Code Story | Startup Podcast for CTOs, CEOs and Technical Founders

Emilie Schario grew up in New Jersey, outside of Newark, and attended college in the state. Currently, she lives in Columbus, Georgia, outside of Atlanta. She mentions she got into technology so she could easily follow her husband's career geographically, and has much success in the industry. Outside of tech, she is married with 3 boys (all 5 and under)... so there is a lot of wrestling in her household. She admits she is often quoted staying she does three things in her life - work, parenting, and if she is lucky, attends CrossFit 3 times a week. In fact, she finds a great sense of community in that world, and brings her kids with her to cheer her on.

A year and a half ago, Emilie's current venture was started, to build the open source orchestrator (or "harness") for AI coding agents. Through some shuffle in the early team, Emilie joined and started in building the fastest AI coding app on the market.

This is the creation story of Kilo.

Sponsors

• Unblocked
• TECH Domains
• Mezmo
• Braingrid.ai

Links

• https://kilo.ai/
• https://www.linkedin.com/in/emilieschario/

Timestamps

1:49 Guest introduction: Emilie Schario's background and career journey

2:51 Overview of Kilo Code as an open source agentic engineering harness

3:14 Differentiating through model freedom and supporting 500 plus AI models

3:58 Kilo Code founding story with Sid Sijbrandij and team history

4:42 Defining the evolving MVP for AI coding tools in a fast-moving market

5:25 The rapid shift from manual prompt engineering to autonomous loops

6:19 Trade-offs and resource allocation: Deprecating the Kilo App Builder

9:09 Modern AI product management: Why multi-year roadmaps no longer work

10:19 Shifting PM responsibilities from tracking engineers to setting context

13:00 The AI throughput illusion: Why expensive models don't equal shipped code

17:00 Measuring true engineering output and productivity in the AI era

21:00 Building resilient engineering cultures around AI coding platforms

23:30 Closing thoughts and where to learn more about Kilo Code

Bhaskar Sunkara grew up in Delhi, India, and moved to the states when he started working. He has lived in San Fransisco for several decades now, and has spent a lot of his professional life building systems (infrastructure, observability and now, analytics). His prior startup, AppDynamics, was eventually acquired by Cisco. In general, he stays curious about how things work, and likes to deconstruct systems to figure out how they work. Outside of tech, he is a big sports fan, enjoying football, baseball, cricket and basketball. In fact, he grew up watching Michael Jordan and the bulls.

Bhaskar noticed that business teams were drowning in dashboards, and as such, were not sure how to take the next steps in the business. He and his team realized that what people needed was not a retroactive view, but a proactive one - something more akin to a 24x7 analyst.

This is the creation story of Bicycle AI.

Sponsors

• Unblocked
• TECH Domains
• Mezmo
• Braingrid.ai

Links

• https://bicycle.ai/
• https://www.linkedin.com/in/bhaskarsunkara/

Timestamps

0:00 Intro and episode teaser on the limits of manual KPI monitoring

1:49 Guest introduction: Bhaskar Sunkara's background and AppDynamics experience

2:50 The core problem: Why revenue teams are drowning in dashboards

3:53 Origin story: Shifting from reactive dashboards to proactive AI analysts

4:36 Identifying target transactional verticals in retail, travel, and payments

6:02 Building the MVP: The 1-year journey and defining core capabilities

7:06 The three MVP pillars: Data connection, KPI definition, and dimensional search

8:33 Strategic trade-offs: Choosing vertical focus over generic horizontal BI

10:00 Harnessing LLMs and agentic AI for root-cause context

15:00 Establishing single-source-of-truth KPI definitions across departments

20:00 How AI agents integrate into existing enterprise data stacks

25:00 The future of autonomous analytics and proactive decision-making

Today, we are dropping our final episode in our series The AI Control Loop, How enterprises govern the AI they've already deployed - sponsored by our friends at Wallarm.

Wallarm is the AI Control Platform for Enterprise AI, protecting every AI workload, API, and application in production, giving CISOs the governance they need and CIOs the speed they demand. Organizations choose Wallarm for a complete inventory of APIs, AI agents, and AI apps, patented AI/ML-based threat detection and blocking that operates at production traffic speeds.

In our final episode, we are joined by Shayne Higdon, Wallarm CEO, who closes the series by examining what the accountability moment demands from enterprise leaders, what a mature AI governance model needs to prove rather than promise, and what the next 12 to 24 months look like for organizations that get this right.

