Pricing & Value Capture
3 tier-5 · 1 tier-4
CJ's most cited work argues that pricing is the highest-leverage lever a tech company has, and that the AI era is forcing a wholesale rethink of how value gets captured. The throughline: price to the value you create for the customer (and your certainty of delivering it), not to your cost or to seat counts — outcome-based pricing for AI that does the work, value-based capture rates calibrated to certainty/size/speed, and multi-year contracts understood for what they actually buy.
TIER 5
Mar 3, 2026
A detailed case study of how Intercom bet the company on its Fin AI agent priced at 99 cents per resolution, the first outcome-based AI pricing in market, reversing five quarters of declining net new ARR and lifting new-customer NRR from 112% to 146%. CJ unpacks the P&L mechanics with CFO Dan Griggs: token-cost unit economics, multi-model selection, resolution rate as the core margin lever, deliberate underpricing for adoption, and why cannibalization fear was overblown. A landmark reference for pricing AI that does the work rather than helps humans do it.
outcome-based pricingAI monetizationNRRunit economicsSaaS pricing
TIER 5
Nov 18, 2025
Presents pricing expert Michael Stanisz's framework for value-based pricing—how much of delivered value a vendor can capture depends on certainty of value (will the buyer actually capture it?), size/meaningfulness of the value to that customer, and speed/timing (NPV) of realization. Concrete calibration: high-certainty, large, fast value lets you capture 70-80%; low-certainty, long-horizon, hard-to-measure value drops you to 20-30%, with most B2B SaaS landing 10-30%; cost avoidance is easier to validate than revenue gain (attribution problems), but both need baseline pressure-testing. A landmark, reusable value-capture framework central to the AI-pricing debate.
pricingvalue-based-pricingvalue-capturecost-avoidanceROI
TIER 5
Dec 16, 2025
A data-rich analysis (15,000+ contracts, 2,600+ suppliers via Tropic) busting the myth that multi-year SaaS commitments earn proportional discounts: the multi-year premium is only ~2-3pp, it functions as the 'price of admission' to discounting rather than a bigger discount, volume beats term, and the real win comes from starting single-year then upgrading at renewal (+2.5pp) while staying multi-year forever erodes discounts (-1.3pp). Includes named vendor lists (who rewards vs. penalizes the upgrade path), uptick-avoidance value, and an 'AI Tax' prediction for 2026. A landmark, immediately actionable procurement/pricing reference.
procurementSaaS-pricingmulti-year-dealsdiscountingnegotiation
TIER 4
Jan 20, 2026
A framework, via former Akamai CISO Andy Ellis, distinguishing actuarial risk (random, within predictable distributions, insurable) from behavioral risk (driven by intelligent adversaries who respond to incentives, where dollar-quantified probabilities are false precision). Illustrated by ransom-insurance pricing floors, the cobra-bounty effect, and the Getty kidnapping, it argues CFOs and CISOs misapply actuarial math to cybersecurity and should instead use scenario-based prioritization (impact severity x surprise level) and robust response over fake precision.
risk quantificationcybersecuritybehavioral riskincentivesCISO
SaaS Metrics, Valuation & Profitability
2 tier-5 · 2 tier-4
CJ's metrics-purist core: how to compute the numbers that drive enterprise value honestly, and what they really say once you stop gaming them. The cluster takes the most contested core metrics—Rule of 40, EBITDA, stock-based comp/dilution, revenue multiples—and shows that the 'right' answer depends on the business's plumbing, with a recurring insistence on the 'least worst' portable baseline over false precision.
TIER 5
Feb 15, 2026
A documentary-style origin story of EBITDA, from John Malone's debt-and-depreciation cable playbook at TCI through its spread to private equity, investment banking, software, and now capital-intensive AI infrastructure financed by GPU-backed loans. It balances the case for EBITDA as the 'least worst' portable cross-industry metric against Munger and Buffett's critique that depreciation is a real expense and free cash flow is what ultimately matters. A landmark explainer with durable reference value on the most contested non-GAAP metric.
