In this episode, Mehmet Gonullu sits down with Nat Natarajan, Chief Operating Officer and Chief Product Officer at Globalization Partners, to explore what it really takes to deploy AI in highly regulated environments.

From labor laws and compliance across dozens of countries to human-in-the-loop AI systems, Nat shares how Globalization Partners built explainable, trustworthy AI that enterprises can actually rely on. This is a grounded, operator-level conversation on moving beyond AI hype toward real productivity and trust.

⸻

👤 About the Guest

Nat Natarajan is the Chief Operating Officer and Chief Product Officer at Globalization Partners, a pioneer in global employment solutions. He previously held senior leadership roles at companies including TurboTax (Acquired by Intuit), PayPal, RingCentral, Ancestry.com, and Travelocity. Nat brings decades of experience at the intersection of technology, regulation, and large-scale enterprise systems.

https://www.linkedin.com/in/natrajeshnatarajan/

⸻

🧠 Key Takeaways

• Why black-box AI fails in regulated industries

• How human-in-the-loop design builds trust and adoption

• The role of proprietary, vetted data in enterprise AI

• Where general-purpose LLMs fall short for compliance-heavy use cases

• Why AI should augment humans, not replace them

• How CHROs and boards are rethinking AI as a “digital workforce”

⸻

🎯 What You’ll Learn

• How to design AI systems that can explain their decisions

• When to keep humans in the loop and when automation works best

• How enterprises can deploy AI responsibly without slowing innovation

• What makes AI adoption succeed inside large, global organizations

• Why regulated complexity is an advantage, not a blocker, for AI

⸻

⏱️ Episode Highlights & Timestamps

• 00:00 – Introduction and Nat’s background

• 02:00 – Why regulated environments are ideal for AI, not hostile to it

• 05:00 – Lessons from TurboTax and encoding legal reasoning into systems

• 08:00 – Designing AI that avoids the black-box problem

• 12:00 – Human-in-the-loop systems and guardrails

• 16:00 – Why proprietary data beats generic models

• 19:00 – Enterprise vs startup AI adoption dynamics

• 23:00 – AI as a collaborator inside HR teams

• 27:00 – Explainability, trust, and employee-facing AI

• 32:00 – The CHRO’s role in an AI-powered workforce

• 36:00 – From hype to real productivity with agentic AI

• 40:00 – Final thoughts and advice for leaders adopting AI

⸻

📚 Resources Mentioned

• Globalization Partners : https://www.globalization-partners.com/

• GIA: http://www.g-p.com/gia

• Prediction Machines (Updated & Expanded Edition) – referenced by Mehmet

AI models are becoming commoditized, but deploying AI systems that deliver real ROI remains hard. In this episode, Mehmet sits down with Bryan Wood, Principal Architect at Snorkel AI, to unpack why data-centric AI, evaluation, and domain expertise are now the true differentiators.

Bryan shares lessons from working with frontier AI labs and highly regulated enterprises, explains why most AI projects stall before production, and breaks down what it actually takes to deploy AI safely and at scale.

⸻

👤 About the Guest

Bryan Wood is a Principal Architect at Snorkel AI, where he works closely with frontier AI labs and enterprises to design high-quality, AI-ready datasets and evaluation frameworks.

He brings over 20 years of experience in financial services, with a unique background spanning banking, engineering, and fine art. Bryan specializes in data-centric AI, programmatic labeling, AI evaluation, and deploying AI systems in high-compliance environments.

https://www.linkedin.com/in/bryanmwood/

⸻

🧠 Key Takeaways

• Why AI success is less about models and more about data and evaluation

• How enterprises misunderstand ROI and why most projects stall before production

• The difference between benchmark performance and real-world trust

• Why evaluation must be bespoke, not off-the-shelf

• How frontier labs approach data as true R&D

• Why partnering beats building AI entirely in-house today

• What’s realistic (and unrealistic) about autonomous agents in the near term

⸻

🎯 What You’ll Learn

• How to move from AI experimentation to production deployment

• How to design data that reflects real enterprise workflows

• How to identify where AI systems actually fail, and why

• Why regulated industries are proving grounds, not laggards

• How startups can overcome data and talent constraints

• Where AI is heading beyond today’s LLM plateau

⸻

⏱️ Episode Highlights & Timestamps

00:00 – Introduction & Bryan’s background

02:30 – Why data is now the real AI bottleneck

05:00 – Models are commoditized. So what actually matters?

