Recent AI news and official updates

TechCrunch AI published: The rise of AI has brought an avalanche of new terms and slang. Here is a glossary with definitions of some of the most important words and phrases you might encounter.

TechCrunch AI published: The people deciding that AI can replace your job are also the ones least likely to understand what your job truly involves, according to Box founder Aaron Levie, who pointed to this as an example of “AI psychosis.” Indeed, ClickUp recently cut 22% of its workforce for AI agents, tech layoffs in 2026 are already nearly matching all of 2025, […]

Google AI Blog published: Watch 9 videos showing the capabilities of Gemini Omni and Gemini 3.5, announced at Google I/O 2026.

TechCrunch AI published: Chipmaker Groq is looking to raise $650 million in internal funding as it pivots from hardware to focus more on AI inference, the process of refining the way AI models respond to prompted requests, per Axios.

TechCrunch AI published: Chipmaker Groq is looking to raise $650 million in internal funding as it pivots from hardware to focus more on AI inference, the process of refining the way AI models respond to prompted requests, per Axios.

TechCrunch AI published: Cognition makes Devin, the first and arguably most successful AI coding agent. But famed coder Wu says it isn't designed to supplant human programmers.

TechCrunch AI published: The people deciding that AI can replace your job are also the ones least likely to understand what your job truly involves, according to Box founder Aaron Levie, who pointed to this as an example of “AI psychosis.” Indeed, ClickUp recently cut 22% of its workforce for AI agents, tech layoffs in 2026 are already nearly matching all of 2025, […]

TechCrunch AI published: If you're looking for a fun way to connect with nature while collecting bird species on an app like Pokémon, give this smart feeder a try.

Google AI Blog published: University of Waterloo students develop AI prototypes like sign language tutors to reshape the future of education and work.

TechCrunch AI published: South Korean chip startup XCENA is betting that AI's real bottleneck is not compute, but memory.

MIT Technology Review published: Pope Leo XIV’s new encyclical on artificial intelligence includes a statement that warrants serious attention from technologists and policymakers: “Technology is never neutral.” Magnifica Humanitas (“Magnificent Humanity”) is a clarion call to all people to act with courage and solidarity as we enter an age already being transformed by artificial intelligence, the greatest change in…

Simon Willison's AI Notes published: The most interesting thing about Anthropic's $65B Series H announcement is this line (emphasis mine): Since our Series G in February, adoption has continued to grow across global enterprise customers, and our run-rate revenue crossed $47 billion earlier this month. Anthropic have made a bit of a habit of sharing their "run-rate revenue" in this kind of announcement, which is an annualized projection of their current revenue - typically calculated by taking the most recent month and multiplying by 12. Update : here's a leaked description of their run-rate formula . Earlier this year: Apr 6, 2026 in Anthropic expands partnership with Google and Broadcom : "Our run-rate revenue has now surpassed $30 billion —up from approximately $9 billion at the end of 2025." Feb 12, 2026 in Anthropic raises $30 billion in Series G : "Today, our run-rate revenue is $14 billion , with this figure growing over 10x annually in each of those past three years." I had Claude Opus 4.8 make me this chart using Matplotlib (Claude: "a data line chart is more straightforward matplotlib work—not really a design piece"): Back in April Axios CEO Jim VandeHei wrote that he could not find "any company — in any industry, in any era — that has scaled organic revenue this quickly at this level as Anthropic" - and that was when they were at a paltry $30 billion. (Also in Axios today is an anonymously sourced note that "An AI consultant tells Axios one of their clients recently spent half a billion dollars in a single month after failing to put usage limits on Claude licenses for employees" - times that by 12 and you get an extra $6 billion in annualized run-rate!) Ed Zitron was extremely skeptical of that $30 billion number - I wonder if his skepticism will update for the new $47 billion figure. I've seen a few people dismiss this as untrustworthy, because the numbers come from Anthropic. That doesn't hold up: these numbers were included in announcements of their fundraises, and lying to investors who just put in $65 billion would be securities fraud. They're even less likely to lie given that the real numbers will no doubt come out in their S-1 when they file for their IPO. Tags: anthropic , ai

TechCrunch AI published: The enterprise AI search startup tripled its annual revenue even as tech giants entered the category.

