Episode Summary

In this episode of AI for Founders, Ryan Estes sits down with Alfonso Gómez-Jordana Mañas, co-founder of Crossmint and former Google/WhatsApp product manager. Alfonso shares how he went from building “I am not a robot” CAPTCHA to building the rails for a programmable economy. They explore AI agents as financial actors, how micropayments could reshape the internet, and why stablecoins are doubling every few months.

This conversation is a roadmap for founders, builders, and investors who want to understand the intersection of AI, blockchain, and the future of online trust.

Key Takeaways

• Identity and Trust: From CAPTCHA to Crossmint, Alfonso’s career has focused on distinguishing humans from machines and securing digital interactions.
• Programmable Economy: Stablecoins, wallets, and NFTs are the building blocks for embedded finance and AI agent commerce.
• AI Agents as Financial Actors: Agents will soon research, negotiate, and transact on behalf of humans, creating both opportunities and new risk models.
• Micropayments as Defense: Instead of fighting bots with detection, shift to economic incentives—introducing costs for interactions reduces abuse.
• Democratizing Creativity: AI tools replace mediums like watercolor or code-writing, but taste and curation remain uniquely human.
• Regulatory Landscape: New clarity around stablecoins and blockchain is accelerating adoption, but frameworks for equity and tokenized investment are still needed.
• Habit Formation Idea: Alfonso suggests programmable contracts that automatically deduct money if commitments (like gym workouts) aren’t met—an example of blockchain-enabled accountability.
• Vision for 2030: A synergistic world where humans set intent and agents execute, reshaping commerce, communication, and creativity.

Frameworks Discussed

• Intent + Verification Model: Humans express intent, AI agents execute, humans verify results.
• Economic Incentive Framework for Spam/Abuse: Reduce abuse not by detection but by shifting ROI and adding micropayment costs.
• Programmable Economy Stack:
  • Stablecoins = money
  • Wallets = storage
  • NFTs = ownership
  • APIs = infrastructure
• Programmable Habits: Smart contracts enforcing behavior through economic penalties or rewards.

Resources and Links

• Crossmint
• Alfonso on Twitter/X
• Cloudflare & Coinbase micropayments (X402)
• AI for Founders newsletter
• Ryan Estes

Elle George on Founder Alignment, Partnerships, and Hard-Won Lessons

Episode Summary

Founder and author Elle George breaks down her new book “Before You Shake Hands”, a practical system for assessing alignment before partnerships, co-founder agreements, or investor deals. She shares hard lessons from a two-decade legal saga, the exact questions she now asks, and how to structure conflict protocols before momentum blinds everyone.

Who this episode is for

Founders, CEOs, operators, first-time investors, and anyone considering a co-founder or strategic partner who wants fewer surprises and fewer trips to court.

What you will learn

• How to run an alignment check before you sign anything
• The five domains to vet in every partnership
• How to document conflict protocols up front
• Investor questions that expose goals and timelines early
• Why spiritual discipline and daily routines support better decisions

Key Takeaways

• Alignment beats traction. Momentum masks risk. Stop and align goals, values, roles, legal, money, and exit plans before papering anything.
• Ask first-principle questions. Why invest. What is the five-year end state. How will conflicts resolve. Who decides at ties.
• Document the downside. Agree on “what if we hate each other,” capital calls, buy-sell terms, and tie-breaker rules before you commit.
• Battle scars matter. Do not avoid hard histories. Ask how a partner handled legal, credit, or operational failures and what changed.
• Culture shows in small signals. Hiring prompt she loves, “What work bores you.” It is a precision question for role fit.
• Discipline scales intuition. Routine, reflection, and written agreements turn gut feel into repeatable decisions.

