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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.

Legal Battle Over OpenAI’s Safety CommitmentElon Musk’s lawsuit alleges that OpenAI has strayed from its founding promise to ensure humanity benefits from artificial general intelligence (AGI). A federal court in Oakland heard testimony that the company’s for‑profit arm may be prioritising market rollout over safety safeguards.Testimony Reveals Shift From Research to Product FocusFormer employee and board member Rosie Campbell testified that after joining the AGI readiness team in 2021, she observed a transition from a research‑centric culture to a “product‑focused organization.” She cited the disbanding of her team in 2024 and the shutdown of the Super Alignment team as evidence.Campbell highlighted a deployment of GPT‑4 in India via Microsoft’s Bing before review by the Deployment Safety Board.She argued that without robust safety processes, scaling powerful models is “suboptimal” for the public good.Financial Pressures and Funding Needs HighlightedUnder cross‑examination, Campbell acknowledged that achieving AGI “will likely require significant funding,” suggesting that financial imperatives are driving the product push. No specific dollar amounts were disclosed, but the implication is that capital constraints are influencing safety trade‑offs.Governance Gaps Undermine AI Safety OversightTestimony from former board members Tasha McCauley and expert witness David Schizer painted a picture of a non‑profit board unable to supervise the for‑profit subsidiary. Allegations included:Misleading statements by CEO Sam Altman about board decisions.Failure to disclose the launch of ChatGPT and conflicts of interest.Board’s limited confidence in the information it received.The board’s brief removal of Altman in 2023, linked to the India deployment incident, underscores the recurring tension between governance and commercial rollout.Regulatory Scrutiny Likely to IntensifyBoth Campbell and McCauley argued that OpenAI’s internal failures justify stronger government regulation of advanced AI systems. As the lawsuit proceeds, policymakers may face increased pressure to define clear safety review mandates for AI deployments.

The Deadline Approaches: Startup Battlefield 200 Applications Close May 27 Startup Battlefield 200 applications are open, but only for three more weeks. Apply by May 27 for your shot at VC access, global visibility, TechCrunch coverage, $100,000 equity-free, and more opportunities for major scaling impact. Who Should Apply: Pre-Series A Founders and Ambitious Startups Pre-Series A founders — and anyone who knows a startup worth backing — this is your reminder: The deadline is approaching fast, and the strongest contenders are already entering the arena. If your startup has been nominated, don’t wait. Complete your application now before the window closes. Know a startup that deserves to step into the spotlight? Nominate them now to give them time to complete the application by the deadline. The Opportunity: A Platform for Growth and Visibility This is not just another pitch competition. Startup Battlefield 200 puts you on the main stage at TechCrunch Disrupt 2026 in front of 10,000+ attendees, top-tier investors, media, and the global TechCrunch audience. You are competing live, getting direct VC feedback, and proving your company belongs among the next breakout startups. What We’re Looking For: Innovative and Ambitious Startups We’re looking for ambitious early-stage startups building innovative, potentially category-defining products. Applications are open globally across every industry. Most selected companies are pre-Series A, though select Series A startups may qualify case by case. A functional MVP and clear product demo are required. Most importantly, we’re looking for founders building with vision, execution, and real market impact. A Proven Track Record: Launchpad for Successful Startups This is the same launchpad where companies like Dropbox, Discord, Fitbit, Trello, and Mint gained early momentum. Thousands apply every year. Only 200 are selected. Just 20 finalists pitch live on the Disrupt Stage. One startup takes the crown. The Benefits: High ROI Opportunity for Early-Stage Founders Selected startups receive one of the highest ROI opportunities available to early-stage founders. It’s free to apply, and the potential return — from investor exposure to media coverage and customer growth — can create real scaling impact. The Final Push: Don’t Miss the Deadline Applications close May 27. The founders who break through are not waiting until the final hour — they are already making their move. If you are building something category-defining, or know a founder who is, now is the time to step forward. Nominate your startup — or one that deserves the spotlight — and complete your application before the deadline runs out.

