This morning, I woke up feeling a little nervous. It wasn't because AI was going to read my inbox—it has been doing that for months. What made me uneasy was knowing that I was no longer going to be the reading it at all for three days. My inbox isn't just business. It's clients, prospects, personal email, financial information, family logistics, subscriptions, and everything in between. I also have a few active deals in progress, so missing the wrong email could have real consequences. Since I had fully committed to letting AI review my inbox first, I wanted to make sure the instructions were as clear and complete as possible, and that ChatGPT and I were on the same page. One thing I like about ChatGPT Scheduled Tasks is that I can prompt directly inside the email notification chat, which means it already has context, that makes it very easy to refine the instructions without starting from scratch. So, without reading my email, I asked a simple question: 💬 The Prompt: I would like to look my email every three days. How should we enhance the prompt? Or is it good? 🤖 ChatGPT’s Response: Your current prompt is good for twice-daily alerts, but if you only plan to personally check email every three days, I would strengthen it. The main enhancement is to make the AI act less like a “notification filter” and more like an email operations assistant with escalation judgment. Here is a stronger version: Review my recent email and notify me only when a message likely needs my attention before my next manual inbox review. I personally review my inbox every 72 hours, so prioritize anything that cannot safely wait three days. Flag emails involving: - Clients or prospects - Time-sensitive requests - Deadlines, renewals, billing, payments, payroll, contracts, or account/security issues - Anything from VIP senders - Messages that require a reply, approval, decision, review, or action Ignore by default: - Promotions - Spam - Newsletters - Automated notifications - Receipts or FYI-only updates, unless they involve money, account access, deadlines, or risk

For the past several months, I’ve been using ChatGPT and Claude to help manage my inboxes. They’ve been reading emails, sorting intent, identifying what matters, and surfacing what needs attention. But up until now, I’ve always check the work of AI—reviewing everything alongside them and verifying decisions daily. That will change for the next 30 days. 🧪 The Experiment Starting today, I’m running a 30-day controlled experiment: - ChatGPT and Claude will be the "first systems to review my inbox" - AI will handle all first-pass triage, prioritization, and escalation - I will only respond to emails that are flagged by AI - I will only open my email every 72 hours (3 days) - I will rely on AI summaries and alerts between reviews - ChatGPT scheduling and Claude coworking workflows will run in parallel This is not convenience automation. It’s a controlled delegation test under time delay. 📬 Important Context My email is not siloed. It is a shared channel for both personal and business communication. That includes: - Clients and prospects - Financial and operational items - Personal messages and family logistics - Newsletters, system alerts, and vendor communication This is a real mixed-context inbox, not a filtered business queue. That matters, because context switching is where prioritization either succeeds or fails. 🎯 The Goal I want to understand one thing clearly. What happens when AI becomes the first decision layer in a real-world inbox with delayed human access? Not just summarization. Not just filtering. But actual prioritization that must hold for 72-hour cycles. Specifically: - What AI consistently gets right - Where urgency is misclassified or delayed too long - How well personal vs business context is separated - What gets buried that should not be - How trust behaves when human correction is delayed 🚧 The Guardrails This is not full autonomy. There is still a safety system in place: - Human review every 72 hours - Explicit escalation rules for VIP, financial, and time-sensitive messages - Dual-system validation (ChatGPT + Claude) - No irreversible actions without review - I am still responding to emails, that is not being delegated

I’m excited to announce the upcoming launch of "Lone Wolf AI League", a new premium Skool community for people determined to become the AI resource inside their company, agency, executive room, board room or marketplace. Inside Lone Wolf AI League, I will share real-world AI strategy, deal strategy, wins, losses, client conversations, consulting realities, business execution, and what it actually takes to compete in the AI economy. This post is an example of the type of content and conversations we will explore inside the community. 🐺 Last night, I came across the Reid Hoffman quote: “If you’re not embarrassed by the first version of your product, you’ve launched too late.” I have seen this quote many times, but last night it brought back a wave of nostalgia because it connects directly to something I have experienced over 30 years of building software, launching companies, and surviving technology disruption. Over the last 30 years, I have built more than 100 software applications, large and small. I have started four technology companies. My first startup, in 1999, raised $100,000 in capital. At the time, that was big money to me. We spent six months building a highly specialized eCommerce SaaS model for the brand merchandising industry. We financed an $80,000 server running Windows NT 4, built on a Classic ASP stack, and later moved to .NET. We wanted to go open source, but MySQL was still in its infancy. Java felt like space-age technology. JSON was just starting to look promising. We were in the middle of a 10-year browser war, and you could not practically email a file larger than 1 MB. After a successful launch, we invested another $100,000 in servers and software. We sold SaaS eCommerce subscriptions for $2,500 per month. We thought we were building the future. Then Yahoo launched website and shopping cart capabilities for about $25 per month, and overnight, the industry shifted beneath our feet. Our model was not only under attack. It took a direct hit because customers saw price, but often did not understand the nuance between small retail websites and enterprise eCommerce.

What Enterprise Leaders Need to Watch 1. Shadow AI Is Shadow IT 2.0 Employees are already using AI tools outside formal approval processes. Companies need visibility into what tools are being used, what data is shared, and how outputs are applied. 2. Agent Sprawl Is Emerging Employees and departments will increasingly create AI agents and automations on their own. Every agent needs a defined owner, approved purpose, controlled access, and a way to shut it down. 3. AI Needs Identity and Access Controls AI agents may access data, interact with systems, and act on behalf of people. They must have permissions, audit trails, oversight, and clear accountability. 4. AI Can Be Manipulated AI systems can be tricked through prompt injection, unsafe instructions, or excessive system access. Security controls must protect both the AI and the systems it can reach. 5. Data Governance Becomes AI Governance The first governance question is what information AI is allowed to access. Sensitive company, employee, customer, financial, and proprietary data must be protected from unapproved use. 6. AI Regulation Is Driving Structure Emerging regulations such as the EU AI Act are pushing companies to classify risk, document AI use, assign accountability, and establish human oversight. Governance is becoming a business requirement, not an optional exercise. 7. Governance Must Become Operational A policy document alone is not enough. Companies need approved tools, use-case reviews, risk scoring, monitoring, audit trails, incident response, and ongoing accountability. 8. Human Oversight Must Be Designed Human review cannot be an afterthought. Organizations must define where people approve, verify, override, or stop AI-supported work and decisions. 9. AI Literacy Is a Security Control Many AI risks come from confident misuse rather than bad intentions. Employees need training on data protection, output verification, approved use, and when human judgment is required. 10. Security Must Enable Safe Adoption

This message just popped up on my ChatGPT mobile app. Introducing Codex Mobile! This is the one I’ve been waiting for on Claude Code… but OpenAI beat them to the punch. And honestly, I think this is a bigger shift than most people realize. Not because people are going to build full enterprise apps from an iPhone. But because AI development is becoming continuous. You can review agent output, fix issues, direct workflows, capture ideas, and keep projects moving from anywhere. The gap between: “I have an idea” and “It exists” keeps collapsing. Software development is starting to feel less like programming……and more like continuously directing intelligent systems.