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The Strategic Alliance Between SpaceX and AnthropicAnthropic announced a landmark agreement to tap the full computing capacity of SpaceX’s Colossus 1 facility in Memphis, Tennessee. The deal marks a rapid shift from previous criticism to collaboration, providing the Claude chatbot maker with a massive boost in AI‑compute resources.Colossus 1: 220,000 Nvidia GPUs Deliver 300 MW to ClaudeUnder the terms disclosed on Wednesday, Anthropic will access:More than 220,000 Nvidia processors housed in the Colossus 1 data centre.300 megawatts of power—enough for over 300,000 homes—to be added within a month.Dedicated capacity for the Claude Pro and Claude Max AI assistants, enabling higher request volumes and removal of peak‑hour caps.The new “dreaming” feature unveiled at Anthropic’s developer day will also benefit from the expanded hardware, allowing AI agents to retain context across sessions.Capacity Surge Translates to Billions in AI Compute ValueIndustry analysts estimate that each megawatt of AI‑focused compute can be valued at roughly $10 million per year, suggesting the 300 MW addition could represent a $3 billion annual capability boost for Anthropic. The partnership also positions SpaceX to monetize its under‑utilised GPU fleet, diversifying revenue beyond launch services.Ripple Effects Across the AI Landscape and U.S. PolicyThe deal arrives amid Musk’s ongoing lawsuit against OpenAI and its CEO Sam Altman, intensifying competition for compute resources. While Microsoft, Google and Musk’s own xAI are negotiating government access to AI tools, Anthropic was excluded from recent Pentagon contracts, highlighting a potential strategic disadvantage that the SpaceX alliance aims to offset.Furthermore, the agreement fuels Musk’s long‑term vision of orbital data centres, signaling a possible new frontier for ultra‑large‑scale AI infrastructure.Future Trajectory: Orbital Data Centres and Competitive PressuresAnthropic plans to explore “multiple gigawatts” of space‑based compute with SpaceX, a venture that could redefine latency‑critical AI services. If successful, the partnership may force rivals to secure comparable high‑density compute, accelerating a race for both terrestrial and orbital AI super‑clusters.In the short term, expect Anthropic to double rate limits for paid users, remove usage caps, and roll out the “dreaming” capability broadly, while SpaceX will likely package its GPU assets as a commercial service for other AI firms.

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 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 Musk-OpenAI Legal Battle Elon Musk spent the better part of three days on the witness stand this week in his lawsuit against OpenAI, and it's already getting messy. Emails, texts, and his own tweets are surfacing in court, and there are plenty more witnesses to come. The Charity Mission Controversy Musk's argument against OpenAI is that by converting the company to a for-profit model, Sam Altman betrayed the "nonprofit for the benefit of humanity" mission Musk signed up to fund. As Musk keeps reminding the courtroom: "You can't steal a charity." What's at Stake in the Courtroom On this episode of TechCrunch's Equity podcast, Kirsten Korosec and Sean O'Kane break down what's actually at stake in the courtroom and what to watch for as Altman and others take the stand, plus deals, defense tech, and what Big Tech's earnings week revealed about the limits of the AI spending era. Podcast Coverage and Analysis Listen to the full episode to hear about the ongoing legal battle between Musk and OpenAI, the implications for AI development, and the future direction of the company originally founded with the mission of benefiting humanity. Subscribe to Equity on YouTube, Apple Podcasts, Overcast, Spotify and all the casts. You also can follow Equity on X and Threads, at @EquityPod.

The High-Stakes Confrontation The lawsuit between Elon Musk and OpenAI, led by Sam Altman, has taken a dramatic turn. Musk spent three days on the witness stand, and the case is becoming increasingly complex. Emails, texts, and Musk's own tweets are being used as evidence, with more witnesses, including Altman, set to testify. Musk's Core Argument Musk's primary argument is that Altman and OpenAI betrayed the company's original mission as a nonprofit organization "for the benefit of humanity" by converting it to a for-profit model. Musk emphasized in the courtroom, "You can't steal a charity." The Implications and Future Proceedings The case has significant implications for the future of AI development and the ethics surrounding for-profit models in tech. More witnesses, including Altman, are expected to take the stand. The case may set a precedent for how nonprofit missions are upheld in the tech industry. Related Discussions and Resources Listeners can tune into TechCrunch's Equity podcast for further discussions on the case, including: Analysis of what's at stake in the courtroom. Interviews with experts and witnesses. Coverage of Big Tech's earnings and the AI spending era. The Equity podcast is available on YouTube, Apple Podcasts, Overcast, Spotify, X, and Threads (@EquityPod).

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.