“Accurate Insights 2026” Event - RSVP Now

Curated for institutional investors, the event will focus on generating actionable, reliable signals from quantitative and qualitative sources in an AI world.

With perspectives from these expert speakers:

• Dan Entrup - Co-Fou…

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“Accurate Insights 2026” Event - RSVP Now

Curated for institutional investors, the event will focus on generating actionable, reliable signals from quantitative and qualitative sources in an AI world.

With perspectives from these expert speakers:

• Dan Entrup - Co-Founder - AggKnowledge

• - Founder -

• Ben Tallman - CEO - AirBrx

• Dan Averbukh - Co-Founder & CEO - ValidityBase

• Yijia Liang - Co-Founder - CompoundAI

• - Founder -

• Matt Harney - Founder - Cloud Ratings

• Brad Lyons - Founder & CEO - Crossover Intelligence

• Sagar Kadakia - CEO & Founder - Qualitate

• Nate Singer - CEO & Chairman - Herron Intelligence

RSVP here.

EVENT RSVP

AI-Driven Churn → Qualitate New Report

We invited Qualitate CEO Sagar Kadakia to speak at “Accurate Insights 2026” because of the caliber of their research work product, including their new “The AI Disruption Report” (56 slides | n=4,800+).

Their definitions are key to interpreting and commenting on the report:

• “Displacement is used to describe plans for true negative actions or diminished Usage of a particular company - defined as instances of Spend Decreases and Churn, without Spend Flattening.”

• “Disruption is a broader term that also includes flattening NTM Spend, to gauge vendor stagnation. We define Disruption as instances of Spend Decreases + Churn + Spend Flattening.”

Key excerpts:

AI-Driven Displacement 1H 2026 vs 2H 2025 (n= ~200-300):

• Planned Churn: 8% →18% attributed to AI

• Spend Decreases: 17% →27% attributed to AI

As a caution, the percentages reflected in the above Displacement Reasons (i.e., Spend Decreases and Planned Churn) sample could cause undue alarm.

Across all discussions within Qualitate’s broader IT buyer pool (n=4,800+), AI-led headwinds are much lower in absolute terms, albeit growing rapidly:

• Disruption All Interviews: 9% (up 3x vs 2H’25)

• Displacement (aka Decrease and/or Churn) - All Interviews: 5% (up 2.5x vs 2H’25)

  

As always with software, it's better viewed at the workflow and category level.

Unsurprisingly, “obvious” areas with compelling AI-Natives are showing flattening of traditional spend (i.e., Customer Service, Business Process), while areas like Finance & Accounting are far less impacted:

Consensus narratives also seem to hold for this churn discussion by category chart:

… as well as spend decreases:

The Qualitate report has extensive specific company coverage - partial examples here:

AI-Led Software Churn As A Funding Mechanism

I strongly believe many audiences - whether operators or investors - overestimate the growth rate of IT budgets (in aggregate + for software). Morgan Stanley’s latest CIO survey reflects 2026e budget growth of:

• Software: +4.1%

• Overall: 3.8% (via Hardware at 1.6%, Communications at 2.6%, Services at 1.8%)

Given the outsized growth of AI-Natives - like the 130% NRR with 165% YoY ARR growth in the >$100m top quartile cohort covered last week via ICONIQ data - those expansions and new deployments need funding.

ETR surveys provide excellent data on AI funding mechanisms, especially “Where AI Money Comes From: The Quiet Shift Inside IT Budgets.”

For Large + Global 2000-sized organizations, a) “Broader IT Budget” is the #1 AI funding source and b) the importance of “Broader IT Budget” is generally gaining share versus “Innovation” and “R&D” budget mechanisms.

In the event of AI cost overruns, ETR data specifically calls out 39% of organizations reallocating from other parts of the IT budget.

This provides more context for the AI-led churn/spend-flattening data covered by Qualitate.

“Accurate Insights 2026” Event - RSVP Now

Curated for institutional investors, the event will focus on generating actionable, reliable signals from quantitative and qualitative sources in an AI world.

EVENT SIGN UP

Edward Robson (CIO - 2717 Partners) On “Cloud Returns” Investing Podcast

SPOTIFY | APPLE | OTHER PODCAST PLATFORMS| VIDEO

We recently released this episode with Edward Robson, Partner + Chief Investment Officer of 2717 Partners. The episode outlines his frameworks for capital allocation in technology and the related impact of AI.

About Cloud Ratings

In mid-2024, we announced a research partnership with G2 - more here:

with this slide showing how our G2-enhanced Quadrants (like our recent Sales Compensation Software) release, this business of software newsletter you are reading, our podcasts, and our True ROI practice area all fit within our modern analyst firm:

Hypergrowth AI Metrics - ICONIQ “State of Scaling 2026”

While the “quarters to $___ ARR” genre of slope charts are fun to look at, they are nearly always silent on underlying drivers + unit economics.

ICONIQ Capital (h/t Vivian Guo + co) released their “State of Scaling 2026” (52-slides here) with a key focus on hypergrowth AI companies (labeled as "Pacesetters”). With key Pacesetter metrics summarized here:

ICONIQ Report Positive Takeaways:

#1 Gross Margin: Pacesetter gross margins are broadly in line with traditional software. As we’ve covered repeatedly, AI gross margins are a critical watchpoint.

#2 Net Revenue Retention: Combining ICONIQ’s NRR data here:

… with our earlier ServiceNow-inspired AI NRR vs Gross Margin Lifetime Value matrix:

Roughly speaking, the ICONIQ Pacesetter’s customer lifetime multiple versus traditional SaaS works out to be:

• <$100m ARR - Median NRR of 120% = ~50% higher LTV

• <$100m ARR - TQ NRR of 136% = ~130% higher LTV

• $100m+ ARR - Median NRR of 130% = ~105% higher LTV

• $100m+ ARR - TQ NRR of 146% = ~210% higher LTV

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ICONIQ Report Negative Takeaways:

#1 Shorter Contracts: “Short-term contracts are becoming the norm: 65% of Enterprise buyers prefer only signing contracts of 1-year or less”

#2 Pacesetter Gross Retention at $100m+: Perhaps a nitpick, but “only” 90% median gross retention is a watchpoint (at least relative to achieving durable, public company scale outcomes).

As always, go read the full report from ICONIQ.

Mapping AI Spend Data From Cledara, Ramp, Vertice + Zylo … With NEW Stripe Adoption Data

Our last edition debuted our B2B AI Spend Cycle tracker, aggregating disclosures from Cledara, Ramp, Vertice, and Zylo.

As we incorporated AI spend data through August, we chose to start showing adoption in our “all-in-one” thanks to Stripe (h/t Ernie Tedeschi + co), which disclosed AI adoption among Stripe customers.

The US Census Bureau’s inherently broad-based AI adoption tracking reinforces the need to understand the platform/respondent mix when interpreting these charts. See last edition’s graph:

Regardless of the absolute levels + mix, the upward trendlines and slopes are generally consistent across all of these platform disclosures.

But Signs Of Caution In Our B2B AI Interest Index - AI Native Plateau

That said, a redacted excerpt showing a plateau from our expanded B2B AI-Native (n=240+) coverage universe warrants caution: this factor is forward-looking for future AI purchases/adoption.

As always, trends vary by category and company.

Curated Content

Credit markets are an increasingly key part of the AI economy → “Financing The AI Buildout” from credit investment firm Sycamore Tree Capital Partners (h/t Trey Parker + Christian Yonkoski) provides a great holistic view of AI x Credit.

“AI (In)Security” from Sapphire Ventures (h/t Kevin Burke) provides a digestible recap of AI x cybersecurity issues + themes in 2026.

