The Metrics Brothers

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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The claim that “95% of AI projects fail” has become one of the most repeated talking points in enterprise AI. But where did it come from, and does it actually hold up?
In this episode, Dave "CAC" Kellogg and Ray "Growth" Rike take a detailed, data-driven look at the MIT NANDA report, titled The GenAI Divide: State of AI in Business 2025. They break down how the "95% fail rate" statistic went viral, why it stuck, and why the underlying evidence does not support such a sweeping conclusion.
What Ray and Dave cover:
Why the NANDA report is often mistaken for a peer-reviewed academic study when it is not
How ambiguous definitions of “failure” turn partial adoption into sensational headlines
Data inconsistencies and methodological gaps that undermine the 95% claim
The difference between failed AI initiatives and early-stage pilots or experiments
Why measuring AI success by the percent of projects is misleading compared to the business value created
The rise of Shadow AI and employee-driven adoption, and why that may be a feature, not a flaw
How the report’s conclusions conveniently align with the authors’ proposed NANDA architecture
The real issues enterprises face with AI: workflow integration, governance, and change management

The episode also discusses why personal productivity gains still matter to the P&L, even if they do not appear as a clear line item, and why fear-driven AI narratives can do real damage within organizations.
Key takeaway:
The NANDA report raises some legitimate concerns about scaling AI from pilot to production, but the infamous “95% of AI projects fail” claim does not survive close inspection. Leaders should read the report skeptically and push back when flawed statistics begin to drive decisions and strategy.
Recommended for:
CFOs, operators, AI leaders, and anyone tired of scary AI statistics that fall apart under scrutiny.
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