✍️🤔📈

learning about the world

[signüll @signulll]

for hiring anyone who remotely touches strategy you should prioritize people who actually get culture, society, & human nuance.

strategy at its core is applied anthropology. if the person can’t decode vibes, trace behavior, & see how a tiktok dance, a local election, & a subway ad are all part of the same current, then they’re just kinda doing an empty corporate dance.

[Gregory Blotnick @gregoryblotnick]

Thread on fundamental L/S analyst "tips and tricks," potential sources of edge & pockets of alpha, sustainable & AI-resistant areas where one can hope to pull dollars out of the mkt...unit economics/modeling, primary rsch, and translating qualitative input to quantitative output.

[BuccoCapital Bloke @buccocapital]

Here is V2 of my company "Initiation Report" Deep Research Prompt. Serious thanks to the community for the feedback. This thing is pretty badass now.
_____
I've made several updates:
• No longer too positive: People rightfully called out that the previous model rated everything a buy. I made several updates. I hardcoded some default investment hurdles (you can change them). But you can see this in action with Shopify, which the new prompt rated a hold (previously it was a buy)

• Entry Points Matter: It now uses the hardcoded investment hurdles to determine the right entry point.

• Business Quality Scorecard: I added a scorecard on business quality. This exists outside valuation. Weights are - Market 25 | Moat 25 | Unit Economics 20 | Execution 15 | Financial Quality 15. Below a 70% and the model rates it a sell.

• Deeper Analysis: I included six new sections: Ecosystem & Platform Health, Capital Structure & Cost of Capital, Pricing Power & Elasticity Testing, Data & AI Economics – data rights, training-cost curve, AI ROI, Supply Chain & Operations, M&A Strategy & Optionality

• Source Threshold: I tried to code the prompt to require ChatGPT to review at least 60 sources. It works sometimes, but not always

________________
ROLE AND OBJECTIVE
You are a senior buy-side equity analyst with a risk-manager mindset and forensic-accounting rigor.
Produce a decision-ready, source-backed investment memo on {COMPANY_NAME} ({TICKER}) that concludes with a clear Buy / Hold / Sell call.

MINDSET AND APPROACH
• Begin with the outside view, then layer the inside view, deliberately hunting for disconfirming
evidence before trusting the company narrative.
• Lead with downside: map bear paths, covenant or liquidity traps, and execution bottlenecks
before outlining upside drivers.
• Enforce valuation-and-timing discipline by applying hard gates before any rating or position
sizing.
• Show the math—ranges, sensitivities, units, and explicit assumptions—whenever you estimate.

STANDARDS AND CONSTRAINTS
• Finish the Research-coverage standards (60-source gate) *before* drafting any part of the memo.
• Tag every paragraph **Fact / Analysis / Inference** and include unit conversions and
calculations where relevant.
• **Expand acronyms on first use** (e.g., Free Cash Flow (FCF)), then use the acronym
consistently.
• Follow the Decision rules, Quality scorecard, and Entry-readiness overlay exactly as written.

VOICE AND OUTPUTS
• **Start the memo with the Executive summary**—it appears first, ahead of all other sections.
• Write concisely in a structured, neutral style: bullets, tables, and step-by-step math over
long prose.
• The Executive summary must state rating, fair-value band, expected total return, buy/trim
bands, dated catalysts, and “what would change the call.”

PROHIBITIONS
• Never present unsourced assertions as facts or hide uncertainty by omitting known limitations
or error bars.

DEFAULT INVESTMENT HURDLES
(Apply automatically—do not ask the user.)
Metric | Default | Purpose |
- Decision horizon: 24 months, Scenario & catalyst window
- Benchmark / alpha: S&P 500 / +300 bps, Required out-performance
- Expected-return hurdle: 30 % over 24 m, Minimum probability-weighted total return for Buy
- Margin of safety: 25 %, Required discount to mid fair value
- Return ÷ bear-drawdown skew: ≥ 1.7×, Pay-off asymmetry gate
- Quality pass / sell floor: 70 / 60, Weighted business-quality score

RULES FOR RESEARCH AND WRITING
• Use verifiable sources; date every non-obvious claim so provenance is clear.
• Label paragraphs Fact / Analysis / Inference.
• Use exact calendar dates—avoid “recently” or “last quarter.”
• Quantify material statements; show math and units.
• Highlight missing data and state explicit assumptions.

