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This week’s sponsor is AI CRM Attio (raised $52M from Google Ventures):

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• Instantly prospect and route leads with research agents

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• Build powerful AI automations for your most complex workflows

Fast-growing companies like Granola, Modal, Replicate and more are all experiencing the future of GTM with Attio. See it for yourself.

Try Attio Pro for FREE

This week I sat down with my friend Ignacio “Guli” Moreno, cofounder of Capchase, the fintech that has deployed more than $2B into SaaS startups.

Capchase started with a simple idea: founders shouldn’t be stuck with revenue in the future. By turning ARR into upfront cash, they gave SaaS companies speed without selling equity.

But the real playbook here isn’t just the product. It’s how Guli and team built trust in a new category, scaled globally, and designed culture as their ultimate GTM weapon:

What you’ll learn

• How to design culture as a GTM advantage

• The founder-led sales tactics that got Capchase its first customers

• Why EU startups needs to think global from day one

• How talent snowballs create ecosystems

Key takeaways

• MVP velocity wins trust: Capchase shipped its first working demo in 11 days on a $7 server, speed built confidence with investors and early customers.

• Category creation requires focus: Narrowing only to SaaS looked small, but enabled deep expertise, better underwriting, and founder trust.

• Culture = GTM strategy: Values like intellectual humility, builder attitude, and yes before no weren’t internal HR slogans, they were the operating system that shaped how customers perceived Capchase.

• Founder-led GTM: Guli and team didn’t wait for playbooks. They handled calls, cold outreach, and product tweaks directly, collapsing the feedback loop.

• Trust before growth: While competitors blitz-scaled and died when interest rates rose, Capchase grew cautiously, signaling reliability to customers.

• Flywheel of talent: Guli launched Exponential Fellowship, finding and sending the brightest builders in Spain on a life-changing experience to the US (get in touch with us!).

• EU’s mindset gap: Too many founders still think local. Guli’s lesson: Nordics think global because their market forces them to, Spain must too.

• High-agency behavior compounds: Whether it’s tinkering with an “electric eraser” at age 9 or sending cold DMs, Guli shows that small, consistent acts of initiative snowball over time.

That’s it! I’d love to hear from you, just reply to this email (I reply to all of them).

🤙

P.s. join as a premium subscriber for venture capital waves + GTM playbooks:

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Welcome to a Founder Podcast edition of Startup Riders:

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This week I sat down with Pablo Jimenez de Parga, co-founder of Throxy — the contrarian AI sales startup quietly replacing SDR teams with results-first automation.

But here’s the twist: it’s not a SaaS, and it’s not an AISDR product.

Throxy is a full-stack AI company that builds custom outbound systems, then runs them for you. Invisible UX, real meetings, full accountability. Their model = start manual, validate every step, then automate what works.

They’ve hit $1.5M ARR and are redefining what “venture-backable” looks like in the AI agency era.

📺 Help your boy, subscribe on YouTube!

What you’ll learn

• Why most outbound fails and how Throxy flips it

• Why AISDRs don’t work yet (and what replaces them)

• Why the “AI agency” model works

Key takeaways

1. Sales is still broken: Missed quotas, outdated tools, bad CRMs. Pablo’s insight: it’s not about better messaging, it’s about better targeting.

2. Account > persona > message: Most teams get this backwards. If the pain is real, the message can be weak, it’ll still land.

3. AISDRs failed the outcome test: Pablo’s team proved they could deliver meetings consistently, while most AI tools delivered... demos.

4. The full-stack agency bet: They don’t sell software. They run your outbound for you. Customers don’t care how, it just works.

5. Manual first, then automate: While others chased scale, Throxy stayed manual. They iterated by hand until they earned the right to automate.

6. Invisible UX = magic: Customers see dashboards, replies, and meetings. The AI scraping, enrichment, and filtering? All hidden behind the scenes.

7. GTM was founder-led brute force: Pablo handled 100% of growth. Cold emails, cold calls, statement of work, send, close, repeat.

8. No ops bloat, only functions that move the needle: Legal, finance, HR? Outsourced. Focus stays on growth, product, and engineering.

9. Fundraise fast or miss it: Base10 led their $6M round in record speed. Pablo’s tip: If you want to back top YC founders, move fast or get left out.

📺 Watch and subscribe on YouTube → Startup Riders on YouTube

🗣️ What do you want us to cover next? Hit reply or comment 🤙

Startup Riders

Surfing Tech Waves: AI markets + founder playbooks

By Ivan Landabaso

“Speed is non-negotiable — it’s the ultimate advantage.”

