Patrick Bonuso

The model isn't the moat. Everyone has access to the same frontier models. The advantage is in how you use them, the judgment, the workflow, the institutional knowledge layered on top. Your edge is the human judgment wrapped around the AI. Protect that.

Elon Musk was asked why his companies move faster than anyone else. His answer: "I'm constantly addressing the limiting factor. Whatever the limiting factor is on speed, I'm going to tackle that. If capital is the limiting factor, I'll solve for capital. If it's not the limiting factor, I'll solve for something else." He then said something most managers never figure out: "If something is going really well and making good progress, there's no point in me spending time on it." "The irony is if something's going really well, they don't see much of me. But if something is the limiting factor, they'll see a lot of me." He spends his time entirely on whatever is blocking the next step. Not on what's interesting. Not on what he's best at. But on whatever is the bottleneck right now. Most leaders do the opposite... They gravitate toward what they're comfortable with and away from the hard problem. From: @dwarkesh_sp and @collision

Most meetings exist because someone didn't write the decision memo. One page. The decision, the options, the tradeoffs, a recommendation. Send it 24 hours early. The meeting becomes 20 minutes instead of 45.

The most valuable engineers on AI projects right now aren't the fastest coders. They're the ones who ask uncomfortable questions before writing a line. "Do we actually need this?" "What breaks when this is wrong?" "Who maintains this in a year?" Slow questions. Fast outcomes.

The fastest way to build team trust: Say "I was wrong" before anyone asks. Not as a performance. As a design principle. The gap between "this isn't working" and "I got this wrong" is where your credibility is made or lost.

"AI-powered" is the new "cloud-based." It tells you nothing about whether the product actually works. Stop evaluating AI features. Start evaluating AI outcomes. Be AI-disciplined, not AI-decorated.

Before you sign any enterprise AI contract, ask one question: "Walk me through what a failure looks like." Most vendors will pivot back to capabilities. The ones who answer clearly have actually shipped into production. Buy the failure model. Not the demo.

The most underrated AI skill right now: knowing when not to use it. Some decisions need slowness. Some writing needs friction. Some thinking needs to stay yours. Speed isn't always the goal. Use it where it earns its place.

I got back 5 hours a week without changing a single deliverable. Three calendar changes. No new apps. No system overhaul. Full breakdown on LinkedIn today.

AI writes the first draft. You write the second. The discipline isn't in the generation. It's in the review. Most devs who struggle with AI coding tools are treating the output like it's finished. It's not. It's a starting point from a smart junior who doesn't know your codebase.

Still cranking out features! Love to see this after an acquisition, respect and trust to let @steipete keep building!

New @openclaw beta bits are up! github.com/openclaw/openc…

The best AI workflow I've found isn't about the tool. It's about being specific enough that the tool can actually help. Vague prompt → vague output → you do the work anyway. Precise prompt → useful output → you're done in 10 minutes. The bottleneck is almost always the brief, not the model.

Team USA pulled up in USA Hockey jerseys for the WBC Final 🇺🇸

Most AI coding policies aren't really about AI. They're about whether your engineering culture has enough trust and clarity to adopt anything new well. Fix the culture. The policy gets easier.

We doubled Claude usage on weekends, and outside 5–11am PT on weekdays for the next 2 weeks.

The gap between an AI demo and AI in production is mostly a people problem, not a tech problem. Someone has to own it. Someone has to maintain it. Someone has to explain it when it breaks. Build for those people first.

I got 6 hours back this week. Not by working less, by removing friction from the work I was already doing. Full breakdown on LinkedIn. (Link in bio)

Here's a #WorldBaseballClassic tiebreaker grid for Pool B, ahead of the final game.

The AI vendors winning enterprise deals right now aren't winning on model performance. They're winning on: -Clear ROI framing - Smooth IT/security reviews - References in the right industry The tech is table stakes. The sale is everything else.