Dave "CAC" Kellogg and Ray "Growth" Rike discuss the ICONIQ 2026 State of GTM Report, a 32-page benchmark study based on a January 2026 survey of 155+ B2B SaaS executives across CROs, CEOs, and RevOps leaders. The pair digs into what the data says about how high-growth companies go to market differently, how usage-based pricing is reshaping sales compensation, and where AI in the GTM stack is actually delivering results versus falling short.

Topics Covered

• GTM Motion Mix: Top-Down vs. Bottom-Up vs. Hybrid. The data shows roughly 60% of companies use a hybrid motion, but high-growth companies skew more toward bottom-up and PLG. Ray and Dave unpack the ICONIQ "variable growth bar" definition and what the motion mix signals about the source of growth.
• Channel and Partnership Revenue Is Bigger Than Expected. ICONIQ reports channel partnerships representing 27-31% of revenue for high-growth companies. That is well above the 11-15% Ray typically sees in comparable reports. Dave calls it the long-awaited comeback of channel in SaaS, and both hosts flag the near-absence of self-serve as a surprise.
• Quota Setting and Commission Structures in a Usage-Based World. For the first time in a major GTM benchmark, ICONIQ covers how companies set quotas and structure commissions in a consumption and outcome-based pricing environment. 30% of respondents use forecasted consumption to set quota. Commission payout timing is split across four models, signaling how unsettled the go-to-market compensation playbook remains.
• Clawbacks Are Back. With usage-based and prepaid consumption models on the rise, 45-50% of companies now have clawback provisions in sales compensation. Ray and Dave discuss why clawbacks are a morale killer for sales teams and what the smarter alternative looks like in practice.
• POC and Free Trial Conversion Rates. POC-to-paid conversion improved from 36% to 50% year over year. Ray and Dave discuss resource allocation for proof-of-concepts, including dedicated versus shared solution architects, and raise the question of where forward-deployed engineers fit into the picture.
• AI in GTM: Where It Is and Isn't Working. Lead gen and call transcription top the adoption charts, but AI-driven forecasting sits at only 38%. Ray flags the gap between AI-native and traditional SaaS companies in GTM AI adoption. Dave points to slide 30 as a reality check: pipeline efficiency and unit economics are not yet showing meaningful improvement from AI investment.

If you are responsible for GTM strategy, sales compensation, or measuring the ROI of AI investments, this episode gives you a practical lens on one of the best benchmark reports published in 2026. Ray and Dave go beyond summarizing the slides. Dave and Ray flag caveats in the methodology, challenge the data where it warrants scrutiny, and connect the findings to real-world operating decisions on quota design, commission structures, channel strategy, and AI adoption. If you only have time for one GTM benchmark deep-dive this year, this is the episode to start with.

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Dave "CAC" Kellogg and Ray "Growth" break down one of the oldest productivity metrics in business and explain why, in the age of AI-native software, it has never mattered more. This episode covers the full arc from Frederick Taylor's factory floors to Cursor's $3.3M per employee, with the rigorous definitional discipline the Metrics Brothers are known for.

What We Cover:

• The metric's 100-year history. Revenue per employee traces its roots to scientific management in the late 1800s, gained traction as a Wall Street efficiency screen in the 80s and 90s, and became a standard signal of business model quality in M&A diligence. The core math is simple: annual revenue divided by headcount. What is not simple is how you define the denominator.

• FTE vs. employee: why the definition matters more than the formula. The E in FTE stands for full-time equivalent, not full-time employee, and that distinction drives real measurement decisions. How do you count a part-time contractor? What about 200 offshore developers on a third-party vendor's payroll? Ray and Dave walk through the practical choices, including why offshore headcount is almost never counted on a 1:1 basis and why that decision can dramatically change your benchmark comparison.

• Public SaaS companies in 2025: the benchmark is $395K. Using the Benchmarkit SaaS 100 index (134 public SaaS companies), the median revenue per employee in 2025 is $395K, up from $327K in 2022, a 21% improvement in three years. ARR per FTE runs about 5-7% higher at $413K. The shift reflects the industry's move from growth-at-all-costs to efficient revenue growth.

