The Official SaaStr Podcast: SaaS | Founders | Investors

Lessons from Arvind Nithrakashyap, Co-Founder and CTO of Rubrik, on scaling cyber resilience platforms, building multiple product curves, and implementing AI across both products and operations.

Company Snapshot:

* Founded: January 2014 (11 years)

* Current ARR: $1.09B+ (Q1 FY2025)

* Growth Rate: 39% YoY ARR growth, 47% revenue growth

* NPS Score: 80 (exceptionally high for enterprise software)

* Net Revenue Retention: 133% (as of Jan 2024)

* Customers: 2,246 customers with $100K+ ARR contracts

* IPO: April 2024 on NYSE (RBRK) at $5.6B valuation

* What Rubrik Does: • Zero Trust Data Security platform for cyber resilience • Data protection, backup & recovery across hybrid cloud environments • Data security posture management and threat detection • AI-powered data governance and compliance solutions • Ransomware recovery with $10M warranty program

After 11 years of relentless execution, Rubrik has achieved what many B2B companies only dream of: crossing the $1 billion ARR milestone with 39% year-over-year growth and an extraordinary 80 NPS score. But perhaps more impressive than these numbers is how Co-Founder and CTO Arvind Nithrakashyap has positioned the company at the intersection of two of enterprise software’s most critical trends: cybersecurity and artificial intelligence.

In a recent deep dive at SaaStr Annual + AI Summit 2025, Arvind shared the tactical playbook behind Rubrik’s scale, revealing counterintuitive strategies for product development, customer success, and AI adoption that challenge conventional SaaS wisdom.

The Multi-Product Growth Engine: Why “Say No to 95%” Doesn’t Work at Enterprise Scale

Most SaaS advice tells founders to focus ruthlessly and say no to 95% of requests. Arvind fundamentally disagrees—at least when it comes to large enterprise customers.

“I don’t believe in saying no to 95% of requests. You have to say yes to everything with large Fortune 500 customers. If you miss a $100K deal today, you’re potentially walking away from a 20-30x lifetime value down the road.”

This philosophy has driven Rubrik’s expansion from a core data protection platform to multiple product pillars spanning data protection, data security, and AI enablement. But saying yes to everything requires a systematic approach to innovation and go-to-market execution.

The Hackathon-to-Product Pipeline

Rubrik’s new product development follows a surprisingly grassroots approach. Almost every product in their current portfolio started as a 24-hour hackathon project:

* 10 hackathons over 11 years with consistent execution since year two

* 130 submissions in their most recent hackathon

* Top 10 projects get formal review and consideration

* Systematic evaluation process to identify commercial potential

“It all starts with a few engineers spending 24 hours hacking up something and saying, ‘hey, here’s an interesting idea,'” Arvind explains. This bottom-up innovation model ensures product ideas come from technical feasibility rather than market research alone.

The Three-Stage Go-to-Market Conveyor Belt

Once a product shows promise, Rubrik deploys what they call their “RX” process—a systematic go-to-market incubation engine:

Stage 1: Go-to-Market Incubation (0 to $10M)

* Dedicated team that only gets paid for selling the new product

* Cross-functional squad: engineers, product managers, sales, marketing

* Rapid iteration on both product and messaging

* Deep customer discovery and market validation

Stage 2: Product Line Sales (10 to $100M)

* Scale the successful formula to 10-20 dedicated sellers

* Continued focus on the specific product line

* Refinement of sales processes and customer success playbooks

Stage 3: Core Sales Integration ($100M+)

* Integration with the main sales organization

* Mature product with proven market fit

* Established messaging and sales processes

This approach solves a critical challenge most SaaS companies face: how do you enable a large sales team to sell new products without disrupting their core business performance?

Customer Success at Scale: The Transparency Advantage

Achieving an 80 NPS score while scaling to $1B+ ARR seems almost impossible. Arvind attributes this to two core principles that were established from day one.

Founder-Level Customer Obsession

Both Arvind and his co-founder were literally the first support team members, taking calls directly from beta customers. This wasn’t just a startup necessity—it was a cultural decision that permeated the entire organization.

“There was an early-stage customer—a law firm at 4 PM—that had an email system issue. I literally booked a flight that night, took the red eye, was with them for two days sitting there until it was resolved, then flew back.”

This level of commitment created a “customer-first” DNA that scaled throughout the organization. Today, when any customer has a problem, engineers still drop everything to help resolve it.

Radical Transparency in Customer Relationships

Perhaps counterintuitively, Rubrik’s approach to customer challenges is radical honesty, even when it means admitting they can’t meet requirements.

Arvind shared a telling example: “A very large retailer in Europe came with huge Oracle database requirements. They said, ‘without this, we’re not going to buy you.’ After evaluation, we realized we couldn’t meet their 7x reduction requirement. So my engineer and I went in front of the customer and said, ‘You know what? What you want today isn’t going to happen today. But we will invest in this, and in six months we’ll be able to deliver it to you. But if you want to go with somebody else, we would totally understand.’ The next day they came back and said, ‘We’ll go with you.'”

This approach works because it builds trust. Customers prefer honest assessment over over-promising and under-delivering.

AI Strategy: Product Enhancement vs. Platform Enablement

Rubrik’s AI strategy operates on two distinct tracks, each addressing different market needs and opportunities.

Track 1: AI-Enhanced Product Experience

Rubrik’s first AI initiative focuses on making their complex platform more accessible through “Ruby”—an AI companion for their SaaS platform.

The Problem: Rubrik’s platform handles data across data centers, cloud VMs, databases, object storage, and SaaS applications. When security incidents occur, IT personas often lack the knowledge to respond effectively.

The Solution: Ruby serves as an intelligent assistant that:

* Explains security concepts and IOCs (Indicators of Compromise)

* Recommends next steps based on specific scenarios

* Queries the product through APIs to provide contextual answers

* Leverages knowledge bases, documentation, and support articles

This isn’t just a chatbot—it’s a sophisticated system designed to enable self-service support and reduce the expertise barrier for complex cybersecurity operations.

Track 2: Enterprise AI Data Platform

The second track addresses a broader market opportunity: enabling enterprises to build AI applications securely.

Arvind observed that “80% of AI POCs don’t make it to production,” often because of data access and security challenges rather than AI model limitations. Enterprises struggle with:

* Data Access: Getting the right data for specific use cases

* Permission Management: Ensuring users only access authorized data

* Sensitive Data Protection: Preventing financial documents or PII from being exposed to unauthorized users

Rubrik realized their 11-year investment in enterprise data management positioned them perfectly to solve these problems:

* Unified Data Platform: All enterprise data in a single, accessible platform

* Automatic Refresh: Data updated daily or hourly

* Permission Understanding: Built-in access controls

* Sensitive Data Masking: Automatic detection and suppression of sensitive information

Their new product, Anapurna, aims to let enterprises “focus on your business use case, focus on your workflow, focus on building the right kind of agents, but don’t worry about the data.”

Internal AI Adoption: Legal-Led Innovation

Rubrik’s internal AI adoption strategy offers a fascinating case study in organizational change management, particularly because it’s led by their VP of Legal—an unconventional choice that has proven highly effective.

AI Governance Committee

Rather than treating AI adoption as purely a technology initiative, Rubrik established an AI governance committee including InfoSec, Legal, and business stakeholders. This committee evaluates AI tools and use cases for security and compliance before deployment.

The AI Summit Approach

Recognizing that grassroots adoption was happening regardless of top-down initiatives, Rubrik held an internal AI summit to:

* Consolidate Tools: Reduce the proliferation of different AI tools across teams

* Share Best Practices: Enable cross-functional learning about what works

* Platform Strategy: Prioritize building on Rubrik’s own platform where possible

* Education: Provide basic education on generative AI, RAG, and implementation approaches

Adoption Challenges

Despite providing tools and training, Arvind notes that “even if you put these tools out there, people just don’t adopt it easily. They try it once, doesn’t work, they’re like, ‘oh no, I’m not going to go there.’ So you’ve got to keep pushing people to go adopt it.”

This highlights a critical insight: AI adoption isn’t just about tool availability—it requires ongoing change management and support.

Measuring AI ROI: Business Metrics Over AI Metrics

Perhaps the most tactical insight from Arvind’s approach is how Rubrik measures AI success. Rather than focusing on AI-specific metrics, they prioritize business outcomes.

Engineering AI Metrics

Primary Business Metrics:

* Roadmap Delivery: Reduction in net delay for planned releases (measuring if Q3 deliveries actually happen in Q3 vs. 15-30 days later)

* Quality Index: Defects per account and support cases per account to ensure speed doesn’t compromise quality

Secondary AI Metrics:

* Percentage of code generated by AI

* Mean Time to Resolution (MTTR) for defects

* Other technical efficiency measures

The Business-First Philosophy

“At the end of the day, we measure ourselves by: did these business metrics actually go down? If they didn’t, then it doesn’t matter. I can do 100% AI code. If we don’t meet those business metrics, it makes no difference to the business.”

This approach ensures AI initiatives stay grounded in actual business value rather than getting caught up in technology for its own sake.

