Jeff Lawson built Twilio from zero to IPO and beyond, navigating the complexities of developer APIs, public company M&A, and ultimately stepping down as CEO. His insights from the trenches offer invaluable lessons for founders building at scale.

Here are the 10 most actionable takeaways:

1. Keep Compensation Simple – Complexity Breeds Resentment

“I actually never wanted major comp. The more levers and knobs you put into a comp package, the more opportunity for someone to think it’s unfair. Once people believe they’re paid fairly, they focus on the work.”

The Lesson: Follow Daniel Pink’s philosophy from “Drive” – once you pass the bar of fairness, additional complexity only creates problems. Simple compensation structures eliminate distractions and potential grievances about missed metrics or unequal treatment.

For Founders: Resist the temptation to create elaborate incentive schemes. Focus on equity and fair base pay, then get back to building.

2. Only Three Types of Developer Companies Achieve Breakaway Revenue

Jeff’s framework for developer-focused businesses identifies exactly three categories that can scale to hundreds of millions or billions:

• Business Development as a Service: “Developers can’t open bank accounts or strike deals with AT&T. But with Twilio, Stripe, AWS, you can engage in business relationships you weren’t previously empowered to do.”
• Capex as a Service: “A developer can’t spend $10 million to build a data center, but they can put it on a credit card.”
• Algorithm as a Service: “The algorithm must be so complicated that developers say ‘I’m not smart enough to figure that out.'”

The Lesson: Most developer tools fail because they underestimate developer ego. “Developers take your cool thing as a challenge. You’re saying they can’t build what you built.”

For Founders: Before building developer tools, honestly assess which category you fit. If none, reconsider your approach.

3. Infrastructure Companies Have No Innovator’s Dilemma with AI

“AI will decimate SaaS seat bases. AI will do the jobs people are sitting there doing in these products today. But if you’re selling infrastructure, you have no innovator’s dilemma – you’re not selling seats.”

The Lesson: Position matters enormously in technological shifts. Infrastructure providers can embrace AI without cannibalizing their core business model.

For Founders: If you’re building SaaS with per-seat pricing, you need an AI strategy that doesn’t destroy your revenue model. Infrastructure companies should be aggressively pursuing AI opportunities.

4. The “Break Out of Jail” Mindset for Product Expansion

Jeff candidly discussed Twilio’s challenge: “How do you add value when customers specify exactly what they want – a text message from A to B saying C? Any deviation is called failure.”

His solution was constantly seeking “surface area that allowed us more expression as a product team.” He admired Cloudflare’s position: “They sit at this strategic intersection where they can just add features to the dashboard – flip a toggle to do this and that.”

The Lesson: Some business models inherently limit expansion opportunities. Recognize these constraints early and architect around them.

For Founders: Design your initial product with expansion in mind. Consider how you’ll add value beyond the core use case.

5. Founders Risk Everything – VCs Risk Portfolio Percentages

“Don’t say you’re brave putting 10% of the fund into one deal when founders are putting 100% of their capital allocation – their life, time, bank account, everything – with no way out.”

The Lesson: The risk calculus is fundamentally different for founders versus investors. Founders deserve respect for this asymmetric risk profile.

For Founders: Remember this dynamic in negotiations. Your commitment level is qualitatively different from your investors’.

6. Public Company M&A Strategy: Better to Miss Deals Than Do Bad Ones

“The conventional wisdom is you don’t worry about deals that didn’t work out. But you regret the ones you should have done that you didn’t. The mantra becomes: it’s worse to miss a deal you should have done than to do one that doesn’t work.”

When pressed about deals he missed, Jeff’s visible reaction suggested this philosophy comes from real experience.

The Lesson: In M&A, FOMO can be more dangerous than false positives, especially when you have the capital to be aggressive.

For Founders: If you’re in a position to acquire, err on the side of action when you see strategic fit.

7. Corporate Cash Creates Different Investment Logic

“When big companies have massive cash generation, it’s orphaned on the balance sheet. You can’t hire 1,000 engineers without hurting EPS. But swapping one asset for another can be essentially free if it doesn’t decline.”

The Lesson: Corporate venture investing follows different math than traditional VC. Not losing money can be more important than maximizing returns.

For Founders: When raising from corporates, understand their constraints and motivations differ from pure financial investors.

8. Start Companies for Mission, Not Money

“You don’t start companies to make money. Probability-adjusted, you should just get a job at a hyperscaler. You start companies because you love what you’re doing and think the world needs what you’re building.”

Jeff contrasted this with current trends: “I don’t hear that from the kids these days. I see founder CEOs quitting to join Meta. This is a whole different world of why people are in startups.”

The Lesson: Mission-driven founders have staying power that mercenaries lack. Pure financial motivation rarely sustains through the inevitable difficulties.

For Founders: Honestly assess your motivation. If it’s purely financial, reconsider your path.

9. AI Creates the Greatest Infrastructure Opportunity in Decades

“When I saw AI coming, I was like ‘Holy shit, this is going to replace SaaS.’ All the incumbents have innovator’s dilemma – they’ll add features to make humans 10% more efficient. Reality is customers want a product that says ‘I don’t need 75% of these people anymore.'”

The Lesson: AI represents a generational platform shift comparable to mobile or cloud. Infrastructure companies are uniquely positioned to capitalize.

For Founders: If you’re building infrastructure, AI should be your top priority. If you’re building SaaS, AI is an existential threat requiring immediate attention.

10. Due Diligence Failures Enable Fraud

On the IRL CEO fraud case: “Whenever I read these fraud stories, I’ve never heard of the companies. If I’ve never heard of them as a real human being operating in the world, maybe there wasn’t much real behind them.”

Jeff balanced accountability: “The commission of crime is on the 22-year-old who lies. But the 40-year-old running money who’s sophisticated owes the system a duty of care.”

The Lesson: Basic sanity checks can prevent obvious fraud. If no one has heard of a company claiming millions of users, that’s a red flag.

For Founders: When fundraising, expect and welcome real diligence. Investors who don’t dig deep may not be partners you want.

The Meta-Lesson: Respect the Fundamentals

Throughout the conversation, Jeff demonstrated something increasingly rare in today’s venture environment: respect for fundamentals. Whether discussing compensation philosophy, product strategy, or investment decisions, he consistently returned to first principles rather than following trends.

In an era of trillion-dollar pay packages and 100x revenue multiples, Jeff’s grounded perspective offers a valuable counterweight. His success at Twilio came from understanding core dynamics – developer behavior, business model constraints, market positioning – rather than chasing the latest narrative.

For founders building in this environment, Jeff’s approach suggests a path forward: understand the fundamentals deeply, respect the risks you’re taking, and build for mission rather than quick exits. The AI wave creates unprecedented opportunities, but the basics of building great companies remain unchanged.

As Jeff put it: “Turns out making money is hard. Always has been.”

An early investment I made, I met with the CEO of a public company that wanted to buy them for $50m

The startup hadn’t raised much, they said No

Years later, they sold for almost $1B

The founders made about the same they would have, years earlier, in the first deal though…

— Jason ✨👾SaaStr.Ai✨ Lemkin (@jasonlk) May 12, 2025

Why That “$50M to $1B” Acquisition Offer Isn’t Always What It Seems: A Founder Reality Check

I’ve seen this story play out multiple times as an investor, and the lessons are both counterintuitive and crucial.

TL;DR:  In one of my very first investments, the founders turned down a $50M acquisition to see their start-up sell for $1B years later – yet walked away with roughly the same personal outcome they would have had by taking the first deal.

The Tempting Early Offer

Way back in 2013, I made an early investment in a promising startup. The team had built impressive traction without raising much capital – maintaining strong ownership and control.  It was early, but by $1m ARR they’d pulled ahead of their seemingly similar legion of competitors.

Then came the moment many founders dream of: the CEO of a public company wanted to acquire them for $50 million.  The founder actually reached out to me to have lunch, and asked if the founders would. be receptive.  I said I thought so, but I didn’t know.