Frameworks from the Episode

1) The Alignment Framework — Five Domains

• Values and trust
• Roles and responsibilities
• Legal and documentation
• Financials and capital calls
• Exit strategies and scenarios

2) Partnership Diligence Checklist

• Why do you want to work with me or invest in this business
• What are your goals for the company and the timeline to reach them
• What is your exit preference and under what conditions
• Describe your biggest business challenge and how you handled it
• Any prior legal or financial issues, what you learned, what changed
• Conflict plan, decision rights, tie-breaker mechanism
• Buy-sell mechanics, valuation method, funding obligations

3) Conflict Protocol Up Front

• Define disagreement thresholds that trigger a decision process
• Name who has final call on specific domains
• Establish a written tie-breaker and escalation ladder
• Pre-agree on mediation or arbitration path and venue

4) Investor Fit Questions

• End-state in five years and expected liquidity path
• Capital intensity expectations after initial check
• Governance, reporting, and operating cadence preferences
• Support offered beyond money and how success is measured

5) Founder Operating Rituals

• Morning affirmations and meditation for clear decision-making
• Weekly reflection on assumptions and alignment drift
• “No gossip” rule to protect culture and focus

Resources and Links

• Founder Challenge by Kitcaster: https://media.kitcaster.com/founder-challenge
• AI for Founders: https://aiforfounders.co
• Ryan Estes: https://ryanestes.info

In this episode of AI for Founders, Ryan Estes interviews Alex, founder of Solmag.ai, a company building virtual power plants that enable peer-to-peer solar energy trading. From his journey after a first startup exit to a mission of helping humanity reach Type I civilization on the Kardashev scale, Alex shares how Solmag is tackling energy distribution, grid optimization, and decentralized networks.

Listeners will learn how communities can share surplus solar power, what regulatory shifts are coming in Europe, how AI is used to price and route electricity, and why Solmag could redefine the economics of clean energy. This is a must-listen for founders, climate-tech investors, real estate developers, EV infrastructure operators, and anyone passionate about the future of decentralized energy.

• From Exit to Energy: Alex’s search for meaning after a startup exit led to a mission to transform global energy.
• Solmag.ai’s Vision: Virtual power plants connecting solar households.
• Peer-to-Peer Trading: Local energy sharing cuts costs and boosts prosumer revenue.
• Hardware + Cloud: Gateway device and cloud aggregation enable real-time trading.
• Regulatory Landscape: Europe’s 2026 legislation will open new opportunities.
• AI in Energy: Routing algorithms optimize distribution like Waze for electricity.
• Scalability Challenge: Expanding from 100 homes to entire cities.
• Investment Path: Pre-seed round in motion, aiming for Series B growth.
• Future Outlook: 25M solar rooftops today, projected 100M by 2030.
• Big Picture: Humanity must grow energy harnessing 8,700x to reach Type I civilization.

• Current: Type 0.73
• Type I: Harness all Earth’s energy
• Future: Compact, space-based, or nuclear solutions

• Gateway devices installed in homes
• Cloud aggregation of energy data
• Peer-to-peer transactions within communities
• Scaling network effects for efficiency

• Utilities: 30–40% margins
• Solmag: 10% transaction fee
• Users retain ~85% of market value
• Local energy should cost less than long-distance grid supply

• Solmag.ai
• Solmag Whitepaper
• Kardashev Scale Background
• AI for Founders
• Ryan Estes

AI for Founders — Ben Wilcox (ProArch)

Episode Summary

CTO/CISO Ben Wilcox breaks down how to build a secure foundation before layering on AI and data. We cover compliance early vs. late, agentic AI realities, Microsoft Copilot in the enterprise, change management for AI adoption, and leadership lessons from Ben’s background as a racing instructor.

Who This Is For

Founders, CTOs, CISOs, product leaders, and operators at startups to mid-market enterprises who want fast AI adoption without compliance blowups.

Topics & Keywords

AI security, compliance, data privacy, PII, PCI, SOC 2, Microsoft Copilot, agentic AI, change management, enterprise AI adoption, Microsoft ecosystem, security foundation, data governance, quality engineering, automation, remote work.