07:45 – Why AI evaluation is harder than building AI

11:30 – Enterprise misconceptions about AI readiness

15:10 – Hallucinations, RAG failures, and finding the real problem

18:40 – Why most AI projects fail to show ROI

22:30 – Partnering vs building AI in-house

26:00 – AI in regulated industries: myth vs reality

30:10 – Startups, cold start problems, and data moats

33:40 – Scaling data operations with small teams

36:00 – What’s next: agents, data complexity, and AI timelines

39:00 – Final thoughts and where AI is really heading

⸻

📌 Resources Mentioned

• Snorkel AI – Data-centric AI and programmatic labeling: https://snorkel.ai/

• Enterprise AI evaluation frameworks

• Frontier AI lab research practices

• MIT studies on AI ROI and enterprise adoption

Live events generate massive attention, yet most venues have no idea who is actually attending. In this episode, Mehmet Gonullu sits down with Matt Zarracina, CEO and Co-Founder of True Tickets, to unpack the hidden infrastructure problem behind ticketing, identity, and audience ownership.

Matt shares how legacy ticketing systems optimized for transactions, not relationships, and why “shadow audiences” have become one of the biggest blind spots in live event tech. The conversation spans SaaS innovation in legacy industries, blockchain learnings, AI-driven personalization, and what it truly takes to build mission-critical infrastructure at scale.

⸻

About the Guest

Matt Zarracina is the CEO and Co-Founder of True Tickets, a ticket custody and identity platform helping venues understand who is actually attending their events.

His background spans the U.S. Naval Academy, helicopter aviation, systems engineering, an MBA, M&A consulting at Deloitte, and corporate innovation leadership before founding True Tickets full-time in 2018.

https://www.linkedin.com/in/zarracina/

⸻

Key Takeaways

• Why most venues only know 30–40% of their real audience

• How “ticket custody” differs fundamentally from ticket sales

• Why legacy ticketing systems were never designed for identity or post-sale visibility

• The real reason ticket resale abuse and bots persist

• How data unlocks personalization, donor growth, and long-term audience relationships

• Why mission-critical SaaS cannot “move fast and break things”

• Where AI fits next: fraud detection, pricing intelligence, and behavioral patterns

⸻

What You’ll Learn

• What the “shadow audience” really is and why it matters

• How True Tickets integrates into legacy ticketing systems without replacing them

• Why frictionless UX is not always the goal and what “optimal friction” means

• How venues can reclaim ownership from secondary markets

• Lessons from building SaaS inside conservative, legacy industries

• Why consultants and operators can become strong founders

⸻

Episode Highlights & Timestamps

(Approximate, optimized for Spotify & YouTube chapters)

• 00:00 – Introduction and Matt’s unconventional journey

• 03:45 – The origin of True Tickets and discovering ticketing’s blind spot

• 07:30 – Defining the “Shadow Audience” problem

• 10:45 – Bots, resale markets, and why legislation alone fails

• 14:00 – Real-world example: turning attendees into donors

• 17:45 – What True Tickets actually does under the hood

• 21:30 – SaaS in legacy industries and mission-critical systems

• 26:00 – Balancing security, friction, and user experience

• 30:45 – The future of ticketing: data, AI, and personalization

• 35:00 – Global expansion and market opportunity

• 38:30 – Founder lessons from consulting to scale-up CEO

• 43:30 – Final reflections and where to learn more

⸻

Resources Mentioned

• True Tickets Website: https://www.true-tickets.com/

• ROI Calculator and Product Demo (available on True Tickets’ site)

• Super Founders by Ali Tamaseb

In this episode of The CTO Show with Mehmet, I’m joined by Ahikam Kaufman, Co-Founder and CEO of Safebooks.ai, a seasoned finance executive turned entrepreneur with deep experience across startups, public companies, and large-scale acquisitions.

We explore why finance has lagged behind other functions in digital transformation, how AI is fundamentally reshaping financial governance, and why the modern CFO is becoming a transformation leader, not just a financial steward.

This conversation goes beyond buzzwords and dives into real-world problems: broken audit trails, fragmented systems, compliance risk, and how AI agents can finally deliver real-time financial truth.

⸻

👤 About the Guest

Ahikam Kaufman is the Co-Founder and CEO of Safebooks.ai.

He began his career in accounting, served as a CFO in Silicon Valley startups, experienced multiple acquisitions including by Hewlett-Packard and Intuit, and spent over a decade as an entrepreneur.