EBITDAJohn Maloneleveraged financefree cash flowfinancial metrics
TIER 5
Dec 7, 2025
Runs all 148 tracked companies through Rule of 40 under EBITDA, FCF, and Adjusted EBITDA lenses to show there is no single 'right' profitability input—each rewards different financial structures (EBITDA flatters capex-heavy CoreWeave; FCF flatters high-SBC SaaS like Snowflake; LendingClub/SoFi tank on FCF because their balance sheet is the business). The honest conclusion: companies that fail under both EBITDA and FCF are in genuine 'growth purgatory,' and Adjusted EBITDA, while gameable, is the 'least worst,' most portable baseline for ranking companies with different plumbing. A definitive, reference-grade treatment of a contested core metric.
rule-of-40EBITDAfree-cash-flowSaaS-metricsvaluation
TIER 4
Dec 14, 2025
Reframes the broken stock-based-comp discourse by separating SBC expense (a comp-philosophy signal, median ~12% of revenue) from actual shareholder dilution (median <2%), showing that dilution is largely a function of growth rate via the multiple: high growth → high multiple → lower percentage dilution per SBC dollar. The actionable rule: when value transferred to employees (SBC as % of market cap) exceeds value added (revenue growth), you're on a path to value destruction—so the companies killed by SBC are the slow-growers, not the big spenders. Substantive, well-quantified analysis with a clear takeaway, plus the benchmark appendix.
stock-based-compdilutionvaluationequitybenchmarks
TIER 4
Feb 1, 2026
A contrarian rebuttal to the 'SaaS is dead' narrative as median EV/NTM revenue multiples slid to a 3-handle ten-year low. CJ argues the bear case requires believing three things at once that the data does not support: retention rates blowing up (they haven't, ServiceNow's renewal even rose to 98%), AI being delivered through something other than SaaS (it isn't, every AI vendor sells subscriptions), and dataless startups outcompeting incumbents who own the data moats. His operator's read: investors gave up because it's hard, creating a lazy discount.
SaaS valuationrevenue multiplesretentionAI disruptiondata moats
IPOs, Exits & Capital Markets
1 tier-5 · 6 tier-4
The deepest cluster in the archive: a near-complete operator's curriculum on going public and getting liquid. CJ walks the full IPO gauntlet (bakeoff to day-one trading), the banking economics underneath it, why pricing is structurally hard, the capital-roadmap discipline that lets companies survive crashes, and the messy exits and alternative liquidity paths (the $150M–$300M 'awkward' zone, tokenization, prediction markets) that sit alongside the traditional IPO. Anchored by a reference-grade Wealthfront S-1 teardown.
TIER 5
Oct 2, 2025
A deep S-1 teardown of Wealthfront's IPO: small revenue ($339M LTM, 26% growth) but extreme efficiency (90% gross margin, 45% adj. EBITDA, ~$1M revenue per employee, 120% NDR, Rule of 40 at 71%), with the key insight that cash management (~60 bps) not advisory fees (~22 bps) drives the ~38 bps blended take rate—making it a rate-sensitive digital private bank, not a robo-advisor. It pencils a $2.5–$3B valuation (floored by the dead 2022 UBS $1.4B deal), offers an 8-tier framework ranking fintech revenue quality (SaaS > payments > AUM > trading > interchange > float > insurance > lending), and lays out seven substantive red flags including rate dependence, slowing growth, and a narrow high-earner TAM.
