My posts, measured.

I've been on both sides of deal outreach. Sending it as a buyer. Receiving it from bankers trying to get my attention. The emails that get replies all share one thing. And it's not what most teams optimize for. The standout emails are short. Four or five sentences. No fund size. No last deal closed. No "checking in." They mention something specific about the business. A detail that tells the owner: I actually read your materials and I understand what you built. Then they say, roughly: "We're looking at a few opportunities in this space. If a conversation is useful, I'm around. If not, no sweat." That's it. No pitch. No urgency. No credential flex. Here's why this works. Most outreach telegraphs need. The subtext is: please take my call. The recipient feels pursued, not respected. The short email does the opposite. Its subtext is: I have a thesis and capital. If our paths overlap, great. If they don't, I won't waste your time. That posture is non-needy but also nice. It signals competence without demanding attention. Sellers have ego. They built something from nothing. They can tell the difference between someone who understands the business and someone who bought a mail merge tool. The first message sets the entire dynamic. Now — if you already own a business in the sector, this gets even easier. Your portco does the credentialing. The seller doesn't wonder if you're real. You've already put capital to work in their world. You understand the margins, the customers, the headaches. That shortens the distance between "who is this" and "let's talk" by half. But even without one, the principle holds. Be specific. Be short. Don't chase. Credibility isn't something you announce. The seller decides it by the end of the first paragraph. This is where most lean PE teams get the AI question backwards. The instinct is to automate the message. Feed the CRM into an LLM. Let it write. The output passes a grammar check but fails the credibility check. It sounds identical to every other buyer using the same tool. The better answer: AI runs the system. Human judgment reviews the message. AI can draft a smart template, enrich it with details about the owner and the business, manage the cadence. But someone who knows the sector reviews every message before it ships. If it sounds like a bot wrote it, you never get the reply. Automate qualification. Automate deliverability. Automate follow-up so nothing falls through the cracks. That's the part that burns time for teams with capital and a mandate but no analyst army. But the first touch needs human review. Someone who knows why this business, why this owner, and what a real conversation sounds like. Differentiation has to show up in the first message. Not the pitch deck. Not the second call. The email. Being easy to work with is the most underrated differentiator in M&A outreach. The seller decides whether you're easy to work with by the end of the first paragraph.

Two searchers bought a home care business for $88M in 2020. It's now tracking toward an exit close to $1B. The differentiated mechanism: The business had two halves — a software company and a service company. When they entered a new state, they deployed the software sales team FIRST. Sold into the market. Got operators implementing. Then acquired one of those software customers as the platform. By LOI, they already knew: → the target's exact financials → their operational patterns → whether the team could actually execute → how fast integration would go (under 30 days, because the data was already in their system) That's not proprietary deal flow. That's manufactured deal flow. Most searchers and ISPs treat "proprietary" as "cold outreach to an unbrokered deal." That's just earlier. The real edge is being inside the operator's business for 2 years before you ever send an LOI. Software is the most obvious Trojan horse, but it's not the only one. Any service you sell into the sector works — audits, recruiting, implementation consulting. Anything that gives you visibility into the actual operations before you offer to buy them. The next $1B search outcomes aren't coming from a broker's teaser. They're coming from operators who sold into their sector first and acquired from inside their customer list. (Story is from Jenna Wigum on Acquiring Minds — the Abound Health build-up is worth the full listen.)

Med spas. HVAC. Landscaping. Lots of funds is chasing fragmented plays. But the weirdest market I've looked at? Dead people. Making money from dead people seems kinda unethical, but it's not really. You're providing a service. To their loved ones, I guess. It just hits different. Because we're talking about a business model that shares commonalities with our deepest fear. Or uncertainty. People die every day. Recurring revenue box ticked. And more people are going to die as the boomers age out. PE doesn't care. And the deals are being done: → Birch Hill Equity Partners took Park Lawn private for $1.2B in 2024 — 170+ funeral homes and cemeteries, now expanding into new states → Axar Capital took StoneMor private, rebranded it Everstory Partners, now runs 460+ locations with drone-mapped digital operations → Serent Capital backed PlotBox in 2025 to scale cloud-based cemetery and crematory management software → Rosewood Private Investments quietly rolled up 77 funeral homes across the northeast through Milestone → SCI spent $71M on acquisitions in just the first nine months of 2025 — sitting on a $16B preneed backlog Also, the smartest players aren't just consolidating — they're layering in software. Tech-enabled death care. That's a phrase I never expected to type. Often in PE a business is a business. Landscaping, vertical SaaS, healthcare — they all get a nod when mentioned in a convo. But could you get up every day and get excited about the fact that you're rolling up funeral homes? — If you're building out your own origination engine to find deals in sectors most funds overlook, I put together a free tactical guide with the systems we use at SearchLoop: https://lnkd.in/e6GBgwxR #PrivateEquity #GrowthEquity #SearchFunds #DealOrigination #PrivateMarkets