• Private SaaS companies: size matters. ARR per employee scales materially with company size. At the $5-20M ARR stage, the median is $144K. By $100M+ ARR, the median reaches $300K. The recurring-revenue tailwind from a large renewal base is a significant driver as companies scale.

• AI-native companies have reset the benchmark entirely. Where the historical range for enterprise software was $200-400K per employee, AI-native companies operate at a fundamentally different level. Cursor reached $1.67M per employee at 60 people, and now runs at $3.3M per employee at 300 people. Midjourney is at $4.7M. Anthropic is in the $3-5M range on a run-rate basis. This is not a modest improvement over traditional SaaS. It is a 10x shift.

• One important caution on the AI numbers. Many of the figures being cited by AI-native companies are monthly run-rate revenue annualized (last month times 12), not trailing 12-month GAAP revenue. When growth is compounding fast, that distinction can dramatically inflate the productivity figure. The Metrics Brothers flag this as a meaningful source of confusion in how the benchmark is being discussed today.

• The AI tailwind may be temporary, at least in part. Current customer acquisition friction for AI software is unusually low, given experimentation budgets and departmental purchasing. As enterprise procurement tightens (74% of enterprise AI purchases now involve IT), GTM investment will likely increase, and revenue per employee for AI-native companies may stabilize or compress. Ray and Dave estimate that steady-state productivity is more likely to be in the 3-5x range over traditional SaaS, not 10x.

• Revenue will replace ARR as the standard numerator. The rise of usage-based and hybrid pricing is rendering ARR less meaningful for a growing share of companies. Snowflake, Datadog, and MongoDB do not report ARR. As AI-native pricing models proliferate, Ray and Dave expect the industry to converge on revenue as the standard numerator across productivity benchmarks.

• What about revenue per agent? Ray raises the forward-looking question: as AI agents take on SDR, sales, and other GTM functions, how do we measure agent productivity? Dave's take is that "revenue per agent" is likely a dead end, partly because agent instances are nearly impossible to count and partly because the right way to price and measure agents is to decompose their capabilities, not to anthropomorphize them as headcount equivalents.

The Bottom Line:

Revenue per employee is a deceptively simple metric with genuinely complex definitional choices underneath it. For B2B SaaS executives, the 2025 benchmarks are $395K (public) and $144-300K (private, depending on scale). For AI-native companies, the numbers are in a different category entirely, though some of that gap reflects accounting choices as much as true productivity gains. The metric is worth tracking closely, both as a board-level efficiency signal and as a leading indicator of business model quality.

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One word can reveal a lot about someone's analytical depth, and on this episode of The Metrics Brothers, Dave "CAC" Kellogg and Ray "Growth" Rike break down one of the most commonly misused pair of terms in metrics analysis: segments and cohorts.

Dave shares what sparked this topic, a Norwest benchmark report that used the word "cohort" when it clearly meant "segment," and explains why the mix-up matters far more than a simple vocabulary error. In this episode, Ray and Dave cover:

• Segments vs. Cohorts Defined: A segment is a slice of data defined by a shared attribute such as company size, vertical, or deal size. A cohort is a group anchored to a shared event and tracked over time, such as all opportunities created in Q1 or all customers acquired in a given year. The two are orthogonal concepts, not synonyms, and confusing them can signal a lack of the numerical fluency that sharp operators and investors expect

• Snapshot vs. Cohort Analysis: Standard dashboard win rates are fast and stable, but they only capture what crossed the finish line in a given period with no visibility into where those deals came from or how long they were in the pipeline. Cohort analysis rides along with a group of opportunities from creation to resolution, revealing how process and personnel changes actually affect outcomes over time

• Win Rates and Pipeline Coverage: Ray walks through a real example where cohort-based win rate analysis exposed a breakdown in discovery quality after a Q3 process change, something a standard dashboard completely masked. Dave explains why pipeline coverage goals should not simply be calculated as the inverse of a snapshot-based win rate, and how close rate (a cohort-based metric) gives a more accurate picture of both yield and timing