IPO-S1fintechwealthfrontunit-economicstake-rate
TIER 4
Oct 12, 2025
A guest deep dive by analyst Collin Cook, sparked by ICE's $2B Polymarket investment and Kalshi's $300M raise, tracing the proliferation of trading platforms from the 1792 NYSE through zero-commission Robinhood to crypto and prediction markets, with the surge in retail volume and prediction-market weekly volumes. It then explains private-company share tokenization (Robinhood's OpenAI/SpaceX SPV-backed derivative tokens), how it works, and why it may not change IPO calculus but could eventually become an alternative listing path. A substantive, well-sourced primer on a fast-moving corner of markets.
prediction marketstokenizationtrading platformsTradFi/DeFiprivate market liquidity
TIER 4
Oct 30, 2025
Using Navan's CFO and President fresh off their roadshow, CJ walks the full six-month IPO gauntlet (bakeoff, prep, public filing, roadshow, pricing/allocations, day-1 trading) and goes deep on how the order book builds, how oversubscription drives price-range raises (69% of recent SaaS IPOs raised, median 16%), and the trade-off between long-term holders and liquidity in allocations. Rich operator color (36 meetings, the killer AI margin slide, the live Navan-vs-competitor pricing demo) makes it a strong practical primer on going public.
IPO processroadshoworder bookNavanCFO operations
TIER 4
Nov 16, 2025
Drawing on ex-Goldman/hedge-fund/advisor Jeff Bernstein, CJ deconstructs why 2025 day-one IPO pops are so erratic: a supply-starved market creates pent-up demand, bull-market psychology de-risks buying, and underwriters must price to a peer-relative 'seasoned valuation' rather than the day-one spike, all amplified by predictive trading algorithms. The core insight is that everyone in the IPO (company, banks, investors, employees) plays a different game with misaligned incentives, so once trading opens no one really controls price discovery. A genuinely useful explainer on a frequently-misunderstood topic, paired with the weekly benchmark roundup.
IPO pricingcapital marketsunderwritinginvestor incentivesSaaS benchmarks
TIER 4
Nov 30, 2025
Dissects the structurally misaligned 'grey zone' exit ($150M-$300M) where founders win generational wealth while late-stage investors who underwrote a $1B+ outcome take a haircut, producing slow consent emails and false binaries. The operator-side prescription: time your rounds to exit before raising again if possible, always take secondaries off the table when offered, and understand your investors' portfolio math before taking their money—plus a broader point that the venture model imposes an 'innovation tax' that kills good-but-not-legendary companies. Thoughtful, candid analysis of an under-discussed dynamic, with the benchmark appendix.
M&Aventure-capitalexitsfounder-economicssecondaries
TIER 4
Jan 4, 2026
Explains IPO underwriting economics: the 'lead left' banker role, 4-5% (or 7-8% for sub-$500M) aggregate fees, the syndicate split (management/underwriting/selling concession), and the shift to joint-lead models, worked through Reddit, Figma, and Navan examples. It also covers the years-long courting, share-of-wallet horse-trading, and the looming OpenAI/Anthropic/SpaceX fee bonanza. A solid, concrete explainer of a frequently-misunderstood process, with the recurring benchmark appendix.
IPOinvestment-bankingunderwriting-feescapital-marketsbenchmarks
TIER 4
Feb 22, 2026
Using anticipated SpaceX and OpenAI mega-IPOs as the hook, CJ interviews Rivian CFO Claire McDonough on how a pre-revenue company raised ~$14B in a single day by building a high-quality book of demand off a pre-committed base. The lasting lesson is the capital roadmap and 'raise off the front foot' discipline: oversize when demand is there because by the time you need the capital it may not be available, the move that let Rivian survive an 80% stock decline.
IPOcapital raisingRiviancapital roadmapCFO interview
Marketplaces & Platform Economics
1 tier-5 · 3 tier-4
CJ writes about two-sided platforms with the authority of a founder who failed at one (Bubba Booking), and this cluster is his most original body of strategy work. The throughline: supply is an eroding moat while mindshare and cash-flow leverage win, take rate is a function of frequency × ticket × labor intensity, and the durable plays are layering a single-player 'plus' tool on top of the network and weaponizing payment terms.
