Terminal Value

About Terminal Value

Terminal Value is a market landscape podcast that breaks down where value accrues in AI, software, and technology markets through deep dives with founders, operators, and investors. Terminal Value is hosted by Nik Singh and is currently not monetized. Content is for general information and education, not investment, financial, legal or tax advice. Company discussions are not recommendations to buy or sell securities. Views are the speakers’ own and reflect the recording date.

Publisher

Nik Singh

Category

business · technology

Language

en

Explicit

No

First episode

5 May 2026

Latest episode

8 Oct 2026

Latest episodes

18 episodes in the feed.

1. 

   8 Oct 2026

   Killing the Coordination Tax | Parris Khachi, Highlight AI

   Work happens across Slack, Notion, Linear, GitHub, meetings, and the desktop. The problem is not a lack of tools. It is the context lost between them. In this episode, Nik Singh sits down with Parris Khachi to explore Highlight's approach to proactive workplace AI. Parris explains why the next AI interface may not be a chat box and how Highlight uses screen, meeting, and integration context to make assistance more proactive. The conversation also covers privacy and user control, individual versus team context, model economics, product-led adoption, competition with incumbents, and why teams should automate coordination without outsourcing judgment. Highlight AI: https://highlightai.com/ Parris Khachi: https://www.linkedin.com/in/parris-k-7928b011/ Terminal Value: https://www.youtube.com/@Terminal-Value-Podcast Chapters 00:00 Cold open 00:56 Introduction 01:31 Where context gets lost across work tools 05:03 Highlight's mission and the coordination tax 06:04 The AI intern that follows the work 13:01 Screen context, privacy, and user control 18:18 Choosing models across capability, speed, and cost 21:43 Ideal users, deployment, and adoption 27:28 How AI could change team structure 29:32 Competition and the interface after chat 33:51 Highlight's long-term vision 36:10 Closing takeaways Disclaimer: For information and education only; not investment advice or a recommendation to buy or sell securities. Views are the speakers' own.

2. 

   17 Sept 2026

   Email Security Is AI vs. AI | Cy Khormaee, Aegis AI

   In this episode of Terminal Value, I'm joined by Cy Khormaee, founder and CEO of Aegis AI, an AI-native email security company that recently raised a $36 million Series A led by Battery Ventures, to discuss why email security is becoming an AI-versus-AI problem, how personalized threats are bypassing legacy rules, and what changes when AI begins handling the intelligence layer of cybersecurity. We cover how email security evolved from reputation-based appliances to machine learning and rules, why LLM-based reasoning can investigate each message in context, how Aegis uses specialized multi-agent models instead of frontier APIs, why proprietary email data creates a compounding advantage, how automated incident response can shrink exposure from hours to milliseconds, what security buyers need to trust autonomous systems, why AI-native architecture can challenge incumbents, how the category could expand from protecting the corporate inbox to protecting the person, and why Cy hires for slope in an uncertain future. 0:00 Hook: Why email security is becoming AI versus AI 0:40 Intro to Cy Khormaee and Aegis AI 1:45 How email security evolved from reputation and rules to AI 4:55 How AI is making phishing attacks more personalized and harder to detect 9:35 How Aegis uses multi-agent reasoning to stop email threats 12:50 Why rule-based security breaks as threats change faster 15:45 How proprietary email data creates a compounding AI advantage 18:00 How automated response changes accountability and earns trust 25:10 Why Aegis builds specialized models instead of relying on frontier models 31:45 How AI-native architecture can overcome incumbent data advantages 39:25 From securing email to protecting the person 41:15 Why Cy hires for slope in an uncertain AI future 42:50 Closing takeaways

3. 

