When I started the AI Automation Society, I had no idea it would grow into this. Under two years later and we just crossed 400k. The largest AI automation community in the world. Yes, the space is exploding. But this community grew because you guys keep showing up, asking questions, dropping answers, sharing builds, and helping the person one step behind you. Huge thank you to the team that keeps this thing running, and to every single one of you who's posted, commented, or just been here. I feel lucky to get to do this. So excited to share with you guys what we've been quietly cooking up over here at AIS...👀 - Nate

Seems that Nick Saraev didn't like Nate's recent video and published this response to it? Does @Nate Herk have been with Nick or something? What's happening? Is Nate selling us overhyped tools as Nick says? I'm concerned.

I just spent 30 minutes to build this And the feature I built inside it is something I genuinely have not seen anyone do yet I built this entire website using Google AI Studio with Gemini 3.5 Flash The layout, the code - Google AI Studio Images - Nano Banana Pro The part that I always wanted to build day and night mode But not the usual dark mode toggle you see everywhere This one changes the entire environment of the same scene Same location. Same composition. But as you switch, the world shifts The lighting changes. The atmosphere changes The whole mood of the scene transforms as if time is actually passing inside the image Getting that right took the most effort The trick was prompting it to feel like the same place at a different time Not two separate images. One scene. Two moments Take a look - Click Here

Everyone in the AI space is being told the same thing right now: start an AI automation agency. But there's a bigger, quieter shift happening that fits way more people. In this video I break down a recent IBM study of 2,000 CEOs, the new chief AI officer wave, the 61-point gap between who can use AI and who actually does, and the two paths into that seat. By the end, you'll know which one fits you and why playing to your strengths matters more than chasing the loudest trend. The IBM study: https://newsroom.ibm.com/2026-05-04-ibm-study-ceos-are-reshaping-c-suite-roles-for-the-ai-era

This is something most freelancers go through, especially in the early days. A project comes in. The core tool is familiar, maybe Make.com, maybe n8n, maybe something else. But the client's stack includes a platform they've never touched. Poor docs, barely any community support. They take it on anyway because the money is there and they want to prove they can deliver. And the problems that follow are sneaky, because they don't look like mistakes at first. They look like hustle. → Discovery risk. They didn't know what they didn't know. The tool behaves differently than expected, edge cases surface mid-build, and they're making changes on the fly. This isn't the client moving the goalposts. This is the freelancer underestimating complexity because the tool was unfamiliar. But from the outside, it looks like poor planning. → Pricing mismatch. They quoted it as a straightforward build because they're already an expert in the main automation tool. But the unfamiliar pieces eat up hours that were never accounted for. Now they're either eating the cost quietly or having an uncomfortable renegotiation conversation that chips away at trust. → Bleeding timeline. They're learning as they go. Delivery slips. The client starts checking in more often. Confidence drops. And the worst part is they can't explain why it's taking long without admitting they're learning on the client's dime. → Opportunity cost. This is the one nobody talks about. While they're spending weeks buried in a poorly documented platform for one client, they could have delivered two or three projects confidently in their comfort zone. They didn't just underprice one project. They gave up the revenue from the projects they didn't take. Now, there's a real counterargument. Early on, taking unfamiliar-stack projects IS how freelancers build range. Getting paid to learn has real value. But this strategy has diminishing returns. At some point, the learning premium stops justifying the delivery risk.