{{first_name | Leader}}, welcome back.
Recent updates: Red Hat is doubling down with a security-heavy OpenShift 4.20, and creative sectors feel the shift as AI music climbs the charts.
Today’s updates:
Turn meeting chaos into organized work instantly with Radiant.*
Federal Genesis Project positions AI as national infrastructure.
Google’s Gemini surge reshapes the AI hardware landscape.
OpenAI unlocks compliance with regional data and voice.
Tools, resources, and a prompt to pinpoint what’s slowing teams down quickly. ⬇️
Radiant is a Mac app that captures your calls locally (no bots) and instantly turns them into the work you normally do after every meeting: clear summaries, structured action items, follow-up messages, timelines, briefs, and client-ready documentation.
You open Radiant after a call, and everything is already organized — with names, owners, deadlines, and decisions outlined for you. No replaying the meeting, no rewriting notes, no manual cleanup.
It’s the fastest way to turn conversations into real progress.
The White House signals its biggest AI move yet
The Genesis Project is the federal government’s new “Manhattan Project for AI,” led by the Department of Energy. The plan is to train a unified national model on federal datasets and use it to accelerate progress in healthcare, energy, climate, and defense. It also ties AI investment to jobs and long-term energy efficiency, which tells you how seriously the government is treating AI as infrastructure.
For enterprises, this points to a future where federal models, compliance standards, and safety frameworks shape how large organizations build and deploy AI. Think more predictable regulation, clearer procurement paths, and a shared baseline for what responsible AI should look like at the national scale.
Google’s AI momentum is real
Gemini 3 just topped multiple benchmarks, and Google is now reportedly in talks to supply Meta with billions worth of AI chips. Between that and its new Ironwood chip, this is the strongest competitive push we’ve seen from Google in years and it shifts the narrative from “catching up” to “setting the pace.”
For enterprise leaders, this matters because a stronger Google means more credible alternatives to Nvidia and OpenAI. That opens the door to better pricing, multi-vendor cloud strategies, and more control over AI roadmaps. If the Meta deal lands, compute availability and cost structures could look very different heading into 2026.
OpenAI tackles compliance and interface friction
OpenAI now supports regional data hosting, letting enterprises pin ChatGPT data to specific geographic locations. That solves one of the biggest hurdles for regulated industries. They also added ChatGPT Voice directly inside text chats, making voice a seamless input method instead of a separate mode.
For large teams, this unlocks easier compliance conversations and clears the way for broader deployment. And while voice seems like a consumer feature, it’s going to reshape how internal tools, agents, and customer systems are designed. Expect multimodal interfaces to become the default much faster than most companies are prepared for.
Productivity Tools
📸 Aragon – Creates realistic, professional AI photos of you.
📬 SaneBox – Filters important email and organizes the rest to keep you focuse
🤖 Onyx – AI workspace for researching, creating, and automating with your team’s knowledge.
📂 Taskade AI – All-in-one productivity platform that streamlines teamwork.
Get featured tomorrow: How do you use AI for business/personally? Interesting stories will be shared with 100K curious readers.
One Chart That Matters
As leaders, is your board more concerned about AI risk or AI competitiveness?
💰 Funding
Vijil raised $17M to build resilient, secure infrastructure for AI agents, enabling safer deployments and faster enterprise adoption.
Codenotary raised $16.5M to expand its AI-powered cybersecurity and software-supply-chain trust platform for global enterprises.
💼 Roles in AI
🐦 Hiring Distribution?
Delivery Bottleneck Detector
When to use this?
When projects are dragging, deadlines are slipping, and you need to pinpoint what’s slowing teams down quickly.
You are an operations efficiency analyst.
I’ll describe a delayed project below. Identify:
Top 3 bottlenecks slowing delivery (people, process, or tech).
Root cause of each issue.
Quick win fixes that can be implemented this week.
Preventive measure to stop recurrence next quarter.
Keep the output under 200 words with clear bullets.
Project: [describe project + what’s delayed].Correct Input Style:
“Project: rollout of AI-based customer scoring system.
Delay: two weeks late.
Issues: cross-team handoffs, unclear QA ownership, dependency on external data pipeline.”
P.S. Get more such prompts in the Prompting Playbook (free for you)
Q. Roughly what percentage of enterprise AI projects never make it past the pilot stage (as of 2025)?
Results: MIT report says 95% of generative AI pilots at companies are failing
Stay curious, {{first_name | leaders}}
PS. If you missed yesterday’s issue, you can find it here.