• NRR, GRR, and Customer Expansion: Dave makes the case that tracking ARR by customer acquisition cohort over time is far more predictive of long-term retention and expansion behavior than NRR alone, which only looks back one year. Ray adds how cohort analysis helped him identify a high-value expansion window between months 18 and 30 of the customer lifecycle, enabling smarter allocation of sales resources towards existing customers

• Combining Both for Maximum Insight: The most powerful approach is a segmented cohort analysis, tracking time-based behavior across meaningful attribute-based cuts of your customer or pipeline data. Segments tell you what kind of customer. Cohorts tell you what happened over time. Together, they tell the full story.

If you use metrics to help inform decisions in your company, and have a goal to help build a culture of numeracy in your company, this is a great listen!

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The Metrics Brothers, Dave Kellogg and Ray Rike open with their introductory bit, which this week included Statler and Waldorf, the grumpy old men in the balcony from the Muppets, before diving into a thorough review of the Norwest B2B Sales and Marketing Benchmark Report, a102-page study published in November 2025, that included 177 participants (77 Norwest portfolio, 100 third-party VC/PE-backed).

Key Topics Covered:

• Overall Report Assessment: Praised for its breadth, year-over-year trend data, and even split of marketing (40%), sales (42%), and combined (18%) respondents

• Marketing Budgets: Smaller companies ($5M–$15M ARR) saw dramatic budget cuts — down from $3.3M to $825K, nearly a 75% decrease

• Top GTM Challenges in 2025: #1: Positioning product as a "must have" — 44%, up 6% YoY

• Revenue Re-Forecasting: 66% of respondents changed their revenue plan mid-year; 43% increased it (down from 48% prior year), while 23% decreased (up from 18%)

• Renewals Ownership Shift: Customer Success owned renewals 56% of the time in 2023 — now just 29% in 2025

• MQL Scoring Model Collapse: Use of formal scoring models (demographic fit + engagement)

• Top Marketing KPIs: #1: Dollar value of opportunities (pipeline) was number one metric at 56%

• CAC & Cost-Per-Lead Awareness - The "Bonus" Topic: 45% of respondents didn't know their CAC; 41% didn't know their cost per lead

• Closing Recommendations: Both hosts recommend reading the report, including pages 80–98 covering AI adoption in sales and marketing, that they were not able to cover in this episode

If you are a GTM executive leading a software company or the CFO responsible for driving revenue growth and profitability - this episode and the associated report is a great source of insights!

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For more than a decade, the Rule of 40 has been the gold standard for measuring SaaS performance:

Growth Rate + Profit Margin ≥ 40

But in today’s environment of higher interest rates, multiple compression, private equity leverage, and AI-driven cost pressures, that benchmark may no longer be enough.

In this episode of the Metrics Brothers, Ray “Growth” Rike and Dave “CAC” Kellogg explore why many investors and operators are now targeting something far more ambitious:

The Rule of 60.

Dave walks through the history of SaaS economics, from the growth-at-all-costs era, to the rise of balanced metrics after 2015, to the capital reset that began in 2022. The discussion then shifts to the math driving today’s expectations: if private equity firms buy companies at high multiples but must sell them later at lower multiples, Rule-of-40 performance simply doesn’t always generate acceptable returns.

In many leveraged SaaS deals today, hitting Rule of 60 can be the difference between a 1.1x return and a 3x outcome.

Ray and Dave also dig into how the Rule of 40 is evolving in practice, including:

• Why growth still matters far more than margin in valuation models
• How companies organically converged on the “20/20” Rule-of-40 profile
• Why PE investors increasingly expect Rule-of-60 performance
• The impact of debt service, CAC inflation, and AI cost structures
• Why achieving Rule of 60 often requires radical operational changes

The takeaway: this isn’t a temporary metric trend.

For many SaaS companies, the math of modern software investing now demands it.

One interesting comment that reflects the reality of the Rule of 60, especially for companies that have been funded with leverage debt is: “This isn’t a fad. The math of the deal breaks without it.”

