Insurance distribution is one of the last analog corners of software — and AI is moving in fast. In this episode of Terminal Value, Nik Singh sits down with Vlada Lotkina, co-founder and CEO of SuperAgent, to discuss why insurance brokerages have become one of the clearest targets for AI labor, and what it means to put an autonomous sales workforce inside an agency.
Vlada lays out the "perfect storm" reshaping the industry: roughly a quarter of agency staff and owners are set to retire in the next four to five years, new agents take months to ramp, and margins are compressing at the same time. Most insurance AI has gone after the carrier side — underwriting, claims, the back office — but SuperAgent is built for the front end of distribution, where the majority of premium still flows through brokers and their customer relationships. She walks through how the product works as a multi-agent system: real-time coaching and AI role-play to bring every rep to the same bar, plus autonomous outreach that resurfaces the thousands of aged leads and former customers most agencies are already sitting on, opens conversations, and drafts quotes for a human to close.
Nik and Vlada also get into the harder questions — where the human stays in the loop, why SuperAgent is built to grow revenue rather than just cut cost, how AI pricing is shifting from subscriptions and credits toward outcome-based models, how roll-ups and M&A are pushing small agencies to become technology-forward, and why being AI-native beats bolting AI onto legacy systems in a multi-trillion-dollar market.
Guest: Vlada Lotkina, Co-Founder & CEO of SuperAgent

In this episode of Terminal Value, I'm joined by Shan Kulkarni, co-founder and CEO of Nullify — an AI-native product security company building AI agents for application security.

We discuss why the old "shift left" promise often created more work for security teams, how Nullify uses agents, Vault, context, memory, and tooling to automate AppSec workflows end to end, and what changes when software starts getting measured like labor rather than seats.

IN THIS EPISODE, WE COVER:
- What application security is, and why legacy scanners create alert backlogs
- How AI agents triage vulnerabilities, validate exploitability, open pull requests, follow up in Slack, and close the loop
- Why Vault and customer-specific context are central to Nullify's product advantage
- Where humans still matter: threat modeling, design reviews, architecture, and stakeholder translation
- Why Nullify's ICP starts around companies with 50+ developers
- Campaigns, campaign lookbacks, and merge-ready rate
- Pricing AI agents against security headcount and operating expense
- How security jobs may evolve as AI takes over more repetitive workflow execution
- Why agentic systems create the next major security surface

Subscribe for conversations on applied AI, vertical SaaS, and where value accrues in software businesses.

#ApplicationSecurity #AppSec #Cybersecurity #AI #TerminalValue

In this episode of Terminal Value, Nik Singh sits down with Dhaval Shreyas, co-founder of Pie, to discuss how QA is evolving from manual scripts and brittle automated tests into product intelligence.

As AI accelerates software development, engineering teams are shipping faster than traditional QA teams can keep up. Dhaval explains why the future of quality is not just “agents writing tests,” but systems that understand the product, the user experience, and the business context behind each flow.

We cover how Pie discovers a product from a staging URL or app build, how it builds and maintains coverage, why product context matters more than scripts, and why QA may increasingly merge with product, engineering, and product management.

We cover:

Why QA is underinvested in and often becomes a bottleneck
How AI-driven development is increasing pressure on quality teams
Why QA is moving from scripts to product understanding
How Pie discovers product flows and builds coverage
Why product context is hard for coding agents like Claude Code to replace
How Pie compares to Selenium, QA Wolf, and agentic coding tools
The role of PQEs and human-in-the-loop deployment
How the QA role may evolve as software teams become more AI-native
Why product context could become valuable beyond QA, including documentation and support

Terminal Value explores where value accrues as AI, software, markets, and infrastructure change.

Subscribe for more conversations with founders, operators, and investors building the next generation of software.

Chapters

00:00 Cold Open: QA Is Falling Behind
00:55 Intro: Pie and the AI-Native QA Layer
01:50 What QA Looks Like Today
04:02 Why Traditional QA Breaks Down
04:53 Agentic QA and Product Intelligence
07:11 Pii’s Deployment Journey
09:04 How Pie Builds Product Context
10:56 Ingesting PRDs, Help Docs, and Test Cases
12:52 Who Buys AI-Native QA?
14:43 Human-in-the-Loop Deployment and PQEs
15:58 Pricing the PQE Model
16:36 Pie vs. Selenium, QA Wolf, and Testing Agents
18:33 Why Claude Code Alone Is Not Enough
19:49 How the QA Role Changes
21:49 Will QA Merge Into Product and Engineering?
22:48 Pie’s Bigger Vision Beyond QA
25:37 Closing Takeaways

#AI #Software #QualityAssurance #QA #ProductIntelligence #EnterpriseSoftware #DeveloperTools #AgenticAI #TerminalValue