Key Takeaways

• Security first, then AI: Bake in privacy, identity, and compliance controls early. Retrofitting compliance later is expensive and slow.
• Know your customer’s rules: Map target markets to regulatory obligations (PII, PCI, HIPAA/PHI, SEC/FIN). Expect security questionnaires even as an early startup.
• Use third-party rails for risk: Offload card data (PCI) to providers like Stripe to reduce scope and audit burden.
• Agentic AI is early but useful: Frameworks shift quickly; move now with pragmatic pilots rather than waiting for “perfect.”
• Quality doesn’t stop at ship: LLM versions drift. Add continuous quality loops to ensure outputs remain accurate as models change.
• Adoption is a change-management problem: Treat rollout as an org-wide initiative with training, policy, and measurement.
• Personal AI stack that works: Microsoft Copilot (Office/Teams), ChatGPT, Claude.
• Leadership lesson from racing: “Eyes up.” In business: keep eyes on AI, security, and data.
• Microsoft alignment matters: Pairing security + data + AI in one ecosystem compresses cost and time-to-value.

Frameworks from the Episode

1) Secure-Data-AI Ladder

1. Secure Foundation: Identity, least-privilege, logging, audit, encryption, segmentation.
2. Data Layer: Catalogs, lineage, quality SLAs, access controls, privacy by design.
3. AI Layer: Use cases with measurable accuracy targets, human-in-the-loop, monitoring.

2) Compliance-Early Checklist (Startup Edition)

• Identify regulated data: PII/PHI/PCI/Financial.
• Map jurisdictions: state privacy laws + breach notification obligations.
• Offload payments (PCI) to third-party.
• Centralize logs and audits from day one.
• Prep for security questionnaires: architecture, data flows, vendor list, DPA, incident process.

3) Agent Lifecycle & Quality Loop

• Define business outcome + acceptable accuracy.
• Ship a constrained pilot with guardrails.
• Instrument telemetry, prompt/response logs, feedback.
• Regression tests on model or framework updates.
• Retrain/tune or adjust prompts; repeat.

4) AI Change-Management Playbook

• Executive mandate and narrative.
• Everyone uses AI as a personal assistant first.
• Role-specific enablement, office hours, champions.
• Policies for sensitive data, identity, and auditing agent actions.
• Adoption KPIs: usage, time saved, outcome quality.

Outline

• Ben’s dual role (CTO/CISO) and ProArch focus
• Why security before AI
• Compliance landmines: PII, PCI, state privacy laws
• Off-the-shelf rails to reduce risk
• Agentic AI today: reality vs. hype
• Continuous quality for shifting LLM baselines
• Copilot + ChatGPT + Claude in practice
• Microsoft ecosystem advantages
• Leadership via racing: “eyes up”
• Change management for enterprise AI
• Remote culture and durable growth

Resources & Links

• ProArch
• Microsoft Copilot for Microsoft 365
• OpenAI ChatGPT
• Anthropic Claude
• n8n
• Zapier
• Stripe
• Waymo

aiforfounders.co | ryanestes.info

In this episode of AI for Founders, Ryan Estes sits down with Galvin Widjaja, Founder and CEO of Lauretta.io, the privacy-first AI company transforming how organizations understand human behavior in physical spaces. Galvin shares how he built an AI startup trusted by the U.S. Department of Homeland Security, TSA, and U.S. Air Force, and how his early experiences in the restaurant industry shaped his empathy-first approach to AI leadership.

Key Takeaways

• How Galvin went from managing quick-service restaurants to leading an AI company working with government and defense agencies
• Why Lauretta.io focuses on privacy-first computer vision instead of identity-based surveillance
• The frameworks Galvin uses for scaling AI startups in high-security environments
• Balancing real-time situational awareness with ethical design and employee trust
• How certifications like ISO/IEC 27001:2022 and BizSafe build credibility with enterprise and government clients
• The role of empathy, equity, and resilience in building long-term company culture
• Predictions for the next 3–5 years of AI in security, operations, and smart buildings

Frameworks Discussed

1. Privacy-by-Design – building anonymity and trust into the product architecture from the start
2. Human-Centered AI – systems are only as good as the people they serve
3. Predictive vs. Reactive Insight – moving from monitoring events to anticipating needs
4. Trust Framework – combining technical certifications with transparent communication to win high-stakes clients

Resources

• Lauretta.io
• ISO/IEC 27001 Overview
• BizSafe Singapore

Learn more at aiforfounders.co and ryanestes.info

AI for Founders — Show Notes

Guest: Jennifer Berry (CEO, SmartLab / Creative Learning Systems)

Topic: Building STEM identity, designing “aha moment” learning ecosystems, and future-ready talent in the AI era

Episode Summary

SmartLab transforms schools into hands-on, project-based STEM ecosystems that manufacture “aha moments” and build student STEM identity — the belief that you belong, can master rigorous challenges, and that your ideas have impact. Jennifer breaks down SmartLab’s five-part ecosystem, why industry pathways matter more than job titles, and how business leaders can partner to fund labs and volunteer talent to accelerate workforce readiness.

