Good morning, {{ first_name | AI enthusiasts }}. Anthropic just took the lead from OpenAI — not just with a more powerful frontier model, but in the market too.
With the new Opus 4.8 crushing on benchmarks, a more powerful Mythos on the way, and a valuation closing in on $1T, Dario and co finally seem to have all the pieces in place for a blockbuster public listing.
In today’s AI rundown:
Anthropic’s Opus 4.8, nearly $1T valuation
Apple’s new AI Siri to take on ChatGPT
Use Codex to build a game in one prompt
AI doubles dev output, but not for everyone
4 new AI tools, community workflows, and more
LATEST DEVELOPMENTS
ANTHROPIC
Image source: Anthropic
The Rundown: Anthropic made two big announcements on the same day — Claude Opus 4.8, which crushes almost all major benchmarks, and a massive funding round that makes it the most valuable AI lab in the world.
The details:
Coming at the same price as 4.7, Opus 4.8 bests GPT-5.5 and Gemini 3.1 Pro on agentic coding, computer use, financial analysis, and Humanity’s Last Exam.
4.8 is the least lazy among all Anthropic models and more honest, with increased likelihood to flag uncertainties instead of making unverified claims.
The model’s Fast mode is 3x cheaper, plus claude.ai gets new effort control, and Claude Code gets parallel sub-agents for complex, long-running tasks.
Anthropic paired the release with a $65B raise, taking its valuation to $965B (beyond OpenAI), and the promise of a Mythos-class AI in “the coming weeks.”
Why it matters: While the race isn’t over, Anthropic has crossed a milestone that would have seemed unlikely two years ago — a valuation higher than OpenAI and a model that leads on nearly every benchmark. Clearly, its safety-first push is paying off commercially, even as Sam Altman calls the strategy “fear-based marketing.”
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APPLE
Image source: Bloomberg
The Rundown: Apple’s long-overdue AI Siri finally appears to be taking shape, with Bloomberg giving a glimpse of the revamped assistant, rebuilt on Google Gemini, with a dedicated ChatGPT-style app and support for third-party AI agents.
The details:
Siri will live inside Dynamic Island, with a swipe-down interface to run AI search, chat, or iOS tasks, using on-device data, screen content, and the web.
The assistant, rebuilt on Google Gemini, will run AI-powered web searches similar to Perplexity, with answers surfacing as rich cards in the Dynamic Island.
Swiping down farther on the cards brings up a dedicated ChatGPT-style Siri app, with users getting the option to route queries to external AI models.
The revamped Siri will land in the Camera app, with advanced AI-based photo editing, wallpapers, and natural language shortcut-creation also in the pipeline.
Why it matters: Apple has been very slow in the AI race, promising features in 2024 that never shipped, while OpenAI and Google pulled ahead. If this overhaul lands, Apple’s 1B+ iPhone users will experience AI through the phone they use every day. But if it doesn’t, there will be a lot on John Ternus’ plate as he takes over as the new CEO.
AI TRAINING
The Rundown: In this guide, you will learn how to use Codex /goal to build a small browser game without nudging the agent every few minutes. The game is the demo, but the real move is learning to give Codex a finish line it can work toward on its own.
Step-by-step:
Open Terminal and enable goals with: “codex features enable goals.” Then, think of a short, simple game idea with rules you can test
If the idea feels fuzzy, ask ChatGPT to rewrite it in 100 words or fewer with objective tests. If it cannot do that, the scope is probably too big for one /goal
Paste the description after /goal, then follow the checklist Codex creates. For a simple game, expect about 5–6 minutes of building, testing, and fixing
When testing, give feedback as another /goal command. Be specific with instructions, like: “Add distinct animations for every action the user can take”
Pro tip: This works beyond games. List three business processes, choose a metric that proves each works, then ask Codex to improve the process against that metric.
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The Rundown: Datadog’s free guide walks teams through building observability into their LLM stack, so engineers can move fast on production AI without flying blind on errors, costs, or security risks.
In the guide, you’ll learn how to:
Monitor LLM workflows for errors, latency, and token costs
Detect prompt injections and sensitive data exposure before they escalate
Evaluate output quality at scale with built-in and custom checks
AI RESEARCH
Image source: Cursor
The Rundown: Cursor released its Developer Habits Report, based on its own product and engineering data, showing dev output has more than doubled, but the gains are concentrated, with a small group of power users pulling far ahead of everyone else.
The details:
Lines of code added by each dev per week have gone from 3.6K to 8.6K in 18 months, with mega PRs with 1K+ lines changed becoming more common.
Agents are doing more end-to-end work, with tool calls up 30% in two months and 5x more AI-made changes reaching commits without manual review.
Cost per agent request varies 9x across models (Opus 4.7 being the most expensive), meaning the cost of one workflow can vary a lot with underlying AI.
However, the gains remain concentrated, with the top 1% of devs producing 46x more code than the median active user and the gap widening every month.
Why it matters: AI doing deeper work and contributing more code falls in line with growing capabilities, but the usage gaps are worth noting. Not everyone is capturing the full productivity gains, and with the cost per agent request varying heavily across models, many teams may not be using the most cost-efficient AI for their tasks.
QUICK HITS
💡 Founder Starter Kit - Pika’s Claude skills to go from product to launch
🎙 Dubbing V2 - ElevenLabs’ new dubbing AI that adapts across 90 languages
📽 Paris 2.0 - Bagel’s efficient, decentralized-trained video generation AI
💻 Computer - Perplexity’s agent, now inside Excel, Word, and PowerPoint
Workday DevCon Digital Experience, June 2 & 4 — Build agents. Build skills. Build your career on Workday’s developer platform. Register today.*
Google doubled Omni generations for Ultra users and fixed Gemini’s usage-limit issues with free Flash-Lite prompts, caps on high-cost requests, and improved tracking.
Elon Musk said that SpaceX’s compute deal with Anthropic is for 180 days, not three years (as mentioned in the S-1 filing), but indicated a longer deal is possible.
CNN filed a lawsuit against Perplexity, alleging the startup’s AI tools generate a “verbatim” copy of its articles while providing information locked behind a paywall.
An AI consultant revealed to Axios that their client accidentally spent nearly $500M in one month after failing to set usage limits on employees’ Claude licenses.
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COMMUNITY
Every newsletter, we showcase how a reader is using AI to work smarter, save time, or make life easier.
Today’s workflow comes from reader Gabriela in Austin, TX:
“I really wanted to know what my best colors are. I uploaded several selfies to ChatGPT and asked it to perform a color palette analysis. The results aligned with my best guesses, so I moved on to hairstyles based on face shape and outfits based on my body style. I uploaded more photos and shared my measurements, and had it get really specific about colors, styles, shapes, etc.
Next, I asked ChatGPT to write those into custom instructions to create a Project within ChatGPT just for style. I used what it wrote, making minor revisions, then started the Project with some additional photos included (some of me, some for inspiration).
Now I use that Project to get feedback on daily outfits, haircuts, new clothes, etc. It’s been working great, and I feel more confident and more myself.”
How do you use AI? Tell us here.
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Read our last Robotics newsletter: Figure’s humanoids get a retail job
Today’s AI tool guide: Use Codex to build a game in one prompt
RSVP to next workshop on June 18: Build a Marketing Creative Studio
That's it for today!
See you soon,
Rowan, Joey, Zach, Shubham, and Jennifer — the humans behind The Rundown