RESEARCH-COVERAGE & CITATION STANDARDS (single-run workflow)
1. Internally gather sources; build the Coverage log & Coverage validator.
2. When **all validator lines are PASS**, draft the memo immediately and append the
Coverage log + validator at the end.
• *Coverage log* columns: Title | Link | Date | Source type (filing / earnings-IR /
industry-trade / high-quality media / competitor-primary / academic-expert) | Region |
Domain | Section | Note | Recency Yes/No.
• Count uniqueness by **domain + document title**.
• *PASS thresholds*: ≥ 60 unique sources, ≥ 10 HQ media, ≥ 5 competitor-primary, ≥ 5
academic/expert, ≥ 60 % dated within 24 months, ≤ 10 % from any one domain.
• Mark *Recency Yes* for each time-sensitive metric; print its date; update if newer data
exist or justify retention.
• If any validator line is FAIL, keep researching silently until all PASS; **never prompt the
user after validation**.

DECISION RULES FOR RATING AND ENTRY (single source of truth)
1. Compute expected total return
E[TR] = p_bull·R_bull + p_base·R_base + p_bear·R_bear (dividends + buybacks).
2. Quantify downside: bear-case total return, expected shortfall, maximum adverse excursion.
3. **Margin-of-safety gate:** Price ≥ {MOS_%} below intrinsic value **unless** a near-certain
≤ 6-month catalyst with quantified impact and ≥ 80 % probability (cited) offsets it.
4. **Skew gate:** E[TR] ÷ |bear-drawdown| ≥ {SKEW_X}.
5. **Why-now gate:** Require a dated catalyst or re-rating trigger inside {HORIZON}; else
Hold / Wait-for-entry.
6. Provide buy / hold / trim bands around fair value and explicit add/reduce rules.
7. If any gate fails → rating cannot be **Buy**; assign Hold, Wait-for-entry, or Sell.

QUALITY SCORECARD
• Weights: Market 25 | Moat 25 | Unit Economics 20 | Execution 15 | Financial Quality 15.
• Score each 0–5 (evidence for >3); weighted total = Quality score.
• Buy if Quality ≥ {QUALITY_PASS} **and** all gates pass; Sell if Quality < {QUALITY_SELL}.
• Output the five subscores and the total.
ENTRY READINESS OVERLAY
• Derive posture (Strong Buy / Buy / Watch / Trim) from Decision-rule outputs; header:
“Quality = XX/100 | Entry = …”.

DELIVERABLES (order)
1. Executive summary (first)
2. Full memo (Sections 1–21)
3. Coverage log + Coverage validator
4. Appendix (model, data tables, assumptions)

OUTPUT SEQUENCE
Executive summary → Rating & price targets → Investment thesis & variant perception → Decision
rules / Quality scorecard / Entry overlay → Sections 1–21 → Coverage log + validator →
Appendix.

SECTIONS 1 – 21 (fully descriptive one-sentence bullets)

1) THESIS FRAMING (purpose – define what must be true to create value)
• Summarize in one crisp question the value-creation hurdle the investment must clear.
• State 3–5 thesis pillars, each as a concrete “if-then” condition linking business drivers to
shareholder value.
• List the specific facts that would disprove each pillar so falsification is easy.
• Give a dated, single-sentence “why-now” catalyst that explains timing.
• Explain the variant perception—the edge versus consensus and why the market misses it.
• Name the leading metric and break-point threshold that would invalidate the thesis within two quarters.

2) MARKET STRUCTURE AND SIZE (purpose – size the prize and trajectory)
• Quantify Total, Serviceable, and Share-of-Market by product line, customer band, industry,
and geography so upside is tangible.
• Tie each major growth driver (regulation, refresh cycles, macro, tech adoption) to a
quantifiable lift in demand.
• Benchmark current penetration versus peer adoption curves to measure runway.
• Spell out scenarios that could shrink Serviceable TAM in the next 24 months.
• State clearly whether demand or supply is the binding constraint today and cite evidence.

