# Todd Gardner

Todd Gardner writes about SaaS company finance, investing, and valuation, including financial reporting, private credit, and how AI could affect software businesses.

- Profile: https://b2binfluencer.marketing/creators/todd-gardner/
- LinkedIn: https://www.linkedin.com/in/toddgardner1
- LinkedIn followers: 22,847
- Topics: artificial-intelligence, unit-economics
- Based on 11 dated posts, latest 2026-09-15

## Summary

Todd Gardner is most useful for practical SaaS finance, reporting, and valuation readiness advice before fundraising, a sale, or lender diligence.

- Best for: A B2B SaaS company preparing for fundraising, a sale, or lender diligence.
- Not for: A consumer lifestyle brand seeking broad-reach product demos for general consumers.
- Check first: Confirm availability, terms, deliverables, and audience data on SaaS finance leaders.

### Takeaways

- Accounting integrity, not sophisticated metrics, determines valuation and fundability
- Capitalize implementation and onboarding costs when growth distorts gross margin
- Run the buyer's valuation model first

## Main ideas

- **Accounting integrity, not sophisticated metrics, determines valuation and fundability**: Gardner lists five problems found across more than a thousand SaaS financial statements: poorly defined gross margin, weak bank reconciliations, income statements that do not connect to the balance sheet and cash flow statement, a mix of cash and accrual accounting, and a stand-alone MRR report that never reconciles to revenue. The argument: value and the ability to raise capital depend on whether the books hold together, not on advanced reporting. An AI product's cloud costs must be split between production support and R&D; overstated cost of goods sold lowers valuation and the odds of raising capital. A separate MRR report is acceptable only if it reconciles to revenue.
- **Capitalize implementation and onboarding costs when growth distorts gross margin**: Gardner says they hate capitalizing costs, then argues for doing it in one specific case: implementation and onboarding. Their reasoning runs from GAAP treatment of costs required to satisfy the promised service to the distortion caused by growth. These costs behave more like sales commissions than ongoing hosting; in modern SaaS, much of the expense comes from infrastructure such as initial data loads, dedicated environments, and pre-purchased cloud capacity. If left uncapitalized, rapid customer growth pushes reported gross margin below its long-term average and distorts unit economics such as the CAC ratio and the ratio of lifetime value to CAC. The approach has two caveats: present a cash flow statement alongside the profit and loss statement, because this treatment understates cash burn, and use it only when growth is depressing margins—not to temporarily improve the numbers of a low-growth business.
- **Run the buyer's valuation model first**: Gardner cites data on more than 2,000 companies showing that 35% of SaaS companies shrank in 2025, then asks what a shrinking SaaS company is worth in 2026. The buyer will run a discounted cash flow exercise, so the seller should run it first. The framework: 36 months of a SaaS profit and loss statement, with months across columns; a terminal value based on the run-rate operating profit in month 36, not the full third year; conservative retention; and little or no assumed new sales. Gardner suggests a minimum 20% discount rate for businesses above $10 million in ARR and 25% below, arguing that the rate should reflect the risk in the assumptions rather than the overall risk of the business. Retention has the greatest effect on the outcome; gross margin also matters in a revenue multiple.
- **SaaS private-credit stress is a confidence and maturity problem, not mainly a credit-quality problem**: Gardner writes that they helped start the private credit push into SaaS and sees current stress as a loss of confidence combined with an asset-liability mismatch. The Silicon Valley Bank comparison makes the case: its loans more than covered deposits, but withdrawals came faster than the portfolio could be liquidated, and the bank failed anyway. Expected losses come from two sources: lending too much relative to ARR, such as debt above roughly one times ARR, and specific companies facing an immediate AI threat. The base case is that most SaaS customers are unlikely to churn soon and a shrinking SaaS company can still service its debt even when its equity is worth little. Exposure also depends on the instrument: stock in a lender can become worthless even with sound credit, while a traditional fund holder is backed by the loan assets.
- **AI threatens established software, but the service layer around the product limits that threat**: Gardner frames the replacement argument around open source. Free code has existed for decades, yet open source represents only about 12% of the application software market by revenue because proprietary vendors provide implementation, training, support, hosting, stability, and accountability. An AI-native startup can replicate Salesforce's code cheaply but still has to build the rest of the company. Salesforce's existing code base is a sunk cost, not a moat; future spending is what matters. Gardner separates three threats: AI-built replacements, direct obsolescence of analytical tools where AI tools already do things better, and the broader possibility that current software becomes irrelevant as the enterprise turns into a centralized data lake that agents read from and write to.

