Breaking AI & Tech News Analyzed

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 Mass Layoff at Oracle Oracle laid off an estimated 20,000 to 30,000 employees via email on March 31. The layoffs were delivered through a simple and impersonal process, with employees being informed through a VPN access denial and a subsequent email stating their role was terminated immediately. The Severance Offer Oracle offered a fairly standard severance package to laid-off employees, which included: Four weeks of pay for the first year, plus one additional week per year of service, capped at 26 weeks. One month of COBRA insurance. However, the company's terms did not include accelerated stock vesting, which meant that employees forfeited any shares that hadn't vested by the termination date. The Data Analysis The severance package offered by Oracle was seen as inadequate by some employees, particularly when compared to other big tech companies. For example: Meta's severance package started at 16 weeks of base pay, plus two weeks for every year of employment. Microsoft provided accelerated stock vesting, a minimum of eight weeks' pay, and an additional one to two weeks for every six months of service. Cloudflare offered a lump sum severance equivalent to base pay through the end of 2026, plus healthcare coverage through the end of the year, and accelerated vesting of stock through August 15. The Impact Analysis The layoffs and severance package offered by Oracle have significant implications for the tech industry. The company's decision to classify employees as remote workers to avoid WARN Act protections has raised concerns about worker rights. Additionally, the lack of accelerated stock vesting in Oracle's severance package has highlighted the disparities in compensation and benefits between tech companies. The Prediction The rejection of negotiations by Oracle may set a precedent for future layoffs in the tech industry. As the industry continues to experience mass layoffs, companies may face increasing pressure to offer more comprehensive severance packages and prioritize worker rights. Ultimately, the Oracle layoffs serve as a reminder of the precarious nature of employment in the tech sector and the need for workers to be prepared for sudden changes in their employment status.

The Launch of Trusted Contact OpenAI has announced a new feature called Trusted Contact, designed to alert a trusted third party if mentions of self-harm are expressed within a conversation. This feature allows an adult ChatGPT user to designate another person as a trusted contact within their account, such as a friend or family member. How the Feature Works In cases where a conversation may turn to self-harm, OpenAI will now encourage the user to reach out to that contact. It also sends an automated alert to the contact, encouraging them to check in with the user. The alert is designed to be brief and to encourage the contact to check in with the person in question, without including detailed information about what was being discussed. The Data Analysis OpenAI has faced a wave of lawsuits from the families of people who have committed suicide after talking with its chatbot. In a number of cases, the families say ChatGPT encouraged their loved one to kill themselves — or even helped them plan it out. The Impact Analysis The Trusted Contact feature follows the safeguards the company introduced last September that gave parents the power to have some oversight of their teens' accounts, including receiving safety notifications designed to alert the parent if OpenAI's system believes their child is facing a "serious safety risk." The Prediction OpenAI's parental controls are also optional, presenting a similar limitation. However, the company claims that every time it receives a safety notification, the incident is reviewed by a human in under one hour. The company will continue to work with clinicians, researchers, and policymakers to improve how AI systems respond when people may be experiencing distress.

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.'

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 End of a Lucrative Partnership Snap has ended its $400M deal with Perplexity, a company that specializes in AI search engines. The deal, announced last November, would have seen Perplexity's technology integrated directly into Snapchat. Details of the Failed Partnership The deal was worth $400 million in cash and equity over one year. Perplexity's AI search engine was to be integrated into Snapchat's 'Chat' interface. The partnership was expected to contribute to Snap's financials in 2026. Snap and Perplexity 'amicably ended the relationship in Q1.' Impact on Snap's Financials Snap's sales guidance 'assumes no contribution from Perplexity.' The company revealed that its global daily active users (DAU) rose 5% year-over-year to 483 million, while monthly active users (MAU) also grew 5% to reach 965 million. The Future of AI Integration Snap CEO Evan Spiegel had previously stated that the deal reflected the company's vision to use AI to enhance discovery on Snapchat. The company remains focused on investing in AI and other technologies, such as intelligent eyewear. What's Next for Snap and Perplexity While the deal with Perplexity has ended, Snap continues to explore other partnerships and technologies to enhance its platform. The company will share more about its plans at AWE on June 16th.

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.