{{first_name | Leader}}, welcome back.
The last 24 hours weren’t loud, but they were telling. Progress on openness, reasoning, and identity keeps stacking up in the background. Here are today’s updates.
Meet Clara: your 24/7 FinOps agent to decode cloud & SaaS costs instantly.
NVIDIA expands open-source AI models
Zoom tops AI reasoning benchmark
World expands proof-of-human super app
Tools, resources, and a prompt to help you understand the real drivers before reacting or making changes. ⬇️
Cloud and SaaS costs move fast, but traditional FinOps tools slow teams down with dashboards and delayed reports.
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Identify waste and optimization opportunities across your stack
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Built for teams running modern cloud infrastructure, Clara helps you understand and control spend without changing your workflow.
NVIDIA doubles down on open AI models
NVIDIA is expanding its open-source strategy with new models, tools, and acquisitions aimed at developers and enterprise teams. The goal is to make its AI stack more flexible and customizable, while still anchoring everything around Nvidia GPUs.
This lowers reliance on closed, black-box models without moving away from Nvidia infrastructure. It shows how infrastructure players are adapting as open models become more trusted and widely used.
Zoom quietly becomes an AI platform
Zoom’s AI team topped a major reasoning benchmark using a federated setup that combines multiple models instead of one large model. On paper, it is a benchmark win, but the bigger story is where Zoom is headed.
Zoom is positioning itself as an AI layer inside everyday work, not just a meeting tool. That hints at smarter automation, summaries, and decision support baked directly into collaboration.
World expands its proof-of-human app
World rolled out updates to its proof-of-human app, adding secure messaging, global payments, and third-party mini apps. It is trying to combine identity, communication, and money into one system, now live in over 100 countries.
This is more directional than actionable. Global digital identity could become an important infrastructure, but adoption, regulation, and trust will decide whether this scales beyond early experiments.
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One Chart that Matters
The steady rise since 2017 in the share of organizations reporting they use AI in at least one business function.
💰 Funding
Solve Intelligence raised $40M to scale AI-powered patent drafting and legal workflows.
Outset raised $30M to expand AI-led qualitative research and insights for product teams.
💼 Roles in AI
🐦 Even chefs trust AI now!
What’s Actually Driving This Number?
When to use this? When a metric moves up or down and you want to understand the real drivers before reacting or making changes.
You are my business performance analyst.
Based on the metric and context below, tell me:
Top 3 likely drivers behind the change
What’s signal vs noise
What not to overreact to
One smart action to test before making bigger changes
Keep it concise and leadership-ready.
Metric + context: [paste the metric and 3–5 lines of context]Correct Input Style:
Metric: Demo-to-close rate dropped from 32% to 26%.
Context:
New pricing rolled out mid-quarter.
Two senior reps left the team.
Security reviews are taking longer.
No major change in lead quality.
P.S. Get more such prompts in the Prompting Playbook (free for you)
Q. Do you believe boards should have a dedicated “AI competence committee”?
Stay curious, {{first_name | leaders}}
PS. If you missed yesterday’s issue, you can find it here.