Key Takeaways for SaaS Leaders

Rubrik’s journey from startup to $1B+ ARR platform offers several counterintuitive lessons for SaaS leaders:

1. Enterprise Strategy: Say yes to large customers, then figure out how to deliver efficiently

2. Innovation Process: Systematic hackathon-to-product pipelines can drive meaningful innovation

3. Go-to-Market: Use dedicated incubation teams before scaling to core sales

4. Customer Success: Radical transparency builds stronger relationships than over-promising

5. AI Product Strategy: Consider both product enhancement and platform enablement opportunities

6. AI Adoption: Legal and compliance stakeholders should be partners, not obstacles

7. AI Measurement: Business metrics matter more than AI-specific metrics

As enterprises navigate the intersection of cybersecurity and AI, Rubrik’s approach demonstrates that success comes not from choosing between these priorities, but from systematically addressing both through disciplined execution and customer-centric innovation.

The companies that will lead the next decade of enterprise software won’t just be AI companies or cybersecurity companies—they’ll be the ones that solve real business problems at the intersection of multiple technology trends, backed by the operational discipline to scale efficiently while maintaining extraordinary customer satisfaction.

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Rob Giglio, Canva's Chief Customer Officer, shares the playbook for scaling from 170M to 230M monthly active users while building enterprise sales without compromising company culture.

Top 5 Takeaways

1. PLG-to-SLG is Far Easier Than SLG-to-PLG Going from product-led growth to sales-led growth is significantly easier than the reverse. Companies with strong PLG foundations have rich product usage data and customer engagement signals that can be leveraged for enterprise sales. The data infrastructure is already there—it just needs to be redirected toward sales motions.

2. Don't Build Two Cultures—Extend One Canva deliberately avoided creating a separate "enterprise culture" with traditional deal-making behaviors. Instead, they extended their existing customer-first culture into enterprise sales, maintaining their philosophy of "give value first, get value second" even in complex B2B transactions.

3. Treat All Customers Like Gold, Regardless of Revenue Don't segment customer success based on how much customers pay. A free user helping their kid with homework might be the CCO of a Fortune 500 company. Canva focuses on helping customers solve problems and move through their journey, knowing that value creation leads to revenue growth.

4. Post-Sale Orchestration Should Match Pre-Sale Excellence SaaS companies excel at customer acquisition funnels but often neglect post-sale orchestration. Since most SaaS businesses have larger install bases than new customer acquisition, the real value lies in orchestrating exceptional post-sale experiences with the same rigor applied to marketing funnels.

5. Data → Signals → Actions Framework Effective enterprise sales in a PLG environment requires a systematic approach: collect product usage data, transform it into actionable signals, and create specific actions for sales teams. This isn't cold outbound—it's warm outreach to companies already using your product.

AI at Canva: 16 Billion Interactions and Counting

Before diving into enterprise strategy, it's worth understanding the scale of AI adoption at Canva. The company has generated 16 billion instances of AI usage, ranking among the top AI use cases globally alongside industry leaders.

Canva approaches AI through two distinct lenses:

Customer-Facing AI: Making Design Accessible

The majority of Canva's AI focus goes toward customer-facing features that help users scale imagery, ads, and content creation. This isn't about flashy AI demonstrations—it's about solving real design problems for millions of users who lack professional design skills.

The philosophy mirrors Canva's original mission: take complex things (professional design) and make them simple (accessible to everyone). AI becomes another tool in this democratization effort.

Internal AI: Augmenting Rep Performance

For internal operations, Canva focuses on identifying complex workflows and experiences for sales and customer success reps, then "stitching AI next to those to make them easier."

The Framework: Data → Signals → Actions

* Data: Rich product usage information from PLG motion

* Signals: AI-powered insights about customer behavior and needs

* Actions: Specific next steps for reps based on AI analysis

Build vs. Buy Strategy: Rather than building custom AI tools, Canva starts with existing market solutions and implements them into workflows. They're "willing to try almost anything if it's a reasonable investment."

Scaling Philosophy: The same customer-first approach that built their PLG motion now guides AI implementation. Get it right for customers first, then optimize for internal teams.

This AI foundation becomes crucial for the enterprise motion, where the ability to process signals from hundreds of thousands of users and turn them into actionable sales intelligence makes warm outbound possible at scale.

The Journey from 170M to 230M Monthly Active Users

When Rob Giglio joined Canva as Chief Customer Officer, the company had already built one of the most successful PLG motions in SaaS history. But to fulfill their mission of "empowering the world to design," they needed to reach users inside enterprises—environments where IT administrators control software access.

The challenge wasn't just building enterprise sales; it was doing so without compromising the culture that made Canva special.

Green Flags: The PLG Advantage

Canva entered enterprise sales with several advantages that pure-play enterprise companies lack:

Rich Product Usage Data: Years of PLG operation had generated massive amounts of user behavior data. While this data wasn't initially configured for sales use, the foundation was solid.

Enterprise Product Intent: Rather than trying to force-fit a consumer product into enterprise environments, Canva was intentionally building enterprise-specific features and capabilities.

Proven Product-Market Fit: With 170M monthly active users (now 230M) completing designs—not just opening the app—Canva had demonstrated real value creation at scale.

Red Flags: The Cultural Challenge

The transition wasn't without risks:

No Enterprise Sales Experience: The team had never built a sales-led motion before.

Touchless Culture: The company culture was built around organic, network-driven adoption without human intervention.

Redefining Network Effects: They needed to understand what "organic network-led adoption" looks like inside an enterprise environment.

The Solution: Customer-Centric Organization Design

Rather than traditional sales titles, Canva adopted the "Chief Customer Officer" structure—organizing all customer-facing teams (sales, success, operations) under one leader focused on the customer. This approach:

* Forces organizational focus back to the customer

* Breaks down silos between pre-sale and post-sale teams

* Eliminates the artificial separation between PLG and enterprise customers

The Enterprise Sales Playbook

1. Unified Customer Population

Canva rejected the traditional model of separate teams for PLG users versus enterprise customers. Instead, they view all users as part of one population with different needs and engagement preferences.

The Philosophy: Whether someone pays $0, $100, or $100,000, they're solving design problems. The support model should scale based on complexity and volume, not arbitrary revenue thresholds.

2. Signal-Based Sales Development

The enterprise motion leverages PLG data through a structured framework:

* Network Effects Within Organizations: Track multi-user adoption and collaboration patterns within company domains

* Digital Touchpoints First: Send automated signals through email, in-product notifications, and retargeting before human outreach

* Warm Outbound: Sales development focuses on companies already using Canva, not cold prospects

3. Process and Skill Integration

Canva implemented Winning by Design's SPICED methodology with their own cultural adaptations. But process alone isn't enough—the underlying skills matter more:

* Clear Problem Definition: Daily focus on the specific problems being solved for customers

* Genuine Conversations: Emphasis on empathetic, authentic customer interactions over scripted pitches

* Cross-Team Collaboration: Customer Success, AEs, and BDRs work together rather than in separate silos

AI and Scaling Philosophy

Canva approaches AI implementation with the same customer-first philosophy:

Customer-Facing AI: 16 billion instances of AI usage for design creation, ranking among top AI use cases globally.

Internal AI: Focus on making complex workflows easier for sales and success teams, using existing tools rather than building custom solutions.

Data-Driven Framework: The same "Data → Signals → Actions" framework guides AI implementation, ensuring AI serves the customer journey rather than replacing human judgment.

The Economics of Customer Success

One of Rob's most compelling arguments centers on the economics of customer support. Most SaaS companies excel at customer acquisition orchestration but underinvest in post-sale experiences.

The Math: In most SaaS businesses, the install base significantly exceeds new customer acquisition in any given quarter. Yet companies often spend 80% of their orchestration effort on acquisition and 20% on retention and expansion.

The Opportunity: Flipping this ratio—or at least achieving parity—can dramatically improve unit economics and reduce churn while increasing expansion revenue.

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The Most Unconventional CMO Story in B2B

Raaz Herzberg joined Wiz as employee #5. She led product for 2.5 years. Then their CEO asked her to take over marketing.

Her response? "No. What are you talking about?"

Today, Wiz is in the process of being acquired for a stunninga $32 billion by Google. And Raaz just shared exactly how she built their marketing engine from zero marketing experience to hyper-growth success.

Here are the key lessons every founder and marketer needs to know:

The Core Problem That Changes Everything

"Nobody had heard of Wiz."

That was it. That was Raaz's entire marketing strategy in the beginning. Not MQLs, not pipeline attribution, not brand awareness metrics. Just: people need to know we exist.

The pain was real: "We would meet a customer, have a great first meeting, and they would say 'I wish I heard about you three months ago when I just signed with one of your competitors.'"

The lesson: Sometimes the most sophisticated marketing strategy is solving the most obvious problem first.

How to Stand Out When You Know Nothing About Marketing

Roz's advantage wasn't marketing expertise—it was domain expertise. She knew cybersecurity inside and out. She knew how everything else in the space looked and sounded.

Her strategy: Be different on purpose.

"Security products were typically black and red, scaring people about bad things that could happen. As a technical practitioner, I never wanted to hear that. My job is hard enough."

So Wiz went the opposite direction:

* Made security fun and approachable

* Used inside jokes the community would get

* Created content that practitioners actually wanted to consume

Example: They released a meditation app for cyber practitioners on April Fool's Day—with guided meditation sessions full of deep cyber jokes. It went viral in their exact target audience.