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In this episode, Dave "CAC" Kellogg and Ray "Growth" Rike go point-counterpoint on two high-profile articles making waves across Wall Street and Silicon Valley: Citrini's provocative February 2025 report, The 2028 Global Intelligence Crisis, and Citadel's rebuttal, The 2026 Global Intelligence Crisis.

Dave and Ray unpack whether AI is truly triggering an unprecedented economic collapse or whether Citrini's dark simulation is, as one economist put it, just "a scary bedtime story." They dig into the SaaS private credit contagion theory, the historic parallels of labor displacement, the role of government regulation, and why this particular AI scare hits closer to home than any previous tech disruption. As always, the brothers bring the receipts, including nearly 20 sources and 20 hours of research - so you don't have to.

Full Episode Summary:

Dave Kellogg and Ray Rike open by framing the episode as a tale of two AI futures: Citrini's alarming speculative simulation versus Citadel's data-driven rebuttal.

The Citrini Case (Bear Case): Published February 22nd, Citrini's report simulates a scenario in which rapid AI agent adoption triggers a global intelligence crisis by mid-2028 featuring 10.2% unemployment and a 38% drop in the S&P 500. The report argues AI is categorically different from prior technology waves because it displaces cognitive workers, who represent roughly 75% of U.S. labor income.

Citrini further warns that SaaS, already accounting for 23% - 25% of the $3 trillion U.S. private credit market could become the chip in the windshield that cracks the broader financial system, with ripple effects into insurance and the broader economy. Dave and Ray note that Citrini's word choices ran 3.4-to-1 negative, and flag that the firm may hold short positions — characterizing the piece as well-crafted "bear porn."

The Citadel Rebuttal (Bull Case): Two days later, Citadel, a $65B AUM asset manager with 35 years of credibility responded with a data-driven defense. Software engineering jobs are up since January 2024, AI CapEx is 2% of GDP and AI-adjacent commodity pricing is up 65%. Citadel argues AI follows historical S-curve adoption patterns, that "recursive capability doesn't equal recursive adoption," and that technology has always complemented rather than replaced labor - pointing to Microsoft Office as a historical analogue.

Dave and Ray's Take: Both hosts find Citadel more credible, but acknowledge real displacement risks ahead. Their key insight: the reason this particular AI scare is generating 10x more fear than past labor disruptions (auto workers, telephone operators, elevator operators) is that this time it's us — white-collar knowledge workers facing displacement. Ray adds that blue-collar jobs (truck drivers, Uber drivers, warehouse workers) face equal or greater long-term risk from AI plus robotics, but those disruptions don't generate the same visceral fear in the media and investor class. Both agree the timing of adoption is the biggest unknown. Long-term, history favors the Citadel view. Short-term, the transition could be painful.

On Government Response: Dave and Ray agree that political and regulatory intervention is inevitable if unemployment spikes materially, whether through labor protections, AI regulation, or fiscal stimulus.

On Economists' Reactions: Real economists, including Noah Smith (Noahpinion) and Wharton's Jeremy Siegel, largely dismissed the Citrini piece, wi Siegel arguing that productivity gains generate new income and demand, Smith calling it a "scary bedtime story." Dave's takeaway for operators: let the Metrics Brothers do the 20 hours of reading so you don't have to.

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In this episode, the Metrics Brothers, Dave "CAC" Kellogg and Ray "Growth" Rike dive deep into the ICONIQ State of AI: Bi-Annual Snapshot Report. Published in January 2026, this 44-page report summarizes insights from ~300 software executives on the front lines of building and scaling AI products.

Ray and Dave explore a market transition from experimental model races to the challenge of building durable, economically sound products. Key discussions in this episode include:

• Differentiation Beyond the Model: Why 69% of builders are focusing on vertical AI applications and why 49% cite the application layer (UX and workflows) as their primary competitive edge over the underlying model.

• The Gross Margin "U-Curve": A look at the shifting economics of AI, where aggregated gross margins are projected to climb to 52% by 2026, even as inference and infrastructure costs remain significant hurdles.