There are 19,000 private equity funds in the US. There are 14,000 McDonald's. KKR's Alisa Wood dropped that stat last year and it still lands the same punch in 2026. And it's not just a US story. Europe has roughly 9,000 PE funds and around 9,000 McDonald's — nearly one-for-one. The saturation is everywhere. More PE vehicles than fast-food outlets. That's not a fun fact — it's the competitive reality every fund is living inside right now. When there are this many funds chasing the same deals, the old channels get noisy fast. Broker networks, conference circuits, the same Grata and SourceScrub lists — everyone's fishing the same pond. The funds pulling ahead aren't working harder on the same playbook. They're building proprietary origination infrastructure that compounds — systematic market mapping, thesis-driven qualification, persistent outreach that actually lands. It's not about "using AI." It's about having a repeatable system of record you can point to. Disagree? PS: If you're thinking about building your own origination setup, I put together a free 10-chapter playbook with the exact stack and sequences we've used across funds: https://lnkd.in/e6GBgwxR

Search funds are quietly moving upmarket. For years the classic target was $1–3M EBITDA businesses — the smaller "searcher special" deals that bigger PE funds usually ignored. Now the new wave is chasing $5M+ platforms that used to be pure middle-market PE territory. Mineola Search Partners just published a good piece on the trend last week (https://lnkd.in/ebvDekUA), and EPA Investissements announced they're scaling up to back 40 new searchers this year (https://lnkd.in/efhduWEc). Same solo operator. Same 18–24 month clock. But now you're competing head-on with real PE money for the same targets. Origination just got harder — and a lot more valuable. You can't just pull the same Grata, Inven, or SourceScrub lists everyone else is using when bigger players are circling the exact same companies. The edge now belongs to the people building proprietary lists from raw sources — Google Maps, regulatory databases, piecing the puzzle together — then layering on outreach that actually lands in the inbox. I've laid out exactly how to build this infrastructure this 2026 PE Origination Playbook — going from raw data to a qualified calls. Grab it here: https://lnkd.in/e6GBgwxR Anyone else feeling this size creep?

6 million businesses are going to change hands in the next decade. Most won't find a buyer. McKinsey estimates that in 2022 alone, 92% of small business exits ended in closure — not sale. Not because the businesses lacked value. Because no buyer showed up in time. Half of all small business owners in the U.S. are already over 55. One in four are 65+. Regional manufacturers, specialty contractors, local distributors built over 30 years — with real cash flow and no obvious successor. This is the largest wave of quality deal flow any of us will see in our careers. And most PE and search fund teams know it. They're running Instantly sequences, scraping Clay lists, working their broker networks. But it's all disconnected. A campaign here, a list there. No systematic way to map a market, qualify at scale, and build persistent outreach that compounds over time. So they end up reactive — catching owners after they've already decided to sell, in a process, talking to three other funds. The edge goes to whoever reaches these owners while they're still running the place — before they've even decided to sell. That's not a relationship strategy. That's an infrastructure problem. The $5 trillion transfer is coming regardless. What's your current system for finding owners before they raise their hand?

The 2026 PE reports from Bain and McKinsey are out. Headlines look great. Details don’t. McKinsey says global PE deal value +19% to $2.6T. Bain shows buyouts +44%, exits +47%. Peel it back and the recovery is narrow, concentrated, and brutal. Deal count actually fell. A handful of megadeals did the heavy lifting. There’s still a $3.8T backlog of unsold companies. Holding periods pushing seven years. Distributions stuck below 15% NAV for four straight years. LPs aren’t being picky — the math is forcing them. Bain puts it plainly: “Multiple expansion and ultra-cheap debt have waned… The firms that stand out in this difficult new environment will be those that can find or sharpen a repeatable model for sourcing deals, determining early how to create value and executing at speed. Relying on the same old, same old has never been riskier.” Most funds are still running on networks, broker deals and micky-mouse proprietary outbound. Few have a system they can actually point LPs to. The real edge belongs to the teams running a full repeatable system: comprehensive market mapping, thesis-driven qualification, persistent multi-channel outreach that gets A/B tested — all CRM-connected and compounding every week. That’s the infrastructure we build at SearchLoop. This isn’t “we use AI.” This is the repeatable model Bain is talking about. The rebound is here. It’s going to reward the builders. https://lnkd.in/e_w6BCfX

The most expensive line item in your fund is invisible. You didn't lose that deal because you were outbid. You lost it because you were too slow. The founder had three conversations with another fund, built rapport, and went into exclusivity. By the time you knocked on the door, it was over. And the part that stings — that founder sold to the person they trusted. Not the highest number. People obsess over purchase price, IRR, and risk-adjusted returns when they find a deal. But no one measures: 1. Origination funnel speed from research decision to outreach action 2. Building enough trust that a founder who wasn't thinking about selling starts to consider it 3. Deals we never saw 4. Being a month late There's no line item for any of it. No IC memo. No post-mortem. And you know what actually affects IRR? Speed. That invisible cost — compounded across a fund life — is tens if not hundreds of millions of dollars. Every fund measures what happens after they find a deal. Almost none measure how fast they find it.