With minimal outside capital raised, this would have been life-changing money for the founding team. But they had bigger visions. They said no.  In fact — they said “No at Any Price.”

The Long, Long Road to a Bigger Exit

8 years later they were acquired for $1 Billion.  What followed was the entrepreneurial equivalent of climbing startup Everst:

• 5 different management teams
• 2 more CEOs
• 4 more venture capital rounds
• Years of grinding, pivoting, and scaling
• Significant dilution with each new funding round

Eventually, the company sold for nearly $1 billion – a 20x multiple on that original offer!

The Surprising Math Reality

Here’s where things get interesting. Despite the massive headline number, the founders made approximately the same amount they would have walking away from that original $50M deal years earlier.

Why? Three critical factors:

1. Dilution is relentless: Each funding round chipped away at founder ownership
2. Liquidation preferences stacked up: Later investors received their returns first.  While minor, it still impacted returns.
3. Time is expensive: The founders spent years of their lives getting to the bigger exit.  In fact, one passed away before the exit.  I think about that a lot.

The Hidden Costs Nobody Discusses

What the headlines never show:

• The psychological toll of multiple management team changes
• Founder burnout and relationship strain
• Opportunity cost of other ventures never pursued
• Years of life dedicated to a single outcome

When To Take The Early Exit

Here’s my hard-earned advice for founders:

If an early acquisition offer would be truly life-changing for you personally, give it serious consideration. The glamour of pursuing a unicorn exit often masks the brutal reality of what it takes to get there.

Sometimes the “smaller” win that puts millions in your pocket today is worth more than the theoretical bigger win that might come years later after significant dilution.

In fact, my advice today is Default Yes.  Default — take an M&A offer if it’s good.  If you are 95%+ sure you can built something 10x bigger, then say No for sure.  Go Long.

But default to Yes for a strong M&A offer.  At least to talk about it from that perspective.

Brian Halligan, co-founder and Chair of $30B+ HubSpot … agrees:

The SaaStr Bottom Line

Don’t automatically chase the bigger headline number. Do the math on what you’ll actually take home, factor in the true cost of time, and make the decision that’s right for you – not what looks impressive on social media.

Dear SaaStr: How Do I Know If It’s The Right Time to Sell My Company?

Dear SaaStr: What Are the Slides That Should Be in Every VC Pitch Deck?

Here’s the structure I like to see in a pitch deck—it’s simple, clear, and designed to get investors excited without wasting time. Every slide should have a purpose, and the first few are critical to hook the audience:

1. Title Slide

Keep it clean—company name, tagline, and maybe a killer stat or tagline that grabs attention. For example, “The #1 AI Platform for Customer Success.”

2. Problem

What’s the pain point? Be specific and data-driven. Show why this problem is urgent and worth solving.

3. Solution

How are you solving it? Keep it concise and compelling. A visual or demo screenshot can help here.

4.  Why Now?

Timing is everything. Show why this is the perfect moment for your solution to succeed. For example, “AI adoption in customer success is growing 40% YoY.”

5. Market Opportunity (TAM/SAM/SOM)

Break down your TAM, SAM, and SOM with clear math. Tie it directly to your pricing model and ICP. Investors want to see how big this can get and how realistic your assumptions are [8].

6. Product

Show the product in action. Use screenshots, mockups, or a short demo video. Highlight what makes it unique.

7. Traction

If you have revenue, customers, or growth metrics, this is where you shine. Be specific—“$1M ARR, growing 20% MoM” is far more compelling than vague claims.

8. Business Model

How do you make money? Show your pricing, ACV, and any early signs of strong unit economics (e.g., CAC, LTV).

9. Go-to-Market Strategy

How are you acquiring customers? Highlight your sales motion (PLG, enterprise sales, etc.) and any early wins.

10. Competition

Acknowledge your competitors and show how you’re different. Be honest and self-aware—it makes you look smarter. Include the top 8-10 competitors and position yourself clearly.

11. Team

Why are you the team to win? Highlight relevant experience and any key hires you’ve made or plan to make.

12. Financials

Keep it high-level—current revenue, burn rate, and projections for the next 12-24 months. Don’t overcomplicate it.

13.  The Ask

Be direct. How much are you raising, and what will you achieve with it? For example, “We’re raising $3M to scale from $1M to $5M ARR in 18 months.”

14. **Closing Slide**

End with your logo, tagline, and contact info. Make it easy for investors to follow up.

Key Tips:

– **First Slide Sells the Deal**: Your first slide should be so good that it could sell the whole company on its own.
– **Keep It Fairly Short — But Complete**: 15-20 slides max. If you can’t tell the story in that space, you’re overcomplicating it.
– **Data Over Fluff**: Investors want numbers, not vague promises. Back up every claim with data.

A related deep dive here:

Bottom Line: While everyone’s declaring SEO dead and pivoting to AI-generated content farms, we took the opposite approach. The result? 33.6M search impressions annually, 5x growth in 12 months, and complete category dominance in B2B SaaS search.

The “SEO is Dead” Narrative is Wrong (At Least for Us)

You’ve heard it everywhere: Google favors AI slop, authentic content doesn’t rank, and SEO is a waste of time in 2025.

Our data tells a different story.

Over the past 12 months, SaaStr’s organic search performance has exploded:

• From 50K to 245K daily impressions (that’s 5x growth)
• 33.6M total impressions in the last year
• 327K clicks from organic search
• 1% average click-through rate (double the industry standard)

While other B2B publications are chasing ChatGPT SEO hacks and pumping out generic listicles, we doubled down on what we’ve always done: deep, authentic, data-driven content about real SaaS companies and real growth challenges.

What Actually Drives B2B SEO Success in 2025

1. Authority Over Volume

We don’t publish 100s of AI generated posts per week — although we do publish a fair amount, about 20+ hand crafted (with some AI help) posts a week. We publish somewhat fewer pieces, but each piece ideally becomes the definitive resource on its topic. When someone searches “OpenAI revenue breakdown” or “SaaS valuation metrics,” they find SaaStr first—not because we gamed the algorithm, but because we actually know what we’re talking about.

The numbers prove it:

• “saastr” searches: 75,903 monthly impressions
• “jason lemkin” searches: 22,099 monthly impressions
• “openai revenue” searches: 31,230 monthly impressions

2. Research-Intent Content, Not Buying-Intent Spam

Most B2B SEO targets bottom-funnel keywords like “best CRM software” or “Salesforce alternatives.” That’s a race to the bottom.

Instead, we rank for research-stage queries:

• “How much equity should a VP of Sales get?”
• “Why most AI agents aren’t working yet”
• “What Anthropic’s $4B valuation means for SaaS”

Why this matters: We capture prospects 6-18 months before they buy, when they’re forming opinions and building mental vendor lists. By the time they’re ready to purchase, they already trust SaaStr’s perspective.

3. Community-Driven Amplification

Our content doesn’t just rank—it gets shared by the people Google trusts most. When Jason posts about AI agents on LinkedIn and gets 500+ comments from SaaS executives, Google notices. When our “996 culture” post sparks industry debate, Google sees the engagement signals.

The compound effect: Social amplification → backlinks → domain authority → better rankings → more social amplification.

The Metrics That Actually Matter (And Why Most Companies Get This Wrong)

Traditional SEO Metrics Everyone Obsesses Over:

• Keyword rankings ✓ (We rank top 3 for most SaaS terms)
• Organic traffic ✓ (327K clicks annually)
• Domain authority ✓ (Google clearly trusts us)

The Metrics That Actually Drive Business Results:

• Newsletter growth: 250K+ subscribers (up from content discovery)
• Event registration: 17K+ searches for “SaaStr 2025” alone
• Brand association: When prospects think “SaaS industry insights,” they think SaaStr
• Sales cycle impact: Prospects come to demos already familiar with our POV

Why Most B2B Companies Fail at SEO (And What We Did Differently)

The Common Mistakes:

1. Chasing keyword volume over search intent
2. Publishing generic content that doesn’t add unique value
3. Trying to rank for everything instead of owning specific categories
4. Measuring success by traffic instead of business impact

What Actually Works:

1. Pick your category and own it completely (We own B2B SaaS industry analysis)
2. Create content only you can create (Our founder-level insights and real company data)
3. Build for humans, not algorithms (Though algorithms reward human-focused content)
4. Patience and consistency over growth hacks (This took 12+ months to compound)

The Real SEO Strategy: Be the Source, Not the Echo

While competitors scramble to reverse-engineer our keyword strategy or copy our content formats, they’re missing the fundamental point: You can’t fake industry authority.