• Pricing Evolution: The rise of outcome and usage-based pricing, with only 23% of companies still relying on seat-based models as customer demand shifts toward value-aligned monetization.

• AI as an Internal Force Multiplier: How R&D teams are leading internal adoption, with 83% of companies now measuring success through productivity gains and 59% through direct cost savings.

Whether you are a CEO or CFO navigating AI product gross margin concerns or a GTM leader rethinking your proof-of-concept strategy, this episode provides the benchmarks you need to understand the "new phase of maturity" in the AI market.

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In this extended episode, the Metrics Brothers tackle the "elephant in the room" - the SaaSpocalypse. With nearly $1 trillion in market value wiped out recently, Ray and Dave go beyond the stock market headlines to analyze the structural shifts hitting the industry.

The duo breaks down the three primary drivers of the current market "carnage", including the AI Fear of Being Obsolete (FOBO), Regression to the Mean for SaaS stocks and Changing Valuation Methodologies, before diving into the newly released Aviner report, The Future of SaaS: A Fork in the Road. Using Aviner’s "Red Pill vs. Blue Pill" metaphor, they debate whether SaaS companies must fundamentally pivot to "agentic" systems or accept maturity and financialize by focusing on profitability.

Covered in This Episode:

The SaaSpocalypse Explained: Why the stock market is currently a "rugby scrum of information" and why stock price is a measure of future expectations rather than current health

ServiceNow as a Bellwether: An analysis of how a "Rule of 56" company can beat expectations and still see a 30% stock drop in a single month.

FOBO (Fear of Being Obsolete): How the "revenge of build vs. buy" and the collapsing cost of coding are demoting traditional SaaS apps to mere systems of record.

The Aviner Report Breakdown:

• Part 1: The hard data on slowing revenue growth (cut from 40% to 20%) and the "aberration" of 2019–2021 multiples.
• Part 2: The binary choice between embracing AI "Systems of Context" or financializing for net income.

The "Architect Strategy": Ray’s argument for a third path where SaaS companies coexist with AI by providing the governance and orchestration layer

Buyer Sentiment vs. Market Narrative: Why 63% of software buyers believe existing vendors will be the beneficiaries of AI, contradicting the current "SaaS is dead" stock market trend.

Key Metrics & Concepts Mentioned

• Rule of 40 vs. Rule of 60: How the standard for SaaS health is shifting
• Stock-Based Compensation (SBC): Why excluding this from profitability metrics is no longer passing the "financialization" test
• The Three-Layer Taxonomy: Systems of Record, Systems of Engagement, and Systems of Context
• Multiple Compression: The shift from 15x revenue multiples back to the historical 5x mean

Resources Mentioned

• Report: The Future of SaaS: A Fork in the Road by Aviner Growth (Jan 2026).
• Book: The Reckoning by David Halberstam.
• Book: Profit Pools by Orit Gadiesh and James L. Gilbert.

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In this episode of the Metrics Brothers podcast, Ray Rike and Dave Kellogg tackle one of the most critical yet misunderstood metrics in the U.S. economy: Labor Productivity. Amidst the rapid rise of Artificial Intelligence, the "Metrics Brothers" break down how productivity is officially measured by the Bureau of Labor Statistics and why historical technology booms, from SaaS to Cloud, haven't always moved productivity growth as much as expected.

Key Takeaways: A deep dive into the ratio of economic output per hour worked, including what the BLS excludes (farms and government) and the nuances of white-collar labor tracking.

• Historical Trends: A comparison of the post-war boom versus the "SaaS era," exploring why the last 20 years have seen a 66% relative decrease in productivity growth despite trillions in tech investment.
• The AI Impact: Three potential scenarios for the future of work, from "exploding output" to "labor displacement," and why AI might fundamentally remake work in ways the Cloud never did.
• Global Benchmarking: How the U.S. stacks up against leaders like Ireland and Norway in output per hour.

Why Listen? Whether you are a SaaS leader, investor, or white-collar professional, this episode provides a roadmap for staying on the "right side of the divide" in the upcoming AI-driven economic shift.