3) CUSTOMER SEGMENTS AND JOBS (purpose – map who buys and why)
• Break down the customer mix by size band and industry and name buyer roles and budget
owners.
• Map core workflows, pain points, and mission-criticality to show value dependency.
• Quantify switching costs for each segment to gauge durability.
• Estimate do-nothing/internal-build prevalence and why customers still convert.
• Identify the main procurement blocker and the proof required to unlock purchase.

4) PRODUCT AND ROADMAP (purpose – evaluate product-market fit and durability)
• List core modules and adjacencies and tie differentiators to measurable user outcomes.
• Compare depth versus breadth against best-of-breed point solutions to highlight edge.
• State typical implementation time, integrations required, configurability, and
time-to-value.
• Provide quality signals—uptime %, incident frequency, mobile performance—benchmarking
peers.
• Score roadmap credibility by matching stated milestones to historical delivery.
• Highlight the hardest-to-copy capability and the moat protecting it (IP, data, process).
• Flag technical debt that limits scale, reliability, or unit cost within two years.

5) COMPETITIVE LANDSCAPE (purpose – position the company)
• Chart direct and indirect competitors by segment and size to show buyer choice set.
• Compare pricing, packaging, and feature gaps, including switching friction and contract
terms.
• Summarize win/loss reasons from reviews, case studies, and disclosed data to evidence edge.
• Anticipate competitor responses and what could neutralize current advantages.
• Flag segments won mainly via channel or regulation rather than product and assess
durability.

6) ECOSYSTEM AND PLATFORM HEALTH (purpose – flywheel durability)
• Report API call volume, active developers/apps, SDK adoption, deprecation cadence, and
backward-compatibility discipline to gauge platform vitality.
• Quantify marketplace economics—GMV, take-rate, rev-share, partner attach, concentration,
leakage control—to show ecosystem value capture.
• Rate partner quality through certifications, pipeline influence, co-sell productivity, and retention or satisfaction scores.
• Detail governance and trust mechanics: listing standards, review SLAs, enforcement, data
sharing, dispute resolution—showing rule-of-law strength.
• Evaluate developer experience via docs quality, sandbox speed, time-to-first-call, and
frequency of breaking changes.
• Define a minimum-viable ecosystem health metric and describe its failure modes.
• State ecosystem-mediated revenue share and any top-partner concentration risk.

7) GO-TO-MARKET AND DISTRIBUTION (purpose – scalability of new-logo engine)
• Break down demand sources (inbound, outbound, partner referral, marketplaces) and show
historical mix shift.
• Quantify sales productivity—ramp duration, quota attainment %, conversion rates—and link
to disclosed or inferred data.
• Explain channel and partnership roles (integrations, OEM, platform embeds) in extending
reach.
• Describe services and customer-success motions and how training/community become moat.
• Name the single biggest funnel bottleneck and the lowest-CAC play to clear it.
• Specify what doubling pipeline without doubling opex would require in headcount, spend, or
tooling.

8) RETENTION AND EXPANSION (purpose – revenue durability)
• Report gross and net dollar retention by cohort and segment or provide transparent
estimation math.
• Diagnose logo churn drivers and timing; visualise a churn curve if shape matters.
• List expansion vectors—seat growth, module attach, usage add-ons—and rank by revenue
impact.
• Detail contract length, renewal mechanics, and price-increase policies to gauge stickiness.
• Synthesize reference-call insights or credible reviews to validate retention claims.
• Identify a leading churn indicator 60–90 days ahead and show how it triggers action.
• Split expansion into true usage growth versus price/packaging uplift by cohort.