This week I sat down with Guillem Oliva, co-founder of Konvo AI — a vertical agent platform that’s quietly rewriting how e-commerce brands convert.

Konvo isn’t just another support bot. It’s a 24/7 agent that sells: combining brand context, product data, and live buyer signals to trigger real conversations and drive conversion. From live chat to proactive follow-ups, it turns intent into orders.

And it’s working: 200 customers, $500K ARR, and 8% average conversion — built with just a 4-person team. We backed them early, and I’m pumped to share the story.

Help your boy out, Subscribe on Youtube!

What you’ll learn

• Why most ecommerce campaigns convert <1% — and how Konvo unlocks 8%+

• How they went from kitchen MVP to 200 brands

• The real wedge behind “AI agents for commerce” (hint: it’s not just chat)

• How Konvo uses outbound, founder-led sales, and popcorn to win

• Why speed + craftsmanship > frameworks

Key takeaways

1. Co-founder match was founder-future-fit — Guillem and his co-founder aligned early on risk-taking, endurance mindset, and complementary skills — long before the startup idea.

2. MVP = results before product — They onboarded 30–40 customers with zero UI — just backend builds, consulting, and manual execution to deliver real outcomes.

3. 8% conversion > legacy channels — Konvo agents drive 8%+ conversion rates per convo, 10x above email/WhatsApp, by acting as actual sales closers.

4. Agent = proactive sales, not passive chat — Unlike typical bots, Konvo agents detect buyer intent mid-chat and auto-trigger contextual follow-up workflows.

5. Vertical AI > generic LLMs — Plugging in ChatGPT isn’t enough — Konvo builds guardrails, tone, product goals, and context into each brand-trained agent.

6. Own the customer, or lose the edge — As AI platforms become aggregators, every brand needs its own agent to retain conversion and control.

7. GTM began as founder-led brute force — First 100+ customers came from cold email/calling — with just one commercial person on a 4-person team.

8. GTM evolved into community flywheel — Focused on one region (Spain), built trust via offline dinners, creative campaigns (like 60kg popcorn drops), and vertical brand-building.

9. Find what works > scale what works — The playbook: test messy experiments that get results (e.g. popcorn), then figure out how to scale only after.

10. Speed is the real moat — Konvo’s core principle: obsess over detail and move fast — “Anything worth doing must be done fast, or it won’t happen.”

📺 Watch and subscribe on YouTube → Startup Riders on YouTube

🗣️ What do you want us to cover next? Hit reply or comment 🤙

Startup Riders

Surfing Tech Waves: AI markets + founder playbooks

By Ivan Landabaso

This week, I sat down with Clint Burgess, co-founder of CharacterQuilt and former PMM leader at Bloomreach (where we met!).

After running 500+ buyer interviews a year and watching insights die in decks, Clint built a GTM engine powered by AI agents trained on raw voice-of-customer data.

This isn’t a “copy faster” tool. It’s a full-stack marketing engine for founders — research, ICP match, messaging, campaign generation — all in minutes.

Help your boy, subscribe on YouTube!

What you’ll learn

• Why most GTM research never makes it to the final campaign

• The difference between a copy bot and an infused buyer agent

• How a single marketer can now run full campaigns end-to-end

• Where “10x marketers” are already using this to win deals, prep fundraises, and revive warm leads

6 key takeaways

• Campaigns die in committee: Even with great research, insights often get diluted before anything ships.

• Founders should act as PMMs first: Talk to customers. Understand the buyer. Translate that into your narrative and GTM strategy.

• AI ≠ copy monkey: CharacterQuilt trains agents on real interviews, so campaigns are based on actual pain — not GPT guessing.

• From docs to campaign in 30 minutes: Upload positioning materials. CQ scores ICP fit, predicts pain points, and suggests campaign angles + leads.

• One person can run full GTM loops: Research, creative, segmentation, execution — without a 20-person team.

• Used beyond marketing: Clint uses CQ for fundraising, event prep, and reviving 500+ warm leads stuck in Gong/Notion.

📺 Listen and subscribe on YouTube → Startup Riders on YouTube

🗣️ Tell me what you want to hear more of in the comments 🤙

Startup Riders

Learn from the top 1% of founders and investors. Each week, I study how great startups are built and funded—and share ideas you can use to build yours.

By Ivan Landabaso

Each month, I tackle founder-focused questions (send them here!).

