We cover Lucra's shift from a consumer app to an embedded B2B platform, how the company sells to venues and proves payback, why customers that try to build the stack themselves often come back, how Crown brings mobile games into the days between venue visits, how Bracketology extends the model into reality TV, what loyalty could look like when rewards are narrower and immediate, and why Dylan thinks the AI funding boom may be crowding out other kinds of innovation.

0:00 Hook: Why building loyalty in-house costs more than brands expect
0:45 Intro to Dylan Robbins and Lucra
1:43 Why traditional loyalty programs struggle to drive adoption
4:17 How Lucra replaces points with immediate rewards
6:23 From customer goals to integration, payback, and ROI
11:20 Market size, pricing, and the path to self-serve integration
15:22 Lucra’s three growth pillars: IRL, mobile games, and reality TV
17:26 Why brands eventually buy instead of build
21:12 How Crown connects physical venues to mobile games
25:27 How Bracketology expands Lucra into reality TV
30:09 What loyalty could look like in five years
31:54 Why the AI funding boom may be hurting startup innovation
34:05 Closing takeaways

We cover the "engineering V" and why every step runs in its own siloed tool, why the real TAM is the trillions spent designing products rather than the billions spent on simulation software, how Dyad works as a compiler that fuses disparate physics into one simulation, why calibration — not modeling — is the hard part, how Instron cut crash-test-rig calibration from over a day to under a minute, why trusted reusable model libraries make hardware feel like software (text, Git, CI/CD, agents), how agent pricing shifts from per-seat licenses to token consumption, why the harness can matter more than the frontier model, why engineering shows high expected but low observed AI impact today, and why Viral believes hardware engineering is going to become like writing software.

0:00 Hook: Why hardware engineering could become like software
0:35 Intro to Viral Shah, JuliaHub, and Dyad
1:25 How engineers move from product requirements to validated designs
4:41 Why engineering software is fragmented across specialized tools
12:16 Where Dyad fits and why JuliaHub built a physics compiler
19:17 How real-world data calibrates simulation models
23:21 Building trusted models that can be reused like software libraries
26:17 Where Dyad is being used across aerospace, semiconductors, and infrastructure
29:10 How agentic engineering changes seat-based software pricing
31:37 Why the agent harness can matter more than the frontier model
35:44 Why AI adoption in engineering still lags software
38:13 Why Viral Shah believes hardware development will become more like software
41:04 Closing takeaways on Dyad and the shift toward computational engineering

Disclaimer: For information and education only; not investment advice or a recommendation to buy or sell securities. Views are the speakers’ own.

We cover why relationship banking is a large, human, relationship-driven market, why more query and data tools never fixed the banker's day, how Sympera narrows a huge book down to a few fully-researched opportunities and tells the banker what to do next, how it blends public, purchased, and internal bank data without ever exposing what the bank holds, why betting on a single AI model is risky and how a multi-model stack with tuned tools answers it, why running everything through the most expensive frontier model doesn't scale, why banks that try to boil the ocean get nothing done, and why vertical AI's real edge is embedded judgment rather than a better chat box.

0:00 Hook: Why AI copilots fail without process redesign
0:45 Intro to David Sosna and Sympera AI
1:29 How relationship bankers grow and protect client relationships
4:04 Why CRM and data tools still depend on banker judgment
9:28 How Sympera narrows a market to five actionable opportunities
12:43 From bank priorities to ranked leads and next-best actions
16:39 Data security, lead accuracy, and continuous model validation
19:47 Feedback loops, inference cost, and scanning at scale
24:46 Why Sympera uses multiple models and keeps outreach human-reviewed
29:22 Bank targeting, pricing, and the work of changing user behavior
34:53 Salesforce, vertical AI, and the future banking agent stack
41:38 AI-native teams, banker productivity, and why copilots fall short
49:05 Closing takeaways

Disclaimer: For information and education only; not investment advice or a recommendation to buy or sell securities. Views are the speakers’ own.

We cover how the fragmented travel rewards stack works today, why Expedia and Booking quietly control most of embedded travel, why points and miles only ever depreciate, how Odynn acts as a "Shopify for travel rewards" that compares dollars, points, and miles in one place, how the company prices and earns alongside its bank customers, what it takes to sell to banks as a young startup, and how an agentic AI concierge could turn travel from something you search into something you delegate.