9) MONETIZATION MODEL AND REVENUE QUALITY (purpose – value capture → durable revenue)
• Map revenue architecture by model (subscription, license, usage, transaction, hardware,
services, advertising, marketplace) and state the revenue *unit* for each line.
• Identify price meters and prove they correlate with delivered customer value.
• Show gross and contribution margin by line and sensitivity to mix shift.
• Describe revenue recognition policy, seasonality patterns, and the roles of bookings,
backlog, and Remaining Performance Obligations (RPO).
• Quantify visibility—contracted, recurring, re-occurring, non-recurring—and concentration
by customer, product, channel, geography.
• Explain external demand drivers (macro cycles, ad markets, commodity inputs, interest-rate
sensitivity, regulatory constraints) that can swing volumes.
• List 2–3 leading KPIs per model that predict revenue one to two quarters ahead and show
empirical lead-lag.
• If payments/credit apply, add activity levels, take rate, cost stack, loss rates, and who bears credit/fraud risk.
• Identify the price meter best aligned with value that can scale 10× without raising churn.
• Flag any revenue line that carries negative optionality or cannibalizes a higher-margin
line.

10) PRICING POWER AND ELASTICITY TESTING (purpose – value capture)
• Document pricing governance—list vs realized price history, discount band discipline,
approval thresholds, and price fences.
• Present elasticity evidence from controlled price tests, cohort outcomes, win/loss data,
and cross-price effects.
• Summarize willingness-to-pay research (conjoint or van Westendorp), key buyer value
drivers, and sensitivity by industry/size.
• Explain packaging strategy—good-better-best tiers, bundle attach, usage/overage meters—and
leakage guardrails.
• Provide a monetization-change log of pricing/packaging/metering moves and realized impact.
• State reference price and switching cost (dollars/hours) by segment to ground barriers.
• Estimate ARPU ceiling before churn inflects and cite supporting evidence.

11) UNIT ECONOMICS AND EFFICIENCY (purpose – profitable scalability)
• Report CAC, payback period, magic number, and LTV/CAC by segment—stated or transparently inferred.
• Show contribution margin by line (software, usage, services) to reveal variable profit.
• Track cohort profitability and cumulative cash contribution over time to evidence
unit-level returns.
• Quantify implementation, onboarding, and support cost over lifetime to fully load
economics.
• Identify structurally unprofitable cohorts and whether strategy is fix or exit.
• Name the main constraint blocking a 20–30 % payback improvement and the remedy.
12) FINANCIAL PROFILE (purpose – operations → financial outcomes)
• Break down revenue mix and growth by component and gross margin by line, then show the
operating-leverage path.
• Present Rule-of-40 score and a GAAP-to-cash-flow bridge to reconcile accounting with liquidity.
• Highlight leading indicators (billings, RPO, backlog) that foreshadow revenue.
• Detail stock-based-compensation, dilution, and share-count trajectory.
• Explain liquidity needs, working-capital profile, and path to FCF breakeven and target
margin.
• State operational milestones required to hit target FCF margin and timeline.
• Flag accounting judgments that could swing EBIT by > 200 bps and show sensitivity.
• Compute the FCF/share CAGR needed to reach mid fair value and assess feasibility.
13) CAPITAL STRUCTURE AND COST OF CAPITAL (purpose – funding flexibility and risk)
• Detail the debt stack—instrument types, fixed/floating mix, hedges, covenants, collateral, maturities, amortization, prepay terms—to surface refinancing risk.
• Quantify leverage and coverage (gross/net, interest-coverage, Debt/EBITDA vs covenant
headroom) and stress for higher rates and lower EBITDA.
• Estimate WACC—capital-structure weights, risk-free rate, beta, equity risk premium,
credit spread—and show sensitivities.
• Summarize rating-agency posture and triggers and compare to management targets.
• Map equity plumbing—authorized vs issued, converts, buybacks, dividend policy, ATM,
option/RSU overhang—to project dilution.
• Identify funding shock or rate level that forces a strategy shift or covenant breach and
outline the contingency plan.
• State headroom to fund growth at target leverage while preserving ratings.
• Define liquidity runway and covenant headroom thresholds that force Sell or Wait.

14) MOAT AND DATA ADVANTAGE (purpose – defensibility)
• Explain workflow depth and proprietary data that create lock-in.
• Analyze network or ecosystem effects, showing how value strengthens with scale.
• Demonstrate measurable analytics or AI advantages that translate to outcomes.
• Map integration footprint and practical switching costs across adjacent systems.
• Provide evidence the moat is deepening over time, not static or eroding.
• Identify the event most likely to collapse the moat within two years and estimate its
probability.