TIER 5
Mar 8, 2026
A sweeping reference essay tracing marketplaces from Mesopotamian bazaars to Uber, anchored by an original take-rate formula (take rate = f(purchase frequency x ticket size x platform labor intensity)) and Bill Gurley's 'rake too far' thesis. It distills durable operating principles: supply is an eroding moat while mindshare wins, never take on inventory or you become a retailer, beware disintermediation, and layer a 'marketplace plus' SaaS/services hook. Lasting reference value for anyone building, investing in, or analyzing two-sided platforms.
marketplacestake ratenetwork effectsunit economicsplatform strategy
TIER 4
Nov 25, 2025
Explains the 'marketplace plus' strategy—giving one side (usually supply) a single-player SaaS/scheduling tool as a hook to professionalize inventory and corner the network, overcoming the cold-start problem and lifting ARPU and retention. Worked through OpenTable, Outdoorsy, Turo, and PartsTech (where the add-on tripled ARPU), then generalized into a universal lesson: solve adjacent problems before/during/after the core transaction (Brex community, Snyk free DB, Shopify, Home Depot classes). A clear, useful business-model framework with broad applicability.
marketplacesbusiness-modelsARPUretentioncold-start
TIER 4
Nov 13, 2025
A detailed S-1 teardown of Asia-focused experiences marketplace Klook ($3.04B GTV, $540M revenue, first positive adj. EBITDA), framed by CJ's own failed experiences startup, Bubba Booking, which gives the unit-economics analysis real weight. He explains why Klook reports gross-profit/GTV (11.2%) rather than revenue/GTV as the honest take-rate measure for a hybrid 1P/3P model, walks the competitive moat, risks (APAC concentration, discretionary travel, ADR+IFRS complexity), and a $2.5-3.5B valuation estimate. A strong, concrete marketplace case study.
IPO analysisS-1 breakdownmarketplace economicstake ratetravel/experiences
TIER 4
Oct 7, 2025
A sharp essay arguing 'your cash flow model is your business model,' told through Booking.com's agency model versus Expedia's float-rich merchant model: Booking won supply-side scale by getting out of the way on payment timing, then selectively reintroduced merchant flows once it had leverage. Distills operator lessons on using payment terms to attract supply, treating the cash conversion cycle as competitive strategy, and exploiting cost-of-capital asymmetries. A clear, transferable strategic-finance lesson with a memorable central case.
payment termscash flowmarketplace strategyfloatBooking.com
Go-to-Market, Org Design & People
1 tier-5 · 1 tier-4
How AI and resource discipline are rewriting org charts and hiring. CJ's flagship piece here argues the classic SaaS GTM org is collapsing into a forward-deployed-engineer motion, with the financial consequences (CAC, metrics, quotas) that follow; the companion piece warns against the 'FrankenRole' instinct to fuse jobs to save money, casting finance as the org's general manager of headcount allocation.
TIER 5
Jan 13, 2026
Argues that agentic AI is collapsing the classic SaaS go-to-market org (SDR/AE/SE/CSM ratios) into a 'forward-deployed engineer' (FDE) motion where companies embed R&D engineers into accounts before a contract is signed, selling proven outcomes and trust rather than capabilities. It reframes the financial consequences: CAC must be timeboxed via solution sprints, traditional SaaS metrics lag or vanish, and new measures like ARR-per-FDE and use-case-expansion ('momentum') matter more, with revenue treated as a portfolio of bets rather than a funnel. A landmark, original synthesis (drawing on Invisible, Palantir, Bonfire) of how 'hiring software' rather than 'buying software' rewrites org design, quotas, pricing, and efficiency metrics.
go-to-marketforward-deployed-engineersAISaaS-metricspricing
TIER 4
Jan 11, 2026
Uses Samsara's CFO framing of selling into 'non-discretionary operations budgets' (vs. discretionary IT/seat budgets) to teach how precisely describing the budget holder shapes a company's financial story and valuation. The thesis: make the budget holder the hero, locate a large pool of must-spend money with quantifiable ROI, and you can ride a sector's tailwinds while insulating from SaaS-specific risks. A useful financial-storytelling playbook for younger companies, paired with the standard weekly benchmark appendix.