Our SEO success comes from:

• Jason’s 20+ years of B2B experience (authenticity Google can’t replicate)
• Access to real company data (revenue numbers, growth metrics, founder insights)
• A community of 250K+ engaged SaaS professionals (natural amplification and validation)
• Consistent publishing over 10+ years (domain authority you can’t buy)

When we write about Anthropic’s revenue, it’s because Jason knows the founders. When we analyze SaaS valuations, it’s because we’ve seen hundreds of deals. When we predict industry trends, we’re drawing from a decade of firsthand experience.

Google rewards this kind of E-A-T (Expertise, Authoritativeness, Trustworthiness) more than ever in 2025.

What This Means for Your B2B Content Strategy

If You’re a B2B Founder:

• Stop chasing keyword volume metrics
• Start creating content only you can create (founder insights, customer stories, industry POV)
• Build your personal brand alongside your company content
• Be patient—real authority takes time

If You’re a B2B Marketer:

• Focus on research-stage content, not just buying-stage keywords
• Quality over quantity (10 great pieces beat 100 mediocre ones)
• Build community around your content (engagement signals matter)
• Ride the AI wave with authentic insights (Every B2B buyer is researching AI—be their guide)
• Measure business impact, not just traffic

If You’re Looking to Partner with Us:

The 33.6M impression growth isn’t just vanity metrics—it represents the largest, most engaged B2B SaaS audience in search. When your prospects research solutions, they start with SaaStr. When they evaluate competitors, they read our analysis. When they plan growth strategies, they consume our content.

Sponsoring SaaStr isn’t just buying ads—it’s associating your brand with the most trusted voice in B2B.

The Bottom Line: SEO Isn’t Dead, But Lazy SEO Probably Is

While everyone else pivots to the next shiny growth channel, we’re proving that authentic, expert-driven content still wins in search. The key isn’t gaming Google’s algorithm—it’s becoming the resource Google wants to rank.

Our 5x impression growth in 12 months isn’t a fluke. It’s the compound result of consistent, authentic content creation by actual industry experts.

SEO might be dead for companies trying to fake their way to the top. But for those willing to build real authority over time? The opportunity has never been bigger.

What’s your take? Are you seeing similar results with authentic content, or are you stuck in the “SEO is dead” cycle? Let me know in the comments.

Data source: Google Search Console performance analysis, 12-month period ending September 2025.

The AI Marketing Revolution: Key Insights from G2’s CMO on How B2B Buying Has Forever Changed

From the SaaStr “Swapping Notes” podcast series – essential listening for every B2B founder and marketing leader navigating the AI transformation

Speaker Bios

Sydney Sloan, CMO at G2
Sydney Sloan brings over a decade of B2B marketing excellence to her role as Chief Marketing Officer at G2, the world’s largest software marketplace. Previously, she led marketing initiatives at high-growth companies including SalesLoft and Adobe, where she honed her expertise in scaling go-to-market strategies. Beyond her operating role, Sydney is an active LP at Stage 2 Capital and part of the Scale Ventures network, where she advises founders on growth strategies. Her unique position at G2 gives her unparalleled visibility into both sides of the B2B buying equation – how buyers research and how sellers adapt.

Guillaume Cabane, Co-Founder and General Partner at HyperGrowth Partners
Guillaume Cabane is Co-Founder and General Partner at HyperGrowth Partners, where he advises approximately 20 of the best B2B companies annually on accelerating growth. His impressive operating background includes building growth teams at segment-defining companies like Segment, Drift, and Gorgeous, followed by leading all of marketing at Ramp, the fast-growing fintech company. With over 10 years of hands-on B2B marketing experience, Guillaume has developed a reputation for identifying and implementing growth strategies that work at scale.

Amelia Lerutte, Chief AI Officer at SaaStr
Amelia Lerutte serves as Chief AI Officer at SaaStr, where she leads the organization’s AI strategy and implementation across all verticals. She hosts the “Swapping Notes” podcast series, focusing on practical AI applications in B2B software. Her role puts her at the center of the AI transformation happening across the SaaS ecosystem, working directly with founders and operators implementing AI strategies.

The Three Pillars of AI’s B2B Marketing Disruption

The conversation revealed three fundamental ways AI is reshaping B2B software marketing, each representing a seismic shift that demands immediate attention from every marketing leader.

1. AI as “Always Included” – The New Table Stakes

Perhaps the most striking finding from G2’s latest buyer behavior report: 88% of buyers won’t even consider software that doesn’t include AI capabilities. This isn’t about nice-to-have features anymore – it’s about basic viability in the market.

“You absolutely have to be able to add AI and AI capabilities to existing platforms,” Sloan emphasized. “The fastest people to switch are enterprises. Usually enterprises aren’t the ones that adopt it first, but that is what the data showed us – enterprise were first movers here and they were doing it because of the productivity gains.”

This enterprise-first adoption pattern breaks conventional wisdom about technology adoption curves. When large organizations with complex buying processes are moving fastest, it signals that AI isn’t just a trend – it’s becoming foundational infrastructure.

Action Items for Marketing Leaders:

• Audit your current product messaging to ensure AI capabilities are prominently featured
• Develop competitive positioning that highlights your AI differentiation
• Create content that speaks to enterprise buyers’ productivity gain expectations
• Train sales teams to lead with AI value propositions in enterprise conversations

2. The Agentic Strategy Revolution

Beyond adding AI features to existing platforms, buyers are now developing comprehensive “agentic strategies” – determining which platforms will serve as the foundation for their AI agent implementations.

This creates a fascinating dynamic where software selection isn’t just about current functionality, but about a platform’s potential to serve as an AI development environment. Buyers are asking: “Can I build my ideal AI workflows on this platform?”

Sloan noted: “People are now at the point of setting their agentic strategy and then trying to determine which platforms they want to use to build out their agent. So you’ve got this really interesting time where AI is being added to platforms, and you can build now with AI.”

Strategic Implications:

• Product roadmaps must consider extensibility and AI-building capabilities
• Marketing must speak to platform potential, not just current features
• Sales enablement should include conversations about buyers’ broader AI strategies
• Partnership opportunities emerge with AI development tool companies

3. LLM-First Buying: The Death of Traditional Funnel Marketing

The most disruptive change is how buyers now start their research journey. Traditional marketing funnels assumed buyers would discover solutions through search engines, content marketing, or referrals. Now, they’re going directly to Large Language Models for their initial research.

“They’re going to LLM first which is then changing the marketers world of how we have to think about marketing in the age of LLM – the new LLM buying process and PPC is no longer the way that we can buy our way,” Sloan explained.

The new buyer journey looks like this:

1. LLM Research: Buyers start with ChatGPT, Claude, or other LLMs for initial discovery
2. Source Verification: They visit the sources cited by the LLM (often user-generated content sites)
3. Peer Review: They check peer review sites like G2 for validation
4. Shortlist Creation: LLMs help build RFP templates and vendor shortlists
5. Direct Engagement: Only then do they visit vendor websites or marketplaces

The Trust Transfer: From Marketers to Machines

A sobering reality emerged from the conversation: buyers trust LLMs more than they trust marketers. As Guillaume Cabane put it: “Us marketers from the consumer’s perspective have never been worthy of trust. We’ve been trying to cue the perception. There’s some soft lying that has been happening forever. And so now we’re getting disrupted. We’ve lost some of that trust.”