The lesson: Your biggest competitive advantage might be knowing your audience better than traditional marketers do.

The Hiring Philosophy That Built a $23B Company

"Every single person you hire should be your best hire ever. Unless you leave an interview very excited—like 'I wish this person was here right now'—don't hire them."

Raaz is obsessed with hiring. She spends at least a couple hours per week on it, even as CMO of a $23B company.

Her test for identifying "doers" vs "strategists": When asking about problems they've solved, doers can talk about every tiny detail. People who just designed the process stop at a high level.

Her first hires: Not cyber experts. She already knew cyber. She hired people who knew how to make things go viral and could execute end-to-end.

The lesson: Hire for curiosity and execution ability over domain expertise when you already have the domain covered.

The Untraditional Marketing Org Structure That Actually Works

A year ago, Raaz did something radical: She moved field marketing out of her org and into sales.

"I found that being a successful field marketer has more to do with tight alignment with your regional sales director. That's the KPI—are they happy with the field marketer?"

She kept brand, content, and demand gen. Sales got field marketing, events, and regional activation.

The result: Better alignment, clearer ownership, and field marketing that actually works for sales.

The lesson: Don't be afraid to break traditional org structures if it serves the business better.

How AI is Changing Marketing (According to Someone Actually Using It)

"Every single person on my team has dramatically transformed how they work. In team meetings, every person has to show what they're doing with AI."

Raaz's take: AI doesn't replace superstars—it makes superstars 100x more effective.

"I've definitely slowed hiring. Amazing people are now 100 times more effective. But I still need amazing people."

Example: They built a "Wiz spell checker" GPT that every employee uses for customer communications. It ensures technical accuracy, brand compliance, and the right tone (humble, never arrogant).

The lesson: Don't try to reskill average performers for AI. Hire curious people who will figure it out themselves.

The Content Strategy That Actually Builds Pipeline

Raaz tracks two key audiences differently:

* CISOs (budget approvers): LinkedIn growth, exponential quarter-over-quarter growth

* Users (practitioners): Twitter, InfoSec communities, technical content engagement

Content philosophy: "How interested are you in hearing about a tool you don't use? Not very. But if I know your domain and bring truly the best content, you'll subscribe."

Examples that work:

* CTFs (Capture The Flag challenges) - complex security riddles that teach hands-on skills

* Deep technical content from actual security researchers

* Industry insights that make practitioners better at their jobs

The lesson: Content marketing works when you give real value to people who aren't your customers yet.

Measuring Brand When You Can't Really Measure Brand

"I track exponential growth. I want 20%+ quarter over quarter growth in key indicators. I don't care about attribution models—I want exponential curves."

Key metrics Raaz actually watches:

* LinkedIn follower growth in target buyer personas

* Community engagement in practitioner spaces

* "Room temperature" - how warm prospects are when sales gets in front of them

"When I started, we'd walk into rooms cold. Now the room is warm and our AEs feel it."

The lesson: Sometimes the best brand metrics are qualitative feedback from your sales team.

What's Next for a $32B Company

Despite the massive valuation, Raaz sees this as "early days."

"90% of workloads aren't even on cloud yet. AI is pushing teams faster to cloud and new technology adoption. We've had a great start, but we're just starting."

The mindset: Even at $32B, think like you're still in the early innings.

The Bottom Line

Raaz Herzberg built one of the most successful B2B marketing engines in recent history without any marketing background. Her secret wasn't marketing expertise—it was:

* Solving obvious problems first (nobody knows we exist)

* Using domain expertise as competitive advantage (knowing the audience better than marketers)

* Hiring for curiosity over experience (doers who figure things out)

* Breaking traditional structures when they don't work (moving field to sales)

* Embracing AI to amplify superstars (not replace average performers)

* Creating content that practitioners actually want (not product-focused content)

* Never compromising on hiring (every hire should be your best hire ever)

The lesson for every founder: Sometimes the best CMO isn't someone with 20 years of marketing experience. Sometimes it's someone who understands your customers better than anyone else and isn't afraid to figure out the rest.

5 More Tactical Marketing Insights from Wiz's $32B Journey:

* Enable everyone, not just marketing: Raaz built AI tools for the entire company to use, like their "Wiz spell checker" GPT that ensures every customer communication is technically accurate and on-brand. Marketing's job isn't just external—it's helping everyone communicate better.

* Mediocrity is now easy to automate: "In the AI world, mediocrity is easy to automate. That's why you need people who can't be automated—the curious ones who figure things out." The bar for human performance is getting higher, not lower.

* Stop making hour-long training: "I can't expect people to watch hour-long training. I'd rather have short 5-minute explanations you can pull on demand." Enablement needs to match modern attention spans and work styles.

* Stay humble even when you're winning: One of Wiz's core brand principles is humility. When they lose a deal, AEs are trained to say "we'd love to learn more" rather than anything defensive. This approach builds long-term relationships even in losses.

* Deep, high quality technical content still wins: While AI floods the market with shallow SEO content, Roz doubles down on complex, hands-on security challenges (CTFs) that require real expertise to create. "There's a lot of work that goes into it. It's very deep content. And those things always work because you're truly giving somebody really good value."

Want more insights like this? Join us for SaaStr Live sessions every Wednesday and get access to conversations with the leaders building the next generation of SaaS companies.

Thanks for reading The Secrets To Scaling in The Age of AI! Subscribe for free to receive new posts and support my work.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cloud.substack.com

Inside Snowflake’s Board Meetings: How AI is Reshaping Enterprise Data and the Future of B2B Partnerships

We had a lot of fun at SaaStr AI Summit 2025 with a rare look inside Snowflake’s boardroom! We featured an unprecedented dynamic: Jeremy Burton sits on Snowflake’s board while simultaneously running Observe, a company built 100% on Snowflake’s platform. Together with Snowflake’s CEO, Sridhar Ramaswamy, they joined us live on stage.

This unique relationship reveals how the $4.5B ARR data cloud leader is adapting to AI disruption, the evolution of technical sales, and what it really takes to build strategic partnerships that scale.

Top 5 Takeaways

* Consumption Revenue Recognition Changes Everything: Unlike traditional SaaS, Snowflake can’t recognize revenue ratably—they only book revenue when customers actually consume credits, even with multi-year contracts. This creates hyper-aligned incentives where sales success is measured by use case creation, not deal size.

* Technical Sales is Now Table Stakes: Enterprise sales reps must understand technology applications deeply enough to have credible conversations with both business executives and technical teams. The days of relationship-only selling are over in AI-adjacent markets.

* Separate Teams for Land vs. Expand: Snowflake completely separates new customer acquisition from account growth teams. Growing a $50M account requires managing 20+ people and knowing customer architecture better than the customer—a fundamentally different skill than closing new deals.

* Strategic Partnerships Need Internal Champions: Real partnerships only exist when someone’s career depends on your success. Without an internal champion whose future is tied to yours, you’re just a vendor getting lip service.

* AI Will Reshape Technical Roles in 18 Months (or Less): Data engineers will become orchestrators rather than code writers. Data analysts will shift from writing SQL to providing semantic context. The next 18 months will bring more change than the previous four years.

At SaaStr, we’ve seen everything from scaling revenue to product-market fit. But this year, attendees wanted something different: a real board meeting. And we delivered something unprecedented—a live board meeting discussion featuring Snowflake’s leadership and Jeremy Burton, CEO of Observe, who sits on Snowflake’s board and runs a company 100% built on Snowflake’s platform.

The conversation revealed fascinating insights about how AI is transforming enterprise data, the future of technical sales, and the mechanics of truly strategic partnerships in the age of consumption-based pricing.

The AI Data Cloud Vision: Beyond Traditional Analytics

Snowflake’s vision is clear: become “the AI data cloud”—a platform where any business user can interact with their enterprise data using natural language, just like ChatGPT but with access to every dataset in their organization.

“Imagine being able to do that on every data set that your company has,” explained Snowflake’s Ramaswamy during the session. “The pieces to do that are already there. We have internal examples of agents that are pretty magical when you bring it all together.”

But customer expectations are evolving rapidly. The CMO of a major mobile carrier told Snowflake: “I have all this information in Snowflake. I want to build a better churn detection engine. How do I automate this process and make it much easier?”

Meanwhile, companies formed through acquisitions face different challenges. One enterprise customer managing 60 different SAP instances asked: “If you were to ask me how much money am I making with this one particular customer, honestly I can’t tell you. What can you do to help me bring it all into a single pane of glass?”

The key insight: AI isn’t just about better analytics—it’s about making data accessible to business users who previously needed data scientists and engineers to get answers.

4 Learnings on Snowflake’s Future Direction

* The CFO Test is Coming: As Snowflake bills become the “second largest after AWS” for many enterprises, the pressure to prove ROI will intensify dramatically. Success will be measured not just by data storage and processing, but by demonstrable business value creation from that data.

* Business Users Will Drive Consumption Growth: The biggest unlock isn’t more data engineers—it’s enabling any business user to query data like ChatGPT. When marketing managers can ask natural language questions of customer data without SQL, consumption explodes geometrically.

* Agent AI Will Create the Next Platform Layer: Snowflake is building internal agent examples that can automatically stitch together data flows, recommend use cases, and provide real-time business intelligence. This isn’t just analytics—it’s autonomous business intelligence that acts on data insights.

