AI should empowerpeople, not replace us.

After 1,000 hours of AI marketing workflows. This is what I wish I knew from day one. The robots are here to stay. And if they don't kill us in our sleep. we better make good use of them. This is how I use AI for marketing. (I'm 51. If I can do it, you can too.)

1. Record every call → Your customers are writing your content. Every objection, every pain point.
2. Store transcripts in one place → Scattered notes are useless. One folder, every conversation. That's your content goldmine.
3. Extract content ideas → Run your transcripts through AI. Ten post ideas in five minutes.
4. Pull ICP pain points for your ads → Cold traffic responds to their own language. Your calls already have it. Your campaigns should too.
5. Find your hooks in their complaints → The thing your clients hate most? That's your best headline.
6. Build a story bank → Every call adds one story. In six months, you never run out of content again.

If you can't beat the robots, join them.

PS: What's your AI automation story?

I've been a software engineer for 13 years. The software development lifecycle has changed more in the last 3 months than in those 13 years combined. Engineers working with Claude Code describe intent, agents implement solutions, and what used to take a team a week now takes an engineer an afternoon. Internally, much of our code is trending towards machine-generated.

That's great for velocity. It's a problem for reliability.

We previously had many safeguards that kept production stable: code review where the reviewer understood the system, manual testing, the senior engineer who held the architecture in their head. All assumed humans at every stage. Of course they did: there was no alternative.

That assumption is falling apart right now. You can't review 10x more PRs with the same number of humans. You can't hold institutional knowledge in your head when the codebase changes faster than anyone can read it.

The horse has bolted, and now it's about how we react. The bottleneck is now not "how fast can the humans type?". Two key ones remain in the SDLC:

1. Is this the right software to build? Returns accrue to product taste and intuition.
2. Is this software right? Returns accrue to correctness and reliability.

The first is a still a human problem. The second is becoming an infrastructure problem.

Companies are about to discover that reliability is the binding constraint on how fast they can move. The ones who figure that out first will ship faster than everyone else — not because they write more code, but because they can trust what they ship. It's an exciting time to be alive.

How I saved $5k per year using AI to make sales and marketing collateral and presentations. I use Gamma and Claude to build presentations in minutes. I used to hire contractors which would cost me $thousands each year; now I’m able to do this myself in minutes. There has never been a more exciting time to be a solopreneur and small business owner.

I’m a cautious skeptic of AI/LLMs, but I’m trying to use them where it makes sense in my software work. As I use chat/agent tools to help implement and debug code, I make a point of clearly documenting which commits (and sometimes lines) include LLM-derived code—often using Git commit trailers like `Co-authored-by` with the model name/provider—to improve traceability for reviewers and to reduce future legal/compliance risk.

AI automation earns its place in my workflow in areas where human review remains part of the process. Research, drafting, reporting, brainstorming, synthesizing data, and building first versions of things that I evaluate before anything goes live. That's where I get the efficiency gains without gambling on my client's business.

The marketing automation tools that worry me are those marketed specifically on the promise of removing human judgment from the loop.

For many of my clients, my judgment, based on years of experience working in highly sensitive and regulated niches, isn't a bottleneck; it's the service they're expecting. #marketing #ai #openclaw

I spend ~3 hours a day on email, so I tested letting AI handle my inbox for a week using Gmail’s Gemini (drafting/summaries) and Shortwave (auto-organization). The first days felt "magical" (faster replies, thread summaries, inbox triage), but AI nearly caused real damage: it agreed to a meeting time without checking my calendar and drafted a pricing email quoting $4,800 for work I charge $8,000. My takeaway: AI is great for summaries, triage, and first drafts, but anything involving money, scheduling, or sensitive wording requires careful human review before sending.