Today, Ahikam is focused on modernizing the Office of the CFO by applying AI to financial data governance, auditability, and compliance at scale.

https://www.linkedin.com/in/ahikam-kaufman-688310/

⸻

🎯 Key Topics Covered

• Why finance was never designed for today’s data complexity

• The two biggest blind spots in modern financial organizations

• What “audit trail” really means and why it’s so hard to achieve

• How AI agents bridge structured system data and unstructured documents

• From quote to cash: tracing transactions across fragmented systems

• Why compliance failures are often data problems, not intent problems

• The evolving role of the CFO in the AI era

• Where humans still matter and where machines outperform

• Why AI makes regulation easier to meet, not harder

• Practical advice for founders building in finance and compliance

⸻

🧠 Key Takeaways

• Finance teams deal with massive data but are not trained as data teams

• Fragmented systems create hidden compliance and cash-flow risks

• AI can monitor 100% of financial transactions, not just samples

• Real-time governance is now technically possible for the first time

• CFOs are becoming transformation leaders, not just scorekeepers

• The future of finance is continuous, automated, and exception-driven

⸻

🎓 What You’ll Learn

• How AI changes financial accuracy from “material” to near-perfect

• Why most financial errors happen even when teams do “everything right”

• How AI reduces headcount pressure without removing human oversight

• What founders must understand before building in fintech or compliance

• How finance can finally get its own “single pane of glass”

⸻

⏱️ Episode Highlights (Timestamps)

• 00:00 – Ahikam’s journey from CFO to AI founder

• 05:00 – The two unsolved problems in corporate finance

• 09:30 – Why audit trails break across modern systems

• 14:00 – What really goes wrong when financial data is wrong

• 18:30 – How AI understands contracts and financial documents

• 24:00 – Humans vs machines in financial decision-making

• 30:00 – The CFO’s evolving role in AI transformation

• 36:00 – Regulation, compliance, and AI realities

• 43:00 – Advice for founders building in finance

⸻

🔗 Resources Mentioned

• Safebooks.ai

• Topics: AI agents, financial audit trails, CFO transformation, data governance

In this episode of The CTO Show with Mehmet, I sit down with Khaled Nazif, COO of DSquares, one of the most influential yet quietly powerful enterprise loyalty platforms in the MENA region.

Khaled shares his journey from Stanford and Silicon Valley back to the region, where he helped scale DSquares into a 150M+ end-user platform serving banks, telcos, governments, and large enterprises across 16 countries.

We go deep into what loyalty really means today, why most companies still misunderstand it, how culture breaks at scale if you are not intentional, and what founders in emerging markets can learn from Silicon Valley without copying it blindly.

This is a conversation about scale, systems, leadership, and long-term thinking.

⸻

👤 About the Guest

Khaled Nazif is the Chief Operating Officer at DSquares, a leading white-labeled loyalty and engagement platform powering some of the largest enterprises and government programs across MENA and Africa.

Before returning to the region, Khaled spent nearly a decade in Silicon Valley, earning his MBA from Stanford, founding a B2B SaaS startup, and later working at Zendesk. He brings a rare blend of operator discipline, startup grit, and enterprise execution to scaling regional platforms.

https://www.linkedin.com/in/khalednazif/

⸻

🧠 Key Takeaways

• Why loyalty is misunderstood and often wrongly treated as a cost center

• How DSquares scaled without VC hype and stayed bootstrapped for 13 years

• What it really means to move from a “pirate” startup culture to a “navy” scale-up