* The Backlog Problem Becomes the Opportunity: Most enterprises have an “insatiable demand” for data insights but limited technical resources. AI that makes data 10x more accessible means 10x more questions get answered—and 10x more Snowflake consumption gets driven.

The Consumption Model Revolution

One of the most revealing moments came when discussing Snowflake’s consumption-based revenue model. Unlike traditional SaaS companies that recognize revenue ratably over contract terms, Snowflake only recognizes revenue when customers actually consume their credits—even with multi-year pre-commits.

“The consumption model is fabulous because we get paid on usage. Our reps are now focused on making sure that you get value from whatever it is that you signed for,” Jeremy explained. “That’s a super aligned incentive.”

This creates a fundamental shift in how sales teams operate. Success isn’t measured by contract value, but by use case creation. Every week, Snowflake lives and dies by: How many new use cases did you create? How many did you win? How many went live?

Separate Teams: The Acquisition vs. Growth Playbook

To manage this consumption model effectively, Snowflake has implemented a critical organizational structure: completely separate teams for new customer acquisition versus existing account growth.

“The teams that close the customers are off the deal when it closes. They keep it for a year to year and a half, but the set of people that get new accounts are completely distinct from the set of people that are growing existing accounts. Honestly, it’s a different skill.” per Ramaswamy.

The account growth managers are essentially running mini-consulting businesses:

* Managing accounts spending $50+ million annually

* Leading teams of 20+ people

* Understanding customer architecture better than the customers themselves

* Making business proposals for new projects and use cases

This specialization makes sense given the complexity. These account managers often know their customer’s technical architecture better than the customer does because they talk to everyone within the organization.

The New Technical Requirements for Enterprise Sales

Perhaps the most striking insight was about the technical requirements for modern enterprise sales. In the age of AI, traditional relationship-based selling isn’t enough.

“You have to be able to have a credible conversation with either a business executive or somebody that is technical and be able to distill out what’s the problem that really matters to them and how Snowflake applies,” per Ramaswamy.

But there’s a crucial distinction between deep technical knowledge and business application knowledge:

What sales reps DON’T need to know: That Snowflake’s image recognition uses a 7-billion parameter model developed internally.

What they DO need to know: If you want to extract numbers from PDF contracts, Snowflake can do that.

“You need to know the application of technology to business. Those are our best account executives.”

This shift is so pronounced that one founder shared a story about closing a seven-figure deal with OpenAI, where their “relatively technical CRO” was left in the lobby because the technical questions from the engineering team were simply too sophisticated for traditional sales approaches.

The 100% Platform Commitment: Observe’s Bet on Snowflake

Jeremy Burton’s story with Observe provides a fascinating case study in platform commitment. When Observe was founded in late 2017, the decision to build 100% on Snowflake was controversial enough that it cost them founding team members.

“There was a bunch of folks who had a theory that every query had to return in 100 milliseconds,” Burton explained. “They wanted to build their own database. My thesis was quite simple: if we’ve got our engineering team adding value above the database and we let the Snowflake team build the database, then we should be able to add more value and therefore charge more money.”

The commitment required accepting worse gross margins initially—paying the Snowflake bill while building their business. But the results speak for themselves: Observe now runs 190 million Snowflake queries daily, ingesting a petabyte of data, with gross margins reaching 60% at tens of millions in revenue.

The key lesson: “If you make that decision early on that you’re going to build on a platform, you’ve got to realize you are picking up a dependency. You have to build relationships.”

The Partner Ecosystem Reality Check

Burton’s insights into building strategic partnerships were particularly valuable. The relationship between Observe and Snowflake works because of genuine mutual benefit:

* Observe helps Snowflake customers burn down large credit commitments

* Snowflake reps get paid on consumption regardless of whether credits are used in Observe’s multi-tenant environment or customer’s own Snowflake instance

* The relationship provides warm, credible introductions into enterprise accounts

But Burton was clear about what it takes to become a strategic partner:

“Unless there’s someone in Snowflake whose career and future depends on you succeeding, you don’t have a partnership. And by the way, that’s true for every company. Never believe a company that says ‘Hey welcome, you’re a partner. Come spend money on me.’ They just want your money.”

The partnership hierarchy insight: Snowflake focuses on a limited number of partners—dozens, not hundreds—but invests significantly in each. For smaller partners, they use a rational model: prove $5 million in joint revenue with one person, and you get the next level of investment.

The Future of Data Engineering and DevOps

Looking ahead 12-18 months, both leaders see dramatic changes coming for technical roles:

Data Engineers will shift from manually setting up pipelines and writing code to becoming orchestrators, with AI handling much of the actual code generation—similar to how Cursor is transforming software development.

Data Analysts will evolve from writing thousands of SQL queries to providing semantic context to datasets, making them “AI-ready” so business users can get value directly.

DevOps Engineers may see the most dramatic change. Burton envisions AI systems that not only identify issues but propose and execute solutions: “Hi, it’s 11:12. I’ve noticed this spike in issues. I’m 90% sure this is the answer. Jeremy, would you like me to execute that?”

The ultimate goal? “We want to build features, not fix problems.”

Key Takeaways for B2B Leaders

* Consumption models create different incentives: Success metrics shift from deal size to use case creation and actual value delivery.

* Technical sales is table stakes: In enterprise B2B, especially in AI-adjacent markets, sales teams must understand technology applications, not just relationships.

* Platform commitment requires total commitment: Half-measures in platform partnerships don’t work. You’re either all-in or you’re not really in.

* Strategic partnerships need internal champions: Someone’s career must depend on your success, or you’re just a vendor.

* AI will dramatically reshape technical roles: The next 18 months (or less) will see more change in data engineering and DevOps than the previous four years combined.

As we head into 2026, the companies that understand these shifts—consumption models, technical sales requirements, and true platform partnerships—will be the ones that capture the AI-driven enterprise opportunity.

The board meeting format revealed something crucial: the real decisions and insights happen when leaders can speak candidly about what’s actually working. Sometimes the most valuable learning comes not from the keynote, but from listening in on the conversations that shape billion-dollar platforms.

4 Unexpected Learnings from the Snowflake Board Room

* Observe Runs 190 Million Queries Daily (Out of Snowflake’s 5 Billion): Despite being “only” at eight figures in revenue, Observe represents nearly 4% of Snowflake’s total daily query volume. This shows how strategic partners can punch way above their weight class in consumption models—revenue size doesn’t always correlate with platform impact.

* Sales Reps Don’t Need to Know the Technical Details: You don’t need to understand that image recognition uses a 7-billion parameter model. You DO need to know that you can extract numbers from PDF contracts. The best enterprise sales reps master pattern matching between business problems and technology applications, not the underlying tech stack.

* Platform Commitment Cost Them Founding Team Members: Observe’s decision to build 100% on Snowflake was so controversial it literally caused founding team members to quit. The lesson: true platform bets require accepting dependency and worse initial margins, but those who commit fully can reach normal SaaS margins at scale.

* The “Golf and NBA Finals” Sales Approach is Dead: When one founder’s team closed a seven-figure deal with a leading LLM, their “relatively technical CRO” was left in the lobby because the engineering questions were too sophisticated. Relationship selling without technical credibility has zero value in today’s enterprise deals.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cloud.substack.com

The HubSpot AI Playbook: How Yamini Rangan Is Leading the Most Aggressive B2B AI Transformation

HubSpot's CEO came to SaaStr + AI Summit to do a deep and honest dive on how she's transforming the company into an AI-first $2.75B+ ARR B2B leader. And what it means for the future of B2B software.

The January 2023 Pivot That Changed Everything

HubSpot had spent four months meticulously planning your 2023 roadmap. Every feature mapped out, every sprint planned, every resource allocated. Then ChatGPT happens in November 2022, and by January 2023, you make a decision that would reshape your entire company.

"We came into January 2023 and we said, pivot," HubSpot CEO Yamini Rangan told the SaaStr audience. "That entire roadmap that we just planned for the last three to four months is not going to be what we are building in 2023."

This wasn't a gradual shift or a cautious experiment. This was a complete organizational bet on AI that would see HubSpot ship their first AI features by March 2023 – just two months after the pivot decision.

The result? HubSpot now has 95% of their engineering team using AI tools daily, customers seeing 70-80% ticket resolution rates with AI agents, and a fundamental reimagining of what B2B software can be.

The "Kid in the Candy Store" Test

Before diving into the tactical details, Rangan shared what might be the most important hiring and team-building insight for the AI era:

"If they're not saying this is the kid in the candy store age, you have the wrong team. They should be excited. They should be going home at night and screwing around and coming up. They should be doing internal hackathons, even though you haven't had one in five years."

This isn't just about enthusiasm – it's about recognizing that the last five years of B2B software development were, in Rangan's words, "boring." The same playbooks, the same incremental improvements, the same compliance features.

"If you're not seeing that [excitement], it's brutal, but you got the wrong team," she emphasized.

The Two AI Use Cases That Actually Work

While the industry debates AI's potential, Rangan is clear about what's already proven: "Coding and support – these two use cases have found product market fit with AI. No question. We're not going back."