Join 17K+ early-stage founders surfing tech waves:

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Summary

1. 🌊 How to setup an AI GTM tech stack

2. 🌊 Sales Tech Market

3. 🌊 State of AI Voice Agents

4. 🌊 AI Market Crash

5. 🌊 OpenAI Deep Research

6. 🌊 YC’s Request For Startups

7. 🌊 Chasing heat

8. 🌊 Great Reads

9. 💵 Iberian Deals

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🌊 How to setup an AI GTM tech stack

Spent an hour with Fivos Aresti (Head of Growth at ColdIQ) to unpack the key lessons from scaling GTM with AI tools for early-stage startups:

Here’s the tl-dr:

1. Manual Before Automation: Don’t overinvest in fancy tools early. Start with cold outreach via Gmail and LinkedIn. Founder-led content > Paid ads (especially pre-product-market fit).

2. Prioritize Channels Wisely: Early-stage startups should (generally) focus on:

   1. Outbound: Low-cost, high-impact. Start here.

   2. Inbound & SEO: Builds long-term compounding growth.

   3. Content: Founder-led content builds trust + leads. Work on spotting underpriced attention (i.e. today, Linkedin).

3. Data Enrichment = Your Secret Weapon: Use tools like Genesy, Clay and Apollo to scale personalization and automate research—turning basic outreach into super-targeted campaigns.

4. Protect Your Domain’s Reputation: Always use a secondary domain for cold outreach to avoid spam filters and maintain your primary email's deliverability.

5. GTM Engineer: The Role of the Future: This hybrid role will become standard—managing tech tools, automating outreach, and optimizing channel performance.

6. Build Your Personal Brand: LinkedIn isn’t too saturated. If you post consistently and add value, inbound leads and credibility follow. Your network is your unfair advantage.

P.s. if you want to support your boy, subscribe to the pod!

🌊 Sales Tech Market Map ($30B+)

I found a great breakdown on the state of GTM tech stack by .

Tl:dr + my favorite insights from the article:

• The sales tech market is massive—$30B+.

• AI is fundamentally reshaping sales workflows, not just improving efficiency.

• The best sales tools don’t replace people, they amplify top performers. Completely agree on this, its about human + machine.

• AI-Native > AI Add-On → Tools built around AI (Gong) outperform those bolting it on. Check-out Sunny Huang on how to best leverage data for sales there.

• Workflow Integration > New Destination → Winning tools blend into existing workflows.

• Vertical Focus > Horizontal Platform → Specialized solutions (i.e. Affinity.co for VCs, I can attest to that) are beating one-size-fits-all approaches.

• AI is transforming prospecting (hyper-targeted leads), conversation intelligence (real-time coaching), and revenue optimization (forecasting + dynamic pricing).

• The sales stack is moving from disconnected tools to fully orchestrated revenue engines. Which is awesome, but I wonder how long it'll still take for non-techie "circles" to adopt these.

My 2 cents:

• Winners probably won’t just provide AI but integrate it into workflows, making it "invisible" yet indispensable.

• The best companies won’t compete on just having a better model but on becoming the default tool sales teams rely on daily.

• There's a moat in becoming becoming the tool sales people instinctively use.

🌊 State of AI Voice Agents

A16z published a very interesting analysis on the State of AI Voice Agents.

Tl-dr: Why Voice is an Interesting Category for AI Startups

• Voice is the most frequent and information-dense form of communication → AI makes it programmable for the first time, unlocking new applications.

• AI-powered voice agents replace human labor → They are cheaper, faster, and more reliable, often outperforming humans in customer service, scheduling, and transactions.

• 24/7 business availability → AI agents remove the 1:1 constraint of human work hours, allowing businesses to serve customers anytime, anywhere.

• Consumers will likely interact with AI through voice first → It could become the primary way users engage with AI assistants, virtual companions, or learning tools.

• Democratization of services → AI voice agents make previously inaccessible services (e.g., coaching, language learning) available to more people.

• Massive platform shift → AI voice tech is moving from infrastructure to applications, meaning now is the time for startups to build products leveraging this shift.

• Voice is the wedge, not the product → The real opportunity isn’t just voice-based AI products but platforms that use voice as an entry point to unlock larger markets.

🌊 AI Market Crash

The AI market crashed (a little) last week.

What happened, in simple terms:

• DeepSeek built a GPT-4-level model for just $5.6M, using H800 chips—a huge cost drop.

• This proves China can compete at the frontier despite U.S. chip bans.

• Investors panicked, realizing AI models are getting cheaper, faster than expected.

What it means for big tech:

• 𝗠𝗲𝘁𝗮: Cheaper AI fuels their consumer AI products

• 𝗔𝗽𝗽𝗹𝗲: More efficient models improve on-device AI

• 𝗔𝗪𝗦: Doesn’t need its own model, powers infrastructure

• 𝗚𝗼𝗼𝗴𝗹𝗲: Open-source make search competitors bigger

• 𝗡𝘃𝗶𝗱𝗶𝗮: AI efficiency lowers GPU demand, but reasoning models still need compute.