If I had to build a proprietary deal pipeline for a marina roll-up in South Florida, I wouldn't start with a database. I'd start with a satellite image. Marinas can only exist on waterfront. You can't build new ones — coastal permits, environmental regs, and finite shoreline see to that. Supply is locked. Every target already exists. You just have to find the right ones. In South Florida, any marina with 10+ slips needs an annual operating permit from the county. Public record. Slip count, operator, location. Filter to 50+ slips and you've got your target universe in an afternoon. Then layer the signals: → Satellite imagery shows dock condition, occupancy, and adjacent waterfront for expansion → Google reviews reveal management problems no spreadsheet will tell you → Florida publishes boat registrations by county — that's demand density → Single location + incorporated 20 years ago = succession opportunity → Median income within 10 miles = pricing power A marina with aging docks, a 4.2-star rating mentioning "new management needed," 60 slips on the Intracoastal, and an owner who incorporated in 1998? That's not in anyone's CRM. But it's visible from space.

Most funds think their CRM is a database. It's more of a graveyard ... Partners assume it's full of rich deal data. Trends. Margins. UoP. Pass reasons. The reality? A list of project names and deal values with half the fields blank. I lived it. Every Friday (or every other Friday, if we're being honest), we'd try to reconstruct two weeks of deal flow from memory and email threads. What's the revenue? What's the website? Who's the founder? Is this even in the system yet? You're not doing investment work. You're doing data entry. And when you're drowning in CIMs, you do the bare minimum. Type the name. Guess the sector. Hit save. Six months later a partner asks "how many food & bev services businesses did we see last year?" and the honest answer is: we don't know. That's where AI actually earns its keep. Not writing emails. Data integrity. We built a simple agent for a fund. The team drops a CIM into a chat. The agent reads the deck, checks if the company exists in the CRM, and either updates the record or creates one — right category, right fields. Revenue. EBITDA. Website. Key contacts. Deal value. It asks one or two clarification questions — "This EBITDA or this one?" "Is this a new deal or an add-on?" — then saves. One minute instead of ten. And the CRM actually has data worth querying a year from now. Not because the team worked harder. Because the friction of being accurate went to zero. If you have to rely on willpower to get good data, you won't have good data.

The deal is usually in the fourth touch Ask any successful PE partner or searcher where their best proprietary deal came from. It wasn't the perfectly crafted first email. It was the fourth touch. 1. LinkedIn connection to the CEO. 2. Email to the founder. 3. A bump two weeks later. 4. A note to the COO. Then the reply: "Actually, your timing is interesting." But nobody has time to manually manage a 5-touch, multi-channel sequence across 200 targets. So what usually happens? One email goes out. No reply. On to the next. The magic isn't in the message. It's in the persistence. And persistence is impossible to scale without a system. If you're relying on manual follow-ups, you aren't building a pipeline. You're just buying a lottery ticket and hoping for the best.

The #1 reason deal sourcing automation fails at PE firms? It doesn't connect to the CRM. Most origination tools give you AI-generated targets. Sounds great. But that's where it ends. It can't check against who you've already reached out to. It can't update when a deal status changes in your CRM. It doesn't know your team passed on that company six months ago. It's a spreadsheet with extra steps. Why? Because the hard part isn't finding targets. It's connecting to the systems your team actually uses. Affinity differs from HubSpot differs from Pipedrive. Every team structures their lists and funnels differently. You can't ship that as a feature. It has to be built around how your team actually works. The automation that sticks is the one where a partner opens their CRM on Monday morning and sees 15 new scored opportunities already there. Deduplicated. Status-aware. No extra login. That's the difference between a list and a system.

Anthropic just proved what we all suspected: AI makes you worse at the thing it's helping you do. They ran a controlled study — two groups of developers, one with AI, one without. The AI group scored 17% lower on understanding the code they just wrote. The kicker? AI only saved them ~2 minutes. And that wasn't even statistically significant. Here's why this matters for PE: Every firm I talk to is racing to bolt AI onto their deal sourcing, due diligence, and portfolio ops. And they should — we're building these systems every day at SearchLoop. But there's a critical distinction most people miss: AI is extraordinary at horizontal scaling — taking a proven process and multiplying it. Screening 5,000 companies instead of 500. Enriching funding data & doing crm cross checks for 2000 companies instead of 20. What it doesn't do is replace the vertical thinking that actually makes money in this business. Pattern recognition across deals. Knowing when a management team is overselling. Reading between the lines of a QoE report. The Anthropic study found AI crushes productivity on tasks where you already have the skills. But it actively hinders learning new ones. For deal teams, the implication is clear: → Use AI to eliminate the grunt work you've already mastered → Don't use it as a crutch for the judgment calls that justify your carry The firms that will win the next decade aren't the ones that automate the most. They're the ones that automate the right things — and keep their sharpest thinking human. https://lnkd.in/eaWveZ5h