Support: The 70-80% Resolution Reality

HubSpot's internal support AI now resolves 45% of tickets, but their customers using HubSpot's customer agent are seeing even better results:

* Average 52% resolution rate across 2,500+ customers

* Top performers hitting 70-80% resolution rates

* The difference? Knowledge base quality and completeness

"It comes down to knowledge and knowledge-based articles," Rangan explained. The companies seeing 80% resolution rates versus 40% have invested in comprehensive documentation and knowledge bases.

The setup time? "A few hours" if you have your knowledge base, support history, website data, and community answers ready to sync.

Coding: The 95% Daily Usage Revolution

Within HubSpot's engineering organization, 95% of developers use AI-powered code generation daily. The impact goes beyond just faster coding:

"We have to be even more careful about what we ship. It's not just the velocity of shipping something as new features. It is what is the value."

This abundance of development capacity is forcing a fundamental shift from feature velocity to value measurement – a change that's rippling through their entire product strategy.

The Death of Volume-Based Go-to-Market

One of the most striking insights from Rangan was how AI is forcing a complete rethink of traditional B2B sales metrics:

"We've gotten used to in the old playbook, increase the volume of leads, increase the amount of pipeline, increase everything. And it's been this focus on volume. It really needs to be focused on conversion because the volume is exploding."

The old playbook is dead. Rangan shared a perfect example – receiving an SDR email that morning asking to meet "next week" at SaaStr. The conference was happening that week.

"That is egregious," she said. "If you have like egregiously wrong information, like 'Oh, your conference is next week,' then you're out."

Personal vs. Personalized: The New Standard

The shift isn't just from volume to quality – it's from personalized to personal.

"We've been talking about personalization for a very long time, which is you segment your customers, you take your customers and you personalize your data and send it to everybody in that segment. That doesn't cut it anymore. It needs to be personal."

Personal means: "Jason, here are the four posts that you did. Here is why I can help you with this single problem that you have. And let me get 10 minutes with you."

Rangan recently received two personal videos from salespeople referencing specific points from HubSpot's earnings call two weeks prior and how they could specifically help. "That caught my attention."

The Handoff Problem: Where AI Breaks Down

Even as AI improves the first touch, Rangan identified a critical weak point: the handoff to humans.

She shared Jason's story of a perfect AI-generated first email that won a deal immediately, followed by a terrible human follow-up asking basic questions already answered in the prospect's public information.

"The AI was so good. Yeah. But not a great handoff."

This points to a larger challenge: as AI capabilities improve, the human elements need to get dramatically better, not just maintain current standards.

The Platform Thesis: Why Point Solutions Will Lose

Rangan's boldest prediction centers on why integrated platforms will dominate in the AI era:

"Point agents will not win in this whole race that we are in."

The reasoning? Agent onboarding mirrors employee onboarding:

"When your employee joins your company, what do you do in the first week for that employee? You provide your products, your ideal customer profile, your brand voice, your tone, your competitive differentiation, your value proposition. You actually train your employee with all of that context so that the employee can contribute meaningfully to your organization. That's the same thing that's happening with agents."

Companies with unified data across the entire customer journey can train agents more effectively than point solutions with limited context.

The Blurring Lines: From Specialists to Generalists

HubSpot's customer agent, originally built for support, is being used for pricing questions, event information, and sales inquiries. This isn't a bug – it's the future.

"The lines are blurring. And at the end of the day, you serve your customer. Your customer does not need to know which org they're dealing with."

This extends beyond customer-facing functions. AI is enabling generalists across the organization:

"In marketing, you might not be the translation expert, but you can now translate. You might not be the video editing expert, but you can edit your videos right now. You might not be specified like social channel media expert, but AI is going to help you do that."

The implication? The specialist-focused tool category that has dominated B2B software may be giving way to generalist-enabling platforms.

The Search Paradigm Shift: From Click to Conversation

Perhaps the most fundamental shift Rangan identified is the death of the traditional web interaction model:

"Search is gone. Like we're not just searching to get the information anymore. AI overviews are actually providing answers. So waiting for search to lead traffic to your website is gone."

The new pattern isn't "search, click, and consume" – it's "ask a question and get an answer."

"You're not pointing, clicking, searching, consuming. You're just asking a question in your natural language and getting an answer."

This means every company needs an AI agent, not just a website. Static websites are becoming conversation carriers.

The Partner Moat: 40% Revenue, 100% Loyalty

While everyone focuses on AI, Rangan reminded the audience about a more traditional but powerful moat: partners drive 40% of HubSpot's ARR, and 70% of their partners focus exclusively on HubSpot.

"How many of them sell 11 other competitors? None."

This partner exclusivity creates a powerful moat. When customers work with HubSpot partners, they're not evaluating multiple vendors – they're getting HubSpot.

The lesson for founders: "Study your ecosystem. If your ecosystem is in any way partner-driven, and you'll see if you go super, if you go into security or enterprise, sometimes it's 100%. Wiz is all partners. You want to compete with Wiz and you want to sell direct, you're just gonna lose."

The Curiosity and Growth Mindset Filter

When asked who will "make the cut" in the AI transition, Rangan's answer was simple: "Curiosity and growth mindset."

"If you're trying to slow roll AI, if you think that this is going to go away, if you're not investing in yourself and developing a level of AI fluency, then you've pretty much lost the game."

This isn't just about technical skills – it's about embracing change as a constant. As Rangan put it: "Change has been the constant. And you just have to thrive in it."

The Neat vs. Necessary Framework

HubSpot obsessively tracks AI usage patterns, focusing on what Rangan calls the "neat vs. necessary" distinction:

"There are novel, neat AI things. We've all used it in our personal life. We've used it once and it has this beautiful image that it creates and then we never go back to it. That's just neat. But AI has to be necessary."

The key metrics they track:

* Daily repeat usage vs. weekly repeat usage vs. novelty use

* What are users clicking on?

* Which features drive consistent value vs. one-time "wow" moments

"AI has to go from just being neat to being necessary, to being in the flow of everyday work in order to have the promised transformative benefits."

What This Means for Your Business

The HubSpot playbook offers several concrete takeaways:

1. Embrace the Pivot Mentality Be ready to completely reshape your roadmap when the market shifts. The companies that win won't be the ones with the best 2024 plans – they'll be the ones that can completely reinvent their 2025 plans.

2. Start with Proven Use Cases Don't try to be creative with AI applications. Start with coding and support where product-market fit is proven, then expand from there.

3. Measure Value, Not Velocity As AI increases your ability to ship features, become more selective about what creates actual customer value.

4. Invest in Platform Thinking Point solutions will struggle in an AI world. Think about how to provide comprehensive context and data to AI systems.

5. Prepare for the Personal Standard Volume-based outreach is dead. Invest in AI tools that enable truly personal, contextual outreach at scale.

6. Design for Conversation Your website and customer interfaces need to enable natural language conversation, not just navigation and consumption.

The Bottom Line

HubSpot's AI transformation isn't just a product story – it's a complete reimagining of how B2B software companies operate, sell, and serve customers. Under Rangan's leadership, they've made the bold bet that AI isn't just a feature enhancement but a fundamental shift requiring new playbooks across every function.

The companies that recognize this shift and act decisively, like HubSpot did in January 2023, will define the next era of B2B software. Those that don't may find themselves with beautiful roadmaps for a world that no longer exists.

As Rangan put it: "Change has been the constant. And you just have to thrive in it."

The question isn't whether AI will transform your business – it's whether you'll lead that transformation or be disrupted by it.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cloud.substack.com

Perplexity's CBO Dmitry Shevelenko joined Sam Blond ex-CEO of Brex at 2025 SaaStr + AI Summit for a deep dive on AI and Sales. More on the full session coming soon but this deep dive on being prepared for meeting prospects in the Age of AI was super interesting.

The 90-Minute Meeting Prep is Dead

Here's a hard truth every CRO, VP of Sales, AE and founder needs to hear: You have no excuse to not be moving faster.

While you're spending 2+ hours researching prospects, reading through LinkedIn profiles, and manually preparing for sales calls, your competitors are using AI to compress that same preparation into minutes. The result? They're having more conversations, closing deals faster, and scaling their businesses while you're still stuck in research mode.

Perplexity's CBO shared how AI is revolutionizing meeting preparation—and it's not just about efficiency. It's about unlocking what he calls "superpowers" that transform how founders approach every customer interaction.

The AI Meeting Prep Framework That Changed Everything

The framework is deceptively simple but devastatingly effective:

Step 1: Front-load Your Intelligence Gathering Every question you would typically ask during a discovery call should be asked of AI before the meeting. Think about it—why are you learning basic information about a company's challenges, their industry, or their recent moves in real-time during a precious 30-minute slot with a decision-maker?

Step 2: Use the Time You Save to Go Deeper Here's where most founders get it wrong. They think AI prep is about asking fewer questions. It's actually about asking better questions. When you're not burning meeting time on basic discovery, you can dig into the nuanced challenges, explore specific pain points, and have the kind of strategic conversation that actually moves deals forward.

Step 3: Show Your Work in Real-Time This is the power move that separates great founders from good ones. During the actual meeting, share your screen and ask AI questions about the prospect's company live. "What are the biggest challenges Company X is facing right now? How have they tried to solve them?"