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 Diller-Altman Trust Dynamic Billionaire media mogul Barry Diller doesn’t think OpenAI CEO Sam Altman is untrustworthy, despite recent reporting to the contrary. Onstage at The Wall Street Journal’s “Future of Everything” conference this week, Diller vouched for the AI exec, who has been accused by some former colleagues and board members of being manipulative and deceptive at times. The AGI Conundrum Diller, who is friendly with Altman, was responding to a question about whether or not people should put their faith in Altman to ensure that artificial intelligence benefits humanity. In particular, he was asked about the theoretical form of AI known as artificial general intelligence, or AGI, which could one day outperform humans on any task. The Limits of Trust in AI Development The media exec, a co-founder of Fox Broadcasting and chairman of IAC and Expedia Group, said that while he believes Altman is sincere in his pursuits, that’s not really the area of concern people should be focused on. Rather, it’s the unknown consequences that will result from AI. “One of the big issues with AI is it goes way beyond trust,” Diller said. “It may be that trust is irrelevant because the things that are happening are a surprise to the people who are making those things happen.” The Unknowns of AI Progress Diller added that the development of AI is a journey into the unknown, with even those creating it unsure of the outcomes. He emphasized that progress in AI is inevitable and that the focus should be on preparing for its consequences. “We have embarked on something that is going to change almost everything. It is not under-reported. Now, whether these huge investments are going to come through — I couldn’t care less. I’m not invested in it, but progress is going to be made,” The Need for Guardrails Diller also highlighted the importance of establishing guardrails for AI development to prevent unforeseen negative consequences. He warned that if humans don’t think about guardrails, then the alternative is that “another force, an AGI force, will do it themselves. And once that happens, once you unleash that, there’s no going back.”

The Lead-Up to Elon Musk's Departure from OpenAI In late August 2017, key figures at OpenAI gathered to discuss creating a for-profit subsidiary to commercialize its technology and raise funds needed to realize Artificial General Intelligence (AGI). Elon Musk demanded full control of the company, but his co-founders, Greg Brockman and Sam Altman, proposed equal shares. The Heated Meeting That Changed Everything During a tense meeting, Musk became angry and upset when told the others would not accede to his demand for control. He stormed out of the room, grabbed a painting of a Tesla, and asked Brockman and Ilya Sutskever when they would be departing OpenAI. Musk stopped his regular donations to OpenAI's operating budget, and within six months, he would leave the board. The Data Analysis: Financial Impact of OpenAI's Growth OpenAI's growth was fueled by investments from Microsoft, including a $1 billion investment in 2019 and a further $13 billion over the next four years. This led to a significant increase in the company's valuation, with Brockman's current stake worth almost $30 billion. The Impact Analysis: Power Struggle and Its Consequences The power struggle between Musk and his co-founders had significant consequences for OpenAI. Musk's departure led to a change in the company's direction, with a greater focus on commercialization and fundraising. This ultimately fueled Musk's suspicions that Altman and Brockman had taken advantage of him, leading to a lawsuit in 2024. The Prediction: What's Next for OpenAI and Elon Musk The trial between Musk and OpenAI is expected to continue, with both sides presenting their cases. The outcome will likely have significant implications for the future of AI development and the relationships between key players in the industry.

The $1T Milestone and the AI CatalystOn Wednesday, Samsung Electronics officially crossed the $1 trillion market capitalization threshold, becoming only the second Asian company to achieve this feat, following TSMC. This valuation surge is not merely a market reaction but a direct result of the artificial intelligence revolution. The stock price jumped more than 10%, driven by a blockbuster earnings report showing profits eight times higher than the same period a year ago. The core of this growth is the insatiable global demand for memory chips that power AI systems.HBM Margins and the Global Chip ShortageThe profitability of Samsung is heavily reliant on High-Bandwidth Memory (HBM), a type of chip critical to running AI systems. As the world's three largest memory makers—Samsung, SK Hynix, and Micron—struggle to meet demand from AI data centers, they have pulled investment from consumer electronics to prioritize HBM production. This strategic pivot has significantly boosted margins but has also created a scarcity that is driving up prices across the industry.Profit Driver: HBM has dramatically improved company margins compared to standard memory chips.Supply Shift: All three major players are diverting resources from consumer chips to meet AI demand.Market Pressure: Rival SK Hynix is aggressively vying for the same market, keeping the pressure on Samsung.Supply Chain Realignment and Apple's InterestThe surge in valuation is further fueled by reports that Apple is in talks with both Samsung and Intel to manufacture chips on U.S. soil. This potential shift would mark a significant deviation from Apple's long-standing reliance on TSMC in Taiwan, signaling a major restructuring of the global semiconductor supply chain. If Samsung lands the deal, it would represent a historic shift in how major tech giants source their silicon.Future Outlook Amidst Labor TensionsDespite the financial triumph, Samsung faces significant headwinds. An 18-day strike is threatening production later this month, as workers demand a larger share of the AI-driven profits. Furthermore, the company's consumer divisions—phones and TVs—are paying a steep price for the same chips that are fueling their record profits. The sustainability of this growth depends on resolving labor disputes and managing the high costs of the ongoing chip shortage.