Questions

• Why is now the accountability moment for enterprise AI?
• What has changed between the early days of AI experimentation and today's enterprise AI deployments that makes accountability such a pressing issue?
• When we talk about AI accountability, what does that actually mean in practical terms? Are we talking about visibility, auditability, enforcement, ownership—or all of the above?
• As organizations race to deploy AI, how should CIOs balance the speed of transformation with the responsibility to govern it effectively?
• Why are traditional governance and security models struggling to keep pace with the way AI is being adopted across the enterprise?
• Given those challenges, how should boards and executive teams evaluate whether their organizations are truly ready to scale AI safely and responsibly?
• And once an organization believes it's ready, what does a mature AI governance model actually need to prove - not just promise?
• From an operational standpoint, how do capabilities like discovery, runtime monitoring, and enforcement come together to create a closed-loop approach to AI accountability?
• Stepping back and looking across this entire conversation, what's the one mindset shift every enterprise leader needs to make when it comes to AI security and accountability?
• And finally, as listeners think about what's ahead, what should they expect the future of AI security and accountability to look like over the next 6, 12, or even 24 months?

Links

• https://www.wallarm.com/
• https://www.linkedin.com/in/shaynehigdon/

Full Abstract

Abstract: Join Shayne Higdon, Wallarm CEO, for this episode, which closes the series by examining what the accountability moment demands from enterprise leaders, what a mature AI governance model needs to prove rather than promise, and what the next 12 to 24 months look like for organizations that get this right.

AI deployment is not waiting for governance to catch up. Across most enterprises, the gap between how fast AI is being adopted and how well it is being governed is widening every quarter. CIOs and CISOs are not debating whether to govern AI. They are trying to figure out how, under real organizational pressure, with tools and frameworks that were built for a different threat model.

That pressure is coming from every direction at once. Boards want AI transformation to move fast. Regulators want documented evidence that it is under control. Security teams want runtime visibility and enforcement capabilities that most of their current tools do not provide. And the AI systems themselves are not waiting: they are accessing data, calling external services, and making decisions continuously, in ways that after-the-fact governance cannot meaningfully constrain.

This is the accountability moment. Not because the risk is new, but because the consequences of undermanaged AI are now concrete enough to land on a board agenda, an audit report, and a regulatory deadline at the same time. What accountability actually requires in practice is the full AI control loop: knowing what AI is running across the enterprise, seeing what it is doing at runtime, enforcing policy before damage compounds, and generating continuous evidence that the governance is real and not retroactive. Organizations that can demonstrate all four are in a fundamentally different position than those still assembling audit evidence from spreadsheets the week before a review.

Timestamps

1:49 Guest introduction: Shayne Higdon's executive background and role as Wallarm CEO

2:45 From experimentation to production: What triggered the enterprise AI accountability shift

4:10 Why traditional CISO governance models fail to keep pace with autonomous AI agents

6:05 Explaining the AI Control Loop: Discovery, visibility, enforcement, and evidence

8:30 Moving from policy promises to continuous, runtime-proven governance

11:15 Balancing innovation speed for CIOs with security mandates for CISOs

14:00 Tackling Shadow AI and establishing a complete inventory of AI apps and APIs

17:30 Runtime threat detection: Blocking prompt injection and data leaks at production speeds

21:00 Board-level expectations and preparing for evolving AI regulatory frameworks

24:15 What AI governance and security will look like over the next 12 to 24 months

27:00 Closing thoughts and how to learn more about Wallarm

Irina Nazarova grew up in Russia, and has lived in Portugal, Turkey, and now, San Francisco. She got a computer science degree, but felt like an imposter in the dev world. She went on to get an economics degree, and went to work for JP Morgan. Feeling little reward from her work, she read the lean startup and jumped out to build her own, and eventually joined Evil Martians. Outside of tech, she is a person who loves hiking, traveling, and old school film and photography. She enjoys working with old film, where there is high touch, and you have a limited number of takes.

Irina is the CEO of Evil Martians, a well known design and engineering consultancy. During the time of the company, she and the team noticed that websocket solutions don't guarantee delivery. They decided to build a new solution, one that does guarantee delivery, through automatic recovery of messages during connection issues.

This is the creation story of AnyCable by Evil Martians.

Sponsors

• Unblocked
• TECH Domains
• Mezmo
• Braingrid.ai

Links

• https://anycable.io/
• https://evilmartians.com/
• https://www.linkedin.com/in/nonconstant/

Ramiro Roballos grew up in Buenos Aires, and 6 or 7 years ago, moved to Miami and now lives in Buffalo, NY. His path to entrepreneurship has been different, as he started out as a musician, and then an orchestra conductor for several years. He eventually got into building how companies, starting his own music school and his own orchestra. Eventually, he got his MBA, worked for McKinsey and some startups before doing his own. Outside of tech, he is married to a cellist, and keeps playing music for fun. He also enjoys Formula 1, and watches every change he gets.