Why This Approach is Pure Gold for B2B Sales

Authenticity Through Transparency: When you're using AI to research their challenges in real-time, and your solution naturally emerges as a fit, that's not scripted—that's authentic. The prospect sees the discovery happening organically.

Credibility Through Intelligence: You're not just another vendor with a pitch deck. You're a strategic partner who comes prepared with deep insights about their business, industry, and specific challenges.

Efficiency Through Preparation: You're maximizing every minute of face-time with prospects by focusing on high-value strategic discussions rather than basic fact-finding.

The Timing Lesson Every Founder Must Learn

Perplexity's success story offers a crucial lesson about market timing and execution speed. Their CBO revealed something fascinating: if they had started six months earlier, GPT-3 wouldn't have been good enough to create their magical AI + internet experience. Six months later, and someone else would be on stage talking about their success.

The window for competitive advantage is narrower than ever. In today's market, timing isn't just about having the right product at the right moment—it's about executing at the right speed when that moment arrives.

Beyond Meeting Prep: The Acceleration Mindset

The meeting prep framework is just the beginning. The real transformation happens when you apply this acceleration mindset to every aspect of your startup:

Prototyping and Concept Development: The rate at which you can prototype, test concepts, and gather feedback has been "dramatically accelerated." What used to take weeks now takes hours.

Product Validation: Instead of spending months building something you think the market wants, you can rapidly test and iterate on concepts before committing significant resources.

Strategic Planning: Tools like Cursor for rapid development and emerging AI-powered project visualization tools mean you can explore multiple strategic directions simultaneously rather than sequentially.

The Mental Model That Changes Everything

Here's the framework every founder should be running through their head constantly:

* How can I do in one hour what used to take a day?

* How can I do in one day what used to take a week?

* How can I do in one week what used to take a month?

This isn't about working more hours—it's about leveraging AI to compress time-to-insight and time-to-action across every business function.

The Competitive Reality Check

Your competitors are already doing this. While you're debating whether AI tools are worth the investment or learning curve, founders who embrace these superpowers are:

* Having more qualified conversations per week

* Closing deals faster with better preparation

* Iterating on product concepts at unprecedented speed

* Building deeper relationships through more strategic interactions

The question isn't whether you should adopt AI-powered meeting prep and business acceleration—it's whether you can afford not to.

Getting Started: Your AI Meeting Prep Playbook

For Your Next Sales Call:

* Prompt AI with: who you are, what your company does, who you're meeting with, their role

* Ask: "How should we structure this call? What should I know going into this meeting?"

* Follow up with deeper questions about their specific challenges and recent company developments

* Prepare 3-5 strategic questions that go beyond basic discovery

For Your Next Product Decision:

* Use AI to rapidly prototype and visualize different concepts

* Generate multiple approaches to the same problem

* Get quick feedback on concepts before committing development resources

* Compress your concept-to-feedback loop from weeks to hours

The Bottom Line

Speed is the new moat, and AI is the tool that builds it. Every founder now has access to superpowers that compress research time, accelerate decision-making, and enable deeper strategic thinking.

The founders who win in this new landscape won't be the ones with the most resources or the biggest teams—they'll be the ones who move fastest while maintaining the highest quality of strategic thinking.

The tools are at your fingertips. Your competitors are already using them.

Thanks for reading The Secrets To Scaling -- from SaaStr! Subscribe for free to receive new posts and support my work.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cloud.substack.com

We’ll do deep dives on all the SaaStr + AI Summit 2025 speakers soon! We wanted to kick it off with some of the key insights with Box CEO Aaron Levie and IBM VP AI Raj Datta:

The Numbers Don’t Lie – ChatGPT Hit 500M Users in Just ~2 Years

Let’s start with the cold, hard metrics that SaaS leaders seem to underestimate: AI adoption is happening at an unprecedented velocity.

As Aaron Levie, CEO of Box, points out, ChatGPT alone amassed approximately 500 million active users in just over two years. No technology in history has scaled this rapidly – not even close.

Why? For the first time, we have billions of people already connected to the internet, creating a ready-made distribution platform. This isn’t just marginally faster than cloud adoption – it’s an entirely different magnitude.

Your New Workforce Is Wired Completely Differently

Per Levie, college graduates entering your workforce operate fundamentally differently than previous generations. They’re:

* Drastically more efficient

* Skipping time-intensive processes that were once standard

* Questioning why organizations spend weeks on tasks AI can do in seconds

As Levie observed while visiting a college class: “These kids don’t work like us. They’re much more efficient.” While there might be tradeoffs in deep knowledge retention, their AI-enhanced productivity is undeniable.

When these graduates join your enterprise and see teams spending two weeks creating a marketing plan that Claude can generate in seconds, they’ll fundamentally question your operating model. Levie notes they’ll wonder: “Why would you spend time reinventing the wheel of a marketing plan? Literally, it’s in the model.” This generational shift will accelerate transformation far beyond what we saw with cloud adoption.

Enterprise AI Adoption Is Moving 5-10X Faster Than Cloud

The contrast with cloud adoption is striking, according to Levie:

* Cloud in 2008-2009: “Try pitching the cloud to a bank. That was a non-starter. That was not a conversation you would have. They had to keep all the data on premise… Get out of my office.” Many financial institutions didn’t deploy initial cloud use cases until the mid-2010s.

* AI in 2024-2025: “There’s not an enterprise that you could meet with today where they have not already basically said ‘here’s our AI first strategy’ or ‘here’s the AI first strategy we’re working on’ or ‘here are our AI principles.'”

Unlike the decade-long cloud adoption curve, enterprise AI adoption is happening in hyper-accelerated timeframes. What took 10 years with cloud may take just 1-2 years with AI.

Even Leading Tech Companies Feel Behind on AI

Perhaps most telling: even recognized AI-forward companies feel they’re falling behind. Lemkin referenced a conversation from the day before at SaaStr with Yamini Rangan from HubSpot – a company that’s “crushing it” in AI by most standards:

“She’s like, ‘I feel like I’m behind.’ And they’re pretty far ahead if you look at all the peer groups… I’m going to be stressed then.”

When market leaders feel they’re struggling to keep pace, it signals the extraordinary speed of this transformation. The most successful executives admit their stress about staying ahead in the AI race.

The AI Moat Problem: 1,000 Better Competitors vs. 2 Mediocre Ones

The final hard truth from Levie: AI is fundamentally changing competitive dynamics in SaaS. As one top Cloud executive noted at SaaStr 2025:

“Our core is strong and gets stronger… but the thousand small competitors are better than ever.”

In the pre-AI era, established SaaS companies might face a handful of competitors with limited feature sets. Levie explains today’s reality:

“In the old days, you might have two small competitors. Now you have a thousand and the thousand aren’t like one cool feature but the rest does almost nothing … Now the thousand are kind of cool because you can talk to them and they can talk to your data and they can MCP into Box. So now you have a thousand really good competitors, not two mediocre ones.”

What This Means For SaaS Leaders

The velocity of AI transformation demands immediate action. If your organization is taking a wait-and-see approach or treating AI like previous technology waves, you’re already behind. The winners will be those who embrace AI as the core operating system of their business – not just another feature or module.

The cost of waiting isn’t just missed efficiency; it’s existential risk in a market where a thousand AI-powered upstarts are coming for your lunch.

Thanks for reading The Secrets To Scaling -- from SaaStr! Subscribe for free to receive new posts and support my work.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cloud.substack.com

"The Last Generation of Human-Only CEOs: Marc Benioff's Bold Vision for the AI-Augmented Enterprise"

Marc Benioff was kind enough to join SaaStr for the first time to do a truly deep dive on what AI means in business software today. From how quickly humans will be replaced in some functions ... to how slow some traditional enterprises will be to change.

It's a good one.

Top 5 Unexpected Learnings:

1. Human + AI + Executive Teams Are The New NormalMarc Benioff revealed he's been working with AI agents for nearly three years, particularly when creating his V2MOM documents (Vision, Values, Methods, Obstacles, Measures). For these important strategic documents, he now has a three-party approach: himself, a Salesforce executive, and an AI agent working together.

The AI asks critical questions about competitive positioning, distribution expansion, and ecosystem impact – often providing insights Benioff hadn't considered. This collaborative approach is delivering superior strategic thinking beyond what either humans or AI could produce independently.

2. The Last Generation of "Humans-Only" LeadershipBenioff believes current leaders represent "the last generation of CEOs that only had human employees." This isn't just a catchphrase – Benioff actively works with AI agents daily to improve Salesforce and trains his employees to do the same.

He sees the human+agent partnership as consciousness-expanding, providing deeper insights and a form of "enlightenment" for executives. The fundamental concept of agent-augmented executive teams is completely reshaping how decisions are made.

3. AI Budgets Are a Red FlagWhere are AI budgets are coming from? Benioff shared a surprising perspective: having a separate "AI budget" might indicate you're heading in the wrong direction.

He believes AI investments should come directly from line-of-business budgets, with business unit leaders held accountable for efficiency, productivity, and employee augmentation through AI. Organizations with isolated AI teams and budgets aren't properly integrating AI into core business operations.

4. The Four-Layer Enterprise StackBenioff vision’s for the enterprise stack of the future: four integrated layers: 1) Apps (still critically important), 2) Data (more important than ever), 3) Agents (the AI layer), and 4) Robotics (the emerging physical manifestation layer).