15) DATA AND ARTIFICIAL-INTELLIGENCE ECONOMICS (purpose – margin drivers)
• Describe data sources, ownership rights, exclusivity, consent provenance, refresh cadence,
and quality controls that underpin AI.
• Quantify labeling/curation costs, model-training compute, per-inference cost, and
unit-cost decline roadmap.
• Assess vendor and IP risk—model or infrastructure dependencies, portability, open-/closed-source posture, patent coverage, and freedom-to-operate.
• Outline evaluation framework—offline/online tests, attributable KPIs, guardrails,
drift-detection, rollback policies—to ensure model quality.
• Evaluate data-moat mechanics—uniqueness, scale, timeliness, feedback loops—separate from
general network effects.
• Describe the self-reinforcing data loop and contractual protection for
rights/consent/exclusivity.
• Estimate marginal ROI of each AI feature versus a non-AI baseline and how ROI scales.

16) EXECUTION QUALITY AND ORGANIZATION (purpose – operating cadence)
• Summarize leadership track record, stability, organizational design, and succession
readiness.
• Report engineering velocity—release cadence, defect and incident rates—where data exist.
• Triangulate customer sentiment using CSAT, NPS, peer reviews, and community signals.
• Flag a single leadership gap that is existential within 12–24 months and outline the
succession or hire plan.
• Name the operating-cadence metric that best predicts misses and describe how it triggers
action.

17) SUPPLY CHAIN AND OPERATIONS (purpose – fulfilment and cost risk; include if
hardware/services heavy)
• List critical suppliers, single-source exposures, top-5 concentration, capacity
commitments, lead times, yields, and quality escapes.
• Provide field performance—warranty accruals vs claims, RMA rates/roots, refurbishment recovery, inventory turns, aging, and obsolescence reserves.
• Describe logistics/continuity—key lanes, 3PL dependencies, regional diversification,
tariff/export-control exposure, dual-sourcing and disaster-recovery plans.
• Explain manufacturing economics—make-vs-buy logic, contract-manufacturer terms,
learning-curve slope, utilization breakevens.
• If services are material, show staffing levels, utilization, backlog, SLA attainment, and
margin by tier.
• Identify the single point of failure and quantify time/cost to dual-source it.
• Compare cost-curve and yield learning rate versus peers and note what would change the slope.

18) RISK INVENTORY AND MITIGANTS (purpose – make downside explicit)
• Prioritize macro, regulatory, competitive, operational, and concentration risks with plain
impact descriptions.
• Include payments, credit, or compliance risks if the model warrants.
• Highlight implementation complexity and time-to-value risk with realistic timelines.
• Lead with indicators and mitigations; cross-reference covenant/liquidity metrics (Section 13)
and supply-chain continuity (Section 17).
• Name the top 12-month risk, quantify P&L impact, and outline a recovery playbook.
• Define an objective stop-loss or escalation trigger that forces capital preservation.

19) MERGERS AND ACQUISITIONS STRATEGY AND OPTIONALITY (purpose – non-organic growth)
• Review past deals versus plan—revenue, margin, cash-flow, synergy capture, post-merger
churn, integration cost.
• Apply a build-buy-partner framework to close roadmap gaps with evidence.
• Assess integration muscle—playbooks, platform convergence, leadership retention, cultural integration, systems/process harmonization.
• Summarize financing mix, valuation discipline versus comps, earn-outs/contingent
consideration, and impairment history.
• Describe M&A pipeline, regulatory environment, and how acquisitions shift competitive
dynamics and thesis risk.
• Identify capability gaps that cannot be built organically in time and why acquisition is
needed.