• Why government loyalty programs are not an oxymoron

• The importance of productization when scaling enterprise platforms

• How culture breaks after ~150 people and what leaders must do proactively

• What MENA founders can learn from Silicon Valley and what they should ignore

• Why failure must be normalized for ecosystems to truly mature

⸻

🎯 What You Will Learn

• How to scale enterprise platforms across multiple countries and cultures

• How loyalty, data, and behavior change intersect at scale

• Why leadership transitions matter more than founder heroics

• How to think long-term when building in emerging markets

• Why execution discipline beats hype cycles every time

⸻

⏱ Episode Highlights & Timestamps

00:00 – Welcome and introduction

02:00 – Khaled’s journey from Stanford to Silicon Valley

05:30 – What DSquares really does and why most people don’t know it

09:00 – Scaling loyalty across banks, telcos, and governments

13:30 – Loyalty vs transactions: what most companies get wrong

18:00 – Using data and gamification to influence behavior

23:00 – Loyalty as a revenue driver, not a cost center

27:30 – Bootstrapping DSquares and resisting VC pressure

33:00 – Replacing a founder and scaling leadership responsibly

38:30 – The 150-employee culture breaking point

45:00 – Pirate vs Navy mindset and operational maturity

51:00 – Silicon Valley lessons that actually work in MENA

57:00 – Failure, risk-taking, and ecosystem maturity

01:03:00 – Advice for founders building in emerging markets

01:08:00 – Closing thoughts and where to connect with Khaled

⸻

🔗 Resources & Mentions

• DSquares – Enterprise Loyalty & Engagement Platform : https://dsquares.com/

• Book referenced: Blitzscaling by Reid Hoffman

In this episode of The CTO Show with Mehmet, I’m joined by Alex Schlager, Founder and CEO of AIceberg, a company operating at the intersection of AI, cybersecurity, and explainability.

We dive deep into why AI agents fundamentally change enterprise risk, how shadow AI is spreading across organizations, and why monitoring black-box models with other black boxes is a dangerous mistake.

Alex explains how explainable machine learning can provide the observability, safety, and security enterprises desperately need as they adopt agentic AI at scale.

⸻

👤 About the Guest

Alex Schlager is the Founder and CEO of AIceberg, a company focused on detection and response for AI-powered workflows, from LLM-based chatbots to complex multi-agent systems.

AIceberg’s mission is to secure enterprise AI adoption using fully explainable machine learning models, avoiding black-box-on-black-box monitoring approaches. Alex has deep expertise in AI explainability, agentic systems, and enterprise AI risk management.

https://www.linkedin.com/in/alexschlager/

⸻

🧠 Key Topics We Cover

• Why AI agents create a new and expanding attack surface

• The rise of shadow AI across business functions

• Safety vs security in AI systems and why CISOs must now care about both

• How agentic AI amplifies risk through autonomy and tool access

• Explainable AI vs LLM-based guardrails

• Observability challenges in agent-based workflows

• Why traditional cybersecurity tools fall short in the AI era

• Governance, risk, and compliance for AI driven systems

• The future role of AI agents inside security teams

⸻

📌 Episode Highlights & Timestamps

00:00 – Introduction and welcome

01:05 – Alex Schlager’s background and the founding of AIceberg

02:20 – Why AI-powered workflows need new security models

03:45 – The danger of monitoring black boxes with black boxes

05:10 – Shadow AI and the loss of enterprise visibility

07:30 – Safety vs security in AI systems

09:15 – Real-world AI risks: hallucinations, data leaks, toxic outputs

12:40 – Why agentic AI massively expands the attack surface

15:05 – Privilege, identity, and agents acting on behalf of users

18:00 – How AIceberg provides observability and control

21:30 – Securing APIs, tools, and agent execution paths

24:10 – Data leakage, DLP, and public LLM usage

27:20 – Governance challenges for CISOs and enterprises

30:15 – AI adoption vs security trade-offs inside organizations

33:40 – Why observability is the first step to AI security

36:10 – The future of AI agents in cybersecurity teams

40:30 – Final thoughts and where to learn more

⸻

🎯 What You’ll Learn

• How AI agents differ from traditional software from a security perspective

• Why explainability is becoming critical for AI governance

• How enterprises can regain visibility over AI usage

• What CISOs should prioritize as agentic AI adoption accelerates

• Where AI security is heading in 2026 and beyond

⸻

🔗 Resources Mentioned

• AIceberg: https://aiceberg.ai

• AIceberg Podcast – How Hard Can It Be? https://howhardcanitbe.ai/

Raising capital looks easy from the outside. In reality, it is one of the most misunderstood parts of building a startup.

In this episode, Mehmet sits down with Daniel Nikic, a global investment researcher who has analyzed over 15,000 companies across the US, Europe, and the Middle East. Together, they unpack the hard truths founders need to understand about fundraising, investor psychology, market geography, and why most rounds fail long before the first term sheet.

This is a grounded, no-hype conversation about what actually drives investment decisions in 2025 and why “easy money” is often the biggest illusion founders believe.

⸻

About the Guest

Daniel Nikic is the founder of Coherent Research and a global investment research professional with deep experience across North America, Europe, and emerging markets. Originally from Canada and now based in Croatia, Daniel has worked with investors, family offices, and founders worldwide, helping evaluate companies across stages, industries, and geographies.

His work focuses on due diligence, market opportunity analysis, and understanding the human and cultural factors behind investment decisions.