financial-storytellingbudget-holderSamsarapositioningbenchmarks
AI in Finance — Hype, Reality & Economics
0 tier-5 · 5 tier-4
CJ's anti-hype, pro-deployment stance on AI in the finance function and the broader AI-economics debate. The cluster pairs a buyer's-guide diptych (when AI actually works vs. when it's overkill) with provocations on what AI does to the fundamentals: whether it really lowers R&D, why low gross margins might signal real usage, and whether even OpenAI can reach escape-velocity revenue. The consistent posture: deterministic-first, certainty-grounded, and skeptical of agent-washing.
TIER 4
Oct 21, 2025
A contrarian buyer's guide arguing most finance AI pitches are overkill, anchored by one question: 'Can I accomplish this with a deterministic tool or simple workflow?' If yes, skip AI. Catalogs bad use cases (high-judgment accounting like goodwill impairment, problems already solved by your ERP, strategic/relationship work, processes with no clean data foundation) and a sharp definition of what an 'agent' actually is versus rebranded chatbots. A practical anti-hype framework operators can use before vendor calls.
AI hypefinance toolingvendor evaluationdeterministic vs AICFO judgment
TIER 4
Oct 28, 2025
The positive companion to the 'when not to use AI' piece, laying out a SIMPLE/MODERATE/EXTREME spectrum where the 'agentic sweet spot' is the messy middle (too dynamic for RPA, too structured to leave manual) such as collections, pipeline forecasting, procurement, and close orchestration. Provides a concrete 5-point 'agent-ready' checklist, real deployment examples (Tropic, Numeric, Brex+Navan), and the vendor-vetting questions to ask. A genuinely useful operator framework for evaluating finance AI.
agentic AIfinance automationvendor evaluationdecision frameworkprocurement/close
TIER 4
Nov 9, 2025
Built around a16z partner Sarah Wang's counterintuitive claim that sky-high (85-90%) gross margins can be an 'orange flag' signaling little real AI usage, while the best AI companies start with lower margins they can later expand via model-switching, pricing power, and platform layering. CJ argues this breaks traditional SaaS benchmarking (high-margin 'big arms', heavy OpEx 'skinny legs' inverts for lean AI-native firms) and proposes 'momentum' (feedback velocity, iteration speed, capital-allocation agility) as the new currency of enterprise value. A thought-provoking reframe of a core SaaS metric for the AI era.
gross marginAI economicsSaaS benchmarksunit economicsa16z
TIER 4
Nov 23, 2025
A back-of-envelope thought experiment stress-testing whether OpenAI could reach $500B revenue in five years: subscriptions max out near $100B even at 800M users at $10/mo (the largest B2C sub business ever), so it would also need to become a top-three advertiser AND take a transaction vig on commerce—'triple dipping' subscription + ad + payment per user. The sober takeaway: getting halfway is plausible, but eclipsing Apple in under a decade is borderline delusional, and success hinges on a deliberate layer-cake sequencing where focus is the scarcest resource. Engaging, numerate analysis of a topical question, with the benchmark appendix.
OpenAIAI-economicsmonetizationadvertisingsubscriptions
TIER 4
Mar 1, 2026
A mailbag-prompted reflection arguing that AI is unlikely to durably lower R&D as a share of revenue. Three assumptions break down: LLM token costs are not a clean swap for cheaper tooling, the best engineers get paid more (fewer heads at higher cost), and Jevons-paradox competition means any savings get reinvested. The provocative conclusion: cheaper-to-build makes it more expensive to compete, so the ante goes up and aggregate spend may rise even as headcount falls.
R&D spendAI economicsengineering costsJevons paradoxoperating leverage
Customer Economics & Retention
0 tier-5 · 3 tier-4
The P&L truth about who your customers are and how to keep the right ones. CJ attacks the assumption that all revenue is good revenue (low-value accounts can be net-negative), surveys how Customer Success is actually staffed and (mis)paid, and shows—via Grindr—how episodic consumer products break the habit-forming-SaaS retention playbook entirely.