This four-layer vision is becoming the architectural blueprint for Salesforce's entire platform direction, with remarkable convergence in real-world implementation.

5. Agent-First Companies Will DominateBenioff sees both an entirely new class of "agent-first" companies that will fundamentally differ from today's organizations. And existing leaders being radically remade that will look much different from how they do today.

He expresses a hint of envy toward founders who are starting companies today because they can build agent-first organizations from the ground up rather than transforming existing ones. This suggests a profound competitive advantage for new entrants who design their entire business model around AI augmentation from day one.

The Practical AI Transformation at Salesforce

"Agents can only go so far without the data and apps. And this is where Salesforce wins with AI. The data is more important than ever. Or your agents just can't perform at the level of accuracy We need them to."

The Real Impact of Salesforce's Agent Force AI

Benioff is particularly excited about Agent Force, which began as a concept a year ago and is now deployed by about 5,000 companies. What's unique is how quickly customers are seeing value – far faster than traditional enterprise software implementation cycles.

One standout example is Singapore Airlines, whose CEO is building an agentic layer over the entire airline. This was particularly motivating for Benioff, seeing how rapidly customers can deploy high-value AI implementations in relatively short timeframes.

Salesforce itself has deployed AI agents extensively, with 9,000 support agents already conducting over half a million AI-augmented customer interactions.

Contact Center Transformation Is Leading the Way in AI

The contact center is at the bleeding edge of AI transformation. When asked about reports of 30% headcount reductions in many contact centers within just one year due to AI replacing human headcount, Benioff confirmed he's exploring similar efficiencies at Salesforce. However, he cautions that large-scale workforce changes take time at many older enterprises and at true enterprise scale. For every early adopter they are seeing at Salesforce, many enterprises are still taking their time to watch and learn.

Benioff references the famous phrase that "people overestimate what will happen in a year and underestimate what will happen in a decade" with respect to AI workforce transformation. He sees the contact center transformation as just beginning, with early pioneers showing the way but mainstream adoption still in process.

AI Without Data and Apps is of Limited Value: AI Bringing Value to Disney Castmembers

So much of the power in AI will be more platforms like Salesforce because they have massive amounts of data.

"Agents can only go so far without the data and apps. And this is where Salesforce wins with AI. The data is more important than ever. Or your agents just can't perform at the level of accuracy We need them to."

Strategic Customer Implementations

Lennar, one of America's largest homebuilders, exemplifies successful AI implementation. After attending Dreamforce, their team conducted an internal hackathon focused on Agent Force, developing five high-impact use cases that are now dramatically affecting the company's financials.

What's notable is the speed of implementation – from idea to innovation to live deployment took only six months. And this is just the beginning of Lennar's agent-first transformation.

Meeting Customers Is Still The Ultimate Motivator

Marc is still always meeting customers. Meeting the King of Bhutan and seeing he uses Slack? "Now That's Motivating".

Salesforce's Evolution and Strategic Vision

The Power of Integrated Acquisitions

Benioff discussed the performance of Salesforce's major acquisitions, which are showing impressive growth despite being several years post-acquisition:

* Mulesoft: 22% growth

* Tableau: 16% growth

* Slack: 17% growth

For Slack specifically, Benioff noted a strategic shift in their distribution model – allowing all Salesforce representatives to sell it and receive quota credit, essentially making it a pure product with the rest of their portfolio. This change "supercharged" Slack's performance.

The recent Tableau user conference showcased a completely new product that combines apps, agents, and AI for the "data fam" community. Benioff expressed particular excitement about this transformation, as there were aspects of Tableau he "always wanted" that are now being realized.

The Data Advantage

Benioff emphasized that many underestimate the data requirements for effective AI implementation. Salesforce has 230 petabytes of data and also federates data from Snowflake (where Salesforce was a core investor), Databricks, BigQuery, S3, and other sources.

This federated data layer creates a "consistent data layer" that allows AI agents to operate effectively at enterprise scale. While startups might take a "see a bear, shoot a bear" approach with targeted AI solutions, Salesforce is focused on helping enterprises get their data together first, then deploying next-generation apps, and finally implementing agents at scale.

Data Cloud has been Salesforce's fastest-growing product ever and will be their "fastest multi-billion dollar product ever," indicating the market understands this data-first approach to AI implementation.

The Human Impact and Salesforce Values

Work Reorganization vs. Reduction

In healthcare, Benioff sees AI not necessarily reducing staff but enabling "more value-added" work. Medical professionals are overloaded and burned out, and an agentic layer could handle routine patient inquiries, pre/post-operative care guidance, and procedure preparation information.

The key insight is that medical centers might not have fewer people a year from now but will use their highly-trained staff in more valuable ways – similar to what Salesforce is experiencing with their own help.salesforce.com implementation.

The Enduring Importance of Corporate Values

Despite Salesforce's massive growth to over $40 billion in annual revenue, Benioff stressed the enduring importance of the company's 1-1-1 model (1% equity, 1% profit, 1% employee time) committed to philanthropy. Established 26 years ago at Salesforce's founding, this model has now generated 10 million volunteer hours, supported 50,000 nonprofits and NGOs on their platform for free, and delivered nearly $1 billion in donations worldwide.

Maintaining these values while scaling remains a "huge challenge" that Benioff believes companies should be held accountable for – whether related to trust, customer success, innovation, equality, or sustainability.

Top 4 Mistakes Marc Made

1. Keeping Acquisitions Separate Too LongWith Slack, Benioff admitted they initially tried to "keep it how it was" – running it as a separate company, investing in its sales team, and maintaining its independence. However, growth truly accelerated only after they "hit the button" and fully integrated it with their platform and sales organization. This suggests a mistake in not integrating acquisitions more aggressively from the beginning.

2. Underestimating Implementation TimelinesBenioff referenced the reality that "people overestimate what you're going to do in a year" regarding AI transformation. Despite his enthusiasm, he acknowledged that large companies like Salesforce face significant challenges in making material changes to their operations with AI in the short term. This suggests early timelines were too optimistic.

3. Not Leading With Practical AI Solutions SoonerWhen discussing AI budgets and implementation, Benioff noted that "for the vast majority of companies" who've been experimenting with AI models and tools over the past 3-4 years, "they've mostly failed." He attributed this to a lack of practical, specific, tactical approaches. This implies Salesforce itself may have initially pursued too many experimental AI projects before focusing on practical implementations like Agent Force.

4. Not Moving Faster On Data IntegrationBenioff acknowledged a "broad misunderstanding of data" that he plans to "clear up" at Dreamforce. Despite Salesforce's 230 petabytes of data, the critical importance of data federation and integration for AI success suggests this should have been prioritized earlier in their AI journey. The emphasis on Data Cloud as their fastest growing product ever indicates they may have initially underestimated how central the data layer would be to AI success.

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Want to know what it really takes to go public today? Let’s dive into the fascinating data and insights from Meritech Capital’s Alex Clayton and Cathy Choi, who have helped guide dozens of companies through successful IPOs over the past 25 years.

First, let’s address a common misconception: while many founders dream of going public, the reality is that only about 20 SaaS companies manage to IPO each year. That’s a pretty exclusive club! But what makes these companies special enough to make the cut?

The Bar is Higher Than Ever – Here’s What You Need

If you’re thinking about an IPO in 2025/2026, you’ll need some serious metrics. The days of going public with $50-100M in ARR are long gone. Today’s successful IPO candidates typically show up with:

* At least $400M in ARR – that’s right, nearly half a billion in recurring revenue

* Growth of 30% or higher year-over-year (though 50-60% is even better)

* A valuation between $3-5B

* Net revenue retention of 110-120% (and trending up)

* Strong SaaS gross margins

* A clear path to profitability (though you don’t need to be FCF positive yet)

But here’s what’s really interesting – the time from founding to IPO hasn’t changed much.

It still takes about 10 years on average to build a company ready for public markets. What has changed dramatically is the scale required.

The Truth About Multiples and Valuations

Let’s talk about a crucial reality check: no matter what your private market valuation might be, public market investors aren’t likely to value you at more than 8-12x ARR in today’s interest rate environment. This is a hard pill to swallow for some founders, but it’s the new normal.

Recent successful IPOs tell the story. Take Cavo, which went public last September at a $7.6B valuation and trades at 10x ARR. Or look at Rubrik, with its April IPO at $5.6B, trading at 7x ARR. Different growth profiles, similar multiple ranges.

The Secret to Long-Term Success: It’s Not What You Think

Here’s where it gets really interesting. After analyzing 139 software companies, Meritech found something surprising: there’s almost no correlation between company size and valuation multiples (R-squared of just 0.09). What really matters? Durable growth.

The data shows a strong correlation (R-squared of 0.74) between share price appreciation and revenue growth from IPO to present. But here’s the kicker – it’s not about growing as fast as possible. It’s about consistent, predictable growth that compounds over time.

Take two contrasting examples:

* CrowdStrike: Went public in 2019 with $500M ARR, growing at nearly 100%. They’ve maintained 50% growth and are now approaching $4B ARR. Result? 8x return since IPO.

* SPS Commerce: Went public in 2010 with just $50M ARR and has grown at a steady 20% CAGR for 15 years. The result? A remarkable 17x return.