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Brand is one of the most powerful assets a company can build and one of the hardest to measure. In this episode of The Metrics Brothers, Dave “CAC” Kellogg, and Ray "Growth" Rike take on one of marketing’s most persistent challenges: how to measure brand in a world obsessed with direct attribution and near-term ROI.

The conversation starts with what a brand really is, originating from literal marks of ownership and evolving into a promise of quality, trust, and differentiation. From there, Ray and Dave explore why strong brands create pricing power, customer loyalty, category leadership, and long-term defensibility, even if those benefits do not always show up cleanly in dashboards.

They then break down practical ways to measure brand that align marketing and finance perspectives, including indirect valuation approaches such as brand value and goodwill frameworks, along with comparative metrics like direct and branded web traffic, share of voice, share of search, and inbound pipeline contribution. The episode also covers market research fundamentals including awareness, consideration, trial, and repurchase, and why dedicating a portion of your marketing budget to measurement is essential to sustaining brand investment.

Finally, the Metrics Brothers dig into brand measurement techniques that work in practice, including self-reported attribution, lift experiments, and analyzing sales conversations to see how brand shows up late in the buying process, often at the exact moment a deal is won.

If you have ever struggled to align brand investment with measurable outcomes, justify brand spend alongside demand generation, or connect long-term brand building to real business results, this episode provides a grounded, metrics-driven framework for doing exactly that.

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The State of Generative AI in the Enterprise 2025

In this episode of The Metrics Brothers, Ray Rike and Dave Kellogg break down the 2025 State of Generative AI in the Enterprise report from Menlo Ventures and explain what the data really says about where enterprise AI adoption is accelerating and where the market is consolidating.

The headline takeaway: AI software is scaling faster than any software category in history. Enterprise AI spend has exploded from roughly $1.7B in 2023 to nearly $37B in 2025, reaching scale in just three years. This revenue milestone took SaaS more than 15 years to achieve. Foundational models now represent the single largest area of spend, highlighting how infrastructure and model access remain core to enterprise AI strategies.

Ray and Dave also explore a major strategic shift inside the enterprise: buy is decisively beating build. In 2025, 76% of enterprise AI solutions are purchased rather than built internally, up sharply from 53% the year prior. Rapid model evolution, ongoing retraining costs, and model drift are making internal AI development far more expensive to maintain than many teams originally expected.

One of the most surprising findings is on go-to-market efficiency. AI software pilots convert to production at nearly twice the rate of traditional software, with roughly 47% of AI pilots reaching production versus about 25% for conventional enterprise software. This runs counter to recent narratives suggesting enterprise AI pilots are stalling and points to clearer ROI and faster time-to-value.

The episode also dives into what Menlo calls the first true “AI killer app”: AI-assisted coding. Coding tools now account for more than half of departmental AI spend, with over 50% of developers already using AI coding assistants and adoption exceeding 65% among top-quartile teams. Real-world examples show meaningful productivity gains, including double-digit increases in development velocity and significant time savings during legacy system upgrades.

Industry-wise, healthcare emerges as the largest buyer of vertical AI, representing 43% of vertical AI spend. This is notable given healthcare’s historically lower IT spend as a percentage of revenue. Much of the value is coming from administrative automation such as medical scribing, where AI directly reduces non-clinical workload and unlocks meaningful productivity gains for care providers.

Finally, Ray and Dave examine the shifting competitive landscape among foundation model providers. Anthropic has surged to roughly 40% share of enterprise AI usage, up dramatically from prior years, while OpenAI’s share has declined as Google continues to gain traction. The discussion centers on focus versus breadth and why enterprise positioning and reliability may matter more than consumer mindshare.

Key takeaways from the episode:

• AI software is the fastest-scaling software category ever
• Enterprises are rapidly moving from build to buy
• AI pilots convert to production at nearly 2x traditional software
• AI coding is emerging as the first true enterprise AI killer app
• Anthropic’s enterprise focus is translating into meaningful market share gains

If you care about how AI adoption actually translates into spend, productivity, and competitive advantage inside large organizations, this episode is a must-listen.

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