TIER 4
Feb 10, 2026
Grindr CFO Vanna Krantz explains why an episodic, in-market consumer product cannot be modeled like habit-forming SaaS: cohorts never flatten, users cycle out and return in predictable bursts, and the right metric is yearly (not monthly) active users. The standout insight is that a cheaper weekly plan raised ARPU rather than cannibalizing the monthly tier, because matching pricing to short windows of intense need converts users multiple times, with travel and seasonality as reliable reactivation triggers. Reframes retention vs attention models for B2C finance leaders.
reactivationconsumer subscriptionforecastingARPUpricing
TIER 4
Jan 25, 2026
An original survey of 132 tech companies on how Customer Success is staffed, structured, and paid, finding near-universal lack of confidence and three-way splits on reporting line, P&L placement, and staffing basis. The core finding is that incentives don't drive retention (product quality and customer fit do), and the central dysfunction is paying CS on expansion/NDR it doesn't control, with 56% comped on expansion but expansion owned elsewhere 63% of the time. Recommends deciding whether CS is a cost center or revenue role and aligning comp and P&L honestly.
customer successbenchmarkscompensationNDRincentive design
TIER 4
Feb 17, 2026
A concrete framework for spotting the P&L black hole where low-value customers consume disproportionate CS resources and have the worst NPS, illustrated by Cassie Young's Sailthru example where the sub-$100K segment was 13% of ARR but 40% of CSM time. The remedy is to make needy customers self-select via a tiered option set (pay for a CSM, accept basic support, or go month-to-month) rather than a blunt firing, with tone and off-ramp quality emphasized to protect the customers you want to keep.
customer segmentationCS economicschurnICPretention
Finance vs. Marketing — The Spend Debate
0 tier-5 · 2 tier-4
CJ's defense of marketing against the reflexive finance instinct to cut it. Both pieces argue that marketing compounds like a flywheel rather than scaling linearly like sales capacity, so the easy-to-cut 'program' bucket is exactly the wrong target—and reframe big brand bets (the Super Bowl ad) as portfolio resource-allocation decisions rather than binary ROI questions.
TIER 4
Feb 8, 2026
CJ reframes the perennial CFO-no-CMO-yes Super Bowl debate as a portfolio-theory resource-allocation bet rather than a binary ROI question. Using Ro's ~$20M fully-loaded cost breakdown (including the hidden follow-on media commitment), he argues the downside is capped at a single-digit hit to annual marketing efficiency while the asymmetric upside is a small sustained efficiency lift across the whole budget plus measurable direct acquisition. Best for B2B firms with a consumer entry point, ample scale, and a story worth telling to 100M people.
brand marketingmarketing ROIportfolio theoryCACresource allocation
TIER 4
Dec 9, 2025
Argues that 'program' marketing spend (ads, events, sponsorships)—the easiest-to-cut, low-immediate-impact bucket—is exactly what finance teams wrongly attack first, because marketing behaves like a compounding flywheel rather than a linear sales-capacity model, so cuts cause non-linear damage 3-9 months out and a multi-year recovery hole. Using the People/Systems/Programs breakdown and a sports-GM analogy (the Dallas Mavericks trading Luka), it frames finance leaders as org GMs who must trust the process rather than mortgage future pipeline for this quarter's EBITDA. A useful, persuasive finance-vs-marketing piece.
marketing-spendfinance-vs-marketingbudget-cutsflywheelCFO
FP&A, Forecasting & Investor Communication
0 tier-5 · 4 tier-4
The craft of forecasting and the craft of communicating the numbers. CJ pairs reusable FP&A frameworks—how to diagnose why a forecast missed, how much modeling rigor a company's data maturity actually warrants, and why early-stage models should be 'cowboy forecasts' rather than false-precision spreadsheets—with the public-market storytelling game: the disciplined art of beat-and-raise, and the supporting-communicator skillset of the number-two executive.