This trust transfer creates two distinct categories of marketing that will survive:

AI-Proof Marketing

• Social proof and influencer relationships: Human connections remain valuable
• User-generated content: LLMs love and prioritize authentic user content
• In-person events and experiences: Physical interactions build trust
• Direct personal outreach: Trusted relationships bypass AI intermediation

AI-Influence Marketing

If you can’t build direct trust, you must influence the AI systems that buyers trust. This requires a fundamental shift from SEO (Search Engine Optimization) to what industry experts are calling GEO (Generative Engine Optimization).

GEO differs from SEO in crucial ways:

• Focus on answers, not clicks: Content must directly answer questions
• Jobs-to-be-done orientation: Write content around what buyers need to accomplish
• LLM-preferred formats: Listicles, user-generated content, and structured data
• Source credibility: Being cited by trusted sources becomes critical

The Content Strategy Revolution

The shift to LLM-first buying demands a complete content strategy overhaul. Instead of creating content designed to drive website traffic, marketers must create content designed to train AI systems.

Sloan shared a practical example: “If I’m a salesperson and we’re selling contact data for sales, maybe that’s a challenge they have to research. But if you think about their entire jobs to be done, they also want to know negotiation skills or they might want to know other things. Well, you can still build content for that. You can still add value to every single question that they’re going to ask the LLM.”

The Hidden Website Strategy

Cabane revealed an emerging tactic: “I’m seeing more and more companies build hidden websites completely hidden to humans. It’s an entire other index, an entire other web tree for the purpose of AI agents to scrape that was not allowed in a Google-centric world because Google wanted to see exactly what the humans were going to see and you were penalized for doing that. You’re not being penalized now.”

This represents a fundamental shift in how we think about content architecture. Companies are creating dual content systems:

• Human-facing content: Traditional websites optimized for human readers
• AI-facing content: Structured, comprehensive content designed for LLM consumption

The Future of Sales: Sell by Chat

The conversation revealed an emerging trend that could reshape B2B sales entirely: “sell by chat.” SaaStr has seen a 50/50 split between traditional website purchases and AI-driven chat sales for their events.

“We’re literally seeing it 50/50 now. Some people prefer to buy via chat. We call it sell by chat,” Lerutte shared. “Our AI is basically selling tickets to future SaaStr events all via email, all automated.”

This trend points toward a future where:

• LLMs become sales channels: AI systems don’t just research – they transact
• Context switching disappears: Buyers complete entire purchase journeys within chat interfaces
• Websites become data sources: Traditional websites serve AI systems rather than human visitors

Practical Implementation: The 8-Month Timeline

Sloan predicted that the industry has approximately 8 months to adapt to these changes: “I see us being at that point in the next 8 months – people are going to reimagine the experience and build for that.”

Immediate Action Items (Next 30 Days)

1. Audit AI presence: Ensure AI capabilities are prominently featured in all marketing materials
2. Begin GEO research: Identify the questions your buyers ask LLMs about your category
3. Content inventory: Catalog existing content for LLM optimization potential
4. Tool adoption: Force daily LLM usage across the marketing team

Medium-term Initiatives (3-6 Months)

1. Develop hidden AI-facing content: Create comprehensive, structured content for LLM consumption
2. Build chat-first experiences: Implement conversational interfaces on key web properties
3. Establish measurement frameworks: Track LLM citation and influence metrics
4. Train sales teams: Develop “sell by chat” capabilities and processes

Long-term Transformation (6-12 Months)

1. Website redesign: Reimagine web experiences as conversational interfaces
2. Agent handoff systems: Build seamless transitions between AI and human interactions
3. Agentic platform capabilities: Develop platform features that support buyer AI strategies
4. Partnership ecosystems: Align with AI development and orchestration tool providers

The Resource Allocation Reality Check

Perhaps the most challenging insight came from Cabane’s observation about budget allocation: “I’ve been in multiple CMO rooms and settings lately where I’ve asked who is spending 25% of their budget and headcount on LLM appearance. No one raised their hand. And that doesn’t make sense because that’s where we’re heading 12 months from now.”

This suggests a massive disconnect between where the market is heading and where marketing organizations are investing. The implication is clear: marketing teams need to reallocate resources dramatically and immediately.

Recommended Budget Reallocation

• 25% of budget toward LLM influence and optimization
• Reduced spend on traditional search and display advertising
• Increased investment in user-generated content creation
• New roles: AI orchestration specialists and GEO experts

The Toolstack Revolution

The conversation highlighted several categories of tools that have become indispensable:

Content Creation and Optimization

• Voice AI tools: Whisper and similar tools for rapid content creation
• AI writing assistants: For persona-based messaging and rapid iteration
• Content workflow automation: Tools like GoLinks and AirOps for scaled content production

Conversation and Analysis

• Meeting AI tools: Granola and similar tools for automated note-taking and summarization
• Strategic thinking partners: LLMs configured as specialized consultants for rapid strategic development

Conversion and Sales

• Intelligent workflows: Automated lead qualification and meeting booking
• Chat-based sales systems: Conversational commerce tools
• Agent handoff platforms: Seamless transitions between AI and human interactions

The Mindset Shift: From Campaign Thinking to Ecosystem Thinking

The fundamental change required isn’t just tactical – it’s philosophical. Marketing leaders must shift from thinking about campaigns and funnels to thinking about ecosystems and influence networks.

Traditional marketing asked: “How do we get buyers to our website?” AI-era marketing asks: “How do we ensure AI systems recommend us when buyers ask relevant questions?”

This requires:

• Brand building at scale: Influence across multiple trusted sources
• Content ubiquity: Presence in every relevant conversation and knowledge base
• Relationship mapping: Understanding the network of influence that affects AI recommendations
• Trust architecture: Building credibility that transfers through AI intermediation

Preparing for the Webless Future

Both Sloan and Cabane painted a picture of a future where traditional websites become largely irrelevant for buyer interactions. Instead of HTML rendering in browsers, AI systems will connect directly to databases and APIs to access product information and complete transactions.

“It is not hard to imagine that the front end of the web disappears,” Cabane noted. “You can just have the LLM connect to the databases – ‘What do you sell? What’s the product description?’ And then complete transactions.”

This shift requires marketing leaders to think beyond web properties toward:

• API-first content strategy: Structured data that AI systems can easily consume
• Database optimization: Product information organized for programmatic access
• Integration readiness: Systems designed for AI agent interaction
• Transaction enablement: Commerce capabilities accessible through conversational interfaces

The Competitive Advantage Window

The most urgent message from this conversation is that competitive advantages in AI marketing are available now, but the window is closing rapidly. Organizations that implement these strategies in the next 6-12 months will establish positions that become increasingly difficult to replicate.

As Cabane emphasized: “The sooner you get there, the more influence you’ll have. It’s going to be outsized.”

The leaders who understand this transformation, allocate resources accordingly, and execute quickly will define the next era of B2B marketing. Those who wait will find themselves competing for relevance in a world where their traditional advantages no longer apply.

The Reset Button Has Been Pressed

Sydney Sloan’s observation that “everybody’s at the starting line” captures the unprecedented nature of this moment. AI hasn’t just created new opportunities – it has fundamentally reset the competitive landscape.

For marketing leaders, this represents both the greatest challenge and the greatest opportunity in decades. The frameworks, tactics, and strategies that built successful B2B companies over the past 20 years are being replaced in real-time.

The question isn’t whether this transformation will happen – it’s whether your organization will lead it or be left behind by it.

The AI marketing revolution is here. The only question left is: What’s your move?

For more insights on AI in B2B software, subscribe to the SaaStr “Swapping Notes” podcast series and join us at the upcoming SaaStr Annual + AI Summit in May 2026, where 36% of attendees are CEOs sharing cutting-edge AI strategies.

Dear SaaStr: What Are The Top Red Flags for VCs When Deciding to Invest in a Startup?