Like the “Economist magazine cover as a contra-indicator”… the Silicon Data Token Index has declined materially since our July 23rd coverage:

Edward Robson (CIO - 2717 Partners) On “Cloud Returns” Investing Podcast

SPOTIFY | APPLE | OTHER PODCAST PLATFORMS| VIDEO

We recently released this episode with Edward Robson, Partner + Chief Investment Officer of 2717 Partners. The episode outlines his frameworks for capital allocation in technology and the related impact of AI.

About Cloud Ratings

In mid-2024, we announced a research partnership with G2 - more here:

with this slide showing how our G2-enhanced Quadrants (like our recent Sales Compensation Software) release, this business of software newsletter you are reading, our podcasts, and our True ROI practice area all fit within our modern analyst firm:

As more 3rd-party platforms publish AI spend data, we thought an “all-in-one” analysis might add value. First, all credit is due to their AI spend transparency → sources here:

• Cledara AI Spend Data

• Ramp AI Spend Data - note we transformed it by calculating “AI” divided by “Software + IT” to be comparable to other sources

• Vertice AI Spend Data

• Zylo AI Spend Data

The end customer mix of each platform helps explain the AI spending gaps:

AI spend levels appear to be closely correlated with end-buyer size: SMB/Mid-Market-heavy platforms (like Cledara + Ramp) have seen far faster AI ramps than more enterprise-weighted platforms, like Vertice + Zylo.

The relatively low absolute enterprise AI adoption creates significant adoption upside, especially as “Enterprise Plus” (10,000+ employee orgs) deploy at their unique scale (for SaaS, Enterprise Plus spend 8.9x vs 500-2,500 employee orgs).

Zylo’s 2025 growth data highlights the enterprise potential:

• Enterprise Plus (+393%) growth materially outpaced Overall (+108%)

• The Enterprise Plus spend disparity vs 500-2,500 employee orgs is even more pronounced than traditional SaaS: 12.7x for AI (vs 8.9x for software)

Bonus Chart Aligned With “Enterprise Torque”: Goldman Sachs token usage estimates (h/t Glenn Solomon of Notable Capital) show a) enterprise agents ramping starting 2H 2026 and b) having the highest relative growth curve 2028+ (vs consumer agents + non-agent workloads):

Edward Robson (CIO - 2717 Partners) On “Cloud Returns” Investing Podcast

SPOTIFY | APPLE | OTHER PODCAST PLATFORMS| VIDEO

We’re especially excited to debut this episode with Edward Robson, Partner + Chief Investment Officer of 2717 Partners. The episode outlines his frameworks for capital allocation in technology and the related impact of AI.

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Curated Content

“For AI adopters, success and failure look identical — at first” a thoughtful note on the AI J-Curve from Nathan Warren and Azeem Azhar of :

An X thread on declining Product-Market Fit (PMF) from RepVue’s Adam Litte:

Related RepVue Resource: Q2 2026 Cloud Sales Index (report | excerpts)

AI ~“ARR” multiples via Tomasz Tunguz:

• Decagon 125x ARR

• Sierra 79x

• Legora 56x

• Harvey 37x

• Ramp 31x

Databricks’ disclosure that 25% of revenue is from new AI products provides an incremental + important disclosure for our benchmarking of AI mix / % revenue

Qualitate provided a quadrant view of AI Disruption via n=8,000+ enterprise buyer evaluations. “Imminent Disruption” is the most crowded quadrant for Software + Cybersecurity:

“Why I Believe in Categories, Just Not Category Design” by Dave Kellogg

A fascinating look at the value of enterprise data via bankrupt Spirit Airlines $10m data sale to Google:

About Cloud Ratings

In mid-2024, we announced a research partnership with G2 - more here:

with this slide showing how our G2-enhanced Quadrants (like our recent Sales Compensation Software) release, this business of software newsletter you are reading, our podcasts, and our True ROI practice area all fit within our modern analyst firm:

Episode Topics:

• The story behind 2717 Partners and the “iron sharpens iron” philosophy.

• Why technology remains a constantly evolving investment category.

• ROIC and why capital allocation is under-discussed in software.

• ROICA: return on intangible capital allocation.

• R&D and sales and marketing as growth CapEx.

• Why recurring revenue can be viewed like a durable bond.

• Gross retention, logo retention, and contract renewal rates.

• Multi-product strategy and why it improves stickiness.

• AI hype versus proven AI productivity gains.

• Lessons from RPA, low-code, no-code, and previous technology cycles.

• How AI may help software companies build more product surface area internally.

• Why public companies need clear intrinsic value thinking in the AI age.

• Why there have been fewer software IPOs.

• How private markets, secondary liquidity, and venture incentives shape IPO timing.

• What Edward looks for in great CEOs.

• The importance of self-awareness, capital allocation, and communication.

Resources:

https://www.linkedin.com/company/2717-partners/ 

https://geoffreyamoore.com/book/the-gorilla-game/

About Cloud Returns by Cloud Ratings:

Cloud Returns covers ALL types of software investing, whether seed, venture capital, growth equity, private equity, debt, and the public markets.

Our host - Matt Harney - is active on Twitter @saasletter and writes "SaaSletter", an investing-oriented SaaS newsletter on Substack.

https://cloudradio.cloudratings.com/

https://twitter.com/SaaSletter

https://www.saasletter.com/

About Cloud Ratings:

Cloud Ratings is a customer outcomes-focused, data-driven software research analyst firm. We exist to allow organizations to make more confident, lower-risk software purchasing decisions.

Our Cloud Ratings Category Reports combine user reviews with verified vendor data to impartially identify leading software products. Recent report examples include: Subscription Management Software, Session Replay Software, Sales Compensation Software, and Digital Sales Room Software

Built upon investigative customer interviews, our True ROI Reports quantify and provide 3rd party validation of a software product’s business value.

https://cloudratings.com/

Inference Goes Exponential → Brute Force Implications

Our recent editions have covered the exponential curve for inference needs (h/t Siddharth Ramakrishnan of Scale Venture Partners) that sets up…

Dave Kellogg’s post on the "Brute-Force Era of AI". He rightly points out that while applying massive compute to architectures works (Sutton's Bitter Lesson), it doesn't inherently solve the efficiency problem.

We are officially entering Phase 2:

Why Does This Matter For Software?

While AI NRR might offset weak gross margins… those NRR scenarios rely on world-class, possibly unsustainable longer-term rates:

High gross margins have underpinned the business quality and investment case for software:

xkcd “Dependency” Source

ICONIQ (h/t Vivian Guo) recently released their “State of AI” report (47 slides, ungated).

Key call-outs: while AI product gross margins are projected to increase by ~1,400 bps (‘25: 45% → ‘26e: 53% → 2027e: 59%):

… the fabulously granular cost structure breakdown makes those margin increases look optimistic. “Talent” - a largely fixed cost - is only 28%-34% of the cost structure for the GA to Scaling stages. All other costs are highly variable, such as inference and cloud costs. Even for model training, there is little fixed-cost leverage, since it is an ongoing process.

Key message: There seem to be too few fixed costs to generate software economics.

Inference is the #1 non-talent cost at all stages (Beta, GA, Scaling). Ahead of infrastructure & cloud, data storage, and model training.

That said, the ICONIQ “State of AI” did include encouraging unit cost mitigants:

And as inference increases … in a volatile fashion, managing the cost of inference becomes even more critical.

As we researched this trend, we connected with Carmen Li, Dual CEO of Compute Exchange and Silicon Data, publisher of multiple AI market cost trend indices, including the Silicon Data LLM Token Price Index.

