The CTO Show with Mehmet Gonullu

In this episode of The CTO Show with Mehmet, Mehmet sits down with Raphael Peyret, Founder and Principal Advisor at SHA/RP. Raphael brings experience across product, cybersecurity, Google, and startup execution from MVP to acquisition. The main tension is clear: companies keep chasing scale before the basics are working.The conversation reframes AI security, startup growth, product management, and GTM as the same sequencing problem. AI-native threats matter, but unpatched systems, weak credentials, poor MFA adoption, unclear positioning, premature sales hiring, and feature overload still break companies first. Raphael argues that founders need defensible security, repeatable sales, and product discipline before they scale people, spend, or complexity.If you are building, investing in, or leading early-stage enterprise technology, cybersecurity, AI, or SaaS companies, this conversation gives a practical way to separate progress from motion.About the GuestRaphael Peyret is the Founder and Principal Advisor at SHA/RP, where he works with startups as an independent advisor and fractional executive across product management and cybersecurity.His background includes Google and a VP of Product role at Harangi Cybersecurity, a Singapore-based cybersecurity startup that moved from MVP through fundraising, acquisition, and integration into Bitdefender.Raphael frames startup execution through the lens of risk, product discipline, and sequencing, which makes him well placed to discuss where founders and security leaders usually move too early.LinkedIn: https://www.linkedin.com/in/rpeyret/Website: https://sha-rp.comKey TakeawaysAI threats get attention, but basic security failures still cause most breaches.Startups need defensible security, not enterprise-grade security theatre.Cybersecurity should help startups move faster without creating reckless exposure.Founders often hire sales before they understand how their product sells.A salesperson cannot fix unclear positioning or unfinished customer pain.Product teams fail when they add features before solving the core problem.Founder bottlenecks appear when decisions stay personal instead of becoming systems.Motion becomes progress only when each step proves a specific assumption.What You Will LearnThe difference between AI security headlines and the breach risks most companies actually face.How startups can define good enough security without copying enterprise playbooks.Why basic hygiene such as MFA, SSO, and credential management still matters most.When hiring sales too early creates more confusion than revenue.How product management helps founders stop becoming the bottleneck.Why feature expansion can hide weak product-market understanding.What separates motion from progress in founder execution.Episode Highlights00:00 — Raphael Peyret connects cybersecurity with startup execution02:00 — AI threats distract from basic security failures05:00 — Security teams still struggle to speak business language09:00 — Startups need defensible security, not overbuilt controls15:30 — Security diagnostics expose the risks founders miss18:00 — MFA and SSO still form the security base20:30 — Good enough security helps startups keep moving24:30 — AI can reduce friction before attacks begin27:00 — Startups hire sales before sales is repeatable31:00 — Marketing cannot fix unclear positioning35:00 — Product teams add features before solving pain40:30 — Founders need systems before they can scale46:30 — Fractional leadership bridges the early expertise gap49:30 — Motion and progress are not the same thing56:30 — Founders need sequencing across every functionListen NowAvailable on all major podcast platforms and YouTube.Follow The CTO Show with Mehmet for more conversations at the intersection of technology, startups, and venture capital.

In this episode of The CTO Show with Mehmet, Mehmet sits down with Alex Grant, SVP of Sales at North. Alex brings more than 16 years of experience building sales teams across fintech, payments technology, and SaaS. The conversation centers on a hard tension: AI can create more opportunities, but it still cannot create trust by itself.The conversation reframes AI in sales and fintech as an execution problem rather than a technology topic. Alex explains why companies are using AI to move faster, secure payment systems, and generate more qualified opportunities, while also showing why complex software still needs a human seller who can translate risk, value, and trust for the buyer.If you are leading revenue, building fintech products, investing in AI-enabled software, or selling complex enterprise technology, this conversation shows where AI can accelerate the system and where human judgment still carries the deal.About the GuestAlex Grant is the SVP of Sales at North, where he is building the company’s first coast-to-coast W-2 outside sales channel. Before joining North, Alex spent more than 16 years building sales teams in fintech, payments technology, software, and SaaS, including time at a Fortune 500 company.At North, Alex focuses on building full-time sales teams that can sell payment technology, AI-supported security, and software solutions with a structured career path, training model, and field-led culture. His perspective is grounded in the operational reality of selling fintech and payments technology to businesses of different sizes.LinkedIn: https://www.linkedin.com/in/ralexgrant/Website: https://north.comKey TakeawaysAI can generate meetings, but it cannot replace trust in complex technology sales.Buyers want AI, but many still struggle to define what they actually need.Payment security is one of the clearest practical use cases for AI in fintech.Smaller businesses often underestimate security risk until they become easier targets.AI lowers the cost of testing new software ideas before committing years of development.Automated SDR workflows will pressure traditional appointment-setting models.Human sellers still matter when buyers need confidence before signing large contracts.Sales teams perform better when leadership gives field teams real access and voice.What You Will LearnHow AI is changing prospecting and appointment setting in fintech sales.Why payment security is becoming a stronger AI use case than generic productivity.The reason smaller merchants often misunderstand their exposure to fraud and breaches.How buyers talk about AI when they know they need it but cannot define the purchase.Why complex software still needs human interpretation during the sales process.When W-2 sales teams create more control than 1099 agent-led distribution.What sales leaders can do to keep field teams engaged, heard, and useful.Episode Highlights00:00: AI sales needs a human interpreter05:00: Payment security becomes the practical AI case07:30: Small businesses misread their breach exposure11:30: Buyers want AI before defining the need14:00: AI lowers software development risk17:00: Automated SDRs pressure old appointment models19:00: Trust still decides large software purchases28:30: North shifts toward a W-2 sales model36:00: Salespeople need access, not just incentives45:30: Door-to-door selling still creates human trust50:00: AI turns ideas into working prototypes fasterResources MentionedNorth: https://north.comW-2 sales model: referenced as North’s full-time sales channel structure1099 agent model: referenced as the traditional distribution model in payments technologyListen NowAvailable on all major podcast platforms and YouTube.Connect with the ShowFollow The CTO Show with Mehmet for more conversations at the intersection of technology, startups, and venture capital.