Ramiro went through the immigration process in the US, and was very disappointed in the quality of the service, given the importance of this process in determining a pillar life outcome. He felt there should be a better way, one that has excellent service and quality, and centralizes the expansive process into one platform.

This is the creation story of Tukki.

Sponsors

• Unblocked
• TECH Domains
• Mezmo
• Braingrid.ai

Links

• https://tukki.ai/
• https://www.linkedin.com/in/ramiro-roballos/

Today, we are dropping another episode in our series The AI Control Loop, How enterprises govern the AI they've already deployed - sponsored by our friends at Wallarm.

Wallarm is the AI Control Platform for Enterprise AI, protecting every AI workload, API, and application in production, giving CISOs the governance they need and CIOs the speed they demand. Organizations choose Wallarm for a complete inventory of APIs, AI agents, and AI apps, patented AI/ML-based threat detection and blocking that operates at production traffic speeds.

In today's episode, Craig Thomas, Sr. Solutions Engineer at Wallarm, returns to the show to dive into why runtime behavior is the critical blind spot, and what CISOs should demand if they want to move from policy to control.

Questions

• Security teams are used to detecting incidents and responding after the fact. Why is that model becoming insufficient for AI-driven systems?
• Building on that, when we talk about response today, enforcement often means actions like restarting pods, rotating credentials, or shutting down services. Why can those measures come too late in an AI environment?
• So if traditional response isn't enough, why does AI behavior require controls that operate much closer to runtime?
• And when people hear "runtime enforcement," they may think of existing security controls. What changes when enforcement happens at the kernel level rather than only at the network, identity, or application layer?
• Can you make that tangible for us? What does it actually mean to revoke or contain a compromised AI session without disrupting the broader deployment?
• How does that kind of real-time containment change the risk equation for AI agents that have access to sensitive data, external services, or production workflows?
• With that in mind, what are some examples of AI behaviors that organizations should be able to stop immediately?
• Of course, security teams also don't want to become a bottleneck. How do organizations balance strong enforcement with the need to keep AI development and deployment moving quickly?
• And once organizations have the ability to discover, observe, and enforce AI behavior in real time, how does that change accountability at the enterprise level? What does good governance look like from there?

Links

• https://www.wallarm.com/
• https://www.linkedin.com/in/cu-craigthomas/

Full Abstract

This episode examines what is actually missing in AI security today. Craig Thomas, Sr. Solutions Engineer at Wallarm, dives into why runtime behavior is the critical blind spot, and what CISOs should demand if they want to move from policy to control.

CIOs and CISOs have moved past debating whether AI security matters. The question now is what to actually do about it, and most organizations are finding that their existing tools answer a different question than the one AI is asking.

Traditional security tools were built around access: who can reach a system, what credentials they present, what traffic looks like at the perimeter. AI shifts the problem to execution: what a system does once it has access, whether that behavior matches what the business intended, and how you know when it doesn't. Most current tooling has no answer for that. It can tell you what is deployed and what is configured. It cannot tell you what your AI is actually doing at runtime, on whose behalf, or whether any of it violates the policies you thought were in place.

That gap is where most AI security programs stall. There is no shortage of governance frameworks, compliance checklists, and vendor claims. What is missing is operational control: the ability to see AI behavior as it happens, enforce policy at runtime, and produce evidence that holds up when an auditor or a board asks for it. The four capabilities that define a closed AI control loop, discover, observe, enforce, govern, are well understood as a category. Getting all four working together in production is where the real work begins.

John Wright grew up in Arkansas, when his family moved from Wisconsin for his Dad's job. He was influenced heavy by his father, who became an entrepreneur with several successful exits. As a kid, he got to see the ups and downs, and how you ride the roller coaster of being a business owner. Outside of tech, he is an active sailor and certified instructor in yacht racing.

Growing up with a family of wood workers, he also likes to build things and make stuff with his hands. Finally, he lives in sobriety and recovery from past addiction, and is active in this community of people.

In the past, John and his team built a platform around email, which they sold in 2001 to a company that is now apart of Google. Post that, he started to noticed the proliferation of SMS in the messaging world, in similar patterns as to what email did - and they decided to build a platform to serve the enterprise in this capacity.

This is the creation story of TrueDialog.

Sponsors

• Unblocked
• TECH Domains
• Mezmo
• Braingrid.ai

Links

• https://www.truedialog.com/
• https://www.linkedin.com/in/johnnwright/

Ajay Kulkarni grew up in tech, as his father was a tech entrepreneur selling PC's in the early 80's. He went to college in MIT, and eventually founded a startup that was acquired by GroupMe (while it was being acquired by Skype... while they were being acquired by Microsoft). He's always been attracted to building things, so startups are right up his alley. Outside of tech, he is married with 2 young kids. He is a big exercise guy... he loves to run, swim and track his steps. Additionally, he loves music - to listen, and to play guitar, piano and drums.