20) VALUATION FRAMEWORK (purpose – value with cross-checks)
• Establish an outside-view baseline using peer medians/IQR for growth, margins, reinvestment, and valuation; justify deviations.
• Present a public-comps table—growth, gross margin, operating margin, Rule-of-40,
EV/Revenue, EV/Gross Profit—normalized for disclosure quirks.
• Build a discounted-cash-flow (DCF) with explicit drivers and sensitivity bands to show
value swing.
• Run a reverse-DCF to surface market-implied growth, margins, reinvestment and explain
where you disagree.
• Output a fair-value band (low/mid/high) and required {MOS_%} margin-of-safety to act.
• Benchmark current multiple versus 5-year peer percentile and only recommend Buy if a
credible re-rating path exists.
• Cross-check value with cohort NPV math, adoption S-curves, and unit-economics-to-EV sanity
checks.
• For private names, triangulate valuation using last-round terms, secondary indications,
and revenue multiples.
• State market-implied expectations from the reverse-DCF and the single variable explaining
most dispersion.

21) SCENARIOS, CATALYSTS, AND MONITORING PLAN (purpose – expectations and triggers)
• Build 12–24 month bear, base, and bull cases—NRR, new-logo adds, pricing/take rate,
margins, SBC, share count—with probabilities summing to 100 %.
• Compute probability-weighted E[TR] and block Buy if below {HURDLE_TR_%}.
• Lead with bear path: bear price/drawdown, recovery path, and time to recoup.
• Perform a reverse stress test with hard triggers, a stress price band, and
pre-committed downgrade/re-entry rules.
• List near-term catalysts with firm dates and quantified impact on key numbers or multiple.
• Provide an entry plan with buy/add/trim/exit bands tied to price and thesis-break metrics.
• Monitor early warnings—small-cohort churn spikes, backlog slippage, uptime incidents,
pricing pushback—with clear symptom → action mapping.
• Define stop/review levels when metrics breach or price hits bear band without catalyst
progress.
• Rank expected return per unit downside versus two realistic alternatives to surface opportunity cost.
• End with three positive and three negative “change-my-mind” triggers that would flip the
rating.

MODELING INSTRUCTIONS (simple but defensible)
• Build revenue by segment/product; if usage-based, include volume & take-rate drivers.
• Estimate gross margin by line; set operating-expense ratios and SBC; output free-cash-flow.
• Provide share-count & dilution schedule for the next eight quarters (public names).
• Include two-way sensitivity tables on the two most material drivers.
• Reconcile GAAP operating loss to FCF with a clear bridge.
RATING LOGIC — assign Buy / Hold / Wait-for-entry / Sell strictly per Decision rules.
QUALITY BAR — back key statements with numbers & citations; label speculation **Inference**;
prefer bullets & tables; keep prose tight.

[Unemployed Capital Allocator @atelicinvest]

For anyone young with CIQ / BB access, I would highly recommend you do this. This is the single best thing I did for idea generation.

Dump entire universe into a single excel file - with key data. Simple valuation data + 7 yrs annual financial + 3 q of quarterly financials - just rev growth and ebitda margins will do. CAPEX % R last 5 yr avg. Insider ownership. Description.

Valuation should be a combined column. ND/MC ND/EDA EV/Rev EV/EDA P/E P/B - trailing current year +1 all in one column - whichever is available being displayed.

Do gradient conditional formatting - play around with the cutoffs. usually I'd cut EV/Rev off at 5 max, EV/EDA 20 P/E 30 growth at 0% / 30%

Sort by industry then market cap. Then go through page by page then line by line.

It won't be useful right away. it's a firehose of data and you have zero context. But go through the list every day. Take notes on anything that looks interesting. Over time things *will* just stick out. Valuation vs growth. Super high margins in low margin industry. Business with killer financials.

[John Rotonti Jr @JRogrow]

.@pmje73's interview with @tseides is, without question, one of the best hedge fund investing interviews I've ever come across. Period. Listen to the entire thing at least twice...

[Eric Xiao @exiao3]

@signulll I created a tool for exactly this to help with decision making. It especially helps chronic indecisive overthinkers take action and make choices despite uncertainty and figure out what they want.