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Silicon Data - Token Index + CEO Commentary

The above LLM Token Price Index highlights:

• Increasing Costs: up 50%+ over past 6 months

• Volatility: like a ~34% decline in ~1 month seen in mid-January to February. With a similar rapid decline in June as companies take steps to mitigate the “tokenmaxxing” trend.

Key points from our research exchange with Carmen Li:

1. The Subscription Illusion vs. Commodity Reality “Revenue may look subscription-like on the surface, but underneath, the cost structure increasingly behaves more like a dynamic commodity market... Token expenditure volatility increasingly resembles the pricing behavior of energy, bandwidth, or other critical industrial inputs rather than conventional cloud software pricing.”

2. The Shift to Deployment Economics “One of the biggest misconceptions in AI right now is that model capability alone determines competitive advantage. In reality, deployment economics are becoming just as important. Two companies may offer similar AI functionality while operating under completely different infrastructure economics depending on how efficiently they route workloads, what compute environments they access, and how exposed they are to pricing volatility underneath the stack.”

3. Infrastructure Arbitrage & Regional Disparity “The same inference workload can carry materially different economics depending on where it runs, what hardware it runs on, and whether compute capacity was secured through long-term agreements or acquired dynamically in tighter market conditions.”

Morgan Stanley On Gross Margins + “Tokenomics”

Morgan Stanley’s very recent + excellent “The Moat & The Journey – Assessing Software Durability + Growth in the Age of AI” (170 pages; our selected excerpts here) covered both Gross Margins and “Tokenmaxxing” Mitigation:

MS View Recap: Some gross margin dilution offset at EBIT level; levers (like model routing or open-source) help mitigate AI COGS impact.

MS View Recap: “Token scrutiny is real, but not uniform.” + “Workflow-rich incumbents become the optimization layer: routing simple work to cheap models, reserving frontier for risk, capping low-value usage.”

With a useful - at least for our hyper-granular readers - sensitivity table wherein gross margin declines are/can be mitigated at the EBIT level:

Lazy AI: A Pet Theory

On our recent podcast with Tim Sanders of G2, he emphasized why AI engines are incentivized to provide high-quality answers (their own brand, trust, and competition).

However, a pet theory of mine relates to the AI engines’ strong incentives to be lazy. See these inference costs. Think through the costs of scaling web search and crawling. Especially into higher-inference knowledge sources like audio and video.

After all, what do I know if they missed the equivalent of page 16 of Google Search results?

AI engine outbound web traffic data, in absolute terms, suggests very few are clicking on the provided source links. If not even checking sources, are users exploring what was missed?

Meaning AI engines can (and should?) be lazy.

For our institutional investor audience, there are read-throughs to the respective moats and unit economics of Google, OpenAI, and Anthropic if this theory is right.

SPOTIFY | APPLE | OTHER PODCAST PLATFORMS| VIDEO

About Cloud Ratings

In mid-2024, we announced a research partnership with G2 - more here:

with this slide showing how our G2-enhanced Quadrants (like our recent Sales Compensation Software) release, this business of software newsletter you are reading, our podcasts, and our True ROI practice area all fit within our modern analyst firm:

ICONIQ “State of AI” - AI Costs + AI Gross Margins

ICONIQ (h/t Vivian Guo + team) released their latest July 2026 edition of the “State of AI” report (47 slides, ungated).

Their granular AI cost structure chart evolution from Beta → General Availability (GA) → Scaling once again leads to this conclusion:

Key message: There seem to be too few fixed costs to generate software economics.

Looking strictly at customer-facing AI product costs, the 14% projected increase in AI Gross Margins (2025: 45% → 2027: 59%) seems questionable.

For our core software readers, the 2027e 60% gross margins for Application Layer Products seem encouraging.

However, the change in the unit economics survey suggests gross margin improvement is supportable: 66% of respondents improved their cost per query by 10%+, with 19% at 30%+.

Key unit economics drivers: #1 reducing inference costs, #2 implementing routing strategies, #3 revenue growth through cost leverage, #4 switching to lower-cost/open-source models. In our view, these are believable, “blocking and tackling” drivers.

ICONIQ “State of AI” - AI Revenue Mix

The revenue mix data for non-AI-native companies was striking:

• 2025A: 32% AI as % of total revenue

• 2026E: 42%

• 2027E: 53%

We’ve tracked public company disclosures of their AI metrics. Through early 2025, creating this summary was a heavy lift - actual, meaningful disclosure was limited.

AI disclosure transparency has improved dramatically. Granted, what is disclosed varies widely - like new deal attachment rates or stats on user actions/adoption… but no-to-limited clean AI financials.

Working with public company disclosures through June 2026 for 20 (at least partially) AI transparent companies, these are the only “clean” apples-to-apples AI mix ratios relative to ICONIQ:

For the “Is Software Dead?” debate, you could interpret ICONIQ AI's high mix as a positive sign that smaller private non-AI natives can innovate and quickly monetize AI within their software platforms. Or you could focus on public companies’ HSD/LDD AI penetration rates ~3.5 years post-ChatGPT launch as evidence that legacy software will be disrupted.

Other public disclosures - like deal attach rates - outline how/whether/when the ICONIQ-mix levels could be achieved. This is an “apples vs oranges” chart set at the same scale, so use this for ideation only:

Coming to an accurate view is company- and category-specific… requiring a range of modeling sensitivities, which inspired this:

Related Resource: Our Cloud Ratings Quantitative Strategy Analyst Nick Aiken created a dynamic model template - including the impact of cannibalization - to help answer:

• Does AI Revenue Offset The Loss Of Legacy Seats?

• How Fast Are We Actually Converting To An AI-First Business?

• Can A High-Performing AI Business Save Us From Flatlining?

All of which can help you see scenarios (by variables like AI mix, cannibalization rate, legacy + AI growth rates) where/when/whether AI leads to an overall re-acceleration (related chart = Goldman Sachs software sector-wide ARR growth):

While this AI as a % of software spend data set from Cledara, a software spend management platform (h/t Brad van Leeuwen) could help you choose assumptions on both cannibalization and relative growth:

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ICONIQ “State of AI” - More Excerpts

These excerpts should speak for themselves. And we will remind you to always go read the full ICONIQ x AI report:

Excellent data for validating AI ROI:

Data on agent risk and human intervention needs:

Model share amongst the ICONIQ sample (valuable read through given combination of growth rates and scale):

Finally, an update of ARR/employee:

May 2026 B2B AI Interest Index from Cloud Ratings

We’ve updated our Cloud Ratings B2B AI Interest Index through March 2026 - full slides below:

B2B AI Interest Index

Thematic Category Interest (n = 47 sub-categories tracked - i.e. “manufacturing AI” or “supply chain AI”) continues its plateau pattern.

Bellwether Microsoft Copilot continues to stay at a higher plateau. Other “Blue Chips” (i.e., Hubspot, ServiceNow) remain range-bound.

SaaS Incumbents (n=340: the same 340 vendors tracked in our top-of-the-funnel-focused, forward-looking SaaS Demand Index) showed some moderation, but remained on a still-positive overall trendline.

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Curated Content

“Ways to think about token pricing” from Benedict Evans

“2026 Midyear Review – 10 Questions Shaping AI (Part 1)” (30 slides, ungated) from Sapphire Ventures (h/t Kevin Burke)

“Work-Bench AI Snapshot H1 2026” (54 slides, ungated) from Work-Bench (h/t Kelley Mak + Proby Shandilya)

About Cloud Ratings

In mid-2024, we announced a research partnership with G2 - more here:

with this slide showing how our G2-enhanced Quadrants (like our recent Sales Compensation Software) release, this business of software newsletter you are reading, our podcasts, and our True ROI practice area all fit within our modern analyst firm:

“State of AI” Reports - Best Of 1H2026

[NEW COVERAGE] “The state of the AI economy” - from Azeem Azhar + the Exponential View team. Released June 25, their 66 slides comprehensively cover AI while adding real insights + viewpoints.

