My posts, measured.

David Friedberg (the All-in guy) says the AI jobs panic is a crock. Daniel Priestley says human labor is trending toward worthless. They're both right. The gap between them is where the leverage gets made. Friedberg's point: nobody's cutting headcount with AI. They're building 100x more. The bottleneck stopped being cost and became how fast you can act on opportunity. Priestley's point: if you sell time, you're competing with software that does the same work cheaper and never sleeps. That work gets priced toward zero. Both true at once. Here's how they fit: The 100x leverage is real. It just doesn't get shared. It flows to whoever owns the system and skips right past whoever works inside one. Same technology. Opposite outcomes. One question decides your side: Are you building the systems, or running the tasks they replace? One caps out at the hours in your day. The other compounds while you sleep. The people who get this are building now, while it still feels optional. That window doesn't stay open. So I wrote the exact build. A field guide to making your own acquisition screen with Claude Code, from free public data, instead of renting a $25k database seat. Free, no email: https://lnkd.in/ejNVWmUE

I failed a history class my sophomore year of college. I thought that was it. That my life was over, that I'd never get a job, that I'd be sent packing back from whence I came. What I couldn't see then: the failing grade was never the improbable part. Me sitting in that classroom at all was. We couldn't afford a US education. My mother raised me alone, and we did ok by Zimbabwean standards, but a US tuition bill was not a number we had. I got into the one school that offered a 99% grant. She paid a few thousand a year for something that should have cost six figures. A few years later, a 3.1 GPA walked into investment banking. Into rooms built for 3.9s. Every gate I went through had a number on it that said no. The grade. The tuition. The GPA. I build scoring systems for a living now. I rank companies. I score targets. I decide who makes the list, and who gets outreach. And I never forget that a score is a snapshot, not a verdict. The one worth backing is often the one the model would have cut. I'd know. I was the one the model would have cut. So now the first thing I ask my models isn't who ranks highest. It's: is there a way this one could work? Is there an x factor here that makes the rest of the scorecard irrelevant? The best part of the story is usually the part the data can't see.

I got this book for my son. But it’s having a bigger impact on me.

The ballsiest way to make a million dollars as an acquisition entrepreneur is probably underwater. Four words: off-bottom oyster farming. You'll need high testosterone for this one. Which is perfect, because oysters deliver more of the crucial building block for T than any food on earth: zinc. One serving = 300% of your daily dose. Let me pencil you the numbers. → An acre of off-bottom water grows 100,000 oysters a year → Unbranded, they clear $0.50 at the farm gate. With a name menus repeat: $1.00 → Cost to grow one: $0.30–0.35 + other ops costs $0.20-0.30 → = $0.35 of EBITDA per oyster The name costs nothing. It's your bay. So a branded acre = $100K revenue, $65K gross margin, $35K EBITDA. For $1M of EBITDA you need ~30 acres. Build cost runs $60K/acre. So: $1.8M in once (+ maintenance), $1M out. Every year. And this isn't some dying trade. Farmed oysters are a $327M US market (USDA Census of Aquaculture) that now out-earns wild harvest AND imports — output nearly doubled over the past decade, pulled up by raw bars going year-round. So why hasn't anyone built this? And why does it take big brass balls? Because an oyster takes 18–36 months to grow. Everything you sell in 2028, you finance today. Two years of inventory sitting in open water, through storms and pestilence, before a dollar comes back. That's why America has 900 oyster farms averaging $360K of revenue. The market isn't fragmented because it's a bad business. It's fragmented because of a working capital wall. And walls like that don't stop markets. They just decide the winner: whoever arrives with a balance sheet. Another problem. You can't just permit 30 new acres. In the premium states, new leases take years and die slow deaths in waterfront politics. Permitted acres are scarce. So the fast path isn't growing acres. It's buying them. Aging founders on 5-acre leases, mature inventory already in the water, no succession plan. You're not buying companies. You're buying water, permits, and a two-year head start someone else already paid for. And it gets better as it gets bigger: → One umbrella brand amortized over 30 acres instead of 3 → Mortality risk spread across bays — one bad closure no longer sinks the year → Seed bought at scale through the hatchery bottleneck → Year-round supply consistency, which is exactly what distributors pay up for Small farms compete. Scale compounds. The consolidation has already started — with nobody driving. Alabama, 2018–2022 (per a study in Aquaculture Economics & Management): production doubled, 22 farms became 10, same acreage. A rollup with no roller. So there you have it. A way to capitalize on every jock who now tracks his sleep score and measures nighttime erections like Bryan Johnson. A high-T business model. Through and through. PS: Oyster map doesn't exist yet. I'll build it if enough people ask. The Ohio trades map does exist though: screen.searchloop.ai