TIER 4
Oct 14, 2025
Via ex-Reverb finance leader Kevin Drost, this argues that early-stage forecasting should be 'cowboy forecasting': a single-page, real-time-playable model used as a goal-setting hypothesis, not a precise prediction, because false rigor on thin data is a trap. It maps how forecasting sophistication should scale with data availability (scrappy back-of-envelope early, street-grade rigor at public-company stage), uses the 80/20 principle, and shows TAM-by-analog estimation. A memorable, well-argued framework on matching modeling effort to maturity.
forecastingfinancial modelingstartup finance80/20 principleTAM estimation
TIER 4
Oct 5, 2025
Built on the Sergey Bubka anecdote—the Soviet pole vaulter who broke his own world record 36 times by the smallest possible margin to keep collecting state bonuses—as a metaphor for the public-market “beat-and-raise” earnings game, sourced from a CFO interview with Zeta Global's Chris Greiner (15 straight beat-and-raise quarters). The thesis: exceed expectations but not by so much that you set an unsustainable bar, playing both the finite (collect credit now) and infinite (keep the right to play) games. Memorable, sticky framing, though the bulk of the issue is the recurring weekly SaaS multiples/benchmark methodology boilerplate.
beat-and-raiseearnings-guidanceCFOSaaS-benchmarksinvestor-relations
TIER 4
Dec 23, 2025
Shares Notion CFO Rama Katkar's three-part framework for diagnosing forecast misses—(1) fat-finger model error, (2) incorrect assumption, (3) deliberate change in how the business is run—designed to be blameless and to locate where intuition/data needs strengthening. The key reframe is that the real question isn't whether the model was wrong (it always is) but 'would I make the same decision next time?', and that forecasting accuracy comes mostly from 'time on the board.' A genuinely useful, reusable FP&A framework.
forecastingFP&Avariance-analysisNotionframeworks
TIER 4
Jan 27, 2026
Drawing on Pega's Ken Stillwell, CJ lays out the communication craft of the CFO/COO as supporting communicator to the CEO: the listening advantage from lower cognitive load, drafting off the CEO in the room and debriefing after, and decoding the 'question behind the question' by playing it back for confirmation. He also catalogs the failure modes of sitting too close to the sun, monopolizing Q&A, over-correcting publicly, and committing before the CEO does. A useful soft-skills explainer for senior operators.
executive communicationCFO leadershipchief of staffinvestor relationssoft skills
Risk, Insurance & Winding Down
0 tier-5 · 2 tier-4
The defensive, hard-to-find-elsewhere reference content for protecting and (when necessary) closing a company. CJ catalogs the tech-company insurance stack as a hierarchy of needs and provides a tactical, lived-experience guide to the off-ramps and obligations of shutting a company down cleanly.
TIER 4
Dec 2, 2025
A practical buyer's guide to tech-company insurance structured as a 'Maslow's hierarchy': Survival (D&O, with EPLI add-on), Revenue Protection (Tech E&O + Cyber), and Operational (general liability/BOP), with current dollar pricing estimates by stage and the key mechanics (defense costs erode and don't reset against your limit; sector multipliers for AI/fintech/healthtech). Concrete, hard-to-find reference content for operators (~$40-60K/yr for a typical Series B), though it's an explainer rather than original analysis.
business-insuranceD&Ocyber-insurancerisk-managementoperations
TIER 4
Feb 24, 2026
A tactical, lived-experience guide to winding down a startup, covering the three off-ramps (Hail Mary raise, asset sale, real shutdown), the board/shareholder vote and liquidation-preference waterfall, and the counterintuitive truth that you need money to stop spending money. It surfaces blindspots most guides miss: the WARN Act, 401(k) termination, D&O tail policies, Delaware's non-prorated franchise tax, and the reputational value of a clean wind-down for raising again. Useful operator reference for a situation most CFOs hit at least once.
shutdownwind-downbankruptcyliquidation waterfallfounder obligations