[NEW] “AI eats the world” - from Benedict Evans (79 slides) with our 7 key excerpts here.

ICONIQ (h/t Vivian Guo and team) published their “State of AI: Bi-Annual Snapshot” report (44 slides, ungated). Their granular cost structure data led us to speculate:

Key message: There seem to be too few fixed costs to generate software economics.

Battery Ventures (h/t Dharmesh Thakker, Danel Dayan + co.) published their “State of AI” report (36 slides here). The report included important Battery frameworks for the impact of AI on business + financial models. For our newsletter audience, especially these pre → post AI GTM metrics:

• AE Quota: $1.0-$1.2m → $1.2m-$1.5m

• Conversion Rates: 20%-30% → 40%-50%

• Attainment: 70% → 80%

OpenAI released their own “The State of Enterprise AI” (24 pages). The strong growth across Healthcare, Manufacturing, and Finance could serve as a counterargument to AI as being overly dependent on the Technology sector adoption.

Mayfield released a report on “Enterprise Agentic AI” (n=266, h/t Gamiel Gran)

Beyond report formats, we will call out these AI articles:

• Jaya Gupta and Ashu Garg of Foundation Capital’s “AI’s trillion-dollar opportunity: Context graphs” may raise some solutions to the poor success rates covered in our “Is AI An Abject Disaster?”

• “The Brute-Force Era of AI (and What Comes After)” - Dave Kellogg

• “The Race to Own the Agentic Future” - Dave Yuan from Tidemark

• "How AI Search is Rewiring B2B Software Buying" - from G2 / Tim Sanders + co (n=1,076 B2B decision makers)

• Stratechery on “Agents Over Bubbles”

Beyond AI “State of _” Reports - Best Of 1H2026

Reports (a bit) less explicitly AI-oriented included:

[NEW COVERAGE] “Public Markets Update - May 2026” - 32 slides from Coatue

Redpoint (h/t Logan Bartlett, Adil Bhatia, and Lydia Day) released their annual “Market Update” (69 slides, our 13 key slide excerpts).

Avenir (h/t Jared Sleeper + Hannah Bao + co) published their excellent “Future of SaaS”- our excerpts here

a16z (h/t David George + co) released their “State of the Markets” - our excerpts here

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Benchmark Reports - Best Of 1H 2026

[NEW COVERAGE] Ray Rike + Benchmarkit released their flagship benchmark report (n=342). Our overall take: “fits with "maturing growth industry" theme, with signs of greater efficiency... but concerning retention trends for "legacy" vendors in an AI-world” - and slide excerpts here.

Related: Emergence Capital also released their “Beyond Benchmarks” report that builds upon their work with Benchmarkit

A staple of our reading and coverage: Fullcast x Pavilion 2026 GTM Benchmarks (n = $78 billion of sales opportunities), which warranted a newsletter edition AND a podcast with Guy Rubin of Fullcast:

FULL EPISODE: VIDEO | AUDIO: Apple Podcasts | Spotify | All Platforms

More GTM resources:

Scale Venture Partners (h/t Mark Gustaferro + Craig Rosenberg), in partnership with Ray Rike and Benchmarkit, released their GTM Benchmark interactive database.

We further analyzed their data to examine points of separation, meaning where the “best of the best” differentiate themselves from the median company. More in this edition, including a 7-page downloadable GTM benchmarking resource:

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We covered ICONIQ’s "2026 State of GTM" in this newsletter edition - TL,DR: largely positive trends:

Zylo (h/t Ben Pippenger + co) released their 2026 SaaS Management Index, covering $75b+ of SaaS spend (= massive read through on industry):

Our 1H 2026 Cloud Ratings Podcasts

Beyond our 2026 GTM benchmark episode with Guy Rubin, we were privileged to host:

“The AI Answer” - Tim Sanders - G2

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“Where Vertical AI Wins” - Patrick McGovern - Bowery Capital

SPOTIFY | APPLE | OTHER PODCAST PLATFORMS| VIDEO

“Engineering Management in the Age of AI” - Karim Fanous

SPOTIFY | APPLE | OTHER PODCAST PLATFORMS| VIDEO

About Cloud Ratings

In mid-2024, we announced a research partnership with G2 - more here:

with this slide showing how our G2-enhanced Quadrants (like our recent Sales Compensation Software) release, this business of software newsletter you are reading, our podcasts, and our True ROI practice area all fit within our modern analyst firm:

Episode Topics:

• The 2026 GTM benchmark report and what $75B in pipeline reveals about sales efficiency today.

• Why sales efficiency dropped by 28% and what’s driving the decline.

• The growing gap between top-performing sellers and the rest of the sales team.

• Why AI-generated volume does not automatically create better revenue outcomes.

• How companies using AI for rigor, qualification, and process are increasing revenue per seller.

• Why territory balance matters and how overloaded sellers lose depth, relationships, and win rates.

• Moving beyond round-robin lead distribution toward smarter routing based on seller expertise.

• Why forecasting based only on deal stage is no longer enough.

• How CRM, email, calendar, call recordings, and relationship data can create a more accurate revenue picture.

• The role of Revenue Insights as a Service for private equity firms and portfolio companies.

• Why generic BDR/SDR pipeline often creates noise instead of quality opportunities.

• How AE-led outbound, partnerships, communities, and customer introductions are becoming more efficient growth channels.

• Why ICP definition should be dynamic, data-backed, and based on real buying signals.

• How leadership teams can use simple, visual revenue insights to align around better GTM decisions.

• Why relationships still drive revenue, even in an AI-enabled sales environment.

Resources:

https://www.fullcast.com/ebooks/2026-benchmarks-report-the-state-of-gtm-in-2026/

https://www.fullcast.com/content/guy-rubin-inside-the-go-to-market-benchmark-report/

https://www.joinpavilion.com/resource/2025-gtm-benchmarks-ebsta-pavilion 

About Cloud Radio by Cloud Ratings:

Made for "full stack" cloud + SaaS operators, Cloud Radio is a SaaS podcast covers all aspects of the business of software.

Our host - Matt Harney - is active on Twitter @saasletter and writes "SaaSletter", an investing-oriented SaaS newsletter on Substack.

https://cloudradio.cloudratings.com/

https://twitter.com/SaaSletter

https://www.saasletter.com/

About Cloud Ratings:

Cloud Ratings is a customer outcomes-focused, data-driven software research analyst firm. We exist to allow organizations to make more confident, lower-risk software purchasing decisions.

Our Cloud Ratings Category Reports combine user reviews with verified vendor data to impartially identify leading software products. Recent report examples include: Board Management Software, Account-Based Marketing Software, Cap Table + Equity Management Software, Lead Routing Software, and Sales Training + Onboarding Software.

Built upon investigative customer interviews, our True ROI Reports quantify and provide 3rd party validation of a software product’s business value.

https://cloudratings.com/

“The AI Answer” - Tim Sanders - G2

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UPDATE: Can Superior NRR Offset Weak AI Gross Margins?

Amazing timing on this disclosure vs last Friday's newsletter:

The ServiceNow Investor Analyst Day from Monday included the above *AI* ACV Curve -> 4.5x implies 146% NRR.