In this episode of The CTO Show with Mehmet, Mehmet sits down with Eugene Cheah, CEO of Featherless AI. The AI bottleneck is no longer just GPU access. Power, memory, inference cost, and model reliability are becoming the real constraints.Eugene reframes the AI infrastructure debate away from a simple race for bigger models and more chips. The conversation connects energy capacity, HBM shortages, open source model adoption, linear attention architectures, and the enterprise need for predictable AI systems. It also challenges the assumption that the best AI strategy is always to use the largest available model.If you are building, investing in, or operating AI infrastructure, this conversation gives a clearer view of where AI economics, hardware constraints, and production reliability are heading.About the GuestEugene Cheah is the CEO of Featherless AI, an AI startup making open source AI models accessible through a single platform.Featherless AI started from AI research and optimization work around RWKV architecture, with a focus on reducing inference cost and making AI models more accessible. Eugene’s work sits at the intersection of open source AI, model efficiency, GPU infrastructure, HBM constraints, and inference optimization.He is well positioned to frame this shift because Featherless AI works directly on the infrastructure layer between developers, open models, and production inference.LinkedIn: https://www.linkedin.com/in/eugene-cheah-a47791126/Website: https://featherless.aiKey TakeawaysAI infrastructure constraints are shifting from GPU access to power, memory, and inference efficiency.HBM scarcity becomes more serious as models and context windows continue to grow.Bigger models do not solve the enterprise problem of reliable execution.Open source models are becoming strong enough to replace many closed model use cases.Fine-tuned smaller models can outperform frontier models on narrow enterprise tasks.Nvidia’s moat weakens when developers can move workloads across more hardware choices.Linear attention architectures matter because quadratic memory scaling is economically unsustainable.Enterprises value model control when closed providers change, deprecate, or restrict models too often.What You Will LearnThe real infrastructure bottlenecks behind AI deployment beyond GPU availability.How HBM pressure affects model size, context length, and inference economics.Why energy capacity can delay AI infrastructure even when chips are already available.How open source models are changing enterprise AI adoption and deployment control.Why smaller fine-tuned models can beat larger models on specific production tasks.When linear attention architectures reduce memory demand compared with transformer attention.What hardware choice, model portability, and local inference mean for AI infrastructure strategy.Episode Highlights00:00 — AI infrastructure moves beyond the GPU race03:30 — Nvidia, AMD, and Huawei follow different hardware strategies07:30 — Power becomes the first AI infrastructure bottleneck08:30 — HBM pressure exposes the memory constraint12:00 — AI follows the same pluralism as databases15:00 — Developers start with big models, then specialize18:30 — Transformer memory scaling becomes an economic problem23:30 — Hardware choice starts weakening platform lock-in29:30 — Reliability matters more than raw intelligence36:00 — Open source gives enterprises model control41:30 — Small models can now build real applicationsResources MentionedFeatherless AI: https://featherless.aiRWKV architecture: AI architecture referenced by Eugene as part of Featherless AI’s research backgroundListen NowAvailable on all major podcast platforms and YouTube.Connect with the ShowFollow The CTO Show with Mehmet for more conversations at the intersection of technology, startups, and venture capital.