⸻

Key Topics Discussed

• Why most fundraising fails before it even starts

• The biggest misconceptions founders have about “easy capital”

• How geography actually impacts investment decisions

• Why the Middle East is not fast money despite capital availability

• Founder psychology, stress, and emotional control as investment signals

• What investors look for beyond pitch decks and valuations

• The difference between angels, VCs, family offices, and accelerators

• Why urgency and FOMO often kill deals instead of closing them

• How AI is changing investment behavior and decision-making

• Realistic timelines for closing funding rounds in emerging markets

⸻

Key Takeaways

• Capital is not free money. Investors expect returns, discipline, and execution.

• Geography still matters, but trust and relevance matter more.

• Founders who rush fundraising often lose credibility.

• Investors back people they trust, not just ideas or decks.

• Being organized and prepared beats hype every time.

• Fundraising is a relationship-building process, not a transaction.

⸻

What You Will Learn

• How to target the right investors at the right stage

• Why mixing angels, VCs, and family offices too early backfires

• How investors think about risk, timing, and founder maturity

• What “smart money” really means beyond capital

• How long fundraising realistically takes and why patience matters

⸻

Episode Highlights & Timestamps

(You can fine-tune timestamps once audio is finalized)

• 00:00 – Introduction and Daniel’s global background

• 04:00 – Patterns from analyzing 15,000+ companies

• 07:30 – Geography vs psychology in startup success

• 10:45 – The Middle East investment misconception

• 15:20 – Why capital follows trust, not hype

• 18:30 – Choosing the right investor type early on

• 22:40 – Check sizes, valuations, and regional differences

• 27:00 – AI, FOMO, and modern investment behavior

• 32:00 – Why urgency kills fundraising deals

• 36:30 – Realistic timelines to close a round

• 41:00 – Final advice for founders raising capital

⸻

Resources & Links

• Daniel Nikic on LinkedIn: https://www.linkedin.com/in/daniel-nikic/

• Website: https://www.danielnikic.com/

In this episode, Gabriel Jarrosson, founder and managing partner at Lobster Capital, breaks down what truly drives breakout startups inside the world’s most competitive ecosystem.

Before becoming a YC-focused investor, Gabriel built seven startups, failed four, and bootstrapped one to one million ARR alone — no co-founder, no employees, no AI.

Today he invests exclusively in YC companies and shares how he evaluates founders, why early traction beats everything, how YC creates unstoppable momentum, and how AI is reshaping the next generation of builders.

⸻

About Gabriel Jarrosson

Gabriel Jarrosson is a serial founder turned YC-specialized investor and managing partner at Lobster Capital. He has built seven companies, exited three, and invested in more than 100 YC startups. Gabriel also hosts The Lobster Talks and has grown a fast-rising media presence supporting early-stage founders.

https://www.linkedin.com/in/gabrieljarrosson/

⸻

Key Takeaways

• Why solo founders can still win big when they embrace urgency, automation, and creative resourcefulness

• The mindset required to scale without waiting for funding or a co-founder

• YC founder patterns: technical teams, relentless execution, and high velocity

• Why YC attracts the world’s strongest builders and why it’s nearly impossible to replicate

• Gabriel’s 2 percent rule for selecting the best companies in every YC batch

• Why early revenue and market pull matter more than ideas and hype

• How AI is changing the definition of what a “lean team” can achieve

⸻

What You Will Learn

• How top investors evaluate teams, traction, and momentum

• How YC creates an environment that rewires founders to move faster

• Why some geographies struggle to reproduce Silicon Valley outcomes

• How to think about automation, support systems, and scaling with AI

• How founders outside the US can become YC-ready

• What Gabriel regrets missing as an angel investor — and what he learned from it

⸻

Episode Highlights & Timestamps

00:00 — Introduction

01:30 — Seven startups, three exits, four failures

03:00 — Bootstrapping to 1M ARR as a solo founder

07:00 — The role of AI in scaling today

10:00 — Why YC is a category of its own

14:30 — What YC founders have in common

18:00 — Why “local incubators” fail to replicate YC

21:00 — How Gabriel selects winners

27:00 — Getting into competitive YC deals

33:00 — The media edge in venture

37:00 — Becoming YC-ready as a non-US founder

46:00 — Gabriel’s biggest miss

50:00 — Closing thoughts

⸻

Resources Mentioned

• Lobster Capital: https://www.lobstercap.com/

• The Lobster Talks podcast: https://www.youtube.com/@lobster-talks