Ajay and his co-founder met 30 years ago at MIT. They reconnected after years of doing their own thing, starting to dig into the iOT world. In doing this, they built a database because they the best solution to store this data... and in doing so, they unlocked their next venture out of this necessity.

This is the creation story of Tiger Data.

Sponsors

• Unblocked
• TECH Domains
• Mezmo
• Braingrid.ai

Links

• https://www.tigerdata.com/
• https://www.linkedin.com/in/ajaykulkarni/

Timestamps

00:00 Accidental Database MVP

00:41 Podcast Intro Setup

01:34 Ajay Background

02:50 Meeting Co Founder

04:11 Why Tiger Data

05:36 MVP Building TimescaleDB

07:06 Postgres Not NoSQL

08:50 Roadmap And Agents

10:40 Hiring The Right Team

11:59 Scaling As CEO

13:23 Resilience And Pride

14:36 Mistakes And Lessons

17:33 Future Physical World

19:18 Stoicism And Influence

21:01 Advice Play Love Triumph

23:54 Closing And Credits

Today, we are dropping another episode in our series The AI Control Loop, How enterprises govern the AI they've already deployed - sponsored by our friends at Wallarm.

Wallarm is the AI Control Platform for Enterprise AI, protecting every AI workload, API, and application in production, giving CISOs the governance they need and CIOs the speed they demand. Organizations choose Wallarm for a complete inventory of APIs, AI agents, and AI apps, patented AI/ML-based threat detection and blocking that operates at production traffic speeds.

In his follow up appearance on the Code Story podcast, Tim Ebbers, Field CTO at Wallarm, discusses why detection alone is insufficient for AI-driven systems, what real enforcement looks like at the runtime level, and what accountability becomes possible once all four stages are in place.

Questions

• Security teams are used to detecting incidents and responding after the fact. Why is that model insufficient for AI-driven systems?
• What does “enforcement” usually mean today, and why can actions like restarting pods or rotating credentials come too late?
• Why does AI behavior require controls that operate closer to runtime?
• What changes when enforcement happens at the kernel level rather than only at the network, identity, or application layer?
• Can you explain what it means to revoke or contain a compromised AI session without touching the broader deployment?
• How does real-time blocking change the risk equation for AI agents that access sensitive data, external services, or production workflows?
• What kinds of AI behaviors should organizations be able to stop immediately?
• How do teams balance strong enforcement with the need to avoid slowing down AI development and deployment? 
• Once organizations can discover, observe, and enforce AI behavior, what does accountability look like at the enterprise level?

Links

• https://www.wallarm.com/
• https://www.linkedin.com/in/tebbers/

Full Abstract

Tim Ebbers, Field CTO at Wallarm, discusses why detection alone is insufficient for AI-driven systems, what real enforcement looks like at the runtime level, and what accountability becomes possible once all four stages are in place.

Detection tells you what happened. It does not stop it. For most security incidents, that tradeoff is manageable. For AI systems that can access sensitive data, call external services, and trigger downstream actions at machine speed, the gap between detection and response is where the damage happens.

The enforcement model most security teams operate today was built for a slower threat. Restarting pods, rotating credentials, and updating policies are all responses to something that has already occurred. Against an AI agent that can exfiltrate data, invoke a production workflow, or violate a compliance boundary in the time it takes to page an on-call engineer, that response model is not enforcement. It’s cleanup.

Closing that gap requires controls that operate at the layer where AI behavior actually executes, not at the perimeter, not at the identity layer, not at the application boundary. Kernel-level enforcement changes what is possible: a compromised session can be revoked by user identity or trace ID, connections can be terminated at the workload level, and enforcement can happen without a pod restart, a deploy cycle, or any impact to the broader environment. That is what it means to complete the AI control loop. Discover what is running, observe what it is doing, enforce what it should not be doing, and govern with evidence that the enforcement worked. Organizations that can only do the first two are solving half the problem.

Ahikam Kaufman spent most of his career in the Bay Area. After becoming a CPA, he started his career as CFO at a startup company. Over time, he has been giving multiple opportunities to not only serve finance, but serve business roles as well - which prepared him for his own entrepreneurial path. IE starting 3 companies and exiting one to Intuit. Outside of tech, he enjoys traveling the world, spending time with his family, and hiking. But, he notes that the demands of being a business owner limits the amount of time he spends in these things.

Ahikam started to think about how automation can positively impact financial operations, specifically around managing data in the office of the CFO. After the first AI models were released, he got excited, realizing that these models would continue to get better and better, alongside operating with agency.

This is the creation story of Safebooks.

Sponsors

• Unblocked
• TECH Domains
• Mezmo
• Braingrid.ai

Links

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