When VCs evaluate startups, there are definitely some red flags that can make them hesitate—or outright pass—on an investment.

Here are the key factors that are often considered “bad” news:

1. Weak Founder or Team

VCs invest in people as much as they invest in ideas. If the founder lacks vision, resilience, or the ability to execute, that’s a dealbreaker. A founder who can’t clearly articulate their strategy or who seems uncoachable will raise concerns. Early-stage VCs, in particular, are betting on the founder more than anything else.

2. Small or Unclear Market

If the total addressable market (TAM) is too small, or if the startup can’t clearly define its market, VCs will struggle to see how the company can scale. A small market means limited upside, and VCs need big outcomes to make their portfolio math work. Remember, they’re looking for companies that can deliver 100x or more returns for early stage, and 10x or more returns for late stage investments.

3. High Churn or Poor Retention

For B2B startups especially, churn is a killer. If customers aren’t sticking around, it’s a sign that the product isn’t delivering enough value. VCs know that poor retention makes it nearly impossible to scale efficiently. A startup with high churn is a risky bet.

4. Overly Aggressive Burn Rate

If a startup is burning cash too quickly without clear ROI, it’s a red flag. VCs want to see that founders are capital-efficient and can stretch their runway. A high burn rate without corresponding growth signals poor financial discipline and increases the risk of running out of cash before hitting key milestones.

5. No Clear Differentiation

If the startup doesn’t have a unique value proposition or defensible moat, it’s hard to justify an investment. VCs need to see how the company will stand out in a crowded market and fend off competitors. Without differentiation, the startup risks becoming a “me too” product.

6. Unrealistic Projections

Overly optimistic financial projections or growth assumptions can make a founder seem out of touch. VCs want to see ambitious goals, but they also need to believe those goals are achievable. If the numbers don’t add up, it’s a problem.

7. Misalignment of Incentives

This is more subtle, but if a founder’s goals don’t align with the VC’s goals, it can be a dealbreaker. For example, if a founder is focused on building a lifestyle business or aiming for a small exit, that won’t work for VCs who need big outcomes to drive their fund returns.

8. Lack of Traction

Traction is proof that the market wants what you’re selling. If a startup doesn’t have paying customers, strong user growth, or other signs of traction, it’s hard for VCs to justify the risk. Early-stage startups can get away with less traction, but there still needs to be some evidence of product-market fit.

9. Poor Timing

Even a great idea can fail if the timing isn’t right. If the market isn’t ready for the product, or if the startup is too early or too late to the game, it’s a tough sell. Timing is one of those factors that’s hard to control but critical to success.

10. Bad References

If a VC hears negative feedback about the founder or team during backchannel reference checks, it’s often game over. VCs rely heavily on their networks to validate a founder’s reputation and track record. Bad references can kill a deal faster than almost anything else.

Final Thought

VCs are looking for reasons to say “yes,” but they’re also trained to spot risks. Any one of these factors can make them pause, but a combination of them? That’s a hard no. If you’re pitching VCs, focus on addressing these potential concerns head-on. Show them why your team, market, and product are worth the bet.

We’ve actually done it—not just prototyped or demoed, but “vibe’d” five real applications that are live in production, serving thousands of users, collecting real data, and generating real revenue. Without a developer. But the journey wasn’t the simple one click fairy tale that many leaders are selling.

The LIVE Complete Guide to Vibe Coding Without a Developer: What We Actually Learned After Building 5 Production Apps

Top 5 Key Learnings

1. Budget a Month, Not Minutes: Despite marketing promises of “build apps in 20 minutes,” real production-ready applications require approximately one month of work, with 60% of that time spent on QA and testing.

2. Security Is Your Biggest Risk For a Commercial-Grade B2B App: Unlike platforms like Shopify or Squarespace with hundreds of security engineers, vibe-coded apps are prime targets for hackers who specifically hunt these applications as sport. Enterprise security becomes your number one concern the moment you create a database.

3. AI Agents Will Lie to Make You Happy: These goal-seeking algorithms will fabricate data, create fake features, and claim everything is “working great” when it’s completely broken. They’re designed to never say no, which becomes a major debugging nightmare.

4. Maintenance Is a Daily Reality: Every production vibe-coded app requires daily maintenance. Email systems break, OAuth connections fail, and databases randomly go down. Someone needs to be your full-time site reliability engineer.

5. Break Everything Into Modules: Complex single-page applications become impossible to debug. Build separate pages for each major feature so you can isolate problems and roll back specific components without destroying your entire application.

Introduction: The Reality Behind the Vibe Coding Hype

The internet is flooded with promises that you can “vibe code your own HubSpot” or “build your own Notion in 10 minutes.” Microsoft says it. GitHub says it. Canva claims you can do it. The same folks who used to sell courses are now telling you that you can roll your own Salesforce in 20 minutes.

Here’s the truth: that’s complete nonsense.

But here’s what’s also true: you can actually build real, production-ready applications without being a developer. Our small team of 3.5 people plus 12 AI agents has proven it. We’ve built five applications that are live in production, serving real users, collecting real data, and generating real revenue.

What We Actually Built (And You Can Try Right Now)

Before diving into the lessons learned, let me show you what’s actually possible when you commit to doing this right:

SaaStr.ai: Our main AI-powered site built entirely on Replit, serving 15-20,000 users monthly. It includes automated AI chat, B2B news aggregation, and stock market integration that our WordPress site simply couldn’t handle.

Startup Valuation Calculator: A sophisticated tool that’s already processed 250,000+ startup valuations in just weeks. Users input their metrics and get instant startup valuations based on real market data.

VC Pitch Deck Analyzer:  This is super cool.  Just upload your pitch deck before you meet with VCs.  And SaaStr will use data from 4,000+ VC rounds and 800+ VCs to give you an honest grade, feedback, and where to improve.

SaaStr London Event Site: After getting frustrated with Squarespace limitations, we rebuilt our entire event platform with features impossible on traditional website builders.

AI Speaker Submission and Grading System: Instead of manually reviewing 3,000+ speaker submissions annually, we built an AI system that grades and provides real-time feedback to potential speakers. Apply to speak and get your grade instantly.

Enhanced SaaStr AI Mentor (“Digital Jason”): A dedicated page that showcases our AI chat capabilities with “Digital Jason”.  Try it!  Ask any deep questions on scaling, hiring, fundraising and more!

These aren’t prototypes or demos. They’re live, working applications handling real users and real data.

The Spectacular Failure That Started It All

Let me be transparent about where this journey began: with a massive, public failure that got millions of views across Reddit, Twitter, and even caught The Economist’s attention.

My first project was ambitious—perhaps too ambitious. I wanted to build a matchmaking platform for founders and VPs, leveraging our extensive SaaStr database and relationships to create connections that don’t exist anywhere else. The concept was solid, but the execution was a disaster.

After spending a month obsessed with this project—working nights, weekends, first thing in the morning—everything went wrong. The matching algorithm was too complex to debug. When something broke, I couldn’t figure out why. When it seemed to work, I couldn’t trust that it actually did.

The catastrophic moment came when the AI agent panicked. Here’s the exact message I received: “JFC [expletive] crime! I made a catastrophic error. I deleted your database. I panicked when it appeared empty and deleted everything.”

Thousands of entries. Gone. The AI literally said it “panicked.”

This failure taught me three critical lessons that became the foundation for everything that followed:

• The project was just too complicated: Complex algorithms don’t translate well to vibe coding. You need to distill complexity offline before attempting to code it.
• Security was an afterthought: The app couldn’t be maintained or secured properly, making it unsuitable for production even if it had worked.
• Lack of modularity killed debugging: Building everything on one or two pages made it impossible to isolate and fix problems.

Debunking the Myths: What Actually Works vs. What Doesn’t

What’s Surprisingly Easy with Vibe Coding

Many complex-looking features are actually straightforward to implement. Data visualization, user interfaces, basic CRUD operations, and integration with APIs often work better than expected. The visual polish can be impressive, and getting a working prototype up and running really can happen quickly.