Relative to *standard* SaaS LTVs, this implies AI LTV multiples that are:

• at 40% AI GM = 1.7x v SaaS

• at 50% AI GM = 2.2x

• at 60% AI GM = 2.6x

• at 70% AI GM = 3.0x

All of which is reflected in our modified “efficient frontier” for AI apps:

The key message: last week, we theorized about potential AI NRR curves, working from Tropic transaction data that is inherently short-term and less oriented toward enterprise orgs. With the caveat that this is an illustrative exhibit, this 146% implied AI NRR was put forward by a very sophisticated public company with a strong history of cohort unit economic disclosures.

“State of Martech in 2026”

Scott Brinker and Frans Riemersma released their flagship, ever-popular “State of Martech in 2026” (125 pages).

To add value to their excellent AI adoption impulse coverage by category, we summarized the findings:

Key messages:

• Preference For SaaS (44%) Over AI-Native (26%)

• Effectively, “no one knows” and “we are still early” → “Nascent” has the greatest representation across B2B + B2C combined.

• B2B is very “Buy AND Build” oriented (37%, #1 rank) → so a very strong demand sign

• B2C is much more mixed between Buy and Build options, with “Nascent” the dominant view (51%, #1 rank)

Scott + Frans’ report includes classics like the total martech app count (a very rare plateau… albeit at 15,505!) and new data on what is falling out of the stack:

UPDATE: Inference Is Eating the World

Last week, we covered “Software Is Eating the World (But Actually This Time)” from Siddharth Ramakrishnan of Scale Venture Partners. With our key emphasis on how inference demands go exponential with complexity:

This post from Charlie Zvibleman of AlphaSense, especially the “ auto Think Longer” note, gives a real-world example of the vertical token consumption ramp (up ~230% in 5 months for AlphaSense based on eyeballing the graph)

Dave Kellogg just published a note - “The Brute-Force Era of AI (and What Comes After)” - closely related to this inference explosion.

We hope to cover Dave’s “Brute-Force Era” in the future → in the interim, here are excerpts to get you to read the full post:

Constraint as a Driver of Efficiency

It is not coincidental that much of this efficiency work is emerging from environments where compute is constrained. When you cannot simply add more GPUs, you are forced to extract more value from the ones you have.

That dynamic has a long history in engineering. Systems built under constraint tend to be more efficient, more elegant, and more disciplined. Anyone who has written low-level code on constrained hardware has seen this firsthand. When you only have kilobytes of memory, you learn to think differently about how software is structured.

and:

We have seen this pattern repeatedly in other domains. Early cloud infrastructure was heavily overprovisioned before cost optimization became a discipline. Early web applications prioritized functionality over performance before latency and efficiency became central concerns. Early software systems accumulated features before being refactored into more coherent architectures.

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About Cloud Ratings

In mid-2024, we announced a research partnership with G2 - more here:

with this slide showing how our G2-enhanced Quadrants (like our recent Sales Compensation Software) release, this business of software newsletter you are reading, our podcasts, and our True ROI practice area all fit within our modern analyst firm:

Episode Topics:

• AI’s role in reshaping the B2B software buying journey, from early research to shortlist creation.

• The rise of “one-shotting” and how buyers now compress days of vendor research into a single AI prompt.

• How G2 reviews, category rankings, and high-authority third-party sources influence AI recommendations.

• Why enterprise buyers are moving faster toward AI-assisted buying as teams get leaner and workloads increase.

• The new importance of “winning the answer” in ChatGPT, Gemini, and deep research tools.

• Mentions vs. citations vs. recommendations: which ones actually influence software buying decisions?

• Deep research is the new buying battleground, where the final recommended vendors matter more than general visibility.

• Why AI-driven traffic may convert better than traditional search traffic.

• The content shifts from polished corporate messaging to fresh reviews, proof points, Q&A content, and human-answer-shaped thought leadership.

• YouTube transcripts, podcasts, LinkedIn, reviews, and niche publications are emerging sources of AI visibility.

• The difference between machine learning, bots, and agents.

• The executive “30% rule” for AI literacy and why leaders should reframe business problems as prediction problems.

• Why companies need to close the trust gap between AI reliability and employee adoption.

• How AI changes hiring and promotion by increasing the value of judgment, curiosity, feedback, and “taste.”

• Inside G2’s own AI-first experiments, including generative summaries, dynamic FAQs, and learning how to make complex product pages easier for machines to understand.

Resources:

https://learn.g2.com/g2-2026-ai-search-insight-report 

https://learn.g2.com/g2-2025-ai-agent-insight-report 

https://www.tryprofound.com/blog/profound-g2-partnership 

https://university.tryprofound.com/

https://6sense.com/science-of-b2b/buyer-experience-report-2025/

https://promptwatch.com/ 

https://spotlightar.com/

https://firstpagesage.com/seo-blog/generative-engine-optimization-geo-explanation/ 

https://graphite.io/five-percent/ai-is-much-bigger-than-you-think

https://www.similarweb.com/blog/marketing/geo/build-prompts-from-keywords/ 

About Cloud Radio by Cloud Ratings:

Made for "full stack" cloud + SaaS operators, Cloud Radio is a SaaS podcast covers all aspects of the business of software.

Our host - Matt Harney - is active on Twitter @saasletter and writes "SaaSletter", an investing-oriented SaaS newsletter on Substack.

https://cloudradio.cloudratings.com/

https://twitter.com/SaaSletter

https://www.saasletter.com/

About Cloud Ratings:

Cloud Ratings is a customer outcomes-focused, data-driven software research analyst firm. We exist to allow organizations to make more confident, lower-risk software purchasing decisions.

Our Cloud Ratings Category Reports combine user reviews with verified vendor data to impartially identify leading software products. Recent report examples include: Board Management Software, Account-Based Marketing Software, Cap Table + Equity Management Software, Lead Routing Software, and Sales Training + Onboarding Software.

Built upon investigative customer interviews, our True ROI Reports quantify and provide 3rd party validation of a software product’s business value.

https://cloudratings.com/

Fullcast 2026 GTM Benchmarks

While last week we covered ICONIQ’s “2026 State of GTM“ positive trends in areas like win rates and sales cycles, the latest GTM benchmarks from Fullcast paint a more sobering picture.

We’ve covered the Fullcast (formerly Ebsta pre-acquisition by Fullcast) / Pavilion benchmarks each edition for years now due to a) granularity and b) sample size (this year = $78 billion of sales opportunities). Key highlights:

Sales efficiency and win rates continue to be poor, in absolute and directional terms:

AI is accelerating seller ramps:

Poor pipeline ICP mix very likely contributes to poor absolute win rate levels

Tactical call out - outbound BDR efforts 80% below average efficiency → in a noisy AI world, is this SDR cold outreach playbook dead + a brand net negative?

Fullcast Revenue Insights As A Service

We are helping Fullcast + Guy Rubin connect with software private equity investment firms to grow their Revenue Insights as a Service (RIaaS) offering:

• quarterly deep dives into your portco’s sales machine,

• using a mix of AI/software (linked into CRMs, email, and call recordings)

• with a professional services element that yields this type of report (attached)

Full Fullcast RIaaS overview deck here + book a demo

Can Superior NRR Offset Weak AI Gross Margins?

Inspired by this AI vs SaaS NRR data via Tropic (a software spend management platform covering ~$20 billion of spend) in Lazard’s “AI Impact on Technology M&A” deck (60 slides)…

The eye-popping AI NRR of 154% inspired a theoretical cross-check: Can Superior NRR Offset Weak AI Gross Margins?