The Predictability Premium

Here’s something that might surprise you: about 80% of successful public SaaS companies consistently beat and raise their quarterly guidance. This isn’t just about good numbers – it’s about being predictable enough that public market investors can trust your forecasts.

How to Build Durable Growth

The most successful public companies have found ways to maintain growth through:

* Multi-product expansion (like HubSpot growing from $100M to $2.5B ARR)

* Increasing wallet share with existing customers

* Strong net dollar retention that improves over time

The Bottom Line

If you’re thinking about going public, focus on building a business that can grow predictably for years, not just quarters. The math is simple: durable growth + strong unit economics + predictability = public market success.

Remember, a down-round IPO isn’t the end of the world. Companies like SPS Commerce prove that consistent execution over time matters far more than your initial public market valuation.

And always keep in mind – the public markets can only absorb about 20 new SaaS companies each year. Make sure you have a compelling reason why investors should choose your stock over the 100+ other great SaaS companies already trading.

Recent Examples:

* Klaviyo (September 2023)

* $7.6B valuation

* 10x ARR multiple

* Only $16M burned pre-IPO

* Strong unit economics

* Rubrik (April 2024)

* $5.6B valuation

* 7x ARR multiple

* $700M+ burned pre-IPO

* Enterprise-focused model

* OneStream

* $4.6B valuation

* 11x ARR multiple

* Strong financial operations focus

The Science Behind Long-Term Success: What Really Matters

Meritech’s analysis of 139 software companies revealed several surprising insights:

Company Size Impact:

* R-squared value of 0.09 between valuation multiples and company size

* Size alone doesn’t drive valuations

* Market opportunity and execution matter more

Growth Correlation:

* R-squared value of 0.74 between share price appreciation and revenue growth

* Strong correlation indicates growth sustainability is key

* Consistent growth beats sporadic high growth

Case Studies in Durable Growth

* HubSpot’s Evolution:

* Started: $100M ARR (2014)

* Current: $2.5B+ ARR

* Initial Valuation: $900M

* Current Valuation: $26B+

* Key Strategy: Multi-product expansion

* CrowdStrike’s Journey:

* IPO ARR: $500M (2019)

* Current ARR: Approaching $4B

* Growth Rate: Maintained 50%+ since IPO

* Return: 8x since IPO

* Key Strategy: Market leadership in expanding category

* SPS Commerce’s Long Game:

* IPO ARR: $50M (2010)

* Growth: 20% CAGR for 15 years

* Return: 17x since IPO

* Key Strategy: Consistent execution in steady market

The Predictability Premium: Why It Matters More Than Ever

Public market success requires exceptional predictability:

* 80% of successful public SaaS companies consistently beat quarterly guidance

* Forward-looking guidance accuracy is crucial

* Market rewards companies that can forecast accurately

* Operational excellence becomes paramount

Building for Long-Term Success: The Playbook

Multi-Product Strategy:

* Start with core product excellence

* Expand into adjacent markets

* Increase wallet share systematically

* Build strong cross-sell motion

Customer Economics:

* Focus on net dollar retention improvement

* Build enterprise-grade customer success

* Invest in customer expansion programs

* Monitor and optimize unit economics

Operational Excellence:

* Implement robust forecasting systems

* Build redundancy in critical processes

* Invest in compliance and controls early

* Develop public company ready reporting

The Final Checklist: Are You Really Ready?

Before considering an IPO, ensure you have:

* Clear market leadership position

* Predictable revenue model

* Strong management team

* Enterprise-grade systems and processes

* Compelling growth story

* Efficient capital structure

* Clear use of proceeds

* Strong board and governance

Looking Ahead: The Future of SaaS and Cloud IPOs

While the bar for going public remains high, companies that focus on building durable, predictable businesses will continue to find success. The key is not just reaching the metrics required for IPO, but building a business that can sustain and grow in the public markets for decades to come.

The most successful public SaaS companies share a common trait: they don’t just focus on getting public, they build for long-term success. Whether you’re growing at 50% like CrowdStrike or 20% like SPS Commerce, what matters most is the durability and predictability of that growth.

Remember, a down-round IPO isn’t fatal – many of today’s most successful public companies started trading below their private valuations. What matters is what you build from there.

Thanks for reading Scaling SaaS -- from SaaStr! Subscribe for free to receive new posts and support my work.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cloud.substack.com

Want to know when to move upmarket and how to actually pull it off?

Jeanne Dewitt Grosser (Chief Business Officer at Stripe) and Lindsey Scrase (COO at Checkr) have done it multiple times at companies like Google Cloud, Stripe, and Checkr. They came to SaaStr Annual to share what they’ve learned about making the move to go more enterprise actually work.

A Bit About Our Enterprise Leaders

Jeanne Dewitt Grosser

* Currently Chief Business Officer at Stripe, running all technology and financial partnerships, plus corporate strategy

* Scaled Stripe’s sales team from 10 to hundreds of reps across the Americas

* Former leader at Dialpad (SaaS communications)

* Built out Google Enterprise (now Google Cloud) SMB sales teams across APAC and Americas

* Started at Google working on Gmail post-launch in operations

* Known for analytical rigor and building teams from scratch

Lindsey Scrase

* Current COO at Checkr (formerly CRO)

* Runs bulk of Checkr’s operations after joining 2 years ago

* Previously led Google Cloud for SMB, midmarket, and startups

* Built and scaled multiple functions within Google Workplace

* Started career at A.P. Moller-Maersk in global operating roles

* Deep expertise in operational scaling and go-to-market strategy'

The 5 Key Things To Know About Moving Upmarket:

1. You Need Real Signals – Not Just Board Pressure

Most founders get the timing wrong on moving upmarket. Just because your board member wants a bigger TAM doesn’t mean you’re ready.

What you actually need:

* Your existing customer base is literally pulling you upmarket. Think Shopify, Lyft, and Square bringing in grown-up CFOs and prepping for IPO – that’s what happened at Stripe.

* You’re winning true enterprise workloads, not just “fins” (side projects or experimental business lines that don’t represent real adoption).

* You have enough product maturity to actually serve enterprise needs – not just feature requests you can handle “soon.”

2. Going Enterprise Is a Company-Wide Decision

The #1 mistake? Thinking enterprise is just a go-to-market play. It’s not. You need:

* A dedicated lighthouse engineering team building enterprise features

* Solution architects and integration engineers (but be thoughtful about which roles you actually need)

* Product and engineering in every major sales call

* Aligned compensation across teams

* Multi-year patience from leadership and board

As Lindsey puts it: “You can’t just dip your toe in enterprise. You have to go all in.”

3. The Enterprise Pricing Journey Is All About Unbundling

Stripe learned this the hard way. Their famous 2.9% + $0.30 pricing was brilliant for SMBs but completely wrong for enterprise. Why? Enterprise buyers:

* Need predictability in pricing

* Want to buy solutions, not features

* Require modularity and customization

* Have completely different procurement processes

The key is to repackage your components into enterprise-ready solutions. But watch out – this is a major undertaking that touches product, engineering, sales, and finance.

4. The Metrics That Matter Have Changed Dramatically

The “growth at all costs” era is dead. In 2019, top SaaS companies spent 50-55% of revenue on sales and marketing. In 2024? It’s down to 30%.

What you need to track obsessively:

* Payback period (top quartile SaaS companies have 3x faster payback)

* Gross margin

* LTV:CAC by segment

* Revenue ramp time

* Win rates by industry

Break these down by segment and channel. Make them visible to everyone. Your teams need to act like owners.

5. Your Operating Model Must Be Bottom-Up

The most successful enterprise moves are built on granular operating models that show exactly what needs to be true to hit targets. You need:

* Detailed funnel math (leads → opportunities → wins)

* Conversion rates by stage

* Average opportunity size assumptions

* Revenue ramp time models

* Cost breakdowns by function

Build this model by segment. Use it as a diagnostic tool. Update it constantly.

Real World Examples: How Stripe and Checkr Did It

Stripe’s Enterprise Evolution:

* Started with signals from growing customers (Shopify, Lyft, Square)

* Won Amazon in Singapore as first major enterprise logo

* Rebuilt pricing from scratch for enterprise needs

* Now sees enterprise customers buying 2+ products on average

* Focused heavily on financial automation products (Billing, Tax) to drive up ACVs

Checkr’s Playbook:

* Targeted specific verticals: staffing, retail, manufacturing, hospitality

* Built dedicated enterprise capacity across SE, product, and engineering

* Created two distinct product lines (initial screening, ongoing Trust product)

* Actually paused their first enterprise push when they realized their product wasn’t ready

The 5 Signs You’re Actually Ready for Enterprise:

* Your existing customers are growing into enterprise needs

* You’re winning real workloads, not side projects

* Your product can handle enterprise requirements today (not soon)

* You can dedicate real engineering resources

* Your board understands this is a multi-year journey

What To Do Next:

* Build your bottom-up operating model now

* Map out your lighthouse account list

* Do the hard work of rethinking pricing and packaging

* Get engineering and product bought in early

* Set up the metrics dashboard your team needs

Remember: This is a multi-year journey. But if you get it right, the rewards are enormous. Just look at Stripe and Checkr’s trajectories.

Thanks for reading Scaling SaaS -- from SaaStr! Subscribe for free to receive new posts and support my work.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cloud.substack.com