In this episode of The CTO Show with Mehmet, Mehmet sits down with Helen Gu, Founder and CEO of InsightFinder AI. Helen brings decades of research in distributed system reliability, anomaly detection, and AI-driven operations. The conversation focuses on why AI reliability is becoming a business risk, not just an engineering issue.The conversation reframes AI observability as a production control layer for enterprises deploying AI agents. Helen explains why traditional DevOps and SRE practices are not enough when systems are probabilistic, model behavior changes, data shifts, prompts evolve, and agents begin taking actions across workflows.If you are building, investing in, operating, or leading AI systems inside enterprise environments, this conversation gives you a practical frame for reliability, drift, runtime monitoring, and accountability.About the GuestHelen Gu is the Founder and CEO of InsightFinder AI, and a professor at North Carolina State University. InsightFinder AI was founded from her research in distributed system reliability using AI technology.Helen has worked on anomaly detection, prediction, diagnosis, and system reliability since the late 1990s. She also spent a sabbatical year at Google evaluating anomaly detection algorithms, which later helped shape the foundation for InsightFinder AI.LinkedIn: https://www.linkedin.com/in/helen-gu-b1aa42b6/Website: https://insightfinder.com/Key TakeawaysAI systems can fail silently while still returning confident answers.AI reliability is becoming a business risk, not only an engineering concern.Multi-agent systems can spread upstream mistakes across business workflows quickly.Traditional SRE practices do not fully cover model behavior, prompts, and data drift.Runtime monitoring matters more once AI moves from sandbox testing to production.Observability alone is not enough without diagnosis, recommendations, and remediation.Model drift can change business outcomes even when infrastructure appears healthy.Human review shifts from doing work to supervising AI decisions and guardrails.What You Will LearnWhy probabilistic AI systems require different reliability practices than software systems.How model drift and data drift change production behavior over time.What silent AI failure looks like inside enterprise workflows.The reason sandbox testing misses real production AI failure cases.How runtime monitoring helps detect hallucinations, bias, leakage, and accuracy issues.Why AI observability must connect infrastructure, data, prompts, models, and business outcomes.What leadership teams need to consider before AI agents begin taking actions.Episode Highlights00:00 — Helen Gu frames AI reliability from research02:30 — AI systems answer confidently even when wrong04:30 — SRE lessons do not fully transfer to AI07:00 — AI reliability needs fine-grained runtime metrics08:30 — Silent failure creates hidden business damage10:00 — Multi-agent mistakes propagate faster than humans12:00 — Model drift changes outcomes without warning15:00 — Sandboxes miss production AI behavior18:00 — Observability must become actionable control21:30 — AI reliability becomes a leadership responsibility24:30 — AI Labs test prompts, models, and datasets28:30 — AI agents become part of enterprise workflows31:30 — Responsible AI starts with accepting failure riskListen NowAvailable on all major podcast platforms and YouTubeConnect with the ShowFollow The CTO Show with Mehmet for more conversations at the intersection of technology, startups, and venture capital.

In this episode of The CTO Show with Mehmet, Mehmet sits down with Jason Remillard, Founder of Data443. Jason brings more than 30 years of cybersecurity, data security, infrastructure, and enterprise risk experience. The conversation focuses on the gap between AI adoption speed and the security operating models still built for slower systems.The episode reframes AI security as an execution and visibility problem, not only a model risk problem. Jason argues that security teams lose when they only block users, rely on slow approval workflows, or assume old SOC models can handle AI agents, MCPs, SaaS sprawl, and machine-speed data movement.If you are leading cybersecurity, enterprise IT, AI adoption, or digital infrastructure strategy, this conversation gives you a practical lens for where the real exposure is forming.About the GuestJason Remillard is the Founder of Data443, a data security company focused on securing data across systems, users, and enterprise workflows. His career spans more than 30 years, from early systems operations and ISP infrastructure to enterprise security and regulated environments.Jason has worked across cybersecurity, data protection, ransomware recovery, threat intelligence, DLP, attack surface management, and AI-related security challenges. His perspective is grounded in the operational reality of how users, security teams, and business units behave when controls create friction.LinkedIn: https://www.linkedin.com/in/jremillard/Website: https://data443.com/Key TakeawaysAI agents expand the attack surface faster than security teams can govern with manual workflows.End users bypass controls when security becomes a blocker to legitimate business execution.DLP cannot solve data loss when users can photograph, move, and re-enter information elsewhere.Security teams need to enable safer decisions, not only enforce binary allow-or-deny rules.Inference can reduce AI security costs when models are trained for specific enterprise use cases.Threat intelligence must track agents, connectors, APIs, and machine actions as risk-bearing actors.Post-quantum risk matters because encrypted data can be stored now and decrypted later.Cyber resilience starts with assuming breach, not assuming the perimeter still holds.What You Will LearnThe reason cultural failure still sits behind many enterprise security failures.How AI agents change visibility across SaaS, APIs, Shadow IT, and enterprise data flows.Why traditional exception management breaks when AI decisions happen in milliseconds.How inference can help security teams operate faster without relying only on GPUs.What MCP and agent-to-agent workflows mean for API governance and connector risk.Why post-quantum security is already relevant for long-lived sensitive data.The practical starting point for cyber resilience when attacks cannot be fully prevented.Episode Highlights00:00 — Jason Remillard frames three decades in cybersecurity04:30 — Security failure starts with not-my-job thinking08:30 — DLP breaks when users bypass friction12:00 — AI agents change enterprise visibility13:30 — Approval workflows cannot match AI speed17:30 — Non-human actors create identity risk20:30 — AI defense depends on trained inference27:00 — Multimodal input changes user behavior28:30 — MCP turns APIs into hidden risk31:00 — Attackers gain the same AI velocity35:00 — Quantum risk makes stored data vulnerable39:00 — Resilience starts by assuming breachListen NowAvailable on all major podcast platforms and YouTube.Connect with the ShowFollow The CTO Show with Mehmet for more conversations at the intersection of technology, startups, and venture capital.