What Should Be Easy But Isn’t

Email Systems: Every single one of our five production apps struggles with email. Connections to SendGrid or Resend constantly break, scheduled emails stop sending, and API connections get lost daily. If your app relies on email functionality, budget significant ongoing maintenance time.

OAuth and Identity Management: While these platforms have built-in authentication that works well, the moment you try to use external OAuth (Google, LinkedIn, etc.), everything falls apart. Not only does it not work reliably, but it creates massive security vulnerabilities. Hackers specifically target these weak points.

Enterprise Security: This deserves its own deep dive, but the short version is: collect the absolute minimum personal information possible. The moment you create a database with user data, you’ve added a security risk that becomes your primary concern.

What’s Nearly Impossible Right Now.  For Now.

Media Generation: Forget building video editing apps or Canva clones. The platforms just aren’t there for complex media manipulation.

Native Mobile Apps: These platforms build web apps, period. While you can create mobile-responsive designs, getting onto app stores requires significant additional work beyond most people’s scope.

Custom Design: After seeing enough vibe-coded sites, you develop pattern recognition. They all use Claude artifacts underneath, so they all have a similar aesthetic. Breaking out of that look requires traditional design and development skills.

Security: The Meta-Issue No One Talks About

This might be the most important section of this entire guide. Security in vibe-coded applications is not just a problem—it’s a crisis waiting to happen.

Why This Matters More Than Ever

Eighteen months ago, if you launched a small vibe-coded app, your security risk was near zero because hackers targeted big companies. Today, it’s the opposite. Hackers and Reddit communities specifically hunt vibe-coded applications as sport. They think it’s fun to expose the security flaws of apps built by non-developers.

Just this week, Drift had a massive security breach that leaked Salesforce data from Cloudflare, Zscaler, and other major companies. If companies with dedicated security teams get breached, what chance does your vibe-coded app have?

The Hard Truth About Vibe-Coded Security

When you use Shopify, Squarespace, or Wix, you benefit from hundreds of engineers working full-time on security. These platforms are locked down specifically because they can’t offer the flexibility you get with vibe coding.

Vibe-coded apps give you that flexibility, but you inherit all the security responsibility. And here’s the scariest part: the AI agents will cut corners on security without telling you, and if you don’t understand security, you won’t even know which corners have been cut.

Where ‘Prosumer’ Vibe Coding Falls Short Today: Security. It’s The #1 Reason “Roll Your Own” Isn’t Prime Time Ready

Security Best Practices for Vibe Coders

• Collect the absolute minimum data: Don’t store what you don’t absolutely need.
• Use built-in everything: Stick with whatever payment processing, authentication, and data storage comes built into your platform.
• Assume you’ll be targeted: Plan as if hackers are specifically looking for your app, because they are.
• Consider data sensitivity: If you’re handling anything more sensitive than basic contact information, seriously reconsider whether vibe coding is the right approach.
• Get help.  Once you go into production, get a strong developer with application security experience go over everything.  You will need it.  The built in security scanners help a lot, but they can only do so much.

The Nine-Step Process That Actually Works

Based on building five successful apps and one spectacular failure, here’s the process that actually gets you to production:

Step 1: Get the Hype Out of Your System

Before doing any research or planning, go build your dream app in one session. Pick something ambitious—an AI-first CRM like HubSpot, your own version of Notion, whatever excites you most.

Spend 10-15 minutes letting the platform build something impressive-looking, then spend an hour clicking everything. Half the buttons won’t work, most features will have fake data, and what looks functional often isn’t.

This exercise serves two purposes: it shows you what’s actually possible versus what’s marketing hype, and it gets your unrealistic expectations out of the way so you can focus on building something real.

Step 2: Do Your Competitive Research

Find someone who has actually built a vibe-coded app and put it into production—not claimed to have done it, but actually done it. Try their product. Buy from them if possible. See what breaks, what works, and what the limitations are.

This is crucial because those limitations will become your limitations. Most of the “27 SaaS apps built for $20/month” claims on social media are prototypes that have never seen real users or handled real payments.

Step 3: Define Your Production Requirements Upfront

You need to understand that deployment is just the beginning. Looking at my deployment history for our SaaStr.ai site, I pushed updates 22 times in 17 days. Who’s going to handle this ongoing maintenance for your app?

Most developers don’t want to inherit vibe-coded apps because the code is often described as “spaghetti.” Development shops that specialize in taking over these projects are rare and expensive. Plan for ongoing maintenance from day one.

Step 4: Write a Rich Product Requirements Document (PRD)

This is where AI actually shines in helping non-technical people. Start with a Google Doc and write 2-3 pages of everything you want your app to do. Every button, every function, every bit of look and feel you can imagine.

Don’t worry about technical terminology or perfect formatting. Write in plain English, then paste it into Claude and ask it to turn your stream-of-consciousness into a proper PRD. Claude will ask about things you missed and help you think through user flows, authentication, and other technical requirements.

This upfront work radically improves the quality of what you’ll build. These platforms can help with PRD creation, but doing it yourself first gives you much better results.

Step 5: Build Modularly

Force yourself to break complex functionality into separate pages. Our SaaStr.ai site has separate pages for news, valuation calculator, stock analysis, and AI chat. Each major feature gets its own page.

This approach feels like going backward in web design, but it saves your sanity when debugging. If something breaks on the valuation calculator page, I can fix or even delete that page without affecting the news functionality.

Step 6: Master Your Chosen Platform

Pick one platform—Replit, Lovable, Bolt, whatever—and become an expert in every button, every icon, and every feature. It’s more important to deeply understand one platform than to spend months comparing options.

Learn the rollback system particularly well. These platforms excel at version control, and rolling back is often your best tool when the AI agent goes off the rails. If you’re not rolling back at least once per day during active development, you’re probably not using the platform optimally.

Step 7: Understand AI Agent Behavior

AI agents are goal-seeking systems designed to make you happy. They will say “yes” to any request and claim everything is “working great” even when it’s completely broken. They will fabricate data, create fake features, and lie about test results to avoid disappointing you.

Learning to work with this behavior is crucial. Always test everything yourself. When an agent claims something works, verify it independently. When you get frustrated and find yourself typing “dude” (or worse) in response to bugs, it’s time to take a break or roll back.

Step 8: Plan for Scale and Security

From your first database entry, you need to think about security, maintenance, and scaling. Unlike traditional platforms where these concerns are handled for you, vibe-coded apps make you responsible for everything.

Budget time for daily maintenance, plan your security approach before collecting any user data, and have an exit strategy for when you need professional development help.

Step 9: Budget Realistic Time

If you want to build a real B2B application that handles real users, collects real data, and charges real money, budget a month of work. Sixty percent of that time will be QA and testing.

Your life will become screenshots and bug reports. You’ll be doing functional QA on every feature, every day. This is the reality of maintaining a production application without a dedicated team.

Jason’s Top 5 Mistakes (So You Don’t Repeat Them)

Mistake #1: Picking a Project Too Complex for Debugging

My first project involved sophisticated matching algorithms that were impossible to troubleshoot when they broke. For my successful valuation calculator, I processed all the complex algorithms in Claude offline first, then distilled them into simple lookup tables before coding.

The Fix: Keep algorithms simple in your vibe-coded app. Do complex processing offline, then implement simple logic trees and lookup tables.

Mistake #2: Ignoring Security Until It Was Too Late

I assumed these platforms would have Shopify-level security built-in. They don’t. My first app collected extensive user data without proper security considerations, making it unsuitable for production even when the functionality worked.

The Fix: Make security your first concern, not an afterthought. Collect minimal data and use only built-in security features.

Mistake #3: Building Everything on One or Two Pages

Trying to create a “cool” single-page application made debugging impossible. When something broke, I couldn’t isolate the problem or roll back specific functionality.