I used to feel like Adam Sandler in 50 First Dates. Every morning, re-introducing myself to my AI, along with a bunch of frustrated swear words, like the last 24 hours never happened. Now I don't. Because I, at long last, figured out how to use Obsidian. Obsidian is a free app that turns a folder of plain text files into a connected knowledge graph. Point Claude at the folder and it inherits every connection you've built. The problem is it's not intuitive. Most people never see it working. So I built a starter vault you can download and open in a minute — a fictional lower-middle-market deal with a thesis, a CIM, a founder, a banker, and a four-gate scoring model, all wired together. (You'll need Obsidian too — it's free.) Open the graph view and watch everything connect. Point Claude at the folder and it reads the whole thing. No setup. No "let me tell you about my work." Just context, instantly. But here's the part that actually matters — it's recursive: Every session starts by pointing Claude at the vault. Every session ends by asking it to write up what you did and link it back in. It already knows how — the rules live in the vault. So every session makes the next one smarter. The vault never forgets, and it keeps getting richer. You stop repeating yourself — for good. I built it for private equity pros. But it works for anyone who keeps starting from scratch — anyone whose AI should remember how you think and build on what you've already done. Your rules. Your systems. Your frameworks. Link's in the comments. No email gate, no sign-up. What do you find yourself re-explaining to your AI every single time?

Marc Benioff had a funny line on All-In: "Sex bots off. Cursor on." lol, but the point: AI is moving toward actual work infrastructure. And a lot of PE firms I talk to are still stuck on step one. Using AI like a smarter intern. Draft the memo. Summarize the CIM. Clean up the deck. Write diligence questions. Polish the email. Useful. But still just a human sitting inside a chat window. The real leverage is much less glamorous: turning messy workflows into systems. At the fund level: A banker sends over a new opportunity. Who reads it? Who checks the CRM? Who looks for conflicts? Who decides whether it's worth a first call? Where does that decision get recorded? Is it a different process next time? At the portfolio company level: A lead comes in. An RFP lands. A quote goes out or invoice gets generated. Someone checks it. Someone follows up. Someone records the review. Someone updates the customer record. Someone knows the weird exception that never made it into the SOP. That's the operating layer. And at most companies, it lives in people's heads, inboxes, spreadsheets, half-used systems, and "Sarah usually handles that." AI doesn't just automate those workflows. It exposes whether the workflow actually exists. The next wave of AI value won't come from better prompts. It'll come from turning messy human operating knowledge into repeatable systems. That's the layer I care about at SearchLoop: turning messy origination and operating workflows into systems that compound. Happy to compare notes if you're thinking through where AI actually changes your operating model.

What used to take a 20-person PE shop can now be done with 2-3 people and AI. Fragmented industries — pool services, dental, specialty trades — have been a real roll-up grind for years because the operational complexity didn't scale. I think that could be chaning fast. Here's why.

