Good morning, {{ first_name | AI enthusiasts }}. Two months ago, OpenAI’s Fidji Simo told staff the company was in a “code red” over Anthropic’s enterprise rise. New spend data says the leaderboard has actually flipped.
Ramp’s latest AI Index just showed Anthropic leading adoption among its paid business users for the first time, a 4x adoption surge since 2025 that may tell the story of why OpenAI has been drastically shifting its priorities throughout 2026.
P.S. — Our next live workshop, ‘Finally Getting AI to Do Real Work’, is today at 2 PM EST. Join for a breakdown of how AI actually works in 2026 and the context habits power users layer on top to get reliable output from any model. RSVP here.
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Anthropic seizes OpenAI’s business AI lead
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Amazon doubles down on Alexa+ for shopping
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Create content with Claude Code and Higgsfield
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Adaption automates AI training with AutoScientist
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4 new AI tools, community workflows, and more
ANTHROPIC & OPENAI
The Rundown: Fintech firm Ramp just published its latest AI Index, showing Anthropic taking the paid business-adoption lead from OpenAI for the first time, an enterprise surge that has quadrupled its usage over the past year while OpenAI has leveled off.
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Ramp tracks corporate card and invoice payments from 50K+ U.S. businesses, making this a spend signal, not full market share.
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Anthropic rose 3.8% in April to 34.4% of adoption, while OpenAI fell 2.9% to 32.3% as overall AI use continued to climb to 50.6%.
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Claude Code anchored much of the swing, with Anthropic expanding from technical teams into finance, legal, and research workflows.
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Ramp highlighted several risks facing Anthropic despite the trend, including recent Claude outages and increasing costs compared to OAI and open-source.
Why it matters: OpenAI is not suddenly cooked, with Ramp not tracking some large enterprise deals and ChatGPT remaining the bigger consumer brand. The yearly trend speaks louder, and was likely the same type of chart that caused OAI’s Fidji Simo-led pivot, which has become evident from recent Codex and other enterprise pushes.
TOGETHER WITH GOOGLE CLOUD
The Rundown: Scale your AI operations with the Gemini Enterprise Agent Platform, Google’s new evolution for building, governing, and optimizing AI agents. Using the open-source Agent Development Kit (ADK), this hands-on codelab teaches you to orchestrate specialized agents like Researchers and Judges into self-correcting workflows that solve complex problems at enterprise scale.
In this codelab, you will:
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Construct self-correcting AI feedback loops.
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Orchestrate task hand-offs between specialized agents.
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Deploy production-ready workflows to Google Cloud Run.
AMAZON
The Rundown: Amazon folded its standalone shopping chatbot ‘Rufus’ inside “Alexa for Shopping”, a new agent that takes over Amazon search and follows shoppers across devices with a shared memory of purchases, preferences, and prior chats.
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Rufus drew 300M+ users in 2025 while still in beta, with its product knowledge and shopping history now feeding Alexa for Shopping answers.
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Amazon says the new assistant draws on catalog data, reviews, delivery timing, past purchases, and Alexa conversations for information.
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Alexa can now field questions in the search bar, run side-by-side comparisons, track pricing, and Auto-Buy items when prices hit a target.
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A new Buy for Me handles checkouts on non-Amazon stores, with Scheduled Actions able to automatically restock products on a cadence.
Why it matters: RIP to Rufus, but consolidating under one Alexa AI brand feels like a better play, and the sheer amount of customer history Amazon has gives its agent quite the moat to work with. But with a consumer base that is already moving to other AI platforms for their agentic shopping needs, will Alexa play nice and integrate with them?
AI TRAINING
The Rundown: In this guide, you will learn how to connect Higgsfield to Claude Code with the Higgsfield CLI, then use Claude Code to send one image prompt to several AI image models at once.
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Create a new project folder, install Higgsfield CLI (npm install -g @higgsfield/cli), authenticate (higgsfield auth login), and add Higgsfield skill (npx skills add higgsfield-ai/skills) for Claude Code
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Open Claude Code in the same folder and ask it to inspect higgsfield.ai/cli, verify the installation, and list available image models
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Give Claude Code an image prompt and tell it to run it across six models, save outputs into a higgsfield-model-test folder, and generate a comparison.md file with notes for each result
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Pick the best direction, then ask Claude Code to refine the winning prompt or run one more comparison
Pro tip: If setup gets confusing, ask Claude Code to check Node/npm and give a walkthrough. Higgsfield also supports video, so you can also try this for short clips.
PRESENTED BY STRIPE
The Rundown: AI companies are hitting revenue milestones faster than ever—but figuring out how to monetize AI products is still one of the hardest problems founders face. Stripe interviewed teams at Anthropic, Vercel, Clay, and more to build a five-step pricing framework for AI products.
Download the guide to learn best practices for how to:
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Pick the right pricing model
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Prevent billing surprises
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Refine your pricing over time
ADAPTION
The Rundown: Adaption, the AI startup from ex-Cohere VP of Research Sara Hooker, just introduced AutoScientist, a new system that automatically customizes AI models for specific jobs by adjusting both what the model learns from and how it learns.
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AutoScientist tests different training data and settings, then iterates until the model meets the user’s goal.
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In internal tests, AutoScientist outperformed its own expert-tuned models by 35% on average, with success rates jumping from 48% to 64%.
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Results held across multiple AI models, a wide range of dataset sizes, and 8 varied industries, including finance, legal, and medical domains.
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Adaptive’s initial Adaptive Data release in February aimed at increasing the quality of datasets, with Autoscientist now moving toward customizing models.
Why it matters: A few thousand people in the world know how to properly train and fine-tune a frontier model, and nearly all of them work at the same handful of labs. If a tool like Autoscientist can start to automate that expertise, models customized for individual businesses and use cases may become a lot more practical to create.
Nvidia became the first company to hit a $5.5T market cap, coming as CEO Jensen Huang arrived in China to join U.S. President Donald Trump in meetings with Xi Jinping.
Sam Altman testified in the Elon Musk vs. OpenAI legal battle that Musk’s “specific plans on safety” made him worry, and proposed passing the company to his children.
David Silver’s Ineffable Intelligence and Nvidia announced a partnership to build training pipelines for RL agents, with early work targeting Nvidia’s Vera Rubin hardware.
Microsoft introduced MDASH, an AI security harness that chains 100+ specialized agents to hunt for software bugs, with the system catching 16 flaws across Windows.
UK’s AI Safety Institute said that AI’s ability to complete cyberattacks is doubling every few months, with Mythos Preview and GPT-5.5 finishing its simulated breaches.
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 Rod R. in Elizabethtown, KY:
“I am an avid cyclist. I turned 61 years old this year, but I am still participating in multi-day cycling events. I needed advice on the type of riding I should be doing to prepare, and I thought I could benefit from a coach, but I can’t afford one. Then it occurred to me to try ChatGPT.
That has worked out tremendously! We had a “conversation” about my goals, medical history, current fitness, event dates, etc. I have been following a training plan for about 6 weeks now.
ChatGPT continues to coach me as I report on each ride, helps me restructure the plan when life requires me to modify it, and also remembers things we have already discussed. I love how this has helped me to prepare, and I have peace of mind from knowing what I should be doing each day. Today is a rest day! 😊“
How do you use AI? Tell us here.
That’s it for today!
Before you go we’d love to know what you thought of today’s newsletter to help us improve The Rundown experience for you.
Rowan, Joey, Zach, Shubham, and Jennifer — the humans behind The Rundown