What it means for you / startups:

• Models will likely get commoditised. The real moat is becoming the tool people instinctively use.

• Right now, AI hype is all about the next big model. Long-term success is about adoption, distribution, and trust.

• These events suggest the biggest winners won’t just be model builders— but apps solving real problems.

P.s. Highly recommend Stratechery’s breakdown of this:

🌊 YC Request For Startups: Spring 25

YC's latest request for startups mentions AI 15+ times.

Good insight into future tech waves:

My 2 cents:

• Models are starting to become commoditized.

• Distribution, trust, and UX are starting to emerge as real moats.

• Owning workflows / mindshare is on the rise.

• AI is shifting from model innovation → infrastructure optimization.

• Vertical AI agents could replace SaaS in many categories.

• AI-native platforms can outscale AI-enabled retrofits.

• Inference costs are the next major bottleneck.

On building moats:

This statement from @levie on X at Box resonated:

"If, on the other hand, the layer above the intelligence requires a heavy degree of critical business logic, workflows, proprietary customer data, integrations in other systems, or deep industry or line-of-business expertise, then there’s likely more of a moat over overtime. Even having highly specialized implementation and services capabilities can be a very useful advantage in the world of AI."

🌊 OpenAI Drops Deep Research

Big impact on knowledge workers. Tl-dr:

• Instead of instant responses, it takes 5-30 minutes to conduct multi-step research. But for good "reason" ;)

• Browses the web, extracts data (tables, PDFs, images), and generates fully cited reports.

• Works like an analyst—asking clarifying questions before diving in.

• Built on OpenAI’s upcoming O3 model, trained for reasoning + retrieval.

• Outperforms humans on complex knowledge tasks (and scores 26.6% on Humanity’s Last Exam).

• Best hallucination-resistant OpenAI model so far, but still needs source checks.

Why it matters:

• This moves AI closer to being a real knowledge worker (and why you are seeing "agents everywhere" right now, even when you discount the hype).

• Its pretty amazing how instead of just regurgitating facts, it can plan, adapt, and synthesize new insights over time.

🌊 Chasing Heat

Despite all the current AI hype - published a great reminder to “Play It Cool” - on the importance of Chasing Heat vs. Being Contrarian in Venture Capital.

A good reminder to keep critical and independent thinking present:

8 "Sleeping" tech waves, overshadowed by the AI noise that I’m excited about:

1. 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀: In dynamic environments like construction, agriculture, or disaster response.

2. 𝗘𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻: Personalized and immersive learning, internationalization, and language adaptation.

3. 𝗦𝗽𝗮𝗰𝗲 𝗘𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻: Mining, autonomous infrastructure, and telepresence exploration.

4. 𝗘𝗻𝗲𝗿𝗴𝘆: Smart grids, dynamic management, advanced materials, and self-repairing infrastructure.

5. 𝗕𝗶𝗼𝘁𝗲𝗰𝗵 / 𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲: New drug discovery, prosthetics, cognitive tools, and personalized medicine.

6. 𝗟𝗼𝗻𝗲𝗹𝗶𝗻𝗲𝘀𝘀 𝗘𝗽𝗶𝗱𝗲𝗺𝗶𝗰: solutions for meaningful social interactions, virtual companionship, and community-building in hybrid spaces (physical / digital).

7. 𝗪𝗲𝗹𝗹𝗻𝗲𝘀𝘀: AI-enabled tools for stress management, personalized mental health therapies, sleep optimization, digital detoxes, and neuroadaptive well-being solutions.

8. Human-computer interaction: AR/VR/XR as a potential AI interface + everything that touches human-computer interaction enhancements. Check-out Meta Ray-Ban’s glasses (I actually think this is underrated).

🌊 Other Great Reads

• Moving beyond founder-led sales by

• AI as Electricity by

• Sales Tech Market Map 2025: The $30B Industry Breakdown by

• AI voice agents report by a16z

• State of Venture 2024 by AngelList

• YC Spring 2025 Request for startups by Y combinator.

• Aaron Levie from Box on AI moats

• Ark invest 2024 ideas

💵 Iberian Deals

You love startups and want to enjoy a Spanish lifestyle?

Come join the Spanish startup ecosystem.