Who This Episode Is For

Founders, operators, school and district leaders, edtech builders, and employers who care about future talent pipelines, authentic project-based learning, and community-powered STEM programs.

Key Takeaways

• STEM identity > content mastery: Confidence, belonging, and agency drive durable outcomes in a world where AI handles tasks and humans solve problems.
• Aha moments are engineered: Environment, curriculum, kits, facilitation, and community engagement compound to create frequent breakthroughs.
• Industry pathways beat job forecasting: Teach applications tied to sectors rather than single jobs that may be automated.
• Facilitators are force multipliers: Ongoing coaching and national communities of practice matter more than one-and-done PD.
• Partnerships power sustainability: Businesses can co-fund labs, co-create curriculum, and volunteer to inspire students.
• Future-readiness is apolitical: Belonging, problem-solving, and resilience are common ground across school types.
• Fast-moving leadership: Set high standards, move at speed with ~70% info, learn in public, iterate.

Frameworks & Models

SmartLab 5-Component Ecosystem

1. Customized Learning Environment: Turnkey rooms or flexible zones in libraries, classrooms, or community centers.
2. Standards-Aligned Curriculum (SaaS): Scaffolded, project-based units mapped to state standards and industry pathways.
3. Kits & Equipment: Curated robotics, electronics, and manipulatives tied directly to curriculum outcomes.
4. Facilitator Enablement: Initial training, continuous coaching, national community, and extended learning.
5. Partnerships & Support: Tech/customer support plus structured community and industry involvement.

Aha-Moment Chain

Curiosity → Hands-on attempt → Productive struggle → Iteration → Breakthrough → Identity shift (“I belong, I can, I matter”).

Pathways-First Planning

Choose industry → map STEM applications → design age-appropriate projects that show purpose and real-world impact.

Episode Outline

• What is STEM identity and why it matters now
• How SmartLab engineers “aha moments”
• Designing environments from blank rooms to flexible corners
• Curriculum that ties STEM skills to industry pathways
• Equipping labs with kits that connect to outcomes
• Investing in facilitators beyond PD
• Building durable community partnerships and sponsorships
• Why businesses should co-fund labs and volunteer talent
• Leadership style: speed, standards, human-centered culture
• Theater, choreography, and storytelling as learning engines

Resources

• SmartLab Learning

• Jennifer Berry

More from AI for Founders: aiforfounders.co  |  ryanestes.info

AI for Founders — Show Notes

Guest: Gaurav Bhattacharya, CEO & Co-founder, Jeeva.ai
Topic: Agentic AI for sales. From zero to $6M ARR in under nine months.

Episode Snapshot

Jeeva.ai builds agentic AI that behaves like a sales teammate. It finds lookalike accounts, enriches contacts, drafts outreach, manages calendars, and pushes structured notes to CRM after calls. We cover deliverability, pricing alignment, and the exact workflow behind rapid growth.

Who This Is For

• Founders, indie hackers, B2B sales leaders, SDRs, AEs
• Teams replacing tool sprawl with an agentic stack
• Operators who need deliverability and measurable outcomes

What You’ll Learn

• How agentic AI replaces six sales tools
• Inbox “draft then manual send” deliverability tactic
• Lead discovery, enrichment, sequencing, booking, follow-ups
• Outcome-based pricing that aligns with results
• PLG plus enterprise as an AI go-to-market

Key Takeaways

• Manual-send drafts restore deliverability at scale
• Waterfall enrichment lowers bounces and raises match rates
• Multi-channel sequencing increases response rates
• Agents handle 95% of the work. Humans approve and close
• Outcome pricing and PLG drive efficient growth