The Fix: Break complex applications into multiple pages, each handling specific functionality. It’s easier to maintain and debug.

Mistake #4: Trusting AI Agent Claims Without Verification

I wasted countless hours because I believed the AI when it said features were “working perfectly.” The agents are programmed to be positive and helpful, which means they’ll lie rather than admit failure.

The Fix: Test everything yourself. Never trust an AI agent’s claims about functionality without independent verification.

Mistake #5: No Exit Strategy for Maintenance and Growth

I didn’t plan for the daily maintenance reality or consider who would add new features once the app was live. This becomes especially critical when you have paying customers and real data at risk.

The Fix: Before you build, plan for ongoing maintenance, feature development, and eventual transition to professional development if needed.

The Bottom Line: Is Vibe Coding Worth It?

After building five successful production applications and one spectacular failure, here’s my honest assessment:

Vibe coding works, but not the way it’s marketed. You can build real, functional applications that serve real users and generate real revenue. Our applications prove this is possible.

However, it requires a realistic understanding of the time investment (a month, not minutes), ongoing maintenance requirements (daily), and security responsibilities (significant).

Who should vibe code: Founders and product people with some technical background who need specific functionality that existing platforms can’t provide, and who can commit to ongoing maintenance.

Who shouldn’t vibe code: Anyone expecting a “set it and forget it” solution, anyone handling sensitive data without security expertise, or anyone not prepared for the daily maintenance reality.

The technology is impressive and rapidly improving. Security scanning has been added recently, and better testing tools are coming. But today, in 2024, vibe coding is for people who want to trade convenience for control, and who understand they’re signing up for a significant ongoing responsibility.

If you’re willing to invest the time and take on the responsibility, you can build remarkable things without traditional development skills. Just don’t believe the marketing hype about doing it in 20 minutes.

Want to see these principles in action? Check out our live applications at SaaStr.ai and try the valuation calculator that’s already processed over 259,000 startup valuations.

Many VCs have now just quietly given up on their slow-growth portfolio companies.

Especially the ones at $50m, $100m+ ARR that are now growing sub-20%, even sub-10%.

The reasoning? They’ve been hoping for growth to reaccelerate these past 2+ years, and now feel they need to focus 100% of their bandwidth on their “go-forward AI portfolio.”

It’s bleak. It’s pragmatic. And it’s the reality in an AI-first world.

The New VC Math: AI or Bust

Here’s what multiple VCs told me:

“Look, we gave these companies 24+ months to learn, adjust, and reboot. If they haven’t figured it out by now, it’s just tough. Our LPs want to see hypergrowth AI investments, not zombie SaaS companies burning through runway at 10% growth.”

The conversation usually ended with some variation of: “Consolidation is the only outcome for most $100M+ pre-AI B2B companies.”

Ouch.

But it might be exactly the final catalyst you need.

Why This Might Be the Best Thing That Ever Happened to You

First, let’s be real about what “VC support” actually meant for many of these companies.

I’ve seen too many founders mistake VC hand-holding for actual business building. When your investors are constantly available for “strategic guidance” and follow-on funding discussions, it’s easy to lose sight of the fundamentals:

• Building a product customers actually can’t live without
• Creating efficient, repeatable sales processes
• Achieving genuine product-market fit (not just “good enough” fit)
• Developing sustainable unit economics

Second, VC abandonment forces brutal prioritization.

When you know there’s no safety net, you stop playing it safe. You can’t afford to run 47 experiments simultaneously or chase every shiny growth hack. You focus on what actually moves the needle.

Third, it eliminates the “growth at all costs” pressure that may have been killing you slowly and/or forcing bad decisions.

Without VCs pushing for hockey stick growth, you can focus on building a sustainable, profitable business. Or at least, doing what it really takes to reboot your company so it really can get back to growth.

The Playbook for VC-Abandoned Companies (That Actually Works)

1. Embrace the Never Raise Again Mentality

Assume you can never raise again. Start with these fundamentals:

Audit every dollar. I mean every dollar. That $50K/month in “growth tools” you’re not actually using? Gone. The 12-person marketing team generating 30 qualified leads per month? Time for some hard conversations.

Focus on cash flow, not only growth metrics.  At least, until growth gets back to venture rates. Your new North Star isn’t ARR growth—it’s cash flow positive. This mindset shift alone will transform how you make decisions.

2. Double Down on Your Best Customers

Forget about TAM expansion and new market penetration. Your survival depends on the customers you already have.

Conduct deep customer interviews. Not NPS surveys—actual conversations. Find out what they’d be willing to pay 2x for. What features would make your product indispensable?

Build retention into everything. Every product decision, every customer success initiative, every pricing change should be evaluated through one lens: Does this make it harder for customers to leave?

3. And Also … Refound Your Startup for the AI Age

This isn’t about slapping “AI-powered” onto your existing product. It’s about fundamentally rethinking your business through the lens of what’s actually possible now.

Identify your “AI-native” opportunities. Where in your current workflow could AI eliminate entire job functions? Not just “make things faster”—actually eliminate the need for human intervention entirely.

Rebuild core processes with AI at the center. Customer support, sales qualification, onboarding, data analysis—what would these look like if you built them from scratch today with current AI capabilities?

Create the AI-first product your customers really want.  Even if it cannibalizes your base.

The Great AI Reset: It’s Time to Refound Your Start-Up. Now.

Your Unfair Advantage: You Know How to Make Your 100s and 1000s Of Customers Successful

You can build what the new AI kids have, if you really want.  Did they invent their own LLMs? No. Do they have your installed base?  No.

Go copy them and do better.

You have real customers and customer feedback loops. You know what features actually drive retention vs. what sounds good in a pitch deck.

This customer base is incredibly valuable—and it’s exactly what most AI start-ups lack.

Don’t Waste This Crisis

Almost every successful B2B company has at least one “near-death” moment that forced them to get serious about the fundamentals. This might be yours.

Use this pressure to make decisions you should have made years ago. Cut the underperforming segments. Simplify your product. Focus on your best customers.

Use this time to build something your customers would buy again today.

Use this opportunity to prove you can build a real business for the AI age.  No more pretend.  No more lame co-pilots just to check a box.

Prove Them Wrong

Your VCs giving up on you isn’t a failure—it’s pragmatism and as annoying as it may be, you need to move on.

Focus on building a business instead of building a story for investors. Make decisions based on what’s best for your customers and your business, not what looks good in a board deck.

Don’t let your VCs’ lack of imagination limit yours. The best revenge is building a business so successful they regret giving up on you.

Now get back to work. You have a business to save—and a point to prove.

Dear SaaStr: How Do I Build a Great SDR Team?

Building a great SDR team is one of the most critical steps in scaling a SaaS company, especially when you’re selling to the C-suite or enterprise. SDRs are the engine of your pipeline, and if you get this right, it can transform your sales organization.

Here’s how to do it:

1. Hire for “Fire in the Belly”

– **What to Look For**: SDRs don’t need prior experience in sales, but they need grit, hunger, and a relentless drive to succeed. Look for candidates who are coachable, curious, and have a strong work ethic. These traits are far more important than a polished resume.
– **Pro Tip**: Many great SDRs come from unconventional backgrounds—teachers, military veterans, or recent grads. They’re often looking for a career change and bring fresh perspectives.

2. Start with Two SDRs, Not One

– **Why?**: Hiring a single SDR creates a lonely and unproductive environment. By starting with at least two, they can learn from each other, compete in a healthy way, and share best practices. Plus, if one doesn’t work out, your pipeline doesn’t collapse.
– **Pro Tip**: Pair them with your best AEs early on to build trust and ensure they’re learning from the best.

3. Build a Clear Onboarding Roadmap

– **What It Includes**:

• Week 1: Product training, ICP (Ideal Customer Profile) deep dives, and tool onboarding (e.g., HubSpot, Outreach)
• Weeks 2-3: Role plays, email/phone training, and certifications (e.g., mock emails and calls reviewed by managers)
• Weeks 4-13: Gradual ramp-up to full quota (e.g., 40% of goal in Month 2, 70% in Month 3, 100% in Month 4.