Here’s a list of recently funded startups:

• TravelPerk (business travel platform) raised €190M

• Quibim (AI-powered medical imaging) raised €47.6M

• H2SITE (on-site hydrogen production) raised €36M

• Mundimoto (motorcycle leasing) raised €21.1M

• Wooptix (semiconductor metrology) raised €10M+

• Crescenta (PE & VC platform) raised €7M

• Zynap (AI-powered cybersecurity) raised €5.7M

• Genesy (AI for sales) raised €5M

• Weecover (embedded insurance) raised €4.2M

• Redegal (digital services) raised €3.8M

• Sycai Medical (AI for healthcare) raised €3M

• Kanguro (last-mile logistics) raised €2M

• Brickken (tokenization of assets) raised $2.5M

• Kymatio - Activate Human Firewalls raised €1.85M

• xNova International (saas) raised €1M

• FisioReact (at-home healthcare) raised €1M

• Robinsun (€600K, solar energy)

• Caleida (€500K, AI-driven marketing)

• Peimi (€415K, SaaS for sales compensation)

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About Me:

I write write a newsletter for early-stage founders and invest in startups (€200K-2M). Previously, I built products at Facebook, helped scale Bloomreach ($2B+), and bootstrapped Revenue Squared. I also love to surf and practice BJJ.

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I write a newsletter about startups and investing. I share market maps, playbooks and tactical resources for founders surfing tech waves.

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Summary

1. 📈 Hubspot’s playbook: on scalable, predictable revenue growth.

2. 💡 Thinking: writing as a recruiting cheat-code.

3. 💵 Iberian Deals: 14 startup deals in Spain (>€50M).

📈 Hubspot’s 0-$100M Sales Playbook

Mark Roberge scaled Hubspot’s customer base from 0 to 12.000, and from 0 to $100M in revenue between 2007 and 2013.

Even though Mark came from a technical background (MiT Engineer), he was given a sales hat upon joining Hubspot as employee #3 (first sales job he ever had).

He wrote a remarkable playbook - The Sales Acceleration Formula. In it he lays out the 4 formulas he followed to achieve his mission.

What I love about this book is how he rejects the conventional wisdom that sales is more art than science. Most sales books focus on the “art of convincing” buyers.

This book does a 180 and presents the “science” of sales growth - basically a standardised and repeatable process built on tracking and experimentation.

The 4 main actionable “formulas” are a collection of repeatable processes, metrics, and calculations used to complete their mission of generating predictable scale:

1. Hiring: “Hire the same successful salesperson every time.”

2. Training: “Train every salesperson in the same way.”

3. Management: “Hold our salespeople accountable to the same sales process.”

4. Demand Generation: “Provide our salespeople with the same quality and quantity of leads every month.”

5. Technology & Experimentation: “developing a culture of experimentation.”

Let’s break it down a little further in case you want to action it.

1. The Sales Hiring Formula

“Hire the same successful salesperson every time.”

Data is king

The first year, Mark wrote down the 10 primary criteria he thought would correlate positively to successful sales hiring.

They defined what a score of a 1 to 3, or a 5 to 7, or an 8 to 10 would look like.

They were disciplined about scoring every single hire against that criteria.

Once 6-12 hires had been closed and they were able to assess their performance - they ran analyses to see what correlated to hiring success.

Here’s a regression analysis they ran in 2009 - which they used to continuously improve and refine their Candidate Assessment criteria:

The conclusion was fascinating: people misunderstood what “good sales skills” were - at least in Hubspot’s case.

When looking at the data, they saw:

1. “Traditional sales skills” (think car sales cliché) such as closing ability, convincing and objection handling, were negatively correlated with success.

2. And things that you would associate with a great consultant or a coach, like preparation, domain experience, and intelligence were highly correlated.

2. The Sales Training Formula

“Train every salesperson in the same way.”

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Welcome to a Growth edition.

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Summary

• ☠️ The Life & Death Of SDRs: A few learnings from a B2B SaaS startup.

• ⚡ Weekly Hack: Lanzarote Valley.

• 💵 Recent Deals in Spain: DudyFit, Atani, Ritmo and more!

• 💭 Thinking or Reading: Grow from discomfort.

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Summary

• 💵 Recent Deals in Spain: Glovo, Inbrain, Kenmei, Balio etc!

• 🏷️ Pricing Strategy: a big lever for startups and how to pull it.

• ⚡ Weekly Hack: T-shaped information diet.

• 💭 Thinking or Reading: No such thing as truly original?

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• ⭐ Minimum Viable Product: Y Combinator’s blueprint.

• 🏃 Crossing The Chasm: If Wealthfront did it, so can you.

• 💵 Recent Deals: Smart Protection, Paack, Ladorian and more!

• 💭Thinking or Reading: Death by powerpoint and Siddhartha.

Check out the Community Group to meet fellow riders and share your experience 🤙

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