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

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#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.

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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

Finance software is starting to deploy itself.

In this episode of Terminal Value, Nik Singh sits down with Ahikam Kaufman, Co-Founder and CEO of SafeBooks, to discuss how AI agents are changing finance operations, revenue integrity, and the modern CFO stack.

SafeBooks is building an agentic revenue integrity platform that connects systems like CPQ, CRM, contracts, billing, ERP, and revenue recognition — then gives finance teams a way to validate transactions, catch errors, automate workpapers, and ask questions across their financial data.

We discuss why finance teams still spend so much time manually checking data, how AI can create a financial data graph across systems, why forward-deployed engineering may become less important over time, and why many finance AI workflows may not require frontier models.

We also cover the future of accounting work, the difference between SafeBooks and legacy close-management platforms, and what happens when finance operations become real-time, agent-driven, and self-serve.

Chapters:
00:00 Cold open: AI agents for finance operations
00:45 Introduction: SafeBooks and the revenue integrity problem
01:44 What revenue integrity means in practice
03:20 The finance stack behind revenue integrity
04:31 What an agentic revenue integrity workflow does
06:09 The financial data graph behind SafeBooks
08:54 Deploying across CRM, billing, ERP, and accounting systems
10:27 Are forward-deployed engineers going away?
11:16 SafeBooks’ target customer and mid-market use case
13:03 Pricing AI software with SaaS + usage models
16:20 Why finance AI may not need frontier models
18:39 SafeBooks vs. BlackLine, FloQast, and close-management tools
21:09 Why deployment, context, and real-time controls matter
23:40 How AI changes finance employment
25:16 The future of automated finance operations
27:51 Closing and final takeaways

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AI agents are starting to change how marketing teams turn digital traffic into revenue.

In this episode of Terminal Value, Nik Singh sits down with AJ Goyal, co-founder and CEO of Fibr AI, to unpack why web conversion is still so manual — and how agentic AI could reshape the workflow between paid traffic, websites, experiments, and revenue.

For years, marketing teams have gotten increasingly sophisticated at targeting ads, segmenting audiences, and optimizing acquisition. But once that traffic reaches the website, the experience often becomes static, generic, and dependent on analysts, agencies, marketing operations, and engineering tickets.

Fibr sits in the middle of that workflow. The company helps teams take the intent already captured in ads, campaigns, and analytics and turn it into personalized web experiences that can be tested and improved continuously.

We cover:

* Why web conversion is still such a manual workflow
* How marketers depend on analysts, agencies, marketing ops, and engineering
* Why personalization often breaks down after the ad click
* How Fibr uses AI agents to create and improve web experiences
* Why reducing CAC is the wedge for enterprise buyers
* How agentic software changes pricing, deployment, and GTM
* Why incumbents may struggle when AI threatens their agency ecosystems
* What happens when AI agents become users of the web

Chapters:

00:00 – Hook
00:30 – Introducing AJ Goyal and Fibr AI
01:39 – How web conversion works today
04:10 – The modern marketing and web stack
06:49 – Where the workflow breaks down
09:29 – What Fibr does
11:39 – How Fibr changes the marketer’s workflow
14:16 – The first wedge: reducing CAC
15:00 – Customer example: 48% CAC reduction
16:35 – Where humans stay in the loop
18:12 – Why more data improves the agent loop
19:09 – Fibr’s enterprise ICP
21:12 – Go-to-market: events, conferences, and LinkedIn
23:04 – Pricing agentic software
25:12 – Enterprise pricing pushback
27:20 – Proof-of-concept motion and ROI
28:59 – Deployment and implementation
29:47 – Competitive landscape: Optimizely, Adobe, and agencies
31:38 – Incumbents, agency channels, and agentic conflict
33:31 – Product utilization and always-on experimentation
35:10 – Could agencies become a channel?
36:17 – How AI changes marketing orgs and agencies
38:59 – Will marketing teams get leaner?
39:28 – The future of agentic web experiences
41:29 – Final takeaways: workflow collapse, GTM conflict, and agents as users

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#AI #MarketingAI #AgenticAI #EnterpriseSoftware #SaaS #ConversionOptimization #DigitalMarketing #TerminalValue

AI is changing FP&A from a tool-assisted workflow into something closer to an operating system for business planning.

