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In case you missed out on our last episode, please find it here.

Today’s Rundown

• Conflicts between searchers and investors

• 2026 search fund predictions

• AI tools for searchers

• Where to find business owners ready to sell

• Learnings from 605+ acquisitions

• 2 deal / launch announcements

Weekly Highlights

• Yale published a new paper on exploring bilateral conflicts of interest between entrepreneurs and investors in search fund projects:

  • The search fund structure concentrates risk asymmetrically, with entrepreneurs typically holding low initial capital at risk but meaningful upside participation, while investors supply most of the capital and bear the majority of downside exposure, creating predictable incentive gaps across the lifecycle

  • Empirical patterns show that conflicts most often emerge at four quantitative inflection points: deal closing probability versus expected return, leverage levels at acquisition, reinvestment versus cash distribution decisions during operations, and timing of exit relative to IRR versus MOIC optimization

  • CEO behavior is statistically skewed toward higher-variance strategies because compensation is convex, meaning a small probability of a 5x+ outcome can dominate decision-making even when median outcomes underperform investor hurdle rates

  • Investor-side control mechanisms such as CEO removal rights and board dominance reduce operational risk but introduce measurable downside for entrepreneurs, including reduced long-term equity value and increased likelihood of premature exits that cap upside

  • Outcomes improve when governance and incentive structures explicitly rebalance these forces, with evidence suggesting that clearer reporting cadence, predefined reinvestment thresholds, and transparent exit criteria materially reduce value-destructive conflicts over time

• José Moreno, managing director of search fund investor AIJ Global, shared his predictions for search funds in 2026: AI shifts, deal dynamics, and the new SME landscape:

  • Search funds entering 2026 face structurally longer deal cycles in mature markets, with closed transactions projected to decline by roughly 10–12% while diligence expectations and proof requirements rise materially

  • AI-driven diligence has shifted from advantage to baseline, cutting diligence timelines by an estimated 30–50% while reducing first-year post-close surprises and compressing integration risk

  • Seller behavior is changing measurably, with earnouts and rollover equity now appearing in close to half of deals versus roughly one quarter only two years ago, reflecting increased emphasis on continuity, governance, and long-term stewardship over headline price

  • Valuation dispersion is widening as AI-heavy businesses command a 15–20% premium only when revenue acceleration, cost efficiency, and operational leverage can be clearly demonstrated under scrutiny

  • Geographic arbitrage is re-emerging, with leverage in mature markets stabilizing around 4–5x EBITDA while frontier markets offer steadier deal flow and lower entry multiples at the cost of higher execution and scaling complexity

• Chris von Wedemeyer, founder of search fund investor Legacy Partners, shared a his view on AI tools searchers quietly rely on:

  • High-performing searchers use 5–7 tightly integrated AI tools, not autonomous agents, to compress low-value tasks and reallocate time toward owner conversations and downside underwriting, where judgment still drives outcomes

  • In sourcing, AI materially reduces manual effort but not decision risk as tools like structured web scraping and list enrichment shorten target identification cycles, while contactability and human follow-up remain the primary bottlenecks at scale

  • AI-assisted diligence delivers speed, not certainty, which is why early synthesis of large data rooms (20+ PDFs, contracts, CIMs) cuts initial diligence time meaningfully, but is best used to surface blind spots and stress-test assumptions rather than replace deep human diligence

  • Financial modeling remains Excel-led, with AI accelerating mechanics rather than alpha, so automation improves scenario iteration speed and downside sweeps, freeing attention for survivability analysis instead of formula work

  • The durable advantage is not tool novelty but workflow discipline as AI lowers context-switching costs and increases reps, enabling searchers to run more consistent outreach, faster diligence loops, and higher decision throughput without degrading judgment quality

• Entrepreneurial capital, a search fund investor, published an article on where to find owners likely thinking about selling:

  • Off-market sourcing consistently outperforms public listings because owners often signal intent months earlier, and combining multiple channels materially increases conversion from outreach to real conversations

  • Public marketplaces are most useful as market intelligence tools, where listings lingering 90+ days or showing price cuts correlate with higher seller flexibility on price and structure rather than as primary deal sources

  • Scalable sourcing relies on volume plus enrichment, with tools like Google Maps, LinkedIn, and data platforms enabling hundreds of targets per week, while enrichment workflows convert raw lists into actionable owner contacts

  • Early-intent signals such as long owner tenure of 10+ years, businesses aged 5–10 years, declining headcount, legal events, or supplier order pullbacks significantly increase the likelihood of productive seller dialogue

  • A disciplined weekly cadence of 25–50 highly personalized outreaches across stacked channels compounds over time, producing more qualified conversations without relying on higher email volume

• Buyers & Builders launched a new podcast episode on what 605+ acquisitions teach you about execution:

  • A disciplined roll-up strategy focused on $1–10M enterprise value companies produced unusually strong outcomes: ~5.5x median returns, ~7x average cash-on-cash, ~72% IRR, and no realized exit below 3x across 83 platform investments and 15 exits

  • Capital deployment is deliberately skewed toward add-ons: only $5–25M invested in the initial platform while reserving the majority of capital for follow-on acquisitions, creating downside protection and multiple paths to achieving 3x–7x outcomes even if early execution is imperfect

  • Industry selection is treated as the primary risk filter: fragmented sectors with long-term tailwinds (not operator brilliance) set the return ceiling, reinforcing the principle that even elite teams underperform in structurally declining industries

  • Leadership leverage is systematized: roughly 80% of CEOs are first-time, younger operators, supported by ~7-person boards of veteran industry executives (often semi-retired), dramatically compressing learning curves and aligning incentives through equity that can reach $250k+ per board member per successful deal

  • “Luck” is reframed as repetition at scale: sustained high returns emerged from hundreds of calls, industry touches, and add-on reps, exemplified by an early $6M acquisition sold for ~$50M within three years, which unlocked institutional capital and validated the repeatable system rather than a one-off win

Deal / Launch Announcements

• 🇩🇪 Tobias Röhrl launched trolega, a sector-agnostic search fund (link)

• 🇧🇷 KYR, a search fund investor, launches a second fund while their first fund currently reports an IRR of 39% (link)

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