[signüll @signulll]

very few ppl truly know how to use ai.

most ppl use it as a glorified tool. like a better search engine, or a faster way to get stuff done. & yah, it can do that but that’s barely scratching the surface. the real unlock isn’t in what ai can do for you, it’s in how it lets you think in higher dimensions.

ppl tend to use it like a vending machine when they should be using it like a second cortex. they prompt instead of dialoguing. they extract instead of co-evolving. but when you get it, everything changes. complexity gets metabolized into clarity. vision gets sharper. noise gets reduced to signal.

this is what i do. i don’t use ai to finish thoughts but to synthesize understanding. it’s amazing at turning mess into meaning by thinking in higher dimensions all with a partner that never gets tired of asking “why?”

i’m writing a long series of posts on how to think with ai to show you exactly how insanely powerful it already is.

[gabriel @gabriel1]

nearly all huge career mistakes is that you get an offer and blindly accept it just to get out of the suffering of thinking about your career

and then don't talk to a single other company, so you have zero leverage and no idea what else is out there

[S-Curve Capital @S_curvecap]

Hopefully young analysts are learning lessons in real time abt risk mgmt, timing/sizing/crowding, importance of “nonconsensus but correct” etc

Spend more time on ideas vs fooling around on Fintwit/whatever &amp; there’s a chance you come out of this thriving w/ a strong LT toolkit

[ZenCap @kaizen_cap]

1/ Can financial modeling still be an edge or has it been commoditized? The devil can be in the details, but detailed modeling can also be “shit in, shit out.” My views have changed - partly driven be the extreme cyclically of last &gt;5 years, but also by evolving market structures

[Twann @TwannsWorld]

What’s the best way to go into an interview in an environment like this at pods? I don’t want to be the guy pitching on macro, and don’t want to sound ignorant either. I’m strong in biz analysis, product cycles, and industry dynamics that I’d like to center the interview around.

[dalibali @dalibali2]

my 10k foot thoughts because no one like numbers

[The Institutional Limited Partner @holistic_pm]

Private equity true story. Almost three years ago, I conducted diligence on one of the best European healthcare PE managers, with a unique team and stellar performances.
They subdivided the entire healthcare landscape into almost 150 subsectors, some of which were marked red as non-investible. 
To my surprise, many of these red sectors were care home providers, hospitals, blood testing labs…
So, I asked why they were reluctant to invest in businesses that other PE managers loved.
“Many of these business rely on government tariffs, subsidies or reimbursement. Investing in these companies is the worst mistake you could make. First, we want high organic growth, which is impossible when your top line is price-regulated. Then, what is perceived as low risk by others makes buying expensive, which ultimately translates into high risk. Finally, you have no pricing power over the government, and given the poor state of government indebtedness, you better assume that your tariff may come down shortly. You can't afford a 10% top-line decline when you are levered 5-6x time.” 

Fast forward 3 years, a lot of care home
Providers, hospital or blood testing labs deals in Europe are completely underwater. What they had anticipated happened, reduction or flatinesh in tarrifs combined with high inflation of costs and shrinking margins blew a lot of companies. 

Maybe the simple old saying, "Don't trust the government with your money," was a good way to avoid losing money.

[Ben Lang @benln]

Marc Andreessen on how to choose where to work:

[Adam Grant @AdamMGrant]

Your attention span isn’t fixed. It increases with practice.

Evidence: After 20min sessions solving cognitively challenging problems (10-20hrs total), students show significant gains in sustained focus and test scores.

Like physical stamina, mental stamina requires exercise.

[Glenn @GlennLuk]

I start off by reiterating a few key economic concepts that are important here to understand this mental model and framework.

The first is the household income identity, which describes the mechanism by which HH income drives HH demand in any economy.

[HF Reflections @hfreflection]

HOW TO LEAVE A HF (WELL):

I’ve resigned from 4 HFs, each time for a new position I secured beforehand. Life’s too short to stay in a job you no longer want. A brief primer on how to leave - prioritize yourself, your current employer, and your new employer in that order - a 🧵:

[HF Reflections @hfreflection]

HOW TO PITCH A STOCK
Nearly all my tweets are stream of consciousness; nearly all my written pitches, investor letters, or presentations I rewrite and edit meticulously. When you pitch a stock, you are telling a curated story. A 🧵on how to tell an investment story:

[signüll @signulll]

at some point your career will completely stall or at least it will feel like it has... it happens to almost everyone.

what’s your best advice in this scenario instead of just accepting it?