AGI Is Here. It's Just Not Evenly Distributed Yet.

Happy Monday, everyone. I hope you had a nice weekend. We're in Q2 of 2026. Let's get into it.

Marc Andreessen tweeted something the other day that stuck with me and ended up inspiring this edition. He said AGI is already here, it's just not evenly distributed. I simmered on that for a bit and kept coming back to it because it captures something I see every single day in my work.

Not in the abstract. The "should we explore AI?" conversation is over. It's now about specific decisions that affect specific people doing specific work. And that shift is hitting everyone, not just the C-suite.

Last Tuesday night, I went to bed and left five autonomous AI agents running. One was scanning federal solicitation databases. Another was drafting a government proposal response. One was triaging my email inbox and flagging action items. One was writing some blog posts that are mostly geared at SEO. And the last was reconciling data across three compliance systems.

By Wednesday morning, I had a dossier of outputs waiting for review. Not perfect. But 70-80% of the way to final deliverables that would have taken a team of humans days to produce. The tools are good. They're getting better fast. And they're forcing a decision on organizations that most aren't prepared to make.

This is just what the tools can do right now if you've invested the time to set them up. That's Andreessen's point. The capability exists. It's production-grade. But it's nowhere close to evenly distributed across organizations, industries, or individuals.

This week I want to break down why. Not the technology gap, that one closed. The adoption gap. The energy gap. Because the bottleneck in 2026 is not whether AI can do the work. It's whether people are willing to change how they work to let it.

Sponsor of This Week's AI Newsletter:

I want to be specific about what AI agents actually do right now, in production environments. Not in demos. Not in pitch decks. In workflows that produce actual deliverables for paying clients.

Document Drafting - I have agents that take a solicitation, parse the requirements, cross-reference our capability library, and produce a first-draft response with proper section formatting and technical narratives. The output isn't final, but it's 70-80% there. What used to take a team three days now takes an evening.

Database Search and Monitoring - Agents continuously scan procurement databases, matching new opportunities against our qualifications and past performance. They flag relevant ones and generate initial capture summaries before I've had my first coffee. I used to need a dedicated analyst checking portals manually every morning.

Email Pipeline Management - An agent reads incoming emails, classifies them by urgency and topic, drafts responses for routine items, and surfaces the ones that need my judgment. Nothing gets sent without my approval. But it turns a 90-minute morning email session into a 15-minute review.

Workflow Automation - Agents that connect systems: pull data from one platform, transform it, push it to another, generate a status report, and notify the right people.

Content Production - I like to maintain a lot of control over this newsletter because it’s my own voice and channel. That being said, I use an agent to help me review my outline, it acts as an editor and offers suggestions. Some I heed, some I do not. I need to force myself to maintain authorship moving forward because I think the ability to write well is a reflection of a sharp, focused mind and that’s key to being on top of AI systems now and moving forward.

Every one of these workflows runs on models and tools available today. I use our own Knowledge Spaces for a lot of the work and I use nearly every single major LLM because they all excel in different areas.

Think of this as a field report, not a pitch. These are patterns any organization can implement if they're willing to put in the setup time. Humans set direction, agents execute at speed, humans validate and ship.

I talk to executives and operators across government and enterprise every week. The technology gap closed a while ago. Everyone has access to the same models. What remains is an adoption energy gap, and it has three layers.

Workflow restructuring is the biggest one. Most organizations haven't redesigned their processes to actually include AI. They've bolted ChatGPT onto existing workflows and called it transformation. That's like buying a CNC machine and using it as a shelf. The real gains come when you rebuild the workflow around what the AI can do, not when you ask it to fit into what you were already doing.

Then there's trust calibration. People either trust AI outputs too much or too little, and both are dangerous. The organizations getting results have built review loops where humans validate AI work at specific checkpoints. They treat the AI like a competent but new team member: capable of solid work, but you check the deliverables before they go out the door.

And the hardest one: learning energy. Setting up autonomous agents is not plug-and-play. It requires understanding prompting, context management, API integrations, error handling, and the domain itself. Most people aren't willing to invest the 40-80 hours of focused learning to get there. They'd rather wait for a turnkey solution. That wait is exactly the gap Andreessen is talking about.

Most organizations are still using AI as a fancy search engine. They type a question into ChatGPT, copy-paste the answer, and call it adoption. That's tourism. Real adoption means the AI is embedded in your daily operations, running tasks without being prompted, and producing work product that feeds directly into your deliverables.

Don't try to transform everything at once. That's how organizations stall out. Pick one workflow. Something you do repeatedly, something that takes real time, something where the output is structured enough that an AI can handle the first pass.

Automate that workflow end-to-end. Not halfway, not "AI-assisted." Fully automated with human review at the end. Then measure what it used to take versus what it takes now. Hours saved. Output quality. Error rates. Get actual numbers, not feelings.

Then take what you learned and apply it to the next workflow. Each iteration gets faster because you're building intuition about what AI handles well, where it breaks, and how to structure the handoff between human and machine. That operational intuition is the real advantage. Not the model. Not the tool. The knowledge of how to use them inside your specific business.

Skip the transformation roadmap. Skip the committee. The organizations that will win in 2026 are the ones with the best operators. People who understand their business deeply enough to know which workflows to automate, how to validate AI outputs, and when to step in manually. That's the distribution edge Andreessen is describing.

AGI is here. It's working. The question is whether you'll restructure to use it or keep watching from the sidelines. If you want to see what production-grade agentic AI looks like, that's what we build at Sprinklenet. Visit sprinklenet.com or reply to this email. I read every one.

One song on repeat. Good tempo for writing an entire newsletter in one sitting.

Take Five.

🎺🎧 Note: Web Edition Only

Help us shape topics

🔥 Very useful

👍 Good insights

😐 Neutral

👎 Not relevant this time