   3 Sept 2026

   Loyalty Is Becoming a Game | Dylan Robbins, Lucra

   In this episode of Terminal Value, I'm joined by Dylan Robbins, founder and CEO of Lucra, which recently raised $20 million led by ARK Invest Venture Fund to build competitive loyalty infrastructure, to discuss why traditional points programs often struggle to drive adoption, how games and immediate rewards can make loyalty feel worth using, and why the real moat sits in the payments, identity, fraud, and compliance stack underneath the experience. 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

4. 

   20 Aug 2026

   Hardware Engineering Is Becoming Software | Viral Shah, JuliaHub

   In this episode of Terminal Value, I'm joined by Viral Shah, co-founder and CEO of JuliaHub, the company behind Dyad — a physics compiler paired with AI agents for engineering and simulation — to discuss why physical product engineering has been so hard to automate, what an AI agent actually needs to model the real world, and whether hardware development can start to look much more like software. 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

5. 

   6 Aug 2026

   An AI Team for Every Banker | David Sosna, Sympera AI

   In this episode of Terminal Value, I'm joined by David Sosna, founder and CEO of Sympera AI, which raised a $10M seed round to bring agentic AI into relationship banking, to discuss why a copilot is the wrong model for enterprise AI, whether AI replaces relationship bankers or expands the market they serve, and why the real win is embedding AI into the banker's process instead of bolting on a chatbot. 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

6. 

   30 Jul 2026

   Shopify for Travel Rewards | John Taylor Garner, Odynn

   In this episode of Terminal Value, I'm joined by John Taylor Garner, founder and CEO of Odynn, an AI-native travel and loyalty infrastructure company that has raised over $9M in seed funding from Bonfire Ventures and Fiat Ventures, to discuss why points and miles behave less like a rewards program and more like a private economy, why the real product in travel is the decision layer rather than the booking, and how Odynn turns a multimillion-dollar embedded-travel build into something a late-seed startup can afford. 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

7. 

   23 Jul 2026

   Security Just Went Autonomous | Curt Aubley & Stephen Collins, Sevii

   In this episode of Terminal Value, I'm joined by Curt Aubley, co-founder and CEO of Sevii, and Stephen Collins, co-founder and CTO. Sevii is building autonomous defense and remediation for security operations, backed by Overline and BMW i Ventures. We discuss why the SOC was built for a slower world, what separates autonomous remediation from automated playbooks, and whether companies are culturally ready to let AI take real security actions. 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

8. 

   16 Jul 2026

   The Decision Layer Is Missing | Tushar Makija, Team Ohana

   In this episode of Terminal Value, I'm joined by Tushar Makija, co-founder and CEO of Team Ohana, to discuss why headcount planning breaks the moment the CFO's model is locked, how AI agents are becoming the orchestration layer between HR and Finance, and why the real system of record should capture decisions, not just transactions. 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.

9. 

   9 Jul 2026

   AI Is Killing The Seat | Manish Choudhary, Flexprice

   In this episode of Terminal Value, I'm joined by Manish Choudhary, co-founder and CEO of Flexprice, an open-source billing and metering platform for AI and API-first companies, to discuss why AI is breaking the old seat-based SaaS model, how pricing is shifting toward usage and outcomes, and why billing is starting to look less like admin software and more like core infrastructure. 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.

10. 

    2 Jul 2026

    Your Bank Account Is Becoming a Budgeting Engine | Gentry Davies, Crew

    In this episode of Terminal Value, I’m joined by Gentry Davies, co-founder and CEO of Crew, to discuss how consumer finance products shape behavior, why traditional budgeting tools often fail, and how Crew is building a calmer, more intentional alternative to today’s banking and investing apps. 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.”

11. 

    25 Jun 2026

    The Revenue Team Is Getting an AI Data Analyst | Harsha Mokkarala, E:cue

    Harsha Mokkarala, founder and CEO of e:cue, joins Terminal Value to discuss why go-to-market analytics is still so fragmented — and how AI data analysts could change the way revenue teams operate. 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.

12. 

    20 Jun 2026

    Insurance Brokerages Are AI's #1 Disruption Target | Vlada Lotkina, SuperAgent

    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

13. 