Frameworks

Agentic Sales Loop

1. Define ICP and lookalikes
2. Waterfall enrichment
3. Personalized copy by role and trigger
4. Multi-channel sequencing
5. Inbox triage and live calendar booking
6. Post-call notes and follow-ups drafted automatically
7. Iterate on reply and positive outcome rates

Deliverability Playbook

• Aged domains and inboxes
• Low bounce rates via verification
• Avoid API-tagged bulk sending
• Draft then manual send to protect reputation

Outcome-Aligned Pricing

• Credits tied to verified outcomes
• Percent-of-revenue where attribution allows
• PLG tiers for prosumers and enterprise plans for teams

Playbooks and Tactics

• Write to role and industry change
• Use agent search for context that earns replies
• Offer real availability to compress cycles
• Auto-draft follow-ups and push structured CRM notes

Notable Stats

• Zero to $6M ARR in under nine months
• Hundreds of organic daily signups

Resources

• Jeeva.ai
• Gaurav on LinkedIn

About the Show

aiforfounders.co | ryanestes.info

In this episode of AI for Founders, Ryan Estes sits down with Amos Bar-Joseph, co-founder and CEO of Swan AI. With just three people on the team, Swan scaled to $1M ARR in nine weeks, built a $1.5M pipeline per month, and now serves more than 200 customers. Amos reveals how Swan is building the world’s first autonomous business, where AI agents work alongside humans to 100x their productivity.

We dive into frameworks for discovering your zone of genius, using AI to eliminate bottlenecks, and designing agentic workflows that drive pipeline at the speed of thought. Amos also shares hard-won lessons on pricing in the AI era, contrarian marketing experiments, and why storytelling became his most valuable growth channel.

If you’re a founder, this is a masterclass in scaling smarter, not bigger.

Key Takeaways

• Autonomous Business Model: Swan’s vision is a company run by AI agents and a lean team, scaling revenue without headcount.
• Zone of Genius Framework: Identify bottlenecks, use AI to remove friction, and double down where passion and skills intersect.
• From Boo to Yay: All marketing should drive transformation—move your audience from fear to hope, from friction to empowerment.
• Contrarian Marketing: Avoid the “King’s Road” of best practices; focus on bold, unconventional experiments.
• Storytelling as Growth: Personal narrative on LinkedIn became Amos’s zone of genius, fueling outsized pipeline.
• Pricing in AI: Move from usage-based confusion to outcome-based pricing tied to business ROI.
• Trust Over Hype: B2B buyers want authenticity; sharing wins and failures builds stronger trust.

Frameworks Discussed

1. Autonomous Business OS – Build systems around people’s strengths, not rigid org charts.
2. Zone of Genius Iteration – Identify bottlenecks → apply AI/automation → reinvest gains into passion-driven work.
3. Challenge the Challenger Narrative – Stand out by rejecting industry-wide challenger tropes and creating a new, hopeful story.
4. Emotional Marketing Lens – Shift from analytical personas to emotional transformations.
5. Pricing in the AI Era – Balance predictability for customers with managing usage-based costs internally.

Resources & Links

• Swan AI
• Amos Bar-Joseph on LinkedIn
• The Big Shift Newsletter
• Project33 Interview
• AI for Founders
• Ryan Estes

Guest: Mark Walker, CEO of Nue.io

Topic: Revenue Lifecycle Management, AI-era pricing, quote-to-cash, experimentation at enterprise speed

Episode Snapshot

Nue.io powers recurring revenue and consumption businesses with a Salesforce-native system for quoting, contracting, self-serve, billing, and usage. Mark explains why the “pace of change of the pace of change” forces companies to test pricing continuously, how outcome-based models collide with human experience, and why bring-your-own-tokens matters for security and portability.