The Political Commentary in Ojo's Cartoon Samuel Ojo's latest cartoon for The Guardian presents a thought-provoking visual commentary on UK Prime Minister Keir Starmer's approach to the nation's cost of living crisis. The cartoon, published on May 2, 2026, captures the current political mood and public sentiment through Ojo's distinctive satirical style. The artwork appears to depict Starmer in a situation that symbolizes the government's response to economic pressures, though the specific visual elements aren't fully described in the provided content. Political cartoons serve as important cultural barometers, reflecting public attitudes toward leadership during challenging times. The Visual Language of Political Satire Ojo employs the traditional techniques of political cartooning—exaggeration, symbolism, and metaphor—to convey complex economic issues in accessible visual form. The cartoon likely uses visual shorthand that British readers would immediately recognize, making it an effective tool for political commentary. Political cartoons have a long history in British media, dating back to the 18th century, and continue to serve as a vital form of social commentary. Ojo's work appears to continue this tradition, addressing contemporary issues through the lens of visual satire. The Cost of Living Crisis as Political Context The cartoon appears against the backdrop of the UK's ongoing cost of living crisis, which has been a defining issue for British politics in recent years. This economic challenge has affected households across the country, with rising prices for essentials, energy costs, and housing creating significant financial pressure for many citizens. Political cartoons often crystallize public sentiment about such issues, highlighting the gap between political promises and reality. Ojo's work likely captures the frustration or skepticism many Britons feel toward the government's handling of these economic challenges. The Role of The Guardian in Political Commentary As a publication known for its independent editorial stance, The Guardian provides a platform for voices like Ojo's that offer critical perspectives on political leadership. The Saturday Opinion cartoon series, to which this piece belongs, represents an important tradition of visual journalism in British media. Cartoons in mainstream media serve multiple functions: they entertain, provoke thought, and hold power to account. Ojo's contribution to this tradition demonstrates the enduring power of visual satire in political discourse. The Future of Political Cartooning in Digital Media Despite the digital transformation of media, political cartoons remain a relevant and influential form of commentary. Ojo's work, published both in print and online through The Guardian's platform, exemplifies how this traditional art form continues to adapt to contemporary communication channels. As the cost of living crisis continues to evolve, political cartoons like Ojo's will likely remain an important part of the public conversation, offering visual perspectives that complement written journalism and provide accessible entry points into complex political and economic issues.

The Rise of Gen Z Investors in a Volatile LandscapeAcross the globe, members of the 1997‑2012 cohort are jumping into stocks, bonds, AI startups and crypto far sooner than their parents did. The trend reflects a mix of personal ambition, heightened economic anxiety and unprecedented digital access to markets.Early Market Entry and Diversified StrategiesAmbrico Ranginui first encountered cryptocurrencies at age 12 and was investing by 16, using birthday money and allowance. After a painful crypto loss, he pivoted to a role at Flatmate Ventures, allocating capital to lithium, robotics and artificial intelligence. Similar stories echo across the generation: many start with high‑risk assets like crypto, then gravitate toward more stable vehicles such as exchange‑traded funds (ETFs) and retirement accounts.Numbers Behind the Boom: Participation Rates and ETF Adoption30% of Gen Z have begun investing before entering the workforce, versus 15% of Millennials and 9% of Gen X (World Economic Forum report).Unemployment for ages 22‑27 is now nearly 8%, up from about 6% seven years ago and well above the U.S. average of 4.3%.About 75% of Gen Zers hold ETFs in retirement accounts, compared with 60% of Baby Boomers (Nasdaq study).41% say they would trust an AI system to manage their portfolio, and many already use tools like ChatGPT for quick analysis.Why This Shift Matters: Economic Uncertainty and Eroding Safety NetsRising inflation, cuts to social‑welfare programs and the decline of employer‑sponsored retirement plans leave younger workers with “less financial stability and smaller social safety nets,” according to Natalya Guseva of the World Economic Forum. At the same time, fintech apps such as New Zealand’s Sharesies provide low‑cost education and instant access, making market entry almost frictionless.While the majority adopt a “slow and steady” approach—opening Roth IRAs, automating contributions and favoring diversified index funds—a smaller cohort embraces speculative bets. In South Korea, Minwoo Lim trades commodities and reports a €1,000 profit from crude‑oil positions, yet warns that only about 4% of day traders earn a living and roughly 10% are profitable.Looking Ahead: AI‑Driven Portfolios and Long‑Term OutlookAI is becoming a de‑facto advisor for many Gen Z investors. Kelly Noel Mbunui Kameni from Kenya photographs her portfolio and asks ChatGPT for diversification suggestions, using the output to make rapid decisions. As AI tools improve, trust in machine‑managed portfolios is likely to rise, potentially amplifying the shift toward low‑cost, passive strategies.Analysts such as Andy Reed (Vanguard) predict that the cost‑savvy, early‑investing habits of Gen Z will “pay off in the long run,” especially if the generation continues to favor ETFs and broad‑market indices over high‑risk speculation. The convergence of economic pressure, technology, and a cultural move toward self‑reliance suggests that Gen Z will reshape asset allocation patterns for decades to come.