I don't know how to code. But last week I vibe-coded a full control center for a client's deal origination system. Launch searches. Initiate outreach. Check the funnel. Run enrichment. Verify ownership. One interface. It looks better than half the SaaS tools I've paid for. So if anyone can build that now — do SaaS investments even work anymore? Here's the thing. That control center only works because we spent a lot of time building what sits behind it. The workflows. The qualification logic. The CRM integrations. The enrichment chains. The error handling. The front end just gives you buttons for infrastructure that already exists. I couldn't have vibe-coded any of that. And that's exactly what PE is paying for when they roll up vertical SaaS. Nearly half of all SaaS M&A last year was vertical SaaS. Roofing ops. Cemetery management. Public safety dispatch. Nobody's acquiring these companies for the UI. They're acquiring the system of record — embedded workflows, compliance logic, integrations that took years to build. Vibe coding made the front end a commodity. The SaaS companies I'd worry about are the ones where the product IS the pretty interface. That's a weekend project now. The ones I'd buy? The ugly vertical platform with mission-critical guts. So did Claude Code kill vertical SaaS? No. But it killed the wrong reasons to invest in it. — If you're hunting vertical SaaS compounders before they hit the broker desks, I put together a free tactical guide with the origination stack we run at SearchLoop: https://lnkd.in/e6GBgwxR #PrivateEquity #B2BSaaS #VerticalSaaS #DealOrigination #SearchFunds

The most overhyped AI project right now is OpenClaw. Not because it's bad. It's actually cool conceptually — a bunch of AI agents in a trench coat that you talk to through Telegram. The problem? Nothing it does is novel. It summarizes emails. Monitors competitors. Posts to Twitter. These are API calls with extra steps. And it burns through a billion tokens doing it. Every single use case can be done faster, cheaper, and more reliably with purpose-built tools wired together: n8n, Make, Claude … Code lol. Oh, and it's a pain to set up — ironic for something that's supposed to simplify your life. When users are pressed on what value they're actually getting, the answer is usually "it remembers my conversations". We're talking about a database. The hype is wildly out of proportion to what it buys you. If you're thinking about automation, don't start with the shiniest all-in-one tool. Start with the specific problem. Build around that. Purpose-built beats bundled. Every time.

Living in Ohio connecting virtual lego blocks for private equity investors was not on my 2026 bingo card. Farm in Zimbabwe → liberal arts college in Massachusetts → investment banking in New York → PE in London → MBA in Texas → solopreneur in Ohio. That wasn't in my childhood journal. Back when I was recruiting for PE in London, I sent hundreds of cold DMs wondering: How do these guys actually find deals? Turns out I was sorta right. Deals don't just land in your lap. You have to go find them. I did a lot of that at my fund. Manually. Unsystematically. Events, LinkedIn searches, email blasts, grabbing coffees with other investors. After business school I decided to give this solopreneur thing a whirl instead of going back to PE. 80–90% of funds — including big ones — have no real system for proprietary sourcing. It's networks, broker deals, a rushed bit of market research, and a volley of emails. The process? Usually held together with duct tape and good intentions. Even the funds with "we use AI to intelligently source deals" on their website. Side note: a company ChatGPT subscription doesn't count as an AI system. You'd be amazed at what people say about themselves. A lot of it is smoke and mirrors. So now I build the actual infrastructure. White-label deal sourcing systems for PE and growth equity firms. AI that finds companies systematically, plugs into your CRM, and gives your team a pipeline that actually compounds. It's a lot of work doing everything yourself. But the gap between a ChatGPT subscription and an integrated system that actually runs is much wider than anyone thinks. And until that gap closes, there's plenty to build. Zimbabwe to Ohio. Stranger things have happened. Probably.

Pessimists get to be right. Optimists get to be rich. I think Shaan Puri from My First Million said that. Doomsdaying about ai is a hot topic right now. Fear gets clicks, I guess. And humans have been doing it since that apple was eaten. But you know what's hard. Optimism. Doomsdaying requires no effort, no experimentation, no risk. Just loud opinions. My guess: those are the people who ai will replace. But the ones in the arena. The ones figuring out what can be done that couldn't be done before. The people who think of ai as outcomes & leverage. Those are the people that will have a place in the post ai world. Ai doesn't have judgment or curiosity. It can't go for a walk or let thoughts percolate in the shower. But optimists can. And with ai, they can give those thoughts some leverage. How exciting.