Key Takeaways

• AI leaders are running different pricing experiments at the same time, there is no single winning model yet.
• Falling token and compute costs create unprecedented pricing pressure, outcomes become the clearest way to anchor value where possible.
• Outcome pricing works best when the unit of value is unambiguous, examples include e-sign envelopes or background checks.
• Hybrid models are rising, teams mix seats, usage, step-tiers, revenue share, and per-invoice fees to match their value story.
• The new sales motion is transparent, collaborative, and risk-diagnostic. Buyers want help stress-testing failure modes before they buy.
• Experimentation without lock-in is essential, your first pricing bet can trap you for years if systems are rigid.
• Bring-your-own-tokens protects sensitive data and lets customers choose model providers per use case.
• AI will not replace every deterministic workflow, keep probabilistic AI where it adds leverage and keep deterministic systems where precision is mandatory.
• Services work changes, less “hands on keys,” more advisory and change design as AI compresses implementation time.
• Culture matters during rapid change, optimize for customer outcomes and team enthusiasm or attrition will hollow out expertise.

Frameworks Discussed

1) Pricing Decision Map: Outcome vs Usage vs Seats vs Hybrid

1. Define the unit of value customers actually care about.
2. Validate measurability and attribution.
3. Choose the least gameable metric with the simplest governance.
4. Layer hybrid elements for fairness and margin protection.
5. Stress-test migrations when experiments evolve.

2) Experimentation Flywheel for Quote-to-Cash

1. Rapidly model variants in one system.
2. Launch controlled cohorts.
3. Measure revenue, churn, margin, and support impact.
4. Retire losing variants fast and migrate with guardrails.
5. Institutionalize learnings in templates and approvals.

3) BYOT Compute Strategy (Bring Your Own Tokens)

1. Separate application value from raw model cost.
2. Let customers pick the LLM per task, respect data boundaries.
3. Optimize for portability, security, and policy compliance.

4) Human Impact Guardrails

1. Identify joy-creating work that should remain human.
2. “Salt” roles with meaningful cases to sustain expertise.
3. Use AI for drudgery, keep humans for edge cases and empathy.

5) New-School Sales Blueprint

1. Lead with candor about where your product is not a fit.
2. Co-diagnose risks and failure patterns with the buyer.
3. Provide a path to experiment safely and switch paths cleanly.

Resources

• Nue.io
• OpenAI
• Anthropic
• Google Gemini
• Snowflake
• DocuSign
• Checkr
• Apollo
• ZoomInfo
• Metronome
• Salesforce

More from the Host

aiforfounders.co | ryanestes.info

Episode Summary

In this episode of AI for Founders, Ryan Estes sits down with Eric Chu, CEO of Tradesk, to unpack how he’s building the next-generation retail investing platform. From raising over $12 million to navigating SEC and FINRA compliance, Eric shares the journey of merging engineering, Wall Street experience, and AI to empower busy professionals with institutional-grade investing tools.

We dive into how Tradesk differentiates from platforms like Robinhood and Fidelity, why AI is changing the future of financial research, and what founders can learn about building trust, scaling compliance-heavy products, and designing for both accessibility and depth.

Key Takeaways

• From Engineer to Wall Street to Fintech Founder: How Eric’s career path shaped the DNA of Tradesk.
• Democratizing Institutional Tools: Why features like insider trading monitoring, thematic investing, and recurring investments matter for retail investors.
• The $12–13M Raise: What it took to build a compliant, global investment platform.
• Balancing Simplicity and Sophistication: Designing for users who outgrow Robinhood but aren’t ready for overwhelming institutional dashboards.
• AI in Finance: How large language models are streamlining research, boosting efficiency, and creating new compliance challenges.
• Founder-Led Trust: Why visibility, transparency, and regulation are critical for onboarding users in fintech.
• The Future of Investing: How retail investors may soon access non-public companies and leverage AI-powered decision-making.

Frameworks Discussed

1. Institutional → Retail Translation: Bringing pro-grade tools to everyday investors in a usable format.
2. Trust Framework: Regulation, founder visibility, accurate data, and user education as cornerstones of adoption.
3. AI Integration Model: Using LLMs for efficiency, layering compliance guardrails, and testing across user scenarios.
4. Investor Journey Map: From novice (Robinhood) → intermediate (Tradesk) → institutional-level sophistication.

Resources Mentioned

• Tradesk Website
• Eric Chu on LinkedIn
• AI for Founders
• Ryan Estes

Links to Explore

• FINRA
• SEC