## Work to read

- [Red flags in SaaS financial statements (2026 update)](https://www.linkedin.com/posts/toddgardner1_i-have-reviewed-the-financial-statements-activity-7475530334886047745-j-I4)
- [The case for capitalizing implementation costs](https://www.linkedin.com/posts/toddgardner1_the-case-for-capitalizing-implementation-activity-7432064016665014274-thEp)
- [What a shrinking SaaS company is worth in 2026](https://www.linkedin.com/posts/toddgardner1_thirty-five-percent-of-saas-companies-shrank-activity-7437494953292918784-ulvr)
- [A view from inside SaaS private credit](https://www.linkedin.com/posts/toddgardner1_i-helped-start-the-private-credit-push-into-activity-7436048772142555136-3gOC)
- [Is AI to software what open source was?](https://www.linkedin.com/posts/toddgardner1_is-ais-influence-on-application-software-activity-7449894536358690816-xxjh)

## Brand relationships

- **SaaS Capital** (investment)
- **Maxio** (mention)

## Partnerships

Todd Gardner's posts do not describe a partnership, sponsorship, or commercial-collaboration format. Their analytical commentary on SaaS finance and investing includes no sponsor disclosure, branded partner mention, or paid format. No published page explains how a brand can inquire about working with them. The LinkedIn profile is a general professional contact point, not a partnership route, so no inquiry link is offered here. The posts show subject-matter fit rather than a media package. Gardner writes from direct experience reviewing SaaS financial statements, structuring SaaS credit, and modeling valuations, addressing finance leaders and investors in that voice. A company seeking their involvement should expect to negotiate terms directly. Their posts do not document formats, deliverables, or pricing; confirm those details with Gardner before planning around them.

## Alternatives

- Jarrod Souza: A better choice when the reporting challenge is ecommerce or DTC revenue quality rather than SaaS metrics. Souza offers a CFO’s checks for distinguishing revenue that compounds from revenue that merely covers the month: new versus repeat customers, margin by SKU, and one-time versus high-LTV cohorts. They also warn against dropping a P&L into AI and treating the output as a verdict, listing five failure modes, including hallucinated conclusions, missing ecommerce context, and misread cash versus profit. For a workshop on judging which revenue is worth defending and using AI only as an assistant under CFO oversight, Souza’s posts speak more directly to the task than Gardner’s.
- Branden Jenkins: A better choice when the workshop should focus on pricing and the value gap per account, or on how the market currently prices AI-native SaaS. Jenkins writes as a CEO about the shift from growth-at-all-costs to capital efficiency to a premium on AI, arguing that traditional SaaS KPIs now earn single-digit revenue multiples while AI-native companies command more. They describe continuously tracking three things per account: what was sold, what the customer is entitled to, and what they actually use. The example is a customer running $250 million in annual API calls who asked for a discount. For a brief on aligning pricing and packaging with delivered value, this is closer to the decision than a review of reporting mechanics.
- Dawoud Nasraty: A better choice when a company wants ongoing fractional CFO support and accounting-system implementation, not a single workshop. Nasraty’s posts describe building financial clarity as a service: helping a founder outgrow a legacy accounting system, implementing cloud-based reporting, and giving owners real-time visibility and confidence in their numbers. One post links competitive defensibility to financial discipline, noting that as AI lowers the cost of building products, reliable accounting data and forward-looking CFO insight help determine whether an advantage is translating into value. This fits engagements that include reporting infrastructure and an ongoing advisory relationship.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/