The key assumptions and rationale: SaaS unit economics are well-established, with a reasonable baseline of roughly 105% net revenue retention (NRR) and 70% gross margin.

We applied a 7-year expected customer life (84% gross retention, so below core Mid-Market / Enterprise GRR levels… while recognizing 7 years is an eternity in terms of technical disruption) and a 10% discount rate.

Under these assumptions, a $10k ACV SaaS customer has an NPV of $39k.

This NRR vs Gross Margin sensitivity table helps frame the “efficient frontier” for AI apps:

Translating a bit, AI apps can have SaaS customer unit economics at these thresholds:

• At 50% AI gross margin, need NRR of ~118%

• At 40% AI gross margin, need NRR of ~130%

• At 30% AI gross margin, need NRR of ~141%

• At 30% AI gross margin, need NRR of ~150%

Of course, 130%-%150% NRRs over 7 years translate into exiting ACVs of $48k-$113k off of a $10k starting base! Meaning you would be underwriting towards ServiceNow’s legendary cohort growth (via latest ServiceNow Investor Presentation):

Inference Is Eating the World

Siddharth Ramakrishnan of Scale Venture Partners (h/t Rory O’Driscoll) published a thoughtful “Software Is Eating the World (But Actually This Time)” where he does a good job outlining the less visible complexity and increasing inference needs in AI workflows (our emphasis = bold):

Take a voice support agent handling something simple but real, like rescheduling a medical appointment. To the customer, it feels like one conversation. Under the hood, it is a small autonomous system running continuously. As the caller speaks, a speech recognition model transcribes audio in real time. An orchestration model then reasons over the transcript, pulls the patient record, checks scheduling constraints, looks up provider availability, decides what to ask next, and calls the relevant tools. Once it has enough information, it synthesizes the result into a response, and a text-to-speech model turns that back into natural audio. In parallel, other models may be monitoring sentiment, checking compliance, or deciding whether the call should be escalated.

The system is doing all the work itself: listening, retrieving, deciding, tool-calling, verifying, and responding in a loop. An 8 minute call might contain only ~3k tokens of raw transcript, but the orchestration layer can easily consume ~40k tokens once you account for repeated reasoning over the growing conversation, retrieved context, and tool outputs, on top of continuous ASR and TTS inference running for the duration of the call. “One AI phone call” is really a multi-model inference stack operating continuously.

To help call out to the human eye what this means for token + inference needs, we translated the article charts from log to linear scale:

As always, go read the full post which includes deeper insights like:

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Avenir on Anthropic vs Microsoft

Jared Sleeper from Avenir published “How to Distribution-Mog Microsoft: Anthropic’s Path to Building the White Collar AI Platform” (16 slides). Our excerpts focus on their primary survey work, showing an interest in a) paying a premium for better AI and b) customers wanting M&A (as our research has shown, software buyers prefer platforms over point solutions, even when “best of breed”):

This 4-in-1 excerpt gives a sense of the strategic angles in the deck:

While focused on Microsoft, this thesis left a:

• stronger appreciation for the Google moat (specifically, Workspace + Gemini + Google Cloud, both in terms of compute + custom enterprise applications to the extent enterprises are also on platforms like BigQuery) in an AI world where “mogging” Microsoft has become a point of debate.

• The customer demand/interest in comms M&A is intriguing from a first principles perspective: Why would Anthropic owning Zoom or Box/Dropbox really change things? (Monday/Asana makes a bit more sense). What does this mean for feature gaps in AI <> SaaS integrations? Just a function of not wanting to do yet another integration (again, buyers do perform platforms)?

Curated Content

A status check on AI ramp at incumbents: Adobe disclosures from their Adobe Summit 2026 (n=1 here... but big!):

• $400m "AI-first ARR" = 1.5% of total ARR

• $1b "GenStudio ARR" = 3.8% of total ARR

• Generative AI credits growing +45% quarter over quarter = 377% annualized (yes, that high given exponential in QoQ at 45%!)

Lazard’s “AI Impact on Technology M&A” deck (60 slides) mentioned above is worth a rehighlight - strong aggregation of AI frameworks + studies.

This competitive framework from Goldman Sachs was interesting. TBD on the weighting of consumer familiarity - equal weighting would require a substantial increase in the “consumerization of IT” within enterprises, more than realized in past cycles… that said, AI is truly an epoch shift.

About Cloud Ratings

In mid-2024, we announced a research partnership with G2 - more here:

with this slide showing how our G2-enhanced Quadrants (like our recent Sales Compensation Software) release, this business of software newsletter you are reading, our podcasts, and our True ROI practice area all fit within our modern analyst firm:

“Where Vertical AI Wins” - Patrick McGovern - Bowery Capital

SPOTIFY | APPLE | OTHER PODCAST PLATFORMS| VIDEO

“Engineering Management in the Age of AI” - Karim Fanous

SPOTIFY | APPLE | OTHER PODCAST PLATFORMS| VIDEO

Redpoint Ventures - Market Update

Redpoint (h/t Logan Bartlett, Adil Bhatia, and Lydia Day) made a strong contribution to 2026’s “State of _” genre with their Market Update (69 slides, our 13 key slide excerpts). Selected highlights below:

G2 On AI x Software Buyer Search

"How AI Search is Rewiring B2B Software Buying" - from G2 / Tim Sanders + co (n=1,076 B2B decision makers)

#1 - Godard Abel was right on our earlier podcast - AI expands the shortlist! (direct clip linked). Anecdotally, in our own buying AI, both G2’s Chatbot and Gemini have identified vendors we otherwise would not have considered.

#2 - huge volume of insights in the report - like 71%(!) of the B2B software funnel starts with a chat prompt - especially at the CMO level. Huge implications for monitoring, content, attribution, pipeline visibility, and more!

ICONIQ 2026 GTM Benchmark Update

A largely positive "2026 State of GTM" from ICONIQ:

- Consistently improving win rates, an important sign relative to consistent win rate declines in 2025.

- Those challenging closing rates meant forecasts and pipeline coverage were significantly strained in 2025 (see slides from our Cloud Ratings’ “2025 Value Creation Summit”)

- Back to ICONIQ, positive trends in cost/opportunity and sales cycles.

Curated Content

Ciridae (an AI workflow architecture start-up, backed by A16Z and General Catalyst; h/t Felix Stocker & Jai Kondapalli) released an interesting AI transformation explorer, by sector and even by private equity firm. We made a quick recut to visualize the findings:

Sapphire Ventures (h/t Kevin Burke + co) March Market Memo included good coverage of AI agents…

along with this Stratechery “Agents Over Bubbles” take.

Apoorv Agrawal from Altimeter on “The Economics of Generative AI”

March 2026 B2B AI Interest Index from Cloud Ratings

We’ve updated our Cloud Ratings B2B AI Interest Index through March 2026 - full slides below:

B2B AI Interest Index

Thematic Category Interest (n = 47 sub-categories tracked - i.e. “manufacturing AI” or “supply chain AI”) rebounded and reversed poor winter trends (while noting inherent seasonal lows in December).

Bellwether Microsoft Copilot continues to stay at a higher plateau. Other “Blue Chips” (i.e., Hubspot, ServiceNow) remain range bound.

SaaS Incumbents (n=340: the same 340 vendors tracked in our top-of-the-funnel focused, forward-looking SaaS Demand Index) performed strongly in Q1 2026.

About Cloud Ratings

In mid-2024, we announced a research partnership with G2 - more here:

with this slide showing how our G2-enhanced Quadrants (like our recent Sales Compensation Software) release, this business of software newsletter you are reading, our podcasts, and our True ROI practice area all fit within our modern analyst firm:

Episode Topics:

• How Bowery approaches pre-seed in 2026 and what “first institutional capital” looks like today.

