on the record, the lawsuit may set precedents for how intellectual property and service‑agreement violations are judged in the AI space. Expect tighter API usage policies, increased monitoring of query volumes, and possibly new regulatory guidance on model‑copying techniques. Meanwhile, firms that can master distillation without breaching contracts could reshape the competitive landscape, forcing incumbents to innovate beyond sheer compute power.