Simon Willison's AI Notes published: Anthropic shipped Claude Opus 4.8 today. My favourite thing about it is this note in the release announcement: Users will find Opus 4.8 to be a modest but tangible improvement on its predecessor. There’s still more to be done: we’re working on developing and releasing models that provide many of the same capabilities as Opus at a lower cost. It's so refreshing to see an AI lab honestly describe a release as a minor incremental improvement over the previous model! Honesty seems to be a theme. Here's my other favorite note from that announcement: One of the most prominent improvements in Opus 4.8 is its honesty . We train all our models to be honest---for instance, to avoid making claims that they can't support. But a general problem with AI models is that they sometimes jump to conclusions, confidently claiming to have made progress in their work despite the evidence being thin. Early testers report that Opus 4.8 is more likely to flag uncertainties about its work and less likely to make unsupported claims. This is borne out in our evaluations , which show that Opus 4.8 is around four times less likely than its predecessor to allow flaws in code it has written to pass unremarked. That linked system card includes the following: Claude Opus 4.8 had the lowest incorrect-rate of the six models on every benchmark—the most direct measure of factual hallucination. It achieved this mainly by abstaining on questions about which it was uncertain rather than by answering more questions correctly. Model characteristics Not much has changed since 4.7. It's priced the same as Opus 4.5/4.6/4.7 - $5/million input and $25 per million output. "Fast mode" is twice that price, which is a significant reduction from their previous models - fast mode on 4.6/4.7 remains at $30/$150. Note that fast mode is only available to organizations that are part of the research preview, "Contact your account manager to request access". Both the reliable knowledge cutoff and the training data cutoff are January 2026, the same as for 4.7. The context window is still 1,000,000 tokens, and the max output is 128,000 tokens. The What's new in Claude Opus 4.8 document has some of the more interesting details. These caught my eye: Mid-conversation system messages . Claude Opus 4.8 accepts role: "system" messages immediately after a user turn in the messages array (subject to placement rules ). This lets you append updated instructions later in a long-running conversation without restating the full system prompt, which preserves prompt cache hits on the earlier turns and reduces input cost on agentic loops. See also this update to the Anthropic Python SDK. Being able to steer the system prompt mid-conversation sounds really powerful. I was worried this would be incompatible with the abstraction provided by my own LLM library , which expects a single system prompt per conversation... but it turns out my recent redesign should handle that just fine . Lower prompt cache minimum . The minimum cacheable prompt length on Claude Opus 4.8 is 1,024 tokens, lower than on Claude Opus 4.7. I checked and 4.7's minimum was 4,096 . And some pelicans Here are pelicans riding bicycles for all five thinking levels, low , medium , high , xhigh , and max : low medium high xhigh max This time I ran them using the LLM CLI , exported the logs to Markdown and then had Claude Opus 4.8 build me an HTML tool that could render that Markdown with the svg fenced code blocks displayed as SVGs on the page. (I later had GPT-5.5 xhigh in Codex update that code to remove any XSS holes. I'm sure Claude could have done that if I'd asked, but GPT-5.5 is my code security blanket at the moment.) The max one was clearly the best, but it did take 25 input, 17,167 output tokens for a total cost of 43 cents ! Tags: ai , generative-ai , llms , anthropic , claude , pelican-riding-a-bicycle , llm-release

TechCrunch AI published: As AI agents move from experiments to production, AWS, Cloudflare, and others are redesigning cloud infrastructure for a future dominated by machine-generated internet traffic instead of human users.

TechCrunch AI published: Asana will incorporate StackAI into its growing suite of AI workflow tools.

TechCrunch AI published: Anthropic has closed a $65 billion Series H round at a $965 billion post-money valuation, marking what could be the AI startup's final private fundraise before a highly anticipated IPO.

TechCrunch AI published: Large exchanges are designing derivative products around AI tokens, which are increasingly being considered less a computational output and more a raw material input, like electricity or bandwidth.

TechCrunch AI published: The new Opus model comes with a tool called Dynamic Workflows, for coordinating swarms of subagents.

TechCrunch AI published: Sesame’s new iOS app brings its conversational AI agents to the public, offering more natural back-and-forth interactions designed to feel less like traditional chatbots and more like talking to a person.