0:00 Hook: Airlines don't make money flying planes

0:45 Intro to John Taylor Garner and Odynn

1:35 How the travel rewards stack works today

3:25 Banks, OTAs, and the Expedia/Booking duopoly

5:55 Where it breaks down for the consumer

8:10 Why points and miles only lose value

10:35 What Odynn is and how the product works

16:15 Pricing, commissions, and supply economics

20:30 Customers, competition, and the $60K disruption

25:00 Winning big banks and proving longevity

30:00 The AI concierge and the future of loyalty

36:00 Building AI-native: agents, engineers, and OpEx

41:55 Closing takeaways

Disclaimer: For information and education only; not investment advice or a recommendation to buy or sell securities. Views are the speakers’ own.

We cover the legacy SOC workflow from alert to remediation and why it hands attackers a 15-minute head start, how Sevii's agentic "cyber warriors" go from detection to reported remediation in 2 to 15 minutes across the tools you already own, why per-asset licensing keeps security budgets predictable, Sevii's rule of only using AI where it's necessary, and why the hardest competition may be cultural trust rather than another vendor.

0:00 Hook: Why attackers get a 15-minute head start

0:40 Intro to Curt Aubley, Stephen Collins, and Sevii

1:26 How legacy SOC workflows reach remediation

4:35 Where speed, scale, and economics break the SOC

7:00 Why existing security tools still leave a bottleneck

9:29 What Sevii's autonomous remediation platform does

15:02 How Sevii moves from detection to remediation

19:31 How cyber warriors change SOC staffing

24:00 Customer control, MDR limits, and AI cost strategy

31:20 Sevii's ICP, buyer, and forward-deployed model

37:33 Asset-based pricing and predictable budgets

40:53 Competition, culture, and the future of autonomous defense

48:35 Closing takeaways

Disclaimer: For information and education only; not investment advice or a recommendation to buy or sell securities. Views are the speakers’ own.

We cover why a late hire is deferred capital, how a sales leader can plan ten hires from a single Slack prompt, why HR and Finance never demand better tools, human capital versus "agentic capital," and Tushar's "accelerate or die" case for moving now.

Disclaimer: For information and education only; not investment advice or a recommendation to buy or sell securities. Views are the speakers’ own.

We cover how software pricing evolved from one-time licenses to seats to usage, why billing becomes an infrastructure problem as companies move to consumption-based models, a real customer example that cut billing inquiries by 80% and surfaced hidden revenue leaks, how Flexprice lets finance teams change pricing without engineering work, and why open source may be a real wedge into the market.

Disclaimer: For information and education only; not investment advice or a recommendation to buy or sell securities. Views are the speakers’ own.

We cover Crew’s pocket-based banking model, the problem with credit card and neobank incentives, how AI helps a small team move faster, and why Gentry ultimately wants to build the “anti-Robinhood.”

Disclaimer: For information and education only; not investment advice or a recommendation to buy or sell securities. Views are the speakers’ own.

We cover why marketing, sales, and finance often have different versions of the truth, why dashboards can create more reconciliation than clarity, how e:cue is building Q as a C-suite GTM analyst, and why the future of AI in revenue teams may move from insight to action.

Disclaimer: For information and education only; not investment advice or a recommendation to buy or sell securities. Views are the speakers’ own.

Disclaimer: For information and education only; not investment advice or a recommendation to buy or sell securities. Views are the speakers’ own.

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

Disclaimer: For information and education only; not investment advice or a recommendation to buy or sell securities. Views are the speakers’ own.

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

Disclaimer: For information and education only; not investment advice or a recommendation to buy or sell securities. Views are the speakers’ own.

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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Disclaimer: For information and education only; not investment advice or a recommendation to buy or sell securities. Views are the speakers’ own.

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

Subscribe to Terminal Value for conversations with founders, operators, and investors on where value accrues across markets, software, AI, and infrastructure.

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

Disclaimer: For information and education only; not investment advice or a recommendation to buy or sell securities. Views are the speakers’ own.

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

Subscribe to Terminal Value for deep dives on where value accrues across AI, software, and markets.

#investing #retailtech #venturecapital

Disclaimer: For information and education only; not investment advice or a recommendation to buy or sell securities. Views are the speakers’ own.

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.

Subscribe for more conversations on the businesses and markets shaping the next wave of enterprise software.

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

Disclaimer: For information and education only; not investment advice or a recommendation to buy or sell securities. Views are the speakers’ own.