• The shift from “SaaS contracts” to outcome-driven and services-driven models.

• Pat’s vertical AI playbook.

• Case study on Enata and how voice-first AI gives field sellers time back while improving CRM quality and sales insights.

• Why more startups may compete directly for industry spend rather than selling tools to incumbents.

• What Pat wants in founders.

• The new definition of technical in the AI era.

• How to run customer pilots well, including shorter timelines, mid-point check-ins, proof-of-value gates, and prioritizing referenceable logos.

Resources:

https://x.com/pw_mcgovern?lang=en
https://www.linkedin.com/in/pwmcgovern
https://bowerycap.com/
https://capitalefficient.substack.com/

About Cloud Returns by Cloud Ratings:

Cloud Returns covers ALL types of software investing, whether seed, venture capital, growth equity, private equity, debt, and the public markets.

Our host - Matt Harney - is active on Twitter @saasletter and writes "SaaSletter", an investing-oriented SaaS newsletter on Substack.

https://cloudradio.cloudratings.com/

https://twitter.com/SaaSletter

https://www.saasletter.com/

About Cloud Ratings:

Cloud Ratings is a customer outcomes-focused, data-driven software research analyst firm. We exist to allow organizations to make more confident, lower-risk software purchasing decisions.

Our Cloud Ratings Category Reports combine user reviews with verified vendor data to impartially identify leading software products. Recent report examples include: Subscription Management Software, Session Replay Software, Sales Compensation Software, and Digital Sales Room Software

Built upon investigative customer interviews, our True ROI Reports quantify and provide 3rd party validation of a software product’s business value.

https://cloudratings.com/

Episode Topics:

• How Bowery approaches pre-seed in 2026 and what “first institutional capital” looks like today.

• The shift from “SaaS contracts” to outcome-driven and services-driven models.

• Pat’s vertical AI playbook.

• Case study on Enata and how voice-first AI gives field sellers time back while improving CRM quality and sales insights.

• Why more startups may compete directly for industry spend rather than selling tools to incumbents.

• What Pat wants in founders.

• The new definition of technical in the AI era.

• How to run customer pilots well, including shorter timelines, mid-point check-ins, proof-of-value gates, and prioritizing referenceable logos.

Resources:

https://x.com/pw_mcgovern?lang=en
https://www.linkedin.com/in/pwmcgovern
https://bowerycap.com/
https://capitalefficient.substack.com/

About Cloud Returns by Cloud Ratings:

Cloud Returns covers ALL types of software investing, whether seed, venture capital, growth equity, private equity, debt, and the public markets.

Our host - Matt Harney - is active on Twitter @saasletter and writes "SaaSletter", an investing-oriented SaaS newsletter on Substack.

https://cloudradio.cloudratings.com/

https://twitter.com/SaaSletter

https://www.saasletter.com/

About Cloud Ratings:

Cloud Ratings is a customer outcomes-focused, data-driven software research analyst firm. We exist to allow organizations to make more confident, lower-risk software purchasing decisions.

Our Cloud Ratings Category Reports combine user reviews with verified vendor data to impartially identify leading software products. Recent report examples include: Subscription Management Software, Session Replay Software, Sales Compensation Software, and Digital Sales Room Software

Built upon investigative customer interviews, our True ROI Reports quantify and provide 3rd party validation of a software product’s business value.

https://cloudratings.com/

Episode Topics:

• How Bowery approaches pre-seed in 2026 and what “first institutional capital” looks like today.

• The shift from “SaaS contracts” to outcome-driven and services-driven models.

• Pat’s vertical AI playbook.

• Case study on Enata and how voice-first AI gives field sellers time back while improving CRM quality and sales insights.

• Why more startups may compete directly for industry spend rather than selling tools to incumbents.

• What Pat wants in founders.

• The new definition of technical in the AI era.

• How to run customer pilots well, including shorter timelines, mid-point check-ins, proof-of-value gates, and prioritizing referenceable logos.

Resources:

https://x.com/pw_mcgovern?lang=en
https://www.linkedin.com/in/pwmcgovern
https://bowerycap.com/
https://capitalefficient.substack.com/

About Cloud Returns by Cloud Ratings:

Cloud Returns covers ALL types of software investing, whether seed, venture capital, growth equity, private equity, debt, and the public markets.

Our host - Matt Harney - is active on Twitter @saasletter and writes "SaaSletter", an investing-oriented SaaS newsletter on Substack.

https://cloudradio.cloudratings.com/

https://twitter.com/SaaSletter

https://www.saasletter.com/

About Cloud Ratings:

Cloud Ratings is a customer outcomes-focused, data-driven software research analyst firm. We exist to allow organizations to make more confident, lower-risk software purchasing decisions.

Our Cloud Ratings Category Reports combine user reviews with verified vendor data to impartially identify leading software products. Recent report examples include: Subscription Management Software, Session Replay Software, Sales Compensation Software, and Digital Sales Room Software

Built upon investigative customer interviews, our True ROI Reports quantify and provide 3rd party validation of a software product’s business value.

https://cloudratings.com/

Episode Topics:

• Why VPE is “people + product + processes”  and how breakdowns show up as business problems (growth, quality, churn, GTM).

• How “buggy processes” reveal themselves at the exec level through growth stalls, quality pain, churn, and GTM friction?

• Executive interview questions that reveal whether an engineering leader can connect engineering to business reality.

• The common scaling fallacy: doubling engineers does not double output.

• “AI made us faster… not necessarily better”: how AI boosts strong engineers, but used carelessly amplifies technical debt.

• Why human review won’t scale in an agent-driven world and why software will need machine verification to move at “GPU speed.”?

• Engineering shifts toward architecture, integration, trade-offs, experiments, and validation.

• The “10x engineer” debate in the AI era.

• AI security risks: non-deterministic systems + autonomous actions require controls beyond RBAC.

• Inside StrongDM’s approach on agents treated like coworkers with identities, visibility, and feedback that improves behavior.

Resources:
https://www.cummulative.io/ 

https://www.linkedin.com/in/karimfan
https://www.strongdm.com/
https://www.cummulative.io/p/ai-agents-are-here-security-isnt 

https://www.cummulative.io/p/preparing-for-a-world-where-humans 

https://www.cummulative.io/p/how-do-you-interview-a-vp-of-engineering 

About Cloud Radio by Cloud Ratings:

Made for "full stack" cloud + SaaS operators, Cloud Radio is a SaaS podcast covers all aspects of the business of software.

Our host - Matt Harney - is active on Twitter @saasletter and writes "SaaSletter", an investing-oriented SaaS newsletter on Substack.

https://cloudradio.cloudratings.com/

https://twitter.com/SaaSletter

https://www.saasletter.com/

About Cloud Ratings:

Cloud Ratings is a customer outcomes-focused, data-driven software research analyst firm. We exist to allow organizations to make more confident, lower-risk software purchasing decisions.

Our Cloud Ratings Category Reports combine user reviews with verified vendor data to impartially identify leading software products. Recent report examples include: Board Management Software, Account-Based Marketing Software, Cap Table + Equity Management Software, Lead Routing Software, and Sales Training + Onboarding Software.

Built upon investigative customer interviews, our True ROI Reports quantify and provide 3rd party validation of a software product’s business value.

https://cloudratings.com/

ICONIQ (h/t Vivian Guo and team) recently released its “State of AI: Bi-Annual Snapshot” report (44 slides, ungated).