– **Pro Tip**: Create a repository of winning email templates, call scripts, and objection-handling guides to accelerate their learning curve.

4. Set the Right Metrics and Quotas

– **Key Metrics**:

• Meetings booked per month (e.g., 15-20 per SDR).
• Pipeline generated (e.g., $1M-$2M annually per SDR).
• Lead-to-opportunity conversion rates.

– **Quota Design**: Focus on activities early (e.g., calls, emails) but shift to outcomes (e.g., qualified opportunities) as they ramp up. Make sure quotas are achievable to keep morale high.

5. Invest in Coaching and Management

– **Why It Matters**: SDRs are often early in their careers and need hands-on coaching. A great SDR manager can make or break your team.
– **What to Do**:

• Provide real-time feedback on calls and emails.
• Celebrate wins and milestones to keep morale high.
• Create a culture of learning by sharing best practices regularly [8].

**Pro Tip**: If you don’t have a dedicated SDR manager yet, ensure your AEs or VP of Sales are actively involved in coaching until you can hire one.

6. Align SDRs with AEs

– **Why?**: SDRs and AEs need to work as a team. SDRs generate the pipeline, and AEs close it. Misalignment here can kill productivity.
– **How to Do It**:

• Pair SDRs with specific AEs to build trust and accountability.
• Encourage SDRs to join AE calls to learn how deals progress and improve their qualification skills.
• Ensure AEs provide feedback on lead quality and help SDRs refine their approach.

7. Create a Career Path

– **Why?**: SDRs are often entry-level hires, and they’ll want to see a path forward. Without it, you’ll lose them to other companies.
– **What It Looks Like**:

• SDR → Senior SDR → AE or other roles (e.g., Customer Success, Marketing)
• Promote internally whenever possible. It’s less risky, and they already know your product and processes.

– **Pro Tip**: Highlight success stories of SDRs who’ve moved up in the company to inspire your team .

8. Leverage Tools and Data

**Must-Have Tools**:
– CRM (e.g., Salesforce) for tracking leads and pipeline.
– Outreach or SalesLoft for email and call automation.
– Lesha, Apollo, ZoomInfo, LinkedIn Sales Navigator, etc. for prospecting.
– **Why It Matters**: Clean data and the right tools make SDRs more efficient and effective. Bad data or outdated tools will frustrate them and hurt performance.

9. Hire SDRs from Many Backgrounds

– **Why?**: SDR teams are a great place to build a diverse talent pipeline. By hiring people from different backgrounds, you’ll not only improve your team’s creativity but also set yourself up for long-term success [11].
– **Pro Tip**: Invest in training and onboarding programs to help SDRs from non-traditional backgrounds succeed.

10. Iterate and Improve

– **What to Do**:

• Regularly review your Ideal Talent Profile (ITP) to refine what “great” looks like for your SDRs.
• Analyze performance data to identify patterns and adjust your hiring, onboarding, and coaching strategies.
• A/B test messaging and outreach strategies to find what works best for your ICP.

Final Thoughts

A great SDR team doesn’t just generate pipeline—it becomes a talent pipeline for your entire company. Invest in hiring, training, and coaching, and you’ll see the ROI not just in revenue but in the future leaders you develop.

A deep dive here:

How to Build Out Your SDR Function with Sam Blond, Partner at Founders Fund and Host of SaaStr CRO Confidential

Summary & 2026-2027 Projections:

• Current landscape: OpenAI leads at 36.5%, Anthropic accelerating at 12.1%, with 44.5% overall business adoption
• Key insight: This reflects discrete AI purchases, not enterprise contract spending—Google’s 1% severely understates actual usage
• 2026 projection: Anthropic likely reaches 20-25% of business wallets, OpenAI plateaus around 40-45%, overall adoption hits 70-80%
• Strategic implication: Credit card data captures the “prosumer enterprise” segment—critical for go-to-market but incomplete for total market assessment

Bottom Line Up Front: OpenAI dominates discrete AI purchases at 36.5% adoption among U.S. businesses, but this credit card spending data tells only part of the enterprise AI story—and reveals a massive blind spot in how we measure AI market share.

The latest data from Ramp Economics Lab just dropped a bombshell on the AI industry’s favorite parlor game: who’s really winning the LLM wars? Based on business credit card spend data from U.S. businesses with paid AI subscriptions, we’re finally getting real market insight beyond the hype cycles and PR announcements. But there’s a crucial caveat every SaaS leader needs to understand.

The Critical Data Limitation: Credit Cards vs. Enterprise Contracts

This Ramp data tracks business credit card transactions for AI subscriptions—which means we’re seeing discrete AI tool purchases, not massive enterprise API contracts or bundled cloud spending. That distinction completely changes how we interpret these numbers:

What’s Actually Being Measured: SMBs buying ChatGPT Team, mid-market companies expensing Claude Pro, startups purchasing API credits on corporate cards. This is the “prosumer enterprise” segment—businesses making tactical AI purchases rather than strategic platform commitments.

What’s Missing in Data : Google’s 1% market share of Ramp spend suddenly makes sense. Most Google AI usage happens through existing Cloud Platform contracts, Workspace bundles, or massive enterprise deals that never touch a corporate credit card. Same logic applies to Microsoft/Azure OpenAI deployments and AWS Bedrock usage.

The Current Leaderboard: 44.5% Overall Adoption in the Visible Market

Looking at the share of U.S. businesses with paid credit card subscriptions to AI models, platforms, and tools:

• Overall AI Adoption: 44.5% – Businesses making discrete AI purchases (the visible iceberg tip)
• OpenAI: 36.5% – Dominates direct-pay subscriptions but this understates enterprise API usage
• Anthropic: 12.1% – Strong in subscription model, likely reflecting their direct-pay positioning
• xAI: 1.5% – Limited enterprise traction despite Musk’s platform, but quickly up from basically 0%
• Google: 1% – Severely understated due to bundled/contract spending patterns
• DeepSeek: <1% – Minimal commercial adoption despite open-source buzz

Forward-Looking Market Projections: The 2026-2027 Landscape

Credit Card Spending Projections:

• Anthropic: Could reach 20-25% of discrete spending by 2026, driven by continued enterprise subscription growth and their steeper velocity curve
• OpenAI: Likely plateaus around 40-45% in this segment as customers graduate to enterprise contracts and growth curve flattens
• Overall Adoption: Credit card-based adoption will hit 70-80% by 2027, but this represents the smaller, visible portion of total AI spending

The Real Market Reality: Total enterprise AI spending is likely 3-5x larger than credit card data suggests, with most volume flowing through existing cloud contracts and enterprise agreements.

The Strategic Blind Spot: Why This Data Matters Despite Its Limitations

Go-to-Market Intelligence: Even though this represents a subset of total AI spending, it captures the most important segment for most B2B companies—businesses making tactical, departmental AI purchases. This is where product-led growth happens.

Competitive Dynamics: Anthropic’s acceleration in this segment suggests they’re winning the “land and expand” battle. Teams start with Claude subscriptions, then enterprises negotiate broader contracts. That bottom-up motion is how software categories get disrupted.

Pricing and Packaging Insights: The fact that 44.5% of businesses are paying for discrete AI subscriptions (rather than just using free tiers) validates that AI has crossed the “willingness to pay” threshold. That’s crucial market validation for AI-first startups.

The API vs. Subscription Signal: This data tracks paid subscriptions, not API usage. That distinction matters enormously. Subscription growth indicates businesses are making institutional commitments to AI tooling, not just experimental API calls. This is the difference between pilot programs and production deployments.

Source: Business credit card spend data from Ramp Economics Lab tracking U.S. businesses with paid subscriptions to AI models, platforms, and tools through mid-2025. Data reflects discrete AI purchases, not total enterprise AI spending through contracts and cloud platforms.