In this episode of Terminal Value, Nik Singh sits down with Austin Gardner-Smith, Founder & CEO of Drivepoint, to discuss why financial planning and forecasting are especially critical in consumer, retail, and CPG businesses.

The conversation covers why forecasting can be “life or death” when inventory, cash flow, and margins are on the line; why vertical software may beat horizontal FP&A tools; how AI changes the value proposition of SaaS; and what happens to finance teams as software moves from organizing work to actually doing the work.

Drivepoint is building agentic planning software for consumer and retail brands, helping teams connect data across Shopify, Amazon, retailers, ERP systems, inventory systems, and finance workflows.

Chapters:

00:00 — Why forecasting is life or death in retail and CPG
00:50 — Welcome to Terminal Value
01:00 — What Drivepoint is building
01:20 — Why this is about more than FP&A software
01:50 — What FP&A actually does inside the CFO office
04:10 — The evolution of FP&A tools: Oracle, Anaplan, Adaptive, Workday, Pigment
06:30 — What Drivepoint does differently
07:50 — From financial models to proactive scenario planning
09:50 — Why Drivepoint focuses on consumer and retail
12:30 — Why inventory makes forecasting high stakes
13:40 — How AI changes FP&A for business leaders
15:40 — Trust, permissions, and governed access in AI finance tools
16:30 — Why the UI layer is collapsing
17:20 — Why data quality matters more in AI-native software
19:30 — How software pricing may change in an AI world
22:30 — Will Anthropic or OpenAI build this?
25:10 — How AI changes finance team org design
27:10 — Where Drivepoint goes next
28:40 — Nik’s closing thoughts: context, vertical AI, and where software value accrues

Topics covered:

• FP&A software

• Vertical AI

• Agentic planning

• Retail and CPG forecasting

• Inventory planning

• Strategic finance

• Office of the CFO

• SaaS pricing models

• AI and finance jobs

• Data quality and context layers

• Drivepoint

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#investing #retailtech #venturecapital

AI-native software is changing the economics of SaaS.

For years, cloud cost management was mostly about visibility: dashboards, budgets, showback, chargeback, and rate optimization. But as AI usage, token costs, observability bills, and cloud consumption become material parts of software gross margin, the problem is moving closer to the code itself.

In this episode of Terminal Value, I sit down with Mike Weider, Founder & CEO of Frugal, to discuss the shift from traditional FinOps to Application Cost Engineering.

We cover why legacy cloud cost tools mostly helped companies measure spend, how Frugal maps cloud and AI costs back to the code driving them, why AI-native companies face a more urgent gross margin problem, and why cost optimization may become part of the developer workflow.

Chapters:
00:00 Cold open: AI’s gross margin problem
00:25 Welcome to Terminal Value
01:20 Why cloud cost management matters more because of AI
01:50 Interview begins
02:00 The first wave of cloud cost management
04:20 Showback, chargeback, and the finance/engineering tension
06:35 What Frugal is building
07:00 Why cloud cost optimization is too reactive today
09:35 Moving cost visibility into the developer workflow
10:00 Mapping cloud, AI, and observability costs back to code
12:10 How FinOps and engineering work together
14:45 Building trust in cost-to-code automation
17:45 Why Frugal uses a forward-deployed engineer model
20:00 Why AI still needs the right context
21:45 Frugal’s ICP and where customers get the most value
23:25 Why AI-native companies have a gross margin problem
24:35 Frontier models, cheaper models, and evals
28:00 Pricing AI-native software
31:20 How AI changes engineering teams
32:35 Engineers as conductors of AI agents
35:20 Where Frugal sits in the software stack
37:20 Why FinOps dashboards still matter
39:00 Competition from FinOps, observability, and coding agents
41:05 The future of cost-aware code
43:00 Key takeaways from the conversation

Terminal Value explores where value accrues across markets, software, AI, and infrastructure through deep dives with founders, operators, and investors.

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#AI #Software #FinOps #CloudComputing #SaaS #EnterpriseSoftware #GrossMargins #TerminalValue