Key call-outs: while AI product gross margins are projected to increase by ~700 bps (‘25: 45% → ‘26e: 52%):

… the fabulously granular cost structure breakdown makes those margin increases look optimistic. “Talent” - a largely fixed cost - is only 26%-28% of the cost structure for the GA to Scaling stages. All other costs are highly variable, such as inference and cloud costs. Even for model training, there is little fixed cost leverage since model training is an ongoing process. Key message: There seem to be too few fixed costs to generate software economics.

Notably, larger organizations (>$500m revenue) are the leading deployers of customer-facing agentic AI.

The increasing AI deployment and returns to scale are consistent with our 2023 formula below (with emphasis on the volume variable) and Morgan Stanley findings that large enterprises are achieving the highest ROIs:

AI ROI = (Business Process Value x Volume) / AI Costs

Back to ICONIQ, flagging this data + commentary as a headwind: “Internal AI is increasingly funded through R&D budgets, with fewer companies relying on net new budget allocation.” This could have implications for both AI vendors (i.e., hitting budgetary guardrails + enterprise reality checks) and non-AI software vendors (i.e., AI truly competing in the same IT dollar pool rather than a separate “innovation” budget).

If you know, you know (“IYKYK”), any and all AI model market share charts attract a lot of “attention” amongst VCs:

And yet another data point for the “buy versus build” component of the “Software Is Dead” daily debate:

December 2025 B2B AI Interest Index from Cloud Ratings

We’ve updated our Cloud Ratings B2B AI Interest Index through December 2025 - full slides below:

B2B AI Interest Index

Thematic Category Interest (n = 47 sub-categories tracked - i.e. “manufacturing AI” or “supply chain AI”) registered a notable decline. Note: holiday seasonality will be at play throughout this month’s report. Beyond seasonality, as AI-native vendor brands strengthen, generalized product searches might be losing share to brand-targeted search.

Bellwether Microsoft Copilot continues to stay at a higher plateau. Other “Blue Chips” (i.e., Hubspot, ServiceNow) had a weak December (down 4% YoY, so not just seasonality here).

SaaS Incumbents (n=340: the same 340 vendors tracked in our top-of-the-funnel focused, forward-looking SaaS Demand Index) registered a seasonal MoM dip, but interest in the Incumbent basket is up 119% YoY.

About Cloud Ratings

In mid-2024, we announced a research partnership with G2 - more here:

with this slide showing how our G2-enhanced Quadrants (like our recent Sales Compensation Software) release, this business of software newsletter you are reading, our podcasts, and our True ROI practice area all fit within our modern analyst firm:

Scale Venture Partners (h/t Mark Gustaferro + Craig Rosenberg), in partnership with Ray Rike and Benchmarkit, recently debuted their GTM Benchmark interactive database.

We further analyzed their data to examine points of separation, meaning where the “best of the best” differentiate themselves from the median company.

Our downloadable 7-page document includes more on GTM motion (i.e., PLG vs Inside Sales vs Outside Sales vs Channel) and volatility:

DOWNLOAD HERE

A caveat: Some of these variances are structural (i.e., an enterprise ERP vendor will have a significantly longer sales cycle, regardless of execution; or a PLG vs Channel vs Sales-Led motion will have many different baseline metrics). Taking into account that dynamic, our key call-outs:

SALES REP TURNOVER: 773% Delta

This is the single most glaring “off the chart” stat in the entire dataset.

For the top 10% of companies, annual sales rep turnover is significantly lower than the mean, with a 773% difference between the best and the average.

The elite performers are playing a different game: Retention. You cannot build compounding momentum when you are constantly replacing your GTM engine parts. The “Best of the Best” have essentially solved the turnover problem, keeping institutional knowledge in-house.

CAC PAYBACK PERIOD: 192% Delta

The separation here is stark: the 90th percentile outperforms the average by 192%.

While the average company struggles with payback periods that drag on, top-tier firms are recovering their acquisition costs nearly 2x faster. Which allows them to reinvest in growth cycles significantly faster than their peers.

QUOTA + RAMP: >100% Delta

It’s all about having ramped productive reps hitting their number.

Exceeding Quota: The separation for “% of Reps Exceeding Quota” in the most recent period is 121%.

Ramp Speed: This is fueled by a 107% advantage in “Time for Rep to Fully Ramp”.

This suggests the “Best” aren’t just hiring better; they also have superior enablement, Finance → GTM hiring coordination, and territory-planning infrastructure.

Related Research Call Out:

Declining quota attainment and slower ramps have been relentless long-term trends in a “maturing growth industry” - see our 2024 “State of Software” (Full slides, 1st presented at Bowery Capital’s 2024 AGM) - perhaps making top decile outperformance all the more impressive.

INBOUND HANDRAISERS: 129% Delta

The “Best of the Best” generate 129% more pipeline from Inbound Hand Raisers than the average.

Put differently, the top decile has a “Brand Dividend” with enough market pull that prospects come to them.

Reminder - we have the Scale GTM Benchmarks available in a downloadable PDF here:

Scale GTM Benchmark - 7 Page Analysis

Curated Content + Upcoming Coverage

Dave Kellogg released his excellently comprehensive 2026 Predictions →

His callout of Jaya Gupta and Ashu Garg of Foundation Capital’s “AI’s trillion-dollar opportunity: Context graphs” may raise some solutions to the poor success rates covered in our “Is AI An Abject Disaster?”

Flagship reports have been in full bloom this January → we hope to cover these individually… but might as well give you the underlying links too:

Avenir (h/t Jared Sleeper + Hannah Bao + co) published their excellent “Future of SaaS”- our excerpts here

a16z (h/t David George + co) released their “State of the Markets” - our excerpts here

Mayfield released a report on “Enterprise Agentic AI” (n=266, h/t Gamiel Gran)

About Cloud Ratings

In mid-2024, we announced a research partnership with G2 - more here:

with this slide showing how our G2-enhanced Quadrants (like our recent Sales Compensation Software) release, this business of software newsletter you are reading, our podcasts, and our True ROI practice area all fit within our modern analyst firm:

Battery Ventures (h/t Dharmesh Thakker, Danel Dayan + co recently released their “State of AI” report (36 slides here).

Beyond strong core “State of _” type slides like this example:

… the report included important Battery frameworks for the impact of AI on business + financial models. For our newsletter audience, especially these pre → post AI GTM metrics:

• AE Quota: $1.0-$1.2m → $1.2m-$1.5m

• Conversion Rates: 20%-30% → 40%-50%

• Attainment: 70% → 80%

November 2025 B2B AI Interest Index from Cloud Ratings

We’ve updated our Cloud Ratings B2B AI Interest Index through November 2025 - full slides below:

B2B AI Interest Index Slides

Thematic Category Interest (n = 47 sub-categories tracked - i.e. “manufacturing AI” or “supply chain AI”) registered a notable decline. Note: holiday seasonality might be at play throughout this month’s report.

Bellwether Microsoft Copilot continued its broad acceleration. Other “Blue Chips” (i.e., Hubspot, ServiceNow) registered a poor November.

SaaS Incumbents (n=340: the same 340 vendors tracked in our top-of-the-funnel focused, forward-looking SaaS Demand Index) registered a poor November, especially relative to a tough October comp. That said, the n=340 Incumbent trendline remains overall positive for 2H 2026.

Bonus Coverage: OpenAI “The State of Enterprise AI”

OpenAI released their own “The State of Enterprise AI” (24 pages). These excerpts should speak for themselves + pique your interest in the full report.

The strong growth across Healthcare, Manufacturing, and Finance could serve as a counterargument to AI as being overly dependent on Technology sector adoption.

About Cloud Ratings

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