    11 Jun 2026

    Application Security Is Becoming an AI Workforce | Shan Kulkarni, Nullify

    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

14. 

    4 Jun 2026

    QA Is Becoming Product Intelligence | Dhaval Shreyas, Pie

    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

15. 

    28 May 2026

    Finance Software Is Starting to Deploy Itself | Ahikam Kaufman, SafeBooks

    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 Subscribe for more conversations on AI, software, markets, and where value accrues.

16. 

    21 May 2026

    Your Website Is the New Sales Agent | AJ Goyal, Fibr AI

    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 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

17. 

    14 May 2026

    The UI Layer Is Gone: Welcome to Agentic FP&A | Austin Gardner-Smith, Drivepoint

    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 (https://www.youtube.com/watch?v=2oVGCuVZ70Q) — Why forecasting is life or death in retail and CPG 00:50 (https://www.youtube.com/watch?v=2oVGCuVZ70Q&t=50s) — Welcome to Terminal Value 01:00 (https://www.youtube.com/watch?v=2oVGCuVZ70Q&t=60s) — What Drivepoint is building 01:20 (https://www.youtube.com/watch?v=2oVGCuVZ70Q&t=80s) — Why this is about more than FP&A software 01:50 (https://www.youtube.com/watch?v=2oVGCuVZ70Q&t=110s) — What FP&A actually does inside the CFO office 04:10 (https://www.youtube.com/watch?v=2oVGCuVZ70Q&t=250s) — The evolution of FP&A tools: Oracle, Anaplan, Adaptive, Workday, Pigment 06:30 (https://www.youtube.com/watch?v=2oVGCuVZ70Q&t=390s) — What Drivepoint does differently 07:50 (https://www.youtube.com/watch?v=2oVGCuVZ70Q&t=470s) — From financial models to proactive scenario planning 09:50 (https://www.youtube.com/watch?v=2oVGCuVZ70Q&t=590s) — Why Drivepoint focuses on consumer and retail 12:30 (https://www.youtube.com/watch?v=2oVGCuVZ70Q&t=750s) — Why inventory makes forecasting high stakes 13:40 (https://www.youtube.com/watch?v=2oVGCuVZ70Q&t=820s) — How AI changes FP&A for business leaders 15:40 (https://www.youtube.com/watch?v=2oVGCuVZ70Q&t=940s) — Trust, permissions, and governed access in AI finance tools 16:30 (https://www.youtube.com/watch?v=2oVGCuVZ70Q&t=990s) — Why the UI layer is collapsing 17:20 (https://www.youtube.com/watch?v=2oVGCuVZ70Q&t=1040s) — Why data quality matters more in AI-native software 19:30 (https://www.youtube.com/watch?v=2oVGCuVZ70Q&t=1170s) — How software pricing may change in an AI world 22:30 (https://www.youtube.com/watch?v=2oVGCuVZ70Q&t=1350s) — Will Anthropic or OpenAI build this? 25:10 (https://www.youtube.com/watch?v=2oVGCuVZ70Q&t=1510s) — How AI changes finance team org design 27:10 (https://www.youtube.com/watch?v=2oVGCuVZ70Q&t=1630s) — Where Drivepoint goes next 28:40 (https://www.youtube.com/watch?v=2oVGCuVZ70Q&t=1720s) — 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 (https://www.youtube.com/hashtag/investing) #retailtech (https://www.youtube.com/hashtag/retailtech) #venturecapital (https://www.youtube.com/hashtag/venturecapital)

18. 

    5 May 2026

    AI Software Has a Gross Margin Problem — Frugal’s Mike Weider on the Future of Cloud Cost Management

    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. Subscribe for more conversations on the businesses and markets shaping the next wave of enterprise software. #AI #Software #FinOps #CloudComputing #SaaS #EnterpriseSoftware #GrossMargins #TerminalValue

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